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Genetic association studies of the susceptibility to acute mountain sickness Wang, Pei 2013

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GENETIC ASSOCIATION STUDIES OF THE SUSCEPTIBILITY TO ACUTE MOUNTAIN SICKNESS by Pei Wang M.Sc. Tianjin Institute of Physical Education, China, 2003  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in  The Faculty of Graduate Studies (Kinesiology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) May 2013  © Pei Wang, 2013 1  Abstract Purpose: Acute mountain sickness (AMS) is the most common and most benign altitude illness. The main symptoms (i.e. headache, nausea, fatigue, dizziness, and disturbed sleep) appear in 6 – 12 h after rapid ascent (usually to above 2500 m) and often subside after 4 – 5 days without further ascent. The etiology is unclear and may be partially due to an innate susceptibility, which could be genetic. The studies described in this thesis focused on genetic predisposition to AMS susceptibility in Nepalese. Given that ancestral background of Nepalese is strongly influenced by its neighbouring populations (e.g. Tibetan and Indian) who may have different levels of AMS susceptibility, due to differing in evolutionary history, population stratification was assessed. Methods: Two hundred and thirty five Nepalese who permanently live below 1800 m were recruited in 2005 (n = 103) and 2008 (n = 132) while attending a religious festival at Gosainkunda (4380 m). Subjects were assigned to the AMS+ and AMS- groups based on clinical diagnosis performed by physicians and using Lake Louise Scoring (LLS) system. Twenty polymorphisms in five candidate genes (ACE, AGTR1, BDKRB2, ADRB2, and NOS3) that encode important components of vascular and pulmonary physiological pathways were investigated. Allele and genotype frequencies at the polymorphic loci were compared between the AMS+ and AMS- groups. Population stratification was assessed by comparing allele and genotype frequencies of fifteen ancestry informative markers and estimating Tibetan and South Asian genetic contributions to Nepalese subjects. Results: No significant difference in allele and genotype frequencies of 19 polymorphisms was found between the AMS+ and AMS- groups. An association was found between the alleles of the NOS3G894T polymorphism (rs1799983) in NOS3 and AMS in the 2005 cohort as well as in native Andean highlanders (Quechua) collected in the late 1990s; however, the association was not replicated in the 2008 Nepalese cohort. No population stratification was found in the Nepalese cohorts. Conclusion: Genetic predisposition to AMS in Nepalese was not shown strongly influenced by either alleles in the genes investigated in the studies described in this thesis or by ancestry (South Asian or Tibetan).  ii  Preface A version of Chapter 3 has been published into two papers. 1) Koehle MS., Wang P., Guenette, JA., and Rupert JL. (2006). No association between variants in the ACE and angiotensin II receptor 1 genes and acute mountain sickness in Nepalese pilgrims to the Janai Purnima Festival at 4380 metres. High Alt. Med. Biol. 7: 281-289 (PMID: 17173513) and 2) Wang P., Koehle MS., and Rupert JL. (2010). No association between alleles of the bradykinin receptor-B2 gene (BDKRB2) and acute mountain sickness. Exp. Biol. Med. (Maywood) 235: 737-740 (PMID: 20511677). Author contributions: Drs. Jim Rupert and Michael Koehle formulated the general idea, recruited subjects (with assistance from Jordan Guenette) and collected DNA. I performed DNA extractions, data collection and analysis, and wrote the manuscript draft that was subsequently revised and edited by the authors as a team. A version of Chapter 4 has been published as: Wang P., Koehle MS. and Rupert JL. (2007). Common haplotypes in the beta-2 adrenergic receptor gene are not associated with acute mountain sickness susceptibility in Nepalese. High Alt. Med. Biol. 8: 206-212 (PMID: 17824821). I was involved in experiment design, data collection and analysis, and wrote the initial manuscript that was subsequently revised and edited by the authors as a team. A version of Chapter 5 has been published into two papers: 1) Wang P., Koehle MS. and Rupert JL. (2009). Alleles at the G298T polymorphism in the eNOS gene are associated with the susceptibility to acute mountain sickness. High Alt. Med. Biol. 10: 261-267 (PMID: 19775216) and 2) Wang P., Ha A., Kidd KK., Koehle MS., and Rupert JL. (2010). A variant of the endothelial nitric oxide synthase gene (NOS3) associated with AMS susceptibility is less common in the Quechua, a high-altitude native population. High Alt. Med. Biol. 11: 2730 (PMID: 20367485). I was involved in the design of the research and experiment, data collection and analysis, and the writing of the two manuscripts that were subsequently revised and edited by the authors as a team. Alice Ha was an undergraduate student who assisted in the analysis of the second study. The Mayan DNA samples were provided by Dr. Ken Kidd (Yale University).  iii  Table of Contents Abstract………………………………………………………......................................ii Preface………………………………………………………….…...............................iii Table of Contents…………………………………..…....………..………..........................iv List of Tables……………………………………………………..........................................vi List of Figures………………………………………..……….……………....................viii List of Abbreviations…………………………….……………………………....................xi Acknowledgements………………..………….....…………………….......................xii Dedication……………………….…………………...…………………………………..xiii 1  Introduction……………………………………………………………...……….….1 1.1  An overview of high-altitude acclimatization and altitude illnesses…….......1  1.2  AMS – Symptoms and diagnosis………………………………………….…4  1.3  AMS – Susceptibility factors and pathophysiology………………………....6  1.4  AMS – Prevention and treatment……………………………...……….…...11  1.5  AMS – Genetics……………………………………………………..............13 1.5.1 Background concepts……………………………………………….…13 1.5.2 Evidence for genetic predisposition in developing AMS………….….15  1.6 2  Rationale of the work described in this thesis……………………………....25  General Materials and Methods………………………………...…….…….….…36 2.1  General methodology……………………………………………….…….…36  2.2  Subjects……………………………………………………………….….….37  2.3  Candidate genes……………………………………………………………..37 2.3.1 Angiotensin converting enzyme, angiotensin II type 1 receptor, and bradykinin B2 receptor genes………………………………………..38  2.4  2.3.2  Beta-2 adrenergic receptor gene……………………………………..40  2.3.3  Endothelial nitric oxide synthase gene………………………………41  Genetic analysis……………………………………………………………..42 2.4.1  DNA sampling and isolation……………………………………..…42 iv  2.4.2 2.5 3  Genotyping…………………………………………………………..43  Statistics………………………………………………………………..……44  Association Analysis of the Angiotensin Converting Enzyme, Angiotensin II Type I Receptor and Bradykinin B2 Receptor Genes with Susceptibility to AMS in Nepalese…………………………………………………………………………..47  4  Association Analysis of the Beta-2 Adrenergic Receptor Gene with Susceptibility to AMS in Nepalese……………………………………………………………....74  5  Association Analysis of the Endothelial Nitric Oxide Synthase Gene with Susceptibility to AMS in Nepalese……………………..………………………90  6  Population Stratification Detection in Nepalese……..………………………..111  7  Concluding Chapter………………………………………….………………..….139  References………………………………………………………………………………149 Appendices………………………………………………………………………………165 Appendix A: Consent Form v2.3 – 2008………………………………………………165 Appendix B: Lake Louise Scoring System Form…………………………………….168 Appendix C: Summary of Studies of AMS Pathophysiology………………………170 Appendix D: STR and Primer Sequences………………….…………………………175 Appendix E: Names of the Polymorphisms Assayed………………………………183  v  List of Tables Table 1.1  Types of common genetic polymorphisms…………………………………34  Table 1.2  Potential effects of single nucleotide polymorphisms (SNPs) occurring in protein coding sequences…………………………………………………..35  Table 3.1  Genot ype and allele frequencies in ACE, AGTR1, and BDKRB2 in Nepalese………………………………………………………………….71  Table 3.2  Published allele frequencies for the ACEI/D polymorphism in populations geographically close to Nepalese……………………………………….72  Table 3.3  Published allele frequencies for the BDKRB2+9/-9 polymorphism in various populations……………………………………………………………….73  Table 4.1  Genotype and allele frequencies at the seven tagSNPs in ADRB2 in the 2005 cohort………………………………………………….............................89  Table 5.1  Genotype and allele frequencies at the seven tagSNPs in NOS3 in the 2005 cohort……………………………………………………………………….110  Table 6.1  Three sets of 15 STRPs for the fragment analysis of this study….............125  Table 6.2  The information of parental populations and the Nepalese of this study for admixture analysis ……………………………………………………….126  Table 6.3 Table 6.4  Allele frequencies of the 14 STRPs in the whole cohort (n = 216)………127 Allele frequencies of the 14 STRPs in the AMS- group of the whole cohort (n = 115)………………………………………………………………….128  Table 6.5  Allele frequencies of the 14 STRPs in the AMS+ group of the whole cohort (n = 101) …………………………………………………………………..129  Table 6.6  Allele frequencies of the 14 STRPs in the 2005 cohort (n = 92)…………130  Table 6.7 Table 6.8  Allele frequencies of the 14 STRPs in the 2008 cohort (n = 124)………..131 Allele frequencies of the 14 STRPs in the AMS+ group of the 2005 cohort (n = 33)…………………………………………………………………….132  Table 6.9  Allele frequencies of the 14 STRPs in the AMS- group of the 2005 cohort (n = 59)……………………………… …………………………………….133  Table 6.10  Allele frequencies of the 14 STRPs in the AMS+ group of the 2008 cohort (n = 68)…………………………………………………………………….134  vi  Table 6.11 Table 6.12  Allele frequencies of the 14 STRPs in the AMS- group of the 2008 cohort (n = 56)…………………………………………………………………135 Allele frequency comparison by Chi-square method between the subpopulations of this study……………………………………………….136  Table 6.13  Genotype frequency comparison by Chi-square method between the subpopulations of this study………………………………………………..137  Table 6.14  Admixture estimation of Tibetan and South Asian ancestries between hybrid populations of this study………………………………………………...138  vii  List of Figures Figure 1.1  Relationship between altitude and barometric pressure and inspired partial pressure of oxygen (P IO2), high-altitude categories commonly used in the literature, and the incidence of acute mountain sickness (AMS) at various altitudes………………………………………………………….……….. 27  Figure 1.2  Partial pressure of oxygen (PO2) values along the oxygen cascade at sea level and at high altitude (4300 m) ………………………….…………………...28  Figure 1.3  Four ph ysi ol o gi cal chan ges duri n g a cut e and chro ni c al t i t ude Acclimatization………………………………………………………………29  Figure 1.4  Regulation of hypoxia-inducible factor -1 (HIF-1) under normoxic and hypoxic conditions and examples of genes and pathways regulated in response to hypoxia …………………………..………………………….30  Figure 1.5  The central nervous system-based model of AMS pathophysiology…..……31  Figure 1.6  The free-radical model of the pathophysiology of AMS and HACE.………32  Figure 1.7  The generic structure of a gene and the process of gene expression……..…33  Figure 2.1  Map of Gosainkunda (4380 m) in Nepal……………………………...……..45  Figure 2.2  The general ascent profile for the Janai Purnima Festival at Lake Gosainkunda (4380m)………………………………………….……………46  Figure 3.1  The Renin-Angiotensin Aldosterone system pathways…………………….60  Figure 3.2  The structure of the angiotensin converting enzyme gene…………………..61  Figure 3.3  PCR based assay for the insertion/deletion polymorphism in ACE.…...……62  Figure 3.4  The structure of the angiotensin II type 1 receptor gene…………….………63  Figure 3.5  The structure of the bradykinin B2 receptor gene……………….………….64  Figure 3.6  Samples demonstrating the genotyping assay for the ACE I/D, ACEA-240T, ACEA2350G, AGTR1A1166C, BDKRB2+9/-9, and BDKRB2C-58T polymorphisms………………………………………………………………65  Figure 3.7  Genotype and allele frequencies of (a) ACE I/D, (b) ACE A-240T, and (c) ACEA2350G polymorphisms in ACE and (d) AGTR1A1166C polymorphism in AGTR1 in Nepalese with and without AMS diagnosed by clinical evaluation…………………………………………………………………..66 viii  Figure 3.8  Genotype and allele frequencies of (a) ACE I/D, (b) ACE A-240T, and (c) ACEA2350G polymorphisms in ACE and (d) AGTR1A1166C polymorphism in AGTR1 in Nepalese with and without AMS diagnosed by Lake Louise Score (LLS > 3)……………………………………………..……………………68  Figure 3.9  Genotype and allele frequencies of the BDKRB2 +9/-9 and BDKRB2 C-58T polymorphisms in Nepalese with and without AMS diagnosed by clinical evaluation and Lake Louis Score (LLS ≥ 3)…………………………….70  Figure 4.1  The structure of the human beta-2 adrenergic receptor and mechanism of action in smooth muscle cells (insert)……………….…...............................80  Figure 4.2  The structure of the beta-2 adrenergic receptor gene and the locations of the seven tagSNPs assayed in this study………………....................................81  Figure 4.3  Samples demonstrating the genotyping assay for the seven tagSNPs in ADRB2……………………………………………………………………82  Figure 4.4  Genotype and allele frequencies of the seven tagSNPs in ADRB2 in Nepalese with and without AMS diagnosed by clinical evaluation……......................83  Figure 4.5  Genotype and allele frequencies of the seven tagSNPs in ADRB2 in Nepalese with and without AMS diagnosed by Lake Louise Score (LLS > 3)………86  Figure 5.1  The structure of the endothelial nitric oxide synthase gene………………..102  Figure 5.2  Samples demonstrating the genotyping assay for the seven tagSNPs in NOS3……………………………………………………………………….103  Figure 5.3  Genotype and allele frequencies of the seven tagSNPs in NOS3 in Nepalese (2005) with and without AMS diagnosed by clinical evaluation and Lake Louise score (LLS ≥ 3)………………………………………………….…104  Figure 5.4  Genotype and allele frequencies of rs1799983 in NOS3, a) 2008 and b) 2005+2008, in Nepalese with and without AMS diagnosed by clinical evaluation and Lake Louise score (LLS ≥ 3)……………………………..107  Figure 5.5  Genotype frequencies of rs1799983 in NOS3 after combining the G/T and T/T into one group, a) 2005, b) 2008, and c) 2005+2008, in Nepalese with and without AMS diagnosed by clinical evaluation and Lake Louise score (LLS ≥ 3)………………………………………………………………….108  ix  Figure 5.6  Genotype and allele frequencies of rs1799983 in NOS3 in native highlanders (Quechua) and lowlanders (Maya) native American populations…………109  Figure 6.1  The effects of population structure at a SNP locus……………..…………123  Figure 6.2  Screen shots of a sample demonstrating the Set-1 fragment analysis assay.124  x  List of Abbreviations AIMS  Ancestry Informative Markers  AISNP  Ancestry Informative Single Nucleotide Polymorphism  AMS  Acute Mountain Sickness  BBB  Brain-blood Barrier  BNP  Brain Natriuretic Peptide  CBF  Cerebral Blood Flow  CNS  Central Nervous System  CSC  Cerebrospinal Compliance  CSF  Cerebral Spinal Fluid  ESQ  Environmental Symptom Questionnaire  GWAS  Genome-wide Association Study  ICP  Increased Intracranial Pressure  HACE  High Altitude Cerebral Edema  HAH  High-altitude Headache  HAPE  High Altitude Pulmonary Edema  LLS  Lake Louise Scoring System  MCA  Middle Cerebral Arteries  MRI  Magnetic Resonance Imaging  OR  Odds Ratio  PCR  Polymerase Chain Reaction  SINE  Short Interspersed Element  SNP  Single Nucleotide Polymorphism  STRP  Short Tandem Repeat Polymorphism  TCD  Transcrainal Doppler  TVS  Trigeminovascular System  UTR  Untranslated Region  VNTR  Variable Number Tandem Repeat xi  Acknowledgements My PhD journey would not have been possible without the support and help from many people and organizations. I would like to extend my sincere thanks to them for accompanying me. It was truly a life enriching experience and I am truly humbled by their support. I would like to give my deepest appreciation to Dr. James Rupert, my supervisor, for his profound knowledge, invaluable support and guidance throughout my PhD study. Sincere gratitude is given to my supervisory committee, Dr. Michael Koehle, for his close guidance and volunteer work at the Himalayan Rescue Association Nepal that laid the groundwork for this project, to Drs. William Sheel and Patricia Schulte for their valuable guidance and support throughout all the stages of my PhD study. Special thanks are given to the principle investigators and their lab members from many laboratories, especially Brown, Koehle, McKenzie, Robinson, Rupert, Schulte, Sheel, and Warburton Labs at U.B.C., Kidd Lab at Yale University, and my statistics consultant, Saeedi Ardavan, for their generous help and support on all the projects of my PhD study. At last but not the least, I am especially grateful to my friends and family for their unconditional support and love.  Pei Wang May 31, 2013  xii  Dedication To my family  xiii  1  Introduction  1.1  An overview of high-altitude acclimatization and altitude illnesses Every year, millions of people travel to high altitude (Figure 1.1) for personal,  recreational and professional reasons (e.g. travel, entertainment, work, trekking, etc.). One of the environmental stresses encountered by people with gain in altitude is a reduction of barometric pressure and hence a concordant drop in the partial pressure of oxygen (PO2) in ambient air, resulting in a decrease in inspired partial pressure of oxygen (PIO2) (Figure 1.1). The decline in PIO2 at high altitude results in reduced driving force needed along the oxygen cascade for the exchange and transport of O2 compared with the PIO2 level, which drives these activities at sea level (Figure 1.2). Altitude acclimatization is a suite of transient physiological mechanisms that help alleviate the effects of O2 deprivation along the oxygen cascade. Acclimatization to hypoxia-induced hypoxemia involves a series of temporary changes in respiratory, cardiovascular, renal, and haematological physiology over a variety of time courses (Figure 1.3). These temporary physiological responses serve to enhance the exchange and transport of O2 along the oxygen cascade and fully (or partially) compensate for the decrease in the O2 availability at high altitude. Some examples of immediate responses to hypoxemia include hyperventilation, resulting from the activation of peripheral chemoreceptors; increased heart rate, resulting from increased sympathetic activity; an increased CO2 ventilatory response, resulting from the activation of central chemoreceptors; and an increase in red blood cells, resulting from elevated secretion of erythropoietin in kidneys. In combination, these responses simultaneously increase the volume of inspired air per unit of time as well as the systemic and pulmonary blood flow. Concomitantly, the decline in alveolar partial pressure of oxygen (PAO2) induces a decrease in the degradation of hypoxia-inducible factor-1 α subunit (HIF-1 α). The increase in HIF-1α promotes the formation of HIF-1, which binds to its recognition nucleotide sequence (“NCGTG”) in the promoter region of target genes encoding molecules that are involved in hypoxic responses, and elevated expression levels of HIF-1-induced molecules promote vasodilatation,  1  increases in red blood cell mass, angiogenesis, and thereby decreasing the efficiency of O2 delivery and consumption (Figure 1.4) (see review by (Semenza 2009)). Overall, hypoxiainduced physiological and molecular changes increase O2 delivery and reduce O2 consumption, thereby facilitating the process of acclimatization. The extent to which an individual is able to acclimatize to high altitude is usually influenced by factors such as the rate of ascent and the absolute altitude reached. Generally, the recommended ascent strategy for people who do not live at high altitude is that the increase in sleeping altitude should be no more than 300 m per day at altitudes above 3000 m, and one more night of rest is added after two to three days of ascent (or every gain in altitude of 1000 m) (Murdoch 1999). A lack of acclimatization greatly increases the occurrence of altitude illnesses, including acute mountain sickness (AMS), high-altitude cerebral edema (HACE) and high-altitude pulmonary oedema1 (HAPE). The signs of altitude illnesses range from unpleasant sensations (e.g. headache, light-headedness, nausea or lack of appetite, and sleep disturbance) to life-threatening conditions (e.g. ataxic gait, disturbed consciousness, dyspnoea at rest and orthopnoea). Altitude illnesses may occur simultaneously and share similar pathophysiological pathways although they possess characteristic signs and symptoms and develop through different time courses. A brief review of the primary characteristics, prevalence and pathophysiology of altitude illnesses follows. The most benign and common type of altitude illness is AMS. It affects many people who ascend rapidly to high altitude with an occurrence rate that ranges from 10 % to 93 % depending on the rate of ascent and altitude attained (Figure 1.1). Symptoms of AMS, which include high-altitude headache (HAH), nausea, fatigue, dizziness, and disturbed sleep, appear within 6 to 12 h after rapid ascent, peak at two to three days and usually subside after four to five days if there is no further ascent (Ward et al. 2003a). The pathophysiology of AMS is unclear and has been characterized by relative hypoventilation in hypoxic conditions, impaired gas exchange, increased sympathetic activity, fluid retention and redistribution, and raised intracranial pressure (ICP) (see review by (Imray et al. 2010)). AMS is a self-limiting  1  The spelling of the word oedema follows British style; however, the abbreviation of high altitude pulmonary edema (HAPE) follows American style, as is commonly used in the literature.  2  illness that tends to resolve without intervention if the person does not continue ascending; rest alone is usually sufficient to treat mild cases. If symptoms become moderate or severe, descent to a lower altitude or oxygen treatment is recommended to provide symptomatic relief. Medicine (e.g. dexamethasone) may be administered in conjunction with the treatments mentioned above, especially when descent is impossible and oxygen is unavailable. If untreated, severe AMS may progress to HACE; however, the nature of the pathophysiological continuum between AMS and HACE is uncertain (i.a. (Kallenberg et al. 2007, Schoonman et al. 2008, Mairer et al. 2012)). High altitude cerebral edema is an encephalopathic and potentially fatal altitude illness. It rarely occurs below 4000 m with an incidence ranging from 0.5 % to 1.5 % at 4000 m to 5000 m although again, the occurrence is influenced by the rate of ascent and altitude attained (Bartsch and Saltin 2008). HACE is characterized by various degrees of confusion, ataxic gait, disturbed consciousness, and psychiatric changes. These symptoms can progress to brain herniation, which is a potentially fatal condition in which the brain shifts across structures (e.g. falx cerebri) within the skull under very high intracranial pressure and a coma. Most of the victims of HACE may also have symptoms of AMS or HAPE although the latter condition is considered a distinct entity (Wilson et al. 2009). An understanding of the mechanism by which HACE develops remains elusive, largely because it is difficult in conducting research on disorders with such a low natural incidence and is also too dangerous to induce in humans for research purposes. The development of HACE may involve vasogenic edema, resulting from a combination of factors, such as raised cerebral capillary pressure, impaired cerebral autoregulation, altered permeability of the brain-blood barrier (BBB), and hypoxia-induced chemical mediators (e.g. bradykinin, nitric oxide (NO) and VEGF) (see review by (Basnyat and Murdoch 2003)). Unlike AMS, which can frequently be addressed simply by no further ascent, individuals who were afflicted by HACE should be transported to lower altitude immediately with supplemental O2 (or hyperbaric treatment) and pharmacotherapy (e.g. dexamethasone) (Ward et al. 2003b). High altitude pulmonary edema is a potentially fatal non-cardiogenic pulmonary edema. It typically occurs in the first two to four days after rapid ascent to altitudes above 3000 m. The prevalence of HAPE ranges from 0.48 % to 6 % (i.a. (West 2004, Bartsch and 3  Saltin 2008)) and causes the most deaths from altitude illness. Mortality can be up to 50 % if treatment is unavailable (Lobenhoffer et al. 1982, Basnyat and Murdoch 2003). Early HAPE symptoms include dyspnoea on exertion, reduced exercise tolerance and a dry cough. Over time, symptoms may progress to dyspnoea at rest, orthopnoea and cough with blood-tinged frothy sputum (Bartsch 1999). In contrast to HACE, HAPE is not necessarily preceded by AMS (Hackett 2008; Stream and Grissom 2008) even though approximately 50 % of patients with HAPE may have AMS symptoms (Ho and Siu 2010). How HAPE develops is unclear. A widely accepted mechanism is that hypoxia-induced patchy pulmonary vasoconstriction causes uneven distribution of blood flow in the lungs and pulmonary edema, resulting in over-perfusion in regions of high capillary pressure. Several other factors, such as decreased alveolar clearance of sodium and water and impaired release of relaxing factors caused by hypoxia-induced endothelial dysfunction, also play substantial roles in rendering an individual susceptible to HAPE (see review by (Scherrer et al. 2010)). Effective treatments for HAPE are similar to those for HACE and include descending immediately to a lower altitude, administering supplementary oxygen and applying of medicine (e.g. nifedipine) to lower the pulmonary arterial pressure (Ward et al. 2003b). The research described in this thesis focuses on the genetic contribution to AMS susceptibility and symptoms. The symptoms, diagnosis, susceptibility factors, pathophysiology, prevention, treatment, and what is currently known about the genetic predisposition of AMS are reviewed in the following sections.  1.2  AMS – Symptoms and diagnosis Symptoms Acute mountain sickness endangers the health of people who are acutely exposed to  high altitude. The primary symptoms include HAH, gastrointestinal disorders (e.g. anorexia, nausea, or vomiting), fatigue, dizziness, and disturbed sleep and are not necessarily present simultaneously. HAH is essential, and about 80 % of people who ascend to high altitude may have it (http://www.ihs.org). The mechanism by which HAH develops is unknown and may  4  involve activation of the trigeminovascular system (Sanchez del Rio and Moskowitz 1999). The second most common symptom is disturbed sleep (insomnia), which occurs secondary to periodic breathing, severe headache, dizziness, and shortness of breath (see review by (Imray et al. 2010)). (Jafarian et al. 2008) observed that 60 % of individuals reported sleep disturbances during a 24 h interval after a rapid ascent to an altitude of 3500 m that included a ride from 1700 to 3500 m in 45 to 60 min. Among the gastrointestinal disorders, anorexia and nausea are common, whereas vomiting is relatively rare (Hackett et al. 1976). Fatigue and dizziness may present simultaneously with other symptoms. The severity of the symptoms can range from very mild to completely incapacitating. Because the symptoms of AMS are non-specific, diagnostic confusion with other disorders, such as migraine, alcohol hangover, hypothermia, exhaustion, and dehydration, is increased and may induce misdiagnoses. Diagnosis The diagnosis of AMS can be performed by physicians trained in high-altitude medicine and by using two commonly applied scoring questionnaires: the Environmental Symptom Questionnaire (ESQ) (Roach et al. 1993, Bartsch et al. 2004) and the Lake Louise Scoring (LLS) System (Roach et al. 1993). The ESQ contains 67 questions, 11 of which are for assessing AMS. Each question has a scoring scale of 0 to 5 and contributes to the total score with its own factorial weight. The sum of the weighted 11 scores is then multiplied by 0.1927 and the product is the total score, which is called the AMS-C score. Individuals with an AMS-C score of 0.7 or higher are diagnosed as having AMS. The LLS system is simple to use in comparison with ESQ providing convenient assessment to prevent people from developing serious AMS and HACE and is used in the studies described in this thesis. LLS covers the primary symptoms of AMS in five items with a scoring scale from 0 to 3 for each item, and the sum of the five scores is the LLS score. The LLS diagnosis requires the presence of headaches and one additional listed symptom in addition to a recent ascent to high altitude (Roach et al. 1993). Individuals with a LLS score of 3 or greater are diagnosed as having AMS. The presence of AMS as defined by the LLS cutoff-score indicates an early to mild stage, whereas AMS as defined by the AMS-C cutoff-score is at a more advanced stage. A LLS score greater than 4 is suggested to have sensitivity and specificity compatible 5  to the AMS-C cut-off score (≥ 0.7) (Bartsch et al. 2004). Recently, the visual analogue scale (VAS), which is commonly used to assess subjective phenomena such as pain, has been introduced to perform an AMS self-assessment. The VAS consists of a 100-mm line with the words “none” and “severe” at opposite ends of the line. When the self-assessment is performed, participants put a single slash mark on the line at the place that best represents the magnitude of their altitude illness at that moment. The VAS score is the distance in millimeters from the left side of the line (“none”) to where the mark was placed (Myles and Urquhart 2005, Streiner and Norman 2007). The VAS has been shown to have high testretest and inter-rater reliability (Wagner, Tatsugawa et al. 2007); however, the correlations between the VAS and LLS or AMS-C are inconsistent (i.a. Kayser, Aliverti et al. 2010; Hext, Stubbings et al. 2011; Van Roo, Lazio et al. 2011) and may be supplemented for individual items such as HAH when the research question concerns the efficacy of a pharmacological treatment for headache ((Harris et al. 2003).  1.3  AMS – Susceptibility factors and pathophysiology  1.3.1  Susceptibility factors  The occurrence of AMS can be contributed by multiple susceptibility factors. The rate of ascent and the altitude reached are the most important environmental factors as discussed in section 1.1 of this chapter. Longer exposures to hypoxia and strenuous exertion at high altitude increase the risk; however, longer exposures with a slow ascent rate are beneficial to the process of acclimatization and reduce the incidence rate. When climbing in certain locations (e.g. Denali in Alaska, United States), latitude should be considered because of the ‘bulge effect’ (i.e. the decrease in barometric pressure from the equator to the poles). At the same altitude, regions closer to the equator have higher barometric pressure and consequently higher PIO2 than regions closer to the poles. AMS typically occurs in remote areas where health-care professionals may not be available and medical treatment is either limited or unavailable; therefore, the lack of and precautionary education and public awareness may contribute to the development of AMS (Hatzenbuehler et al. 2009). 6  In addition to these external factors, understanding the individual susceptibility factors may help in the prediction and prevention of AMS. A pre-history is suggested to be strongly related to the occurrence of AMS, and individuals who were previously afflicted with AMS showed a significantly higher risk compared with those who reached a similar altitude without being afflicted (i.a. (Schneider et al. 2002, Rexhaj et al. 2011)). In addition, many other factors may influence the incidence of AMS, such as age, body mass, gender, and physical fitness. Older people appear to have a higher tolerance for moderate altitudes than young people; however, the findings are inconsistent. In addition, several factors may contribute to their apparent tolerance; for example, older people may be more likely to ascend at a slower rate. Individuals with a low body mass index (BMI) showed less susceptibility to AMS than did those who were considered normal size or obese (Hirata et al. 1989, Ge et al. 2010), whereas other studies reported that BMI had an influence only in men or that it had no influence at all (Kayser 1991, Schneider et al. 2002, Wang et al. 2010a). Both men and women are at risk of developing AMS. Physical fitness has no protective effect against AMS and may be considered as a risk factor of developing AMS because fit individuals may overexert themselves while ascending or at high altitude.  1.3.2  Pathophysiology  The pathophysiology of AMS is not entirely understood. A number of mechanisms for AMS pathophysiology have been proposed during the past few decades and can be summarized into three models: the tight-fit model, the central-nervous-system (CNS) model and the free-radical model. These models focus on pathophysiological pathways to the development of HAH, which is the key symptom of AMS; however, emphasize different neurological and cerebral characteristics of the brain in response to hypoxia. Hansen and Evan in 1970 hypothesized that compression of the brain may induce the symptoms of AMS, due to increased cerebral venous volume, reduced absorption of cerebral spinal fluid (CSF) or cerebral edema (Hansen and Evans 1970). Later, this hypothesis was summarized by Ross, who further proposed the “tight-fit” model that emphasizes an anatomical predisposition, and suggested that neuroaxis compliance (i.e. the ratio between 7  CSF pressure and the increase in brain volume (Shulman and Marmarou 1971)) determines susceptibility to AMS (Ross 1985). The depletion of CSF from intracranial space reduces the brain-swelling-induced elevation of ICP resulting in less stimulation of pain-sensitive structures, thereby lowering the risk of developing AMS. A greater amount of CSF in the brain is a protective predisposition to tolerate the hypoxia-induced brain-swelling. Individuals with limited neuroaxis compliance may be less tolerant to hypoxia-induced increases in brain volume and thus have greater susceptibility to AMS. In addition to anatomical predisposition proposed by the tight-fit model, the centralnervous-system (CNS) model (Figure 1.5) that was proposed by Hackett and Roach (Matsuzawa 1992, Hackett and Roach 2001) focuses on the role of physiological predisposition in the formation of brain edema. The CNS-based hypothesis suggests that hypoxia-induced hypoxemia initiates a progressive brain swelling, which induces an exhaustion of intracranial buffering space and increased ICP. Consequently, pain-sensitive structures are stimulated, and then HAH presents. Specifically for the cause of brain swelling, CNS-based hypothesis emphasizes that the elevation in cerebral blood flow (CBF) and blood volume (CBV) elevate capillary pressure, and this induces increased permeability of BBB, leading to cerebral edema and subsequent brain swelling. Recently, an alternative hypothesis, which was developed by Bailey and associates, focuses on redox-activation of the trigeminovascular system (TVS). This model (Figure 1.6) suggests that free radicals stimulate the TVS and cause HAH (Bailey et al. 2009a). Low cerebral PO2 at hypoxic exposures induces more formation of free radicals (e.g. hydroxyl radical (OH•)), the consequent failure of Na+/K+-ATPase pumps, the movement of fluid from extracellular to intracellular space, astrocyte swelling, and the extra formation of NO. All these events stimulate the TVS, causing AMS and even HACE (Bailey et al. 2009a). The brain is highly dependent on oxygen to maintain a continuously active state. The brain accounts for only 2 % of total body weight; however, it expends about 25 % of the basal oxygen budget. The reliance on oxygen makes the brain extremely sensitive to hypoxemia. The three models focus on brain-centered pathophysiological pathways and emphasize on the contributors that are involved in mechanical and biomolecular mechanisms 8  causing stimulation of pain-sensitive structures (i.e. the TVS system) and HAH. Numerous studies have investigated the relationships between the main physiological characteristics in the three models and AMS (many of these are summarized in Appendix C). The main findings of these studies do not completely and consistently support the roles of physiological contributors (e.g. impaired BBB) described in the models of AMS pathophysiology. Most of the findings showed increases in CBF (global or regional) and brain volume and decreases in iCSF (intracranial CSF) during hypoxic exposures; however, the changes of brain edema and later events (e.g. increased ICP) are inconsistent and the causes are uncertain. Additionally, the physiological events (e.g. CBF) of the models have not been shown in association with AMS consistently unless it comes to moderate and severe AMS. In addition to brain-related physiological characteristics, the roles of several other systemic physiological characteristics in the development of AMS have also been investigated, such as ventilation, fluid retention and sympathetic activation. Ventilation Changes in ventilation are the initial and immediate strategy to protect the body and ventilation increases within minutes in response to hypoxia (see review by (Streamt et al. 2009)). Hypoxic ventilatory response (HVR) indicates increased sensitivities of the peripheral chemoreceptors and is often used to estimate ventilation acclimatization. Enhanced HVR increases PAO2 and raises arterial oxygenation (Bisgard and Forster 1996). HVR was reported to increase at high altitude (Sato et al. 1992, Sato et al. 1994); however, limited predictive power for AMS risks was found (see review by (Burtscher et al. 2008)). HVR at low altitude (100 m) was not correlated with the development of AMS at 4559 m, suggesting that hypoventilation and impaired gas exchange in response to hypoxia may contribute to the more severe hypoxemia in susceptible individuals rather than cause AMS (Bartsch et al. 2002). AMS susceptible individuals showed lower minute ventilation and higher PETCO2, which can be an indirect proxy of HVR, compared to AMS resistant individuals, suggesting that blunted ventilatory response indicates AMS susceptibility (Moore et al. 1986). The highest PETCO2 at 4 h after arrival at 3326 m was related to the presence of AMS at 24 h at high altitude (Douglas and Schoene 2010). Burtscher and associates reported that SpO2, which can also be considered as an indirect proxy of HVR, 9  resulting from a low ventilatory response could successfully predict AMS in 86 % of individuals who were exposed to hypoxia corresponding to 3000 m to 4000 m for 20 – 30 min (Burtscher et al. 2004). Similarly, hyperventilatory capacity (percentage rise of SpO2 after one minute voluntary hyperventilation) at 2833 m was negatively correlated with the development of AMS (r = - 0.664, P < 0.01) later on an ascent to 5768 m (Hayat et al. 2006). Fluid retention Fluid retention, possibly resulting from anti-diuresis, sodium retention, increased plasma and extra-cellular volumes and plasma aldosterone concentration, may contribute to AMS susceptibility in acute hypoxemia (see review by (Milledge 1992)). High diuresis levels were associated with the resistance to AMS (Bartsch, Swenson et al. 2002). Similarly, a lower urine output has been found in mountaineers susceptible to AMS compared to those who were resistant (1336 ml vs. 1655 ml); however, the difference was insignificant (Nerin et al. 2006). Conversely, individuals who showed significant fluid retention in the first 3 h exposure at simulated altitudes of 4880 m developed AMS after 8 to 12 h. The underlying mechanism of these associations is unclear, and a synergistic interaction, which involves reduced barometric pressure, plasma aldosterone and antidiuretic hormone, is suggested to contribute to the fluid retention at hypoxic exposure (Loeppky et al. 2005). Reduced plasma aldosterone levels were shown at high altitude and this may facilitate natriuresis and diuresis (i.a. (Zaccaria et al. 1998)). Plasma renin activity and aldosterone levels were suppressed while atrial natriuretic hormone was stimulated at acute exposure to 5050 m and unfortunately, the correlation between these hormones and AMS was not investigated (Zaccaria et al. 1998). Decreased saliva aldosterone levels were shown on an ascent to 5150 m, independent of AMS (Woods et al. 2011). Brain natriuretic peptide (BNP) induces diuresis, vasodilatation and decreased aldosterone secretion (Hall 2005) and may be involved in the development of AMS although the findings are not consistent. Feddersen and associates reported that BNP levels were associated with maximal AMS LLS only at 5050 m (Feddersen et al. 2009). Similarly, individuals with increased BNP levels at 5150 m had higher LLS than those with no increase on day 10 (mean LLS: 3.3 vs. 0.75, P = 0.034) and day 11 (mean LLS: 3.3 vs. 0.0, P = 0.003) of the expedition (Woods et al. 2011) and significantly higher BNP levels were shown in individuals with severe AMS (LLS > 6) 10  compared to those who were resistant (BNP: 58.4 vs. 22.7 pg/ml, P = 0.048) (Woods et al. 2012). Conversely, no significant change in BNP was found on an expedition to Muztagh Ata (7549 m) (Pichler et al. 2008). Sympathetic activity Sympathetic activities are mediated through catecholamines (e.g. epinephrine and norepinephrine). Both sympathetic bursts and catecholamine levels increased during hypoxic exposure (i.a. (Hansen and Sander 2003)); however, the effect and control of sympathetic activities in the development of AMS are still uncertain. AMS susceptible individuals showed significantly higher epinephrine concentrations during an 8 h hypoxic exposure at simulated altitude of 4600 m and higher arterial norepinephrine levels prior to and during the first hour of exposure compared with those who were resistant, suggesting that increased sympathetic activities may be involved in AMS pathogenesis (Kamimori et al. 2009). In addition to catecholamines, activation of catecholamine receptors (e.g. beta-adrenergic receptors, which are widely distributed in the heart and lungs) may contribute to AMS susceptibility. Individuals with AMS showed a greater hypoxic vasodilatation and elevated heart rate, suggesting that an augmentated beta-adrenergic tone may be involved in early AMS pathophysiology (Loeppky et al. 2003) while individuals, who took the betaadrenergic receptor blockers, showed less severity of AMS (Fulco et al. 1989). In summary, none of the pathophysiological models proposed to date have been confirmed by consistent empirical data, and the associations between important physiological characteristics and the pathogenesis of AMS are still uncertain. Despite this, research efforts to understand the underlying mechanism of AMS have substantially promoted the prevention and treatment of this condition.  1.4  AMS – Prevention and treatment AMS can be prevented (see review by Eide III (Eide and Asplund 2012)). The rule of  thumb to minimize the risk of developing AMS is that people should not ascend more than 300 m per day while above 3000 m with an additional night at the same altitude every two to 11  three days of ascent (or every gain of 1000 m in altitude) to ensure the body has enough time to acclimatize to the decreased availability of oxygen (Murdoch 1999). Considering that this rate may be slow to many people, Hackett and Roach recommended that the average difference in altitude between consecutive sleeping sites should be no more than 600 m and an extra night of rest should be added at the arrival altitude for an increase between 600 and 1200 m in altitude (Hackett and Roach 2001). Pharmacologic prophylaxis such as acetazolamide and dexamethasone (Palmer 2010) is recommended if a rapid ascent is undertaken or if there is a prior history of AMS. Acetazolamide (Actz) is a carbonic anhydrase inhibitor and the most commonly used medicine for the prevention of AMS (see review by (Ritchie et al. 2012). The recommended dose is 250 mg twice daily for adults and 2.5 mg/kg/12 h for children (i.a. see review by (Luks et al. 2010)). Dexamethasone is a glucocorticoid steroid hormone and shown effectively in the prevention of developing AMS (i.a.(Ellsworth et al. 1987, Ellsworth et al. 1991, Wille et al. 2012b)); however, the prophylactic usage of dexamethasone is limited unless Actz is contraindicated (e.g. sulfa intolerance) and only used in adults with a duration of no more than 10 days to prevent glucocorticoid toxicity or adrenal suppression. The recommended dose is 2 mg every 6 h or 4 mg every 12 h (i.a. see review by (Luks et al. 2010)). Higher doses (4 mg every 6 h) may be considered only at urgent circumstances such as rescue personnel being airlifted to high altitude (> 3500 m) with immediate performance of physical activity (see review by (Luks et al. 2010). Several other preventive and/or indigenous treatments or medicine options have been investigated or recommended, for example, pre-exposure to moderate altitudes (> 1500 m), Ginkgo biloba extract, coca-derived products; however, their utility in the prevention of AMS has not been standardized and established systemically (i.a. see review by (Luks et al. 2010)) and current research in our lab suggests that garlic may not be effective in preventing AMS (MacInnis et al., manuscript in preparation). When AMS occurs, the symptoms usually can be alleviated rapidly with simple interventions following a proper and timely diagnosis (Hackett and Roach 2001). Initially, afflicted individuals should stay at the same altitude without further ascent. If the symptoms show no improvement or worsen, the best treatment is to transport the patients to a lower altitude as soon as possible. Usually, descending 500 to 1000 m is sufficient to alleviate  12  symptoms. If physical descent is not an option, supplemental oxygen or hyperbaric therapy can be used. In addition, administration of medicine in the treatment of AMS is also recommended and is well reviewed (i.a. see reviews by (Luks et al. 2010), Schommer and Bartsch (Schommer and Bartsch 2011)). Both Actz and dexamethasone are used for the treatment of AMS. In compared with Actz, dexamethasone is more effective for any degree of AMS, especially moderate to severe AMS, which requires descent treatment too. When the risk of AMS remains and dexamethasone is the only drug being used, the administration should be continuous because the symptoms may recur. Other medicines are also used such as ibuprofen and metoclopramide, mainly for mild to moderate AMS.  1.5  AMS – Genetics  1.5.1 Background concepts The human genome contains approximately 25,000 genes, which are the hereditary units containing not only the DNA sequences for encoding products (e.g. proteins, functional RNAs), which contribute to characteristics in a living organism, but also the associated sequences responsible for the control of the time, location and quantity of the expression. From a structural point of view, a gene contains coding sequences (exons) separated by noncoding sequences (introns) and 5 prime (5’) and 3 prime (3’) untranslated regions (UTRs) and different components of a gene play different roles in gene expression (Figure 1.7). Human genetic variation On average, more than 99 % of gene sequences between humans are identical and the genetic differences between individuals are due to DNA variations in the remaining 1 % (Lander et al. 2001). When DNA variations have a frequency ≥ 1 % in a population, they are called polymorphisms (i.a. see review by (Lunetta 2008)). Polymorphisms can be classified into three forms according to physical characteristics (as shown in Table 1.2): 1) the  13  presence/absence (i.e. Insertion/deletion2 (Indels), copy number variation (CNV)) of DNA segments; 2) the repeated patterns of DNA segments (i.e. short tandem repeat (STR), variable number tandem repeat (VNTR)); and 3) the single nucleotide difference/substitution (e.g. C to G) in DNA sequences (i.e. single nucleotide polymorphism (SNP)). Among these types of polymorphisms, SNPs are the most common (there are approximately 10 million SNPs in the human genome) and most frequently studied type of polymorphism (International HapMap Project, http://hapmap.ncbi.nlm.nih.gov/). The potential effects of SNPs at the protein level are listed in Table 1.2. Variants at a polymorphism locus3 are referred to as alleles and will be paternal and maternal depending on the parent from which they were inherited. Each polymorphism can have more than two alleles; however, each individual can only have two alleles at each polymorphic locus (excluding genes located on the X and Y chromosomes, for which males carry only a single gene). Different combinations of the alleles at a polymorphism are called genotypes (note: the term ‘genotype’ can also refer to multiple allele combinations as well, e.g. the genotype of an individual is the sum of all genotypes in his/her genome). When the alleles of a polymorphism are the same on both parental and maternal chromosomes, the genotype is called homozygote. When the alleles are different, the genotype is called heterozygote (in cases where there is only a single chromosome (such as the Y in males) the term ‘hemizygote’ is used). Whether or not an allele manifests as a phenotype (e.g. susceptibility to AMS) depends on the location and the nature of the allele in a gene. Alleles located in coding sequences may affect the structure and potentially the function of the proteins while those located in regulatory regions, introns or UTR regions may affect control of the time, location, and quantity of gene expression. Alleles, which result in a phenotypic change, are termed “functional”, and those, which appear to have no phenotypic effects, are “non-functional”. When an allele is found to be statistically in association with a phenotype, this does not necessarily mean that the allele is a biologically causal marker but may be because the  2  While “deletion” is commonly used in the literature, most “deletions” are due to ‘non-insertion’ of a segment of DNA rather than the removal of a segment. 3  The physical location of a polymorphism is called a locus (pl. loci)  14  allele is in linkage with the true phenotype-related allele. When alleles of different genetic loci do not segregate independently between generations such that the frequency at which they appear together in a population is higher than expected given their individual frequencies and random association, these alleles are said to be in linkage disequilibrium (LD) (Slatkin 2008). The reason for this is as follows: when a mutation (i.e. a new allele) first occurs, it will be physically linked to every allele on the chromosome where the mutation is located. Every generation thereafter, meiotic recombination (the exchange of genetic material between chromosome pairs during meiosis) will separate the new allele from other alleles on the chromosome. The probability of a ‘separation’ will depend on the distance separating the two alleles and the number of generations since the initial event. The alleles in LD move between generations as haplotypes and any allele on a haplotype can serve as a proxy marker for all other alleles on the haplotype. Using proxy markers minimizes the number of polymorphisms to be investigated in an association study. The International HapMap Project (http://hapmap.ncbi.nlm.nih.gov/) provides a free, comprehensive online database of haplotype data in humans. Currently, eleven populations are represented in the database, as are allele frequencies for thousands of polymorphic loci. The program allows identification of the maximally informative single nucleotide polymorphisms (called tagSNPs) for any given gene and population. By determining the frequency of tagSNP alleles in a population (or subsets of a population in a comparative study), researchers can make assumptions about the frequencies of other alleles on the haplotype, thereby maximizing the informativeness of the experiment while minimizing the number of assays needed to fully assess the potential role of variants within, or near, the gene. TagSNPs have been employed into the association studies of two candidate genes in this thesis and the details are discussed in Chapter 2.  1.5.2 Evidence for genetic predisposition in developing AMS Acute mountain sickness is an environmentally triggered condition with non-specific symptoms. Some individuals may be more susceptible than others and develop the condition at moderate altitudes even with a relatively slow rate of ascent. This suggests an individual predisposition in the development of AMS. The inner predisposition can be genetic; however, 15  the extent and mechanism that the genetic components contribute to AMS susceptibility are unknown. Genetic evidence specifically for the development of AMS, which is limited, can be grouped into three categories: individual susceptibility, familial clustering and candidategene associations (see reviews by (Rupert and Koehle 2006, MacInnis et al. 2011). Newer methods of genetic analysis such as genome wide association studies (GWAS) and whole genome/exome sequencing have yet to be done for AMS. Individual susceptibility A personal previous history of AMS is suggested to be a strong predictor on subsequent ascents to high altitude (Honigman et al. 1993). Among 235 affected subjects who ascended to 4559 m, individuals with a history of AMS showed almost three times higher incidence of AMS than those without, irrespective of recent exposure and rate of ascent (Schneider et al. 2002). Wagner and colleagues also reported that hikers, climbing at Mt. Whiteney (4419 m), who had experienced altitude illness showed one and one half or two times higher AMS risk than those who had not experienced the condition previously (Wagner et al. 2006, Wagner et al. 2008). Similar findings (odd ratio = 1.74, P = 0.001) were shown in trekkers who traveled to Jade Mountain (3952 m) in Taiwan (Wang et al. 2010a). Rexhaj and associates evaluated the reproducibility of AMS between two exposures to altitude of 3450 m 9 to 12 months apart. Their study showed that the presence of AMS at the second exposure is highly predicted by whether AMS occurred during the first exposure, with positive and negative predictive values of 78 % (95 % CI: 52 – 93 %) and 100 % (95 % CI: 60 – 100 %), respectively (Rexhaj et al. 2011). These studies alone are not necessarily proof for a genetic etiology of AMS since the differences could be due to developmental effects (e.g. exposure as a child); however, individual differences in susceptibility do indicate that there is an innate predisposition, which could be, in part, genetic. Family clustering Familial clustering data, in which a trait is more common within families than between families, supports a genetic contribution to phenotype. The familial data for AMS is rare, due to the challenges (e.g. the whole-family transportation to extreme environments is  16  expensive, often the whole family leaves when one member is afflicted, and difficulties recruiting whole families for chamber studies). An alternative approach is to recruit close genetic relatives as subjects such as twins. Only one study to date assessed AMS using Children’s Lake Louise Score in twins (17 pairs) and their parents (Yaron et al. 2002). Seven children developed AMS and six of them were sibling pairs. The information of zygosity of the twins was not mentioned in the paper but was obtained through personal communication with Yaron in 2006 (see review by Rupert and Koehle, (Rupert and Koehle 2006)). The seven affected siblings include two pairs of monozygous twins, one pair of dizygous, and one of a discordant pair of monozygous twins. The chance to have such an incidence of AMS in siblings as six out of seven is lower than 10 %; however, the authors stated that a greater shared environment could account for the observed concordance between siblings. Candidate-gene associations Candidate-gene association studies detect the potential role of alleles of single genes in the development of AMS. The candidate genes that have been investigated usually encode proteins that are involved in physiological pathways thought to be associated with acclimatization to high altitude. The distributions of alleles and genotypes frequencies of genetic markers in candidate genes are compared between the afflicted and resistant cohorts. An association is identified when the distributions are significantly different between the cohorts. As of the fall of 2012, genetic markers of 18 autosomal genes and haplotypes in mitochondrial DNA (mtDNA) have been investigated in different populations under various ascent conditions and, alleles of nine autosomal genes and two haplotypes in mtDNA were over-represented in individuals who had AMS. The genes that have been investigated are well reviewed by (MacInnis et al. 2010) and Li (Li et al. 2011). These genetic markers here are classified into several broad physiological categories though their products may overlap with each other both at genetic (i.e. gene: gene interactions) and physiological levels. The associations between the angiotensin II type I receptor, bradykinin B2 receptor and β-2 adrenergic receptor genes have only been investigated in the studies described in this thesis and therefore, these three genes are not discussed in this review section but in the individual data chapters instead.  17  Genes involved in vascular tone Angiotensin I converting enzyme Angiotensin I converting enzymes (ACEs) are important components of the reninangiotensin-aldosterone (RAAS) (Figure 3.1), which is involved in maintenance of homeostasis including fluid and electrolyte balance, blood volume, and blood pressure. ACEs catalyze the conversion of angiotensin I to angiotensin II (Ang II) and degrade bradykinin, which is an important vasodilator of the kallikrein-kinin (KKS) system (Figure 3.1). Ang II is a potent vasoconstrictor, stimulating systemic vasoconstriction via AGTR1s and the release of aldosterone, which increases sodium and water reabsorption in the kidneys. The degradation of bradykinin, which stimulates vasodilatation via BDKRB2s, facilitates the vasoconstriction stimulated by Ang II. Research of the associations between alleles in the ACE gene (ACE) and AMS is limited and the conclusion is unclear. The functional Indel (I/D) polymorphism (rs4646994) in ACE is commonly studied. The ACEI/D polymorphism is the presence or absence of a 287 bp DNA segment (Alu repeat) and the alleles are associated with ACE levels (Rigat et al. 1990, Tiret et al. 1992). Studies of European mountaineers showed that the occurrence and severity of AMS was independent of the ACEI/D polymorphism (i.a. (Dehnert et al. 2002, Tsianos et al. 2005)) while the D allele, which is associated with higher ACE levels, was over-represented in Han Chinese who were afflicted with AMS; however, the description of the case and control groups was limited and may have included other forms of altitude illness (Buroker et al. 2010). There are two lines of indirect evidence that support the associations between the alleles of the ACEI/D polymorphism and AMS: 1) Ascent to extreme altitudes (i.a. (Montgomery et al. 1998, Thompson et al. 2007)): having the I allele of the ACEI/D polymorphism may not guarantee a successful ascent to Mt. Everest; however, individuals with the I allele may tend to have more sufficient/efficient acclimatization and consequently have less limitation (e.g. less risk of developing AMS) to reach higher and higher altitudes on their way to be an elite mountaineer and higher successful rate of ascent to extreme altitudes than those who do not have; 2) High altitude adaptation (i.a. (Droma et al. 2008)): Being able to adapt to high altitude permanently or reach extreme altitudes may not suggest that someone is less susceptible to AMS since overcoming AMS is only one determinant to a successful 18  permanent settlement at high altitude. The pioneer lowerlanders who migrated to high altitude might have the I-related genotypes (the II or ID genotype) as an inner predispostition to overcome altitude illnesses (e.g. AMS) during migration and adaptation and finally were able to settle at high altitude permanently. Endothelial nitric oxide synthase gene The endothelial nitric oxide synthase gene (NOS3) encodes endothelial nitric oxide synthase (eNOS), which is the main source of NO in blood vessels. Endogenous NO level is suggested to play an important role in acclimatization to high altitude through involvement in the physiological responses to acute hypoxia (e.g. cerebral blood flow, pulmonary arterial pressure) (i.a. (Scherrer et al. 1996, Dweik et al. 1998, Van Mil et al. 2002)). Several functional polymorphisms (rs2070744 (T-786C), rs1799983 (G894T or Glu298Asp), and 4b/4a) in NOS3 are associated with gene expression, enzyme activity and endogenous NO concentration (i.a. (Ahsan et al. 2005, Ahsan et al. 2006, Dosenko et al. 2006)). Among these three functional polymorphisms, the most commonly studied is rs1799983 (Glu298Asp), which is an amino acid changing polymorphism resulting from a G to T transversion at base 894 in NOS. The T (298Asp) allele showed a negative dosage effect on eNOS activities (Wang et al. 2000) and was associated with maladaptive responses to acute hypoxia in different populations (Droma et al. 2002, Ahsan et al. 2004) while the G allele is suggested to be beneficial to high-altitude adaptation (Ahsan et al. 2005); however, the role of alleles of rs1799983 in AMS susceptibility is unknown. Recently, the roles of alleles of two other polymorphisms (rs743507 and rs1800779) in developing AMS were assessed in a group of Han Chinese who were acutely exposed to high altitude (4600 m); however, no association was found (Ding et al. 2011). Guanine nucleotide-binding protein, beta polypeptide 3 gene The β-polypeptide 3 gene (GNB3) encodes β-3 subunits of guanine nucleotidebinding proteins, which modulate the transmission of chemical signals from receptors to the downstream compounds in signaling cascade. The T allele of the C825T polymorphism (rs5443) was shown in associated with hypertension (i.a. (Siffert et al. 1998)). The A allele  19  of the A-350G polymorphism, which is in strong LD with the C825T polymorphism, was over-represented in individuals who ascended to approximately 3670 m and developed AMS; however, no association was found between the alleles and blood pressure (Buroker et al. 2010) and therefore, the underlying mechanisms of these associations are unclear. Genes involved in oxidative stress response Hypoxia-inducible factor 1, alpha subunit gene, Egl nine homolog gene and von Hippel-Lindau tumor suppressor protein gene Hypoxia-inducible factor-1 and -2 alpha subunits (HIF-1 α) in combination with beta subunits (HIF-1 and -2 β) form the complete HIF-1 and HIF-2 transcription factors, which mediate the expression of genes in response to reduced oxygen availability to maintain oxygen homeostasis (i.a. see reviews by (Semenza 2007) and (van Patot and Gassmann 2011)). Under normoxic conditions, HIF-α subunits are hydroxylated by hydroxylases such as prolyl hydroxylase domain proteins (PHDs/EGLNs) (see review by (Loboda et al. 2010)) and rapidly degraded by proteasomes such as VHL (see review by (Kaelin and Ratcliffe 2008) and, under hypoxic conditions, HIF-α subunits are stabilized resulting from the degradation of hydroxylases, and the levels of the subunits increase (see review by (Semenza 2009)). Genetic contributions to the development of AMS from the genes that encode HIF-1 and -2 alpha subunits, PHD2 and VHL remain unclear. Allele and genotype distributions of the C1772T polymorphism (rs11549465) in HIF-1A, which encodes HIF-1 alpha subunits, were not significantly different between Sherpas with and without a history of AMS (Droma et al. 2008) and were independent of AMS status in a group of Caucasian male subjects exposed to simulated altitude of 3962 m (12.7 % oxygen) for an 8-hour exposure (Hennis et al. 2010). Similarly, alleles of polymorphisms in exon (rs11549467, rs17099141 and rs4902080) and 5’-UTR (rs10129270, rs41362550, rs2301113) regions of HIF1A showed no contribution to the development of AMS in a group of Han Chinese who ascended to 4600 m in two hours (Ding et al. 2011). Buroker and associates recently reported that the frequency of the C allele of rs480902 (C/T) in EGLN1 was higher in individuals who developed AMS in comparison with those who did not and the difference in allele frequency was significant when applying the Fst statistics on the genetic distances (0.018, P < 0.05); however, no over20  representation of the C allele was found when odds ratio testing was applied (P = 0.175) (Buroker et al. 2012). The roles of alleles of polymorphisms (rs28940298, rs779805, rs779808, rs1678607, and 1149A > G) in VHL were investigated in Sherpas who did/did not develop AMS; however, no association was found (Droma et al. 2008). Heat shock protein genes Heat shock proteins (HSPs) are a group of intracellular proteins and up-regulated at stress (e.g. heat, inflammation, hypoxia and oxidative stress) (Giffard et al. 2004, Yenari et al. 2005). An induction of HSP70 at pre-exposure to hypoxia showed protective effects on hypoxia-related brain injury in rats (Zhang et al. 2009). AMS-resistant individuals showed an approximately two times higher level of HSP70 compared to AMS-susceptible individuals (P < 0.05) at 0 h prior to a 10 h hypobaric hypoxia (4572 m), suggesting that higher HSP70 level is a pre-existing hypoxia-tolerance; nevertheless, the high fluctuation of HSP70 level in AMS-susceptible group should be considered when interpreting the correlation (Julian et al. 2011). The roles of alleles in HSPA1A, HSPA1B and HSPA1L, which encode hsp70-1, hsp70-2, and hsp70-hom, respectively, in AMS susceptibility have been assessed. Li and associates reported the HSPA1B GG genotype was over-represented in individuals with AMS whereas no association was found between the alleles of HSPA1A b1/b2 polymorphism and AMS (Li et al. 2004). These findings were reproduced in the study by Zhou and coauthors, and an association between the HSPA1L B/B genotype and AMS was found (Zhou et al. 2005); however, the allele designation of the HSPA1L G2437C polymorphism in this study was unclear. Ding and associates recently studied associations between alleles of five polymorphisms in HSPA1L (rs2075800, rs1061581, rs2227956 rs2227955, and rs9469057) in addition to one polymorphism in HSPA4 (rs35853823) in Han Chinese who were acutely exposed to 4600 m and no association was found (Ding et al. 2011). Glutathione S-transferase mu 1 and glutathione S-transferase theta 1 genes Glutathione S-transferase mu 1 (GSTM1) and glutathione S-transferase theta 1 (GSTT1) are of the glutathione S-transferase (GST) family, which is a group of intracellular  21  enzymes that detoxify endogenous and exogenous substances by conjugation with the glutathione. The genes (GSTM1 and GSTT1) encoding these two molecules are often absent in human genome with a rate of ~ 50 % and 13 – 20 %, respectively (Garte et al. 2000). Decreased plasma GSTs activities were associated with AMS in Chinese soldiers (Jiang et al. 2004). The presence of GSTT1 (+/+, and +/-) and the absence of GSTM1 (-/- genotype) were independently associated with higher prevalence of AMS (Jiang et al. 2005). The result for GSTT1 did not support the previous physiological findings (Jiang et al. 2004); however, the further analysis on these two genes in predicting individual risk of having AMS supported their initial study and showed that subjects with the GSTM1-/- and GSTT1-/- genotypes were five times more likely to develop AMS than those with the GSTM1 (+/+ or +/-) and GSTT1/- genotypes (Jiang et al. 2005). Genes involved in hypoxic ventilatory response ACE, succinate dehydrogenase subunits (SDHB, SDHC, SDHD), and HIF1A genes Hypoxic ventilatory response (HVR) is the compensatory increase in ventilation (rate and depth) in response to a decrease in partial pressure of arterial oxygen. In hypoxic conditions, relatively low HVR may result in decreased oxygen delivery and increase the risk of developing AMS; however, the relationships between HVR and AMS are controversial, possibly due to the wide variation in methodologies for the measurement of HVR (Schoene et al. 1984, Milledge et al. 1988). Family and twin studies have shown a strong genetic modulation of HVR (Fagan and Weil 2001), and potential roles of these five genes in HVR were assessed in three studies. Individuals with the I/I genotype of the ACEI/D polymorphism showed greater increase in minute ventilation and decrease in end tidal CO2 in response to hypoxia compared to those with the I/D and D/D genotypes (Patel et al. 2003). Conversely, no association was found between the magnitude of HVR and the investigated alleles of polymorphisms in SDHB, SDHC, SDHD and HIF-1A (Richalet et al. 2009) and between isocapnic HVR and the genotypes of the ACEI/D polymorphism (Bigham et al. 2008).  22  Genes involved in oxygen management ACE and angiotensinogen genes Oxygen saturation levels are shown to play an important role in the development of AMS and may be a useful variable in predicting AMS (Burtscher et al. 2008). Individuals who had SpO2 higher than 86 % (i.e. a 92 % negative predictive value) were less afflicted with AMS at 4380 m (Koehle et al. 2010). In addition, low SpO2 values during sleep at high altitude were correlated with AMS (Johnson et al. 2010). A relatively high heritability for SpO2 was found in two separate Tibetan highland populations, and about 44 % of the interindividual variation was determined by additive genetic effects (Beall et al. 1994, Beall et al. 1997). The role of genetic markers in determining SpO2 at high altitude has been evaluated; however, the conclusion remains uncertain. The genotypes of the ACEI/D polymorphism were associated with SpO2 in Caucasians who were grouped into rapid-ascent (12 d) and slow-ascent (18.5 d) teams ascending from 1250 to 5180 m. The climbers with the II genotype in both rapid- and slow-ascent teams showed a higher SpO2 than those in rapidascent team with the I/D or D/D genotypes (Woods et al. 2002), suggesting that any role of the genotypes of the ACEI/D polymorphism in AMS may be secondary to their effect on SpO2. These data also suggest that the gene-environment interaction is an important confounding factor that complicates the reconciliation of data from different studies and alleles of multiple genes could contribute to inter-individual variation in SpO2. Another gene that was assessed as a determinant of SpO2 is the AGT gene (AGT) in the RAAS system (Figure 3.1). Alleles of the M235T polymorphism in AGT were associated with SpO2 at 3670 m, and individuals with the T/T or M/T genotype showed higher SpO2 (P < 0.05) (Buroker et al. 2010), indicating that variants of multiple genes could contribute to individual differences in SpO2. Genes involved in oxidative phosphorylation Mitochondrial DNA Cerebral oxidative stress has been suggested to be stimulated by hypoxia, and altered redox homeostasis is associated with AMS (Bailey et al. 2009c). Increased free radical 23  formation is suggested to contribute to the alteration of BBB permeability and the development of AMS (Askew 2002). Reactive oxygen species (ROS), which are byproducts of oxidative phosphorylation in mitochondria, are involved in the formation a variety of free radicals and up-regulated by hypoxia. Haplogroups4 in mitochondrial DNA are suggested to contribute to the differences in oxidative phosphorylation and may contribute to the pathogenesis of AMS. Haplogroup M7 was shown over-represented in individuals with AMS (P < 0.003) while haplogroups D and M9 were related to individual tolerance to AMS (P < 0.001) in southwestern Han Chinese (Li et al. 2011). Genes involved in oxygen and lipid circulatory transport ABO blood group (ABO) gene The three alleles (A, B, and O) in the ABO blood group gene (ABO) that determine a person’s blood type (e.g. A, B, AB, or O) were associated with mean ACE activity in hypertensive Han Chinese patients although the underlying mechanism is unclear (Chung et al. 2010); however, blood type was not associated with the occurrence of AMS in trekkers who ascended to 3952 m at Yushan (Jade Mountain) in Taiwan (Wang, Chen et al. 2010a). Unfortunately, no other AMS studies have ascertained subjects’ ABO blood types. Apolipoprotein B (APOB) gene Apolipoprotein B, which is the primary apolipoprotein in chylomicrons and lowdensity lipoproteins (LDLs), binds to the receptors on hepatic cells and triggers the uptake of LDLs into the liver, where the degradation takes place. A strong genetic component was shown in the determination of plasma apolipoprotein B concentration (Lamon-Fava et al. 1991) and the AA genotype of the APOB A/G polymorphism (rs693) was associated with blood pressure (systolic and diastolic) in Han Chinese who had AMS; however, no  4  A haplogroup is a group of haplotypes that have the same SNP and share a common ancestor, for example, Haplogroup M7 is defined by the T9824C polymorphism, which leads to a synonymous mutation in the cytochrome c oxidase subunit III gene.  24  association was found between the allele of this polymorphism and AMS susceptibility (Buroker et al. 2010).  1.6  Rationale of the work described in this thesis Physiologists have made great efforts to elucidate the underlying mechanism of the  development of AMS for decades; however, no clear conclusion has been reached. Substantial amount of evidence suggests that there is an innate contribution to the development of AMS, which may be in part genetic. Understanding the genetic predisposition on susceptibility to AMS can promote the understanding of the etiology and would aid in AMS prevention (e.g. advising travelers). Vascular-/pulmonary-related characteristics have been suggested to play important roles in the development of AMS. The studies described in this thesis assessed alleles in the genes that encode compounds involved in vascular-/pulmonary related functions that may be related to AMS susceptibility. A series of genetic association studies were performed in Nepalese lowlanders who had or had not afflicted with AMS during a trip to high altitude (4300 m). The research questions addressed are as follows: 1) Are the alleles and genotypes of the polymorphisms in the genes selected for this thesis in association with susceptibility to AMS? 2) If an association was found, whether the subjects with/without AMS have the same ancestral composition of different genetic ancestries? If there is a difference, would the genetic ancestry contribute to the difference that is possibly involved in AMS tolerance or susceptibility? All the genes that were investigated in this thesis encode bio-molecules that are suggested to play important roles in regulation of physiological characteristics including vascular homeostasis and pulmonary functions in response to acute hypoxic exposures. The  25  genes are: 1) the angiotensin converting enzyme gene; 2) the angiotensin II type I receptor gene; 3) the bradykinin B2 receptor gene; 4) the beta-2 adrenergic receptor gene; 5) the endothelial nitric oxide synthase gene;. Alleles of polymorphisms in these genes are either functional or informative (e.g. tagSNPs). Associations were determined when alleles were over-represented in either AMS-resistant or AMS-susceptible Nepalese. When an association was found, detection of population stratification would be performed using fifteen ancestry informative markers.  26  Figure 1.1 Relationship between altitude and barometric pressure and inspired partial pressure of oxygen (PIO2), high-altitude categories commonly used in the literature, and the incidence of acute mountain sickness (AMS) at various altitudes. Altitudes above sea level in feet and meters are shown at the top and bottom x-axes, respectively. Barometric pressure and PIO2 are shown on the left and right y-axes, respectively. Various locations relevant to the work described in this thesis are indicated for reference: Sea level, Kathmandu (1338 m), Dhunche (2000 m), Gosainkunda (4380 m) in Nepal, highest human habitation (~ 5500 m), submit of Mt. Everest (the highest mountain in the world, 8848 m). The three gradient regions in pink show commonly employed classifications of altitude starting at 1500 m that range from high (1500 m to 3500 m) to very high (3500 m to 5800 m) to extreme (above 5800 m) (Imray et al. 2011). In the studies described in this thesis, high altitude was defined as above 2500 m, as that is the height where high-altitude illnesses commonly begin to occur. The bottom arrows show ranges of AMS incidence across various altitudes (Hackett et al. 1976, Maggiorini et al. 1990, Honigman et al. 1993).  27  Figure 1.2 Partial pressure of oxygen (PO2) values along the oxygen cascade at sea level and high altitude (4300 m). The oxygen cascade is the process of O2 delivery from the external environment through the body to its ultimate destination in the mitochondria, and involves a series of stepwise movements of O2 from regions of higher PO2 to regions of lower PO2. At sea level, inspired PO2 (PIO2) is about 150 mmHg and declines after each stage of gas exchange from the lungs to the tissues. At 4300 m, the oxygen cascade begins with a low PIO2 (approximately 90 mmHg), and the extent of the reduction of PO2 at each stage of gas exchange is reduced. Consequently, the driving force for the delivery of O2 is lowered. This figure is adapted from (McArdle et al. 2007)  28  Figure 1.3 Four physiological changes during acute and chronic altitude acclimatization. Increases in heart rate (black curve) and hyperventilation (blue curve) occur within minutes in response to hypoxia and represent acute acclimatization. These changes increase the volume of inspired air per unit of time in the lungs and promote systemic and pulmonary blood flow. Consequently, the frequency and amount of O2 exchange and the amount of O2 delivered to tissues are both elevated to compensate for the lower availability. Over time, the hyperventilation and increased heart rate gradually recede but the CO2 ventilatory response (orange curve) begins to induce a decrease in bicarbonate concentration in cerebral spinal fluid, which stimulates central chemoreceptors and triggering hyperventilation. The increase in red blood cells (pink curve), which is due to an elevated secretion of erythropoietin in the kidneys secondary to HIF 1 triggered up-regulation of the EPO gene, is chronic response to hypoxia and gives a sustained enhancement of the exchange and delivery of O2 during long term (weeks or more) acclimatization. Note that the time scale is non-linear. This figure is adapted from (Peacock 1998).  29  Figure 1.4 Regulation of hypoxia-inducible factor -1 (HIF-1) under normoxic and hypoxic conditions and examples of genes and pathways regulated in response to hypoxia. HIF-1 is a heterodimeric protein that is composed of a constitutively expressed β-subunit and an O2regulated α-subunit. Under normoxic conditions, HIF-1 α is hydroxylated and then degraded by proteasomes (i.e. VHL). Under hypoxic conditions, HIF-1 α levels rise because 1) extant HIF-1α is stable because the decrease in hydroxylase activities and 2) HIF-1α messenger RNA expression is elevated, so more protein is synthesized. The elevated formation of HIF1(due to increased α-subunit availability) induces expression of a number of genes that encode molecules involved in hypoxic responses, for example, inducible nitric oxide synthase (iNOS), erythropoietin (EPO), vascular endothelium growth factor (VEGF), and glucose transporters (GLUTs). Increased iNOS levels promote the production of NO, which causes vasodilatation. EPO secreted by the kidneys induces erythropoiesis and increases the production of red blood cells. This consequently promotes the exchange and delivery of oxygen. VEGF triggers the process of angiogenesis, resulting in tissue vascularization, which facilitates the delivery of oxygen. The increase in GLUTs promotes the transportation of glucose to the cytosol. Concomitantly, HIF-1 inhibits pyruvate dehydrogenase activities and this induces a decrease in O2 consumption in the tricarboxylic acid cycle. Overall, elevated expression of these molecules promotes vasodilatation, an increase in red blood cells, angiogenesis, and a decrease in O2 consumption. These effects facilitate the process of acclimatization by increasing O2 delivery and reducing O2 consumption. This figure is adapted from (Semenza 2009). 30  High altitude hypoxemia  Brain  Vasodilation  Cerebral blood volume Cerebral blood flow Sodium excretion Capillary pressure  Extracellular fluid  BBB permeability Cerebral edema Brain swelling  cCSF via large CSC  cCSF via large CSC  No ICP  Transient ICP  No AMS  AMS  HACE  Figure 1.5 The central nervous system-based model of AMS pathophysiology. The figure is adapted from the review article by Roach and Hackett, (Roach and Hackett 2001) and describes the series of proposed physiological pathways of the development of AMS. This model suggests that the hypoxia-induced hypoxemia may drive the rise in cerebral blood flow (CBF) and blood volume (CBV). The consequent increase in capillary pressure may induce the disruption of brain-blood barrier (BBB) and leads to cerebral edema and brain swelling. The elevated CBF and CBV may promote brain swelling independently. In addition, sodium and fluid retention may assist the process of brain edema. With the depletion of the intracranial buffering space, the ICP increases and AMS symptoms present. Severe AMS may develop to a fatal condition, high altitude cerebral edema (HACE). 31  Sensitivity to hypoxemia Intracapillary distance and regional O2 diffusion limitation  Endothelial dysfunction  Inadequate activation of “O2-sensing” apparatus  Cerebral PO2  Free radicals Na+/K+-ATPase pump failure pupumpspumpspu Fluid “re-distribution” from vasogenic component  Permeability Peroxidation Inflammation  Astrocyte swelling “Osmotic-oxidative stress”  Nitric oxide  Trigeminal activation  AMS  HACE  Figure 1.6 The free-radical model of the pathophysiology of acute mountain sickness (AMS) and high-altitude cerebral edema (HACE). The model emphasizes that low cerebral PO2 may stimulate the formation of free radicals, which may be responsible for the failure of Na+/K+ATPase pumps. This latter event may induce the cerebral fluid “re-distribution” from extracellular to intracellular space. Astrocyte swelling can increase cerebral nitric oxide formation. In combination with the increase in free radical-mediated lipid peroxidation, membrane destabilization and inflammation consequently activate the trigeminovascular system and induce AMS. In addition, free radicals may cause neurovascular endothelial dysfunction and depress hypoxic ventilatory control and pulmonary gas exchange. This figure is adapted from the review article (Bailey et al. 2009a). 32  Figure 1.7 The generic structure of a eukaryotic gene and the process of gene expression. Typical genes are DNA sequences containing coding sequences (exons), which are separated by non-coding sequences (introns), and 5-prime and 3-prime untranslated regions (UTRs). During gene expression, exons are transcribed into unprocessed messenger RNAs (mRNAs), which carry coding information for protein synthesis. After transcription, the introns are deleted and the processed mRNAs are formed and ready for the protein synthesis. 5’ and 3’ UTR regions are located at both ends of mRNAs and are not translated into protein. Both introns and UTR regions are not translated into protein sequences but may affect the stability or the efficiency of mRNA translation. In addition, regulatory elements (short DNA sequences) that are located in various regions in (or near) the gene play essential roles in gene expression, either by following a preordained pathway (such as during development) or in response to numerous internal or external stimuli. This figure is adapted from (MacInnis et al. 2011).  33  Table 1.1 Types of common genetic polymorphisms Polymorphism Single nucleotide polymorphism (SNP) Substitutions Insertion/deletions Variable number of tandem repeats (VNTR)  Description A change at one base pair. One base is substituted for a different base. One base pair is inserted or deleted.  Usually two variants; one of two alternate bases present at each allele. Usually two variants; one base is present or absent at each allele.  Multiple tandem repeats of identical DNA sequences.  Microsatellites  Very short repeats (~1-10 base pairs)**  Minisatellites  Longer repeats (~10-100 base pairs)**  Insertion/deletion (indels)  Number of variants; Variant type  The insertion or deletion of more than one base pair.  Multiple variants*; number of repeats (i.e. length of variant) at each allele. Multiple variants; number of repeats (i.e. length of variant) at each allele. Usually two variants; presence or absence of multiple bp at each allele.  *As humans are diploid for all chromosomes (except the X and the Y in males), individuals can only carry a maximum of two variants for any given polymorphism regardless of how may variants exist in the population. ** definitions by length vary in the literature. The content of this table is adapted from the review article by (MacInnis et al. 2011).  34  Table 1.2 Potential effects of single nucleotide polymorphisms (SNPs) occurring in protein coding sequences on the encoded protein. SNP  Result  Silent (synonymous) Conservative Missense (non-synonymous) Non-conservative  No change in amino acid sequence due to the degenerate genetic code - no effect on protein structure. One amino acid is substituted for an amino acid with similar chemical properties - lesser effect on protein structure+. One amino acid is substituted for an amino acid with different chemical properties - greater effect on protein structure+.  Frameshift  The three-base (triplet) reading frame is altered by the addition of n+1 or n+2 bases (where n is 0, 3, 6, etc.) so that the reading frame for all following triplets is changed.  Nonsense  A triplet encoding an amino acid is changed to a stop codon that terminates translation prematurely.  Example TCT and TCA both encode serine CTT and ATT both encode hydrophobic amino acids (leucine and isoleucine respectively) GAG encodes a hydrophilic amino acid (glutamic acid), GTG encodes a hydrophobic amino acid (valine) The insertion of an A changes GTG GAG CCA to GTA GGA GCC, altering the amino acid sequence from Val-Glu-Pro… to Val-Gly-Ala… TAT encodes tyrosine, TAA is a stop codon  *examples show changes in 3 base DNA codons and the resulting changes in the amino acid structure of the protein The content of this table is adapted from the review article by (MacInnis et al. 2011).  35  2  General Materials and Methods  2.1  General methodology Genetic association studies assess the statistical relationship between variants at a  polymorphic locus and a trait by comparing variant frequencies between groups with and without phenotypes and are a powerful approach to detect the genetic contribution to complex traits (Risch and Merikangas 1996, Risch 2000). A significant allele frequency difference between individuals with the phenotype and those without suggests that this allele (or a variant in linkage disequilibrium with this allele) influences the phenotype of interest. In this work, a series of genetic association studies were performed to investigate genetic contribution of specific genes to susceptibility to AMS. DNA was prepared from buccal epithelial cells (cheek cells), and genotypes of the samples were determined by polymerase chain reaction (PCR) usually followed by digestion with diagnostic restriction endonuclease. Allele and genotype frequencies of polymorphisms in candidate genes encoding biomolecules that are involved in regulation of vascular homeostasis and pulmonary function were compared between AMS+ (cases) and AMS- (controls) Nepalese recruited at 4300 m. An association was reported when the frequency of an allele or a genotype was significantly different between the AMS+ and AMS- groups. One of the common problems with conducting population-based genetic association studies is population stratification, in which the case and control groups are different in composition of ancestral or environmental backgrounds (Attia et al. 2009). Nepal is located between the Tibet Autonomous Region of China and India and encompasses a wide range of geographical ecosystems (Figure 2.1). Given that ancestry background of Nepalese is strongly influenced by its neighbour populations (e.g. highland Tibetans and lowland Indians), who may have significantly different levels of AMS susceptibilities, population stratification was assessed in the samples of this thesis. The genotyping assay (based on fifteen short tandom repeat polymorphisms (STRPs)) and statistical analyses used for the detection of population stratification are discussed in Chapter 6.  36  2.2  Subjects Two hundred and thirty five Nepalese lowlanders who permanently live below 1800  m were recruited in 2005 (n = 103) and 2008 (n = 132). The two recruitments followed similar procedures and were performed at the Himalayan Rescue Association Gosainkunda Temporary Health Camp, where emergency medical care was provided for the pilgrims who attended the Janai Purnima Festival at Gosainkunda (4380 m) (Figure 2.1). An interpreter explained the purpose of the study. Informed consent was obtained. Both males and females were included. Most of the subjects were first time visitors, and none of them had been at high altitude in three months prior to participating in this study. Access to the site is mainly by foot (or very rarely by horseback) and most of the attendees ascend about 3000 m in 24 h (Figure 2.2). Such a rapid ascent leads to a high prevalence of AMS (reported as high as 68 % in previous research at this event (Basnyat et al. 2000)). Physicians trained in high altitude medicine identified the subjects with clinical AMS based on history, physical and global clinical impression. The Lake Louise Score (LLS) (Roach et al. 1993) was determined after clinical diagnosis, and the test was administered by the treating physician. Subjects who were diagnosed as having AMS by physicians were designated clinical-AMS, and those who had LLS ≥ 3 were designated LLS-AMS. The control group was recruited from pilgrims who attended the festival but were without symptoms of AMS (often individuals at the health camp for reasons other than AMS or accompanying friends of family members). All procedures were approved by the University of British Columbia Clinical Research Ethics Board and the Nepal Health Research Council (Appendix A).  2.3  Candidate genes Candidate genes are genes that encode bio-molecules/compounds potentially  implicated in the development of the phenotype(s) of interest. In this work, a series of association studies were performed using genetic markers in candidate genes selected a priori because they encode proteins that are suggested to play important roles in regulation of physiological characteristics that may be involved in the susceptibility to, or etiology of AMS, including vascular homeostasis and pulmonary function in response to acute hypoxic 37  exposure. The genetic markers selected are either reported as potentially functional based on literature or identified as informative (i.e. tagSNPs) using the tagSNP picker of the haplotype database of the International HapMap Project. The candidate genes and variants are described in the following section.  2.3.1  Angiotensin converting enzyme (ACE), angiotensin II type 1 receptor  (AGTR1), and bradykinin B2 receptor (BDKRB2) genes The ACE15, AGTR1 and BDKRB2 encode proteins of the renin-angiotensinaldosterone system (RAAS) and the kallikrein-kinin system (KKS), which are important to the maintenance of homeostasis including fluid and electrolyte balance, blood volume, and blood pressure. Angiotensin converting enzyme (ACE) is the key enzyme in the RAAS (Figure 3.1) and converts angiotensin I to angiotensin II (AngII), which mediates vasoconstriction via angiotensin II type I receptors (AGTR1). ACE also degrades bradykinin, which is the key component of the KKS and stimulates vasodilatation through bradykinin B2 receptors (BDKRB2). The degradation of bradykinin by ACE promotes the vasoconstriction induced by Ang II. Variants of functional polymorphisms in the ACE, AGTR1, and BDKRB2 genes have drawn substantial research attention in the genetic contribution to vascular homeostasis and susceptibility to AMS. Angiotensin I converting enzyme gene Three functional polymorphisms in ACE, including an indel (I/D), A240T, and A2350G, were genotyped in this work. The commonly studied ACEI/D polymorphism (rs46469946) is the presence (I, insertion) or absence (D, deletion) of a 287 bp Alu segment (Alu elements are family of repetitive elements with length approximately 300 bp) in intron 16 and accounts for 47 % of variation in ACE levels with the D allele associated with higher levels (Rigat et al. 1990, Tiret et al. 1992). The association between the variants of this polymorphism and AMS has been investigated; however, the results are inconsistent. 5  By convention, human gene abbreviations are capitalized and italicized. This can be confusing when the same abbreviation is used for the protein (e.g. ACE (the gene) encode ACE (the protein)). 6 ‘rs numbers’ are dbSNP identifiers (see: http://www.ncbi.nlm.nih.gov/snp/)  38  Dehnert and colleagues found that the prevalence of AMS was independent of the ACEI/D polymorphism (Dehnert et al. 2002). Later, Tsianos and associates reported that the occurrence rate of AMS on day one in European mountaineers was associated with the variants of the ACEI/D polymorphism and individuals with the I/D genotype were less affected; however, the severity and the onset of AMS on day two were independent of the ACEI/D polymorphism (Tsianos et al. 2005). Unlike the studies that showed an association between AMS and the I allele, Buroker and associates found the D allele to be associated with the development of AMS in Han Chinese who were hospitalized at 3670 m; however, the description of the case and control groups was limited and, based on the clinical descriptions, the cases may have included other forms of altitude illness (Buroker et al. 2010). In addition to the ACEI/D polymorphism, two other polymorphisms, A240T (rs4291) and A2350G (rs4343)7, have been shown to account for 6 % and 19 % of the variability of plasma ACE levels, respectively, but their roles in AMS pathogenesis are unknown (Zhu et al. 2001). Angiotensin II type 1 receptor gene Angiotensin II type I receptors are G protein-coupled trans-membrane receptors predominantly distributed in the surface of vascular smooth muscle cells and cardiomyocytes. I examined the commonly investigated AGTR1A1166C polymorphism (rs5186) in the 3-prime UTR region of the gene. Both of the A and C alleles of this polymorphism have been associated with hypertension in different populations (i.a. (Wang et al. 1997, Hahntow et al. 2010)). This polymorphism does not appear to be functional; however, it may be in linkage disequilibrium (LD) with other functional polymorphisms. The varying LD pattern between populations may contribute to inconsistent associations (for example with hypertension) in different populations. Bradykinin B2 receptor gene Bradykinin B2 receptors are G protein-coupled trans-membrane receptors and mediate vasodilatation stimulated by bradykinin. I looked at two variants in this gene: the 7  These two polymorphisms were referred to as ACE 4 and ACE 8, respectively in Zhu, 2001.  39  BDKRB2-9/+9 polymorphism (the presence/absence of 9 bp, rs5810761) in exon one and the BDKRB2-58 C/T polymorphism (rs1799722) in the promoter region, both of which are associated with vascular characteristics. The +9 allele of the BDKRB2-9/+9 polymorphism was associated with the increase in BDKRB2 activities and high systolic blood pressure (Braun et al. 1996, Pretorius et al. 2008). The T allele of the BDKRB2-58 C/T polymorphism is suggested to be an independent risk factor in the development of hypertension in several ethnic groups although the underlying mechanisms are not known (Gainer et al. 2000, Wang et al. 2001, Cui et al. 2005).  2.3.2  Beta-2 adrenergic receptor gene  Beta-2 adrenergic receptors (B2AR) are G-protein-coupled proteins distributed on the surface of bronchial smooth muscle cells, vascular smooth muscle cells, and cardiomyocytes. They are activated by catecholamines (e.g. epinephrine and nor-epinephrine) and mediate sympathetic activities that are elevated in response to hypoxia, such as increased heart rate and stroke volume, tissue vasodilatation, and bronchodilation, thereby improving oxygen delivery during altitude acclimatization (Hansen and Sander 2003, Mazzeo and Reeves 2003). Increased B2AR tone (e.g. increase peripheral vasodilatation and heart rate) was associated with developing AMS, suggesting that B2ARs may be involved in the pathogensis of this condition (Loeppky et al. 2003). The ADRB2A46G polymorphism (rs1042713), which induces an amino acid change from Arg to Gly at protein position 16, has been suggested to play an important role in B2AR affinity to endogenous and exogenous agonists, G proteins, signal transduction, and the down-regulation response to exogenous agonists (Green et al. 1993, Green et al. 1994). Human airway smooth muscle cell lines with the G/G genotype of the ADRB2A46G polymorphism showed ~ 23 % higher agonist-promoted down-regulation of B2AR during a 24 h exposure to agonists (Green et al. 1995). The investigation of ADRB2 also employed tagSNP analysis. Seven tagSNPs (including the ADRB2A46G polymorphism) within a 5 kb region around this gene were  40  identified using the tagSNP picker of HapMap database (with a cut-off range of allele frequency from 10 % to 90 %) based on the haplotype data for Chinese population, which is the Hapmap population most geographically and genetically close to Nepalese.  2.3.3  Endothelial nitric oxide synthase gene  Endothelial nitric oxide synthase (eNOS) is the main source of NO in blood vessels. NO is a strong vasodilator and NO-dependent endothelial function plays an important role in vascular homeostasis and has shown beneficial effects on acclimatization to acute hypoxia (Scherrer et al. 1996, Van Mil et al. 2002). A number of studies investigated the role of endogenous NO levels and susceptibility to high altitude illnesses but the results are inconsistent. Individuals with decreased exhaled NO levels in response to acute hypoxia were shown more susceptible to altitude illnesses (Busch et al. 2001, Droma et al. 2002, Ahsan et al. 2005, Ahsan et al. 2006) while no association was found by Brown, 2006 (Brown et al. 2006). The NOS3G894T polymorphism (rs1799983) in the eNOS gene (NOS3) induces an amino acid change from glutamic acid (Glu) to arginine (Arg) at protein position 298. The T allele was shown to be a critical predictor of cardiovascular and pulmonary diseases (Casas et al. 2004, Arif et al. 2007) and the T/G and T/T genotypes are associated with lower NOS3 expression and decreased eNOS activities although the difference did not reach the significant level (Song et al. 2003, Dosenko et al. 2006). In contrast, individuals with the G/G genotype in combination with the 4b4b genotype of the NOS34b/4a polymorphism (another functional polymorphism in NOS3) showed higher exhaled NO levels in response to acute hypoxia (Ahsan et al. 2005, Ahsan et al. 2006) but the associations between variants of polymorphisms in NOS3 and the development of AMS have not been studied prior to this thesis. The investigation of NOS3 in this thesis employed tagSNPs instead of genotyping all the SNPs in this gene. Seven tagSNPs were assessed in this thesis, including the commonly  41  investigated polymorphism (rs1799983). They were identified using the same method for ADRB2. Detailed information of all the polymorphisms used in studies described in this thesis is summarized in Appendix D.  2.4  Genetic analysis  2.4.1  DNA sampling and isolation  Buccal mucosal scrapings were collected from four locations in the mouth using an endocervical sampling cytobrush (CooperSurgical Inc., Trumbull CT, US). The cytobrushes with scrapings were then were stored dry in envelopes for transport (King et al. 2002) to Canada for DNA isolation (Saftlas et al. 2004). The protocol of genomic DNA isolation from the cytobrushes is adapted from (Saftlas et al. 2004). The procedure is as follows: brush heads with cheek cells are incubated at 55 °C for 8 h or overnight in 700 µl lysis buffer (100 mM NaCl, 10 mM TrisCl (pH 8.0), 25 mM EDTA, 0.5 % SDS) with 3.8 µl proteinase K (20 mg/ml). After the incubation and centrifugation at 18,000 g for 1 min to free the attached cells, the brush heads are discarded. RNAse (2.3 µl of a 10mg/ml sol’n) is then added to the cells, and the tube is incubated at 55 °C for 1 h. KOAc (potassium acetate, 320 µl of a 5 M sol’n) for precipitating proteins is added. The tube is incubated on ice for 10 min and then centrifuged at 18,000 g for 5 min. The supernatant, which contains DNA, is transferred to a fresh tube and the precipitate discarded. Isopropanal (510 µl) and glycogen (2.5 µl) are added, and the tube is put on ice or stored at - 20 °C for at least 10 min to facilitate DNA precipitation. DNA is then collected to the bottom of the tube by centrifugation at 18,000 g for 10 min and the supernatant is discarded. DNA pellets are rinsed with 1 ml ethanol (70 %) to remove any remaining salt, and then recollected by centrifugation at 18,000 g for 10 min and the ethanol rinse is discarded. The DNA pellets are dried and then re-suspended in 100 µl TE buffer (10 mM  42  Tris/Cl, 1 mm EDTA, pH 8.0). Working stocks are prepared and stored at 4 °C, and the remaining DNA was stored at - 80 °C.  2.4.2  Genotyping  All of the genotyping assays began with PCR amplification of the polymorphic region. Basically, a pair of PCR primers flank the target DNA segment containing the polymorphic locus on the template strand, and then the DNA is repeatedly amplified by polymerases copying the regions between the two primers (including the two primers). The number of amplicons increases exponentially, for example, after 40 cycles, the region of interest has been amplified 240 times, yielding enough DNA to be visualized on a stained electrophoresis gel. Genotyping SNPs involves a subsequent step, in which the amplified PCR products are digested by a diagnosis restriction endonuclease that cut the DNA at short recognition sequences. The digested products are then separated based on their lengths by gel electrophoresis and genotypes of SNPs are identified based on the lengths of the digested fragments. For Indel type polymorphisms, genotypes are identified based on the length of the PCR products. The samples demonstrating this genotyping assay are shown in figures in each data chapter. PCR Standard PCR reactions done in the studies of this thesis are as follows: DNA (100 ng) is amplified in a 25 μl reaction containing 2.5 µl PCR-buffer (20 mM Tris/Cl pH 8.4, 50 mM KCl), 0.2 mM dNTPs (25 mM), 0.7 µl MgCl2 (25 mM), 1.0 µl of each primer (10 μM), and 0.5 units Taq polymerase (Invitrogen Corporation, Carlsbad, CA, US). There are 40 cycles per PCR reaction. Each cycle includes 1 min at 94 °C, 30 sec at 54 – 60 °C (depending on the annealing temperatures of each pair of primers), and 15 sec at 72 °C, followed by a 5 min incubation at 72 °C. PCR amplification was performed in MJ Mini Cycler (Bio-Rad Laboratories, Hercules, CA, US). The primer sequences and the annealing temperatures for the specific PCR reactions as well as the reaction temperatures of restriction enzymes are listed in Appendix D.  43  Digestion and gel electrophoresis Following application and digestion (if needed) the products are separated by PAGE (polyacrylamide-gel electrophoresis) using 8 % gels run in 1×TBE buffer, visualized using ethidium bromide staining.  2.5  Statistics Allele and genotype frequencies were established by gene counting and compared by  2 × 2 and 2 × 3 contingency tables, respectively (available through http:// faculty.vassar.edu/lowry/VassarStats.html). Tests for Hardy-Weinberg equilibrium and the comparisons of allele and genotype distributions between the AMS and non-AMS groups were performed by Chi-square analysis. When observed or expected values included a cell were less than five, Fisher’s exact test was used. The criterion of statistical significance was set at P < 0.05; however, when multiple tests were applied (e.g. the study on the seven tagSNPs in ADRB2), the Bonferroni method of correcting for multiple testing8 was employed (i.e. the threshold P value (0.05) is divided by the number of tests).  8  For discussion at this correction factor, see the review (Attia et al. 2009).  44  Tibet  China  India  Figure 2.1 Map of Gosainkunda (4380 m) in Nepal. Gosainkunda (insert) is located in Langtang National and north of Kathmandu (square), which is the capital of Nepal. It is a sacred lake for both Hindu and Buddhist pilgrims. Every year, during the Janai Purnima festival in August, thousands of Hindu and Buddhist pilgrims ascend to the lake for a holy bath.  45  Figure 2.2 The general ascent profile for the Janai Purnima Festival at Gosainkunda (4380 m) in Nepal. Usually, attendees leave from Kathmandu (1338 m) by bus, and take approximately 10 h to arrive at Dhunche (2000 m), and then spend 24 – 48 h trekking to 4380 m (occasionally by horseback). Such a rapid ascent leads to a high prevalence of AMS at the festival (reported as high as 68 % in previous research (Basnyat et al. 2000)).  46  3  Association Analysis of the Angiotensin Converting Enzyme and  Angiotensin II Type 1 Receptor, and Bradykinin B2 Receptor Genes with Susceptibility to AMS in Nepalese9  3.1  Introduction The renin-angiotensin-aldosterone system (RAAS) (Figure 3.1) plays an important  role in the maintenance of homeostasis including fluid and electrolyte balance, blood volume, and blood pressure. The RAAS cascade begins with the formation of angiotensinogen, which is secreted by the liver. The first rate-limiting step is the formation of angiotensin I (Ang I) and involves the cleavage of Ang I, the biologically inert decapeptide, off of antiogensinogen. The process is catalyzed and regulated by the enzyme renin, which is secreted by the kidney. The Ang I, then, is converted to angtiotensin II (Ang II, an octapeptide), a potent vasoconstrictor, by angiotensin converting enzyme (ACE). The formation of Ang II is the second rate-limiting step in the RAAS cascade. Ang II is the active component of the RAAS and performs direct (e.g. vasoconstriction) and indirect effects (e.g. stimulating the secretion of aldosterone in the adrenal gland) via the activation of angiotensin II type 1 receptors (AGTR1). The increase in aldosterone levels promotes the re-absorption of sodium in the collecting ducts in the kidney, thereby increasing blood volume. Vasoconstriction and the concomitant increase in blood volume consequently elevate blood pressure. Meanwhile, the degradation of bradykinin (a strong vasodilator) by ACE further facilitates the main effects (e.g. vasoconstriction, raised blood volume) of the RAAS cascade. Bradykinin, a key component of the kallikrein-kinin system (KKS), mediates physiological effects via the bradykinin B1 and B2 receptors (BDKRB1 and BDKRB2). BDKRB2s are widely and constitutively expressed in tissues and are involved in the vasodilatation activated by bradykinin (Erdos et al. 2010) while BDKRB1 is expressed usually after tissue damaging stimuli and is involved in inflammatory responses (Regoli and Barabe 1980, Bhoola et al. 1992, Ignjatovic et al. 9  Data from this chapter was published as: No association between variants in the ACE and angiotensin II receptor 1 genes and acute mountain sickness in Nepalese pilgrims to the Janai Purnima Festival at 4380 metres. High Alt. Med. Biol. 7: 281-289 (PMID: 17173513)  47  2002, Ignjatovic et al. 2004, Stanisavljevic et al. 2006). The KKS interacts with the RAAS at several levels and the interaction at the level of ACE is the most widely recognized (Schmaier 2003, Shen and El-Dahr 2006, Shen et al. 2007). The characteristics (e.g. blood volume, fluid and electrolyte balance, and vascular tone) regulated by the RAAS and the interaction between the RAAS and KKS systems (e.g. the degradation of bradykinin by ACE) have been suggested to be involved in the process of acclimatization to acute hypoxic exposure as well as susceptibility to AMS (Singh et al. 1969, Baumgartner et al. 1994, Dubowitz et al. 2009), and there is considerable interest in the underlying contribution that alleles in the RAAS genes may have in the hypoxia response. Three genes (the angiotensin converting enzyme gene (ACE), angiotensin II type 1 receptor gene (AGTR1), and bradykinin B2 receptor gene (BDKRB2)) have been particular focuses in genetic studies of susceptibility to AMS. The angiotensin converting enzyme gene (ACE), which encodes the second ratelimiting enzyme in the RAAS, has been widely investigated as a genetic contribution to human acclimatization and adaptation to high altitude and human performance at extreme altitudes. The most commonly investigated polymorphism in ACE is the ACEI/D polymorphism (Ins/Del, rs4646994), which is the presence (or insertion) or absence (or deletion10) of a 287 bp Alu segment in intron 16. This polymorphism is functional, and the I allele is associated with lower ACE levels and activity (Rigat et al. 1990, Tiret et al. 1992) and has been suggested to be beneficial in acclimatization and adaptation to high altitude (Qadar Pasha et al. 2001, Droma et al. 2008, Buroker et al. 2010). In 1998, the landmark study by Montgomery and associates reported the association between the I allele and human performance at extreme altitude (Montgomery et al. 1998) and launched the research on genetics high-altitude acclimatization and adaptation. The I allele and II genotype were over-represented in elite mountaineers in comparison to normal controls (P = 0.003 and 0.02, respectively). No individuals carrying the DD genotype were among the 15 climbers who ascended beyond 8000 m without using  10  Although the term ‘deletion’ is commonly used, the polymorphism is most likely due to the insertion if the Alu element and the alternate allele is the absence of the insertion rather than a deletion per se.  48  supplementary oxygen. A follow-up study also showed a higher but not significant frequency of the I allele among the mountaineers who successfully ascended beyond 8000 m (0.55 vs. 0.36 in successful vs. unsuccessful, respectively, P value was not mentioned) (Thompson et al. 2007). These findings were not supported by a recent study that showed summit success at Mt. Kilimanjaro (5895 m) to be independent of the ACEI/D genotype although the successful rate was 100 % in I/I subjects in comparison to 52 % and 43 % in I/D and D/D subjects, respectively (P = 0.09) (Kalson et al. 2009). These data, in spite of being inconsistent, suggest that the I allele and II genotype of the ACE I/D polymorphism may be important in determining human performance at altitudes when human physiological capacity approaches to its extremity at extreme altitudes. Overcoming AMS is only one determinant to a successful ascent to extreme altitude and being able to reach extreme altitudes does not implicate that someone is less susceptible to AMS; however, being susceptible to AMS may limit the altitude level that an individual is able to reach comfortably and preclude someone of staying in the sport long enough to become an elite mountaineer (Davies et al. 2009). The ACEI/D polymorphism has also been associated with high-altitude adaptation, and the frequencies of the I allele and I-related genotypes (I/I and I/D) were significantly high in native highlanders (Sherpas, P = 0.036; P = 0.035, respectively) (Droma et al. 2008). The over-representation of the ACE I allele and I-related genotypes (I/I and I/D) in native highlanders does not directly support a protective role against AMS; however, may be due to migrational selection in the founding populations (i.e. the pioneer lowlanders who had I/I or I/D genotypes may have been an advantage when colonizing the mountains migration and thus prospered at high altitude compared to D/D individuals. The association between the ACEI/D polymorphism and AMS is unclear. A number of factors (e.g. altitude, ascertainment method, ascent profile, population) complicate and increase the difficulty to compare studies and draw a conclusion. Dehnert and associates performed the first study of the association between this polymorphism and the prevalence of AMS as defined as an AMS-C score of environmental symptom questionnaire equal to or higher than 0.70 (Dehnert et al. 2002). This team investigated the incidence in 83 Caucasian mountaineers who ascended from below 2000 m to 4559 m in three days and 49  were assessed on the day of arrival. The results showed that the prevalence was independent of the ACE genotype (Dehnert et al. 2002). Later, Tsianos and associates conducted a study on 284 Caucasian subjects who followed a 2-day ascent profile to climb Mt. Blanc (day 1: from the valley at 1100 m to the Gouter hut at 3817 m; day 2: climb to the summit at 4807 m and back to the hut). They reported that an early onset (day 1) of AMS defined as LLS ≥ 4 was dependent of the ACEI/D polymorphism and the I/D subjects were less afflicted; however, no association was found between the I/D polymorphism and general AMS onset and severity when the data for day 2 were included (Tsianos et al. 2005). In contrast, an association was found between the D allele and AMS (OR= 1.71, CI = 1.0 – 2.8, P = 0.038) in a study on 158 Han Chinese (cases: n = 98; controls: n = 60) who were hospitalized at 3670 m and afflicted within two days after arrival but no ascent profile was reported and the subjects may have included other forms of altitude illness (Buroker et al. 2010). Besides the ACEI/D polymorphism, two other polymorphisms (the ACEA-240T (ACE-4 or rs4291) and ACEA2350G (ACE-8 or rs4343)) are also functional and account for 6 % and 19 % of the variability of serum ACE levels, respectively (Zhu et al. 2001). If the I allele, which lowers ACE levels, is suggested to be beneficial to resisting AMS, the A allele at both the ACEA-240T (ACE-4 or rs4291) and ACEA2350G polymorphisms should also be good for AMS resistance. Overall, the role of ACE in the RAAS pathways and the regulation of vascular tone, the potential role of these functional polymorphisms in acclimatization and adaptation to high altitude and human performance at extreme altitudes suggest that these polymorphisms may be involved in susceptibility to AMS. The impact of ACE on the regulation of vascular tone is mainly because ACE levels determine the converting rate from Ang I to Ang II. The physiological effects such as vasoconstriction and the release of aldosterone stimulated by Ang II are mediated via AGTR1s, which are G protein-coupled trans-membrane receptors and predominantly distributed in the vascular smooth muscle cells and cardiomyocytes (de Gasparo 2000). The A1166C polymorphism in the AGTR1 gene (AGTR1), which encodes an AngII receptor, has been shown in association with vascular-related characteristics, and the C allele of the AGTR1A1166C polymorphism (rs17231380) in the 3’ UTR has been associated 50  with hypertension in different populations although the hypertension-related phenotypes in the association are not consistent (i.a. (Bonnardeaux et al. 1994, Castellano et al. 1996, Wang et al. 1997, Hahntow et al. 2010, Wang et al. 2010b)). Bonnardeaux and associates examined five polymorphisms including the AGTR1A1166C polymorphism and found the C allele was over-represented in Caucasian individuals with hypertension (Bonnardeaux et al. 1994). The association was confirmed by another case-control study that compared 108 Caucasian hypertensive patients with a strong family history and early onset disease to healthy controls. The frequency of the C allele was 0.4 in hypertensives and 0.29 in normotensives (P = 0.02) (Wang et al. 1997). A recent meta-analysis study in a Chinese population suggested that the variant genotype (A/C, C/C) is associated with hypertension with the pooled odds ratio of 1.48 (95 % CI: 1.20-1.83) (Wang et al. 2010b). Conversely, the A allele was associated with high blood pressure and the C/C genotype was associated with lower blood pressure in Caucasians in other studies (Castellano et al. 1996, Hahntow et al. 2010). The location of the AGTR1A1166C polymorphism suggests that it is not functional but alleles at this locus may be in linkage disequilibrium (LD) with other functional polymorphisms. Varying LD pattern between populations could contribute to some extent to the above-mentioned inconsistencies. In addition, the C allele may contribute to phenotypes through an interaction with other variants. For example, this allele may enhance the effect of the D allele at the ACE I/D locus on the development of myocardial infarct (Tiret et al. 1994). Overall, the importance of AGTR1 in the RAAS cascade and its interaction with ACE at gene level indicate that it may play a role in the development of AMS. In addition to converting Ang I to Ang II, ACE also degrades bradykinins (strong vasodilators), and this activity promotes the vasoconstriction stimulated by Ang II. Bradykinin is generated from high-molecular-weight precursors (kininogens) by proteolysis and induces vasodilatation through the activation of BDKRB2. The degradation of bradykinin by ACE facilitates the RAAS control of vascular homeostasis, and the alleles in ACE might be part of the genetic contribution to susceptibility to AMS; however, whether and through what mechanism that bradykinin and its receptors are involved in acclimatization to high altitude and the development of AMS are not known. The gene  51  encoding BDKRB2 contains a number of functional polymorphisms, including a 9-base insertion/deletion (+9/-9, rs5810761) in the first exon of the gene, and a C to T transition in the promoter region (C-58T, rs1799722) (Braun et al. 1996). The presence of the 9 bp segment (+9) is associated with a decreased expression of the gene (Pretorius et al. 2008). The steady-state analysis of BDKRB2 mRNA levels showed that the -9-transcript in heterozygous subjects was expressed more consistently and in higher levels (Lung et al. 1997). The two polymorphisms are associated with vascular characteristics of diseases. The +9 allele was associated with greater vascular resistance and higher systolic blood pressure (SBP) (Pretorius et al. 2008). Caucasian subjects with the +9/+9 genotype showed the highest average SBP and pulse pressure while African-Americans with the same genotype presented 25 % higher forearm vascular resistance (Pretorius et al. 2008). The 58 C allele was shown over-represented in individuals with essential hypertension while the -58 T allele presented protective effect (i.a. (Mukae et al. 1999, Bhupatiraju et al. 2012); however, the associations are not consistent between different populations (Niu et al. 2010). In summary, the influence of BDKRB2 on the regulation of vascular homeostasis and the down-regulation of bradykinin by ACE suggest that alleles in BDKRB2 have the potential to be involved in the development of AMS. Given the important roles of ACE, AGTR1, and BDKRB2 in the regulation of vascular homeostasis and the implications of previous genetic studies examing their potential effects in high altitude acclimatization, this study was designed to investigate the associations between the variants of functional polymorphic loci in ACE, AGTR1 and BDKRB2 and susceptibility to AMS in our cohorts of Nepalese pilgrims.  3.2  Methods and materials (see “general methods” section for details)  3.2.1  Subjects  Subjects with and without AMS were recruited at the Himalayan Rescue Association GosainKunda Temporary Health Camp in 2005. The classification of subjects  52  into AMS and non-AMS groups (i.e. AMS+ and AMS-) was based on their clinical evaluation results and LLS scores. Subjects who were diagnosed as having AMS by physicians were in the Clinical-AMS group and those who had LLS > 3 were in LLSAMS group. One hundred and three volunteers (male: n = 80, female: n = 23; mean age: 33 yrs (range 18 – 76 yrs)) participated and were grouped into each category: Clinical-AMS (AMS+, n = 44) and Clinical-non-AMS (AMS-, n = 59); LLS-AMS (AMS+, n = 22) and LLS-non-AMS (AMS-, n = 81) in the ACE and AGTR1studies. Subjects assessed in the BDKRB2 study were recruited at the Himalayan Rescue Association Gosainkunda Temporary Health Camp in 2005 and 2008. Two hundred and twenty eight Nepalese pilgrims (male, n = 170, female, n = 58; mean age: 32 yrs (range 16 – 73 yrs)) took part in this study. Subjects who were diagnosed as having AMS by physicians and had LLS ≥ 3 were in AMS group (AMS+, n = 100) and subjects who were clinically free of AMS and with LLS < 3 were in non-AMS group (AMS-, n = 117). Subjects who were discordant for the two diagnostic criteria (n = 11) were not included in the analysis.  3.2.2  Genotyping  1) ACE The structure of ACE and the locations of the three polymorphisms (ACEI/D, ACEA-240T, and ACEA2350G) tested in this study are shown in Figure 3.2. The assay for analyzing the ACEI/D polymorphism followed a three-primer PCR based protocol (Evans et al. 1994) as shown in Figure 3.3. If the I allele presents, PCR products are the 65 bp and 372 bp segments. The 65 bp PCR product is amplified by ACE-2 : ACE-3. The 372 bp PCR product is potentially amplified by ACE-1 (striped) : ACE-3 but is rarely seen because of competition for primers. If the D allele presents, the PCR product is 84 bp segment, which is amplified by ACE-1 : ACE-3. PCR amplification followed the protocol in Chapter 2 with an annealing temperature at 58 ºC. PCR products were analyzed directly  53  through gel electrophoresis as described in Chapter 2 (see Figure 3.6 for examples of the assay). The assay for analyzing the ACEA-240T and ACEA2350G polymorphisms is adapted from (Keavney et al. 1998). The ACEA-240T reverse and ACEA2350G forward primers were modified to generate recognition sites for the diagnostic endonuclease enzymes, Xba I and Bst UI, respectively. PCR amplification was performed using the ACE4-F : ACE4-R and ACE8-F : ACE8-R primer pairs with an annealing temperature at 55 ºC and 58 ºC for the ACEA-240T and ACEA2350G polymorphisms, respectively. PCR products (10 µl) were digested using 1 to 5 unit(s) of the endonuclease enzymes under the conditions described by the manufacturer (New England Biolabs, MA, US). Digested products were then analyzed directly through gel electrophoresis as described in Chapter 2 (see Figure 3.6 for examples of the assay). 2) AGTR1 The structure of AGTR1 and the relative location of the AGTR1A1166C polymorphism are shown in Figure 3.4. DNA was amplified using the AGTR1-F and AGTR1-R primer pair with an annealing temperature at 55 ºC. PCR products (10 µl) then were digested using 1 to 5 unit(s) of the enzyme, Dde I, under the conditions described by the manufacturer (New England Biolabs, MA, US). Digested products then were analyzed directly through gel electrophoresis as described in Chapter 2 (see Figure 3.6 for examples of the assay). 3) BDKRB2 The structure of BDKRB2 and the relative locations of the BDKRB2+9/-9 and BDKRB2C-58T polymorphisms are shown in Figure 3.5. DNA was amplified using the BD9-F and BD-9-R primer pair and BD-58-F and BD-58-R primer pairs with annealing temperature at 60 ºC and 58 ºC, respectively. The PCR products of the BDKRB2+9/-9 polymorphism were analyzed directly through gel electrophoresis as described in Chapter 2. The reverse primer, BD-58-R, for the analysis of the BDKRB2C-58T polymorphism was modified to generate a recognition site for the enzyme, Bfa I. The PCR products of the 54  BDKRB2C-58T polymorphism were digested using 1 to 5 unit(s) of Bfa I and analyzed by gel electrophoresis (see Figure 3.6 for examples of the assay).  3.3  Results 1) ACE and AGTR1 Allele and genotype frequencies for the four polymorphisms in ACE and AGTR1 in  the Nepalese cohort recruited in 2005 are shown in Table 3.1. The genotype frequencies are in Hardy-Weinberg equilibrium (HWE). No significant differences in genotype or allele frequencies at any of the four loci were detected between the AMS+ and AMSgroups (either “Clinical AMS” or “LLS-AMS” (LLS > 3)) and the results are shown in Figures 3.7 and 3.8. Even after the removal of the potentially confounding “mild” cases who had LLS of 1 or 2, no significant association was found (minimum P = 0.47). 2) BDKRB2 Allele and genotype frequencies for the BDKRB2+9/-9 and BDKRB2C-58T polymorphisms in the Nepalese cohort recruited in 2005 and 2008 are shown in Table 3.1. Genotype frequencies were in HWE for the BDKRB2+9/-9 polymorphism (P = 0.99, χ2 = 0.0001, n = 209) but not for the BDKRB2C-58T polymorphism (P < 0.001, χ2 = 49.6, n = 189), due to a higher proportion of heterozygote than expected. This was significant in both the AMS+ (P < 0.001, χ2 = 19.57, n = 90) and AMS- (P < 0.001, χ2 = 30.58, n = 99) groups. Neither alleles nor genotypes of the two polymorphisms were over-represented in the AMS+ group. Results are shown in Figure 3.9.  3.4  Discussion Allele and genotype frequencies of six polymorphisms in ACE, AGTR1, and  BDKRB2 were compared between Nepalese pilgrims with and without AMS. No significant differences in either allele or genotype frequencies for any of the polymorphism tested were detected. 55  ACE and AGTR1 Allele frequencies of the ACEI/D polymorphism in Nepalese were consistent with previous reports in populations that are geographically close to Nepalese except with Tibetans, and the comparison results are shown in Table 3.2. The alleles of the ACEI/D and ACEA2350G polymorphisms were almost in complete concordance with each other (i.e. ACEI-ACEA2350) while the A allele of the ACEA-240T polymorphism usually segregates with the A allele of the ACEA2350G polymorphism, this indicates some degree of linkage disequilibrium between these two pairs of loci and consistent with previous findings (Zhu et al. 2001). This study was the first to assess the relationship between the ACEI/D polymorphism and AMS in a non-European population, and no association between any of the alleles investigated and AMS was found. The results were consistent with the previous studies performed in European climbers (Dehnert et al. 2002, Tsianos et al. 2005) but not with the recent study on Han Chinese population, in which the D allele was overrepresented in AMS individuals in Han Chinese population (Buroker et al. 2010); although the conclusion is weakened by the method of assessing attitude pathology (some of the subjects may have had HAPE and not AMS), the criteria of recruiting and an undefined ascent profiles (which may not have been the same in the cases and controls). Besides the ACEI/D polymorphism, no association was seen at any other ACE loci in this study of Nepalese. Further studies are needed to clarify the role of the alleles in ACE in genetic susceptibility to AMS. This study described in this thesis is the first to look for a possible role of AGTR1 alleles in susceptibility to AMS. The alleles of the AGTR1A1166C polymorphism have been associated with high blood pressure though the associations were not consistent in different populations see reviews by (Mottl et al. 2008, Wang et al. 2010b, Li et al. 2012). The allele frequencies of this polymorphism in this study are similar to those in Chinese but significantly different from those in Caucasians (Bonnardeaux et al. 1994, Chou et al. 2002). This may mean that the LD patterns between the AGTR1A1166C polymorphism and proximal functional polymorphisms vary in different populations and which could affect 56  any associations between the alleles in AGTR1 and phenotypes (e.g. blood pressure or AMS). No association was found between the assessed alleles in AGTR1 and AMS in this study; however, further studies in different populations are needed before the gene can be broadly excluded as a potential factor contributing to susceptibility to AMS. The genes employed in this study have been investigated extensively and linked to cardiovascular disease (see review by (Bleumink et al. 2004)) and exercise performance (see review by (Jones et al. 2002)). Our study assessed four genetic loci in two genes encoding key components of the RAAS and KKS in AMS. The results of this study in Nepalese do not exclude the genetic role of the three ACE polymorphisms in susceptibility to AMS in other populations. Linkage between these three polymorphisms in Nepalese population has not been studied previously and could be different from other populations; however, our results suggest that there is linkage disequilibrium between the ACEI/D and ACEA-240T polymorphisms and the ACEA-240T and ACEA2350G polymorphisms, and the disequilibrium is consistent with those reported in other populations, such as Andean natives (Rupert et al. 2003), African (Zhu et al. 2001) and Europeans (Keavney et al. 1998). These data would suggest that differences in patterns of LD would not account for differences in associations between these alleles and phenotypes and that our data in the Nepalese is likely applicable to other populations. BKDRB2 This study was also the first to investigate the association between variants in BDKRB2 and AMS in any population. The allele frequencies of the BDKRB2+9/-9 and BDKRB2C-58T polymorphisms were not significantly different between the AMS+ and AMS- cohorts, and thus no association was found between these variants and susceptibility to AMS in Nepalese. The genotype and allele frequencies of the BDKRB2+9/-9 polymorphism in Nepalese were significantly different from those in other populations (e.g. Caucasian, northeast Asian, and African-American) as shown in Table 3.3 (Lung et al. 1997, Pretorius et al. 2008). Considering the phenotypic consequences in association with the +9 or -9 alleles vary between populations (e.g. systolic blood pressure and pulse pressure was significantly higher in Caucasians with the +9+9 genotype but not in African57  Americans with the +9/+9 genotype (Pretorius et al. 2008)), any extrapolation of the results of this work to other populations should be drawn with caution. Genotype frequencies for the BDKRB2+9/-9 polymorphism were in HWE whereas the genotype frequencies of the BDKRB2C-58T polymorphism were not, due to a significantly higher frequency of the C/T genotype in both the AMS+ and AMS- groups. The over-representation of the C/T genotype was more pronounced in subjects who were not afflicted with AMS. There are a number of factors can disturb HWE (e.g. nonrandomly mating, mutation, genetic drift, gene flow, and natural selection) (Hartl 2000). In our cohort, self-selection in festival attendees could contribute to the deviation from HWE (i.e. the higher than predicted frequency of heterozygote). This hypothesis suggests that, if individuals with C/C or T/T genotypes are prone to AMS, they may have turned back when they began to feel discomfort during the ascent, left the festival early, or did not attend because of AMS suffered in previous years. Any or all of these events could result in the C/C and T/T individuals being under-represented at our collecting site and an overrepresentation of the C/T heterozygote in attendance. The greater representation of the C/T individuals in the AMS- cohort supports this model of the BDKRB2C-58T heterozygous advantage for AMS resistance. As we did not find an association between the C/C or T/T genotypes and AMS (i.e. we just found them under-represented in the cohort as a whole), if this model is correct there could have to be other variables (genetic or environmental) ameliorating the effects of the C/C and T/T genotypes on the pilgrims. Another hypothesis for the over-representation of the C/T genotype in this Nepalese cohort is that the high frequency of the C/T genotype is common in the source population (i.e. Nepalese in general) and is not related to altitude or AMS, but rather to some other, unknown factor. Further association studies in other ethnic groups are needed to completely clarify the alleles in BDKRB2 as a potential factor contributing to susceptibility to AMS. Genetic association studies can only exclude an over-representation within the limits of the study’s power to detect frequency variation. For the polymorphisms in ACE and BDKRB2, the sample sizes for the analysis of the three genes in AMS have sufficient power to detect an increased allele prevalence of 33 % (ACEI/D, ACEA2350G) or 35 % (ACEA-240T) in AMS+ group (n = 44) compared to AMS- group (n = 59) and moderately 58  higher frequencies of the hypothesized causal alleles in the AMS+ cohort (13.3 % to 22.0 % for +9; 56.1 % to 69.0 % for -58T). As the presumptive risk C-allele is relatively rare (7 %) at the AGTR1A1166C loci, only a relatively large over-representation (> 2.5 fold) of this allele in the AMS+ would have been detected in this study. In summary, no association was found between all the tested alleles in ACE, AGTR1 and BDKRB2 and susceptibility to AMS in Nepalese who attended the Janai Purnima Festival and did or did not develop AMS. Our data have sufficient power to exclude a substantial role for the tested alleles in ACE and BDKRB2 in AMS susceptibility in Nepalese; however, our results do not exclude a genetic influence of ACE and BDKRB2 on the acclimatization to high altitude in other populations. In addition, the deviation from HWE at the BDKRB2C-58T polymorphism could hypothetically result from a protective effect of the C/T genotype, which if shown to be repeatable in this, or other population, merits future mechanistic investigations.  59  Figure 3.1 The Renin-Angiotensin Aldosterone system pathways. Angiotensinogen is a circulating globular protein produced by the liver and is broken into angiotensin I (Ang I) by renin from the kidneys. Angiotensin I is cleaved into Angiotensin II (Ang II) by angiotensin converting enzyme (ACE). Ang II (a strong vasoactive peptide) not only induces vasoconstriction but also stimulates the release of aldosterone in the adrenal glands. Aldosterone stimulates the increase in re-absorption of sodium and water in the kidneys and this event leads to the increase in blood volume. Vasoconstriction and the increase in blood volume induce the increase in blood pressure. The activity of another pathway that facilitates the increase in blood pressure is the degradation of bradykinin, a strong vasodilator, by ACE. Bradykinin is a 9-amino acid peptide and is cleaved from the high-molecular-weight kininogen by kallikrein and stimulates vasodilatation mainly through bradykinin B2 receptors (BDKRB2). * The gene encoding this molecule is investigated in this study.  60  ACEA2350G rs4343  5’  3’  ACEA-240T rs4291  ACEI/D rs4646994  Exon  Intron  UTR region  Figure 3.2 The structure of the angiotensin converting enzyme gene. ACE encompasses 20.55 kb on chromosome 17q23.3. Dark red bars, pale purple regions, and blue bars represent exons, introns, and untranslated regions, respectively. The three polymorphisms investigated in this study (i.e. ACEA-240T, ACEI/D, and ACEA2350G) locate at 240 bp before the initiation codon of exon 1, in intron 16, and in exon 17, respectively.  61  a) Three primers’ locations ACE-1  ACE-2 ACE-3  b) PCR products amplified by the three pairs of ACE primers ACE 2-3 product (65 bp) ACE 1-3 product (372 bp)  Insertion (I) allele  ACE 1-3 product (84 bp) Deletion (D) allele  Figure 3.3 PCR based assay for the insertion/deletion polymorphism in ACE. The Alu insertion segment is shown in light orange. ACE-1, ACE-2 and ACE-3 are primers. The three arrows indicate the relative locations of the three primers on ACE. PCR products for the insertion allele include 65 bp and 372 bp. The 65 bp PCR product is amplified by ACE2 : ACE-3. The large PCR product potentially amplified by ACE-1 (striped) : ACE-3 in the presence of the insertion is rarely seen. The PCR products of 84 bp for the deletion allele is amplified by ACE-1 : ACE-3. This figure is adapted from (Evans et al. 1994).  62  AGTR1A1166C rs17231380 5’  3’  Exon  Intron  UTR region  Figure 3.4 The structure of the angiotensin II type 1 receptor gene. AGTR1 compasses 45.13 kb on chromosome 3q21 – 25. Dark red bars, pale purple regions, and blue bars represent exons, introns, and untranslated regions, respectively. The AGTR1A1166C polymorphism investigated in this study locates at the 3’- untranslated region.  63  BDKRB2C-58T rs1799722  BDKRB2+9/-9 rs71103505  5’  3’  Exon  Intron  UTR region  Figure 3.5 The structure of the bradykinin B2 receptor gene. BDKRB2 compasses 39.53 kb on chromosome 14q32.1 – 32.2. Dark red bars, pale purple regions, and blue bars represent exons, introns, and untranslated regions, respectively. The two polymorphisms (i.e. BDKRB2+9/-9 and BDKRB2C-58T) investigated in this study locate at the exon one and promoter region, respectively.  64  DD  II  ID  AT  AA  TT  84 bp 65 bp  ACE I/D  AG  137 bp 114 bp  ACEA-240T (XbaI)  AA  GG  AA  AC  CC 350 bp  122 bp 103 bp  ACEA2350G (BstUI) +9/-9  +9/+9  212 bp 138 bp  AGTR1A1166C* (DdeI)  -9/-9  CC  CT  TT  89 bp 80 bp  BDKRB2+9/-9  133 bp 112 bp  BDKRB2C-58T (Bfa I)  Figure 3.6 Samples demonstrating the genotyping assay for the ACEI/D, ACEA-240T, ACEA2350G, AGTR1A1166C, BDKRB2+9/-9, and BDKRB2C-58T polymorphisms. Genotypes shown across top, SNP and diagnostic restriction endonuclease used in the assay shown at bottom, fragment size (in base pairs (bp)) shown at side. Images were modified for presentation purposes. *There was no individual with the C/C genotype in our Nepalese population. Notes: these images have been modified for presentation purpose.  65  a) ACEI/D genotype II Clin AMS+ 19 Clin AMS19  ID 19 24  DD 6 16  P value Allele: 0.11 Genotype: 0.22  b) ACEA-240T genotype AA Clin AMS+ 10 Clin AMS20  AT 29 26  TT 5 13  P value Allele: 0.92 Genotype: 0.08  c) ACEA2350G genotype AA Clin AMS+ 18 Clin AMS19  AG 20 24  GG 6 16  P value Allele: 0.15 Genotype: 0.25  66  d) AGTR1A1166C genotype AA Clin AMS+ 40 Clin AMS51  AC 4 8  CC 0 0  P value Allele: 0.79 Genotype: 0.55  Figure 3.7 Genotype and allele frequencies of (a) ACEI/D, (b) ACEA-240T, and (c) ACEA2350G polymorphisms in ACE and (d) AGTR1A1166C polymorphism in AGTR1 in Nepalese with and without AMS diagnosed by clinical evaluation. No significant difference was found between genotype or allele frequencies for any of the groups compared (P > 0.05).  67  a) ACEI/D genotype LLS AMS+ LLS AMS-  II 9 29  ID 8 35  DD 5 17  P value Allele: 1.00 Genotype: 0.84  b) ACEA-240T genotype LLS AMS+ LLS AMS-  AA 5 25  AT 12 43  TT 5 13  P value Allele: 0.48 Genotype: 0.66  c) ACEA2350G genotype LLS AMS+ LLS AMS-  AA 9 28  AG 8 36  GG 5 17  P value Allele: 0.92 Genotype: 0.79  68  d) AGTR1A1166C genotype LLS AMS+ LLS AMS-  AA 20 71  AC 2 10  CC 0 0  P value Allele: 1.00 Genotype: 1.00  Figure 3.8 Genotype and allele frequencies of (a) ACEI/D, (b) ACEA-240T, and (c) ACEA2350G polymorphisms in ACE and (d) AGTR1A1166C polymorphism in AGTR1 in Nepalese with and without AMS diagnosed by Lake Louise Scoring System (LLS > 3). No significant difference was found between genotype or allele frequencies for any of the groups compared (P > 0.05).  69  a) BDKRB2+9/-9 genotype AMS+ AMS-  -9-9 71 84  -9+9 21 29  +9+9 2 2  b) BDKRB2C-58T genotype AMS+ AMS-  TT 18 20  TC 65 75  CC 7 4  Figure 3.9 Genotype and allele frequencies of the BDKRB2+9/-9 and BDKRB2C-58T polymorphisms in Nepalese with and without AMS diagnosed by clinical evaluation and Lake Louis Score ≥ 3. No significant difference was found between genotype or allele frequencies for any of the groups compared (P > 0.05). The P values of the comparisons (i.e. AMS+ vs. AMS-) of genotype and allele frequencies of the polymorphisms are (a) BDKRB2+9/-9: genotype, P = 0.90; allele, P = 0.75; (b) BDKRB2C-58T: genotype, P = 0.55; allele, P = 0.70.  70  Table 3.1 Genotype and allele frequencies in ACE, AGTR1, and BDKRB2 in Nepalese Polymorphism  Genotype (frequency, %)  Allele frequency  ACEI/D  I/I  38 (36.9)  I  0.58  (rs4646994b)  I/D  43 (41.7)  D  0.42  D/D  22 (21.4)  ACEA-240T  AA  33 (32.0)  A  0.56  (ACE-4a, rs4291b)  AT  50 (48.6)  T  0.44  TT  20 (19.4)  ACEG2350A  AA  37 (36.0)  A  0.57  (ACE-8a, rs4343b)  GA  44 (42.7)  G  0.43  GG  22 (21.3)  AA  91 (88.0)  A  0.94  AC  12 (12.0)  C  0.06  CC  0 (0.00)  -9-9  155 (74.2)  -9  0.86  -9+9  50 (23.9)  +9  0.14  +9+9  4 (1.9)  CC  11 (5.8)  C  0.43  CT  140 (74.1)  T  0.57  TT  38 (20.1)  AGTR1A1166C (rs17231380b)  BDKRB2+9/-9 (rs72348790b)  BDKRB2C-58T (rs1799722b)  a. nomenclature from (Zhu et al. 2001). b. dbSNP identification number (http://www.ncbi.nlm.nih.gov/projects/SNP/)  71  Table 3.2 Published allele frequencies for the ACEI/D polymorphism in populations geographically close to Nepalese Population  I allele  D allele  P value*  Ref  Nepalese  0.66  0.34  0.07  (Umemura et al. 1998)  Nepalese  0.64  0.36  0.16  (Droma et al. 2008)  South Asian  0.61  0.39  0.43  (Sagnella et al. 1999)  Tibetan  0.63  0.37  0.003  (Buroker et al. 2010)  Indian  0.55  0.45  0.50  (Saha et al. 1996)  * The P values are for comparisons between the published allele frequencies of the ACEI/D polymorphism and that of this study.  72  Table 3.3 Published allele frequencies for the BDKRB2+9/-9 polymorphism in various populations Population  -9 allele  +9 allele  P value*  Ref  Caucasian  0.47  0.53  < 0.0001  (Lung et al. 1997)  Caucasian  0.50  0.50  < 0.0001  (Pretorius et al. 2008)  0.42  0.58  < 0.0001  (Lung et al. 1997)  1.00  0.00  < 0.0001  (Fu et al. 2004)  AfricanAmerican Asian (Japanese)  * The P values are for comparisons between the published allele frequencies of the BDKRB2 +9/-9 polymorphism and those of this study.  73  4  Association Analysis of the Beta-2 Adrenergic Receptor Gene with  Susceptibility to AMS in Nepalese11  4.1  Introduction The beta-adrenergic receptors are a family of G-protein coupled cell surface  catecholamine receptors that are involved in the sympathetic mediation of diverse physiological responses. The beta-2 adrenergic receptor (B2AR) is one member of this family and is mainly distributed in lungs including in airway smooth muscle cells, in the endothelial lining of the pulmonary vasculature, in secretory cells (type II alveoli), and in the pulmonary epithelium (see review by (Johnson 2006)). Like other G-protein-coupled receptors, the structure of the receptor includes three extracellular domains, the aminoterminus, three intracellular loops, and the carboxy-terminus (as shown in Figure 4.1). The activation of B2Ars in association with G-protein stimulates a series of signaling cascade, leading to a suite of effects including dilatation of both the pulmonary and bronchial vasculatures, relaxation of the bronchial smooth muscle, lung fluid clearance, systemic vasodilatation, and sympathetic responses in heart (e.g. increase in heart rate, cardio output) (see review by (Snyder et al. 2008)). The beta-2 adrenergic receptor gene (ADRB2) is an intronless gene located on chromosome 5. The gene structure is shown in Figure 4.2. Alleles of the polymorphisms in ADRB2 are commonly investigated as potential genetic contributors to cardiovascular, pulmonary, and metabolic diseases, and may be involved in individual responses to pharmacological treatment (see reviews by (Eisenach and Wittwer 2010, Litonjua et al. 2010)). More than 80 polymorphisms in ADRB2 have been identified by re-sequencing in multiple populations (Hawkins et al. 2006, Ortega et al. 2007). The alleles of the commonly investigated polymorphisms (e.g. rs1042713 (A to G at base 46 or Arg to Gly at amino acid 16 position), rs1042714 (G to C at base 79 or Glu to Gln at amino acid 27 position), and rs1800888 (C to T at base 491 or Thr to Ile at amino acid 164 position)) are 11  Data presented in the chapter was published as Common haplotypes in the beta-2 adrenergic receptor gene are not associated with acute mountain sickness susceptibility in Nepalese. High Alt. Med. Biol. 8: 206-212.  74  associated with the expression of the gene and the functions of the protein (Drysdale et al. 2000, Kay et al. 2010) and may explain the variability in the affinity of B2Ars to endogenous/exogenous agonists and the airway smooth muscle cell and vascular responses to exogenous agonists (i.a. (Green et al. 1993, Green et al. 1994, Green et al. 1995, Cockcroft et al. 2000, Hesse et al. 2010)). The ADRB2 mRNA levels and receptor density in transfected HEK293 cells with the Gly16+Glu27 haplotype are 50% higher than the cells with the Arg16 + Gln 27 haplotype (Drysdale et al. 2000). In human lung preparations (i.e. sub-pleural sampled from the periphery of upper, middle or lower zones following lobectomies) with the C/T genotype at rs1800888 (Thr164Ile) showed almost two-fold higher expression of ADRB2s than those with the C/C genotype (126 ± 15 vs. 69 ± 4 fmol/mg protein) (Kay et al. 2010). The transfected Chinese hamster fibroblasts 1102 cells with the substitution of Ile for Thr at amino acid 164 position showed almost four times lower binding affinity of B2AR to a variety of agonists and a decrease in agoniststimulated adenylyl cyclase activity of 50 %, which indirectly reflects the affinity between B2Ars and G proteins (Green et al. 1993). Human airway smooth muscle cell lines with the G/G46 genotype (Gly16) at rs1042713 showed higher agonist-promoted down-regulation of B2Ars than those with the A/A46 genotype following a 24-h exposure to agonists, whereas cells with the G/G79 genotype (Glu27) at rs1042714 presented less agonist-promoted down-regulation and functional desensitization those with the wild-types (i.e. the A/A46 (Arg16) or C/C79 (Gln27) genotypes) (Green et al. 1994, Green et al. 1995). Regional and systemic vascular responses are suggested to be influenced by the variants in ADRB2 (i.a. (Cockcroft et al. 2000, Hesse et al. 2010)) and individuals with the G/G49 (Gly16) and/or G/G79 genotype (Glu27) are associated with greater forearm vasodilatation in response to the B2AR agonist infusion (e.g. isoproterenol and terbutaline) compared with those who had the A/A49 genotype (Gly16) and/or C/C79 genotype (Gln27) (Cockcroft et al. 2000, Dishy et al. 2003, Trombetta et al. 2005, Hesse et al. 2010). B2Ars are suggested to mediate endothelial NO-dependent pulmonary vasodilatation in response to hypoxia in mice (Leblais et al. 2008)and may contribute to improving O2 delivery during altitude acclimatization (Mazzeo and Reeves 2003). The increase in catecholamine excretion in response to high altitude is suggested to be involved  75  in the development of AMS (Loeppky et al. 2003)) but no effect of the B2AR blockade on AMS at high altitude was detected in studies at done at 4300 m (Fulco et al. 1989). Given that B2Ars may be involved in the physiological responses to high altitude and play a role in the development of high altitude illnesses, the investigation described in this chapter was designed to investigate the association between genetic variants in ADRB2 and susceptibility to AMS. Haplotype analysis was employed to this study. Seven informative SNPs (tagSNPs) in ADRB2 were identified using HapMap tagSNP picking program from the online database provided by the International HapMap project (Thorisson et al. 2005), and associations between variants of the seven tagSNPs and AMS were assessed.  4.2  Materials and methods (see “general methods” section for details)  4.2.1  Subjects  One hundred and three Nepalese lowlanders were recruited at the Himalayan Rescue Association GosainKunda Temporary Health Camp in 2005 (male = 80; female = 23; mean age was 33 (range 18 – 76 yrs)). The subjects were grouped into AMS and nonAMS groups (AMS+ and AMS-) based on their clinical evaluation results and LLS scores. For the diagnosis of AMS, two separate criteria were used: physician clinical diagnosis and LLS. All physicians who took part in the clinic had training in high-altitude medicine, and diagnoses were based on history, physical, and global clinical impression. Subjects who had been diagnosed as having AMS by a physician were considered Clinical AMS. The physicians also assigned all subjects with a LLS. Patients who had headache, a score greater than 3 and recent gain in altitude were categorized as AMS by the LLS. Average LLS for the clinical AMS+ group was 4.25 out of a possible 15. For the clinical AMSgroup, mean score was 0.10. The clinical AMS+ cohort was significantly older than the AMS- cohort (average 38.4 vs. 28.4 yrs). While there were more women among the clinical AMS+ group, the difference was not significant (P = 0.093). Subjects who were diagnosed as having AMS by physicians were in the Clinical-AMS group (Clinical AMS+,  76  n = 44 and Clinical AMS-, n = 59) and those who had LLS > 3 were in LLS-AMS group (LLS AMS+, n = 22) and LLS AMS-, n = 81).  4.2.2  TagSNPs  The HapMap (http://hapmap.ncbi.nlm.nih.gov/) tagSNP picker identified maximally informative SNPs in the 5 kbp region encompassing the ~ 2.033 kbp ADRB2 gene. To ensure the tagSNP selection covering the most common haplotypes in ADRB2, the cut-off value was set at 0.95 and 0.1 for minimal coefficient of determination (r2)12 and minor allele frequency (MAF), respectively. A Chinese population (CHB, Han Chinese in Beijing, China) was used because, at the time that the experiments were designed, this population was the most closely related HapMap population to the Nepalese. Seven tagSNPs were identified: rs2400707 (A/G), rs253044 (A/G), rs12654778 (G/A), rs11168070 (G/C), rs1042713 (G/A), rs1042718 (C/A), and rs1042719 (G/C). The locations of these tagSNPs are shown in Figure 4.2.  4.2.3  Genotyping  DNA was amplified by PCR in a MJ Mini Cycler (Bio-Rad Laboratories, Hercules, CA, USA) using primers shown in Appendix D with the annealing temperature ranging from 54 ºC to 60 ºC. PCR products (10 µl) were digested using the diagnostic restriction endonuclease enzyme. The primer sequences, annealing temperatures, restriction endonuclease enzyme information are given in Appendix D. The forward primers for rs253044, rs12654778, rs11168070, and rs1042719 polymorphisms were modified by changing a base to generate diagnostic recognition sites for the enzymes (Msp I, Mse I, BsiHKA I, and Msp I, respectively) in the PCR products (see Appendix D). The digested products were separated by gel electrophoresis and sizes estimated by comparing to 100 bp  12  The minimal coefficient of determination (r2) at which all alleles are to be captured. Setting the threshold to 1.0 will  result in a non-redundant set of tag SNPs where all untyped SNPs will have a perfect proxy.  77  DNA ladder (Invitrogen Life Science Technologies). Examples of the genotype assays are shown in Figure 4.3.  4.3  Results Allele and genotype frequencies of the seven tagSNPs in the 2005 cohort (n = 103)  are shown in Table 4.1. Allele frequencies in the total cohort did not differ significantly from those of the HapMap database Chinese Population (although genotype frequencies did differ for rs253044, P = 0.02). Genotype frequencies in the Nepalese samples (pooled and the AMS +/- groups categorized by both clinical evaluation and the LLS) for all seven polymorphic loci are in Hardy–Weinberg equilibrium. Allele and genotype frequencies of the tagSNPs in the AMS+ and AMS- groups that were defined by either Clinical- or LLScategory are shown in Figure 4.4 and 4.5, respectively. No association was found between any alleles at the tagSNPs and AMS, suggesting that none of the alleles present on the common haplotypes in ADRB2 are involved in the etiology of the condition in the cohort tested.  4.4  Discussion This study was the first to investigate the association between variants in ADRB2  and AMS in any population and represents the first application of the powerful HapMap database to the investigation of the genetic basis of high-altitude illnesses. No association between any alleles at the seven tagSNPs tested in ADRB2 and AMS was found, suggesting that none of the alleles present on the common haplotypes in the gene are involved in the etiology of the condition. Association studies can only exclude an overrepresentation of an allele in the affected cohort within the limits of the study’s power to detect frequency variation. For the more common minor variants in this study (e.g. rs1042719), the sample size in this study has sufficient power to detect an increased allele prevalence of 33 %; whereas, in the case of the rs11168070, in which the minor allele frequency is 16 % in the controls, a 2.5-fold increase in (or an absence of) the minor allele would have achieved significance at P < 0.05. 78  Patterns of linkage disequilibrium and haplotype distributions can vary between populations (Salisbury et al. 2003) especially if the populations are small, isolated or recently founded. This is a major concern when applying the HapMap data to other populations (Need and Goldstein 2006). A number of studies have addressed this issue and have concluded that tagSNPs are generally portable and have a broad applicability across populations ((Conrad et al. 2006, Gonzalez-Neira et al. 2006); see review by (Need and Goldstein 2006)). As the population of Nepal is described as primarily of Mongoloid descent, with some admixture with Caucasoid features from the Indian subcontinent (Roychoudhury and Nei 1985)), the selection of tagSNPs was based on the HapMap Chinese data. The results of this study supported this decision as allele frequencies did not differ between Nepalese and the HapMap Chinese data; whereas, there was significant disparities in allele frequencies for least half the tagSNPs between the Nepalese and the other populations represented in HapMap such as Africans (i.e. Yoruba in Ibadan, Nigeria) and Caucasians (i.e. Utah residents with Northern and Western European ancestry). In summary, none of the alleles at the seven highly informative polymorphisms in ADRB2 selected for analysis in the AMS+ and AMS- Nepalese cohorts (based on the haplotype structure of the HapMap Chinese database) were associated with AMS (either clinically diagnosed AMS or AMS assigned by a LLS > 3). Altitude acclimatization involves many different physiological and molecular pathways, and altitude illnesses are likely polygenic traits that involve numerous genes, while our data suggest that alleles close to or in the gene encoding B2ARs are unlikely to play a unilateral role in the etiology of AMS in Nepalese.  79  Figure 4.1 The structure of the human beta-2 adrenergic receptor and mechanism of action in smooth muscle cells (insert). The amino acid positions in red are encoded at the polymorphic sites that are discussed or assayed in this study. Resultant amino acid changes are shown if applicable. The three underlined amino acids (Arg16Gly, Arg 175, and Gly 351) are the polymorphic sites (rs1042713 (A to G, synonymous), rs1042718 (C to A, nonsynonymous), and rs1042719 (G to C, non-synonymous)) investigated in this study, respectively. The rs1042713 is a synonymous polymorphism while both rs1042718 and rs1042719 are non-synonymous polymorphisms. Insert: Agonist bound receptor is coupled to and activates adenlyl cyclase (AC) via a stimulatory guanine nucleotide regulatory protein (Gs). Elevated AC activities increase cAMP, which activates protein kinase A (PKA). The activated PKAs phosphorylate the proteins that are involved in airway relaxation. This figure is adapted from Liggett and Raymond (1995) and Barnes (1995).  80  rs2400707 rs1042713  rs12654778  rs1042713  rs1042719  5’  3’ rs253044  rs11168070  rs1042718  Exon  Intron  UTR region  Figure 4.2 The structure of the beta-2 adrenergic receptor gene and the locations of the seven tagSNPs assayed in this study. ADRB2 is an intronless gene that encompasses 2.033 kb on chromosome 5 (5q31 – 32). The seven tagSNPs (rs1042713, rs2400707, rs1042718, rs11168070, rs12654778, rs1042719, and rs253044) were selected in the 5 kb region incorporating ADRB2 and up- and down-stream flanking regions.  81  GA  GG  GG  AA  GA  AA 449 bp  87 bp  295 bp  63 bp  154 bp rs2400707 (HhaI)  rs253044 (MspI)  GG  GC  CC  AA  CC  AC 173bp  99 bp  104 bp 69 bp  67 bp  rs11168070 (BsiHKAI)  GC  GG  rs1042718 (MspI)  CC  AA  GG  GA 94 bp  113 bp 90 bp  74 bp  rs1042719 (MspI)  GG  rs12654778 (MseI)  GA  AA 135 bp 117bp  rs1042713 (NcoI)  Figure 4.3 Samples demonstrating the genotyping assay for the seven tagSNPs in ADRB2. The tagSNPs are rs1042713, rs2400707, rs1042718, rs11168070, rs12654778, rs1042719, and rs253044. Diagnostic restriction enzymes are in brackets. Genotypes shown across top, SNP and diagnostic restriction endonuclease used in the assay shown at bottom, fragment size (in base pairs (bp)) shown at side. Images were modified for presentation purposes. 82  a) rs1042713 genotype AMS+ AMS-  GG 10 19  GA 28 30  AA 6 10  P value Allele: 0.66 Genotype: 0.43  b) rs2400707 genotype AMS+ AMS-  GG 23 29  GA 20 27  AA 1 3  P value Allele: 0.63 Genotype: 0.83  CA 21 27  AA 4 10  P value Allele: 0.31 Genotype: 0.50  c) rs1042718 genotype AMS+ AMS-  CC 19 22  83  d) rs11168070 genotype AMS+ AMS-  CC 29 41  CG 15 17  GG 0 1  P value Allele: 1.00 Genotype: 0.81  e) rs12654778 genotype AMS+ AMS-  GG 18 29  GA 22 23  AA 4 7  P value Allele: 0.81 Genotype: 0.66  f) rs1042719 genotype AMS+ AMS-  CC 11 16  CG 20 26  GG 13 17  P value Allele: 0.84 Genotype: 0.97  84  g) rs2053044 genotype AMS+ AMS-  GG 21 27  GA 22 29  AA 1 3  P value Allele: 0.70 Genotype: 0.88  Figure 4.4 Genotype and allele frequencies of the seven tagSNPs in ADRB2 in Nepalese with and without AMS diagnosed by clinical evaluation. The tagSNPs are (a) rs1042713, (b) rs2400707, (c) rs1042718, (d) rs11168070, (e) rs12654778, (f) rs1042719, and (g) rs253044). No significant difference was found between genotype or allele frequencies for any of the groups compared (P > 0.05).  85  a) rs1042713 genotype AMS+ AMS-  GG 6 23  GA 12 46  AA 4 12  P value Allele: 0.93 Genotype: 0.92  b) rs2400707 genotype AMS+ AMS-  GG 11 41  GA 10 37  AA 1 3  P value Allele: 1.00 Genotype: 0.92  AA 2 12  P value Allele: 0.33 Genotype: 0.51  c) rs1042718 genotype AMS+ AMS-  CC 11 30  CA 9 39  86  d) rs11168070 genotype AMS+ AMS-  CC 14 56  CG 8 24  GG 0 1  P value Allele: 0.92 Genotype: 0.69  AA 2 9  P value Allele: 1.00 Genotype: 0.79  e) rs12654778 genotype AMS+ AMS-  GG 9 38  GA 11 34  f) rs1042719 genotype AMS+ AMS-  CC 4 23  CG 11 35  GG 7 23  P value Allele: 0.53 Genotype: 0.63  87  g) rs253044 genotype AMS+ AMS-  GG 10 38  GA 11 40  AA 1 3  P value Allele: 1.00 Genotype: 1.00  Figure 4.5 Genotype and allele frequencies of the seven tagSNPs in ADRB2 in Nepalese with and without AMS diagnosed by LLS > 3. The tagSNPs are (a) rs1042713, (b) rs2400707, (c) rs1042718, (d) rs11168070, (e) rs12654778, (f) rs1042719, and (g) rs253044. No significant difference was found between genotype or allele frequencies for any of the groups compared (P > 0.05).  88  Table 4.1 Genotype and allele frequencies of the seven tagSNPs in ADRB2 in the 2005 cohort. Polymorphism* rs1042713  rs2400707  rs1042718  rs11168070  rs12654778  rs1042719  rs2053044  Genotype (frequency, %) GG GA AA GG GA AA CC CA AA CC GC GG GG GA AA CC GC GG GG GA AA  29 (28) 58 (56) 16 (16) 51 (50) 47 (46) 4 (04) 41 (40) 48 (47) 14 (13) 70 (68) 32 (31) 1 (01) 47 (46) 45 (44) 11 (10) 27 (26) 46 (45) 30 (29) 48 (47) 51 (49) 4 (04)  Allele frequency G A  0.56 0.44  G A  0.72 0.27  C A  0.63 0.37  C G  0.83 0.17  G A  0.67 0.33  C G  0.49 0.51  C T  0.71 0.29  * The detail information of each polymorphism (e.g. type of polymorphism, location, and phenotypes associated) investigated in this study is listed in Appendix D.  89  5  Association Analysis of the Endothelial Nitric Oxide Synthase Gene  (NOS3) with Susceptibility to AMS in Nepalese13  5.1  Introduction Nitric oxide (NO) is a gaseous signaling molecule that exerts a range of effects on  vascular homeostasis, including increased blood flow, vasodilatation, platelet inhibition, and vascular smooth muscle relaxation and proliferation (see review by (Moncada and Higgs 2006)). NO also plays important roles in pulmonary ventilation, including airway smooth muscle relaxation and the matching between ventilation and perfusion (see review by (Ozkan and Dweik 2001)). The main source of endogenous NO is a family of nitric oxide synthases (NOSs), including neuronal nitric oxide synthase (nNOS), inducible nitric oxide synthase (iNOS), and endothelial nitric oxide synthase (eNOS). The nomenclature reflects the tissues in which the three enzymes were originally identified (e.g. eNOS was originally purified and cloned from endothelial cells along blood vessels), although the distributions of the three enzymes are not limited to the original tissues. Approximately 51 – 57 % of the amino acid sequences of the NOS enzymes is identical and contain functional domains that catalyze the synthesis of nitric oxide (NO) from L-arginine in the presence of oxygen and other cofactors (e.g. nicotinamide adenine dinucleotide phosphate (NADPH) and calmodulin) (Michel and Feron 1997, Forstermann et al. 2003)). The human genes encoding these three enzymes are classified in the order of their identification and named as NOS1 (nNOS), NOS2 (iNOS), and NOS3 (eNOS). Both NOS1 and NOS3 are expressed consecutively and are the main source of endogenous NO. The expression of NOS2 is not consecutive but can be induced in a wide range of cells and tissues by a variety of agents (e.g. cytokines) and conditions (e.g. hypoxia) (Pautz et al. 2010). Hypoxia reduces the availability of oxygen and may limit the production of NO from L-arginine; however, the extent to which hypoxia affects endogenous NO synthesis is still unclear. A decrease in partial pressure of exhaled nitric oxide level (exNO) of 19 % (P 13  Data presented in the chapter was published as Alleles at the G298T polymorphism in the eNOS gene are associated with the susceptibility to acute mountain sickness. High Alt. Med. Biol. 10: 261-267.  90  < 0.001) was found after ascent from sea level to 4200 m (Brown et al. 2006) and, in wellacclimatized high altitude miners, exNO levels were gradually reduced during 2- or 3week shifts at 4000 m (Vinnikov et al. 2011). Conversely, other studies showed that exNO levels remained unchanged during normobaric hypoxic exposures (Hemmingsson and Linnarsson 2009, Donnelly et al. 2011). These inconsistent results may be due to the discrepancies between procedures, including modes of hypoxic exposure (i.e. normobaric hypoxia vs. hypobaric hypoxia) (Kayser 2009), duration of exposure, NO analyzer used and methods of measurements (MacInnis et al. 2012). Despite these discrepancies, low exNO levels at sea level and reduced exNO in acute hypoxic exposures may contribute to hypoxic pulmonary hypertension via vasoconstriction and the development of high altitude illnesses ((Busch et al. 2001, MacInnis et al. 2012)) while inhalation of NO improves the distribution of blood flow, promotes pulmonary arterial oxygenation in the lungs, and relieves the symptoms of high-altitude illness (Scherrer et al. 1996). In addition to the effects of exNO, plasma NO (pNO) is also suggested to play an important role in vascular response to hypoxic exposures (Blitzer et al. 1996, Van Mil et al. 2002); however, whether these changes in pNO levels are associated with the development of AMS is unknown. When NO production was inhibited by administration of NG-monomethyl-L-arginine, forearm vascular resistance during acute hypoxic exposure increased significantly in compared to that at sea level (67 % vs. 39 %, P < 0.05) while reduced forearm vasodilator’s response to hypoxia from 27 % to 11 % (P = 0.01) (Blitzer et al. 1996). A later study showed that an acute hypoxic exposure resulting in peripheral O2 saturation (SpO2) at 80 % induced a 13 % increase in cerebral blood flow (CBF) and the elevated CBF levels returned to the values at sea level after the inhibition of NO synthesis (P < 0.05) (Van Mil et al. 2002). These two studies suggest that pNO is involved in vascular responses to hypoxia and may play an important role in acclimatization. Recently, Bailey and associates reported a general decline (approximately 50 %, P < 0.05) in total NO levels in response to a 6-h hypoxia (FIO2 = 0.12) and lower baseline values of plasma (nitrate, nitrate, and S-nitrosothiols) and red blood cell NO metabolite pools (NO2, nitrosyl haemoglobin, and S-nitrosohaemoglobin) were associated with AMS (P < 0.05) (Bailey et al. 2009b). Consistently, MacInnis and associates reported that lower baseline exNO was  91  associated with the development of AMS during a 6-h hypoxic exposure at a simulated altitude of 4550 m (MacInnis et al. 2012). The underlying mechanism of the role of pNO in developing AMS is unclear. A blunted cerebral uptake of NO2, which is a TVS-activating molecule, was found in response to a 9-h hypoxic exposure, due to less arterial delivery; however, the changes in NO2 uptake was independent of the development of AMS and headache scores (Bailey et al. 2009b). The authors suggested that, since NO2 is an important reserve of NO, the decrease in arterial NO2 delivery might reflect an increased intravascular conversion from NO2 to NO to induce vasodilatation in response to hypoxemia to preserve oxidative metabolism; however, the lack of association between the reduced cerebral NO2 level and AMS suggests that TVS activation may not be the main cause of developing the sickness (Bailey et al. 2009b). The beneficial effects of endogenous NO on high-altitude acclimatization are also indicated by its role in the process of high-altitude adaptation (Beall et al. 2001, Ahsan et al. 2004, Ahsan et al. 2005, Erzurum et al. 2007). Tibetans showed 200 % higher exNO than lowlanders and this characteristic may help the highlanders improve oxygen uptake and consequently increase oxygen delivery to peripheral tissues to compensate for ambient hypoxia (Beall et al. 2001). In addition, Tibetans were also reported to have a more than 10-fold higher circulating concentration of bioactive NO products in plasma and in red blood cells compared to lowlanders, including plasma nitrite and the nitrate and nitroso (RNO) proteins (Erzurum et al. 2007). The authors suggested that the characteristics induced by highly bioactive NO products, such as higher NO synthesis and NO-regulated vasodilatation, may be important to the underlying mechanism of high-altitude adaptation in Tibetans (Erzurum et al. 2007). Consistently, significantly higher pNO levels were also shown in Indian native highlanders compared to lowlanders (95.35 µM vs. 90.53 µM, P < 0.0001) (Ahsan et al. 2004, Ahsan et al. 2005). In general, these findings indicate that the pioneer lowlanders who had high endogenous NO levels or who had evolved to produce more NO in response to the stresses of hypobaric hypoxia might have had advantages to overcome high-altitude illnesses during the process of migration to the highlands and were then able to prosper at high altitude compared to ’competitors’ with lower NO levels. 92  Endothelial nitric oxide synthase (eNOS) is the primary source of NO in blood vessels and it is encoded by NOS3. The expression of NOS3 is regulated by physical (e.g. sheer stress) and chemical (e.g. PaO2) factors (see review by (Chatterjee et al. 2008)). The alleles of several functional polymorphisms in NOS3 (i.e. rs2070744 (T-786C), rs1799983 (G894T or Glu298Asp), and 4a/4b) were associated with the gene expression, enzyme activities, and NO concentration (Wang et al. 2000, Ahsan et al. 2006, Dosenko et al. 2006). Among these three functional polymorphisms, the most commonly studied is rs1799983 (Glu298Asp), which is an amino acid changing polymorphism resulting from a G to T transversion at base 894. This transversion changes the codon GAG (encoding glutamic acid) to GAT (encoding aspartic acid) at amino acid 298 of eNOS. The T allele (298Asp) of rs1799983 showed a negative dosage effect on eNOS activities in human placenta (Wang et al. 2000) and is associated with maladaptive responses to acute hypoxia in different populations (Droma et al. 2002, Ahsan et al. 2004). Conversely, the G allele is suggested to be beneficial to high-altitude adaptation (Ahsan et al. 2005). Given the potential role of variants in NOS3 in high-altitude acclimatization and adaptation, the study described in this chapter aimed to assess the associations between variants in NOS3 and susceptibility to AMS by comparing allele and genotype frequencies between Nepalese individuals who did or did not have AMS. Haplotype-based analysis was employed to analyze genetic associations in this study. Seven informative SNPs (tagSNPs) in NOS3 were identified using HapMap tagSNP picking program from the online database provided by the International HapMap project (Thorisson et al. 2005), and associations between variants of the seven tagSNPs and AMS susceptibility were assessed.  93  5.2  Materials and methods (see “general methods” section for details)  5.2.1  Subjects  Part 1: AMS and non-AMS One hundred and three Nepalese lowlanders (male = 80; female = 23; mean age of 33 (range 18 – 76 yrs)) participated in this study while they attended the Janai Purnima Festival at Gosainkunda (4380 m), Nepal, in 2005. The classification of subjects into AMS and non-AMS groups (AMS+ and AMS-) was based on their clinical evaluation results and LLS scores. Subjects who were diagnosed as having AMS by physicians and with LLS of 3 or higher were assigned to the AMS+ group. Eleven subjects who were diagnosed as AMS by physicians but with LLS less than 3 were eliminated from the study, resulting in a cohort of 92 (AMS+: n = 33) and AMS-: n = 59). The second recruitment was performed during the same festival at Gosainkunda in 2008. One hundred and twenty five subjects were recruited (male = 90; female = 35; mean age of 31 (range 16 – 61 yrs)). The method of recruitment and the criteria for the group classification were the same as those applied in 2005. If an association was found between the variants of a tagSNP in the 2005 cohort, the same genotyping assay of that tagSNP would be performed in the 2008 cohort to see if the association could be replicated. Part 2: Highland and lowland native Americans DNA samples from Quechua (n = 78) and Mayan (n = 49) volunteers were collected during previous studies on high-altitude populations (Kidd et al. 1991, Barr and Kidd 1993, Rupert et al. 1999). Quechua subjects (n = 60) that lived between altitudes of 3600 m and 4500 m in the vicinity of Ollantaytambo, Peru were recruited (Rupert et al. 1999) and only the volunteers with four Quechua grandparents were included in order to minimize genetic admixture. Blood or buccal samples were taken after obtaining informed consent for genetic analysis. The Kidd Lab provided additional Quechua DNA samples (n = 18) and all Mayan DNA samples (n = 49). These DNAs were prepared from cultured 94  lymphoblast cell lines established from blood samples obtained from Quechua living in central Peru (Barr and Kidd 1993). The Mayan DNA samples were prepared from cultured lymphoblast cell lines established from blood samples obtained from Yucatec speaking Mayans living on the low-lying Yucatan peninsula (under 500 m) in Eastern Mexico (Kidd et al. 1991).  5.2.2  TagSNP selection and genotyping  1) TagSNP selection The HapMap (http://www.hapmap.org) tagSNP picker identified seven maximally informative SNPs in a 23.53 kbp region encompassing NOS3. To ensure the tagSNP selection covered the most common haplotypes in NOS3, the cut-off value was set at 0.95 and 0.1 for minimal coefficient of determination (r2) and minor allele frequency (MAF), respectively. A Chinese population (CHB, Han Chinese in Beijing, China) was used, because this population was the most closely related HapMap population to the Nepalese at the time that these experiments were designed. The seven tagSNPs selected were rs1799983 (G/T), rs1808593 (T/G), rs7830 (C/A), rs743507 (A/G), rs3918188 (A/C), rs3918186 (A/T), and rs1800781 (G/A). The locations of these tagSNPs in NOS3 are shown in Figure 5.1. 2) Genotyping DNA was amplified by PCR in a MJ Mini Cycler (Bio-Rad Laboratories, Hercules, CA, USA) using primers shown in Appendix D with the annealing temperature range from 54 ºC to 60 ºC. PCR products (10 µl) were digested using restriction endonuclease enzyme. The primer sequences, annealing temperatures, and digestion restriction endonuclease enzyme information of the digestion reaction are shown in Appendix D. The forward primers for rs1800781, rs3918188, rs7830, and the reverse primer for rs3918186 were modified to generate diagnostic recognition sites for the enzymes (Ban I, BstU I, Hae III, and Bcl I, respectively). The digested products were separated by gel electrophoresis, and sizes were estimated by comparison with a 100 bp DNA ladder (Invitrogen Life Science 95  Technologies). Examples of the electrophoresed products for each assay are shown in Figure 5.2.  5.3  Results Part 1: AMS in Nepal Allele and genotype frequencies of the seven tagSNPs in the 2005 cohort (n = 103)  are shown in Table 5.1. The genotype distribution of each tagSNP was in Hardy-Weinberg equilibrium (HWE) (P > 0.05). Allele and genotype frequencies of the seven tagSNPs in the AMS+ and AMS- groups are shown in Figure 5.3. No over-representation of any genotype or allele was found at six of the tagSNPs (rs1800781, rs3918186, rs3918188, rs743507, rs1808593, and rs7830). The frequency of the G/T genotype at rs1799983 in the AMS+ group was significantly higher than that in the AMS- group (55 % vs. 22 %, P = 0.004), even after the Bonferroni correction for multiple testing. The frequency of the T allele, which encodes an aspartic acid at residue 298 of eNOS, was significantly higher in the AMS+ group than in the AMS- group (30 % vs. 16 %, P = 0.024); however, the difference did not maintain significance after the Bonferroni correction. Since an association was found between the G/T genotype at rs1799983 and AMS, the genotype and allele frequencies at this locus were assessed in the 2008 cohort (n = 125; AMS+: n = 68, AMS-: n = 57). One hundred and sixteen samples were amplified successfully and the data were analyzed independently and pooled with the 2005 data. The results of the 2008 cohort and the combined (2005+2008) cohort are shown in Figure 5.4. The genotype frequencies were in HWE in both the 2008 and the combined cohorts (for 2008 cohort: χ2 = 4.01, P = 0.13; for the combined cohort: χ2 = 1.90, P = 0.39). No significant difference in the genotype or allele frequencies at rs1799983 was detected between the AMS+ and AMS- groups in either the 2008 or the 2005+2008 cohort. In addition, as the T allele of rs1799983 was rare in the samples, the G/T and T/T genotypes were combined into one group to assess the effects of the T allele on susceptibility to AMS in the 2005, 2008 and the 2005+2008 cohorts. The frequency of the G/T&T/T genotype  96  was significantly higher in the AMS+ group compared to that in the AMS- group in the 2005 (P = 0.004) and 2005+2008 cohorts (P = 0.049) but not in the 2008 cohort alone (P = 0.689) (Figure 5.5). Part 2: Highland and lowland native Americans An association was found in the study of the 2005 Nepalese cohort suggesting that the G allele at rs1799983 may confer resistance to AMS; therefore the second hypothesis: that the variants associated with susceptibility to AMS may have been involved in highaltitude adaptation in Andeans, was tested. The allele and genotype frequencies at rs1799983 were compared between Quechua (highlanders, n = 78) and Maya (lowlanders, n = 49) populations (Figure 5.6). Genotype frequencies were in HWE in both Quechua and Maya groups (Quechua: χ2 = 0.23, P = 0.89; Maya: χ2 = 0.0, P = 1.0). The G allele and GG genotype were over-represented in Quechua in comparison to Maya (P = 0.03 for allele frequency; P = 0.01 for genotype frequency).  5.4  Discussion A significant difference in genotype and allele frequencies at rs1799983 (G894T or  Glu298Asp) was found between the AMS+ and AMS- groups of the 2005 study (P = 0.004 and 0.024, respectively). The frequencies of the T allele and G/T genotype were more common in the affected group (AMS+ vs. AMS-: 30 % vs. 16 %; 55 % vs. 22 %, respectively). Caution was necessary when assessing the differences in the genotype and allele frequencies between the AMS+ and AMS- groups, due to two reasons that may increase the occurrence of type I error: one reason is that, although the seven tagSNPs are the best to represent all common haplotypes over the region encompassing NOS3, these SNPs are not independent and, to some extent, in linkage disequilibrium with each other and with other genetic markers contributing to susceptibility to AMS beyond this region while the second reason is that seven SNPs in NOS3 had been tested and this increased the chance of finding a significant difference (P < 0.05) to 30 %. To minimize the possibility of a type I error (false positive), the Bonferroni correction was applied in this genetic  97  assessment and the corrected significance level was accepted at P < 0.007, which was calculated by dividing the original significance level (P < 0.05) by the number of tests. After the correction, the over-representation of the G/T genotype in the AMS+ group was still significant while that of the T allele was non-significant. In the 2005 cohort, the sample sizes for the association analyses of this study had sufficient power to detect an increased allele prevalence of 15 % (rs1800781, rs7830 and rs743507), 20 % (rs1808593) and 27 % (rs3918188) in the AMS+ group (n = 33) in compared to the AMS- group (n = 59) whereas only a slightly increase in allele prevalence of 8 % in the AMS+ group would have achieved significance at P < 0.007. Among the seven tagSNPs, rs1799983 is the only polymorphism that was previously reported to be functional. The presence of the T allele induces a conservative amino acid change from an aspartic acid (Asp) substituted for a glutamic acid (Glu) in eNOS at position 298. Individuals with the T/T genotype showed 80 % less eNOS activity compared to those with the G/G genotype in placenta tissue (Wang et al. 2000). Lower basal NO production and blunted change in forearm blood flow in response to inhibition induced by N(G)-monomethyl L-arginine (L-NMMA) were shown in individuals with the G/T or T/T genotype compared to those with the G/G genotype (-25 % vs. -37 %; P = 0.02) (Veldman et al. 2002). The G/T genotype was over-represented in the AMS+ group in the 2005 cohort after the Bonferroni correction, indicating that individuals who were susceptible may have disadvantages in high altitude acclimatization due to low NO levels. The relationship between endogenous NO and AMS has not been thoroughly investigated and is not fully understood. Brown and associates measured exNO following a rapid ascent (~ 2 h) to 4200 m (Brown et al. 2006). exNO level decreased significantly (19 %); however, this was independent of AMS. A short exposure to high altitude (3 h) was employed in Brown’s study and induced relatively low incidence of AMS (19 %). The individuals who did not present the AMS symptoms during this short exposure might develop AMS if they had spent more time at altitude. A longer exposure may be necessary to show the effect of high altitude on exNO levels and increase the confidence in the conclusion. Also, the small sample size of the AMS+ group (n = 13) limits the strength to extrapolate their conclusion. Schneider and associates reported that L-arginine infusion 98  slightly reduced the AMS score at 4350 m (Schneider et al. 2001) suggesting that eNOS activities are involved the process of alleviating AMS symptoms; however, whether the responses are dependent on the variants in NOS3 is not known. The over-representation of the G/G genotype in the AMS- group of the 2005 cohort (AMS- vs. AMS+: 73 % vs. 42 %) suggests that having the G/G genotype may be useful during the process of migration and adaptation to high altitude, in which case the G/G genotype would be expected to be more common in native highlander populations. To test this hypothesis, the allele and genotype frequencies at rs1799983 were compared between Quechua (native highlanders) and Mayan (lowlanders) populations. The frequencies of the G allele and the G/G genotype were significantly higher in Quechua compared to Mayan who live at lowland areas (Figure 5.6). This result supports the hypothesis and is consistent with the previous findings, which showed that the endogenous NO levels were associated with high altitude adaptation (Beall et al. 2001, Ahsan et al. 2005, Erzurum et al. 2007). The over-representation of the G allele and G/G genotype in highlanders does not directly support a protective role on resistance to AMS; however, this indicates that the pioneer highlanders who were with the G/G genotype may have had genetic advantages to overcome high-altitude illnesses (e.g. AMS) in the process of migration and adaptation and were able to prosper at high altitude. Quechua is the largest linguistically defined native population in Americas. Many of the Quechua live in the city of Cusco (3400 m), Peru, or in towns and villages in the Andean highlands and are considered to be well adapted to life at altitude. The Quechua DNA samples investigated in this study were collected from (1) individuals who lived between altitudes of 3600 m and 4500 m in the vicinity of Ollantaytambo, Peru and have four Quechua grandparents; and (2) those who lived in Lima, Peru but only speak Quechua rather than Spanish. If the high frequency of the G allele (Glu298) in this Quechua cohort is due to selection, there are two possible general but non-exclusive explanations related to the high-altitude environment. One is that the allele contributes to the process of adaptation and has been selected for in the population over the generations since Quechua people migrated to the altiplano. The other explanation is that the allele reduces susceptibility to altitude illnesses and was selected for high-altitude adaptation during the migration. While 99  differential reproductive capability (e.g. due to reproductive failure, early mortality, or sexual non-competiveness) has been proposed as the principal driver of selection in many evolutionary theories, differential migration can also promote the change in allele frequencies. When the ancestors of the Quechua populated at the altiplano, those who felt discomfort might have chosen to return to lower altitudes. If these were the case, there would be a rapid drop in frequency of any allele that contributed to the discomfort. In addition, it was documented in contemporary sources that the Inca leaders were aware of that the curative effects of changing altitudes on the debilitating conditions induced by altitude (West 1998). In such cases, the alleles in association with susceptibility to the discomfort may have remained at their initial reduced level and/or continued to be slowly eliminated due to either biological or cultural selection (Moseley 2001). The first two findings of this study support that the G allele at rs1799983 is associated with a lower susceptibility to altitude illnesses and may have been selected for high altitude adaptation during migration to the Andean altiplano. To verify the association between the alleles at rs1799983 and AMS, another study was performed on a group of Nepalese who were recruited at the same location, season and festival in 2008. The sample sizes for the analysis in the 2008 cohort have sufficient power to detect an increased allele prevalence of 10 % in the AMS+ group (n = 63) compared to the AMS- group (n = 53). No significant differences in the allele and genotype frequencies at rs1799983 were found between the AMS+ and AMS- groups in both the 2008 and the 2005+2008 cohorts. Several factors may contribute to the failure to repeat the association in the 2008 cohort. The symptoms of AMS are non-specific and this increases diagnostic confusion with other disorders and may affect the accuracy of the ascertainment of AMS. The improper and inaccurate definition of symptoms can induce spurious associations (see review by (Attia et al. 2009)). Dr. Michael Koehle, who organized and led the recruitments in both years, commented that the supervision of the recruiters in 2008 was not as close as that in 2005 (Koehle, personal communication); therefore, the phenotypes of the 2008 group may not be as distinct (i.e. AMS+ vs. AMS-) as that of the 2005 group. This could induce the inconsistency of the results between the two years. It is also possible that the association found in the 2005 cohort was a type I error since the association was drawn from such a  100  small sample size, which has less power to reach a firm conclusion. In addition, population stratification may contribute to a type I error in the association found in the 2005 cohort. The geographical location of Nepal determines that the genetic composition of Nepalese population is highly influenced by Tibetans and Indians. Tibetans are adapted to high altitude while Indians are native lowlanders. These two neighbour populations may have different levels of susceptibility level of the development of altitude illnesses. Different extent of ancestral contribution from Tibetans and Indians to the AMS+ and AMS- groups may lead to false positive signal and spurious association and this is called population stratification. Detection of population stratification was performed and is described in Chapter 6; however, the data do not support that population stratification is an explanation for the results of the studies described in this chapter. In summary, the G/G genotype of rs1799983 (Glu298Asp) in NOS3 was overrepresented in the AMS- group in the 2005 cohort even after a conservative statistical correction for multiple testing. In addition, the G allele and G/G genotype were common in native Quechua highlanders. The association between the G allele and a lower susceptibility to AMS was not repeated in the follow-up study performed in the 2008 cohort. The T/T genotype is rare in both cohorts and the T-allele dominant comparison (G/G vs. G/T&T/T) was applied. The frequency of the G/T&T/T genotypes was significantly higher in the individuals who had AMS than that in individuals who did not in the 2005 and 2005+2008 cohorts but not in the 2008 cohort. The findings of this study suggest that individuals who have the T-genotypes (G/T and T/T) of rs1799983 may be more susceptible to developing AMS. Further studies in larger cohorts of Nepalese and different highland/lowland populations are needed to understand the role of the alleles in NOS3 in the development of AMS.  101  rs3918188  rs1800781  rs1808593  5' ’  3' ’ Glu298Asp rs1799983  rs7830  rs3918186 rs743507  Exon  Intron  UTR region  Figure 5.1 The structure of the endothelial nitric oxide synthase gene. NOS3 compasses 23.53 kb on chromosome 7q36. Dark red bars, pale purple regions, and blue bars represent exons, introns, and untranslated regions, respectively. The seven tagSNPs investigated in this study (rs1800781, rs1799983, rs3918186, rs3918188, rs743507, rs1808593, rs7830) are located at intron 3, extron 7, intron 14, intron21, intron 24, and intron 22, respectively.  102  AG  AG  AA  GT  GG  TT 248 bp 163 bp  106 bp 83 bp  85 bp rs1800781 (BanI) AA  CC  rs1799983 (BanII) GG  AC  TT  GT  187 bp  83 bp  110 bp 77 bp  62 bp  rs1808593 (DpnII)  rs3918188 (BstUI) CC  AC  AA  GG  AA  GA 170 bp 104 bp  103 bp 81 bp  85 bp  rs7830 (HaeIII)  rs743507 (HaeIII)  AA  AT  AA  160 bp 33 bp  rs3918186 (BclI)  Figure 5.2 Samples demonstrating the genotyping assay for the seven tagSNPs in NOS3. The tagSNPs are rs1800781, rs1799983, rs3918188, rs1808593, rs3918188, rs743507, and rs3918186. Genotypes shown across top, SNP and diagnostic restriction endonuclease used in the assay shown at bottom, fragment size (in base pairs (bp)) shown at side. Images were modified for presentation purposes. Diagnostic restriction enzymes are in brackets.  103  a) rs1800781 genotype GG AMS+ 26 AMS49  b) rs1799983 genotype GG AMS+ 14 AMS43  GA 7 10  GT 18 13  AA 0 0  TT 1 3  P value Allele: 0.632 Genotype: 0.780  P value Allele: 0.024 Genotype: 0.004  c) rs3918186 genotype AMS+ AMS-  AA 23 51  AT 10 8  TT 0 0  P value Allele: 0.067 Genotype: 0.061  104  d) rs3918188 genotype AA 3 8  AMS+ AMS-  AC 16 28  CC 14 23  P value Allele: 0.594 Genotype: 0.908  e) rs743507 genotype AMS+ AMS-  AA 15 36  f) rs1808593 genotype TT AMS+ 16 AMS36  AG 18 20  TG 17 18  GG 0 3  GG 0 5  P value Allele: 0.424 Genotype: 0.113  P value Allele: 0.764 Genotype: 0.051  105  g) rs7830 genotype AMS+ AMS-  CC 7 20  AC 21 32  AA 5 7  P value Allele: 0.292 Genotype: 0.432  Figure 5.3 Genotype and allele frequencies of tagSNPs in NOS3 in Nepalese (2005) with and without acute mountain sickness (AMS) diagnosed by clinical evaluation and Lake Louise score ≥ 3. The tagSNPs are (a) rs1800781, (b) rs1799983, (c) rs3918186, (d) rs3918188, (e) rs743507, (f) rs1808593, and (g) rs7830. The frequency of the G/T genotype at rs1799983 in the AMS+ group was significantly higher in comparison to that in the AMS- group (P = 0.004) and this significance is strong still after the Bonferroni correction. No significant difference was found between genotypes and alleles of other tagSNPs for the groups compared.  106  a) rs1799983 genotype (2008) AMS+ AMS-  GG 43 38  b) rs1799983 genotype (2005+2008) GG AMS+ 57 AMS81  GT 16 12  TT 4 3  GT 34 25  TT 5 6  Figure 5.4 Genotype and allele frequencies of rs1799983 in NOS3, a) 2008 and b) 2005+2008, in Nepalese with and without acute mountain sickness (AMS) diagnosed by clinical evaluation and LLS ≥ 3. No significant difference in genotype or allele frequencies was found for any of the groups compared (P > 0.05). The P values of the comparisons (i.e. AMS+ vs. AMS-) of genotype and allele frequencies of rs1799983 in the 2008 and 2005+2008 cohorts are (a) rs1799983 (2008): genotype, P = 0.11; allele, P = ; (b) rs1799983 (2005+2008): genotype, P = 0.95; allele, P =0.08.  107  a) rs1799983 genotype (2005) AMS+ AMS-  GG 14 43  GT&TT 19 16  b) rs1799983 genotype (2008) AMS+ AMS-  GG 43 38  GT&TT 20 15  c) rs1799983 genotype (2005+2008) GG GT&TT AMS+ 57 39 AMS81 31  Figure 5.5 Genotype frequencies of rs1799983 in NOS3 after combining the G/T and T/T genotypes into one group, a) 2005, b) 2008 and c) 2005+2008, in Nepalese with and without acute mountain sickness (AMS) diagnosed by clinical evaluation and LLS ≥ 3. Significant differences in genotype frequency between the G/G and G/G&G/T genotype groups were found in the 2005 and 2005+2008 cohorts (P = 0.004; P = 0.049, respectively).  108  a) rs1799983 genotype (Quechua and Mayan) GG GT Quechua 70 8 Mayan 36 12  TT 0 1  n  Figure 5.6 Genotype and allele frequencies of rs1799983 in NOS3 in native highlanders (Quechua) and lowlanders (Mayan) Native American populations. The G allele and G/G genotype were significantly overrepresented in highlanders (Quechua) compared to those in lowlanders (Mayan) (P = 0.03 for allele frequency; P = 0.01 for genotype frequency).  109  Table 5.1 Genotype and allele frequencies at the seven tagSNPs in NOS3 in the 2005 cohort Polymorphism*  rs1800781 G/A  rs1799983 G/T  rs3918186 A/T  rs3918188 A/C  rs743507 A/G  rs1808593 T/G  rs7830 C/A  Genotype (frequency, %) GG GA AA GG GT TT AA AT TT AA AC CC AA AG GG TT TG GG CC CA AA  84 (81) 19 (19) 0 (0) 62 (60) 36 (35) 5 (5) 85 (83) 18 (17) 0 (0) 14 (14) 47 (46) 42 (41) 57 (55) 43 (42) 3 (3) 58 (56) 40 (39) 5 ( 5) 32 (31) 59 (57) 12 (12)  Allele frequency G 0.91 A 0.09 G 0.78 T 0.22 A 0.92 T 0.08 A 0.64 C 0.36 A 0.76 G 0.24 T 0.76 G 0.24 C 0.60 A 0.40  *dbSNP identification number (http://www.ncbi.nlm.nih.gov/projects/SNP/)  110  6  Detection of Population Stratification in Nepalese  6.1  Population stratification in genetic association study Population-based association studies on unrelated individuals are easy to carry-out,  cost-effective, and can overcome the drawbacks of family-based association studies such as the difficulties in recruiting family members and the requirement of relatively late onset of phenotypes of interest; however, population-based association study design is vulnerable to Type I (false positive) error due to population stratification. This pitfall refers to the fact that a difference in ‘biogeographical’ background between cases and controls in a population-based genetic association study may lead to a false positive signal of a null marker or mask a true causal variant (Attia et al. 2009) and an hypothetical example is shown in Figure 6.1 taken from (Marchini et al. 2004). The conclusions of numerous population-based genetic association studies have been shown to be false due to population stratification (i.a. (Knowler et al. 1988, Blum et al. 1990, Gelernter et al. 1993, Khoury and Yang 1998). For example, Knowler and associates (Knowler et al. 1988) reported that the presence of the Gm3,5,13,14 haplotype in the immunoglobulin G gene was inversely associated with non-insulin-dependent diabetes mellitus among residents of the Gila River Indian Community (Pima Indians). The prevalence of diabetes mellitus in the Knowler study was more than three times higher in individuals with this haplotype than those without (29 % vs. 8 %); however, further analysis showed that the presence of this haplotype was an index marker of Caucasian ancestry; therefore, the inverse association actually reflected the associations between Caucasian heritage and risk of non-insulindependent diabetes mellitus rather than the effect of the haplotype itself on susceptibility to diabetes. After stratified by degree of admixture (measured by the reported number of Caucasian ancestors at the grandparental generation), the inverse association disappeared (Knowler et al. 1988). The confounding effect of population stratification can be assessed and controlled if there is reliable information about the sub-population structure of the study cohort. Several effective methods for the detection and control of population stratification (e.g. genome 111  control, admixture analysis) (i.a. (Wacholder et al. 2002) (Pritchard and Rosenberg 1999)) using genetic markers that exhibit large differences in allele frequencies between populations have been applied or developed in the past few decades. These types of genetic markers are called ‘Ancestry Informative Markers’ (AIMs), which are selected from noncoding regions, and assumed selectively neutral and exclusively controlled by mutation and genetic drift. Numerous types and panels of AIMs have been assessed for the use of detecting population stratification in different populations (e.g. African, European, Asian) (i.a. (Hoggart et al. 2003, Halder et al. 2008, Tian et al. 2008, Kosoy et al. 2009, Londin et al. 2010, Kidd et al. 2011)).  6.2  AIMs for the detection of population stratification Several types of genetic markers have been evaluated and employed as AIMs,  including short tandem repeat polymorphism (STRP), single nucleotide polymorphism (SNP), and short interspersed element (SINE), each of which has its own characteristics when being used as AIMS. Short tandem repeat polymorphism Short tandem repeat polymorphisms, also referred to as microsatellites, are made up of single sequence motifs (2 – 6 bp), which are repeated numerous times and arranged head-to-tail with rare interruption by any other base or motif (Hearne et al. 1992). STRPs are highly variable, which provides more opportunities for genetic drift to generate detectable allele frequency differences between diverging populations. The mutation rate of STRPs is high, which can lead to rapid accumulation of population-specific variation. The recent development of genotyping assays using multiplex PCR technique improves both the efficiency and reliability of STRP analysis and promotes the application of STRPs in the detection of population stratification. These advantages make STRPs informative genetic markers for measuring population structure and population stratification (Listman et al. 2007, Haasl and Payseur 2011). Over 8,000 STRPs, which account for 3 % of human genome, are documented and distributed ubiquitously across 23 pairs of human  112  chromosomes with an average occurrence every 10,000 bp (Butler 2010). A number of panels of STRPs distributed on autosomes, the Y chromosome, and the mitochondrial genome (mtDNA) have been assessed for the differentiation of human populations and are suggested to be reliable in detection of population stratification (i.a. (Barbujani et al. 1997, Rosenberg et al. 2002, Barnholtz-Sloan et al. 2005, Zhang et al. 2005, Perez-Miranda et al. 2006, Toscanini et al. 2008, Halder et al. 2009, Listman et al. 2010). Single nucleotide polymorphisms Single nucleotide polymorphisms (SNPs) are the most common polymorphisms in the human genome, with an average occurrence every 1,000 bp (Jorde and Wooding 2004). Only 2 – 12 % of autosomal SNPs are thought to be highly informative (Rosenberg et al. 2003). The available SNPs from the International Hapmap Project database are sufficient for selecting informative SNP as AIMs (Rosenberg et al. 2003, Enoch et al. 2006). Genetic structure of populations develops over time as changes in allele frequencies due to random genetic drift and the presence of population-specific alleles due to mutation that may or may not introgress to other populations (Haasl and Payseur 2011). There will be more chance for genetic drift to generate detectable frequency differences between diverging populations when genetic markers have greater diversity and population-specific variations accumulate faster when genetic markers have higher mutation rate (Haasl and Payseur 2011). In comparison to STRPs, random SNP loci are less diverse due to low mutation rate and shown less informative power to detect populations structure (Haasl and Payseur 2011); however, the advantages of using SNPs, such as the low recurrent mutation rate, the availability of accurate automated genotyping and allele calling, and the achievable small amplicon size, make them reliable, less laborious and cost-effective in ancestry analysis (Kidd et al. 2006). For these reasons, SNPs are the most commonly used AIMs, especially in genome-wide association studies. Several panels of ancestry informative SNPs (AISNPs) have been assessed for detection of genetic ancestries in different populations from between specific ancestral populations, such as African Americans vs. Europeans vs. East Asians, to many globally distributed populations (i.a. (Paschou et al. 2007, Halder et al. 2008, Kidd et al. 2011). The efficiency of an AISNP panel depends on the group of  113  populations for which the markers were selected. No single set of AISNPs has been found to be sufficient to distinguish between all populations in all regions. Short interspersed element Short interspersed elements (SINEs) are repetitive DNA sequences of approximately 300 bp and have been evaluated and used for ancestry analysis (i.a. (Gomez-Perez et al. 2010, Garcia-Obregon et al. 2012). Alu elements, which were originally characterized for containing the recognition site of the restriction endonuclease Alu I (5’-ACTG-3’), are commonly used as AIMs for ancestral analysis. Alu elements are estimated to compose approximately 11 % of the human genome and are distributed in introns, 3′ untranslated regions and intergenic regions. There are over one million Alu elements per haploid human genome with an average distribution of one copy every 4,000 bp (Lander et al. 2001). The probability of two independent Alu elements inserted by chance in the same genomic position is extremely low; therefore, Alu elements that are shared by different individuals should be identical by descent (Batzer et al. 1994, RoyEngel et al. 2002). Most Alu elements are also considered selectively neutral and random events, such as genetic drift and gene flow between populations will be the only determinants of the element frequency distribution (Comas et al. 2000). In addition, polymorphism stability of Alu elements is high due to the extremely low probability of the occurrence of a complete deletion. These characteristics of Alu elements make them efficient and reliable markers for ancestry analysis and the reconstruction of human evolutionary history. Panels of Alu elements are assessed for human admixture analysis in populations (Batzer et al. 1994, Nasidze et al. 2001, Herrera et al. 2007, Terreros et al. 2009, Gomez-Perez et al. 2010). Improvements in genotyping techniques and the development of statistical approaches (e.g. STRUCTURE, ADMIXMAP, maximum likelihood estimation) greatly facilitated the search and application of AIMs for the detection and control of population stratification. The general application of available AIM-panels depends very much on what populations need to be identified and characterized. A number of AIM-panels comprising different types of genetic markers have been assessed in different populations; however, 114  limited work has been done in Nepalese. An panel of 15 hypervariable autosomal STRPs has been widely used for admixture and population genetic structure assessment in a variety of populations, e.g. European, African, Middle Eastern, and Asian populations, which include Nepalese and neighbouring populations (Wang et al. 2005, Perez-Miranda et al. 2006, Montelius et al. 2008, Gayden et al. 2009); therefore, these markers were employed in this study to investigate population stratification in our Nepalese cohorts.  6.3  Genetic ancestral background of Nepalese Genetic diversity of populations inhabiting at an area is often influenced by  geographic and physical features encompassing that area (Gayden et al. 2007). The Federal Democratic Republic of Nepal14 (commonly referred to simply as “Nepal”) is located in the Himalayan region of South Asia and surrounded by Tibet (China), to the north and by India on the three other sides. Geographically, this rectangle-shape country can be divided into three broad areas that run laterally, including Tarai region (lowland sub-/tropical area), Hill region (moderate altitude (1000 – 4000 m), and Mountain region (above 4000 m, including eight of the ten world’s highest mountains (i.a. Mt. Everest)). The Mountain region, which includes the Himalayan mountain range forms, a formidable barrier separating Tibetan plateau from the Indian subcontinent. This natural barrier results in the contemporary genetic composition of Nepalese being primarily influenced by neighbour populations, such as Indian (to the south) and Tibetan (to the north) (Gayden et al. 2007).  6.4  Materials and methods (see “general methods” section for details)  6.4.1  Study location (Nepal)  The Himalayan Rescue Association GosainKunda Temporary Health Camp, where the recruitment of subjects for the studies described in this thesis was performed, was set up at Gosainkunda (4380 m) during the annual festival of Janai Purnima. Gosainkunda is a 14  Nepal was a monarchy and known as the Kingdom of Nepal until abolition of the kingdom in 2008.  115  lake located in Langtang National Park in Hill region (see map, Figure 2.2) and attributed by Hindu mythology as sacred to Lord Shiva. The holy waters of this lake are considered of particular significance during the festival of Janai Purnima, which involves in changing of Sacred Thread, (Janai) on the first full moon day (Purnima) in August and bathing in the lake. The lake is also significant to Buddists and every year thousands of Hindu and Buddhist pilgrims from Nepal and India visit this area over a one-week period in late summer (toward the end of the rainy season). Usually, the pilgrims ascend to Gosainkunda from Kathmandu (1400 m, the capital of Nepal) in 24 to 48 hr and spend 12 to 36 hr at high altitude and then descend. Such a rapid ascent leads to a high prevalence of AMS (reported as high as 68 % in previous research at this event (Basnyat et al. 2000)). Ancestry background of this Nepalese cohort is unknown and the high genetic diversity of the Nepalese population suggests a potential risk of population stratification in genetic association studies when recruiting subjects at this site. The study described in this chapter is designed to assess population stratification in the Nepalese cohorts that were the subjects of the association studies described elsewhere in this thesis.  6.4.2  Subjects  Two hundred and twenty seven Nepalese pilgrims were recruited at the Himalayan Rescue Association Gosainkunda Temporary Health Camp in 2005 and 2008 (male, n = 170, female, n = 57; mean age: 32 yrs (range 16 – 73 yrs)). Subjects who were diagnosed as having AMS by physicians with LLS of 3 or greater were assigned to the AMS+ group (n = 101) and subjects who were not diagnosed as having AMS with LLS lower than 3 were assigned to the AMS- group (n = 115). Subjects who were discordant for the two diagnostic criteria (n = 11) were not included in the analysis.  6.4.3  Short tandem repeat polymorphism (STRP) genotyping  Fifteen STRPs that had previously been used in evaluating the genetic relationships between Nepalese and their neighbour populations were employed in this study (D8S1179,  116  D21S11, D7S820, CSF1PO, D3S1358, THO1, D13S317, D16S539, D2S1338, DS19S433, vWA, TPOX, D18S51, D5S818, and FGA) (Gayden et al. 2009). Detection of these STRPs were done by DNA amplification using polymerase chain reaction (PCR) and multi-capillary electrophoresis fragment analysis as described below. PCR DNA (10 ng) was amplified in a 25 μl reaction containing 1 µl 10 × PCR-buffer (20 mM Tris/Cl pH 8.4, 50 mM KCl), 0.2 µl dNTPs (25 mM), 0.8 µl MgCl2 (25 mM), 1.5 µl of each fluorescent-labeled primer (FAM, VIC, NED, and PET; 10 pmole/ul), and 0.5 units Taq polymerase (Invitrogen Corporation, Carlsbad, CA, USA ). DNA amplification involves one pre-denaturation for 1 min followed by 40 cycles. Each cycle includes 30 sec at 95 °C, 30 sec at 53 – 61 °C (depending on the annealing temperatures of primers, see Appendix D), and 1 min 30 sec at 72 °C. The cycles are followed by a 5-min incubation at 72 °C. The primers for D8S1179, D21S11, D7S820, CSF1PO, D3S1358, THO1, D13S317, D16S539, vWA, TPOX, D18S51, D5S818, and FGA were adapted from the Short Tandem Repeat Internet DataBase (http://www.cstl.nist.gov/strbase/index.htm). The primers for D2S1338 and DS19S433 were selected based on the gene sequences from GenBank (G08202 and G08036, respectively). All primer sequences are listed in Appendix D. Either the forward or the reverse primer for each STRP was labeled with fluorescent dyes including FAM (blue), VIC (green), PET (red), and NED (black) by Applied Biosystems (ABI, Life Technologies Corporation, Carlsbad, CA, USA). PCR products of the 15 STRPs for each DNA sample were mixed for fragment analysis in three sets, which are shown in Table 6.1. All the fragment analyses were performed at the NAPS Unit of Michael Smith Laboratories of the University of British Columbia. Multi-capillary electrophoresis fragment analysis The process of the fragment analysis includes the denaturation of the amplicons and capillary electrophoresis. PCR products (1 µl) are denatured by being mixed with 9.9 µl Hidi Formamide. Denatured amplicons are mixed with 0.1 µl of internal-lane size standard (i.e. ABI program Genescan 500 LIZ) and then separated by multi-capillary electrophoresis  117  in an ABI PRISM 310 Genetic Analyzer (ABI, Weiterstadt, Germany). The fluorescent peaks of the capillary electrophoresis were analyzed using PeakScannerTM Software v1.0, which is provided by ABI (http://marketing.appliedbiosystems.com/mk/get/PS1_login). The fragments that showed fluorescent peaks between 1000 and 5000 rfu15 with a variation of size reading within 1 bp (e.g. 125 ± 0.5 bp) were used for the determination of alleles. Examples of the fragment analysis results of this study are shown in Figure 6.3 – 6.4. The PCR amplicons that are homozygotes of one allele at each STRP were sequenced to confirm the size calling by fragment analysis. Samples of the DNA sequencing results of one allele at each STRP are shown in Appendix D.  6.4.4  Statistical analysis  STRP allelic frequencies calculation and the exact test of Hardy-Weinberg equilibrium (HWE) by the Markov Chain method were performed using the GENEPOP program v4.0.10 (http://genepop.curtin.edu.au/) (Rousset 2008). The distributions of allele and genotype frequencies were compared by Chi-square analysis between different hybridpopulation pairs of this study (i.e. AMS+ (2005) vs. AMS- (2005), AMS+ (2008) vs. AMS- (2008); AMS+ (2005) vs. AMS+ (2008); AMS- (2005) vs. AMS- (2008); AMS+ vs. AMS- (2005+2008); 2005 vs. 2008). The criterion of significance is accepted at P < 0.05 and the Bonferroni correction was applied when multiple tests were performed. Admixture analysis was performed on the whole- (2005 + 2008) and sub- Nepalese populations of this study using Tibetan and South Asian as parental populations. Allele frequencies of the 15 STRPs that were reported by earlier studies on South Asian and Tibetan populations were averaged, respectively, and the averaged allele frequencies of South Asian and of Tibetan populations were used to represent those of the parental populations for admixture analysis. Allele frequencies of the 15 STRPs in the hybrid populations for admixture analysis are that of the whole and sub- Nepalese cohorts (e.g. (2005+2008), AMS+ (2005)) of this study. The background information of all the hybrid  15  Relative fluorescent unit (RFU) represents the fluorescent intensity recorded when the labeled DNAs pass the detection window during the capillary electrophoresis.  118  and parental populations that were used in admixture estimation is shown in Table 6.2. The contributions of the parental populations (i.e. admixture estimation) to the hybrid populations were estimated by the gene identity method provided in the free ADMIX95 software (http://www.genetica.fmed.edu.uy) using allele frequencies of the fifteen STRPs. The admixture estimates between different hybrid-population pairs (e.g. AMS+ vs. AMS-) were compared using the Wald test.  6.5  Results Fragment analysis was performed successfully on 14 of the 15 STRPs for two  hundred and sixteen DNA samples (AMS+: n = 101; AMS-: n = 115). The analysis was not successful for DS19S433. Allele frequencies, population genetic parameters of the 14 STRPs in the whole and sub- populations are shown in Table 6.3 – 6.11. The statistical significance for HWE tests was set at P < 0.0036 (α = 0.05/14) due to multiple testing. The genotype distributions of 13 STRP were in HWE (P > 0.0036) in all the cohorts, except for the vWA polymorphism in the 2005, AMS- (2005), and AMS- (2005+2008) cohorts; therefore, the vWA polymorphism was not included in the Chi-square and admixture analyses. The distributions of allele and genotype frequencies of the 13 remaining STRPs were compared using Chi-square analysis and no significant difference between the subpopulation pairs was observed (i.e. AMS+ (2005) vs. AMS- (2005), AMS+ (2008) vs. AMS- (2008 AMS+ (2005) vs. AMS+ (2008); AMS- (2005) vs. AMS- (2008); AMS+ vs. AMS- (2005+2008); 2005 vs. 2008) except for the allele frequencies at the FGA polymorphism between the 2005 and 2008 cohorts as shown in Table 6.12 ‒ 6.13. Genetic contributions from native highlanders (Tibetans) and lowlanders (South Asians) to the AMS+ and AMS- cohorts who were recruited at Gosainkunda in 2005 and 2008 were estimated. South Asian is the major donor to the Nepalese recruited in this study and contributes more than 60 % to the whole and sub- populations and no significant  119  difference in admixture estimates was found between the AMS+ and AMS- population pairs.  6.6  Discussion Fourteen STRPs were genotyped initially for detection of population stratification  and for estimation of admixture in this study. The genotype distributions of 13 STRPs were in HWE, except for those at the vWA polymorphism in the 2005 cohort, the AMS- group of the 2005 cohort, and the AMS- group of the whole cohort (2005+2008). The vWA polymorphism is located in the 40th intron of the vWF gene, which encodes glycoproteins that are involved in blood clotting. Both allele and genotype distributions of the vWA polymorphism were not significantly different between the AMS+ and AMS- groups in the 2005 cohort. The vWA polymorphism was excluded from further analyses in this study (i.e. Chi-square analysis and admixture estimation). As discussed in Chapter 4, an association was found between the G/T genotype at rs1799983 in NOS3 and susceptibility to AMS in the 2005 cohort; however, this association was not replicated in the 2008 cohort. Two analyses (Chi-square and admixture analyses) were performed to assess whether differential population stratification exists in the samples that could account for these inconsistent association results. Chi-square was used to compare the distributions of allele and genotype frequencies of the 13 STRPs between the AMS+ and AMS- groups that were classified by the year of recruitment (e.g. AMS+ (2005) vs. AMS+ (2008)) and as pooled cohorts (i.e. AMS+ (2005+2008) vs. AMS- (2005+2008)). No significant difference in distributions of allele and genotype frequencies was found between any case-control population pair as shown in Table 6.12 – 6.13. Allele and genotype frequency distributions of the 2005 and 2008 cohorts were compared using Chi-square analysis too. No significant difference at 12 STRPs were found except for at the FGA polymorphism. Allele frequencies at the FGA polymorphism were significantly different between the 2005 and 2008 cohorts (P = 0.0036, (the significance was accepted at P < 0.0038 after Bonferroni correction)) while no  120  significant difference was shown in genotype frequencies. The FGA polymorphism is located in the 3rd intron of the human alpha fibrinogen gene, which has not been shown in association with any AMS-related traits. These data suggest that the differences in the inconsistent NOS3 associations between the 2005 and 2008 cohorts are not likely to be due to the differences in ancestral background between the two cohorts. Due to the country’s geographical location, the contemporary genetic composition of Nepalese is significantly influenced by its South Asian and Tibetan neighbouring populations. Tibetans are native highlanders whose ancestors may have been selected for resistance to AMS when they settled the Himalayan highlands (approximately 30,000 years ago (Aldenderfer 2011)). AMS is incapacitating but not fatal (unless the condition progresses to HACE) so if selection occurred, it would more likely happen due to differential migration rather than differential survival. Our data from the study of the alleles of rs1799983 in NOS3 in an Andean high-altitude population (Quechua), which is described in Chapter 5 ((Wang et al. 2010)), showed that the G allele and G/G genotype that contribute to AMS resistance are over-represented in highland natives compared to lowlanders, suggesting that, in the past, selective forces might increase the accumulation of AMS resistance genotypes in the Tibetans. If this assumption is correct, the probability of developing AMS would be influenced by the extent of genetic contribution from Tibetan ancestry and, therefore, different proportions of Tibetan and South Asian ancestries between cases and controls could direct the associations, if alleles are more common in either Tibetans or South Asian. To address this possibility that ‘Tibetaness’ conferred resistance to AMS in the Nepalese pilgrims, the second analysis, admixture estimation, was performed to assess the genetic contribution of Tibetan and South Asian ancestries to the AMS+ and AMS- groups of the 2005 and 2008 cohorts. The admixture analysis results showed that South Asian ancestry contributes two to three times more to our Nepalese subjects (whole and sub-cohorts, i.e. 2005 + 2008, AMS (2005 + 2008), AMS- (2005 + 2008), 2005, AMS+(2005), AMS-(2005), 2008, AMS+(2008), and AMS-(2008)) in comparison to Tibetan ancestry. The subjects of this study were recruited at the holy Gosainkunda during a Hindu festival; therefore, a high contribution from South Asian ancestry to the subjects is not surprising as Hindu is the predominant religion in India;  121  whereas, most religious Tibetans are Buddhists, and while the lake is sacred to Buddhists, the Janai Purnima festival is primarily an Hindu event. Admixture estimates were compared between sub-population pairs (i.e. 2005 vs. 2008; AMS+ vs. AMS- (2005+2008); AMS+ (2005) vs. AMS- (2005), AMS+ (2008) vs. AMS- (2008); AMS+ (2005) vs. AMS+ (2008); and AMS- (2005) vs. AMS- (2008)) and no significant difference was found, suggesting that AMS susceptibility was not influenced by whether the individuals had Tibetan ancestry. In summary, the possibility that population stratification influenced the outcomes of the studies described in this thesis was assessed using Chi-square analysis and admixture estimation between the AMS case and control (non-AMS) population pairs. Neither the distributions of allele and genotype frequencies nor the ancestral compositions were significantly different between all the case-control population pairs. When comparing genotypes between the two groups, the extent to which the subject had Tibetan vs. South Asian ancestry did not influence their susceptibility to AMS. Population stratification does not seem to account for the inconsistent associations that were observed between the two cohorts (2005 and 2008) for the G/T genotype (rs1799983) and AMS in NOS3. Sample size or ascertainment quality (i.e. whether AMS was diagnosed properly and consistently) is more likely to be the explanation. Although there is no evidence for population stratification in Nepalese of this thesis, assessment and control of population stratification analysis remains highly recommended in population-based genetic association studies in this population, due to the special geographical location of the country and highly diverse ethnicity of the population. This is especially true for altitude studies as there is substantial evidence for that selection for hypoxia tolerance occurred in the Tibetans (see reviews by (Simonson et al. 2010, Simonson et al. 2012)); however, our data would be consistent with the roles of genetics in acclimatization (e.g. AMS resistance) and adaptation are dissimilar in the Himalayas.  122  Figure 6.1 The effects of population structure at a SNP locus. This figure describes a sample for a hypothetical scenario: if the study population consists of subpopulations (population 1 and 2) that differ genetically, and if disease prevalence also differs between these subpopulations, the proportions of cases and controls sampled from each subpopulation will tend to differ and allele or genotype frequencies that differ at any locus between the two subpopulations will differ between cases and controls. This can mimic the signal of association and lead to more false positives or to missed real effects. As the figure shows, the case group has an excess of individuals from population 2, and population 2 has higher frequency of the aa genotype than population 1. This leads to a significant difference in allele and genotype frequencies between cases and controls and a false positive association. This figure is adapted from (Marchini et al. 2004).  123  Figure 6.2 Screen shots of a sample demonstrating the Set-1 fragment analysis assay. The “sample view” window shows name, size standard, analysis method, quality, and scale of each sample. The plot view window presents the peaks of the fluorescent labeled PCR mixture injected in each electrophoresis run. The order of the peaks from left to right is D3S1358 in black, D2S1338 in blue, D19S433 in red, TPOX in green, D21S11 in blue, D16S539 in red, D18S51 in green, and FGA in black. Orange peaks represent the internallane size standard, Genescan 500 LIZ.  124  Table 6.1 Three sets of the 15 STRPs for the fragment analysis of this study Fragment analysis Set 1 STRP ID Fluorescent dye Allele Size Range (bp)  D3S1358 NED 104-152  D5S818 6-FAM 121-169  D19S433 PET 169-209  TPOX VIC 219-267  D2S11 6-FAM 161-279  D16S539 PET 260-308  D18S51 VIC 289-376  FGA NED 308-464  Fragment analysis Set 2 STRP ID Fluorescent dye Allele Size Range (bp)  D2S1338 6-FAM 126-194  THO1 VIC 155-199  D8S1179 PET 211-263  CSF1PO NED 320-364  Fragment analysis Set 3 STRP ID Fluorescent dye Allele Size Range (bp)  vWA 6-FAM 123-181  D13S317 PET 162-210  D8S820 VIC 214-254  Note: The background colors of each type of fluorescent dyes correspond to the peak colors of the capillary electrophoresis readings of fragment analysis. The size ranges are referred from the Short Tandem Repeat Internet DataBase (http://www.cstl.nist.gov/strbase/index.htm).  125  Table 6.2 The information of parental populations and the Nepalese of this study for admixture analysis Sample size (n)  Population  References  Parental population 1 South Asia Bangladesh  127  (Dobashi et al. 2005)  Punjab  86  (Shepard and Herrera 2006)  Tibet  153  (Gayden et al. 2009)  Tibet (Lassa, China)  196  (Kang et al. 2007)  Tibet (Qinghai, China)  850  (Yan et al. 2007)  Parental population 2 Tibet  Hybrid population Nepal 2005 + 2008  216  AMS+ (2005 + 2008)  101  AMS- (2005 + 2008)  115  AMS+(2005)  33  AMS- (2005)  59  AMS+ (2008)  68  AMS- (2008)  56  2005  92  2008  124  126  Table 6.3 Allele frequencies of the 14 STRPs in the whole cohort (n = 216) Alleles 6 7 8 9 9.3 10 11 12 13 13.2 14 14.2 15 16 17 18 19 19.2 20 20.2 21 21.2 22 22.2 23 23.2 24 24.2 25 26 27 28 28.2 29 29.2 30 30.2 31 31.2 32 32.2 33.2 34.2 35.2 HWE  D8S1179  D21S11  D7S820  CSF1PO  D3S1358  THO1  D13S317  0.1528 0.2222 0.0787 0.3750 0.1713  0.0023 0.1412 0.1690  0.0347 0.2755 0.0741 0.3009 0.2060  0.0023  0.0949  0.0023  0.0093 0.1921 0.0671  0.0046  0.0046 0.1435 0.0417 0.1134  0.2153 0.2523 0.2338 0.0255  0.1806 0.2593 0.3750 0.0926  0.0395  0.1343 0.2778 0.2106 0.0440  0.2222  0.0046  0.0139  0.2907  0.0208  0.0069  0.3791 0.1860 0.0977 0.0047 0.0023  0.1782 0.2153 0.0694 0.0116  0.0671  D16S539  0.2175  0.4209  0.5706  0.0333  0.0720  0.7387  0.6159  vWA  TPOX  D18S51  0.0023  0.0744 0.1302 0.1791  0.0093 0.1273 0.0069 0.0486 0.2199 0.2315 0.2384 0.0810  0.1279  0.0347  0.0787 0.3426 0.0162  0.0023 0.0208 0.0556 0.1690  0.3032 0.3032 0.1644 0.0023  0.1759 0.1412 0.0694 0.0301 0.0671  0.0280 0.0794 0.0748 0.0070 0.1028 0.0093 0.1449 0.0047 0.1893 0.0070 0.2033 0.0047 0.0958 0.0140 0.0280 0.0070  0.0231 0.0231  0.0628  0.0185  0.1488  0.0046  0.1395  0.0023  0.0814 0.0186 0.0047  0.0023  0.0116  FGA  0.1944  0.0279  0.0153  D5S818 0.0023 0.0671 0.1574  0.4051 0.1574  0.0139  0.0069 0.1181 0.0139 0.2245 0.0069 0.2662 0.0208 0.0833 0.1042 0.0139 0.0926 0.0347 0.0023 0.0116 0.2634  D2S1338  0.9895  0.0297  0.7117  0.0900  HWE: Hardy Weinberg Equilibrium p-values  127  Table 6.4 Allele frequencies of the 14 STRPs in the AMS- group of the whole cohort (n = 115) Alleles 6 7 8 9 9.3 10 11 12 13 13.2 14 14.2 15 16 17 18 19 19.2 20 20.2 21 21.2 22 22.2 23 23.2 24 24.2 25 26 27 28 28.2 29 29.2 30  D8S1179  D21S11  CSF1PO  D3S1358  0.0522  0.0087 0.1652 0.0391 0.1087  0.2087 0.2783 02043 0.0304  0.1826 0.2565 0.3609 0.1261  0.2435  0.0043  0.1435 0.2130 0.0652 0.0130  THO1  D13S317  0.1696 0.2261 0.0739 0.3783 0.1522  0.0043 0.2043 0.0696  D16S539  D2S1338  vWA  0.0478  0.0043 0.1609 0.1391 0.1217  0.3000 0.0652 0.2696 0.2261  0.0087  0.2957  0.2522  0.1043  0.0087  0.3783 0.1826 0.0913  0.2348 0.0565 0.0304  0.0087  TPOX  D18S51  0.0783 0.3348 0.0174 0.0043  0.1043 0.1478 0.1609 0.1000  D5S818  FGA  0.0043 0.0739 0.1522  0.4174 0.1522  0.0261  0.0043  0.0043 0.0348 0.0652 0.1783  0.1522 0.0043 0.0522 0.2000 0.2478 0.2174 0.0957  0.1696  0.0304  0.0217  0.3087 0.3087 0.1478 0.0043  0.1696 0.1304 0.0522 0.0348 0.0826  0.0263 0.0921 0.0833 0.0088 0.0965 0.0044 0.1360  0.0391 0.0348 0.0652 0.0130  0.1754 0.0088 0.2149 0.0044 0.0965 0.0132 0.0263 0.0132  0.1522 0.0087 0.1609 0.0565 0.0087 0.0043 0.1217 0.0087 0.2217 0.0043 0.2739  30.2  0.0174  31 31.2 32 32.2 33.2 34.2 35.2  0.0783 0.1000 0.0174 0.1043 0.0348 0.0043 0.0087  HWE  D7S820  0.4492  0.7161  0.0933  0.6265  0.0616  0.2549  0.2319  0.2728  0.0443  0.0000  0.7830  0.0303  0.4391  0.4399  HWE: Hardy Weinberg Equilibrium p-values  128  Table 6.5 Allele frequencies of the 14 STRPs in the AMS+ group of the whole cohort (n = 101) Alleles 6 7 8 9 9.3 10 11 12 13 13.2 14 14.2 15 16 17 18 19 19.2 20 20.2 21 21.2 22 22.2 23 23.2 24 24.2 25 26 27 28 28.2 29 29.2 30 30.2 31 31.2 32 32.2 33.2 35.2 HWE  D8S1179  D21S11  D7S820  CSF1PO  D3S1358  THO1 0.1337 0.2178 0.0842 0.3713 0.1931  D13S317  D16S539  0.1188 0.2030  0.0446 0.2475 0.0842 0.3366 0.1832 0.0842  0.0198 0.1782 0.0644  0.0050  0.1188 0.0446 0.1188  0.2228 0.2228 0.2673 0.0198  0.1782 0.2624 0.3911 0.0545  0.0300  0.1485 0.3069 0.1832 0.0297  0.1980  0.0050  0.0198  0.2850  0.0099  0.0050  0.3800 0.1900 0.1050 0.0100  0.2178 0.2178 0.0743 0.0099  0.0842  D2S1338  vWA  TPOX  D18S51  0.3911 0.1634  0.0050  0.0400 0.1100 0.2000  0.0198 0.0990 0.0099 0.0446 0.2426 0.2129 0.2624 0.0644  0.1600  0.0396  0.0050  0.0198  0.0792 0.3515 0.0149  D5S818  0.0594 0.1634  0.0050 0.0446 0.1584  0.2970 0.2970 0.1832  0.2228 0.1832 0.1535 0.0891 0.0248 0.0495  0.0300 0.0650 0.0650 0.0050 0.1100 0.0150 0.1550 0.0100 0.2050 0.0050 0.1900 0.0050 0.0950 0.0150 0.0300  0.0248  0.0150  0.0099  0.0600  0.0248  0.1450  0.0099 0.1139 0.0198 0.2277 0.0099 0.2574 0.0248 0.0891 0.1089 0.0099 0.0792 0.0347 0.0149 0.4139  0.2195  0.8161  0.4363  0.3571  0.0467  0.7911  0.9640  0.1150  0.0050  0.1100 0.0300 0.0100  0.0050  0.3871  0.7941  FGA  0.3264  0.6568  0.7141  0.3778  HWE: Hardy Weinberg Equilibrium p-values  129  Table 6.6 Allele frequencies of the 14 STRPs in the 2005 cohort (n = 92) Alleles 6 7 8 9 9.3 10 11 12 13 13.2 14 14.2 15 16 17 18 19 19.2 20 20.2 21 21.2 22 22.2 23 23.2 24 24.2 25 26 27 28 28.2 29 29.2 30 30.2 31 31.2 32 32.2 33.2 34.2 35.2 HWE  D8S1179  D21S11  D7S820  CSF1PO  0.0163 0.1685 0.0598  0.0054  0.0054 0.1141 0.0543 0.1141  0.2446 0.2609 0.2120 0.0272  0.2011 0.2609 0.3370 0.1033  0.2500  0.0109  0.1576 0.2228 0.0707 0.0109  D3S1358  THO1 0.1467 0.2120 0.0707 0.3587 0.2120  D13S317  D16S539  0.1304 0.1413  0.0435  0.0435  0.1250 0.2989 0.2446 0.0326  0.2880 0.0707 0.3152 0.2174  0.0163  0.3152  0.0272  0.0543  0.0054  0.3424 0.1902 0.1087  0.0707  D2S1338  vWA  TPOX  D18S51  0.0054 0.0109 0.0543 0.1793  0.0055  0.1576  0.1630  0.1209 0.1209 0.1758  0.0543 0.1576 0.2717 0.2228 0.1141  0.1957 0.1576 0.0652 0.0217 0.0652  0.1209  0.0217  0.0380  0.0109  FGA  0.0054 0.0652 0.1685  0.4293 0.1467 0.0761 0.3370 0.0109  D5S818  0.3315 0.2717 0.1576  0.0275 0.0989 0.0604 0.0110 0.1044 0.0055 0.1319  0.0220  0.0217  0.0659  0.0109  0.1593  0.0054  0.1429 0.0165 0.2582  0.1429  0.0054  0.0769  0.0549 0.0110  0.0549 0.0110  0.1630 0.2283 0.0054 0.2120 0.0163 0.0870 0.0978 0.0109 0.1033 0.0598 0.0054 0.0109 0.3364  0.3792  0.0334  0.3982  0.4446  0.1720  0.1243  0.2286  0.0343  0.0022  0.8398  0.1163  0.2115  0.3080  HWE: Hardy Weinberg Equilibrium p-values  130  Table 6.7 Allele frequencies of the 14 STRPs in the 2008 cohort (n = 124) Alleles 6 7 8 9 9.3 10 11 12 13 13.2 14 14.2 15 16 17 18 19 19.2 20 20.2 21 21.2 22 22.2 23 23.2 24 24.2 25 26 27 28 28.2 29 29.2 30 30.2 31 31.2 32 32.2 33.2 34.2 35.2 HWE  D8S1179  D21S11  D7S820  CSF1PO  D3S1358  THO1  D13S317  D16S539  0.1573 0.2298 0.0847 0.3871 0.1411  0.0040 0.1492 0.1895  0.0282  0.0040 0.2097 0.0726  0.0040  0.1935 0.2460 0.2500 0.0242  0.1653 0.2581 0.4032 0.0847  0.0366  0.2016  0.0121  0.2724  0.1250  0.1935 0.2097 0.0685 0.0121  0.0081  0.4065 0.1829 0.0894 0.0081 0.0041  0.0161  0.0040 0.1653 0.0323 0.1129  0.0645  0.1411 0.2621 0.1855 0.0524 0.0161  0.2661 0.0766 0.2903 0.1976  D2S1338  vWA  TPOX  D18S51  0.3871 0.1653  0.0040  0.0806 0.3468 0.0202  0.0040  0.0403 0.1371 0.1815  0.0161 0.1048 0.0121 0.0444 0.2661 0.2016 0.2500 0.0565  0.1331  0.0444  D5S818  0.0685 0.1492  0.0282 0.0565 0.1613  0.2823 0.3266 0.1694 0.0040  0.2177 0.1613 0.1290 0.0726 0.0363 0.0685  0.0285 0.0650 0.0854 0.0041 0.1016 0.0122 0.1545 0.0081 0.2236  0.0121  0.0323  0.0242  0.0605  0.0242  0.1411  0.0040  0.1626 0.0081 0.1098 0.0244 0.0081 0.0041  0.1371 0.1008 0.0242 0.0081  0.0121 0.0847 0.0242 0.2218 0.0081 0.3065 0.0242 0.0806 0.1089 0.0161 0.0847 0.0161  FGA  0.0040  0.0121 0.1509  0.1599  0.4013  0.6414  0.0805  0.0303  0.1231  0.4720  0.5282  0.5417  0.2717  0.5733  0.5120  0.3002  HWE: Hardy Weinberg Equilibrium p-values  131  Table 6.8 Allele frequencies of the 14 STRPs in the AMS+ group of the 2005 cohort (n = 33) Alleles 6 7 8 9 9.3 10 11 12 13 13.2 14 14.2 15 16 17 18 19 19.2 20 20.2 21 21.2 22 22.2 23 23.2 24 24.2 25 26 27 28 28.2 29 29.2 30 30.2 31 31.2 32 32.2 33.2 34.2 35.2 HWE  D8S1179  D21S11  D7S820  CSF1PO  D3S1358  THO1 0.1364 0.1970 0.1212 0.2273 0.3182  D13S317  D16S539  0.0909 0.1970  0.0758 0.2424 0.1061 0.3333 0.1667 0.0606  0.0455 0.1818 0.0455  0.1061  0.0758 0.0758 0.1212  0.2273 0.2273 0.2424 0.0152  0.1970 0.2273 0.3485 0.0606  0.0152  0.1212 0.3182 0.2424 0.0152  0.2121  0.0152  0.0455  0.2576  0.0152  0.0152  0.3636 0.2424 0.1212  0.2121 0.1970 0.1061  D2S1338  vWA  TPOX  D18S51  0.4242 0.1515 0.0606 0.3485 0.0152  D5S818  FGA  0.0606 0.1970  0.0152 0.0455 0.1515  0.3030 0.2424 0.1970  0.0156  0.2273  0.2273  0.0625 0.1250 0.1719  0.0606 0.1667 0.2576 0.1970 0.0758  0.2424 0.1515 0.0606 0.0152 0.0303  0.1250  0.0152  0.0303  0.0303 0.0606 0.0606 0.0152 0.1061  0.0152  0.1515  0.0152  0.0625  0.1818 0.0152 0.2576  0.1875 0.1562  0.0152  0.0606  0.0781 0.0156  0.0606  0.1818 0.2424 0.0152 0.2121 0.0152 0.1212 0.0455 0.0909 0.0455 0.0303 0.7078  0.5317  0.0357  0.6482  0.4138  0.3892  0.9480  0.9375  0.1725  0.1883  0.8637  0.2514  0.3959  0.0608  HWE: Hardy Weinberg Equilibrium p-values  132  Table 6.9 Allele frequencies of the 14 STRPs in the AMS- group of the 2005 cohort (n = 59) Alleles 6 7 8 9 9.3 10 11 12 13 13.2 14 14.2 15 16 17 18 19 19.2 20 20.2 21 21.2 22 22.2 23 23.2 24 24.2 25 26 27 28 28.2 29 29.2 30 30.2 31 31.2 32 32.2 33.2 34.2 35.2 HWE  D8S1179  D21S11  D7S820  CSF1PO  D3S1358  0.0085 0.1610 0.0678  0.0508  0.0085 0.1356 0.0424 0.1102  0.2542 0.2797 0.1949 0.0339  0.2034 0.2797 0.3305 0.1271  0.2712  0.0085  0.1271 0.2373 0.0508 0.0169  THO1 0.1525 0.2203 0.0424 0.4322 0.1525  D13S317  D16S539  D2S1338  vWA  TPOX  D18S51  0.0254  0.0593  0.1271 0.2881 0.2458 0.0424  0.3136 0.0508 0.3051 0.2458  0.3475  0.0339  0.0508  0.1186  0.1271  0.0085 0.1525 0.1186 0.1780  0.0508 0.1525 0.2797 0.2373 0.1356  0.1695 0.1610 0.0678 0.0254 0.0847  0.1186  0.0254  0.0424  0.3305 0.1610 0.1017  FGA  0.0085 0.0678 0.1525  0.1525 0.1102  0.4322 0.1441 0.0847 0.3305 0.0085  D5S818  0.0085 0.0085 0.0539 0.1949  0.0339  0.0254  0.0678  0.0169  0.1441  0.0085  0.3475 0.2881 0.1356  0.0259 0.1207 0.0603 0.0086 0.1034 0.0086 0.1207 0.1207 0.0172 0.2586  0.1356  0.0862  0.0424 0.0085  0.0517 0.0172  0.1525 0.2203 0.2119 0.0169 0.0678 0.1271 0.0169 0.1102 0.0678 0.0085 0.4998  0.6157  0.1010  0.4148  0.2304  0.7341  0.0450  0.2429  0.0142  0.0001  0.7558  0.1927  0.0647  0.6379  HWE: Hardy Weinberg Equilibrium p-values  133  Table 6.10 Allele frequencies of the 14 STRPs in the AMS+ group of the 2008 cohort (n = 68) Alleles 6 7 8 9 9.3 10 11 12 13 13.2 14 14.2 15 16 17 18 19 19.2 20 20.2 21 21.2 22 22.2 23 23.2 24 24.2 25 26 27 28 28.2 29 29.2 30 30.2 31 31.2 32 32.2 33.2 34.2 35.2 HWE  D8S1179  D21S11  0.1397 0.0294 0.1176  D7S820  CSF1PO  0.0074 0.1765 0.0735  0.0074  0.2206 0.2206 0.2794 0.0221  0.1691 0.2794 0.4118 0.0515  0.1912  D3S1358  0.0735  0.0074  0.2206 0.2279 0.0588 0.0147  THO1 0.1324 0.2279 0.0662 0.4412 0.1324  0.0373 0.2985 0.3881 0.1642 0.0970  D13S317  D16S539  0.1324 0.2059  0.0294  0.1618 0.3015 0.1544 0.0368  0.2500 0.0735 0.3382 0.1912  0.0074  0.0956  D2S1338  0.0074  0.0294 0.1029 0.2132  0.0294 0.0368 0.0147 0.0368 0.2794 0.1912 0.2941 0.0588  0.1765  0.0515  0.0221  0.0149  vWA  TPOX  D18S51  D5S818  0.3750 0.1691  0.0588 0.1471  0.0882 0.3529 0.0147  0.2941 0.3235 0.1765  0.0441 0.1618 0.2206 0.1544 0.1544 0.1029 0.0294 0.0588  0.0299 0.0672 0.0672  0.0221  0.0221  0.0074  0.0588  0.0368  0.1119 0.0224 0.1567 0.0149 0.2164  0.0074  0.1567 0.0075 0.1119 0.0224 0.0149  0.1250 0.0956 0.1250 0.0368 0.0147  0.0147 0.0809 0.0294 0.2206 0.0074 0.2794 0.0294 0.0735 0.1397 0.0147 0.0735 0.0294  FGA  0.0074 0.3324  0.0480  0.8217  0.5767  0.0926  0.1463  0.8390  0.8645  0.8594  0.9913  0.1477  0.8846  0.1870  0.6411  HWE: Hardy Weinberg Equilibrium p-values  134  Table 6.11 Allele frequencies of the 14 STRPs in the AMS- group of the 2008 cohort (n = 56) Alleles 6 7 8 9 9.3 10 11 12 13 13.2 14 14.2 15 16 17 18 19 19.2 20 20.2 21 21.2 22 22.2 23 23.2 24 24.2 25 26 27 28 28.2 29 29.2 30 30.2 31 31.2 32 32.2 33.2 34.2 35.2 HWE  D8S1179  D21S11  D7S820  CSF1PO  D3S1358  THO1  D13S317  D16S539  0.1875 0.2321 0.1071 0.3214 0.1518  0.0089 0.1696 0.1696  0.0268 0.2857 0.0804 0.2321 0.2054 0.1607  0.2500 0.0714  0.0536  0.1607 0.2768 0.2143 0.0268  0.1607 0.2321 0.3929 0.1250  0.0357  0.1161 0.2143 0.2232 0.0714  0.2143  0.0179  0.2411  0.0268  0.1607 0.1875 0.0804 0.0089  0.0179  0.4286 0.2054 0.0804  0.0089 0.1964 0.0357 0.1071  D2S1338  TPOX  D18S51  0.4018 0.1607 0.0714 0.3393 0.0268 0.0089  0.0536 0.1786 0.1429  0.1875 0.0089 0.0536 0.2500 0.2143 0.1964 0.0536  0.0804  0.0357  0.0089  0.0089  vWA  D5S818  FGA  0.0804 0.1518  0.0625 0.0714 0.1607  0.2679 0.3304 0.1607 0.0089  0.2143 0.1696 0.0982 0.0357 0.0446 0.0804  0.0268 0.0625 0.1071 0.0089 0.0893  0.0446  0.0446  0.1518  0.0625  0.0089  0.2321  0.1607  0.0089  0.1696 0.0089 0.1071 0.0268  0.1875 0.0714 0.0089  0.0089  0.0089 0.0893 0.0179 0.2232 0.0089 0.3393 0.0179 0.0893 0.0714 0.0179 0.0982  0.0179 0.2249  0.6840  0.2901  0.9549  0.1013  0.1461  0.0508  0.5247  0.1313  0.1911  0.3218  0.0810  0.4905  0.1844  HWE: Hardy Weinberg Equilibrium p-values  135  Table 6.12 Allele frequency comparison by Chi-square method between the sub-populations of this study Short Tandem Repeat Marker  Population D3S1358  TPOX  D16S539  D18S51  FGA  D5S818  THO1  D8S1179  CSF1PO  D13S317  D7S820  D2S1338  D21S11  2005 AMS vs. NAMS 2008 AMS vs. NAMS AMS 2005 vs. 2008 NAMS 2005 vs. 2008  0.3147  0.9673  0.3585  0.6780  0.9483  0.7495  0.0063  0.3785  0.1058  0.5260  0.3722  0.5681  0.3530  0.5944  0.9340  0.4777  0.0195  0.7657  0.8644  0.3031  0.7672  0.2730  0.2308  0.4892  0.0305  0.7207  0.5919  0.9396  0.7288  0.5825  0.3447  0.7765  0.0043  0.5062  0.3750  0.6708  0.5141  0.4393  0.1820  0.2791  0.8296  0.1671  0.1580  0.2691  0.6926  0.2332  0.8297  0.4424  0.6603  0.4463  0.3386  0.0500  AMS vs. NAMS  0.6350  0.9824  0.3556  0.1498  0.8496  0.7971  0.7086  0.4230  0.2479  0.1056  0.2748  0.0120  0.9751  2005 vs. 2008  0.5951  0.8584  0.3092  0.5928  0.0036  0.6308  0.4319  0.7365  0.9091  0.4839  0.3630  0.1327  0.0303  136  Table 6.13 Genotype frequency comparison by Chi-square method between the sub-populations of this study Population  Short Tandem Repeat Marker D3S1358  TPOX  D16S539  D18S51  FGA  D5S818  THO1  D8S1179  CSF1PO  D13S317  D7S820  D2S1338  D21S11  2005 AMS vs. NAMS 2008 AMS vs. NAMS AMS 2005 vs. 2008 NAMS 2005 vs. 2008  0.1834  0.9357  0.5869  0.6286  0.3395  0.2157  0.2508  0.5962  0.3986  0.6905  0.1923  0.2389  0.5694  0.2627  0.3131  0.7598  0.2420  0.6422  0.2979  0.5958  0.5551  0.6424  0.3233  0.5063  0.1895  0.4486  0.2120  0.6368  0.6936  0.7475  0.3717  0.1585  0.0705  0.4504  0.5125  0.8510  0.1427  0.7795  0.1848  0.2983  0.4997  0.2468  0.4054  0.1320  0.1226  0.5768  0.4359  0.7181  0.0513  0.3311  0.0930  0.2064  AMS vs. NAMS  0.7484  0.3618  0.4780  0.6042  0.7385  0.6763  0.4956  0.5476  0.3305  0.1415  0.2996  0.1695  0.8017  2005 vs. 2008  0.8643  0.3688  0.1343  0.8376  0.1297  0.2144  0.1895  0.2292  0.5179  0.0247  0.0898  0.5000  0.1132  137  Table 6.14 Admixture estimation of Tibetan and South Asian ancestries between hybrid populations of this study Parental Population  Hybrid Population  NEP AMS+ AMS2005 2008 AMS+(2005) AMS-(2005) AMS+(2008) AMS-(2008) AMS+(2005) AMS+(2008) AMS-(2005) AMS-(2008)  Tibetan (Highlander) 0.277 ± 0.07 0.242 ± 0.09 0.280 ± 0.07 0.241 ± 0.05 0.307 ± 0.10 0.208 ± 0.12 0.363 ± 0.07 0.384 ± 0.10 0.259 ± 0.11 0.208 ± 0.12 0.384 ± 0.10 0.363 ± 0.07 0.259 ± 0.11  South Asian (Lowlander) 0.723 ± 0.07 0.758 ± 0.08 0.720 ± 0.07 0.759 ± 0.05 0.693 ± 0.10 0.792 ± 0.12 0.637 ± 0.07 0.616 ± 0.10 0.741 ± 0.11 0.792 ± 0.12 0.616 ± 0.10 0.637 ± 0.07 0.741 ± 0.11  Note: There is no significant difference in admixture estimation between hybrid populations.  138  7  Concluding Chapter  7.1 General conclusion  The studies described in this thesis were designed to identify genetic contributions to susceptibility (or resistance) to AMS. A series of population-based candidate gene association studies were performed and allele and genotype frequencies compared between cohorts of Nepalese with, and without AMS. Candidate genes were chosen due to their potential to influence physiological characteristics that are related to vascular homeostasis and pulmonary function in response to acute hypoxic exposures. Subjects who took part into the studies are Nepalese pilgrims and were recruited while attending a Hindu festival at Gosainkunda (4300 m). This concluding section presents a brief summary of the results and a brief general discussion of the value, and limitations of the overall study. Discussions of the individual results are in the corresponding chapters and will not be repeated in this section. The research questions are: 1) Whether the allele and genotype of the polymorphisms in candidate genes are associated with susceptibility to AMS, 2) whether the subjects with/or without AMS can be differentiated based on ancestral composition and, 3) if there is an ancestral difference between the cases and controls, whether having a putative Tibetan heritage is associated with resistance to AMS. Five candidate genes were chosen for the studies described in this thesis: ACE, AGTR1, BDKRB2, ADRB2, and NOS3, encoding the angiotensin I converting enzyme, angiotensin II receptor 1, bradykinin B2 receptor, beta2-adrenergic receptor, and endothelial nitric oxide synthesis genes. These genes and their proteins play important roles in the maintenance of vascular homeostasis. The polymorphisms in these candidate genes are either thought to functional (i.e. altitude performance and/or vascular-related physiological characteristics or diseases) or were selected as being maximally informative (i.e. tagSNPs). Angiotensin I converting enzyme converts Ang I to the strong vasoconstrictor Ang II and this catalytic step is the rate limiting step of the RAAS pathway. Three functional 139  polymorphisms (an intronic indel (I/D), A-240T and A2350G) in ACE that contribute to the variability of serum ACE levels were investigated. The commonly studied ACEI/D polymorphism was reported over-represented in some native highlanders and might influence human performance at extreme altitudes while the roles (if any) of the other two polymorphisms to high-altitude-related characteristics are unknown. In the study described in Chapter 3, allele and genotype frequencies of these three polymorphisms were not significantly different between the AMS+ and AMS- groups. Our results for the ACEI/D polymorphism were consistent with the studies of AMS in European climbers but not with a recent (2010) study on Han Chinese showed that the D allele was associated with the occurrence of AMS. While the latter study was weak (e.g. the ascent profiles of the subjects were not defined and the diagnosis of AMS is suspect (timing and symptoms suggest altitude illnesses other than AMS were present in the subjects), the results suggest that further studies of a role for alleles at the ACEI/D polymorphism in altitude pathologies are warranted. Angiotensin II type 1 receptors play critical roles in mediating the Ang II effects such as vasoconstriction and the release of aldosterone. The AGTR1A1166C polymorphism (located in 3’ UTR region of AGTR1) is non-functional and alleles at this locus are reported to be associated with vascular-related characteristics; however, the associations are inconsistent between different populations. As reported in Chapter 3, no significant difference in allele frequencies of the AGTR1A1166C polymorphism was found between the AMS+ and AMS- groups. Allele frequency distribution of this polymorphism in Nepalese studied in this thesis was similar to that in Chinese; however, was significantly different from that in Caucasian. This suggests that the varying associations may be due to the different linkage relationships between the AGTR1A1166C polymorphism and functional polymorphisms in AGTR1 and that future studies should take population composition into consideration. Bradykinin B2 receptors are expressed in tissues and mediate the vasodilatation activated by bradykinin, a key molecule in KKS. Bradykinin is degraded by ACE, and this degradation strengthens the effects of ACE on vasoconstriction. Alleles of two functional polymorphisms (i.e. +9/-9 and C-58T) in BDKRB2 have been reported to be associated 140  with expression of the gene and vascular-related characteristics (e.g. vascular resistance); however, as described in Chapter 3, no association was found between alleles of the +9/-9 polymorphism and susceptibility to AMS. The genotype distribution of the BDKRB2C-58T polymorphism was not in HWE in the AMS+ and AMS- groups, due to excess representation of the C/T genotype, especially in the AMS- group. The deviation from HWE may be due to a self-selection (e.g. if individuals with these C/C and C/T genotypes are susceptible to AMS, they may leave the festival early or not attend due to previous AMS experiences or may be due to a general characteristic of Nepalese that has no relation with the development of AMS). On the other hand, the over-representation of the C/T genotype is consistent with a heterozygous advantage at this polymorphism for AMS resistance. While imagining a mechanism that would give these results is difficult, a follow-up study would be warranted to see if this result could be replicated or reproduced. Beta-2 adrenergic receptors are important G-protein coupled catecholamine receptors that are mainly distributed in pulmonary tissue, such as airway smooth muscle cells and endothelial lining of the pulmonary vasculature. By modulating pulmonary bronchial- and vaso-dilation, these receptors may contribute to the improvement oxygen delivery during high altitude exposures. Seven tagSNPs in ADRB2 were identified using HapMap tagSNP picker and employed in the study described in Chapter 4. Alleles of the seven tagSNPs were not associated with susceptibility to AMS, suggesting that neither alleles at these loci or the common haplotypes in ADRB2 that they represent are involved in the AMS etiology. Endothelial nitric oxide synthases are the main source of endogenous NO. Both exhaled and plasma NO are suggested to play important roles in vascular responses to acute hypoxic exposures and high-altitude adaptation. Seven tagSNPs in NOS3 were identified using HapMap tagSNP picker, including a commonly studied functional polymorphism (rs1799983 (Glu298Asp)), and employed in the study described in Chapter 5. The results showed that the T allele and G/T genotype at rs1799983 were overrepresented in the AMS+ group of the 2005 cohort although the difference in allele frequency did not remain significant after a Bonferroni correction multiple testing. The G/G genotype was more common in the AMS- group, indicating that the G/G genotype 141  may be instrumental to the process of migration and adaption to high altitude, while individuals who carry the T/T or G/T genotype may be more prone to AMS. To assess this hypothesis, the allele and genotype frequencies of rs1799983 were identified in native highlanders (i.e. Quechua population) and native lowlanders (i.e. Mayan). Both the G allele and G/G genotype were over-represented in Quechua compared to Mayan, suggesting that carrying the G/G genotype may be a genetic advantage for pioneer highlanders to overcome high-altitude illnesses (e.g. AMS) during migration and adaption at high altitude. To further test for reproducibility of the association between the alleles at rs1799983 and AMS, genotyping was performed in the 2008 cohort. No significant differences in the allele and genotype frequencies at rs1799983 were found between the AMS+ and AMSgroups in both the 2008 and combined 2005 and 2008 cohorts. The inconsistency in the association findings of NOS3 may be due to a number of factors, such as sample size and ascertainment. Population-based genetic association studies are vulnerable to false positive error due to population stratification. The contemporary genetic composition of Nepalese is significantly influenced by Tibetans and Indians, who are native highlanders and lowlanders, respectively, and have different levels of susceptibility to AMS. This may increase the potential confounding effect of population stratification on the genetic associations studied in this thesis. Ancestry informative markers (AIMs) are widely used for genetic structure, admixture and forensic analyses. Fifteen AIMs that have been suggested as being informative in distinguishing Nepalese from its neighbor populations were employed to detect population stratification in this thesis and the study is described in Chapter 6. Allele and genotype frequencies of the AIMs were compared by Chi-square analysis between the AMS+ and AMS- populations and no significant difference were found. Admixture analysis was performed to analyze the contributions from Tibetans and Indians to the Nepalese. The results showed that Indian is the major genetic donor to the Nepalese. Admixture estimates were not significantly different between any case and control population pair. The results from the Chi-square and admixture analyses suggested that there is no measurable population stratification is in Nepalese cohort who participated in the work presented in this thesis.  142  The studies described in this thesis are efforts to explore the contribution of genetics to AMS in Nepalese pilgrims acutely exposed to high altitude. Twenty polymorphisms were investigated representing five candidate genes that encode key components of vascular and pulmonary physiological pathways. The issue of subjectiveness when physicians are diagnosing AMS and/or when subjects are selfdiagnosing was minimized by performing both clinical and LLS evaluations. The risk of having a type I error due to multiple testing was controlled using the Bonferroni correction. The findings from the studies suggest that alleles at the twenty polymorphic loci do not strongly contribute to either resistance or susceptibility to AMS in this Nepalese cohort. These data are consistent in general in refuting the hypothesis that these five genes are involved in AMS; however, they cannot completely rule out a role for candidate genes in the etiology of AMS in Nepalese for the reasons discussed below (although it is unlikely that the effect of any contribution could be large, otherwise we should have seen an association). Whether there is a small effect or the genes contribute in a more complex way (e.g. in gene: gene interactions) would be worth further investigation. As well, the data does not exclude an important effect of these loci or these genes in other populations for example, due to interactions with other alleles that are rare in the Nepalese or because different patterns of linkage disequilibrium generate different maker: causal variant relationships.  7.2 Limitations  7.2.1 Sample size The findings of the studies described in this thesis did not support the hypothesis that the alleles in the candidate genes should be associated with AMS status); therefore, it is necessary to know the extent to which this rules out the roles of the five candidate genes in genetic contribution to AMS susceptibility. This is largely determined by sample size, which will determine the degree of contribution that could be ruled out by the data. Two hundred and thirty five Nepalese were recruited for this work in 2005 (n = 103) and 2008 143  (n = 132). The sample sizes of the AMS+ and AMS- groups are relatively small, and have sufficient power to detect increased allele prevalence in the polymorphisms invested in this thesis as discussed in each individual data chapter. Larger sample sizes are needed for future studies, both to power detection of small effects or gene interactions and to strengthen any conclusions pertaining to lack of effect.  7.2.3 Number of candidate genes Twenty polymorphisms in five candidate genes were investigated in the studies described in this thesis. Investigating genetic contribution to AMS susceptibility in a small number of candidate genes was based on a number of considerations. When the studies were initiated in 2005, the research lab was just launched and did not have sufficient research funding to support studies genotyping a large number of polymorphisms given the technologies and costs at the time. As genome-wide association studies were not an option, a series of candidate gene association studies were performed that investigated genes that encode molecules that are involved in specific physiological pathways that have been suggested contributing to the development of AMS (i.e. candidate genes). As these genes were of a priori interest, there was a good chance of having sufficient statistical power given the relatively small sample size that was anticipated (given the conditions where the subjects were being recruited). While a wider search would have investigated more genes, correcting for multiple testing would have likely rendered the statistical power of the study too low to draw any conclusions. In addition to being promising candidates for AMS based on physiological roles and previous altitude-related research, the genes were chosen because they encoded parts of pathways that the lab was capable of following up on if there was a strong association detected. While the overall objective of the project was to identify genetic contributors to AMS susceptibility, the hope was that, if any were found, the underlying physiology (or pathophysiology) could be worked out as well.  144  As the costs of genotyping has dropped considerably in the last five years, if this work was to be continued, the candidate gene approach would still be a powerful tool; however, many more polymorphisms could be investigated using high-throughput genotyping services (e.g. Genome Quebec) while two stage GWAS (in which candidates are identified in stage one and tested for reproducibility in stage II) would also be an option. In reality, it would not be surprising if genotyping was to be replaced by genome-wide sequencing within the next few years. Full sequence analysis might be the way the goals of the study would be achieved in the near future.  7.2.2 Recruitment The rate of ascent and altitude reached are the most important factors determining the development of AMS. The more rapid the ascent is and higher the altitude is attained, the higher the AMS occurrence rate is. With altitude ascent is fixed, the AMS occurrence rate varies substantially as ascent profile changes as shown in Figure 1.1. The two recruitments (2005 and 2008) of this thesis were performed at Gosainkunda (4380 m) during the Janai Purnima festival. The route to Gosainkunda is relatively uniform, and the itinerary (Figure 2.2) that most of the subjects likely followed was ascending from the Kathmandu Valley (1320 m) via Dhunche (2000 m) over a period of 36 ‒ 48 h. Theoretically, some pilgrims could have gained 3000 m in 24 h, while others could have taken as long as 72 h for the same ascent. Unfortunately, ascent profiles of the subjects were not collected during both the 2005 and 2008 recruitments, and the subjects could face nonequivalent AMS risk. In addition, there is chance that the subjects of the AMS- group as controls might develop the condition after being recruited if they stayed at high altitude longer. Ascent profile and the time that the subjects stayed at high altitude should be considered in future studies. Ideally, diagnosis should be done by the same researchers from year to year and relying on self-diagnosis should be limited. New ways if assessing AMS should be considered, such as visual analogue scales for quantifying discomfort and possibly expanding the range of scores to allow greater resolution (e.g. use a 0 – 10 scale rather than a 0 – 3 scale for the LLS).  145  In the 2005 and 2008 cohorts, more men were recruited than women. It is possible that the autosomal genes investigated in the studies described in this thesis may have sexlimited effects; however, the overall sample sizes are small and insufficient to separate the male and female subjects while maintaining power to detect associations. In addition, social and cultural factors contributing to having less female subjects in the cohorts cannot be ruled out, so sex-based cohorts could differ in phenotype.  7.2.3 Informative polymorphism selection The identification of informative polymorphisms in ADRB2 and NOS3 were completed using HapMap tagSNP picker. At the time that the tagSNPs were selected, allele and genotype frequencies of four populations (i.e. European, Han Chinese, Japanese, and African) were available in the database. The Han Chinese population recruited in Beijing, China, was the one most closely related to Nepalese geographically and genetically, and therefore, the configuration of tagSNPs for these two genes was based on allele frequencies in the Chinese population. The selection of tagSNPs is based on LD-associations between genetic polymorphisms, which may be substantially different between populations. Informative SNPs in one population may not represent the ones in another population, especially when the two populations are genetically distant to each other. Northeast Asian ancestry has been shown contributing to the genetic composition of Nepalese substantially; however, Han Chinese population may still not be the most proper population for tagSNP selection compared to Nepalese itself or its neighbour populations (e.g. the admixture estimation results described in Chapter 6 showed that Indian ancestry contribute about 60 % to our Nepalese cohort), and therefore, the tagSNPs identified in this thesis may not be serve as the most informative ones in Nepalese.  7.3 Value of the studies described in this thesis AMS is a common and potentially debilitating illness that affects many travelers who ascend rapidly to high altitudes without sufficient time for acclimatization due to compressed schedules. While the condition can be moderately benign, AMS often occurs 146  in remote areas with limited modern medical facilities, exacerbating the impact on sufferers. The etiology of AMS is currently undetermined. Studying the physiological mechanisms and searching for a genetic predisposition to the development of AMS promotes understanding of the etiology, epidemiology, prevention, and treatment of this condition and of altitude pathophysiology in general. In the near future, DNA markers for AMS susceptibility may be become a practical tool for travelers as the decrease in cost and increase in the SNP spectrum covered by ‘direct-to-customer’ commercial genotyping services (e.g. 23andMe), this will result in many individuals knowing their ‘AMS-risk genotype’, which may further contribute to increased personalized prevention and treatment strategies. The data in this thesis represents much of the data on the genetics of AMS in Nepalese that is in the scientific literature but should be of interest to researchers studying genetic susceptibility to altitude illnesses in other populations. The Janai Purnima festival in Nepal provides excellent opportunities for conducting a variety of high-altitude physiological and genetic projects. Thousands of Nepalese pilgrims attend this festival annually making it possible for studies to have large sample sizes. Most of the pilgrims are first-time travelers to altitude, do not take medications to prevent or treat AMS and ascend at similar rates. These factors, which minimize many of the common confounders of AMS studies, combined with the high frequency of the condition and the ready availability of unaffected, yet similarly exposed controls, contribute to the quality of the data that can be obtained and therefore its value to other researchers in the field. In coming years, emerging technologies, such as genome-wide association studies and next-generation sequencing, will provide powerful technical platforms to determine genetic contribution to susceptibility to diseases in a whole genome scale. With the application of these technologies and careful control of the phenotypic limitation (such as those discussed above), researchers may eventually characterize the genetic contribution to the development of AMS in people who are acutely exposed to high altitude. If specific markers could be identified that have strong predictive values, the markers could be used to advise travelers whose destinations are the highlands of the world on how to best prepare  147  for the physiological demands of low oxygen environments so as to maximize their productivity or enjoyment while in the mountains.  148  References Ahsan, A., G. Mohd, T. Norboo, M. A. Baig, and M. A. Pasha. 2006. Heterozygotes of NOS3 polymorphisms contribute to reduced nitrogen oxides in high-altitude pulmonary edema. Chest 130:1511-1519. Ahsan, A., T. Norboo, M. A. Baig, and M. A. Qadar Pasha. 2005. 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Linkage and association analysis of angiotensin I163  converting enzyme (ACE)-gene polymorphisms with ACE concentration and blood pressure. Am J Hum Genet 68:1139-1148.  164  Appendix A  Consent Form  Note: All aspects of this study were reviewed and approved by the UBC Clinical Ehics Review Board (CREB). Only single examples of the consent forms and of the CREB approval certificates are attached. The ethics approval went through a number of iterations as amendments describing minor changes in the protocols or changes in lab personnel were approved by the CREB.  165  166  167  Appendix B  Lake Louise Scoring System Form  A. AMS self-assessment The sum of the responses is the AMS self-report score. Headache and at least one other symptom must be present for the diagnosis of AMS. A score of 3 or more is usually taken as AMS (although can be a fairly mild manifestation of the condition). It is suggested that this part of the scoring system should be always used and reported separately. The question relating to sleep will not always be relevant, e.g. in short 1 day studies or in evening assessment when twice-daily scoring is used. Symptom Scoring Headache 0 None at all 1 Mild headache 2 Moderate headache 3 Severe headache, incapacitating Gastrointestinal symptoms 0 Good appetite 1 Poor appetite or nausea 2 Moderate nausea or vomiting 3 Severe, incapacitating nausea and vomiting Fatigue and/or weakness 0 Not tired or weak 1 Mild fatigue/weakness 2 Moderate fatigue/weakness 3 Severe fatigue/weakness Dizziness/light-headedness 0 None 1 Mild 2 Moderate 3 Severe, incapacitating Difficulty sleeping 0 Slept as well as usual 1 Did not sleep as well as usual 2 Woke many times, poor night’s sleep 3 Could not sleep at all  168  B. Clinical assessment This portion of the scoring system contains information gained by examination. The clinical assessment score is the sum of scores in the following three questions. Sign Scoring Change in mental status 0 No Change 1 Lethargy/lassitude 2 Disorientated/confused 3 Stupor/semiconscious 4 Coma Ataxia (heel/toe walking) 0 None 1 Balancing maneuvers 2 Steps off the line 3 Falls down 4 Unable to stand Peripheral edema 0 None 1 One location 2 Two or more locations  C. Functional score The functional consequences of the AMS self-reported score should be further evaluated by one optional question asked after the AMS selfreport questionnaire. Alternatively, this question may be asked by the examiner if clinical assessment is performed. Overall, if you had any of these symptoms, 0 Not at all how did they affect your activities? 1 Mild reduction 2 Moderate reduction 3 Severe reduction (e.g. bedrest)  169  Appendix C  Summary of Studies of AMS Pathophysiology  Authors  Year  Altitude  Hypoxia  Time  Subject  Parameter  Singh  1969  5867 m  HA  2 wk  n = 34  ICP  Reeves  1985  4800 m  HYP  7h  n = 12  CBF  Cummings  1985  5030 m  HA  150 m to 3475 m Jensen  Hartig and Hackett  1990  1992  3200 m to 5430 m 5000 m  NA  n=3  in 24 h  n = 12  HA  NOR  5d  n=9  8h  n=3  Baumgartner  1994  4559 m  HA  72 h  n = 23  Zavasky  1995  5000 m  HYP  till AMS occurs  n=3  4d  n = 24  Wright  1995  3440 m 5200 m  HA  Main findings ICP measured by lumbar CSF pressure increased 6 to 12 cmH 2O, indicating the subjects might have HACE. CBF of internal carotid and vertebral arteries increased, independent of HAH, indicating that CBF does not appear to play a primary role in AMS.  ICP  This is the only report of direct ICP measurement at high altitude. ICP (telemetric ICP) remained normal at rest at all altitudes; however, in the single subject with AMS, there was a dramatic increase in ICP even on minimal exertion. An inverse correlation was found between ventricular size and headache score, providing the first objective evidence supporting the tight-fit hypothesis of AMS.  gCBF  The increases in CBF (24 % up to 3475 m; 53 % from 3200 to 5430 m) were similar in AMS+ and AMS- individuals, indicating that alterations in CBF cannot be directly implicated in developing AMS.  ICP  MCAv  iCSF ICV ICP  A slight rise was found ICP measured by lumbar CSF pressure, irrespective of AMS Overall MCAv increased significantly in all subjects (AMS+ vs. AMS-: 148 % vs. 127 % of baseline values, respectively). Individuals with AMS showed a significantly high increase in MCAv than those without on both day 1 and day 2 at 4559 m. A decrease in iCSF and an increase in ICV were found. The onset of AMS is associated with the changes of ICV with the displacement of iCSF from the IC space. ICP shown by TMD increased and was associated with acute hypoxia at 3440 m; however, raised ICP was not independent of AMS.  170  Authors  Year  Wright  1995  Baumgartner  1999  Altitude  Hypoxia  Time  3400 m  HA  NA  5200 m  HA  NA  4559 m  NOR  3&6h  Subject  Parameter  Main findings  n = 24  ICP  ICP shown by TMD increased and was associated with acute hypoxia at 3440 m; however, raised ICP was not independent of AMS.  n = 10  MCAv  Average percentage of low-altitude values remained unchanged, suggesting that CBF is not important in the pathogenesis of AMS.  MCAv  Individuals with AMS showed 30% higher of MCAv and have greater cerebral hemodynamic responses to higher MCAv resting control compared with non-AMS individuals. (AMS+: n = 17; AMS: n = 43; Sherpas: n = 20)  Jansen  1999  4243 m  HA  24 - 48 h  Muza  1999  4400 m  NOR  32 h  Jansen  2000  4243 m  HA  NA  Ter Minassian  2001  up to 8000 m  HYP  >3d  n=8  MCAv  Morocz  2001  4572 m  HYP  32 h  n=9  BV (grey matter)  n = 80  BV  n = 19;  CA  cerebral edema Fischer  2004  4500 m  HYP  10 h  n = 10 CSF  Bailey  2004  4600 m  NOR  18 h  n = 22  ICP  An increase in BV was found and independent of AMS. AMS- (n = 10) showed increased MABP and unaffected MCAv and Sherpas (n = 9) had both increased MABP and MCAv. All Sherpas and the majority of the newcomers showed impaired CA, indicating that an intact autoregulatory response to changes in BP may not be a factor in the regulation of cerebral vasculature and the occurrence of cerebral edema in newcomers to high altitude. MCAv remained unchanged until above 5000 m and significant increase was only presented at 8000 m, suggesting that CBF may only play a critical role in the pathogenesis of AMS at extreme high altitude. An increase in BV (36.2 ml, 2.77 %) presented, suggesting vasogenic edema. (AMS+: n = 8; AMS-: n = 1) No change of cerebral edema was found although CSF decreses 10.3 % - 13.2 %. No significant correlation between AMS symptom scores and absolute or relative change in CSF volumes. Moderate to severe AMS (placebo group) is associated with larger CSF-volume (>10 ml) at normoxia. (AMS+: n = 8; AMS-: n = 2) ICP measured by lumbar CSF pressure remained unchanged, independent of AMS.  171  Authors  Year  Altitude  Hypoxia  Time  Subject  Parameter  Main findings  Van Osta  2005  4559 m  HA  20 h  n = 35  CA  CA is associated with AMS-C, suggesting that an impaired regulation of cerebral circulation may play a role in the development of AMS.  Bailey  2006  4600 m  NOR  18 h  n = 22  BV iCSF Vascular damage1 Neuronal damage2 BBB3 Inflammation4  An increase in BV (7.0 ± 4.8 ml) was observed and independent of AMS. No changes in BBB dysfunction, lumbar pressure and cerebral vascular damage were found. The findings suggest that a freeradical-mediated damage to barrier function may not play an important role in the presence of the mild brain swelling observed in individuals with AMS during acute exposure to normobaric hypoxia. (AMS+: n = 11; AMS-: n = 11)  Permeability5 BV Kallenberg  2007  4500 m  HYP  16 h  n = 22 CE & VE  right MCAv Fedderson  Schoonman  2007  2008  up to 5050 m  HA  4500 m  NOR  11 d  n = 32 Brain dysfunction  6h  n=9  CE & VE  An increase (7 ml; 0.5 %) in BV was found. The increase in ICV is associated with AMS. In addition the general brain swelling, an additional cytotoxic edema in individuals with an anatomical predisposition (i.e. a high ratio between brain volume and intracranial volume) may increase the risk of developing AMS. (AMS+: n = 11; AMS-: n = 11) Right MCAv increased (16 % from 100 m to 3440 m; further elevated 22 % from 3440 to 5050m) and right temporal delta activity decreased in AMS individuals. EEG-detected regional right temporal cerebral dysfunction (temporal delta activity) may serve as a predictive marker for AMS in mountaineers during HA climbing and trekking. (AMS+: n = 10) Mild vasogenic edema was found, independent of AMS. Hypoxic exposure was associated with severe AMS with additional mild cytotoxic edema. (AMS+: n = 7; AMS-: n = 2)  172  Authors  Year  Altitude  Hypoxia  Time  Subject  Parameter  Main findings  Dyer  2008  3800 m  NOR  30 min  n = 12  CBF  Regional CBFs (white and grey matters) elevated during acute hypoxia but are not primary features of AMS susceptibility. (AMS+: n = 6; AMS-: n = 6)  Sutherland  2008  6400 m  HA  NA  n = 13  ICP  ICP shown by ONSD increased and was associated with severe AMS, suggesting ICP plays an important role in the pathophysiology of AMS.  global CBF Bailey  2009  4300 m  NOR  9h  n = 10  Free radicals6 Vascular damage7  Fagenholz  2009  4240 m  HA  NA  n = 287  ICP BV  Dubowitz  2009  3800 m  NOR  40 min  n = 12  iCSF CBV  Cochand  2011  3800 m  HA  6h  n = 18  CA  Mairer  2012  5500 m  NOR  8h  n = 20  CE  Global CBF remained unchanged. Free radical levels increased and were associated with AMS; however, no vascular damage was not found, suggest that free radicals may be the factors for AMS by a mechanism that appears independent of BBB dysfunction and cerebral oxidative metabolism. ICP shown by ONSD is strongly associated with AMS (5.34 mm vs. 4.46 mm between AMS+ and AMS- (P < 0.0001). (AMS+: n = 69; AMS-: n = 218) Increases in BV (8.2 ml; 0.3 %) and CBV (2.3 ml) were found with significant shifts in iCSF, which decreased 10 ml. These changes are not associated with the suscepitiblity to AMS. (AMS+: n = 6; AMS-: n = 6) CA at sea level was associated with LLS and AMS-C scores and a lower baseline CA may be considered a potential risk factor for AMS. White matter volume of the brain increased in the frontal lobe and grey matter volume increased in the posterior lobe. The changes were more pronounced during active hypoxic exposure, irrespective of AMS. The changes at rest and especially during exercise in normobaric hypoxia were associated with accumulation of water in the extracellular space, independent of AMS development, suggesting that AMS and HACE do not share a common pathophysiological mechanism.  173  Abbreviations: ICP: intracranial cerebral pressure; CBF: cerebral blood flow: MCAv: the velocity of middle cerebral artery; iCSF: intracranial cerebral spinal fluid; ICV: intracranial volume; BV: brain volume; CA: cerebral autoregulation; BBB: blood brain barrier; CE: cerebral edema; VE: vasogenic edema. Molecules that were measured: 1. CPK (creatine phosphokinase), LDH (lactate dehydrogenase) and S100β; 2. NSE (neuron-specific enolase); 3. Albumin, IgG, IgA and IgM; 4. (TNF)-α, interleukin (IL)-1β, IL-6 and leucocyte account; 5. VEGF; 6. Lipid-derived alkoxyl-alkyl free radicals and lipid hydroperoxides; 7. NSE (neuron-specific enolase), S100β and 3-nitrotyrosine (3-NT).  174  Appendix D  Primer Sequences and Enzymes for the Analysis of Candidate Polymorphisms and AIMs  1. Primer sequences and enzymes for the analysis of the ACE, AGTR1, BDKRB2, NOS3, and ADRB2 genes Angiotensin converting enzyme gene (ACE) Polymorphism  Primer sequences  Annealing temperature  Restriction enzyme  58 °C  N/A  55 °C  XbaI  58 °C  BstUI  ACE-1 * 5’ CATCCTTTCTCCCATTTCTC 3’ ACE I/D (Ins/Del) ACEA-240T (A/T) ACEA2350G (G/A)  ACE-2 * 5’ TGGGATTACAGGCGTGATACAG 3’  DNA segment length for alleles I allele: 327 + 65 bp D allele: 84 bp  ACE-3 * 5’ ATTTCAGAGCTGGAATAAAATT 3’ ACE4-F ** 5’ TCGGGCTGGGAAGATCGAGC 3’ ACE4-R ** 5’ GAGAAAGGGCCTCCTCTCTCT 3’ ACE8-F ** 5’ CTGACGAATGTGATGGCCGC 3’ ACE8-R ** 5’ TTGATGAGTTCCACGTATTTCG 3’  A allele: 114 + 23 bp T allele: 137 bp G allele: 103 +19 bp A allele: 122 bp  * Previously described in (Evans et al. 1994) ** Previously described in (Keavney et al. 1998). The underlined base in the ACE-8-F primer is a mismatch that replaces an A to create a diagnostic Bst UI recognition site in the presence of the G allele at G/A2350. The underlined base in ACE-4-R replaces an A to create a diagnostic Xba I recognition site in the presence of the T allele at A/T-240.  Angiotensin II type 1 receptor gene (AGTR1) Polymorphism AGTR1A1166C (C/A)  Primer sequences AGTR1-F 5’ ATAATGTAAGCTCATCCACC 3’ AGTR1-R 5’ GAGATTGCATTTCTGTCAGT 3’  Annealing temperature  Restriction enzyme  55 °C  DdeI  DNA segment length for alleles C allele: 212 +138 bp A allele: 350 bp  175  Bradykinin B2 receptor gene (BDKRB2) Polymorphism  Primer sequences  BDKRB2+9/-9 (Ins/Del)  BD-9-F 5’ TCCAGCTCTGGCTTCTGG 3'  BDKRB2C-58T (C/T)  BD-58-F 5’ AAGGTGGCCGCAGCCTTCC 3’  BD-9-R 5’ AGTCGCTCCCTGGTACTGC 3' BD-58-R 5’ CTCATCTTTCAAGGGCTGGCTA 3’  Annealing temperature  Restriction enzyme  60 °C  N/A  58 °C  BfaI  DNA segment length for alleles + 9 allele: 89 bp - 9 allele: 80 bp C allele: 112 + 21 bp T allele: 133 bp  The underlined base in BD-58-R replaces a G to create a diagnostic Bfa I recognition site in the presence of the C allele at -58 C/T.  Beta-2 adrenergic receptor gene (ADRB2) Polymorphism rs2400707 A/G rs253044 A/G rs12654778 G/A rs11168070 G/C rs1042713 G/A rs1042718 C/A rs1042719 G/C  Primer sequences 707-F 5’ AGTAGAGACAAGAGTTACACC 3’ 707-R 5’ GTACTTTAGGTGCCCTCCTTA 3’ 44-F 5’ GACAGCGAGTGTGCTGAGGAAACC 3’ 44-R 5’ TAACCCAGTGTATTCCCTTTC 3’ 778-F 5’ GCTGTGGTTCGGTATAAGTTT 3’ 778-R 5’ CATTCGGAAGGAAACGAGAGT 3’ 70-F 5’ AAAAGCTCCCGGGTTGCTGGTGAG 3’ 70-R 5’ GAGGGCGGGCCACCACTGCTT 3’ 713-F 5’ CCTTCTTGCTGGCACCCCAT 3’ 713-R 5’ CCAGCACATTGCCAAACACG 3’ 718-F 5’ TGATCATTCTGATGGTGTGGA 3’ 718-R 5’ GTAGAAGGACACGATGGAAGA 3’ 719-F 5’ GCAGGTCTTCTTTGAAGGCCTACCG 3’ 719-R 5’ GTCTTCACACAGCAGTTTATT 3’  Annealing temperature  Restriction enzyme  54 °C  HhaI  56 °C  MspI  55 °C  MseI  60 °C  BsiHKA I  56 °C  NcoI  54 °C  MspI  58 °C  MspI  DNA segment length for alleles A allele: 449 bp G allele: 154 + 295 bp A allele: 87 bp G allele: 63 +24 bp A allele: 74 + 20 bp G allele: 94 bp C allele: 67 + 32 bp G allele: 99 bp A allele: 135 bp G allele: 117 + 18 bp A allele: 173 bp C allele: 104 + 69 bp G allele: 90 + 23 bp C allele: 113 bp  1  dbSNP identifying numbers (http://www.ncbi.nlm.nih.gov/projects/SNP/) and base change; 2 annealing temperature for PCR amplification The underlined base in 044-F replaces a T to create a diagnostic Msp I recognition site in the presence of the G allele at rs253044 A/G. The underlined base in  176  778-F replaces a C to create a diagnostic Mse I recognition site in the presence of the A allele at rs12654778 G/A. The underlined base in 070-F replaces a A to create a diagnostic BsiHKA I recognition site in the presence of the C allele at rs11168070 G/C. The underlined bases in 719-F replaces two bases TG to create a diagnostic Msp I recognition site in the presence of the G allele at rs1042719 G/C.  Nitric oxide synthase 3 gene (NOS 3) Polymorphism rs1800781 G/A  rs1799983 G/T  rs3918186 A/T rs3918188 A/C rs743507 A/G rs1808593 T/G rs7830 C/A 1  Primer sequences  Annealing temperature  Restriction enzyme  60 °C  BanI  781-F 5’ GAGCATCACCTATGACACCCT 3’ 781-R 5’ CATCTGAGGCCAGGCCTTAGGCAC 3’  DNA segment length for alleles G allele: 83 + 23 bp A allele: 106 bp  983-F 5’ AAGGCAGGAGACAGTGGATGGA 3’  59 °C  983-R 5’ CCCAGTCAATCCCTTTGGTGCTCA 3’  BanII  G allele: 163 +85 bp T allele: 248 bp  186-F 5’ ATTTACAACATGTGTGCACCTCTGGAC 3’ 186-R 5’ GGGAAGGAAGCTGGAAGGAACTTGATC 3’ 188-F 5’ AGCAGCAAGGCACACGTACAAGCG 3’ 188-R 5’ ATTGTACTTCACTGAGACTGA 3’ 507-F 5’ ACCTGGAGAATCCAGCCATGAAT 3’ 507-R 5’ GCCACCCCAATGAGGCACAGG 3’ 593-F 5’ GGCTAAGCTTTGGCTCTCTCATT 3’ 593-R 5’ GATTTAGTGACTGTAGTTCCCA 3’ 830-F 5’ CCTTCAGGCAGTCCTTTAGGC 3’ 830-R 5’ GGGTCCAGGCACTGGCATTGC 3’  60 °C  BclI  55 °C  BstUI  60 °C  HaeIII  54 °C  DpnII  60 °C  HaeIII  T allele: 133 + 27 bp A allele: 83 bp C allele: 62 + 21 bp G allele: 104 + 66 bp A allele: 170 bp G allele: 110 +77 bp C allele: 81 + 22 bp A allele: 103 bp  2  dbSNP identifying numbers (http://www.ncbi.nlm.nih.gov/projects/SNP/) and base change; annealing temperature for PCR amplification The underlined bases in 781-R replace two bases, CC, to create a diagnostic Ban I recognition site in the presence of the G allele at rs1800781 G/A. The underlined bases in 186-R replace two bases, GC, to create a diagnostic Bcl I recognition site in the presence of the T allele at rs3918186 A/T. The underlined base in 188-F replaces a G to create a diagnostic BstU I recognition site in the presence of the C allele at rs3918188 A/C. The underlined base in 830-F replaces a T to create a diagnostic Hae III recognition site in the presence of the G allele at rs7830 C/A.  177  2  Genomic location, primer sequence, annealing temperature, and allele sequence sample of each STRP for the detection of population stratification  D21S11 Location: chromosome 21  S11-F 5’-ATATGTGAGTCAATTCCCCAAG-3’;  Annealing temperature: 53.3 °C  S11-R 5’-TGTATTAGTCAATGTTCTCCAGAGAC-3’  Sample ID: N504 Allele: 30.2 repeats, [TCTG]6 [TCTA]5 [TCTA]3 TA [TCTA]3 TCA [TCTA]2 TCCA TA [TCTA]11 TCG [TCTA]2 Allele sequence: TCCGTTAAGTGAACCCC TCTA TCTA TCTA TCTA TCTA TCTA TCTG TCTG TCTG TCTG TCTG TCTA TCTA TCTA TA TCTA TCTA TCTA TCA TCTA TCTA TCCA TA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCG TCTA TCTA TCCA GTCTATCTACCTCCTATTAGTCTGTCTCTGGAGA ACAT TGACTAATACAA D16S539 Location: chromosome 16  539-F 5’-GGGGGTCTAAGAGCTTGTAAAAAG-3’  Annealing temperature: 57 °C  539-R 5’-GTTTGTGTGTGCATCTGTAAGCATGTATC-3’  Sample ID: N7 Allele: 11 repeats, [GATA]11 Allele sequence: AATCTAAATGCAGAAAAGCACTGAAAGAAGAATCCCGAAAACCACAGTTCCCATTTTTATATGGGAG CAAACAAAGGCAGATCCCAAGCTCTTCCTCTTCCCTAGATCAATACAGACAGACAGACAGGTG GATA GATA GATA GATA GATA GATA GATA GATA GATA GATA GATA TCATTGAAAGACAAAACAGAGATGGATGATAGATACATGC TTACAGATGC D13S317 Location: chromosome 13  317-F 5’-ATTACAGAAGTCTGGGATGTGGAGGA-3’  Annealing temperature: 58 °C  317-R 5’-GGCAGCCCAAAAAGACAGA-3’  Sample ID: G24 Allele: 8 repeats, [TATC]8 ATCCGTGNNTCTCTGGACTCTGACCCNTCTAACGCCTATCTGTATTTACAAATACAT TATC TATC TATC TATC TATC TATC TATC TATC AATCAATCATCTATCTATCTTTCTGTCTGTCTTTTTGGGCTGCCN  178  D3S1358 Location: chromosome 3  1358-F 5’-ACTGCAGTCCAATCTGGGT-3’  Annealing temperature: 55 °C  1358-R 5’-ATGAAATCAACAGAGGCTTGC-3’  Sample ID: C8 Allele: 16 repeats, [TCTA]1[TCTG]3[TCTA]12 GCAGTCCAATCTGGGTGACAGAGCAAGACCCTGTCTCAT TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTG TCTG TCTG TCTA AAA TCA ACA GAG GCT TGC ATG TA D7S820 Location: chromosome 7  820-F 5’-ATGTTGGTCAGGCTGACTATG-3’  Annealing temperature: 53 °C  820-R 5’-GATTCCACATTTATCCTCATTGAC-3’  Sample ID: N127 Allele: 12 repeats, [GATA]12 Allele sequence: GGGTATGATAGAACACTTGTCATAGTTTAGAACGAACTAAC GATA GATA GATA GATA GATA GATA GATA GATA GATA GATA GATA GATA GACAGATTGATAGTTTTTTTTTATCTCACTAAATAGTCTATAGTAAACATTTA D8S1179 Location: chromosome 8  1179-F 5’-ATTGCAACTTATATGTATTTTTGTATTTCATG-3’  Annealing temperature: 54 °C  1179-R 5’-ACCAAATTGTGTTCATGAGTATAGTTTC-3’  Sample ID: N514 Allele: 14 repeats, [TAGA]11[CAGA]1[TAGA]2 ATCGTATCNNCNANTGCGTGAATATGCCTTAATTTATTTACCTATCCTGTAGATTATTTTCACTGTGGGGAA TAGA TAGA TAGA TAGA TAGA TAGA TAGA TAGA TAGA TAGA TAGA CAGA TAGA TAGA TACGAATGTACACATGAA CSF1PO Location: chromosome 5  CSF-F 5’-CCGGAGGTAAAGGTGTCTTAAAGT-3’  179  Annealing temperature: 57 °C  CSF-R 5’-ATTTCCTGTGTCAGACCCTGTT-3’  Sample ID: N104 Allele: 12 repeats, [AGAT]12 Allele sequence: CAGTTTTCCTACCTGTAAAATGAAGATATTAACAGTAACTGCCTTCATAGATAGAAGATAGATAGATT AGAT AGAT AGAT AGAT AGAT AGAT AGAT AGAT AGAT AGAT AGAT AGAT AGGAAGTACTTAGAACAGGGTCTGACACAG D2S1338 Location: chromosome 2  1338-F 5’-AGCCAGTGGATTTGGAAACAGAAATG-3’  Annealing temperature: 58 °C  1338-R 5’-ACCTAGCATGGTACCTGCAG-3’  Sample ID: N504 Allele: 24 repeats, [GGAA]2[GGAC]1[GGAA]14[GGCA]7 AG GGAA GGAA GGAC GGAA GGAA GGAA GGAA GGAA GNAA GGAA GGAA GGAA GGAA GGAA GGAA GGAA GGAA GGCA GGCA GGCA GGCA GGCA GGCA GGCA AGGCCAAGCCATTTCTGNTTCCNATCCNCTGGCTA D18S51 Location: chromosome 18  S51-F 5’-TTCTTGAGCCCAGAAGGTTA-3’  Annealing temperature: 55 °C  S51-R 5’-ATTCTACCAGCAACAACACAAATAAAC-3’  Sample ID: N509 Allele: 14 repeats, [AGAA]14 Allele sequence: TGCACTTCACTCTGAGTGACAAATTGAGACCTTGTCTC AGAA AGAA AGAA AGAA AGAA AGAA AGAA AGAA AGAA AGAA AGAA AGAA AGAA AGAA AAAGAGAGAG D5S818 Location: chromosome 5  818-F 5’-GGTGATTTTCCTCTTTGGTATCC-3’  Annealing temperature: 55 °C  818-R 5’-AGCCACAGTTTACAACATTTGTATCT-3’  Sample ID: A17 Allele: 11 repeats, [AGAT]11  180  FGA Location: chromosome 4  FGA-F 5’-GGCTGCAGGGCATAACATTA-3’  Annealing temperature: 56 °C  FGA-R 5’-ATTCTATGACTTTGCGCTTCAGGA-3’  Sample ID: N205 Allele: 24 repeats, [TTTC]3TTTT TTCT[CTTT]16CTCC[TTCC]2 Allele sequence: AACTGTAACCAAAATAAAATTAGGCATATTTACAAGCTAG TTTC TTTC TTTC TTTT TTCT CTTT CTTT CTTT CTTT CTTT CTTT CTTT CTTT CTTT CTTT CTTT CTTT CTTT CTTT CTTT CTTT CTCC TTCC TTCC TTTCTTCCTTTCT vWA Location: chromosome 12  vWA-F 5’-GCCCTAGTGGATGATAAGAATAATCAGTATGTG-3’  Annealing temperature: 57 °C  vWA-R 5’-GGACAGATGATAAATACATAGGATGGATGG-3’  Sample ID: N101 Allele: 20 repeats, TCTA [TCTG]4[TCTA]13[TCCA][TCTA] Allele sequence: TCTA TCTG TCTG TCTG TCTG TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCTA TCCA TCTA TCCATCCATCCTATGTATTTATCATCTGNNN TPOX Location: chromosome 2  TPOX-F 5’-ACTGGCACAGAACAGGCACTTAGG-3’  Annealing temperature: 61 °C  TPOX-R 5’-GGAGGAACTGGGAACCACACAGGTTA-3’  Sample ID: N501 Allele: 11 repeats, [AATG]11 Reverse strand: TCTGTCCTTGTCAGCGTTTATTTGCCCAAA CATT CATT CATT CATT CATT CATT CATT CATT CATT CATT CATT CAGTGAGGGTTCCCTAAGTGCCNGNNTGTGCCAGNNN THO1 Location: chromosome 11  THO1-F 5’-ATTCAAAGGGTATCTGGGCTCTGG-3’  Annealing temperature: 59 °C  THO1-R 5’-GTGGGCTGAAAAGCTCCCGATTAT-3’  181  Sample ID: N10 Allele: 9 repeats, [AATG]9 Allele sequence: TACACAGGGCTTCCGAGTGCAGGTCACAGGGAACACAGACCCTGGTG AATG AATG AATG AATG AATG AATG AANG AATG AATG ANGGAAATAAGGGAGGAACAGCATGGGNNTNNNNCANCCAGATCCCTTTGNATT D19S433 Location: chromosome 19  433-F 5’-CCTGGGCAACAGAATAAGATTC-3’  Annealing temperature: 53 °C  433-R 5’-TAGGTTTTTAAGGAACAGGTGG-3’  Sample ID: N526 Allele: 16 repeats, [CCTT]16 Allele sequence: GCACCCATTACCCGAATAAAAATCTTCTCTCTTTCTTCCTCTCT CCTT CCTT CCTT CCTT CCTT CCTT CCTT CCTT CCTT CCTT CCTT CCTT CCTT CCTA CCTT CTTT CCTT CAACAGAATCTTATTCTGTTGCCC AGGA  182  Appendix E  Gene ACE ACE ACE AGTR1 BDKRB2 BDKRB2 NOS3 NOS3 NOS3 NOS3 NOS3 NOS3 NOS3 ADRB2 ADRB2 ADRB2 ADRB2 ADRB2 ADRB2 ADRB2  Names of the Polymorphisms Assayed  Name of the polymorphism ACEI/D polymorphism ACEA240T polymorphism ACEA2350G polymorphism AGTR1A1166C polymorphism BDKRB2C-58T polymorphism BDKRB2-9/+9 polymorphism rs1800781 polymorphism NOS3G894T polymorphism rs3918186 polymorphism rs3918188 polymorphism rs743507 polymorphism rs1808593 polymorphism rs7830 polymorphism rs2400707 polymorphism rs253044 polymorphism rs12654778 polymorphism rs11168070 polymorphism ADRB2A46G polymorphism rs1042718 polymorphism rs1042719 polymorphism  Other name/s  rs number  Type  Location  Phenotypes associated  ACE I/D ACE-4 ACE-8 N/A N/A N/A N/A Glu298Arg N/A N/A N/A N/A N/A N/A N/A N/A N/A Arg16Gly N/A N/A  rs4646994 rs4291 rs4343 rs17231380 rs1799722 rs71103505 rs1800781 rs1799983 rs3918186 rs3918188 rs743507 rs1808593 rs7830 rs2400707 rs253044 rs12654778 rs11168070 rs1042713 rs1042718 rs1042719  Indel SNP SNP SNP SNP Indel SNP Missense, SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP Missense, SNP Silent, SNP Silent, SNP  Intron 16 Promoter Exon 17 3’ UTR Promoter Exon 1 Intron 3 Exon 7 Intron 14 Intron 14 Intron 21 Intron 23 Intron 24 5’ UTR 5’ UTR 5’ UTR 5’ UTR Exon Exon Exon  Account 43 % of ACE Account 6 % of ACE Account 19 % of ACE Blood pressure Blood pressure Blood pressure Unknown NO level Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown B2AR affinity to agonists Unknown Unknown  183  

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