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Mitochondrial genome variation in healthy aging Fornika, Daniel John 2012

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Mitochondrial Genome Variation in Healthy Aging  by Daniel John Fornika Bachelor of Science, Simon Fraser University, 2006  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  Master of Science in THE FACULTY OF GRADUATE STUDIES (Medical Genetics)  The University Of British Columbia (Vancouver) August 2012 © Daniel John Fornika, 2012  Abstract Mitochondria are thought to play a role in the aging process through their production of reactive oxygen species (ROS), and their regulation of cell fate via senescence and apoptosis. We hypothesize that genetic variation in the mitochondrial genome may explain a portion of the phenotypic variance in the development of long-term good health. To test this hypothesis, we have performed genetic association tests on a set of common mitochondrial polymorphisms, in a study of 419 exceptionally healthy seniors (cases) and 415 population-based mid-life individuals (controls). Variant discovery was performed using Sanger sequencing of 834 individuals for the 1.1 kb non-coding mitochondrial control region, and identified 277 SNPs present in at least one individual. A set of 92 mitochondrial coding-region SNPs were chosen via pooled high-throughput sequencing, combined with a previouslypublished set of European-specific mitochondrial tag SNPs. After filtering for minor-allele frequency of > 10%, a set of nine control-region SNPs and seven coding-region SNPs were tested for association with healthy aging. None showed a statistically-significant association signal. Additionally, one controlregion variant that had shown association in an Italian centenarian population was tested in our sample set, but the association was not replicated.  ii  Preface Funding for these experiments was obtained from the Canadian Institute for Health Research (CIHR) by Angela Brooks-Wilson, in collaboration with the Marco Marra, Joseph Connors, Stephen Jones, Nhu Le and Graydon Meneilly. The experiments described in this thesis are a part of the Genomics, Genetics and Gerontology (G3 ) Study of Healthy Aging. All experiments were concieved of and designed by Angela Brooks-Wilson. This thesis is broadly composed of three sub-experiments. They are: pooled nextgeneration sequencing of mitochondrial DNA, Sanger sequencing of individual mitochondrial control-regions, and determination of mitochondrial genotypes by Sequenom genotyping. Dan Fornika performed the Polymerase Chain Reaction (PCR) reactions necessary to provide template mitochondrial DNA for both pooled mitochondrial DNA sequencing and control-region Sanger sequencing. Mitochondrial DNA pools were constructed by Dan Fornika. Sanger sequencing was prepared by Julius Halaschek-Wiener with assistance from Dan Fornika. Sanger sequencing was performed by the sequencing group of the British Columbia Genome Sciences Centre (BCGSC). Library construction and Illumina Genome Analyzer (GA) sequencing was performed by the sequencing group of the BCGSC. Sequenom genotyping was performed by the McGill University/Genome Qu´ebec Innovation Centre. All bioinformatics and statistical analyses were performed by Dan Fornika, except for quality control of Sequenom genotype data, which was performed by Denise Daley. iii  Table of Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ii  Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  iii  Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  iv  List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  vi  List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  vii  Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  viii  1  Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1  1.1  Aging as a Genetic Disease . . . . . . . . . . . . . . . . . . . . .  1  1.1.1  The “Healthy Aging” Phenotype . . . . . . . . . . . . . .  1  Mitochondria and Aging . . . . . . . . . . . . . . . . . . . . . .  2  1.2.1  Mitochondrial Genome Structure and Regulation . . . . .  2  1.2.2  Reactive Oxygen Species . . . . . . . . . . . . . . . . . .  5  1.2.3  The Role of Mitochondria in Apoptosis . . . . . . . . . .  5  1.2.4  The Role of Mitochondria in Cellular Senescence . . . . .  6  1.2.5  Somatic Mitochondrial DNA Mutations and Aging . . . .  7  1.2.6  Mitochondrial Heteroplasmy and Tissue Heterogeneity . .  7  1.2.7  Reported mtDNA Associations . . . . . . . . . . . . . . .  7  Hypothesis and Specific Aims . . . . . . . . . . . . . . . . . . .  8  Variant Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . .  9  2.1  9  1.2  1.3 2  Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv  2.2  2.3  2.4 3  Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  9  2.2.1  Subjects and Samples . . . . . . . . . . . . . . . . . . . .  11  2.2.2  Control Region PCR and Sanger Sequencing . . . . . . .  11  2.2.3  Sanger Sequence Assembly . . . . . . . . . . . . . . . .  11  2.2.4  Long PCR . . . . . . . . . . . . . . . . . . . . . . . . .  12  2.2.5  Construction of DNA Pools . . . . . . . . . . . . . . . .  12  2.2.6  Library Construction and Sequencing . . . . . . . . . . .  12  2.2.7  Statistical Analysis . . . . . . . . . . . . . . . . . . . . .  13  Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  14  2.3.1  Sequencing of the Mitochondrial Conrol Region . . . . .  14  2.3.2  Next-Generation Sequencing of Pooled mtDNA . . . . . .  15  Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  27  A Case-Control Association Study for Mitochondrial Variants and Healthy Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  29  3.1  Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  30  3.1.1  Power Calculations . . . . . . . . . . . . . . . . . . . . .  30  3.1.2  Genotyping and Quality Control . . . . . . . . . . . . . .  30  3.2  Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  31  3.3  Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  33  Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  34  A Supplemental Figures . . . . . . . . . . . . . . . . . . . . . . . . . .  43  B Mitochondrial Marker Data . . . . . . . . . . . . . . . . . . . . . .  46  4  v  List of Tables Table 1.1  Selected Mitochondrial Diseases . . . . . . . . . . . . . . . .  2  Table 1.2  Mitochondrial Protein Genes . . . . . . . . . . . . . . . . . .  4  Table 2.1  List of PCR Primers . . . . . . . . . . . . . . . . . . . . . . .  12  Table 2.2  Summary of Illumina Sequence Mapping (Untrimmed Reads) .  13  Table 2.3  Summary of Illumina Sequence Mapping (Trimmed Reads) . .  14  Table 2.4  Mitochondrial Non-synonymous SNPs . . . . . . . . . . . . .  15  Table 2.5  Number of Variants by Gene . . . . . . . . . . . . . . . . . .  26  Table 3.1  Mitochondrial Control Region (MAF > 0.10) . . . . . . . . .  32  Table 3.2  Mitochondrial Coding Region (MAF > 0.10) . . . . . . . . . .  32  Table 3.3  Replication of rs62581312 (C150T) . . . . . . . . . . . . . . .  32  Table B.1  Mitochondrial Marker Selection . . . . . . . . . . . . . . . . .  46  Table B.2  Mitochondrial Marker Set for Sequenom Genotyping . . . . .  49  Table B.3  Markers Repeated Due to Quality Control Failure . . . . . . .  52  vi  List of Figures Figure 1.1  Map of the Human Mitochondrial Genome . . . . . . . . . .  3  Figure 2.1  Experimental Design for Variant Detection . . . . . . . . . .  10  Figure 2.2  Screenshots for Variant Discovery with Phred/Phrap/Consed .  16  Figure 2.3  Putative Heteroplasmic Positions . . . . . . . . . . . . . . . .  17  Figure 2.4  Per-base Quality Distributions . . . . . . . . . . . . . . . . .  18  Figure 2.5  Per-base Quality Distributions . . . . . . . . . . . . . . . . .  19  Figure 2.6  Sequence Coverage for Case Pool . . . . . . . . . . . . . . .  20  Figure 2.7  Sequence Coverage for Control Pool . . . . . . . . . . . . . .  21  Figure 2.8  Minor Allele Frequencies for Case Pool . . . . . . . . . . . .  22  Figure 2.9  Minor Allele Frequencies for Control Pool . . . . . . . . . .  23  Figure 2.10 MAF Comparison for Cases . . . . . . . . . . . . . . . . . .  24  Figure 2.11 MAF Comparison for Controls . . . . . . . . . . . . . . . . .  25  Figure 3.1  Statistical Power for Association Test . . . . . . . . . . . . .  31  Figure A.1  Short-Read Sequence Coverage of mtDNA . . . . . . . . . .  44  Figure A.2  Control Region MAF Distribution from Sanger Sequencing .  45  vii  Glossary British Columbia Genome Sciences Centre  BCGSC  the Canadian Institute for Health Research  CIHR  Genome Analyzer  GA kb  kilobase Flavin Mononucleotide  FMNH  Minor Allele Frequency  MAF  MELAS  Mitochondrial Encephalopathy Lactic Acidosis and Stroke-like episodes  MERRF  Myoclonic Epilepsy and Ragged-Red Fibres  mtDNA NADH  Mitochondrial DNA Nicotinamide Adenine Dinucleotide  Polymerase Chain Reaction  PCR  revised Cambridge Reference Sequence  rCRS  Reactive Oxygen Species  ROS rRNA  Ribosomal RNA  tRNA  Transfer RNA  OR SNP  Odds Ratio Single Nucleotide Polymorphism viii  Chapter 1  Introduction 1.1 1.1.1  Aging as a Genetic Disease The “Healthy Aging” Phenotype  Our goal is to study biological mechanisms of aging by identifying genetic variants that are associated with healthy aging. This study focuses on individuals who have reached the upper end of the normal human lifespan in good health, as opposed to other longevity-based studies that focus on centenarians who may not be exceptionally healthy[1, 2, 3]. This project has been carried out using samples and phenotype data from the Genomics, Genetics and Gerontology (G3 ) Study of Healthy Aging. In this study, cases are defined as having a “healthy aging” phenotype if they reached the age of 85 years without being diagnosed with cancer, (excluding non-melanoma skin cancer) cardiovascular disease, major pulmonary disease (excluding asthma), Alzheimer disease or diabetes. They have been further characterized by means of the Mini Mental State Examination for determination of moderate to severe cognitive impairment[4], the Timed Up and Go test of basic mobility skills[5] , the Geriatric Depression Scale[6] and the Instrumental Activities of Daily Living Scale[7]. Controls are between the ages of 40 and 54 years, and were not recruited with respect to health status. As such, they are representative of the general population with respect to their probability of reaching the age of 85 years without acquiring 1  one of the five common age-related diseases listed above. Ideally, our controls would be a random sample of the population at the time that our cases were in mid-life, and we believe that these controls are a good proxy for that ideal sample. Specifically, we believe that the allele frequencies of our control sample should be a good approximation of the allele frequencies of the (now largely deceased) population that our cases originated from.  1.2 1.2.1  Mitochondria and Aging Mitochondrial Genome Structure and Regulation  Human mitochondria have a 16.5 kb circular genome, which encodes 13 proteincoding genes, (See table 1.2) 22 Transfer RNA (tRNA)s, and two Ribosomal RNA (rRNA)s (see figure 1.1). The protein-coding genes encode subunits of the mitochondrial electron transport chain complexes I, III and IV, and two subunits of ATP synthase. In contrast with the nuclear genome, there is very little non-coding sequence in the human mitochondrial genome. The majority of the non-coding sequence is contained within the 1.1 kb control region, where three known promoters coordinate expression of the entire mitochondrial chromosome. Outside of the control region, the mitochondrial genes are tightly spaced, with clusters of tRNA genes located between protein-coding genes. Table 1.1: Selected Mitochondrial Diseases OMIM ID 535000 540000 220110 256000 545000 530000 157640  Name LHON MELAS Complex IV Deficiency Leigh Syndrome MERRF Kearns-Sayre Syndrome CPEO  rCRS Positions Mutated 11,778, 3,460, 14484 3,243, (various mutations in MT-CO1-3) 4,681 8,344 (various deletions) (various deletions)  Symptoms Blindness Myopathy, Lactic acidosis Myopathy CNS Lesions Seizures, myopathy Blindness, cardiomyopathy Eye turn, hypogonadism  Hundreds of additional mitochondrial proteins are encoded by the nuclear genome, and coordinated control of the two genomes is required for normal mitochondrial function[8, 9]. Mitochondrial gene expression is controlled by transcription factors (TFAM, TFB1M, TFB2M) and an RNA polymerase (POLRMT) that are 2  Figure 1.1: Map of the Human Mitochondrial Genome. Non-coding control region (position 16,024-576) is shown in grey. Protein-coding genes are shown in blue, while RNA-coding genes are shown in red. All gene labels are from the HUGO Gene Nomenclature Committee (www.genenames.org)  3  encoded on the nuclear genome. A transcriptional regulatory network links a master regulator, PGC-1α (also known as PPARGC1A) to the mitochondrial genome. Table 1.2: Mitochondrial Protein Genes Gene MT-ND1 MT-ND2 MT-COX1 MT-COX2 MT-ATP8 MT-ATP6 MT-COX3 MT-ND3 MT-ND4L MT-ND4 MT-ND5 MT-ND6 MT-CYB  Uniprot Acession P03886 P03891 P00395 P00403 P03928 P00846 P00414 P03897 P03901 P03905 P03915 P03923 P00156  ETC Complex Complex I Complex I Complex IV Complex IV Complex V Complex V Complex IV Complex I Complex I Complex I Complex I Complex I Complex III  rCRS Position 3,307–4,262 4,470–5,511 5,904–7,445 7,586–8,269 8,366–8,572 8,527–9,207 9,207–9,990 10,059–10,404 10,470–10,766 10,760–12,137 12,337–14,148 14,149–14,673 14,747–15,887  The mitochondrial genome also contains a 1.1 kb control region (position 16024576 on GenBank NC 012920) which includes promoters for both the heavy and light strands, and the heavy strand origin of replication. The control region also contains numerous transcription factor binding sites. There are three hyper-variable sequences (HVS1, HVS2 and HVS3) within the control region that contain a relatively high density of polymorphisms, in comparison to the rest of the mitochondrial genome[10]. The human mitochondrial genome is inherited exclusively from the mother. Paternal mitochondria are selectively degraded after fertilization, by ubiquitinmediated proteasomal degradation[11, 12]. There is no conclusive evidence for recombination in human Mitochondrial DNA (mtDNA)[13]. An extensive map of the geographic distribution of mitochondrial haplogroups in human populations has been recorded. Together with geographic and genotype data from the nonrecombining portion of the Y chromosome, this information has helped to trace early human migration out of Africa and across the globe[14]. The mitochondrial genome is also highly polymorphic in all human populations. A previous study of European mitochondrial genome diversity identified 144 single nucleotide polymorphisms present in > 1% of a sample of 928 publicly available European mitochondrial genome sequences [15]. 4  Numerous mitochondrial genetic diseases have been identified[16, 17]. Several of these diseases, with their characteristic mutations are listed in table 1.1. Symptoms vary widely, and include blindness, deafness, diabetes and ataxia.  1.2.2  Reactive Oxygen Species  Mitochondria are thought to contribute to the aging process through the production of Reactive Oxygen Species (ROS), as a byproduct of oxidative phosphorylation[18, 19]. Prolonged exposure to intracellular ROS can cause damage to protein and lipids, and can cause somatic mutations in both the nuclear and mitochondrial genomes. During oxidative phosphorylation, electrons are passed from reduced Nicotinamide Adenine Dinucleotide (NADH) and Flavin Mononucleotide (FMNH) to a group of mitochondrial inner membrane-bound enzymes that comprise the electron transport chain. Electrons are passed down the chain in a series of redox reactions, releasing energy that is used to pump protons into the intermembrane space. These reactions maintain the mitochondrial elechemical gradient that drives the production of ATP. The majority of electrons passing through the electron transport chain will finally be combined with H+ and 12 O2 to form H2 O, but a small percentage will form side-reactions that result in the production of highly unstable superoxide radicals, O–· 2 . Superoxide quickly reacts with H2 O to form hydrogen peroxide, (H2 O2 ) itself a strong oxidizing agent. Although small amounts of ROS are a normal byproduct of cellular metabolism, the accumulated effects of these reactions can degrade tissue, cause somatic mutations and lead to cellular senescence[20, 21].  1.2.3  The Role of Mitochondria in Apoptosis  Mitochondria integrate several intracellular signals including DNA damage response and pro-survival signals, as well as metabolic signals such as the ADP/ATP ratio and intracellular Ca2+ concentrations. Under high cellular stress conditions, these signals can initiate cell death via the intrinsic apoptotic pathway. The pro-apoptotic proteins BAX and BAK are recruited to the mitochondrial membrane, resulting in increased membrane permeability and release of Cytochrome-c and SMAC/DIABLO from the mitochondrial intermembrane space into the cytosol. The release of Cytochrome-c and SMAC/DIABLO leads to the activation of effector caspases that initiate the  5  process of apoptosis. Apoptosis is a key protective mechanism against cancer. When a cell acquires mutations or DNA damage that may lead to escape from the cell cycle and uncontrolled cell division, the apoptotic pathway can be activated to prevent the development of a malignancy. Model organisms such as p53 knockout mice fail to activate the intrinsic apoptotic pathway in response to DNA damage and develop malignancies at a much higher rate than wild-type mice[22]. There is also evidence that variation in the mitochondrial genome itself can alter the probability that a cell will undergo apoptosis. Studies of a lymphoblastoid cell line showed that a A4263G mutation in the mitochondrial isoleucine tRNA could alter mitochondrial membrane potential and lead to an increased rate of apoptosis[23]. Some have argued that many of the phenotypic hallmarks of aging (muscle loss, wrinkled skin, functional decline of internal organs) are due to the accumulated effects of apoptosis and senescence[24]. They hypothesize that successful aging, (defined as reaching the age of 85 without being diagnosed with cancer, cardiovascular disease, diabetes, major pulmonary disease, or Alzheimer disease.)[25] requires a fine balance between cancer surveillance by apoptosis and a maintenance of healthy pre-senescent tissue[26].  1.2.4  The Role of Mitochondria in Cellular Senescence  Several lines of evidence indicate that mitochondria play a role in induction of cellular senescence. Senescent cells are characterized by growth arrest in the G1 phase of the cell cycle, accumulation of H2A.X foci and increased p53 activity indicative of DNA damage, and decreased telomere length[27]. The telomerase reverse transcriptase hTERT is translocated to mitochondria in response to oxidative stress, where it increases the rate of mtDNA damage and promotes apoptosis[28]. This relationship between telomere maintenance and mtDNA maintenance is a recent discovery, and is not yet completely understood[29]. Cells grown in high oxygen concentrations become senescent at an increased rate, and senescence can be delayed by addition of antioxidants or mild uncoupling agents to the growth medium[30].  6  1.2.5  Somatic Mitochondrial DNA Mutations and Aging  Mutations in the mitochondrial genome accumulate with age in somatic tissues. Mitochondrial DNA mutations have been observed to correlate with age in tissues such as heart muscle,[31] brain,[32] and skeletal muscle[33]. In addition to point mutations, accumulation of ROS-damaged deoxyguanosine in the form of 8-Hydroxydeoxyguanosine has been observed.  1.2.6  Mitochondrial Heteroplasmy and Tissue Heterogeneity  The number of mitochondria per cell varies from zero in red blood cells to several hundred in skeletal muscle cells, and each mitochondrion contains several copies of the mitochondrial genome. Mutations can arise in somatic cells because of oxidative damage or replication errors by DNA polymerase-γ, and can be propagated to daughter cells after division. Since each cell contains many copies of the mitochondrial genome, there may be a combination of mtDNA alleles in a particular cell or tissue[34, 35]. This phenomenon is known as heteroplasmy. Several mitochondrial diseases, such as Myoclonic Epilepsy and Ragged-Red Fibres (MERRF) or Mitochondrial Encephalopathy Lactic Acidosis and Stroke-like episodes (MELAS), do not present physiological symptoms unless the causative mutation accumulates beyond a certain threshold level, sufficient to disrupt normal mitochondrial function[36]. Although the accumulation of somatic mtDNA mutations is suspected to play a role in the aging process, our study is designed to detect heritable genetic factors that influence long-term good health. Mutations that arise in skeletal muscle, epithelium, neurons and other somatic tissues are not passed on in the germline. Only mutations that arise in the ova (or pre-oval germ cell lineage) can be passed on to the next generation.  1.2.7  Reported Associations of Mitochondrial Genome Variants with Longevity  Several longevity-associated mtDNA variants have been reported in populations around the world. A control region polymorphism at position 150 was associated with longevity in the Italian population and has been hypothesized to cause a reorganization of an origin of replication on the mtDNA[3]. The comparison of 52 7  centenarians (age range 99-106 years) and 117 controls (age range 18-98 years) showed a statistically significant difference (Odds Ratio (OR) = 5.09, P = 0.0035, Fisher’s exact test) in the frequency of homoplasmic C150T transition in leukocytes. Furthermore, the researchers noted that the abundance of heteroplasmic C150T mutation in fibroblasts was correlated with age. The association signal at control region position 150 has been replicated in both the Japanese and Finnish populations[37]. In Finns, a comparison of 46 seniors (age 90 or 91 years) and 57 middle-aged controls showed a significant association (OR= 1.50, P = 0.037, χ 2 test) of the 150T allele with longevity. A similar result was found in a smaller Japanese sample set of 19 seniors and 9 controls (OR= 1.41, P = 0.032, χ 2 test). A polymorphism in the MT-ND2 gene at position 5,178 of the coding region was found to be associated with longevity in the Japanese population[38]. The study investigated the relative frequencies of the 5178A and 5178C alleles, and found that the 5178A allele in 9 of 11 centenarians, versus 12 of 43 controls. This same polymorphism was also associated with glucose tolerance in Japanese men, and may contribute to resistance to type II diabetes. The MT-ND2 gene encodes a subunit of NADH  1.3  dehydrogenase, complex I of the mitochondrial electron transport chain.  Hypothesis and Specific Aims  We hypothesize that healthy aging is influenced by sequence variation in the mitochondrial genome. Therefore, one or more common mitochondrial alleles will be associated with healthy aging. The specific aims of this study are as follows: 1. Survey the mitochondrial genomes of cases and controls for sequence variants. 2. Determine whether common variation in the mitochondrial genome is associated with healthy aging in our study population.  8  Chapter 2  Variant Detection 2.1  Introduction  The mitochondrial genome is amenable to targeted resequencing by second-generation technologies. Its relatively small size (16.5 kilobase (kb)) and low repetitive sequence content make it much more likely that a unique sequence alignment can be determined for the short reads generated by current second-generation sequencing systems. In order to identify the extent of mitochondrial genome variation in our sample set, we used two sequencing technologies for two distinct segments of the mitochondrial genome. For the non-coding control region (position 16,024-576), we performed bi-directional Sanger sequencing on all 419 cases and 415 controls. A pooled Illumina Genome Analyzer (GA) sequencing strategy was used to identify variants in the entire mitochondrial genome. See figure 2.1 for an outline of the variant detection strategy.  2.2  Methods  This study was approved by the joint Clinical Research Ethics Board of the British Columbia Cancer Agency and the University of British Columbia. All subjects gave written informed consent.  9  2x 600 bp PCR  Sanger Sequencing TACAGTGATTTCCATCGGATC  TACAGTGATATCCATCGGATC  Control Region  Coding Region  Sequenom Genotyping  1x 16.4 kb PCR  Illumina GA Seq  Figure 2.1: Experimental Design for Variant Detection. Two sequencing technologies were used to identify mitochondrial genome variation. The controlregion was PCR-amplified in two segments and sequenced by Sanger sequencing in individuals. The entire mitochondrial genome was PCR-amplified, pooled, and sequenced on the Illumina Genome Analyzer. Coding region variants were carried forward to Sequenom genotyping in individuals.  10  2.2.1  Subjects and Samples  The subjects of this study were 419 healthy elderly individuals (cases) and 415 mid-life controls. Cases were > 85 years old at the time of recruitment, and had not been diagnosed with cancer, cardiovascular disease, Alzheimer disease or diabetes. Controls were 40-50 years old at recruitment, and were ascertained without regard to health status. All participants are of European descent, based on subject-reported ethnicity of their four grandparents. Total DNA was extracted from peripheral blood leukocytes using the Gentra Puregene Blood Kit (Qiagen), according to the manufacturer’s protocol.  2.2.2  Control Region PCR and Sanger Sequencing  PCR primers were designed not to overlap with common polymorphic loci. In-silico PCR was performed using web service based at Kyushu University, to ensure that no nuclear DNA segments would be co-amplified[39]. The mitochondrial control region was PCR-amplified with Platinum Pfx polymerase (Invitrogen). PCR reactions were performed in 20 µL total volume containing: 20 ng template genomic DNA, 10 µM each of forward primer (MAP001 F or MAP002.1 F) and reverse primer (MAP001 R or MAP002.1 R) (Table 2.1), 0.4 U Platinum Pfx enzyme, 10 mM each dNTPs, and 1x Phusion Buffer GC. Forward and reverse primers incorporated the -21M13F (TGTAAAACGACGGCCAGT) and M13R (CAGGAAACAGCTATGAC) extensions, respectively, at their 5’ ends. Sequencing reactions were carried out as described previously [40].  2.2.3  Sanger Sequence Assembly  Sanger sequence traces were aligned to the revised Cambridge Reference Sequence (rCRS) reference sequence (GenBank accession NC 012920) with the Phred/Phrap/Consed suite, version 20.0 [41, 42, 43]. Polymorphisms were first detected automatically using Polyphred version 6.18. To minimize false-positives all non-reference alleles were manually confirmed by visual inspection of chromatograms by two people.  11  2.2.4  Long PCR  The mitochondrial genome was amplified using long-PCR with Phusion polymerase (Finnzymes). PCR reactions were performed in 20 µL total volume containing: 20 ng template genomic DNA (2 ng/µL), 10 µM each of forward primer MAP011.1 F and reverse primer MAP011.1 R (Table 2.1), 0.4 U Phusion enzyme, 10mM each dNTPs, and 1x Phusion Buffer GC. The thermocycler program was: 1.) initial melt at 98°C for 30 seconds, 2.) melt at 98°C for 10 seconds, 3.) anneal/extend at 72°C for 8 minutes, 15 seconds 4.) repeat steps 2 and 3, 29 times 5.) final extension at 72°C for 10 minutes. Table 2.1: PCR primers used for Sanger sequencing and long-PCR. Primer ID MAP011.1-F MAP011.1-R MAP001-F MAP001-R MAP002.1-F MAP002.1-R  Tm (°C) 66.3 64.7 57.1 58.9 59.3 57.3  Sequence GGGAGCTCTCCATGCATTTGG AGACCTGTGATCCATCGTGATGTC (-21M13-Fwda )GAAAAAGTCTTTAACTCCACCATT (M13-Revb )TACTGCGACATAGGGTGCTC (-21M13-Fwd)GAGCTCTCCATGCATTTGG (M13-Rev)AGGGTGAACTCACTGGAACG  rCRS Position 34-54 16,558-12 15,961-15,984 107-126 36-54 707-726  a ‘-21M13-Fwd’ b ‘M13-Rev’  2.2.5  = TGTAAAACGACGGCCAGT = CAGGAAACAGCTATGAC  Construction of DNA Pools  DNA products from long-PCR were quantitated with Quant-iT™PicoGreen® reagent (Invitrogen). Two DNA pools were constructed. One pool consisted of 10 ng mtDNA from each of 419 case samples, and the other consisted of 10 ng mtDNA from each of 415 control samples. DNA was concentrated by speed-vac.  2.2.6  Library Construction and Sequencing  Library construction and DNA sequencing was carried out by the sequencing platform of the BC Genome Sciences Centre. Pooled mtDNA was sheared using sonication and size-separated using electrophoresis. The ∼ 300-bp fraction was isolated for library construction using the Illumina Genome Analyzer single-end library protocol (Illumina). Sequencing was performed on an Illumina GA using two lanes of a flow cell per pool, generating 36-bp reads. 12  Table 2.2: Summary of Illumina Sequence Mapping (Untrimmed Reads) Chr. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y MT other total mapped total unmapped grand total  2.2.7  Length 249,250,621 243,199,373 198,022,430 191,154,276 180,915,260 171,115,067 159,138,663 146,364,022 141,213,431 135,534,747 135,006,516 133,851,895 115,169,878 107,349,540 102,531,392 90,354,753 81,195,210 78,077,248 59,128,983 63,025,520 48,129,895 51,304,566 155,270,560 59,373,566 16,569 6,110,758 -  Reads Mapped, Cases 512,872 (4.30%) 81,462 (0.68%) 74,357 (0.62%) 48,754 (0.41%) 206,336 (1.73%) 32,858 (0.28%) 170,779 (1.43%) 35,068 (0.29%) 27,073 (0.23%) 25,878 (0.22%) 97,174 (0.81%) 28,901 (0.24%) 35,384 (0.30%) 23,239 (0.19%) 13,155 (0.11%) 13,580 (0.11%) 311,656 (2.61%) 19,447 (0.16%) 7,709 (0.06%) 11,142 (0.09%) 13,807 (0.12%) 5,967 (0.05%) 54,631 (0.46%) 11,447 (0.10%) 4,286,809 (35.94%) 5,068 (0.04%) 6,154,553 (51.59%) 5,774,653 (48.41%) 11,929,206 (100.00%)  Reads Mapped, Controls 539,464 (4.23%) 85,478 (0.67%) 77,525 (0.61%) 53,598 (0.42%) 225,336 (1.77%) 35,324 (0.28%) 227,925 (1.79%) 38,300 (0.30%) 26,942 (0.21%) 25,809 (0.20%) 98,442 (0.77%) 30,747 (0.24%) 36,679 (0.29%) 24,482 (0.19%) 14,121 (0.11%) 13,829 (0.11%) 299,374 (2.35%) 20,532 (0.16%) 7,346 (0.06%) 11,389 (0.09%) 14,062 (0.11%) 5,941 (0.05%) 62,053 (0.49%) 11,022 (0.09%) 4,681,659 (36.68%) 5,087 (0.04%) 6,672,466 (52.27%) 6,092,354 (47.73%) 12,764,820 (100.00%)  Statistical Analysis  In order to assess the effect of read trimming on mapping, alignments were done with both full 36-base reads and trimmed reads. For trimmed reads, the BWA read-trimming parameter (q=25) was used. Short sequence reads were aligned to the GRCh37 (hg19) reference using the BWA sequence alignment program, version 0.6.1-r104[44]. Aside from the read-trimming parameter, all reads were mapped using default BWA parameters. Per-base quality scores for both untrimmed and trimmed reads were calculated with FastQC software[45] (See figures 2.4, 2.5) SNPs were detected by analyzing BWA ‘pileup’ output files with a custom perl 13  Table 2.3: Summary of Illumina Sequence Mapping (Trimmed Reads) Chr. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y MT other total mapped total unmapped grand total  Length 249,250,621 243,199,373 198,022,430 191,154,276 180,915,260 171,115,067 159,138,663 146,364,022 141,213,431 135,534,747 135,006,516 133,851,895 115,169,878 107,349,540 102,531,392 90,354,753 81,195,210 78,077,248 59,128,983 63,025,520 48,129,895 51,304,566 155,270,560 59,373,566 16,569 6,110,758 -  Reads Mapped, Cases 496,262 (6.92%) 95,787 (1.34%) 86,431 (1.20%) 64,637 (0.90%) 263,349 (3.67%) 38,931 (0.54%) 112,712 (1.57%) 43,377 (0.60%) 35,488 (0.49%) 32,305 (0.45%) 101,180 (1.41%) 35,422 (0.49%) 36,647 (0.51%) 29,971 (0.42%) 16,489 (0.23%) 17,579 (0.25%) 392,551 (5.47%) 22,205 (0.31%) 8,908 (0.12%) 14,805 (0.21%) 16,454 (0.23%) 7,768 (0.11%) 66,354 (0.93%) 11,477 (0.16%) 3,750,715 (52.29%) 5,121 (0.07%) 5,802,925 (80.90%) 1,370,272 (19.10%) 7,173,197 (100.00%)  Reads Mapped, Controls 508,839 (6.92%) 94,431 (1.28%) 82,319 (1.12%) 65,229 (0.89%) 270,725 (3.68%) 37,264 (0.51%) 107,757 (1.47%) 42,340 (0.58%) 32,654 (0.44%) 29,541 (0.40%) 96,423 (1.31%) 35,238 (0.48%) 36,389 (0.50%) 28,723 (0.39%) 15,896 (0.22%) 16,484 (0.22%) 340,912 (4.64%) 21,735 (0.30%) 7,365 (0.10%) 13,690 (0.19%) 14,409 (0.20%) 6,995 (0.10%) 64,348 (0.88%) 10,403 (0.14%) 4,015,230 (54.64%) 4,722 (0.06%) 6,000,061 (81.64%) 1,348,969 (18.36%) 7,349,030 (100.00%)  script. At each position, the numbers of reference and non-reference bases were counted. Only those bases with phred-scaled quality scores of 40 were included for SNP detection.  2.3 2.3.1  Results Sequencing of the Mitochondrial Conrol Region  The highly polymorphic mitochondrial control region rCRS (position 16024-576) was sequenced using bi-directional Sanger sequencing. We discovered 277 SNPs in  14  the control region that were present in at least one sample.  2.3.2  Next-Generation Sequencing of Pooled mtDNA  The median depth of sequence coverage was 31,134 reads for the case pool and 12,683 reads for the control pool. This represents approximately 30x coverage for each sample that is included in the pool. To reduce the number of false-positive variant calls that are due to sequencing errors, we only considered high-quality bases (base-quality score > 35 and mapping-qualty score > 20) for SNP-calling. We identified 90 SNPs in the case pool and 113 SNPs in the control pool with MAF > 1%. 84 of these SNPs are common to both pools, with 6 SNPs only being observed in the case pool and 29 SNPs only being observed in the control pool. (see figs 2.8 and 2.9). Comparison of minor allele frequencies for control region SNPs in Sanger and Illumina GA datasets is shown in Figures 2.10 and 2.11. We found close correlation (Spearman’s r = 0.88 in cases, Spearman’s r = 0.88 in controls, N = 277). We also observed that pooled Illumina GA sequencing produced consistently lower MAF estimates than Sanger sequencing of individual samples in this region. Table 2.4: Functional Consequences for Non-synonymous SNPs ID rs28357980 rs28358886 rs9645429 rs2853826 rs28359178 rs3135031 rs28357681 rs2853508 rs3088309  Position 4,917 8,697 9,055 10,398 13,708 14,766 14,798 15,326 15,452  MAF (Seniors) 0.073 0.078 0.070 0.055 0.041 0.084 0.109 0.245 0.134  MAF(Controls) 0.060 0.053 0.051 0.059 0.028 0.072 0.077 0.218 0.118  Gene MT-ND2 MT-ATP6 MT-ATP6 MT-ND3 MT-ND5 MT-CYTB MT-CYTB MT-CYTB MT-CYTB  Amino Acid Change N [Asn] ⇒ D [Asp] M [Met] ⇒ I [Ile] A [Ala] ⇒ T [Thr] T [Thr] ⇒ A [Ala] A [Ala] ⇒ T [Thr] T [Thr] ⇒ I [Ile] F [Phe] ⇒ L [Leu] T [Thr] ⇒ A [Ala] L [Leu] ⇒ I [Ile]  PolyPhen 0.129 (benign) 0.890 (possibly damaging) 0.845 (possibly damaging) 0.000 (benign) 0.000 (benign) 0.000 (benign) 0.000 (benign) 0.000 (benign) 0.029 (benign)  For each gene in the mitochondrial genome, the number of variants observed at ≥ 1% frequency were tabulated (Table 2.5). The most variable protein-coding gene is MT-ND3, with 20.2 variants per kb in cases, and 17.3 variants per kb in controls. The most variable RNA gene is MT-TT, with 45.5 variants per kb in cases and 75.5 variants per kb in controls.  15  (a) Alignment  (b) Traces  Figure 2.2: SNP Calling by Phred/Phrap/Consed + PolyPhred. (a) Reads were aligned to the revised Cambridge Reference Sequence (rCRS). Each sample was sequenced in both forward and reverse directions. Only a subset of samples are shown Sample IDs are at left in yellow type. (b) Variants were identified automatically using PolyPhred, and confirmed manually by visual inspection of sequence traces. Two samples (127 WIL and 128 SIN) with differing alleles at contig position 1,288 (rCRS position 16,288) are shown.  16  (a) a  (b) b  Figure 2.3: Putative Heteroplasmic Positions. Heteroplasmy was observed in some samples by identifying double-peaks in sequence traces. (a) Sample ‘157 EPP’ shows putative heteroplasmy level of ∼ 25% at contig position 1,189 (rCRS position 16,189). (b) Sample ‘489 SAM’ shows putative heteroplasmy level of ∼ 50% at contig position 1,126 (rCRS position 16,126). Note that the relative heights of the two peaks at the heteroplasmic positions are consistent in forward and reverse reads.  17  (a) Case Pool, Untrimmed  (b) Case Pool, Trimmed  Figure 2.4: Effect of Read-trimming on Per-base Quality Distributions (Case Pool) Average base quality score was calculated at each read position, across all reads. For each position, red line indicates median quality score, yellow box indicates interquartile range (25-75%), upper and lower whiskers represent 90% and 10% quantiles, respectively, and blue line represents mean quality score. The upwards shift in average quality for trimmed reads indicates that poor-qualty sequence near the 3’ end of reads has been removed in trimmed reads.  18  (a) Control Pool, Untrimmed  (b) Control Pool, Trimmed  Figure 2.5: Effect of Read-trimming on Per-base Quality Distributions (Control Pool) Average base quality score was calculated at each read position, across all reads. For each position, red line indicates median quality score, yellow box indicates interquartile range (25-75%), upper and lower whiskers represent 90% and 10% quantiles, respectively, and blue line represents mean quality score. The upwards shift in average quality for trimmed reads indicates that poor-qualty sequence near the 3’ end of reads has been removed in trimmed reads.  19  Figure 2.6: Sequence coverage across the mitochondrial genome (Case Pool). Blue line indicates high-quality (phred-scaled quality score = 40) sequence coverage. Graph lines every 10,000-fold depth.  20  Figure 2.7: Sequence coverage across the mitochondrial genome (Control Pool). Blue line indicates high-quality (phred-scaled quality score = 40) sequence coverage. Graph lines every 10,000-fold depth.  21  Figure 2.8: Minor allele frequencies (Case Pool). Locations and minor allele frequencies for all SNPs detected by Illumina GA sequencing. Base identities are indicated as follows: A = Red, C = Blue, G = Orange, T = Green. Heights of data bars indicate minor allele frequencies, scale bars every 10% allele 22 frequency.  Figure 2.9: Minor allele frequencies (Control Pool). Locations and minor allele frequencies for all SNPs detected by Illumina GA sequencing. Base identities are indicated as follows: A = Red, C = Blue, G = Orange, T = Green. Heights of data bars indicate minor allele frequencies, scale bars every 10% allele 23 frequency.  Figure 2.10: MAF comparison (Cases). Minor allele frequencies were determined by both Sanger sequencing and by pooled Illumina GA sequencing for 277 SNPs in the control region. The Spearman’s rank correlation between the two estimates is 0.88 in the case sample set, and 0.91 in controls. Dashed line indicates slope = 1; the least-squares regression line is indicated by a solid line.  24  Figure 2.11: MAF comparison (Controls). Minor allele frequencies were determined by both Sanger sequencing and by pooled Illumina GA sequencing for 277 SNPs in the control region. The Spearman’s rank correlation between the two estimates is 0.91 in control sample set. Dashed line indicates slope = 1; the least-squares regression line is indicated by a solid line.  25  Table 2.5: Number of Variants by Gene Gene MT-TF MT-RNR1 MT-TV MT-RNR2 MT-TL1 MT-ND1 MT-TI MT-TQ MT-TM MT-ND2 MT-TW MT-TA MT-TN MT-TC MT-TY MT-CO1 MT-TS1 MT-TD MT-CO2 MT-TK MT-ATP8 MT-ATP6 MT-C03 MT-TG MT-ND3 MT-TR MT-ND4L MT-ND4 MT-TH MT-TS2 MT-TL2 MT-ND5 MT-ND6 MT-TE MT-CYTB MT-TT MT-TP All Protein-coding All RNA-coding  Size (bp) 71 954 69 1,559 75 956 69 72 68 1,042 68 69 73 66 66 1,542 69 68 684 70 207 681 784 68 346 65 297 1,378 69 59 71 1,812 525 69 1,141 66 68 11,395 4,021  Total Variants Cases Controls 0 0 7 10 0 0 17 16 0 0 8 7 0 0 1 1 0 0 12 19 0 0 1 2 0 0 0 1 0 0 11 16 1 1 0 0 3 4 0 1 3 3 7 9 10 9 1 1 7 6 1 1 2 3 18 24 0 0 0 0 1 2 28 27 10 6 0 0 20 22 3 5 0 0 139 155 33 41  26  Variants per kb Cases Controls 0.0 0.0 7.3 10.5 0.0 0.0 10.9 10.3 0.0 0.0 8.4 7.3 0.0 0.0 13.9 13.9 0.0 0.0 11.5 18.2 0.0 0.0 14.5 29.0 0.0 0.0 0.0 15.2 0.0 0.0 7.1 10.4 14.5 14.5 0.0 0.0 4.4 5.8 0.0 14.3 14.5 14.5 10.3 13.2 12.8 11.5 14.7 14.7 20.2 17.3 15.4 15.4 6.7 10.1 13.1 17.4 0.0 0.0 0.0 0.0 14.1 28.2 15.5 14.9 19.0 11.4 0.0 0.0 17.5 19.3 45.5 75.8 0.0 0.0 12.2 13.6 8.2 10.2  2.4  Discussion  We have shown here that it is possible to discover variants across the entire mitochondrial genome in over 400 samples in a single sequencing experiment. By combining long-PCR with second-generation sequencing technology, we were able to estimate the alllele frequencies of over 300 mitochondrial SNPs in our study population. This technique will be useful for rapidly surveying a large sample set for mitochondrial SNPs. Given its small size and high copy number per cell, mtDNA is a good candidate for pooled targeted resequencing efforts. The size of the mitochondrial chromosome (16.5 kb) makes it amenable to long PCR. The whole mtDNA genome can be amplified in one reaction, which simplifies the DNA pooling process. A similar variant detection has been employed by another group, using a pool size of 20 samples[46]. Figures 2.6 and 2.7 show that the entire mitochondrial genome was sufficiently covered by mapped reads to perform variant detection. There are strong peaks in coverage in both the case pool and control pool near position 200 within the control region. We attribute this peak to excess PCR primers that were carried through into the sequencing reaction. A previous report showed accurate determination of allele frequencies of pooled genomic DNA on the ABI SOLiD, Roche 454 and Illumina GA II platforms [47]. Our estimation of MAF from Illumina sequencing of DNA pools correlates strongly with MAF calculated using genotypes determined using Sanger sequence data (Spearman’s r = 0.88); this correlation is close to the value of r2 = 0.9637 published by Druley et al[47]. The most likely source of discrepancy between these two datasets is due to small differences in the quantity of DNA that each sample contributes to the DNA pool. In our analyses, MAF estimated from Illumina GA data is about 25% lower than our measurement from Sanger sequencing. We suggest that this discrepancy may represent a bias against mapping of reads containing non-reference bases. We suggest that a read that contains a real non-reference base in the form of a SNP is less likely to align than a read that contains no non-reference SNPs, and that this probem will be increased in low-quality sequence data. This phenomenon, referred to as ‘reference bias,’ has been observed in previous studies of next-generation  27  sequence data[48]. The number of variants observed in at least 1% of samples varied from 0 (MTTY, MT-TF for example) to 27 (MT-ND5) (see table 2.5). When normalized by the length of the gene, the most variable genes are MT-ND3 (20.2 variants/kb in cases, 17.3 variants/kb in controls) and MT-TT (45.5 variants/kb in cases, 75.8 variants/kb in controls). Note, however, that the short length of the tRNA genes (∼ 70 bp) leads to a highly variable estimate of variants/kb. Overall the distribution of variants was similar in protien-coding and RNA-coding genes at roughly 10 variants/kb. Although our study was not designed to investigate the role that heteroplasmic variants play in the aging process, we did detect a small number of putative heteroplasmic variants by Sanger sequencing. For low levels of heteroplasmy, (below ∼ 25%) it would be difficult to distinguish a true heteroplasmic variant from background noise in the sequence trace. The few instances of heteroplasmy that we were able to identify with some certainty appeared to be close to 50% heteroplasmic (See 2.3 for a representative example).  28  Chapter 3  A Case-Control Association Study for Mitochondrial Variants and Healthy Aging In order to identify variants that are associated with the healthy-aging phenotype, case-control association tests were performed using PLINK software[49]. Each SNP is analyzed by comparing the major and minor allele frequencies in cases versus controls, by applying a Chi-squared (χ 2 ) test. The power of a Chi-squared test to detect a genetic association is based on 2 a comparison of a null χ(1−α) distribution to an alternative χ 2 distribution with  non-centrality parameter λ , proportional to the effect size[50]. It is expressed as follows: 2 Power = P(χ 2 (d f , λ ) ≥ χ1−α (d f )),  (3.1)  where: λ = ∆2 N =  (p − q)2 q  N  (3.2)  and for a 2 × 2 contingency table, the number of degrees of freedom (d f ) are one. Coding-region variants were nominated for genotyping based on three criteria.  29  Variants that showed a suggestive P-value (< 0.05) based on a comparison of the estimated Minor Allele Frequency (MAF) from pooled Illumina GA II sequencing were genotyped, as were a set of 64 tag Single Nucleotide Polymorphism (SNP)s that were designed to capture all common variants present at > 1% in the European population, with linkage disequilibrium of at least r2 = 0.8[15]. Finally, any variant with an estimated MAF of > 0.05 based on pooled sequencing that did not fit the first two criteria was also included. In total, 92 SNPs were nominated for genotyping (see Supplemental Table B.1). Due to our limited statistical power to detect moderate effects in low-frequency variants, a MAF cut-off of 10% was applied before association testing. This limited the number of SNPs that qualified for testing to nine control-region SNPs and seven coding-region SNPs (See tables 3.1 and 3.2, respectively). One SNP in the control region was selected for testing based on previous reports of its association with longevity in Italian[3], Finnish and Japanese[37] populations.  3.1 3.1.1  Methods Power Calculations  Statistical power was calculated with the PS power and sample size calculator[51]. Power curves were calculated for a sample of 419 cases and 415 controls, at minor allele frequencies of 0.01, 0.05, 0.10, 0.25 and 0.50, with a false-positive rate α = 0.05.  3.1.2  Genotyping and Quality Control  Genotyping was performed on the Sequenom MassARRAY platform at the McGill University/Genome Qu´ebec Innovation Centre. A set of 92 SNPs were included in the first assay set. A set of 37 genotyping assays were repeated due to quality control failure. Quality control was performed in collaboration with Dr. Denise Daley (University of British Columbia, St.Paul’s Hospital). Assays with call rates below 95% were considered ‘failed’ and were re-designed. Genotype cluster plots were visually inspected for irregularities. 30  Figure 3.1: Power to Detect Association. Statistical power was calculated using PS software[51]. Curves are shown for the following control MAFs: 0.01 (blue), 0.05 (orange),0.10 (yellow), 0.25 (green) and 0.50 (purple). For all curves, α = 0.05 number of cases = 419, number of controls = 415.  3.2  Results  This study is powered to detect an odds ratio of at least 1.75 (or 0.45) for a variant at minor allele frequency of 0.10 with a false-positive rate of 0.05 (see 3.1). Of the 92 SNPs that were chosen for the initial round of Sequenom genotyping, 37 failed quality control (See B.3) due to low call rates. These assays were redesigned and repeated. Of the second set, only three assays failed quality controls (mt9947, rs41345446 and rs41347846). After performing χ 2 tests for association between mtDNA alleles and healthy aging, no variants that were tested showed association with the healthy aging phenotype, at a p-value significance threshold of 0.05. The lowest p-value for control region SNPs was rs117135796 at position 152, with a p-value of 0.258 and odds ratio of 0.81. For coding region SNPs, the lowest p-value was 0.280, with odds ratio 1.11 for rs2853495 at position 11,719 within the MT-ND4 gene.  31  The rs62581312 variant at position 150 within the control region showed a p-value of 0.171 and odds ratio of 0.72. Table 3.1: Mitochondrial Control Region (MAF > 0.10) Chr M M M M M M M M M  ID rs3087742 rs117135796 rs2857291 rs28625645 mt16126 rs55749223 rs2857290 rs34799580 rs3937033  Position 73 152 195 489 16,126 16,189 16,270 16,311 16,519  Minor Allele A C C C C C T C T  Major Allele G T T T T T C T C  FA a 0.456 0.185 0.170 0.102 0.195 0.139 0.107 0.151 0.340  FU b 0.442 0.218 0.181 0.098 0.167 0.118 0.093 0.167 0.348  χ 2c 0.163 1.405 0.173 0.031 1.083 0.811 0.425 0.384 0.062  P 0.726 0.258 0.714 0.908 0.318 0.404 0.561 0.567 0.826  Odds Ratio 1.06 0.81 0.93 1.04 1.21 1.21 1.16 0.89 0.96  P 0.332 0.383 0.694 0.644 0.280 0.961 0.473  Odds Ratio 1.14 1.10 1.04 1.08 1.11 1.01 1.10  P 0.171  Odds Ratio 0.72  a Minor  Allele Frequency in ‘Affecteds’ (seniors) Allele Frequency in ‘Unaffecteds’ (controls) c χ 2 test statistic  b Minor  Table 3.2: Mitochondrial Coding Region (MAF > 0.10) Chr M M M M M M M  ID rs2853517 rs3928306 rs2015062 rs2853825 rs2853495 rs2853499 rs28357681  Position 709 3,010 7,028 9,477 11,719 12,372 14,798  Minor Allele G C A G A C A  Major Allele A T G A G T G  FA a 0.144 0.264 0.445 0.104 0.493 0.242 0.158  FU b 0.128 0.246 0.436 0.097 0.468 0.241 0.145  χ 2c 0.942 0.760 0.155 0.214 1.167 0.002 0.516  a Minor  Allele Frequency in ‘Affecteds’ (seniors) Allele Frequency in ‘Unaffecteds’ (controls) c χ 2 test statistic  b Minor  Table 3.3: Replication of rs62581312 (C150T) Chr M  ID rs62581312  Position 150  Minor Allele T  Major Allele C  a Minor  Allele Frequency in ‘Affecteds’ (seniors) Allele Frequency in ‘Unaffecteds’ (controls) c χ 2 test statistic  b Minor  32  FA a 0.082  FU b 0.110  χ 2c 1.88  3.3  Discussion  It is notable that this study did not replicate the previously-reported association at position 150 of the mitochondrial control region[3]. There are several potential explanations for this result. The study by Zhang et al. focused on a group of Italians aged 99-106 years, whereas our samples qualify at age 85 and are mainly of British ancestry. Although the association was replicated in both Finnish and Japanese populations,[37] there may be population-specific genetic or environmental factors that combine with the position 150 polymorphism to effect the aging phenotype. Because the mitochondrial genome does not recombine, it is possible to identify sets of variants that are inherited together and form mitochondrial haplotypes. These haplotypes have been traced to geographic/ancestral lineages across the world[14]. Previous studies have identified haplogroups that are associated with longevity[52, 1, 53]. In our study, we elected to combine a previously-published set of common European mitochondrial tag SNPs[15] with additional variants that were discovered by pooled next-generation sequencing.  33  Chapter 4  Discussion We have designed a cost-effective method of surveying the mitochondrial genomes of hundreds of samples for single-nucleotide polymorphisms. Our method combines long-range PCR with a high-processivity, low-error DNA polymerase with pooled next-generation sequencing on the Illumina Genome Analyzer platform. Our singleamplicon long-PCR mtDNA isolation method also eliminates complications due to co-amplification of of mtDNA-derived pseudogenes (NUMTs) in the nuclear genome. While we have established that is is possible to isolate and sequence the whole mitochondrial genome via a single long-PCR reaction, our mtDNA isolation protocol was not designed to detect common deletions that have been observed in other studies[54]. Future studies may be able to take advantage of paired-end sequencing to detect relatively large-scale deletions such as the common 4.9 kb deletion that has been characterized between rCRS positions 8,470 and 13,446[55]. In a paired-end sequencing experiment, deletions can be detected when paired reads map further apart than the expected ∼ 300 bp insert size[56]. Previous reports have demonstrated accurate determination of allele frequencies of pooled genomic DNA on the ABI SOLiD, Roche 454 and Illumina GA IIx platforms[47, 57]. Our estimation of MAF from Illumina sequencing of DNA pools correlates strongly with MAF calculated using genotypes determined using Sanger sequence data (Spearman’s r = 0.88); this correlation is close to the value of r2 = 0.9637 published by Druley et al[47]. The most likely source of discrepancy 34  between these two datasets is due to small differences in the quantity of DNA that each sample contributes to the DNA pool. Our study was not designed for sensitive detection of heteroplasmic variants, though we did observe a small number of variants that were suggestive of heteroplasmy via analysis of Sanger sequence traces. These variants appear similar to heterozygous variants in diploid nuclear sequence data, with overlapping peaks of two different fluorophores (Fig 2.3), and suggest a roughly equal mixture of two alleles. For lower levels of heteroplasmy, it becomes difficult to distinguish true heteroplasmy from background noise in the Sanger sequence trace. The goal of this study was to investigate the role that common, heritable mitochondrial variants may play in the human aging process. Heteroplasmy can be inherited, and can also arise de novo, and can vary by tissue type[58, 59]. Recent studies have shown that next-generation sequencing can be a powerful tool to detect heteroplasmy[35]. In order to detect heteroplasmic variants in a pooled sequencing experiment, one would need a way to tie each read to a specific sample, rather than estimate the allele frequencies of the whole pool as was done in our experiment. New DNA barcoding methods (also called ‘indexed’ sequencing) have now made this possible[60]. It is well established that heteroplasmic variants accumulate with age[58, 61, 62, 63], so if we had used a sequencing technology that was sensitive to heteroplasmy then it is likely that we would have observed differences in levels in heteroplasmy between our cases (> 85 years of age) and controls (40-54 years of age). It would remain unclear, however, if those somatic heteroplasmic variants would be passed down to future generations and also to what extent heteroplasmic variants are involved with healthy aging. In our analyses, MAF estimated from Illumina GA data is about 25% lower than our measurement from Sanger sequencing. We suggest that this discrepancy may represent a bias against mapping of reads containing non-reference bases. We suggest that a read that contains a real non-reference base in the form of a SNP is less likely to align than a read that contains no non-reference SNPs, and that this problem will be increased in low-quality sequence data. This phenomenon is referred to as ‘reference bias,’ and has been observed in other next-generation sequencing experiments[48]. When conducting a case-control genetic association study, it is important to 35  control for possible population stratification. If the case and control groups are composed of samples from different ethnic backgrounds, it is possible to observe false-positive associations due to differences in population-specific allele frequencies that play no functional role in the phenotype of interest. Another study that is also part of the G3 Study of Healthy Aging, and used the same sample set has analyzed a set of ancestry-informative markers and found no evidence for population stratification[64]. Other studies have found evidence for gene-gene interactions in the etiology of type II diabetes mellitus. One study used a non-parametric machine learning method known as Multifactor Dimensionality Reduction (MDR) to study genetic association with the metabolic disease. Out of 23 loci on 15 candidate genes in the study, the researchers were able to identify a two-locus interaction between PPARγ and UCP2 that significantly reduced risk of T2DM in Koreans (odds ratio: 0.51, 95% CI: 0.34, 0.77, p=0.0016)[65]. Another study, using a more traditional logistic regression model, identified a three-locus interaction between variants in UCP2, PGC-1α and position 10,398 of the mitochondrial genome in the North Indian Population[66]. 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In: Human genetics 121.5 (2007), pp. 609–614 (cit. on p. 36).  42  Appendix A  Supplemental Figures  43  1e+04 1e+00  Coverage  Case Pool  0  5000  10000  15000  44  Index  1e+04 1e+00  Coverage  Control Pool  0  5000  10000  15000  Index  Figure A.1: Sequence Coverage. Sequence reads were aligned to the rCRS (NC 012920.1) with MAQ. Median coverage was 13,134 reads (31.3 reads per sample) for the case pool, and 12,683 reads (30.6 reads per sample) for the control pool.  0.5 0.0  0.1  0.2  MAF  0.3  0.4  0.5 0.4 0.3 0.2 0.0  0.1  MAF  16100  16200  16300  16400  16500  0  100  200  Position  300  400  500  600  700  500  600  700  Position  45 0.5 0.4 0.3 0.0  0.1  0.2  MAF  0.3 0.2 0.0  0.1  MAF  0.4  0.5  (a) Cases  16100  16200  16300  16400  16500  0  Position  100  200  300  400  Position  (b) Controls  Figure A.2: Minor Allele Frequencies from Sanger Dataset. A total of 277 SNPs were identified by Sanger sequencing.  Appendix B  Mitochondrial Marker Data Table B.1: Mitochondrial Marker Selection Position 512 675 709 750 896 930 1,189 3,010 3,109 3,348 3,394 3,505 3,849 3,915 4,336 4,529 4,769 4,793 4,928 5,426 5,465 5,495 5,656  MAF case  MAF control  P value  rs Number  0.017 0.165 0.096 0.014 0.002 0.048 0.058 0.223 0.068 0.001 0.014 0.004 0.001 0.021 0.016 0.012 0.035 0.024 0.000 0.010 0.000 0.016 0.017  0.000 0.162 0.079 0.028 0.018 0.035 0.071 0.184 0.093 0.004 0.009 0.026 0.011 0.033 0.012 0.034 0.052 0.008 0.001 0.017 0.000 0.010 0.012  0.01524 0.92549 0.46286 0.16074 0.03740 0.49043 0.48061 0.16902 0.16225 0.24731 0.75245 0.01194 0.03014 0.29868 0.77281 0.03835 0.24322 0.08982 1.00000 0.38249 1.00000 0.54615 0.77281  NA NA rs2853517 rs2853518 NA rs41352944 rs28358571 rs3928306 NA rs41423746 rs41460449 rs28358585 NA rs41524046 rs41456348 NA rs3021086 NA rs41461545 NA rs3902405 rs3020602 NA  46  Reason for Inclusion P-value < 0.050 MAF > 0.050 Saxena et al. Tag SNP SAXENA TAG P-value < 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP MAF > 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP P-value < 0.050 P-value < 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP P-value < 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP  Position  MAF case  MAF control  P value  rs Number  5,785 5,981 6,182 6,260 6,272 6,365 6,719 6,776 7,028 8,251 8,269 8,303 8,697 8,705 8,869 9,123 9,150 9,477 9,548 9,667 9,716 9,899 9,947 10,034 10,084 10,296 10,314 10,398 10,915 11,377 11,485 11,674 11,719 11,812 11,914 12,007 12,372 12,414 12,501  0.000 0.002 0.000 0.012 0.000 0.017 0.003 0.029 0.498 0.027 0.021 0.003 0.117 0.012 0.000 0.011 0.013 0.067 0.016 0.011 0.027 0.020 0.003 0.016 0.012 0.091 0.033 0.161 0.002 0.018 0.019 0.003 0.428 0.071 0.034 0.010 0.213 0.005 0.017  0.011 0.016 0.011 0.017 0.011 0.019 0.002 0.024 0.471 0.057 0.031 0.016 0.072 0.007 0.000 0.010 0.007 0.068 0.000 0.018 0.012 0.013 0.016 0.030 0.006 0.081 0.074 0.185 0.011 0.019 0.021 0.012 0.433 0.060 0.027 0.029 0.236 0.017 0.041  0.03014 0.03740 0.03014 0.57664 0.03014 0.80102 1.00000 0.82959 0.40672 0.02499 0.39659 0.03740 0.03293 0.72527 1.00000 1.00000 0.72527 1.00000 0.01524 0.57664 0.20577 0.57807 0.03740 0.25570 0.45119 0.71196 0.00901 0.35980 0.12210 1.00000 0.81184 0.12210 0.88880 0.57741 0.68557 0.04604 0.45499 0.10603 0.03954  NA NA NA NA NA rs41464546 rs28358872 NA rs2015062 rs3021089 rs8896 NA rs28358886 NA NA rs28358270 NA rs2853825 NA rs41482146 rs41502750 rs41345446 NA rs41347846 rs41487950 NA NA rs2853826 rs2857285 NA rs28529320 rs28358286 rs2853495 rs3088053 rs2853496 rs2853497 rs2853499 NA rs28397767  47  Reason for Inclusion P-value < 0.050 P-value < 0.050 P-value < 0.050 Saxena et al. Tag SNP P-value < 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP P-value < 0.050 Saxena et al. Tag SNP P-value < 0.050 P-value < 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP P-value < 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP P-value < 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP MAF > 0.050 P-value < 0.050 MAF > 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP P-value < 0.050  Position  MAF case  MAF control  P value  rs Number  12,633 12,705 13,020 13,105 13,637 13,706 13,708 13,734 13,869 13,870 13,879 13,934 13,965 13,966 14,182 14,470 14,793 14,798 15,022 15,043 15,218 15,257 15,511 15,758 15,775 15,784 15,833 15,884 15,924 15,937  0.025 0.037 0.010 0.005 0.007 0.022 0.090 0.007 0.030 0.020 0.012 0.023 0.006 0.014 0.032 0.020 0.047 0.167 0.018 0.025 0.039 0.041 0.001 0.007 0.000 0.005 0.014 0.007 0.059 0.012  0.020 0.060 0.017 0.010 0.026 0.000 0.065 0.013 0.091 0.069 0.042 0.020 0.005 0.012 0.042 0.020 0.054 0.123 0.048 0.040 0.049 0.042 0.014 0.015 0.014 0.004 0.007 0.021 0.068 0.034  0.81254 0.15203 0.38249 0.44948 0.03296 0.00374 0.19650 0.50388 0.00026 0.00034 0.00937 0.81254 1.00000 1.00000 0.46312 1.00000 0.75375 0.07692 0.02131 0.17600 0.50023 1.00000 0.01491 0.33904 0.01491 1.00000 0.50548 0.08866 0.67229 0.03835  rs3926883 rs2854122 rs75577869 rs2853501 NA NA rs28359178 rs41421644 NA NA NA NA rs41509754 rs41535848 NA rs3135030 rs2853504 rs28357681 NA rs28357684 rs2853506 rs41518645 NA rs41337244 NA rs28357375 rs41504845 rs28617642 rs2853510 NA  48  Reason for Inclusion Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP P-value < 0.050 P-value < 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP P-value < 0.050 P-value < 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP P-value < 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP P-value < 0.050 Saxena et al. Tag SNP P-value < 0.050 Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP Saxena et al. Tag SNP P-value < 0.050  Table B.2: Mitochondrial Marker Set for Sequenom Genotyping ID mt512 mt675 rs2853517 rs2853518 mt896 rs41352944 rs28358571 rs3928306 mt3109 rs41423746 rs41460449 rs28358585 mt3849 rs41524046 rs41456348 mt4529 rs3021086 mt4793 rs41461545 mt5426 rs3902405 rs3020602 mt5656 mt5785 mt5981 mt6182 rs28623747 mt6272 rs41464546 rs28358872 mt6776 rs2015062 rs3021089 rs8896 mt8303 rs28358886 mt8705 mt8869  Chr  Position  Gene  MAF  Call Rate  AA  BB  AB  U  M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M  512 675 709 750 896 930 1,189 3,010 3,109 3,348 3,394 3,505 3,849 3,915 4,336 4,529 4,769 4,793 4,928 5,426 5,465 5,495 5,656 5,785 5,981 6,182 6,260 6,272 6,365 6,719 6,776 7,028 8,251 8,269 8,303 8,697 8,705 8,869  Non-coding RNR1 RNR1 RNR1 RNR1 RNR1 RNR1 RNR2 RNR2 ND1 ND1 ND1 ND1 ND1 TRNQ ND2 ND2 ND2 ND2 ND2 ND2 ND2 Non-coding TRNC COX1 COX1 COX1 COX1 COX1 COX1 COX1 COX1 COX2 ATP6 ATP6 ATP6 ATP6 ATP6  0.000 0.000 0.141 0.023 0.000 0.046 0.074 0.248 0.000 0.002 0.013 0.018 0.000 0.027 0.017 0.000 0.035 0.000 0.000 0.000 0.001 0.001 0.000 0.000 0.000 0.000 0.018 0.000 0.016 0.000 0.000 0.429 0.049 0.025 0.006 0.090 0.000 0.000  0.994 0.998 0.999 0.992 0.000 1.000 1.000 0.998 0.999 0.988 0.996 1.000 0.000 0.996 0.959 0.724 0.995 0.000 0.999 0.000 0.766 1.000 0.000 0.982 0.000 0.000 1.000 0.898 0.997 0.000 0.000 1.000 0.987 0.999 0.998 0.987 0.000 0.000  0 975 138 22 0 932 72 733 0 963 960 959 0 26 16 707 34 0 976 0 747 976 0 0 0 0 959 877 16 0 0 558 917 24 6 87 0 0  971 0 838 947 0 45 905 242 976 2 13 18 0 947 921 0 938 0 0 0 1 1 0 959 0 0 18 0 958 0 0 419 47 952 969 877 0 0  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  6 2 1 8 977 0 0 2 1 12 4 0 977 4 40 270 5 977 1 977 229 0 977 18 977 977 0 100 3 977 977 0 13 1 2 13 977 977  49  Project State good good good good failed good good good good good good good failed good good good good failed good failed good good failed good failed failed good good good failed failed good good good good good failed failed  ID rs28358270 mt9150 rs2853825 rs41482146 rs41502750 rs41345446 mt9947 rs41347846 rs41487950 mt10296 mt10314 rs2853826 rs2857285 rs41537746 rs28529320 rs28358286 rs2853495 rs3088053 rs2853496 rs2853497 rs2853499 rs41520546 rs28397767 rs3926883 rs2854122 rs75577869 rs2853501 mt13637 mt13706 rs28359178 rs41421644 mt13869 mt13870 mt13879 mt13934 rs41509754 rs41535848 mt14182 rs3135030  Chr  Position  Gene  MAF  Call Rate  AA  BB  AB  U  M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M  9,123 9,150 9,477 9,667 9,716 9,899 9,947 10,034 10,084 10,296 10,314 10,398 10,915 11,377 11,485 11,674 11,719 11,812 11,914 12,007 12,372 12,414 12,501 12,633 12,705 13,020 13,105 13,637 13,706 13,708 13,734 13,869 13,870 13,879 13,934 13,965 13,966 14,182 14,470  ATP6 ATP6 COX3 COX3 COX3 COX3 COX3 TRNG ND3 ND3 ND3 ND3 ND4 ND4 ND4 ND4 ND4 ND4 ND4 ND4 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND6 ND6  0.009 0.000 0.104 0.016 0.016 0.017 0.000 0.000 0.000 0.000 0.000 0.000 0.005 0.015 0.023 0.018 0.467 0.073 0.000 0.020 0.250 0.009 0.030 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.007 0.020 0.000 0.000  0.997 0.000 0.992 1.000 0.996 1.000 0.000 0.000 0.000 0.998 0.991 0.981 0.989 1.000 1.000 1.000 0.999 0.995 0.000 0.829 0.988 0.992 1.000 0.000 0.000 0.000 0.000 0.000 0.869 1.000 1.000 1.000 0.000 0.000 0.000 0.997 0.999 0.000 0.000  965 0 101 961 957 17 0 0 0 0 968 958 5 962 955 18 520 901 0 16 724 960 29 0 0 0 0 0 849 977 977 0 0 0 0 7 19 0 0  9 0 868 16 16 960 0 0 0 975 0 0 961 15 22 959 456 71 0 794 241 9 948 0 0 0 0 0 0 0 0 977 0 0 0 967 957 0 0  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  3 977 8 0 4 0 977 977 977 2 9 19 11 0 0 0 1 5 977 167 12 8 0 977 977 977 977 977 128 0 0 0 977 977 977 3 1 977 977  50  Project State good failed good good good good failed failed failed good good good good good good good good good failed good good good good failed failed failed failed failed good good good good failed failed failed good good failed failed  ID rs2853504 rs28357681 mt15022 rs28357684 rs2853506 rs41518645 rs35070048 rs41337244 mt15775 rs28357375 rs41504845 rs28617642 rs2853510 mt15937 rs55749223  Chr  Position  Gene  MAF  Call Rate  AA  BB  AB  U  M M M M M M M M M M M M M M M  14,793 14,798 15,022 15,043 15,218 15,257 15,311 15,758 15,775 15,784 15,833 15,884 15,924 15,937 16,189  CYTB CYTB CYTB CYTB CYTB CYTB CYTB CYTB CYTB CYTB CYTB CYTB TRNT TRNT Non-coding  0.064 0.154 0.000 0.038 0.049 0.025 0.000 0.011 0.000 0.006 0.000 0.003 0.060 0.000 0.003  0.999 0.988 0.000 0.999 0.999 0.999 0.972 1.000 0.000 1.000 0.000 0.964 0.953 0.602 0.953  62 816 0 37 48 952 950 11 0 971 0 3 875 0 3  914 149 0 939 928 24 0 966 0 6 0 939 56 588 928  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  1 12 977 1 1 1 27 0 977 0 977 35 46 389 46  51  Project State good good failed good good good good good failed good failed good good good good  Table B.3: Markers Repeated Due to Quality Control Failure ID mt896 mt3849 mt4529 mt4793 mt5426 rs3902405 mt5656 mt5981 mt6182 mt6272 mt6719 mt6776 rs2015062 mt8705 mt8869 mt9150 mt9947 rs41345446 rs41347846 rs2853495 rs2853496 rs2853497 rs3926883 rs2854122 mt13020 rs2853501 mt13637 mt13706 mt13870 mt13879 mt13934 mt14182 rs3135030 mt15022 mt15775 rs41504845 mt15937  Chr M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M  Position 896 3,849 4,529 4,793 5,426 5,465 5,656 5,981 6,182 6,272 6,719 6,776 7,028 8,705 8,869 9,150 9,947 9,899 10,034 11,719 11,914 12,007 12,633 12,705 13,020 13,105 13,637 13,706 13,870 13,879 13,934 14,182 14,470 15,022 15,775 15,833 15,937  Gene RNR1 ND1 ND2 ND2 ND2 ND2 Non-coding COX1 COX1 COX1 COX1 COX1 COX1 COX2 ATP6 ATP6 COX3 COX3 TRNG ND4 ND4 ND4 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND5 ND6 ND6 CYTB CYTB CYTB TRNT  MAF 0.005 0.007 0.022 0.018 0.007 0.001 0.020 0.000 0.003 0.000 0.000 0.044 0.434 0.007 0.001 0.011 0.000 0.000 0.000 0.471 0.029 0.020 0.014 0.061 0.012 0.012 0.022 0.000 0.000 0.020 0.016 0.044 0.021 0.000 0.001 0.003 0.002  52  Call Rate 0.998 0.999 1.000 0.994 0.988 1.000 1.000 1.000 1.000 1.000 0.994 0.994 0.999 0.994 0.999 1.000 0.000 0.000 0.000 0.999 0.990 0.998 0.947 0.833 1.000 1.000 0.993 1.000 1.000 0.998 0.998 0.976 0.995 0.995 1.000 0.891 0.957  AA 973 972 22 17 2 979 20 980 977 980 0 931 553 967 1 969 0 0 0 518 28 958 13 50 12 11 952 0 980 958 0 914 955 975 978 870 2  BB 5 7 958 957 957 1 960 0 3 0 974 43 424 7 978 11 0 0 0 461 941 20 915 766 968 968 21 980 0 19 947 42 20 0 0 3 936  AB 0 0 0 0 9 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 31 0 0 0 2 0 0  U 2 1 0 6 12 0 0 0 0 0 6 6 1 6 1 0 980 980 980 1 10 2 52 164 0 0 7 0 0 2 2 24 5 5 0 107 42  Project State good good good good good good good good good good good good good good good good failed failed failed good good good good good good good good good good good good good good good good good good  

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