Medicine, Faculty of
Medical Genetics, Department of
DSpace
UBCV
Dyment, David Alexandre
Master of Science - MSc
University of British Columbia
The overall objective of the Canadian Collaborative Project on the Genetic Susceptibility to Multiple Sclerosis (CCPGSMS) is to elucidate the pathogenesis of Multiple Sclerosis (MS). The aims of this thesis, as part of the CCPGSMS, were to 1) confirm the involvement of genetics in the susceptibility to MS 2) to examine the effects of the shared familial environment on MS risk and 3) to determine if a maternal effect can increase the risk to MS. Fourteen thousand nine hundred and ninety active Multiple Sclerosis (MS) clinic patients were screened as part of the CCPGSMS between the dates of Sept., 1994 and May,1996. From this group, 203 twin pairs, (80 monozygotic and 123 dizygotic), were identified. The proband-wise monozygotic twin concordance rate was 28.25% (SE. 5.84%) and the dizygotic twin concordance rate was 5.61% (S.E. 2.45%). The risk to the non-twin sibs of an index case was estimated at 2.49% (S.E. 0.74%) which was not significantly different than the dizygotic twin risk estimate, 0.05<p<0.10. This confirms the results of previous studies, and supports a strong genetic contribution to MS susceptibility. Five hundred and forty-five MS patients were identified with adoptive parents, and/or adopted sibs, and/or adopted children. The total adoptive first degree relatives of MS patients was 1185. Only one of these individuals had clinically definite MS. This was significantly different than the 25.2 affected individuals expected if the shared familial environment was important to the familial aggregation of MS, p=2.98 X 10"10. This supports the hypothesis that familial aggregation of M S is due primarily to genetics and not to the sharing of the familial environment. Nine hundred and thirty-six index cases were identified that had at least one half-sibling. There was no significant difference in the risk estimate between maternal half-siblings, 1.47% (S.E. 0.42%) and paternal half-siblings, 1.31% (S.E. 0.46%), 0.75<p<0.90. This argues against the contribution of a significant maternal effect to M S risk.
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F A M I L Y STUDIES O F M U L T I P L E SCLEROSIS by D A V I D A L E X A N D R E D Y M E N T B . S c , The University of British Columbia, 1992 A THESIS S U B M I T T E D IN P A R T I A L F U L F I L M E N T O F T H E R E Q U I R E M E N T S F O R T H E D E G R E E O F M A S T E R OF S C I E N C E in T H E F A C U L T Y O F G R A D U A T E STUDIES (Department of Medical Genetics) We accept this thesis as conforming to the required standard : T H E U N I V E R S I T Y O F BRITISH C O L U M B I A March 1997 © David Alexandre Dyment, 1997 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of f W i f a l GtTxWtS The University of British Columbia Vancouver, Canada Date tlnrcM 4 1 ^ 7 DE-6 (2/88) Abstract The overall objective of the Canadian Collaborative Project on the Genetic Susceptibility to Multiple Sclerosis (CCPGSMS) is to elucidate the pathogenesis of Multiple Sclerosis (MS). The aims of this thesis, as part of the CCPGSMS, were to 1) confirm the involvement of genetics in the susceptibility to M S 2) to examine the effects of the shared familial environment on MS risk and 3) to determine i f a maternal effect can increase the risk to MS. Fourteen thousand nine hundred and ninety active Multiple Sclerosis (MS) clinic patients were screened as part of the CCPGSMS between the dates of Sept., 1994 and May,1996. From this group, 203 twin pairs, (80 monozygotic and 123 dizygotic), were identified. The proband-wise monozygotic twin concordance rate was 28.25% (SE . 5.84%) and the dizygotic twin concordance rate was 5.61% (S.E. 2.45%). The risk to the non-twin sibs of an index case was estimated at 2.49% (S.E. 0.74%) which was not significantly different than the dizygotic twin risk estimate, 0.05<p<0.10. This confirms the results of previous studies, and supports a strong genetic contribution to M S susceptibility. Five hundred and forty-five M S patients were identified with adoptive parents, and/or adopted sibs, and/or adopted children. The total adoptive first degree relatives of M S patients was 1185. Only one of these individuals had clinically definite MS. This was significantly different than the 25.2 affected individuals expected i f the shared familial environment was important to the familial aggregation of MS, p=2.98 X 10"10. This supports the hypothesis that familial aggregation of M S is due primarily to genetics and not to the sharing of the familial environment. Nine hundred and thirty-six index cases were identified that had at least one half-sibling. There was no significant difference in the risk estimate between maternal half-siblings, 1.47% (S.E. 0.42%) and paternal half-siblings, 1.31% (S.E. 0.46%), 0.75<p<0.90. This argues against the contribution of a significant maternal effect to M S risk. in Table of Content* Page Number i v Vlll ix xi Abstract ii Table of Contents List of Figures List of Tables Acknowledgments 1. Introduction 1.1. A General Description of Multiple Sclerosis 1.2. Genetics of Multiple Sclerosis-Evidence from Population Studies 8. 1.2.1. Early Studies 8. 1.2.2. Early Family Studies 9. 1.2.3. Autosomal Recessive Mode of Inheritance 11. 1.2.4. Autosomal Dominant Mode of Inheritance 13. 1.2.5. Genetic Susceptibility 15. 1:2.6. Twin Sudies 20. 1.2.6a. Non-Population-based Studies 2 3. 1.2.6b. Population-Based Studies 27. 1.3. Adoption Studies 37. 1.4. Half-Sib Studies 42. 1.4.1. Half-sibs and Quantitative Traits 43. IV 1.4.2. Half-sibs and Qualitative Traits 47. 1.5. Genetics of Multiple Sclerosis-Evidence from Molecular studies 51. 1.5.1. H L A 51. 1.5.2. T C R p Chain Polymorphism 62. 1.5.3. TCR a Chain Polymorphism 68. 1.5.4. Myelin Basic Protein (MBP) 69. 1.5.5. Immunoglobulin heavy chain polymorphisms 72. 1.5.5.1. Immunoglobulin Heavy Chain Constant Region 72. 1.5.5.2. Immunoglobulin Heavy Chain Variable Region 76. 1.5.6. TNF 77. 1.5.7. Interleukin-1 Receptor Agonist 78. 1.5.8. Others 78. 1.5.9. Summary of the Search for Candidate Susceptibility Loci 79. 1.5.10. Genomic Screens 79. 1.6. Mouse Models 83. 1.7. A Transmissible Agent 85. 1.8. A Maternal Effect 87. 2. Material and Methods 89. 2.1. Canadian M S Clinic System 89. 2.2. Diagnosis 90. 2.3. Screening 92. 2.4. Confirmation of Affected Relatives 95. V 2.5. Adoption Study Design 95 2.6. Half-Sib Study Design 98. 2.7. Twin Study Design 100. 2.8. Objectives 101. 2.9. Maximum Likelihood Risk Estimates 102. 2.10. Log-Liklihood Ratio Test 104. 2.11. Kaplan Meier Lifetime Risk 104. 2.11. Applied Logistic Regression 105. 3.Results 109. 3.1. General Results 109. 3.2. The Adoption Study Results 110. 3.2.1. A Description 110. 3.2.2. The Adopted Study Analysis 111. 3.2.3. The Biological Relatives of Adopted Index Cases 114. 3.3. Half-Sib Study Results 115. 3.3.1. A Description 115. 3.3.2. Maximum Liklihood Risks for Half-sibs 117. 3.3.3. Maximum Liklihood Risks for Full-sibs 119. 3.3.4. The Risk to Half-sibs who Lived with Full-sibs 120. 3.3.5. The Risk to Half-sibs who have Lived Together with Half-sibs who have Not Lived Together 121. 3.3.6. A Maternal Effect and M S 121. 3.3.7. The Applied Logistic Regression 123. vi 3.4. Twin Study Results 125. 3.4.1. A Description 125. 3.4.2. Risks to M Z , DZ cotwins and Full-Sibs of the Index Cases 127. 3.4.3. Genetic Heterogeneity 131. 4Discussion 133. 5. Conclusion 144. 6. Bibliography 146. 7. Appendix 168. A-Clinical Documentation 168. B-Initial Summary Screen 170. C-Detailed Biological Questionnaire 172. D-Recurrence Risk Ratios for Different Modes of Inheritance 192. VII List of Figures Title Page Number Fig. l . Axons and Saltatory Conduction Fig.2. Clinical Course of M S Fig.3. Adoption Study Designs Fig.4. Half-siblings Fig.5. Human Leukocyte Antigen (HLA) Locus 52. Fig.6. Flow Diagram (Adapted from Sadovnick et al., 1996) 94. 2. 5. 39. 43. viii List of Tables — ~ —11 ; P j J C T A \ T i irv»Kck»-1.1. Problems of Selected Pre-1960's Family Studies 9. 1.2. A Survey of Family Studies in Multiple Sclerosis 19. 1.3. Twin Studies of Multiple Sclerosis 36. 1.4. Selected Published Haplotype Sharing between Affected Sibs 61. 1.5. Selected Linkage Analysis of H L A (6p) and Multiple Sclerosis 62. 1.6. Studies of T C R B (7q) and Multiple Sclerosis 67. 1.7. Studies of T C R a (14q) and Multiple Sclerosis 68. 1.8. Studies of M B P (18q) and Multiple Sclerosis 71. 1.9. Studies of Immunoglobulin Heavy Chain (14q) and Multiple Sclerosis 77. 1.10. Areas of Potential M S Susceptibility Alleles 82. 2.1. The Canadian Collaborative Study Group 90. 2.2. Rationale for the Adopted Study 98. 2.3. Rationale for Detecting a Genetic Effect Using Half-sibs 100. 2.4. Rationale for Detecting a Maternal Effect using Half-sibs 100. 2.5. Rationale for Detecting i f Shared Environment Contributes to Risk of M S 100. 3.1. General Results 110. 3.2. Individuals Qualifying for the Adopted Study 110. 3.3. Adopted Eligibility Totals 111. 3.4. Kaplan-Meier Lifetime Risks for Biological Relatives 112. 3.8. Expected Versus Observed Number of Adoptive First Degree Relatives with Multiple Sclerosis ix 114. 3.6. Number of Biological Relatives of the Adopted Index Cases 114. 3.7. Index Cases Eligible for Half-sib Study 115. 3.8. Half-sib Summary 116. 3.9. Age-Adjusted Risk Estimates for Half-sibs 117. 3.10. Age-Adjusted Risk Estimates for Half-sibs of Male Index Cases 118. 3.11. Age-Adjusted Risk Estimates for Half-sibs of Female Index Cases 118. 3.12. Age-Adjusted Risk Estimates for Full-sibs 119. 3.13. Age-Adjusted Risk Estimates for Half-sibs (who lived together) 120. 3.14. Age-Adjusted Risk Estimates for Half-sibs (who have never lived together) 121. 3.15. Maximum Likelihood Risks for Maternal and Paternal Half-sibs 122. 3.16. Results of the Logistic Regression 123. 3.17. Comparison of Models 124. 3.18. Description of the Twin Screen 126. 3.19. Description of the Co-twins 127. 3.20. Proband-wise Risk Estimates for Co-twins 128. 3.21. Risk Estimates for the Full-sibs of Twin Index Cases 129. 3.22. Risk Estimates for Full-sibs from M Z Sibships Only 130. 3.23. Risk Estimates for Full-sibs from DZ Sibships Only 130. 3.24. Evidence for Genetic Loading 132. Acknowledgments I would like to thank and acknowledge Dr. Desssa Sadovnick for her guidance and support over the last two and a half years. I am very grateful to have had the opportunity to study and work under her supervision. This research was funded by the Multiple Sclerosis Society of Canada Scientific Research Foundation and the Multiple Sclerosis Society of Canada. I would like to acknowledge my supervisory committee: Drs. Wendy Robinson, Diana Juriloff and Barbara McGillivray. The following clinic/site coordinators were responsible for the data collection used for this thesis: C. Harris, N . Cheyne, M . Hader, B. Davis, A. Royal, M . Perrera, M . Penman, K. Stevenson, L. Murray, G. Smith, C. Edgar, J. Haynes, D. Southwell, R. Arnaoutelis, C. Laforce, C. Masse, P. Weldon, K. Taylor, and G. Alcock. The following clinic neurologists and M S researchers are part of the Canadian Collaborative Study Group on the Genetic Susceptibility to Multiple Sclerosis. They were responsible for the availability and accurate diagnosis of the multiple sclerosis patients used in this thesis: S. Hashimoto, D. Paty, J. Oger, L. Metz, R. Bell, S. Warren, K . Warren, W. Hader, T. Auty, A. Nath, T. Gray, P. O'Connor, R. Nelson, M . Freedman, xi D. Brunet, R. Paulseth, G. Francis, P. Duquette, T. Murray, V. Barm, W. Pryse-Phillips, G. Rice, and G. Ebers. Irene Yee's input in the analytical aspects of the thesis was invaluable. I wish to acknowledge Laila Mashal, Nancy Greig, Laila Abulhusn, Lee Ann Mclntyre, Thomas Hicks, John McArthur and Rochelle Farquhar for their ongoing assistance and encouragement. I would also like to thank Drs. Neil Risch and George Ebers for helpful discussions. I would also like to thank my friends and family for all their support. Thank-you Mom, Nicholas, Dad, Jean-Francois Dyment, Ted Everson, Tom Milne and Lingli Ma. A special thank-you to my girlfriend Shannon Young for all her help during the last two years. xii Introduction 1.1. A General Description of Multiple Sclerosis Oligodendroglial cells are responsible for producing the myelin which insulates the axons of the central nervous system (CNS). Along the length of the axon are the nodes of Ranvier or "gaps" where myelin is absent. These nodes are the sites at which the nerve impulse "jumps" by a mechanism of propagation called saltatory conduction. This mode of conduction greatly increases the velocity of the nerve impulse. Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system. At the sites of inflammation, the myelin and oligodendroglial cells are damaged. In M S , this demyelination occurs with preservation of the axon. As a result, nerve impulses are slowed or lost. It is the loss of signal, via the loss of saltatory nerve conduction, within the central nervous system (CNS) that is responsible for the symptoms of multiple sclerosis (Fig. 1). The prevalence of M S in Canada is approximately 1/1000 or 0.1% among Caucasians of Central and Northern European origin (Sweeney et al.,1986). The lifetime risk for M S is thus approximately 0.2% or 2 times the population prevalence (Sadovnick et al., 1994). This makes M S the most common non-traumatic disabling disease of young adults (Martin et al.,1995). l Figure 1. Axons and Saltatory Conduction A-Demyelinated Axon Impulse is The worldwide distribution of MS reflects latitude and, to some extent, ethnicity. In countries with a homogeneous racial composition, a north-south latitude gradient (or south-north in the Southern Hemisphere) can be observed. A good example of this effect is seen in Australia. The prevalence of M S in Hobart (South Australia) is 75.6 per 100,000 population (Hammond et al.,1988) compared with a prevalence of 18.3 per 100,000 population in Queensland (Northern Australia) (Hammond et al.,1987). Similar phenomena have also been observed in New Zealand, (Skegg et al., 1987) and Japan (Kuroiwa et al., 1983). A latitude effect on MS prevalence has also been documented in North America (Kurtzke et al., 1979). However, this observed gradient has largely been explained on the basis of ethnicity (Sadovnick et al.,1993;Bulman et al. 1992). 2 There are also specific ethnic groups who seem to be resistant to MS, despite residing in high risk areas for the disorder. Examples include the Hungarian Gypsies (Palffy, 1982), the Lapps of Norway (Gronning et al., 1985), and the Amerindians of Canada (Hader et al., 1985). A preponderance of female MS patients has long been recognized. A female to male ratio approaching 2:1 is observed (Acheson et al., 1977). Current diagnostic criteria give the range for M S onset to be from 10 years of age to 59 years of age (Poser et al.,1983). Nevertheless, it is now well recognized that MS may occur before the age of 10 (Duquette et al.,1987) and after the age of 59, (Noseworthy et al, 1983;Hooge et al., 1993). Despite this wide range, the average age of onset for MS is between 28-30 years of age (Weinshenker et al., 1989), with the average onset age of males being a year or two older than for females. The time from initial onset of M S symptom(s) to diagnosis is, on average, four years after the initial onset of symptoms, (Sadovnick et al., 1992). the most common symptoms at initial presentation of MS are motor skill impairments (e.g. coordination) seen in 40% of cases and/or sensory impairments (e.g. tingling, numbness) seen in 37% of cases (Hashimoto, 1986). Optic neuritis and diplopia are also relatively common initial symptoms (Bauer et al., 1993). Forty-five percent of patients present with only one initial symptom and 55% present with two or more initial symptoms (Hashimoto, 1986). There tends to be a difference in onset symptom(s) when older and younger MS patients are compared. Patients over 35 years of age tend to present with motor skill impairment as an initial symptom(s) whereas M S patients under 3 30 years of age tend to present with sensory impairment and/or optic neuritis (Weinshenker et al.,1989;Poser et al. 1984). After the initial presentation, M S symptoms over time can include spasticity, muscle weakness, fatigue, ataxia, bowel and bladder problems, pain, and sexual dysfunction, (MacAlpine,1972). Clinical depression is frequently observed in MS. There is a 50% lifetime risk for M S patients to experience depression (Sadovnick et al.,1996). M S patients are also reported to have a completed suicide rate which can be as high as 7.5 times that of the age-matched general population (Sadovnick et al.,1991). Nevertheless, overall life expectancy is not greatly reduced in the M S population (Sadovnick 1992). There is approximately a 75% survivorship rate for MS patients 25 years post onset of symptoms (Broman et al., 1981). The usual clinical course observed in MS is either a primary chronic progressive (PCP) form or a relapsing-remitting (RR) form (Fig.2). PCP is characterized by continued deterioration with no remission of symptoms for at least six months (Bauer et al.,1993) and is observed in approximately 18% of patients (Weinshenker et al.,1989). RR MS is characterized by episodes of exacerbations followed by periods of partial or total remission (McAlpine,1972). An initial RR course is found in approximately 75-80% of M S patients at onset, (Weinshenker et al.,1989;Hashimoto, 1986). However, given time, the RR form will usually convert to a secondary chronic progressive (2°CP) form of the disorder. In fact, about 30% of the patients initially following a RR course will progress to 2°CP after five years from the initial onset (Hashimoto, 1986). The 4 average duration for this conversion to take place is approximately 6.8 years (Weinshenker et al.,1989). While RR or PCP are the most usually observed initial disease courses, two rarer forms of MS can occur; they are benign MS and malignant MS (Fig.2). Malignant MS is characterized by death within five years of clinical onset and is present in about 3% of all patients, (Hashimoto, 1986). Benign MS, defined as a low level of disability after 13 years from clinical onset, occurs in approximately 4% of patients (Hashimoto, 1986). MS clinical disability is measured using the Extended Kurtzke Disability Status Scale or EDSS (the scale for MS ranges from 0 to 10; 0 being normal, 1.0 being no disability, 3.5 being mild disorder though not sufficient to impede normal activities, 7.0 being wheelchair dependent and 10 being death due to MS) (Kurtzke, 1983). Benign MS requires an EDSS of less than 3.0 after 13 years from clinical onset (Hashimoto, 1986). Figure 2. Clinical Course of M S RR 75% RR 54% 5 MS prognosis cannot be accurately predicted on an individual basis. Nevertheless, certain markers such as sex, age of onset and initial symptom(s) can be used to predict a "better" or "worse" outcome on a group basis. The worse prognostic profile, in terms of time to disability (i.e. requiring assistance to walk 100 meters; EDSS=6.0), is a male patient, over the age of forty who presents with motor impairment. In contrast the best prognostic profile (relatively) is a female patient, initially presenting with sensory impairment under the age of 20 years, (Miller, 1992). MS affects approximately 25,000-30,000 individuals across Canada (Sadovnick et al.,1993) resulting in a tremendous economic cost for the disorder. Over half of MS patients surveyed are unemployed. Individuals still in the labour force often work reduced hours due to fatigue (Catanzaro,1992). The economic loss of unemployment is compounded with costs associated with M S such as hospitalizations, clinic visits, prosthetics, home care and disability benefits. In the United States, the societal cost of M S is estimated at $35,000 per patient per year (Bourdette et al., 1993) or approximately 8.75 billion per year. Another study in the United States estimated the total lifetime cost per case to be 2.5 million dollars (Whetten-Goldstein et al.,1996). In Canada the cost to the patient is minimized because of the universal health care program. Nevertheless, the cost to society can be estimated at 875 million dollars (U.S.$) per year. The estimation of economic cost of the disorder per patient ignores the emotional and financial impact of MS to the primary caregivers of MS patients. Caregivers (e.g. spouses, parents) often have (i) lost job opportunities, (ii) lost income, (iii) experienced a decreased quality of life, and (iv) have negative feelings about the lifestyle changes that 6 the M S has caused (Gregory et al.,1996). Taken together, the impact of this relatively common, chronic disabling disorder on the patient, his/her family, and to society in general is enormous. 1.2. Genetics of Multiple Sclerosis: Evidence from Population Studies 1.2.1. Early Studies The first account of a familial clustering (aggregation) of M S was a concordant mother-son pair reported by Eichhorst (Eichhorst,1913). This began the debate over the relative roles (if any) of genetic and environmental factors in causing M S (e.g. Mackay,1950;Josephy,1950). Early work on the familial nature of M S consisted primarily of individual family reports. Between 1921 and 1948, the literature reported 64 sib pairs concordant for MS, 13 concordant parent-child pairs, and 15 concordant second and/or third degree relatives (Mackay,1950). While of interest, these early reports were greatly hampered by the absence of diagnostic criteria for MS. On review, it was not unusual to find that the affected relatives did not have MS, but rather hereditary cerebello-pyramidal disease or hereditary spastic ataxia, (Muller,1953;Pratt et al.,1951). It was only within the last 30 years that the first diagnostic criteria for M S were published (Schumacher et al.,1965). In addition to a lack of diagnostic criteria, early studies on the familial aggregation of MS were hindered by the lack of appropriate population prevalence data, denominator data, age-corrections as well as a publication bias toward the reporting of concordant pairs (Table 1.1). 8 Table 1.1. Problems of Selected Pre-196Q'S Family StiirfiV* Author (& Year) Diagnostic Criteria Ascertainment Bias? Denominator Data Comparison Grouo Age-Correction Curtius et al. (1933) their own unspecified yes incidence (different reeion) none McAlpine (1946) unspecified unspecified none none none Mackay (1950) his own biased none none none Pratt et al. (1951) unspecified unspecified yes estimated incidence none Muller (1953) his own representative yes none yes Allison et al. (1954) their own representative yes prevalence (same region) none Sutherland (1954) his own representative yes prevalence (same region) none Schapira et al. (1956) their own representative yes prevalence (same region) none 1.2.2. Early Family Studies Despite the problems with early family studies addressed in section 1.2.1, it is still important to refer to a few studies which were for many years considered pivotal investigations. The first systematic family study of M S was a German investigation by Curtius (1933). He identified five affected relatives from a series of 56 M S patients (using his own diagnostic criteria). The patients had a total of2006 first, second, and third degree relatives. This sample provided a familial prevalence of 24/10,000 or 0.24% (5/2006). Curtius then compared this prevalence to a previously estimated Swiss population incidence of M S which was 2.3/10,000 (Ackermann,1931). The relatives' risk was lOx greater than what was seen in the general population, providing evidence for the familial aggregation of MS. As a note, prevalence is defined as the number of individuals affected in a population at a point in time, incidence is the number of new cases in a population per unit of time. By these definitions, incidence is a rate while prevalence is a proportion. If the disease/disorder under study is stable (i.e. the occurrence and duration are not changing) then the prevalence (P) equals the incidence (I) multiplied by the duration (D), P = I x D. Therefore to compare the population incidence of MS directly to the prevalence of MS in the relatives of index cases is valid i f the relative group is composed of individuals who are alive and in the same geographic area. Both these conditions were not met in the Curtius (1933) investigation. The relative population was composed of individuals both alive and dead and not necessarily living in Germany (the location of the study). The problem of an appropriate comparison group was compounded as the incidence used for comparison was derived from a different country (Switzerland). Others criticize the validity of comparing the calculated population incidence estimates with familial prevalence on the basis of the sampling scheme, (McAlpine,1946). The population incidence was estimated using morbidity values derived by inquiring regional physicians and hospitals about M S cases (Ackermann,1931). Because MS is a clinically heterogeneous disorder with adult onset, many cases would not be reported (McAlpine,1946). This would tend to under-estimate population incidence. Conversely, the relatives' M S prevalence estimate was derived from cases reported by a specialist intensely studying the sib population, again increasing the risk in the family sample (relatively). Still other researchers doubt the accuracy of 10 the M S diagnosis of the 5 affected family members in the Curtius investigation. There were many other early family studies which attempted to demonstrate a familial aggregation of M S (Curtius et al. 1937;McAlpine et al. 1946;MacKay 1950), but in all cases, due to study design and sample size, results were largely inconclusive (Table 1.2). The controversy continued over the role of inheritance factors in M S etiology for many decades. 1.2.3. Autosomal Recessive Mode of Inheritance A n autosomal recessive mode of inheritance has been postulated for MS (Bell, 1949;McAlpine, 1946). The best recognized family study supporting an autosomal recessive inheritance was done by Mackay and Myrianthopoulos (1960). They ascertained 54 pairs of twins identified by public appeal and recorded the family history data for 1,112 of their relatives. Eighteen affected relatives were identified from 12 families for a "familial" risk of 22.2% (18/54); when only the "definite" cases (according to Mackay,1950) were used, the risk decreased to 16.7% (9/54). The risk to parents of index cases was estimated at 1.6% (1/62), the risk to sibs was 2.2% (4/182) and the risks to children and first cousins were 2.0% (1/49) and 0.6% (3/519) respectively. Using a population prevalence of 1/2000, (taken from a previous American survey (Kurland et al., 1954)), the risk to a family member was estimated to be 32X greater than the population risk. Although Mackay and Myrianthopoulos recognized the potential bias i the reporting of 1st, 2nd, and 3rd degree affected relatives, they nevertheless maintained that the familial risk of M S was significantly higher than the population prevalence. in l l Using these data, a genetic analysis was done based on an a priori assumption that the mode of inheritance for M S was autosomal recessive. Parents of 50 index cases were assumed to be heterozygous (i.e. they excluded the index cases with an affected parent). Corrections were made for family size and variable age of MS onset. The expected number of affected sibs was calculated by multiplying the "family size-corrected" number of affected individuals in a recessive model, per decade, times the proportion of identifiable MS cases. This proportion of identifiable cases was estimated using population statistics on the proportion of people surviving per decade in the US and the age of onset distribution of M S cases in Northern Ireland derived from Allison and Millar (1954). The expected number of affected sibs and the number of observed affected sibs per decade of life were not significantly different than predicted. The data were thus interpreted as evidence for the autosomal recessive mode of inheritance. However, when all the "decade" groups were summed, the observed number of affected sibs (11) was significantly lower than the expected number of affected sibs (25.5) under a recessive model, p< 0.01. Nevertheless, Mackay and Myrianthopolous went on to calculate a penetrance of 43% (11/25.5) and further used this value to calculate the "expected" numbers of affected children, parents, and second degree relatives in their total sample. Using a 43% penetrance, they found the expected and observed values to be in close agreement (though no statistical test was performed). Mackay and Myrianthopolous attempted to infer a mode of inheritance using an analysis that involved two representative populations (from different continents), selected families with unaffected parents, and an a priori assumption of recessive inheritance. 12 The only results of their analysis supporting an autosomal recessive mode of inheritance were obtained when the sibling population was stratified by decade. However by stratifying their sample, they decreased the sample size and therefore decreased the power to detect a significant difference between the observed and expected affected relatives. They did not test for polygenic or autosomal dominant modes of inheritance in their analysis. Even given the limited knowledge about the role of genetics in common complex diseases at the time, the researchers should have recognized that a recessive model did not account for the preponderance of affected females and the similarity o f familial risks between generations (e.g. sibs (2.2%) and children (2.0%) of M S patients). This was a good example of researchers "fitting" the data to a mendelian mode of inheritance and, as such, the results should be viewed skeptically. 1.2.4. Autosomal Dominant Mode of Inheritance A n autosomal dominant mode of inheritance has also been postulated for M S . Pratt et al. (1951) identified 310 M S patients by combining data from two previous series and administered an oral questionnaire to them about family members' health. If a relative was suspected to have/had M S , data were confirmed by medical records. The sib risk was estimated at 0.93% (5/538), and the parental risk was estimated at 0.65% (3/465). The population incidence of M S for England and Wales was estimated at 1/2000 or 0.05%. The sib risk was therefore 18.6X greater than the general population incidence and the parental risk was 12.9X greater. The population incidence was calculated by assuming that the average duration of M S was 18 years (arbitrary), by including the total population of England and Wales over the age of 15 years, and by 13 using a population mortality rate (due to MS) of 867 per 10 years. Problems with the methodology included the unreliability of death certificate data (MS is often not listed a a cause of death) and the use of the 18 year duration is arbitrary and there is little evidence to support this estimate (Muller,1950;Broman et al.,1981). Lack of diagnostic criteria to determine the "affected" status of the relatives was also a concern. Nevertheless, despite the shortcomings of the data, Pratt et al. (1951) interpreted the data to support a dominant acting gene with very low penetrance. The rationale for this was (1) the observation of multiplex families and (2) the observation in their sample that the parental risk (0.65%) was approximately half the sib risk (0.93%). However, i f the disease allele is dominant and rare, then it should be expected that the parental risk should be equal to the sib risk. The data, as presented, could in fact be interpreted to support a recessive gene with high allele frequency and low penetrance. Pratt et al. (1951) reported a parental consanguinity rate of 1.5% (2/134). This consanguinity proportion was compared to a reported rate of 0.60% (299/49315) published by Bell (1949) for an English hospital population. However, Pratt et al. (1951) stated that as the sib risk did not approach 25%, a recessive gene would have to have an extremely low penetrance and was therefore an unlikely mode of inheritance, (they didn't correct for family size). The data also showed a skewed parent-child distribution which was attributed to the observed skewed sex distribution of multiple sclerosis in England (Brain, 1947) and to chance effects, as their sample size was small. 14 sex Like Mackay and Myrianthopolous (1960), the researchers attempted to fit their data into a simple mendelian mode of inheritance. To do so they ignored the skewed distribution and the extremely low penetrance necessary to account for the low frequency of multiplex families and familial recurrence risks. They also relied upon risks which lacked age-corrections, specified diagnostic criteria, and were of uncertain/unspecified ascertainment. 1.2.5. Genetic Susceptibility Over time, the observations of the skewed sex distribution of M S , the relatively low recurrence risks among first degree relatives and twins, the variable age of onset, the differential parent-child concordance (based on sex), and the inability for recessive and dominant modes of inheritance to explain the familial aggregation of M S led to the consideration of a genetic susceptibility model for MS (Sadovnick et al.,1988). It was suggested that there were many genes involved in determining a susceptibility to M S and that exposure of a genetically susceptible individual to an environmental trigger or agent was required i f clinical M S was to manifest. This was not a new idea; McAlpine reported the possibility of "an occasional hereditary predisposition," yet his investigation was flawed due to its lack of population prevalence, no denominator information, small sample size, and no standardized diagnostic criteria (McAlpine, 1946). Another early investigator who suggested a genetic "vulnerability" was Mackay (1950) who used the term "Beretschaft" (or constitutional tendency) to explain the observed familial aggregation of MS. Unfortunately this study was also poor, as its sample size was very small, there was no statistical test performed, and the sample was "of convenience" 15 derived from the author's private practise. The genetic susceptibility model has had its opponents as well. Muller (1950) states that such a model, "would be difficult to substantiate and is too far-fetched to be acceptable". Kurtzke (1965) also used the low familial risk data and supposed MS "epidemics" to discount the role of genetics in the familial aggregation of MS. It was not until 1988 that Sadovnick et al.(1988) addressed the past difficulties of family studies and found evidence to support a genetic susceptibility model for MS. In the study by Sadovnick et al. (1988), patients were singly ascertained at the M S Research Clinic in Vancouver, B.C. Only clinically definite, autopsy proven, lab-supported, or probable MS cases were included (see section 2.2 for definitions). The diagnosis was by standardized criteria (Poser et al.,1983;Schumacher et al.,1965). Reportedly affected relatives were examined by clinic neurologists experienced in the differential diagnosis of M S and/or appropriate medical records were obtained. The study consisted of 815 consecutively ascertained, unrelated Caucasian index cases and 11,345 of their first-, second-, and third-degree relatives. Of the 815 index cases 19.9% (162/815) had at least one first-, second-, or third-degree relative with MS. The relatives' MS risks, based upon the empiric data, were calculated using age and sex as covariates. For example, the lifetime MS risk for a female child of a female index case was estimated at approximately 5% at birth, but decreased as the child aged, i.e. the child's remaining risk to develop MS was 2.5% at age 28,1.5% at age 33 and 0.5 % at 43. This presentation of risk was very amenable to genetic counselling. 16 In the Sadovnick et al. (1988) study there was minimal ascertainment bias. Cases were ascertained from a MS clinic representative of the population of B.C. Data collection was systematic and the patients were followed-up for up to 7 years. Familial age-corrected risks for M S (using the Kaplan-Meier method (1958)), were presented with 95% confidence limits. The same researchers had also reported the population prevalence of M S in B.C. (Sweeney et al.,1986) and the family risks were presented with the population prevalence for comparison. In terms of providing genetic evidence, there was a convincing familial aggregation observed (risk was 16X that of the general population), and there was an increased risk with increased genetic sharing (as demonstrated by the confidence intervals). The investigation accomplished the original 'goals' of a classical family study. In summary, while the family studies to date (Table 1.2) have provided evidence for a genetic component to M S susceptibility, no definitive conclusions about the relative roles of genetic and non-genetic factors in the etiology and/or familial aggregation of MS could be drawn. The familial aggregation of M S demonstrated by the family studies could also be interpreted as evidence for an interfamilial environmental or transmissible agent (e.g. virus). In addition, with the exception of the work by Sadovnick et al.(1988) and Robertson et al.(1996), the majority of classical family investigations were hampered by methodological problems. Recruitment was biased, the majority of samples were "of convenience", and the studies were rarely population-based. The investigations also did not use a consistent recognized criteria for diagnosis, the definition of an "affected" individual was not constant, the disorders' low population frequency made 17 sample sizes small, (thereby not allowing statistical analysis), the delayed age of onset made the use of crude risks inappropriate, and standard error and/or confidence limits were not presented. Most of the studies show an increased familial rate relative to the population prevalence. However, i f they did demonstrate an increased risk with degree of genetic sharing, they failed to provide a statistical test. Because of these problems and the inability to separate the genetic/environmental component to M S etiology, other study designs have been investigated. 18 Table 1.2-A Survey of Family Studies in Multiple Sclerosis Relatives Relationship to the Proband Author Year Familv+ Parent Sib Child First 0 Second0 Third0 Curtius0 1933 5/56 - - - - - -Curtius et al* 1937 4/106 0/212 4/444 r- 4/656 -- -McAlpine 1946 8/142 21- 61- - 8/~ - -MacKay 1950 5/267 0/-- 61- - 61- - ~ Pratt et al 1951 20/310 3/465 5/538 — 8/1003 31- 31-Muller* 1953 27/750 5/1493 22/2815 4/692 31/5000 — ~ Allison & Millar 1954 44/700 - 34/2939 - 34/2939 - -Sutherland 1956 14/127 0/254 7/545 2/162 9/961 l / ~ 21-Abb& 1956 Schaltenbrand* 29/472 - - - - - — Myrianthou-polous et al. 1960 9/54 1/62 4/182 1/49 6/293 — 3/519 Schapira et al. 1963 24/607 7/1206 17/2151 - 24/3357 ~ -Hyellstedt 1956 152/2731 10/5460 44/12146 -- 54/17606 - -Roberts et al. 1979 10/51 2/94 1/164 01- 3/258 4/893 3//825 Sadovnick etal. 1981 73/416 11/826 27/1179 3/667 41/2672 24/3067 24/2206 Roberts et al. 1982 22/206 - - - 12/1290 7/3318 3/2647 Wikstrom et al. 1984 15/51 -- 5/201 ~ - - -Sadovnick etal.* 1988 162/815 28/998 54/1886 7/1268 89/4152 48/3967 41/3142 Robertson etal* 1996 128/674 25/1237 43/1347 6/1056 74/3640 31/4350 23/3401 Carton et al* 1996 21/1332 35/1852 5/987 61/4171 Total 751/8535 113/13639 302/28389 28/4881 463/48199 114/15595 97/12740 8.79 ±.61% 0.83 ±.15% 1.06 ±0.12% 0.57 ±.21% 0.96 ±.09% 0.73 ±.13% 0.76 ±.15% (*) represent the investigations published with age-corrections, (*) cited by Schapira et al.(1963), (°) cited by McAlpine et al. (1972), O Number of families with at least one additionally affected affected family member 19 1.2.6. Twin Studies and Multiple Sclerosis The classical twin study was first proposed by Galton in 1876 as a method to ' separate the contribution of nature and nurture in studies of human disposition and intelligence (Blacker, 1952). The basic rationale for twin studies involves the comparison of concordance rates between dizygotic (DZ) twins and monozygotic (MZ) twins. The concordance rate is the number of doubly affected twin pairs in the twin sample, i.e. both members of the twin pair have the disease/trait under study. A monozygotic concordance rate which is significantly greater than the dizygotic concordance rate suggests a genetic component to the trait (disease) under study. In contrast, i f the dizygotic concordance rate is not significantly different from the monozygotic concordance rate, a non-genetic or shared environmental component to etiology is supported. The major assumption of twin studies is that the DZ co-twins and M Z co-twins share their environment equally, i.e. a DZ twin shares the environment with his/her co-twin to the same extent as does a M Z twin pair. This environment includes both the rearing/household environment and the prenatal environment. However, DZ twins can be of unlike-sex pairs and as a result would be expected to share their environment to a different extent than the always like-sexed M Z twin pairs (Susser,1985). This is an important consideration in MS, especially given the evidence that suggests a putative environmental component to M S acts in early adolescence, (Kurtzke,1985). It is reasonable to assume that like-sexed twins (MZ or DZ) will share more of their common environment than unlike-sexed DZ twins. To control for this, M S studies now tend to 20 compare the concordance rates between the like-sexed D Z twin pairs and the M Z twin pairs (Sadovnick et al., 1992). We also know that M Z and D Z twins have different prenatal environments (Vogel and Motulsky, 1986). A D Z twin pair, formed from the fertilization of two eggs, will have separate placentas as well as separate chorionic and amniotic membranes. In contrast, a M Z twin pair, formed from the fertilization of one egg and subsequent division of the zygote, will have different prenatal environments dependent upon the time of zygote division. If the division of the zygote is early, the M Z pair will have separate prenatal membranes. This occurs in approximately 1 in 3 M Z births (Susser,1985). In the remaining 2/3 M Z births, the division occurs during the blastocyst stage. The resultant twins will share chorionic membranes and placentas. These differences could result in a violation of the original twin study assumption. One example of the effect o f this difference in prenatal environments is seen in infant mortality. Monochorionic M Z twins have a greater frequency of infant mortality (Bleker,1979) and congenital malformations (Propping, 1976) than do dichorionic M Z twins. The relatively low twinning rate is a major limitation when twins are used to investigate rare diseases/disorders. The twinning rate in North America and Europe is 1200 per 100,000 births (Hrubec, 1984). This is equivalent to 1 twin pair per 83.3 births. The frequency of M Z and D Z twin births in a Caucasian population is 4 and 8 per 1000 births respectively (Nora and Fraser, 1989). The M Z twinning rate is relatively constant throughout the world, however, the D Z rate is observed to vary between ethnic groups (Emery, 1976). For example, the rate in Asians is approximately 4 per 1000 and in 21 American Blacks it is approximately 16 per 1000 (Nora and Fraser, 1989). In recent years there has been a general increase of D Z twin births in the North American population due to the increased use of fertility drugs (Thompson et al., 1986). Another factor which increases the D Z birth rate is increasing maternal age. For example, when women have children at puberty the D Z birth rate is close to 0 in the Caucasian population, while when women have children at 37 years of age the D Z birth rate is approximately 15 per 1000 births (Nora and Fraser, 1989). It is quite apparent that if one is using twins to study multiple sclerosis it will be difficult to amass a large, representative sample. With an M S prevalence of 0.1% and approximately a 1% twinning rate in Canada, one can expect an M S patient (who is a twin) once for every 100,000 individuals present in the general population. This rarity lends itself to the major limitation of M S twin studies; ascertainment bias. To get enough twins to study, researchers have often advertised and accepted volunteers. This would not be a problem if M Z concordant twin pairs were ascertained in a representative manner. Unfortunately M Z concordant pairs tend to be over-represented in volunteer-based and hospital-based studies (Bundey,1991). In fact, in volunteer studies using same-sex twins, a "rule of two-thirds" is observed. This rule states that in a volunteer twin study the sample will consist of 2/3 females and of 2/3 M Z twins (Lykken, 1987). Indicators that can be used to determine i f a sample is biased are the zygosity ratio (approximately 1:2 M Z : D Z ratio in the Caucasian population) and the sex ratio (1:1 females to males or 2:1 in the M S population). If the sample to be studied is biased (as they often are in M S research (Mackay and Myrianthopolous, 1966), the relative M Z and 22 D Z concordance rates should not be used to infer the relative roles of genetics and the environment. 1.2.6.a. Non-Population Based Studies of M S Non-population-based twin studies ascertain twins through either hospitals or by public (volunteer) appeals. Volunteer ascertainment schemes often result in an over-representation of females, of monozygotic twins, and in particular, an over-representation of concordant monozygotic twins, (Bundey, 1991). The other method of ascertainment is through hospitals. These samples often over-represent the difficult or very serious cases and, again, may not be representative of the population of diseased patients (Torgersen,1983). Nevertheless, non-population-based twin studies have the advantage of being relatively inexpensive compared to population-based studies. The first twin investigation of multiple sclerosis reported a series of 96 twin index cases sampled from a large German hospital (Thums, 1938). O f the 96 twin pairs identified, 59 had a co-twin who had lived past the age of 5, and of these, Thums personally examined 49 co-twins for the presence of multiple sclerosis. He observed a dizygotic (DZ) concordance rate of 2.8% (1/36) and a monozygotic (MZ) concordance rate of 0% (0/13). The one affected dizygotic co-twin was a probable case of M S . With no observable difference between M Z and D Z concordance rates, Thums concluded that a genetic factor did not contribute to M S etiology. However, since there was a lack of concordance in general (i.e. no numerator data) it may be best to conclude nothing regarding the relative role of genetics and the environment. However, this was an important investigation as it was the first twin study of multiple sclerosis. 23 A major twin study was carried out by Mackay and Myrianthopoulos (1958). This was a preliminary report of an investigation of 52 twin pairs (n.b. they reported 54 twin pairs, however two index cases were doubtful cases). The twins were ascertained by public appeal and the criteria for "definite" M S was outlined by Mackay (1950). The sample consisted of 27 M Z twin pairs, of which 2 were definitely concordant for M S , (7.4%), and 25 D Z twin pairs of which one pair was definitely concordant for M S , (4.0%). When "possibly affected" co-twins were included, the concordance increased to 18.5% (5/27) for M Z twins and 16.0% (4/25) for D Z twins, and were not significantly different. Note that in this non-population-based sample, the zygosity distribution was 1.3MZ to 1DZ and the gender distribution was 4 females to 1 male indicating an unrepresentative/biased study sample. In the follow-up study, (Mackay and Myrianthopoulos, 1966), the number of twin pairs increased from 52 to 62. M Z and D Z concordance rates also increased to 16.6% (6/36) and 11.5% (3/26) respectively. When possible M S cases were included, the concordance rates increased to 23.1% (9/39) for M Z twins and 20.1% (6/29) for D Z twins. Again, it is important to note that the zygosity and sex distribution were not representative of the Caucasian and M S population, respectively. The investigators repeated the genetic analysis of their 1960 family study (discussed previously) and found a pattern that they interpreted to be an autosomal recessive mode of inheritance, (if penetrance was 43%). They still could not explain the skewed sex distribution and similar sib and parental risks in their sample. 24 Myrianthopoulos and Mackay's evidence for a genetic component to M S was based on three points: The first were the findings from their "family" study (Mackay and Myrianthopolous,1960). This was previously discussed in the section regarding an autosomal recessive mode of inheritance. The second point that supported a genetic component was the greater M Z concordance rate (16.6%) compared to the DZ concordance rate (11.5%). However, this is not convincing as there was no standard error or statistical test. When I performed a Z-test the two proportions were not significantly different; Z=0.44, p=0.66. This was compounded with the problem of the non-representativeness of the sample. The third piece of evidence that supported a genetic component involved "familial" MS. The investigators observed that the familial rate of M S (i.e. the number of families with additionally affected relatives) in concordant pairs was 66% (6/9) and that the rate in discordant pairs was 10% (3/30). The inclusion of possible cases and the small sample size makes this comparison of familial risk in DZ and M Z families weak. The result of this comparison also contradicts their previous model of a recessive mode of inheritance, (i.e. the familial risk should be equal between discordant and concordant pairs with a recessive mode of inheritance). Williams et al. (1980) placed an advertisement in a national (U.S) "MS Society Newsletter" and identified 94 twin pairs of which 30 were like-sexed and willing to participate. Of these, 6 (20%) were of uncertain concordance, so only 24 were included in the study group. Of the 12 M Z twin pairs 6 co-twins were affected (50%) and 2 of 12 DZ co-twins (16.6%) were affected. The co-twin was counted as affected i f he/she met 25 the criteria for at least "possible" MS (McAlpine et al.,1972). The proportions were statistically different, p=0.02. Given the method of ascertainment the estimates were probably an over-estimate. Inclusion of "possible" M S cases also increased the M Z risk estimate. For example, of the 6 affected co-twins, only 2 were definite, 3 were probable, and 1 was possible. If only definite cases were included, the risk decreases to 16.6% (2/12). For the DZ affected co-twins only 1 was definite. However, the index cases in the DZ twin sample were a mixture of definite and probable cases. When only definite index cases and definite co-twins were counted as concordant, the risk increased to 16.6% (1/6). This is equal to the M Z concordance rate. Other problems in this study which tended to under-estimate true risk included (1) the failure to take into account age-corrections for the co-twins (19/24 were still under the age of 50 and therefore still "at risk" to develop MS); and (2) the sample included non-Caucasians. As the highest risk for MS is in the Northern European population (Sadovnick et al.,1992) the inclusion of the low risk Non-Caucasian individuals would tend to under-estimate risk. These major volunteer studies do not allow any conclusions regarding the role of genetics in M S susceptibility. The samples are just too biased in favour of M Z concordant twin pairs. Other volunteer studies that have suffered due to this ascertainment bias include those by Cendrowski (1968), Currier et al.,(1982) and Bammeretal.,(1960). 26 1.2.6.b. Population-Based Twin Studies To avoid the bias of volunteer and hospital-based twin studies, population-based studies are utilized. This requires the use of population-based disease and national twin registries or disease clinic(s) that are representative of the population with the disease. Using twins ascertained through these representative populations minimizes the bias of concordant M Z twin pairs. The negative aspect of this type of investigation is that it is expensive. It requires the creation and maintenance of national twin registries which are cross-referenced to established disease registries. Alternatively, a population wide screening of a large, representative population is also expensive and time consuming. The first twin study in M S to use a population-based scheme of ascertainment was an investigation by Bobowick et al. (1978). They utilized the United States National Research Council Twin Registry which consisted of 16,000 white male twins who reported for military service. The researchers screened individuals bora between 1917 and 1927 for individuals with M S . The sample thus consisted of twin pairs between the ages of 43-53 (for the remaining risk of M S to be at a minimum). At the time of the study, 23 twin pairs were identified, and of these, only 9 were interviewed (39%). The remainder could not be contacted, had died, or refused to participate. There were 5 M Z twin pairs of which one was concordant. There were no concordant twin pairs among the 4 D Z pairs. One out of the 9 sibships had a family history o f multiple sclerosis. The researchers state that the low risk is evidence against a genetic component. A serious consideration of this study involves the extremely small sample size. Only 39% (9/23) of the twin pairs ascertained could be interviewed. As a result, 27 conclusions about the genetic versus the environmental contributions to M S were speculative. However, what makes this paper noteworthy was that it used a national registry, making it the first population-based twin study of multiple sclerosis. As a result, it was the first twin investigation to have a MZ/DZ ratio representative of the Caucasian population (n.b. it was 1:1 as the pairs were all like-sexed). In another population-based M S twin study, the Danish Twin Register was searched for like-sexed twin pairs bom between 1870 and 1930, (Heltberg et al. 1982). This list was then cross referenced with the Danish Multiple Sclerosis Register. Using multiple ascertainment of families, they found a 21.1% (4/19) concordance rate in M Z twins and 3.5% (1/28) in DZ twins. As with the investigation by Bobowick et al. (1978), the sample size was very small and the proportions were not significantly different. Kinnunen et al. (1987) screened the Finnish Twin Registry of 17,127 individuals for M S patients. Concordant twin pairs were identified, and confirmed by a review of medical records. The criteria for diagnosis were those of Schumacher et al (1965). Eleven M Z pairs were identified, of which 1 pair was concordant (9%). Ten DZ pairs were identified but none were concordant. A majority of the co-twins (13/21) were still under the age of 50 and still at risk to develop multiple sclerosis. A l l three studies discussed above are relatively free of bias (in terms of the zygosity and sex ratio). However, by using national twin registries and cross-referencing to an MS registry, a large sample size could not be attained. Although studying the v prospective occurrence of MS in a defined cohort of twins is a favoured study design, the rarity of MS (and of twinning) make this design not feasible. 28 Recognizing the need for a relatively unbiased large scale investigation, Ebers et al. (1986) ascertained twin pairs retrospectively through a nation-wide system of MS clinics. Patients were referred to these regional clinics, and only those with a diagnosis of MS according to the criteria of Schumacher et al.(1965) and Poser et al.(1983) were questioned. These clinics have been shown to be representative of the MS population (Hader et al.,1988;Weinshenker et al.,1989). This representativeness was most likely the result of the universal health care program in Canada. For the Ebers et al. (1986) study, twins were identified by asking the 5463 Canadian MS clinic patients if they were a twin (zygosity was then confirmed by blood testing). This yielded 70 twins, not significantly different from the number expected for a sample of that size, p>0.50. The expected number of twins, given that there are 12 twin births per 1000 births (Hrubec et al.,1984)., was 66 twin pairs. There were 27 MZ and 43 DZ pairs which also did not differ significantly from the expected 1:2 ratio observed in the Caucasian population. The multiple sclerosis concordance rate for MZ twins was 25.9% (7/27) and for DZ twins it was 2.3% (1/43). This strongly demonstrated the important role of genetics in determining MS susceptibility. The researchers also examined familial risk for MS in first degree relatives of concordant versus discordant twins If the risk was different it could imply genetic heterogeneity. For the non-twin relatives of concordant twins the risk was 57% (4/7) whereas the risk in non-twin relatives of discordant twins was 40% (8/20). These family risks were not statistically different, p>0.50. 29 The investigators also recognized the need for an age-adjustment. A n age of onset curve was created from the affected individuals initially screened. Upon comparison of this distribution with the ages o f the M Z co-twins, it was concluded that the 25.9% concordance rate was an under-estimate of risk as the majority of the co-twins were still relatively young and still "at risk" for developing M S . The concordance rates calculated by Ebers et al. (1986) were the most valid, unbiased, and conservative (pair-wise) estimates up to this point. This was a result of having a representative population (in terms of numbers of twins, the proportion of M Z : D Z twins, and the expected 2:1 sex distribution), having a relatively large sample size, using diagnostic criteria which were specific and stringent (Schumacher et al., 1965;Poser et al.,1983) and recognizing the importance of an age-correction. There were 6 twin pairs who, upon hearing of the Ebers et al. study, "volunteered" to participate, (they were not included). O f these 6, 3 were M Z and were concordant (100%), 2 pairs were D Z with 1 pair concordant (50%), and the last pair was of unconfirmed zygosity. This was a good example of bias that can be created by volunteer ascertainment. A 7 1/2 year follow-up study of the Ebers et al. (1986) twin series was reported by Sadovnick et al. (1993). At follow-up, the mean age of the twins exceeded 50 years and thus the risk for developing the disease in the future was relatively low (Poser et al.,1983). The M Z concordance increased to 30.8% (8/26), (one sibship was excluded), and the D Z concordance increased to 4.7% (2/43). 30 These updated estimates were then combined with a second series screened from an additional 2500 patients, the M Z concordance was estimated at 28.9% (13/45) and the DZ concordance was 3.0% (2/66). The DZ concordance rate was not statistically different from the non-twin age-adjusted siblings risk of 5.1% (Sadovnick et al. 1988). When proband-wise concordance rates were calculated for this investigation, the M Z rate increased to 31.9% (15/47) and the DZ concordance rate did not change. A proband-wise concordance rate is the risk to the co-twins of an index case. As a result of this sampling scheme, there is a probability that a concordant pair will be counted twice. This is different than pair-wise concordance which is used to estimate the proportion of doubly affected pairs in a population of twins. When a pair is identified the sibship can be counted only once. The pair-wise method should be employed in volunteer-based schemes in order to avoid an over-estimate of concordance rates (McGue,1992). In population-based investigations, proband-wise concordance estimates, rather than pair-wise concordance rates, should be used for several reasons. When calculating a proband-wise risk estimate one is calculating the risk to the individual co-twins. As we are often interested in the risk to the "individual" (and not to the "pairs" in a population), it is the proband-wise rate which can be used for genetic counselling. Also, it is only proband-wise concordance rates which can be compared to other relatives' recurrence risk and to the population prevalence. To compare the pair-wise rate to the population incidence would be similar to a mistake often observed in early family studies where the "family" risk was compared to population prevalence (MacKay,1950). Pair-wise risk 31 estimates also vary from study to study as the estimated risk is dependent upon the probability of ascertainment. In fact, unless pair-wise risk estimates are calculated from a sample with 100% ascertainment, the risk wil l tend to be inaccurate in contrast to the accurate proband-wise concordance rate, (McGue, 1992;Allen et al., 1979). A twin study that, on the surface, appears not to provide evidence for a genetic contribution to M S was an investigation by the French Research Group (Alperovitch et al. 1992). A total of 7951 M S patients were screened for twins. This population was ascertained by advertisement and represented 36% of France's M S patient population. One hundred and sixteen twin pairs were identified, of which only 54 pairs were included in the study group. Of these, 17 were M Z with 1 concordant pair ( 5.9%) and 37 DZ with also 1 concordant pair (2.7%). Although the initial sample was ascertained by public appeal and riot truly population-based, there nevertheless seems to be little bias in the population . characteristics. However, the results did not agree with the previous two population-based studies (Ebers et al.,1986;Sadovnick et al.,1993). This may be due to a few reasons. Of the 116 originally ascertained twins, 62 were not included in the study (due to death, refusal to participate, and failure to contact). These excluded twins are an important consideration in the calculation of accurate concordance estimates, (i.e. the 54 twins represent only 18% of the M S twin population), and therefore the results may be due to chance. Another possibility was that bias may have resulted in the original ascertainment. However, there was not an over-representation of M Z sibships, or of concordant pairs, and a representative sex ratio was present. The last explanation was 32 that the French population differs in some way from the Canadian population. This "difference" would tend to lower both the M Z and DZ concordance rates. For example, their genetic make-up may have a "protective effect", or the susceptibility loci may have a lower allele frequency in France. Population-based environmental factors present in other populations may be absent in France, or perhaps an additional exogenous factor may protect the individual in the French population. It is interesting that the France population prevalence (25/100,000) is lower than other European countries (Bauer, 1987) and lower than the prevalence observed in Canada (Sadovnick et al.,1995). Another consideration of the French data was addressed by Sadovnick et al. (1995). When the 95% confidence intervals (C.I.) for the French and Canadian data are compared, the rates clearly overlapped. The Canadian M Z concordance rate was 25.9% (with C.I. between 13.0% and 42.5%), which overlapped the French rate of 5.9% (with C.I. between 0 and 17%). When I performed a Z-test the two proportions were not significantly different (just), Z=T.95, p>0.05. The DZ concordance rate's 95% confidence limits for the French estimate (2.7%) was between 0 and 7.9%, while the Canadian DZ concordance rate's (4.7%) 95% confidence limits bracketed 0 and 10.9%. The most recently published population-based twin study was conducted by members of the Association of British Neurologists (Mumford et al.,1994). Recruitment lasted 27 months and 146 pairs of twins with M S were ascertained. One hundred and five of these were investigated by a single clinician using criteria outlined by Poser et al. (1983). The study group was composed of 44 M Z and 61 DZ pairs which was not significantly different than the 1:2 MZ:DZ expected in the Caucasian population. There 33 were 9 definitely affected M Z co-twins (20%). If "possible" cases were included, this increased to 25% (11/44). The DZ concordance was 2/61 (3%), and there were no "possible" cases. This study was interesting because of its method of recruitment which differed from the Canadian method but the results were similar. While the Canadian patients were ascertained from M S clinics that attract the majority of Canadian patients, the British system recruited those M S patients randomly attending neurologists during a finite period of time. The similarity in the concordance rates of the population-based samples could reflect the similar ethnicity and high population prevalences observed in Canada and England. This may support a hypothesis that the twin concordance rate has a regional or population-based environmental component. This would be in accordance with prevalence studies performed in Australia (Hammond et al.,1988). Of the 14 major twin studies undertaken since 1933, 7 of the research groups supported a genetic component to M S while the others favoured an environmental etiology (Table 1.3). However few of these studies were population-based and rarely reported age-corrections, standard errors or confidence intervals, and statistical tests, raising concerns about the studies conclusions. However, perhaps the main reason for the lack of reproducibility between studies was the bias resulting from ascertainment. Because of the low frequency of M S and the relatively low twinning rate, recruitment of enough twins for statistical analysis can be very difficult. Of the 14 studies, only 5 were truely population-based, (excluding the French group). Of these, 3 studies had very small sample sizes (Bobowick et al.,1978;Heltberg et al.,1982;Kinnunen et cal.,1987). Only 34 the studies by Ebers et al. (1986) and the follow-up by Sadovnick et al. (1993) and the work of Mumford et al.(1994) were relatively free of bias (in terms of MZ/DZ ratio, sex ratio, and the twinning rate) and had a relatively large sample size. Taken together these are the only studies from which conclusions can be made, and they supported a genetic component to MS. Looking at the MZ/DZ ratio it is apparent that M S susceptibility is not due to the action of a single locus. If the allele is rare and acting dominantly the ratio should be 2/1 and i f the disease allele is acting recessively then the ratio would be 4/1 (Vogel and Motulsky,1986). When the M Z and DZ concordance rates are summed across studies (Table 1.3.), a ratio suggestive of a recessive gene is observed (4.2/l±l .6). However, because of the problems and biases inherent in these studies, a more accurate ratio would be obtained by examining only the Canadian and U.K. data. These studies had comparable ethnicity, population prevalence, and were relatively free of bias. The ratio observed using these data sets is 8.0/l±2.9, and using only same sex pairs, it is 5.3/l±1.7 (Sadovnick, 1993). As the ratio is greater than 4/1, at least 2 genes acting to influence M S susceptibility is suggested. It should be noted that the highest M Z proband-wise concordance rate (31.9%), which was reported by the Canadian group, was still lower than 100%. This relatively low concordance rate stresses the importance of environmental and/or stochastic factors in the development of multiple sclerosis. It would be extremely useful to investigate the risks to twins reared together versus twins reared apart. However, taken together, the relative rarity of MS, of twinning and the separate adoption of members of a twin pair, this type of study is not feasible. What is needed to resolve the nature/nature question is 35 an unbiased study design and a sufficiently large sample size to make inferences regarding the nature of the genetic and environmental variables. Table 1.3.Twin Studies of Multiple Sclerosis Author Thums 1936 Mackay & 1958 Myrianthopolous 1966 Bammeretal. 1960 Cendrowski 1968 Bobowick et al. 1978 Williams et al. 1980 Currier etal. 1982 Heltberg et al. 1982 Ebers etal. 1986* Sadovnick etal. 1993* Kinnunen et al. 1987 French Group 1992 Mumford et al. 1994* Year Ascertainment flMZ Rate #DZ Rate Full-Sib Zygosity u ~ — i J . : ~ - ' ^ Total hospital-based 0/14 - 1/36 volunteer 2/27 7.4% 1/25 volunteer 6/36 16.6% 3/26 hospital-based 1/6 16.6% 2/7 hospital-based - - 0/3 population 1/5 20.0% 0/4 volunteer 6/12 50.0% 2/12 volunteer 8/22 36.0% 3/29 population 4/19 21.0% 1/28 population 7/27 26.0% 1/43 population 13/45 28.9% 2/66 population 1/11 9.1% 0/10 volunteer/population 1/17 5.9% 1/37 population 9/44 20.1% 2/61 2.7% 4.0% 11.5% 28.6% 16.6% 10.3% 3.6% 2.3% 3.0% 2.7% 2.0% n/a 4/182 9/236 n/a n/a 1/? n/a n/a n/a 87/4582 5.1%D n/a n/a n/a 59/284 20.7% 19/387 4.9% M z / D z Ratio = 20.7/4.9 = 4 . 2 ± 1 . 6 * = significant twin studies (being population-based, large, and relatively unbiased) D = age-corrected risk estimate Q=zygosity determined by questionnaire, B=blood typing and F = D N A fingerprinting Q 3 Q,B B B B B Q,B Q,B Q,B,F Q,B Q,F 36 1.3. Adoption Study Design Adoption studies provide a powerful design capable of separating the genetic and environmental components of disease etiology. To date, (as far as I am aware) the adoption study design has never been applied to studies of multiple sclerosis (or other autoimmune or putative autoimmune disorder). This is primarily due to the lack of resources necessary to undertake an unbiased population-based adoption study. Adoption studies have, however, frequently been used for behavioural disorders, in particular, schizophrenia. The first adoption study in schizophrenia was done by Heston (1966) who reported that the offspring of schizophrenic biological mothers had an increased rate of schizophrenia. These children had all been "adopted-away" within three days of their birth and placed in Oregon State institutions. Adopted-away children of unaffected mothers were used as the comparison group. Many subsequent adoption studies have been performed in schizophrenia, (Kety et al.,1988,1992;Tienari ef al.,1987;Rosenthal et al.,1971) providing a useful paradigm for adoption studies. Adoption studies employ three main strategies: 1) "adoptees method", 2) "adoptees'relative's method" and 3) "cross-fostering". The "adoptees method," (Heston, 1966) focuses on the adopted-away children of affected parents with the assumption being that the risk to the children will reflect either their "biological" condition or their "environmental" (rearing) condition. The risk to the children is compared to an appropriate control population comprised of adopted-away offspring of unaffected parents (Figure 3). A n excellent example of this study design was performed by Tienari et al., (1987) who found that the adopted-away children of 37 schizophrenic mothers had an increased risk of schizophrenia. In a sample of 124 adopted-away offspring of schizophrenic mothers, 9 or 7.3% had become psychotic. The control sample consisted of 147 adopted-away offspring of unaffected mothers with 2 (1.4%) psychotic individuals (n.b. psychosis was included in the definition of schizophrenia). The adoptees method also provided the opportunity to assess the adoptive child's rearing environment. The researchers observed that the biologically "at risk" child's adoptive rearing environment could act to "protect" against the development of schizophrenia, (Tienari et al. 1991). This study thus demonstrated not only the importance of genetics but also the importance of the genotype/environmental interactions in the development of schizophrenia. The second adoption strategy is the "adoptees' relatives method" which compares the risk to the biological relatives of affected (adopted-away) index cases to the risk to the biological relative's of unaffected (adopted-away) controls (Figure 3). Kety et al. (1992), using this method, observed that 5.1% (14/275) of biological relatives of adoptee index cases presented with chronic schizophrenia while only 0.4% (1/253) of biological relatives of unaffected adoptee controls presented with schizophrenia. When the adoptive/adopting relatives were examined, there were no cases of schizophrenic (0/111). Given the population prevalence for schizophrenia of 1/80, their data strongly supports the role of genetics in the transmission of schizophrenia. A third adoption strategy is "cross-fostering" which combines the adoptees method with a more appropriate control group. This method compares the risk to adopted-away children born to an "affected" parent and raised in an unaffected 38 household to the risk for adopted-away children born to an "unaffected" parent and raised in an affected household. This is a very difficult study to undertake due to the relative rarity of adoptions and the accessibility to the necessary family information. However, it was attempted by Wender et al.(1974) in their study of schizophre Unfortunately a level of significance was not attained due to the small sample rema. ie size. Figure 3. Adoption Study Designs Method Study Sample 1. Adoptees 0 Control o O Comparison Risks to the Adopted-away Children 2.Adoptees Relatives 3. Cross-Fostering Op QHD • p . G v T J <>" OjO QHJ op +u o" Risks to the Biological Relatives Risks to the Adopted-away Children Biological Relationship Adoptive Realtionship Adoption investigations require appropriate representative adoption registries. Ethical and legal issues must also be addressed, (such as confidentiality and informed consent) when tracing/contacting the biological and adopting relatives of adoptees and, 39 perhaps more importantly, the contacting of the "at risk" adoptee. These studies are often performed in Scandinavian countries where socialized health care, the existence of national adoption registries, and societal views and ethics make adoption studies more feasible. In Canada, where there is no national adoption registry, identifying information regarding birth parents and adoptees is restricted. This restriction is part of provincial legislation regarding adoptions. In B.C. , this legislation is part of the Adoption Act (1979). Like other provinces, this legislation contains an "as if statement that refers to a clause stating that, "the adopted child will be raised as //the child had been born to the adopting parents". This is present to protect the adoptee, the adopting family and the birth parents. For the adoptee or birth parent (usually mother) to get access to confidential (i.e. identifying) information, most provinces require consent forms from both parties through a provincial registrar. The adoptee must also be over 18 years of age. This concept of protection/privacy in the adoption process has been in the best interests of all parties. Birth parents (i.e. young single mothers) may wish to remain anonymous because of the past stigmata associated with having an illegitimate child. The adoptee is also protected in that he or she may not even know they are adopted. Having a stranger/birth parent inform an unknowing adoptee of his/her birth status could be emotionally devastating. If the adoptee does know his/her birth status, meeting their birth parents (if it is unwanted) can also be a traumatic experience. The adopting parents may also want a degree of confidentiality regarding their initial need to adopt and to be 40 shielded from the birth parents (Edwards et al.,1979). However, societal views on single parenting and a trend to "open" adoptions (where the adopting and birth parents communicate) have made some individuals question the importance of the privacy legislation (Daly et al.,1993). With changing views and laws, investigators performing adoption studies may have greater access to previously inaccessible information. Recently, one of the recommendations by Health and Welfare Canada (1993) was for the creation of a national adoption registry (Daly et al.,1993). Adoption studies rely on a few important assumptions. The first assumption is that the adoptees and the adoptive and biological relatives are representative of the general population. This is often not the case as the adopting parents tend have a higher socioeconomic level, have higher IQ's, and be relatively older (Tienari et al. 1994). The second assumption concerns selective placement. For example, in studying a behavioral disorder it would be important to know i f a genetic and/or environmental correlation exists between the adoptive/adopting and biological relatives of the adoptee, (i.e. perhaps one is not truly separating the genetic and environmental variables). An example of this is in P.E.I where public adoptions can only occur between parents of the same religion (and, potentially, the same ethnic group). Possible maternal effects must also be considered. The adoptee shares nine months in utero with his/her biological mother and perhaps this intrauterine environment is the critical etiologic component for the development of the given disorder. If this was indeed the case, evidence for a genetic effect may be concluded when in fact the trait may be caused by an in utero environmental exposure (e.g. virus). Another serious limitation of an adopted study is 41 the rarity of adoptions. In 1981 there were 3521 private and public adoptions of children under one year of age in Canada. By 1990 this number had decreased to only 1698 (Daly etal.,1993). So, with the limited access to family information, the rarity (and decreasing frequency) of adoptions, and the rarity of MS, it is not surprising that an adoption study in M S has not previously been done. 1.4. Half-Sib Study Design Another study design that has not been utilized in M S or (as far as I am aware) any other autoimmune or putative autoimmune disorder involves half-sibs. Half-sibs share only one common parent (Figure 4). It is somewhat surprising that this relative type is not exploited more in genetic epidemiology as it is a powerful tool in the investigation of both quantitative and qualitative traits. The design can be used to (1) determine i f a genetic component exists, (2) partition the genetic and environmental sources of phenotypic variance and (3) provide a simple test for the detection of maternal effects. Another attractive aspect of the half-sib study is its feasibility compared to twin and adoption studies. 42 Figure 4. Half-siblings O -o Step-sib Maternal Index Half-sib Case (25%) 6 6 Full-sib Paternal (50%) Half-sib (25%) (%) refers to the degree of genetic sharing with the Index Case 1.4.1. Half-sibs and Quantitative traits The partitioning of phenotypic variance is a necessity in animal breeding experiments. By dividing the variance into its separate components, a breeder can determine the degree to which a trait will respond to selection, Sources of genetic variance include the "within" locus source of variation, V d , the "between" locus source of variation, V;, and most importantly, the variation due to additive effects, V a . To quantify these sources of variance, the covariance between pairs of related individuals is calculated. For half-sibs the covariance is comprised of 1/4 of the variation due to additive effects (Falconer, 1989). For full-sibs the covariance is comprised of 1/2 the variation due to additive effects (Falconer, 1989). As mentioned above, there is also a "within" locus source of variation, V d . Because half-sibs share only one common parent, there is a zero probability that the second allele transmitted at any locus will be identical by descent among half-sibs. It therefore does not need to be considered in the 4 3 covanance equation for half-sibs. However, it is possible for full-sibs to share both alleles at a particular locus (1/4 of the time). The variation due to dominance effects is therefore 1/4V d in the full-sib covariance equation (Falconer, 1989). The V ; is the source of variance due to interactions between loci (i.e. epistasis). As such, i f the variation in a trait was determined by only two loci, interactive effects would be between the additive effects of one locus and the additive effects of the other locus, between the dominance effects at one locus and the additive effects at the other locus, and lastly, the dominance effects at one locus and the dominance effects at the other locus. Therefore, in a two locus model, Vj would comprise; V, = Va, + V a d + V d d (Falconer, 1989) As half-sibs have no dominance effects, the Vj source of variation would be limited to the interaction of additive effects, (V a a). As full-sib variation has both dominance and additive effects, the Vj would be a maximum, (V a a + V a d + V d d ) . Phenotypic variance is also comprised of an environmental component. This environmental source of variance can be divided into two components; V E = V E C + Vew (Falconer, 1989). Vec is typically given as the source of variance that causes similarity within a group and differences between groups. It therefore needs to be considered in the half-sib and full-sib covariance equations. V e w is the environmental variance that is constant between and within groups, it therefore can be excluded from the covariance equations. To summarize: 44 Cov(Half-sibs) = 1/4 V a + 0 V d + Vj m i n + (Falconer, 1989) Cov(Full-sibs) = 1/2 V a + 1/4 V d + V, m a x + V e c (Falconer, 1989). Where V a is the variation due to additive genetics, V d is the variation due to dominance effects, V; is the variation due to interaction between loci, and is the variation due to the shared environment within groups. A typical half-sib quantitative study would consist of a hierarchical analysis of variance where three mean squares "MS" are calculated; the "MSw" which is for progeny within full-sib families, the "MSd" which is between half-sibs of the same sire (and different dams) and the "MSs" which is for between sire families. With these values and the previously stated covariance equations, the V a , V d , V ; and can be calculated for a particular quantitative trait (Falconer 1989). From this, suitable selective breeding strategies are then designed to best exploit the additive genetic variation. Using this conventional half-sib analysis, a quantitative trait in animals is relatively simple to investigate (Faust et al.,1989;Tawonezvi,1989;Gama et al. 1991). However, this is not the case when one applies the strategy to humans. In North American families, sibships are relatively small and of unequal size, there are age differences and parity effects between the offspring, often a parent is not available for study (due to death or divorce), and the offspring may or may not be raised in different homes. To overcome these problems, Roberts et al. (1978) studied a West African tribal population. In this culture, polygamy is common thereby creating an abundance of half-45 sibs. The researchers were able to partition the sources of variance and calculate a heritability estimate for height. To overcome the problem in a western society, Nance et al.(1976) proposed the monozygotic twin half-sib model. Because monozygote twins are genetically identical, their offspring will in fact be half-sibs genetically. M Z twins who parent these "half-sibs" will be the same in age and sex. There will be equal amounts of maternal and paternal half-sibs, parents will be more likely to participate, age and size of the families wil l be more closely matched and there will be no traumatic event such as death of a parent or divorce occurring in one sibship. This design has been applied to birthweight by Nance et al.(1983) who partitioned variation of this quantitative trait according to prenatal environment, genetics, and parity effects. Potter (1990) used this design in a study of periodontal disease and showed both maternal and genetic effects in tooth development. Potter was also able to demonstrate non-random mating by investigating husband-wife and twin co-twin correlations. Corey et al. (1978) were concerned that the twin half-sib model still did not provide enough power to discriminate maternal effects. They therefore extended their analysis to the grandchildren of monozygotic twins. This provided 20 distinct relationships based on sex compared to the six relationships in the twin half-sib model. Performing a nested analysis of variance on two levels allowed the maternal effects to be more accurately resolved. This study does make the assumption that environmental effects are constant across generations. 46 1.4.2. Half-sibs and Qualitative traits Half-sib studies can also be used to detect a genetic effect in qualitative traits, (i.e. multiple sclerosis, diabetes). However, before an investigation begins, the researcher must make assumptions regarding the familial environment. If the common environment is thought to differ in an unknown way between half-sibs and full-sibs then this common environment must be eliminated from the study design. To do this, the risk to half-sibs of the index case is compared to the risk to half-sibs of an unaffected individual. In both situations, the half-sibs must be reared apart. If there is a genetic component to the trait under study, the risk should be increased in the index cases half-sibs. If a differing environmental exposure between relative types did indeed exist and we compared relative types (e.g. half-sibs versus full-sibs), any detectable difference may be due to environmental sharing differences rather than genetics. The exclusion of the common environment from the study design is preferred, especially in studies involving psychiatric disorders where differences in the relative's environments may contribute differently to risk. If the researcher can make the assumption that the environmental effects are constant, then it is possible to compare the risk to half-sibs who have lived with the index case to the risk to full-sibs who have lived with the index case. If the full-sib risk is greater than the half-sib risk, it can be concluded that genetics plays a role in disease etiology. If the researcher can make the initial assumption, then this design is favoured as it requires a smaller size to detect a genetic effect (Tierney et al. 1994). The index 47 cases are also relatively more informed about relatives they have lived with versus those they have not lived with. Insight into a mode of inheritance can also be obtained using the half-sib strategy. By observing the difference between half-sib and full-sib risks, polygenic or single locus modes of inheritance can be accepted or rejected. For example, i f a disease allele is rare and dominant-acting in a single locus model, then the risk to half-sibs should be exactly half the risk to full-sibs. However, i f the disease is caused by multiple segregating genes the half-sib risk should be less than half the full-sib risk. This is the same concept as the M Z / D Z ratio and conclusions regarding a mode of inheritance using this ratio should be used cautiously, i.e. the standard error of this proportion should always be presented. A more sophisticated use of the half-sib risk estimate is provided by Risch (1990) who used the lambda values, (k) of varying degree of relatedness (R) to predict a mode of inheritance. A lambda value or recurrence risk ratio, is defined by Risch (1990) as the recurrence risk to a relative of an index case divided by the population prevalence. Accurate recurrence risk estimates for M Z twins, first and second degree relatives, and, i f possible, third degree relatives are required for this analysis. By comparing the predicted recurrence risk ratios with the observed recurrence risk ratios, possible modes of inheritance can be inferred. A n example of this is given in Appendix D. The half-sib model can also be used to investigate maternal effects. A simple comparison between maternal and paternal half-sib risk is required. If there is a maternal effect then the risk to maternal half-sibs should be greater than the paternal half-sib risk. 48 This comparison has been used in disorders such as alcoholism (Vogel et al.,1979) and schizophrenia (Gottesman,1994). A n important consideration in this comparison is the "shared environment" of the half-sibs. It may be hypothesized that an affected index case will more likely have lived with their maternal half-sibs rather than their paternal half-sibs. It might also be assumed that the index case may be closer in age to their maternal half-sibs and be more informed of the "affected status" of their maternal half-sibs compared to their paternal half-sibs. These three points may provide a positive result in the absence of a true maternal effect. It may also be hypothesized that there could be a paternal effect. In such a case the paternal half-sib risk wil l be greater than the maternal half-sib risk. Half-sib studies are attractive because of their feasibility. The lack of numbers present in adoption and twin investigations does not seem to be a problem with the half-sib design. There is a moderately high rate of divorce among Canadians, 273.9/100,000 per year (Health Statistics Division, 1994). This has increased substantially during the last 30 years (e.g. 51.2/100.000 in 1966). With such a high divorce rate, the possibility of remarriage and second families is also relatively high (Tiemey et al.,1994). Over half of all couples who divorced between 1969 and 1977 had at least one child (McKie et al.,1983). So, unlike the adoption study design, the pool of potential individuals to study is increasing rather than decreasing. Tierney et al. (1994) compared the sample sizes required to detect a genetic component of a disease using different relative types and study designs. They calculated the sample size required to detect a genetic component of a disease at a given population 49 prevalence and a given heritability, using the twin and half-sib study designs. The two sample sizes were then made into a ratio. For example, using the half-sibs "reared apart" strategy previously discussed, an assumed multifactorial mode of inheritance and a heritability of 0.65, an investigator would need a study population where the half-sibs outnumbered the M Z twins by 37.4 to 1 (a minimum) to justify using a half-sib study over the twin strategy. The paper provides useful ratios for researchers designing strategies to determine a genetic component for a disorder. Tierney et al. also provided an example from the B.C. MS population. In the example presented, the half-sibs outnumbered the M Z twins by a factor of 21.6 to 1. In a "relatives reared together design" the minimum and maximum ratios are between 10.9 and 15.2. therefore, in this sample of multiple sclerosis patients it is more feasible to investigate genetic effects using half-sibs compared to a twin strategy. 50 1.5. Genetics of Multiple Sclerosis-Evidence from Molecular Studies Evidence for a genetic component to multiple sclerosis has come from association and linkage studies. This discussion will now focus on the studies involving the H L A complex, the myelin basic protein gene (MBP), the germline T cell receptor genes (TCR), immunoglobulin genes (Ig), and the tumor necrosis factor gene (TNF). These candidate loci have all been investigated because of their involvement in the immune response. To date, no candidate gene(s) has been identified that contributes significantly to M S risk. As previously discussed, genetic epidemiological data strongly support the concept that more than one gene is involved. 1.5.1. HLA Human leukocyte antigen (HLA) binds internally processed antigens and presents them on the surface of B cells and macrophages (antigen presenting cells). Once at the cell surface, T cells will bind to the HLA/epitope complex and trigger an immune response. This response wil l be dependent upon the class of H L A (either class I or II) and whether the bound antigen is, in fact, foreign. The locus for the human leukocyte antigen is on chromosome 6p (Fig.5). It is a 3500 Kb region separated into three main classes; class I, II, and III. The alleles of the class I and II regions are responsible for encoding the H L A complex. They are very polymorphic, resulting in a high degree of diversity of antigen presentation (Lechler,1994). This is a necessary requirement i f the T cell is to respond/or not respond to the range of peptides in our environment. Thirty-s genes have been identified in the Class HI region encoding immune system related -six 51 proteins (e.g. complement proteins) and non-immune system related proteins (e.g. 21-steroid hydroxylase). Figure 5. Hum an Leukocyte Antigen Locus 1HLA1 ( A d a p t e d from T i e n a r i , l 994) C l a s s TT C l a s s ITT C l a s s T Okb b (kilobase)-Approximate physical distance between 1 The H L A association with multiple sclerosis is the only consistently replicated molecular genetic observation in MS research. Early studies demonstrated an association with H L A class I antigens A3 and B7 (Naito et al.,1972;Bertrams et al.,1972;Jersild et al.,1972). Following these reports, M S was shown to be associated with the class II antigens H L A D R and Dw2 (Jersild et al., 1973;Winchester et al.,1975). This was later subtyped into a strong and consistent association with the H L A DR15, DQ6, and Dw2 haplotype (Hauser et al.,1989;Cullen et al.,1991;Hillert et al.,1993;Allen et al.,1994). The first association study by Naito et al. (1972) showed an association of MS with HLA-A3 antigens. This antigen was present in 40.4% of patients (38/94) compared to 23.5% of unrelated controls (205/871) in an American population, relative risk 52 (RR)=2.2. An HLA-A3 association was also reported in a population of German patients where 35.6% of patients (140/393) had the H L A antigen compared to 27% (69/255) of controls, RR=1.5 (Bertrams et al. 1972). However, when the significance value was corrected for multiple comparisons, the result was no longer significant. Bertrams et al. (1972) did observe a significant association with the HLA-w5 (R*,4c*,Te50) antigen. Jersild et al. (1972) confirmed the association of MS to HLA-A3 in a combined sample of Danish and American patients. Yet in the Jersild et al. (1972) Danish sample of 107 patients, there was observed a stronger association with HLA-A7 (a.k.a. HLA-B7). In another paper, Jersild et al.(1973) described an association of M S with another antigen, the LD7a determinant (a.k.a. HLA-Dw2) in 28 Danish patients of whom 19 had LD7a compared to 5 of 28 controls for a 1^=9.7. Of the individuals possessing HLA-A7(B7), 56% had LD7a(Dw2) and over 50% possessed HLA-A3, suggesting strong disequilibrium between these marker loci. The next major association of M S with the H L A complex was with HLA-DR. Winchester et al.(1975) reported this association when the Ag7a antigen was found on the B cells of all 25 M S patients studied and only 11/25 controls. The 8 ratio (Thompson and Bengtsson, 1981) allows the assessment of the attributable risk of the H L A II alleles from association studies data. The 5 ratio is; Frequency in M S pts. — Frequency in Population 1-Frequency in Population (Thompson and Bengtsson, 1981). Goverts et al. (1985), investigating the H L A DR2 allele, reported the attributable risk of H L A DR2 to be between 0.24 and 0.28. This demonstrates that although the H L A 53 association is constant, the allele contributes relatively weakly to total risk of MS, (i.e. the alleles are just too prevalent in the unaffected population). A discussion of linkage analysis of H L A should be preceded by a review of the early descriptive/association type studies that examined the H L A segregation within families, although no formal linkage analysis was performed. A n association study determines i f a marker (i.e. exposure) is more frequently found in the affected population compared to a control group. The odds ratio often calculated estimates the relative risk (RR). In contrast, linkage analysis examines the co-segregation of a marker and a disease phenotype within families. If the marker is linked to the disease allele (i.e. less than 50 c M apart) segregation wil l be non-random. The lod score is the log of the odds ratio of obtaining the observed data i f the marker and disease alleles are linked (at various c M distances) versus the probability of obtaining the observed data i f they are unlinked. Linkage may also be determined by measuring the extent of gene sharing between affected relatives. A n example of this non-parametric design is the affected sib pair method (Landner et al.,1994) which compares the haplotype distribution between affected siblings, i.e. i f linked, the haplotype sharing will be non-random between affected sibs. This is a useful method when the mode of inheritance and penetrance is unknown. In an early study, Bird et al.(1975) reported a family with M S in three successive generations. The affected relatives did not share the HLA-A3 or B7 haplotype, but did share the HLA-W16, A l 1 haplotype. Bird et al.(1975) suggested that this H L A determinant was the susceptibility allele within this single family, although no statistical 54 test was performed. When I performed a lod score analysis on the presented data I did not observe significant evidence for linkage = 0.89 at 0 = 0.01. Olsson et al.(1976) examined 8 families with 2 or more affected relatives from a Swedish population. H L A A-B-C-D antigens were typed. There were no differences in the degree of haplotype sharing when antigens in 8 pairs of affected relatives were compared to the 28 unaffected relatives. In other words, the unaffected relatives had the H L A A-B-D antigens in common with the index case as often as the affected relatives. Hens et al. (1978) typed 13 families with two or more affected members for H L A - A antigens and failed to observe a difference in haplotype sharing. Within their 13 multiplex families, there were 7 were sib pairs for whom marker information was described. There is no segregation distortion when I performed a sib pair analysis of the reported data, 0.50<p<0.75, (this sample size was extremely small). Eldridge et al. (1978) and Drachmann et al. (1976) also failed to confirm an association of M S and H L A within 7 and 8 families respectively. The failure of these studies (Olsson et al.,1976;Drachmann et al.,1976; Hens et al.,1978;Eldridge et al.,1978) to show a difference between affected and non-affected relatives was likely due to the small sample size (given the consistent association observed in most H L A studies). Alter and colleagues performed the first classical linkage analysis of multiple sclerosis and H L A (Alter et al.,1976) by typing 10 families for their H L A - A antigens. When all ten families were included, a lod score of 1.148 was obtained at a recombination fraction (0) of 0.10. However, one family had two "possible" cases where the diagnosis of MS was unclear. When this family was excluded, the lod score attained significance at 3.057 at a 0=0.05. The mode of inheritance was assumed to be a single 55 were locus model but dominant or recessive models were not specified, (also unspecified the population prevalence, the penetrance, and the frequencies of markers and disease allele). Visscher et al. (1979) examined H L A A , B, and DR antigens in 12 families. Looking only at sib pairs, they observed significant linkage with the H L A complex, X 2 = 13.11, p<0.001. However, when they applied the same data to a parametric lod score analysis, they failed to demonstrate linkage (assuming a dominant mode of inheritance, and a penetrance of 5%), Another linkage analysis was performed by Tiwari et al. (1980) who pooled data from 10 previous reports on M S and marker segregation. The 72 pedigrees were analyzed using only marker information for H L A A , B antigens. A dominant model with 5% penetrance and a recessive model with 43% penetrance were tested. Under the dominant model, a score of 2.78 was obtained at 0=0.15, while the recessive model obtained a maximum lod score of 4.09 at 0 =0.20. Gene frequencies were estimated at 0.005 and 0.034 respectively. When they varied the penetrance parameter, they estimated 0 to be between 0.15-0.30 for a dominant model and 0.15-0.25 for a recessive model. In the same year, another group investigated linkage in 40 families (Haile et al.,1980). Because some MS families demonstrated vertical transmission, they assumed a dominant model for their analysis. H L A A , B markers were used. What made this analysis different from previous analyses was that it incorporated an age of onset correction into its lod score program, Liped. Their investigation yielded a Z m a x of 2.41 at 0=0.10 when penetrance was at 0.05 and gene frequency set at 0.005. Haile et al. (1980) pointed out the problem of the observed high recombination fraction (0). If association of M S to H L A haplotype was due to linkage disequilibrium, 56 there needs to be intense selection to maintain such the high 6 between marker and disease locus that was observed with linkage analysis. Ho et al. (1982) proposed that this inflation of G was really due to other non-HLA genes acting synergistically or additively. If two relatives were discordant for the particular allele under study, and yet they were both affected they would be classified as recombinants, when in fact other non-HLA determinants are involved in M S pathogenesis. Alternatively, it could be that more than one liability haplotype exists at the H L A locus and it is common in the population. Again any discordance between affected relatives would be classified as a recombination event. Haile et al.(1981) followed up on their original analysis of 40 multiplex families using the computer program Linkas instead of L i ped. While both programs correct for the variable age-of-onset of M S , Linkas also allows for etiological heterogeneity and incorporates linkage disequilibrium parameters. Assuming linkage equilibrium, both dominant and recessive modes of inheritance could be rejected. When linkage disequilibrium was incorporated, the best fit was with tight linkage to H L A in 75.2% of the pedigrees and no linkage to H L A in the remaining pedigrees. However a model involving loose linkage and no genetic heterogeneity could not be excluded. Stewart et al.(1981) studied 15 Australian families. When they added these to previous reports from the literature, they had a combined sample of 100 sib pairs. When analyzed, a significant difference was found in haplotype sharing; X 2 = 11.52 p<0.002. However, only 35 of these 100 pairs had unequivocal marker information. These 35 also displayed a non-random segregation of haplotypes X = 6.2, p<0.01. The Australian 57 study also investigated H L A DR2 homozygosity in 60 unrelated patients to determine a mode of inheritance. They observed only 3/60 homozygous H L A DR2 patients. Because this value was lower than a recessive mode of inheritance would allow, the recessive model was rejected, p<0.02. A dominant mode of inheritance could not be rejected. Taken together, these five studies, provide evidence for linkage of M S to the H L A locus. However, Ebers et al.(1982) demonstrated that this pooled evidence for a single locus model linked to H L A may not be as strong as the reports implied. Ebers et al.(1982) used 40 sib pairs from a population-based sample. A l l were examined by the same neurologist. Of the 40 pairs, 10 shared no haplotypes, 18 shared 1 haplotype, and 12 shared 2 haplotypes, not significantly different from the expected distribution. Goverts et al. (1985) and Compston et al. (1982) also failed to observe a segregation distortion between H L A haplotype and MS. In the Goverts et al (1985) study 20 sib pairs were identified. The distribution of haplotypes was as expected by chance; 6 shared 2 haplotypes, 9 shared 1, and 5 shared 0 haplotypes. Interestingly, in DR2+ discordant sib pairs, the frequency of the DR2+ haplotype was equal to the frequency of DR2+ observed in sporadic cases (sporadic cases families had shown a lower frequency of DR2+ then multiplex families). This suggests a possible epistatic model of H L A with another determinant in the multiplex families. Tienari et al. (1993) used serological typing and (for the first time in an H L A - M S linkage analysis) polymerase chain reaction (PCR) to directly amplify and type the alleles. A variety of tests were performed on the 21 Finnish multicase families including a transmission disequilibrium test (TDT), a sib pair analysis, and a two point linkage 58 analysis. The TDT demonstrated that the H L A DQA1 *0T02 allele was transmitted preferentially from heterozygous parents to MS patients, pO.OOOT. A linkage analysis using 23 sib pairs informative for H L A haplotypes was performed. This was significant, p<0.05. A second analysis was performed where families with DQA1*0102 homozygous parents were excluded. When this was done, linkage analysis in 13 pairs was significant, p<0.005. In both analyses a recessive or dominant mode of inheritance could not be excluded. The parametric lod score analysis of M S and H L A haplotype obtained a maximum lod score of 1.54 at 0=0.10. This assumed an intermediate mode of inheritance with a penetrance of 5%. However, because the results of the TDT demonstrated a non-haplotype specific segregation, (i.e. H L A DQA1*0102 was frequently transmitted to affected relatives from different parents within the same family), the linkage analysis was redone using H L A DQA1*0102 PCR products. The analysis was significant, Zm a x=3.84, penetrance at 5%, 0=0.05, and an intermediate mode of inheritance. When the investigators took into account linkage disequilibrium, the lod scores increased even more (Zm a x=6.43 for H L A A l *0102 and MS). As the putative (common) susceptibility allele was present on many haplotype backgrounds, controlling for the specific allele (and not just the haplotype), provided the analysis with the greatest evidence for linkage. Kellar-Wood et al.(1994) looked at the H L A DR and DQ haplotypes in 115 sib pairs. Of these, 109 families were used in IBD (identity by descent) sib pair analysis. There was no distortion of haplotype sharing from what was expected by chance, p>0.1. When all 115 sib pairs were used in an IBS (identify by state) analysis, there was no 59 evidence for linkage, (pX). 1). However, TDT analysis provided them with evidence confirming association. Kellar-Wood et al. also performed a "classical" association study that confirmed the association of M S with DR15, DQ6, Dw2 haplotype. From these results the researchers concluded H L A does not represent a susceptibility locus and its overall contribution to risk is small and probably epistatic, (i.e. it is not necessary for disease expression). To provide a rough estimate of H L A ' s contribution to risk of developing MS, the sib pairs reviewed in the literature that share no alleles identical by descent were summed (Table 1.4). Of the 336 sib pairs with marker information, 52 shared no haplotypes. However, the expected probability of sharing 0 alleles (between sibs) is given by 0.25A,S, (assuming recombination is negligible between marker and disease locus) (Risch,1987). As Xs is 37 for MS, (3.7% ( s i b r i s k ) /OA %(popUiation prevalence) X the probability of observing 6 haplotype sharing is 0.67%. Using this as a probability, "p", the Poisson probability of observing 52/336 is 2.7x10"51. It is therefore very unlikely that the H L A locus is the sole determinant responsible for the high sib recurrence risk. In a "two determinant" epistatic (multiplicative) model X.sibs equals (A,Mtcus)*(^BIOCUS) (Risch,1987;1990). As the A - A ] ^ equals 1.6 (0.25/ 52/336) then the residual Xeiocus is 22.9, (given that k sibs is 37). This demonstrates that "other determinants" contribute more to relative risk than does HLA. As a note, this value of 52/336 (the summed sib pairs that showed 0 alleles identical by descent) is most-likely an under-estimate of the true value as pooling the data may have incorporated a positive publication bias. 60 Table 1.4. Selected Published Haplotype Sharing between Affected Sibs Author Year 2 Stewart etal. 1981 37 14* Ebers etal 1982 12 Compston et al. 1982 85 Govaerts et al. 1985 6 Tienarietal. 1993 9.77 Kellar-Wood. 1994 36.0 Totals In summary, H L A does contribute to multiple sclerosis susceptibility as demonstrated by its consistent association. However, its overall contribution to risk may be relatively small and probably epistatic as shown by the inconsistent results of linkage analysis (Table 1.4,1.5). 1 0 T r v t o l TJo.Vo 50 13 100 17* 4* 35* 18 10 40 18 103 9 5 20 12.07 1.17 23 59.2 13.7 1 1 <N 52 1 1 .) 336* 61 Author Year #Families Tvne Conclusions S ioni Alter etal. 1976 9 Parametric Zmax=3.057, 9=0.05 Y Visscher etal. 1979 11 Sib pairs X 2=13.1,p<0.001 Y 12 Parametric 2^=1.548 ,9=0.05 (dom.) N Tiwari et al. 1980 72 Parametric Z m a x =4.09,9=0.20 (rec.) Y Haile et al. 1980 40 Parametric 2^=2.41 ,9=0.10 (dom.) N Stewart etal. 1981 100 Sib pairs X 2=11.52 ,p<0.002 Y 35* Sib pairs X 2=5.74 ,p<0.05 Y Ebers et al. 1982 40 Sib pairs (X 2=1.30), 0.50<p<0.75 N Compston etal. 1982 103 Sib pairs , p=0.04(l-tailed) Y Ho et al. 1982 161 Parametric Zmax=9.08, 9=0.05 (int.) Y Goverts et al. 1985 20 Sib pairs T=0.76, n.s. N Tienari et al. 1993 23 Sib pairs X 2 =6.49, p<0.05 Y 21 Parametric ZMX=1.54,e=O.10(int) N Kellar-Wood etal. 1994 115 Sib pairs X 2 = 0 . 1 1 , p > 0 J N Tienari 1994 21 Two locus 2^=10,26 ,9=0.001 Y etal. Parametric (dom. & int) 1.5.2. TCR B Chain Polymorphism The T cell receptor is a transmembrane glycoprotein found on T lymphocytes. Its function is to recognize and bind to the H L A complex. It thus mediates the immune response to foreign antigens presented by the H L A complex. The T-cell receptor is composed of two pairs of polypeptide chains, the a and the 6 chain, the genes for which are located on chromosomes 14q and 7q respectively (Robinson et al. 1992). 62 The first association of M S with the T C R B chain was reported by Beall et al. 1989 (Table 1.6). The region was studied because of evidence in the experimental allergic encephalitis ( E A E ) mouse model suggesting a role of T c R genes in demyelination (Mokhtarian,1984). Haplotypes as defined by restriction fragment length polymorphisms (RFLP's) for the variable and constant region of the p gene were compared in chronic progressive (CP) M S patients and 100 healthy Caucasians. The relative risk conferred by the T C R haplotype was 2.96, p=0.055 (close to significance). However 84% of the M S patients were H L A DR2+. It is important to recognize the possible confounder effect of H L A DR2+ as the H L A locus has also been shown, consistently, to be associated with M S and the H L A DR2+ locus was not controlled in the 100 healthy individuals. The analysis was redone focusing on the 32 DR2+ patients with 43 DR2+ controls. The relative risk was 3.22 and significant,p=0.015, suggesting a possible interaction between H L A and the T C R loci in M S susceptibility. Another study investigating the T C R P chain was done by Fugger et al. (1990) who focused on 37 relapsing-remitting (RR) patients instead of chronic progressive patients. Fugger and co-workers also stratified their sample with respect to DR2+. They failed to observe a significant association in contrast to the result of the study by Beall et al. (1989). Hillert et al.(1991) also failed to demonstrate a significant association between M S and T C R p. Their study sample consisted of both R R patients, 23% (23/100), and C P patients, 77% (77/100). Even when the study sample was stratified according to clinical type and H L A DR2+, the data failed to confirm the first positive association. 63 Martinez-Naves et al.(1993) undertook the same investigation. However, their study consisted of an over-representation of RR patients, 89% (86/97) and 11% (11/97) were CP M S patients. The association was significant, RR = 7.6, yet when only H L A DR2+ patients and controls were used, the association lost its significance. This loss of significance may have been due to the loss of sample size (from 100 to 34 patients) or, as suggested by the authors, perhaps the TCR had a stronger effect in the absence of H L A DR2+ (the study did not look at DR2- patients/controls). Despite the above reports, Beall et al.(1993) re-confirmed the results of their original study, using 83 chronic progressive M S patients and 197 controls (Table 1.6). They also looked at H L A DR2- controls and H L A DR2-patients. When this sample was used, there was no significant difference in TCR P haplotype distribution between controls and patients (contrary to the results of Martinez-Naves et al.,1993). Recently Wei et al.(1995) found no association using CP arid RR patients. Their study was unique as they used 14 polymorphic markers along the variable region of the TCR genes compared to the three RFLP's used in previous studies. How can these conflicting results be explained or interpreted? The first issue for consideration for the lack of reproducibility are the differences between study populations. As these studies are comparing haplotypes, population frequencies may vary. For example the positive findings were from Spanish and American populations while the negative associations were from Swedish, Danish, and Belgian populations. A second consideration is the difference in clinical phenotype. The positive associations were from populations that were chronic progressive (either primary or 64 secondary). It should be noted that the type of chronic progressive course (ie. 1°CP or 2°CP) was unspecified in the studies by Beall et al. 1989;Beall et al.,1993;Martinez-Naves et al.,1993;Wei et al.,1994, and Vandevyver et al.,1994. It has been suggested that primarily chronic progressive MS is a different disease than relapsing-remitting (which proceeds to secondary chronic progressive), (Olerup et al. 1989). Age of onset, initial symptoms and mortality are different between these two clinical types (Verjans et al. 1983). Olerup et al. (1989) also observed different H L A associations between relapsing remitting (RR) and primarily chronic progressive (PCP), i.e. PCP showed associations with DR4, DQw8, DR7, Dqw9, Dqw6 and DRw8 haplotypes while RR was associated with Drwl7, Dqw2 H L A haplotypes. Three linkage studies also investigated the co-segregation of TCR haplotypes and MS. Sebouen et al. (1989) provided the only positive result (Table 1.6). Forty affected sib pairs with RR M S were examined. Three sib pairs did not share any haplotypes; 22 pairs shared one haplotype; and 15 pairs shared both haplotypes. This was significantly different than the 10,20, 10 haplotype sharing distribution expected, p<0.004. Lynch et al.(1991) performed a lod score linkage analysis using 14 multiplex MS families. Penetrance values were set from 0.1 to 0.7 and dominant and recessive models were used. Allele frequencies were based on the varying penetrance, mode of inheritance, and population frequency. The maximum lod score was -4.59 for the dominant model and -8.20 for the recessive model. These results clearly excluded TCR B alleles being linked to M S in multiplex families. Wood et al. (1995), using the affected sib pair method, found that the degree of sharing of TCR 6 alleles identical by descent was not significant. However, when the sib pairs were limited to only H L A DR2+ pairs, there was a significant degree of TCR P variable chain allele sharing between sib pairs. The results support an epistatic role for TCR alleles dependent on the presence of H L A alleles (contrary to the results of Martinez-Naves et al (1993)). If the TCR alleles act independently to increase susceptibility, it is likely that the contribution is negligible. To further demonstrate the negligible effect of TCR alleles, one can use the equations of Risch (1987). If the age-corrected sib risk is 3.89% (Sadovnick, 1988), and the population prevalence is 0.1% (Sweeney et al,1986), then A, s i b s is 38.9 (i.e. A, s i b s . is sib risk/population prevalence). The study by Sebouen et al.(1989) found that 3/40 study patients did not share any haplotypes. Since the probability of two sibs being affected and sharing 0 alleles is given by P (oaiieies)= 0.25/ Xsibs, (Risch,1987), one can calculate >l s i b s from this equation. The A, s i b s = 0.25/3/40, equals 3.33. This is much less than the recurrence risk ratio of 38.9 derived from the literature. This suggests that the TCR locus accounts for less than one-tenth of the sib risk (though this does assume a single locus mode of inheritance). If an epistatic (multiplicative) model of inheritance is assumed it is also possible to calculate the residual risk to sibs due to the unspecified loci (or environmental effects). This is given by A, s i b s = ( AA s i b s )*( X B s i b s ) (Risch, 1987) where XBsihs is the sib risk conferred by the other locus and A A s i b s is the risk to sibs conferred by the original locus. Therefore, A-Bsibs = 38.9/3.33 = 11.68. This residual risk is much 66 greater than the risk from just the T C R B alleles alone, demonstrating that T C R B is not a major susceptibility allele. Taken together, the evidence that a T C R B gehe(s) has an effect on disease etiology/susceptibility is not convincing. The evidence suggests that the contribution, i f any, would be relatively small and act epistatically with the H L A locus. Table 1.6.- Studies of T C R B (7q) and Multiple Sclerosis Author Year Studv Patients Controls Region Result Bealletal . 1989 Association 32 CP patients 43 N.Am. Caucasions Vb8,Vbl l ,Cb Significant Charmley 1991 Association 32 CP 70 N.Am. Vb8, V b l l Significant et al* patients Caucasions Fugger 1990 Association 37 RR 99 Danish Vb8, V b l l N.S. et al. patients Caucasions Hillert 1991 Association 23 RR + 100 Swedish Vb8,Vbl l N.S. et al. 77 CP patients Caucasions Seboun 1989 Linkage 40 RR — Vb97 Significant etal. ASP sib pairs Lynch 1991 Linkage 14 multiplex Vb8 ,Vbl l N S . etal. Classical families Martinez- 1993 Association 86RR + 93 Spanish Vb8, V b l l Significant Naves et al. 11 CP Caucasians Wood et al. 1995 Linkage ASP 127 sib pairs — Vb8, V b l l N.S. Beall et al. 1993 Association 83 CP patients 197 N.Am. Caucasians Vb8 ,Vbl l Significant Vandevyer 1994 Association 71 CP 67 Belgian Vb8 ,Vbl l N.S. e ta l . patients Caucasians Wei et al. 1995 Association 48 patients (CP + RR) 60 N.Am. Caucasians 14 Vb polymorphisms N.S. Conclude: T C R B chain polymorphisms contribute relatively little to M S susceptibility. Any contribution is dependent on the presence of other loci. *-The study by Charmley et al. (1991) used patients of Beall et al (1989) to further localize the associating region 67 1.5.3. TCR a Chain Polymorphisms There have also been many investigations attempting to associate TCR a alleles with multiple sclerosis (Table 1.7.). Oskenberg et al. (1989) reported a positive association with variable region polymorphisms using 28 MS patients and 140 controls. They also reported that the variable region polymorphisms were restricted in the brain tissue of three CP (chronic progressive) patients (Oskenberg et al. 1990). However, Hillert et al. (1992), and Hashimoto et al.(1992), in separate association studies, both failed to demonstrate an association with TCR a polymorphisms. Hashimoto et al. (1992) also performed a linkage analysis using 30 sib pairs and found no evidence for cosegregation. Lynch et al.(1992) did a similar linkage study to their TCR B study and also failed to find evidence for linkage. Taking into account these linkage studies and the results of the larger unbiased association studies, a MS susceptibility locus in the TCR a region is unlikely. Table 1.7. Studies of TCR a (14q) and Multiple Sclerosis Author Year Study Patients Controls Region Result Oskenberg 1989 Association 76 MS 120 N.Am.& V A 12.1 ,C A Significant. etal. patients Austr. Caucasians Oskenberg 1990 Association 3 CP 3 control V A 12.1,CA Significant. etal. brains brains Hillert 1992 Association 23 PCP + 100 Swedish C A N.S. etal. 83 RR Caucasians Lynch 1992 Linkage 14 multiplex V A , J A , C A N.S. etal. classical families Hashimoto 1992 Association 94 MS 97 Can. VA1.2,6.1,6.2 N.S. etal. Caucasians r Linkage 30 sib — VA1.2,6.1,6.2 N.S. ASP pairs Conclude: TCR a polymorphisms do not contribute to MS susceptibility. 68 1.5.4. Myelin Basic Protein (MBP) Myelin basic protein is a major constituent of the myelin sheath which is destroyed during episodes of inflammation in the central nervous system. Because of studies with the experimental allergic encephalitis (EAE) mouse model, it has been proposed that there is an autoimmune reaction against the myelin protein (Waksman,1985). This is also supported by the observation that T cells in M S patients are specifically reactive to M B P , (Olsson et al.,1992). As a result, M B P gene polymorphisms have been investigated as potential MS susceptibility alleles. The first association with the myelin basic protein gene and M S was performed by Boylan et al.(1990). To avoid the confounding effects of ethnic stratification, the 65 patients were all of Northern European descent. A l l the patients were over the age of 50 to minimize the remaining risk for unaffected individuals. This is an important consideration especially i f results are to be truly considered negative. The association analysis was possible because of a polymorphism 5' to MBP. Using an RFLP analysis, it was found that the M S patients differed significantly in the 5' region. Twenty-nine M S patients had a larger fragment polymorphism compared to the 12/63 controls, p<0.005. This finding was confirmed two years later by another group (Tienari et al. 1992) using the homogeneous Finnish population which has a high population prevalence of MS. The results of the association analysis demonstrated an association with an M B P allele compared to the controls. This differed slightly from the Boylan et al. (1990) data as the association was not with the "largest" allele (as the Boylan et al. study found), but with the second largest allele. This may have been due to the resolution differences in 69 the different techniques used. Tienari et al.(1992) used PCR to amplify the D N A 5' to the M B P gene followed by RFLP analysis, while Boylan et al. (1990) used only the RFLP analysis. The linkage analysis (Tienari et al.1992) used 21 families and assumed a dominant model with reduced penetrance (0.05). The maximum lod score was 3.42 with a 0=0.001. These two studies (Boylan et al. 1990;Tienari et al. 1992) are the only positive findings of the M B P association and linkage to MS to date. The next set of studies failed to confirm M B P association (Table 1.8). Rose et al. (1993) used PCR only and identified 8 alleles 5' to exon 1 of the M B P gene. Their method allowed the polymorphisms to be more accurately resolved compared to previous studies. The investigators performed a linkage analysis on 14 American families. With varying modes of inheritance and penetrance estimates, the lod score ranged from -1.22 and -13.32. This study failed to provide evidence that M B P polymorphism is linked to MS. Graham et al. (1993) repeated the association study of Boylan et al. (1990) and Tienari et al.(1992) in the Northern Irish population using the resolution technique of Rose et al.(1990). Observing the alleles at two M B P marker loci, the researchers failed to observe an association between 106 controls and 122 MS patients. Eoli et al. (1994) performed an association study as well as parametric and non-parametric linkage studies in an Italian population. They used 54 sporadic MS cases and 18 familial MS cases and compared them to 55 controls. The familial cases consisted of at least one first degree relative and in two of the families a third degree relative was affected. The investigators also failed to demonstrate an association. For the linkage study, the 18 families were 70 used under dominant and recessive modes of inheritance and varying penetrance. Lod scores varied from -22.3 to 0.20 for both models, providing little or no evidence for linkage. The affected sib pair analysis used 11 sib pairs. No significant results were observed. They used the "lower" resolution technique and yet still failed to observe an association or linkage of M S with MBP. Taking the results of all the studies together (Table 1.8), there is little evidence to support that the M B P locus contributes significantly to M S susceptibility. However, it should be noted that the study by Tienari et al. (1992) used the homogeneous Finnish population. It is possible that this population is not representative of the Northern European MS population with respect to the major loci contributing to M S susceptibility, i.e. perhaps the Finnish population is genetically different. Table 1.8.-Studies of M B P (18q) and Multiple Sclerosis Author Year Population Type of Study Outcome Boylan et al. 1990 Canada Association Significant Tienari et al. 1992 Finland Association Significant Linkage Significant Graham et al. 1993 Ireland Association N.S. Rose et al. 1993 U.S.A. Linkage N.S. Wood et al. 1994 U.K. Association N.S. ASP N.S. Eoli et al. 1994 Italy Association N.S. Linkage N.S. ASP N.S. Conclude: M B P does not contribute to MS susceptibility 71 1.5.5. Immunoglobulin Heavy Chain Immunoglobulins are found on B cells and secreted as antibodies. Their function is to recognize and bind to foreign antigens, triggering the immune response. B cells bound to a foreign antigen secrete other antibodies which target the foreign antigen. Once these antibodies bind to the foreign proteins, other cells are recruited to destroy the potentially harmful antigen. A n immunoglobulin is composed of two chains, a heavy chain and a light chain. The heavy chain is divided into two main domains, the constant region and the variable region. It is the variable chain which binds to antigen and the constant region which defines the specific function of the antibody. Different constant regions define an immunoglobulin isotype while polymorphisms within an isotype are termed allotypes. 1.5.5.1. Immunoglobulin Heavy Chain Constant Region Evidence to suggest that immunoglobulins may be involved in multiple sclerosis is that immunoglobulin G (an isotype) has been observed to be restricted in the cerebrospinal fluid of MS patients (Link et al.,1977) and that autoantibodies may be involved in the destruction of myelin (Ollson et al.,1990). The first published association of M S with the imunoglobulin heavy chain was with the Gm(l,17:21) allotype (Pandey et al.,1981). The allotype was found to confer a relative risk of 3.6 in 55 MS patients compared to 906 Caucasian controls. Gm(l) and Gm(17) are located on the IgGl constant region and Gm(21) on the IgG3 constant region. However they are transmitted together due to the strong disequilibrium present in the Caucasian population (Pandey et al. 1981). Other evidence for an immunoglobulin gene 72 contribution to M S susceptibility was demonstrated using 40 M S patients and 1220 healthy controls (Propert et al.,1982). The investigators found a significant increase in the frequency of Gm(l) and Gm(l,2) allotypes in the patients compared to controls. The allotypes Gm (1,3:5), Gm (1,2,3:5) and Gm (3;5) were observed with equal frequency in controls and patients. However because these allotypes were not found in the patients who were positive for Gm(l) and Gm(l,2) allotypes, it was concluded that the Gm( 1,3:5), (1,2,3:5) and (3:5) allotypes act to "prevent" M S in the control group. Sandberg-Wollheim et al.(1984) did a similar investigation to that of Propert et al. (1982) comparing Gm allotypes in 64 M S patients, 51 patients with another neurological disorder, and 97 healthy controls. They observed a significant increase in Gm(l,2) and Gm(21) allotypes, which was in agreement with the results of Propert et al.(1982) and Pandey et al.(1981) They observed no association with IgG levels and clinical course, however they did observe that M S patients with the Gm( 1,2:21) haplotype had increased IgG levels in the CSF. There were 10/64 patients who had a family history of MS and these patients were shown to have increased Ig levels compared to the patients with no family history of MS. A large French study tested IgG allotypes among 155 M S patients and 373 healthy controls (Sesboue et al. 1985). The researchers observed no IgG allotype distribution differences between the patients and controls. In addition, when they separated the patients by sex and disease severity, they also observed no differences. The investigators suggest that the contradictory results may be due to the differing distributions of Ig allotypes between populations. Salier et al. (1986) also looked at the French population. 73 Their study consisted of 147 M S patients and 226 geographically-matched controls and involved typing for immunoglobulin allotypes and HLA. When Gm phenotypes were considered alone, there was no association. When H L A was also considered, a protective effect from Gm(23) and H L A B35 was observed, while individuals with H L A DR2 and Gm(l) demonstrated a significant relative risk of 4.3. The investigators also examined clinical severity and found that severe MS was associated with H L A B7 and Gm(l)/Gm(2) while mild M S was associated with H L A B12 and Gm(3)/Gm(3). Another study investigating a potential HLA/IgG interaction used a homogeneous Scottish population (where immigration and admixture was thought to be minimal). When the researchers H L A typed their sample they found a significant difference in H L A Dqwl antigens in their MS group compared to controls, RR = 2, (Francis et al.,1986). When the researchers investigated the risk to individuals with both the Gm allotypes (3;5,10,11,13) and H L A Dqw 1, the relative risk increased to 3.3. When neither Gm or H L A determinant was present, the risk decreased to 0.7. They concluded that the Gm allotype and H L A Dqwl interact to increase risk. The next study investigated the association between immunoglobulin Gm and MS using RFLP's (Gainer et al.,1987). The previous studies had used serological tests which were able to detect 23 IgGm allotypes. Using RFLP's over 40 genotypes at the IgGm locus could be delineated (thereby improving resolution). The study consisted of 59 patients from Europe and America (two different continents!?) and 140 controls just from America. The strongest association was with IgG3 genes, (rather than the IgGl (as seen in previous studies, Pandey et al.,1981;Propert et al.,1982;Sandberg-Wolheim et al.,1984). 74 This positive association highlights an important consideration when choosing patients and controls for association studies. There is a high degree of ethnic variation in the frequency of Ig Gm allotypes (Grubb, 1970;Francis et a l , 1976) In fact, common allotypes such as Gm(l,17:21), Gm(l,2,17:21) and Gm(3;5,10,l 1,13,14) show a complimentary frequency gradient as one proceeds north/south in Europe. The Gm( 1,17:21), Gm( 1,2,17:21) are common in the north and decrease in frequency in the south, while Gm(3;5,10,l 1,13,14) is common in the south and less frequent as one proceeds north (Johnson et al.,1977;MacDonald et al.,1985). Certain Gm allotypes also show an east-west gradient in Europe (Grubb, 1970). Taking this into account, the positive results of Gaiser et al. (1987) are questionable as they may be due to population stratification.. Hillert et al. (1993) found no significant association between M S and the IgG constant region in a study of 60 Swedish MS patients and 87 controls. The patients were subgrouped into RR (relapsing-remitting) and PCP (primarily chronic progressive) and H L A DR2+/DR- comparison groups. Y u et al.(1993) also failed to detect an association between M S and IgG. They also did an IBD analysis of 20 sib pairs which was also not significant. Taken together, the lack of consistent results from association studies of the constant gene polymorphisms and MS, the possibility of population stratification, and the failure to detect linkage at this locus, lead to the conclusion that the immunoglobulin constant region polymorphisms contribute little to risk (Table 1.9.). 75 1.5.5.2. immunoglobulin Heavy Chain Variable Region Walter et al. (1989) compared the allelic distributions of 15 immunoglobulin heavy chain constant and the variable gene segment polymorphisms between 2 MS patient groups and a control group (Table 1.9). A significant association was observed in the second segment of the heavy chain variable region in a sporadic M S group and a family history M S group. This variable gene segment (VH2-B5) is in close proximity to the constant region. This was the first study investigating the immunoglobulin variable gene clusters. Hashimoto et al. (1993) compared the VH2-B5 polymorphism in 135 M S patients (familial + sporadic) and 92 healthy Caucasians controls. The results demonstrated an association between the variable region and M S , p=0.001. The investigators followed-up this analysis with an affected sib pair linkage analysis. Using 33 affected sibs, no difference in haplotype sharing (than what would be expected by chance) was found. This suggests that the Ig variable region has a relatively small contribution to MS susceptibility. The work of Hashimoto et al. (1993) and Walter et al. (1990) used the same patient samples. Therefore an investigation from another independent group is required to confirm the issue of an association with the variable gene polymorphism and multiple sclerosis. 76 Table 1.9. Studies of the Immunoglobulin Heavy Chain (Ho) and Multiple Sclerosis Author Year Studv Determinant Outcome 1981 Association Gml,17;21 Significant 1982 Association G m l , Gml,2 Significant 1984 Association Gml,2;21 Significant 1985 Association Glm,G2m,G3m N.S. 1986 Association Glm,G2m,G3m N.S. 1986 Association Glm(l,2,3,17) N.S. G3m(5,10,ll,13,i4,21) N.S. 1987 Association Constant region Significant 1989 Association Constant region N.S. Variable region Significant 1993 Association Variable region Significant ASP Variable region N.S. 1993 Association Constant region N.S. 1993 Association Constant region N.S. ASP Constant region N.S. Pandey et al. Propert et al. Sandberg-Wollhi etal. Sesboue et al. Salier et al. Francis et al. Gaiser et al. Walter et al. Hashimoto et al. Hillert et al. Y u et al. Conclude: The constant region polymorphisms do not contribute to susceptibility, and the variable region polymorphisms need confirmation from an independent group. 1.5.6. Tumor Necrosis Factor TNFa polymorphisms (present at the H L A locus) may also be potential candidates for MS susceptibility alleles. The evidence has come primarily from research investigating the role of TNFa at the CNS lesions of M S patients (Selmaj et al.1991) and the increased levels of TNF in the patient cerebrospinal fluid and sera (Shareif et al.1992). Fugger et al.(1990) did an RFLP analysis in a group of 41 R R M S patients and 131 healthy Danes. The restriction enzyme detects two alleles of the TNFa gene. No association of M S with the TNFa polymorphism was observed. It should be noted that 77 the use of this RFLP was relatively uniriformative and did not exclude the possibility that there was a difference in the TNFa gene in MS patients and healthy controls. Recently another group set out to identify polymorphisms in the TNFa gene in a population-based study of 78 MS patients (Weinshenker et al. 1995). They observed 23/78 had a specific TNF polymorphism, with a G—»A transition in the promoter region of TNF that functions to control the constitutive and inducible expression of TNFa. The significance of the 23/78 patients was lost as no control group data were presented. No association of MS severity and clinical course with the promoter polymorphism was observed. No control group was mentioned. To date, the possibility of TNFa being a susceptibility locus for MS, is speculative. 1.5.7. Interleukin-1 Receptor Agonist Another recent association is with II-1 receptor agonist. Crusius et al.(1995) examined 37 Dutch MS patients and 65 controls. Allele 2 (of the 3 allele system) showed a significant association with MS. The II-1 receptor agonist gene has also been shown to be associated with other chronic inflammatory diseases (Duff et al. 1994). More independent investigations are necessary to examine i f this association is in fact true. 1.5.8. Others In the past there have been association studies with other immune response related loci. These candidate loci have included the Tap2 locus (chromosome 6p), and the C3 locus of the compliment system (chromosome 19) and al-Antitrypsin (chromosome 14). While the Tap2 and compliment loci failed to demonstrate an association with MS (Spurkland et al.,1994;Bulman et al.,1991), the a l - Antitrypsin 78 locus association was significant (McCombe et al.,1985). Alphal-Antitrypsin is a proteinase inhibitor which has been shown to have an effect on demyelination (Cammer et al.,1981). However an investigation by Francis et al.(1988) failed to demonstrate an association using 125 M S patients and 68 controls. They also investigated the potential interaction of a-antitrypsin with Gm/HLA haplotypes. They failed to demonstrate this interaction, and argued against an epistatic role for the protease inhibitor genes in MS susceptibility. 1.5.9. Summary of the Search for Candidate Susceptibility Loci It is quite evident that association and linkage studies have failed in their attempt to define a major susceptibility allele for multiple sclerosis. The M B P locus, the Ig constant region polymorphism, and the TCR loci do not contribute significantly to risk. The interleukin-1 receptor agonist locus, the immunoglobulin heavy chain variable region, and the TNF need further study. The H L A locus is the only locus that seems to be involved in MS susceptibility (Table 1.4.,1.5), yet its relative contribution is small and probably epistatic. Without population studies (Sadovnick et al.l992,1996;Ebers et all996) the conclusions based upon molecular investigations would suggest a minimal genetic contribution to multiple sclerosis. However, population studies indicate that there are loci, not yet identified, that contribute to overall risk. With this in mind, molecular geneticists have undertaken genomic screens for MS susceptibility alleles. 1.5.10. Genomic Screens Three genomic screens for MS susceptibility alleles have recently been reported in Nature Genetics in August (Ebers et al.,1996;Sawcer et al.,1996;Haines et al.,1996). Ebers et 79 al.(1996) utilised three data sets of 100,44, and 78 affected sib pairs as part of the Canadian Collaborative Project on the Genetic Susceptibility to Multiple Sclerosis. A single point analysis of the first data set, using 257 markers with a mean distance of 15.2 cM, yielded 5 loci with maximum lod scores (MLS) greater than 1.0 on chromosomes 2,3,5,11, and X. Multipoint analysis then confirmed the possibility of linkage to these 5 loci. The greatest evidence for linkage was with chromosome 5p which provided a MLS of 4.24 (the pointwise analysis) and 3.1 (the multipoint analysis). Using this first data set, evidence for linkage with H L A was negative. However, because of the a priori evidence for the involvement of the H L A locus, the second and third data sets were used to further investigate the possibility of linkage to the H L A locus. Marker sharing increased at the H L A locus from 54% to 62% and 60% with these two datasets, and the combined MLS for 6p21 reached a maximum at 0.65. Next, Ebers et al. tested the H L A locus for linkage disequilibrium using the transmission disequilibrium test. Interestingly, the results were only significant for a marker 4cM distal to the H L A complex. The Multiple Sclerosis Group (a collaborative investigation of American and French Researchers) performed their genomic screen using three linkage programs (Haines et al.,1996). They used SimlBD (to test affected-relative pairs), Fastlink (for lod score analysis), and Sibpal (to test affected sib pairs). For a marker to be considered "possibly linked" the marker had to test positive on at least 2 of 3 tests. To be considered positive for a test an arbitrary score of greater than 1.0 had to be attained for the lod score analysis and significance values corresponding to less than 0.05 for the affected sib pair and the relative pair analysis. Using 443 markers and 52 multiplex families (with 81 affected sib pairs), they 80 identified 19 regions on 14 chromosomes where potential susceptibility alleles may be located. As did Ebers et al.(1996), Haines et al. (1996) further investigated the H L A locus because of the a priori evidence and the H L A markers had scored positive on two of their three tests, (it was not significant for the affected relative pair test). Using an additional 45 sib pairs and three markers, the researchers attained a M L S of 3.57 in a multipoint analysis for an H L A marker. The third complete genome screen was performed by Sawcer et al. (1996). They used 311 markers in 129 families and also identified 19 areas of potential linkage. These regions had maximum lod scores that corresponded to significance levels of 5%. Six of these regions had an MLS greater than 1.8. This relatively high level of MLS was estimated to occur only once by chance in a complete genomic screen. In the next stage of their investigation, they typed 32 markers in the 6 regions of an additional 98 families. Linkage was detected on chromosome 17q22, MLS=2.7 and on chromosome 6p21, MLS=2.8. They also tested for linkage disequilibrium in these two regions with 9 markers. Only the TNFa marker of 6p21 (HLA) showed significant linkage disequilibrium with MS. A Finnish group also reported a putative susceptibility locus after their screen of human chromosomes syntenic for susceptibility regions in the E A E mouse (Kuokkanen et al. 1996). The regions that were comparative to the E A E susceptibility regions were located on chromosomes Xql3.2-q22, Ip22-q23, and 5pl4-pl2. Two-point linkage analysis was performed using 49 markers. A maximum lod score of 3.40 was reached for 5pl4-pl2, the recombination fraction (9) was 0.10, and a dorninant mode of inheritance with 0.76 81 penetrance was assumed A sib pair analysis also yielded positive results. A level of significance was not reached for any markers on chromosomes 1 or X . Table 1.1ft Areas of Potential MS Susceptibility Alleles Ebers et al. Sawcer et al. Haines et al. Kuokkanen et al. 2p(D2S119) 17q22 (D17S942)0 5ql3-23(D5S815)x 5pl4-pl2 (D5S416) 3 (D3S1261) 6p21 (D6S276)D 7q21-22 (D7S554)* 5p(D5S406) lcen(DlS236) 7q32-34(D7S523)x 10q(D10S212) 5cen (D5S427) l l p l5 (DHS922) x l lq(DllS2000) 7p(D7S629) ,12q24-qter(PAH)x Xp(DXS1068) 14q(D14S8292) 19ql3(APOC2) x 6p(D6S461)* 12p(D12S364) 22a (D22S282) 6p21 (D6S273)XX •showed some evidence of linkage, MLS=0.65, (only upon further investigation) D Relative to the other markers in the Sawcer et al. study, these two markers demonstrated the greatest evidence for linkage x These markers tested positive for 3/3 tests,xx tested positive for 2/3 test. The results of the genome screens (Table 1.10) tell us that many genes are potentially contributing to MS susceptibility and that there most likely is not one single locus contributing significantly to overall risk. Ebers et al. (1996), in searching for a locus contributing a A.s>3, excluded 88% of the genome and Sawcer et al., excluded 93% of the genome when searching for a locus contributing a A.s>5. There is a noticeable lack of 82 reproducibility between studies. Only 2 regions, of the 19 regions (most suggestive of linkage) shown in Table 1.10. showed any constancy between the studies, they were the HLA locus and the markers on 5p. The H L A locus demonstrated (some) linkage in the three complete screens. However, Haines et al.(1996) and Ebers et al.(1996) could only show this linkage when they re-investigated the region as it was not significant in their initial screens. Ebers et al.(1996), even upon further study, only attained an MLS of 0.65 for the HLA locus. The only other region that showed any reproducibility between studies was chromosome 5p. Ebers et al. (1996) used a marker that was approximately 15cM away from the marker used by the Finnish group (Bell et al. 1996). This region definitely warrants further investigation. There were other regions which demonstrated relatively weaker evidence for linkage compared to those markers presented in Table 1.10. Of these, there were some markers where the evidence for possible linkage was reproduced between studies. These were for markers at regions lp, 2p, 3q, 5q, 7q, 19q, and X p (Ebers et al.,1996;Haines et al.,1996;Sawcer et al.,1996). 1.6. Mouse Models for M S Experimental allergic encephalomyelitis (EAE) develops in a variety of laboratory animals, (guinea pigs, primates, rabbits, rodents) that have been inoculated with a CNS homogenate and Freunds adjuvent (an oil that emulsifies protein). After 9-21 days, these acute E A E animals become weak, paralysed and eventually die. A chronic relapsing E A E animal model has also been developed for certain strains of susceptible rat and mice. Only a specific peptide of MBP will induce this E A E form which more closely resembles the MS 83 phenotype (Jansson et al.,1991). The chronic E A E model also resembles multiple sclerosis in the pathology of the CNS lesion and the lymphocyte infiltrate present (Owens et al.,1995). Nevertheless, E A E does not occur spontaneously (as MS does), the animal must always be inoculated with the M B P and/or myelin proteolipid apoprotein mixture or by the adoptive transfer of M B P restricted T cells from an affected mouse. There are many genes involved in the model rodents susceptibility to EAE. M H C alleles (eael) are strongly associated with E A E and recently, two non-MHC regions have been linked to E A E , eae2 and eae3 (Sundvall etal. 1995). There are now transgenic mouse models of EAE. Govermann et al. (1993) constructed a strain of mouse that expressed a T cell receptor specific for MBP. These mice developed E A E i f they were inoculated with MBP, Freund's adjuvent, and pertussis toxin. Interestingly, if the mice were housed in non-sterile cages, a spontaneous E A E was observed. Lafaille et al. (1994) created a TCR transgenic mouse line that was also deficient in Ragl-(Ragl is responsible for the expression of rearranged TCR gene products). As a result, only the TCR transgene expressed its rearranged TCR gene product. One-huridred percent of these mice also developed spontaneous EAE. Fourteen percent of the TCR transgenic mice that were Rag-1+ also developed spontaneous EAE. The results imply that the M B P specific CD4+ T cells were sufficient to develop EAE, and that there may be other populations of T cells (present in the Ragl+ mice) that provide a protective effect. Transgenic mice provide a powerful system for defining the variables involved in autoimmunity. Another animal model often used to study MS is the Theilers Murine Encephalitis Virus induced demylinating disease model (TMEV). Like EAE, the model resembles MS in 84 both pathology and immunology. It also occurs in similar E A E susceptible strains of rodent (Tienari 1994). However, unlike EAE, it requires a virus (a picomavirus) to be induced and not a protein mixture. Though this model has similarities to MS, no virus has been consistently found in multiple sclerosis patients (Souberbielle et al.,1995). 1.7. A Transmissible Agent for M S Evidence that MS is caused by a transmissible agent (e.g.virus) has come from four main lines of evidence: (1) the study of putative M S epidemics, (2) the similarity of MS to virus-induced animal models (3) the elevated viral antibody titres present in MS patients and (4) the involvement of viruses in other demyelinating disorders. An "epidemic" of MS was reported to have occurred on the Faroe Islands after WWII (Kurtzke et al.,1975). According to Kurtzke (1993), the 1,500 British troops stationed on the Faroe Islands brought and transmitted MS to the island's uninfected inhabitants. Since this initial epidemic in 1945, three additional epidemics have been assumed; each one successively transmitted from affected cohort to susceptible cohort. Using mathematical models, it was suggested that an individual between the ages of 11-45 needs 2-4 years of exposure to become infected with this MS causing agent. The Faroe Island epidemics have been criticized for many reasons. There is doubt regarding (1) the observation of zero cases of MS before 1941, (2) that the rise in incidence may have been due to improved clinical diagnosis, and (3) the biological plausibility of these epidemics. Other alleged epidemics have also been reported in Iceland (Kurtzke et a l , 1982), the Shetland Islands and the Orkney Islands (Cook et 85 al.,1985; Cook et al., 1988) and also the Key West Islands of North America (Shermata et al.,1985). Evidence that a transmissible virus may be involved in M S development is also provided by the Theilers mouse model for M S (Dal Canto et al.,1995). In this system, a picornavirus causes a persistent infection in susceptible strains of mice. This is followed by demyelination of the CNS and a progressive inflammatory disorder similar to the chronic progressive form of multiple sclerosis (Miller et al.,1995). The CNS lesions of the infected mouse show a similar pathology to the CNS lesions in humans with M S (Prineas,1984). However, as mentioned earlier, there is no consistently identified virus associated with MS. The inability to isolate a virus in M S patients is not due to a lack of trying. In the past investigators have demonstrated increased levels of many different viral antibodies in the CSF and periphery of M S patients. These antibodies have been directed against the measles virus (Gronning et al.,1993), rubella virus (Kinnunen et al.,1990), herpes virus (Challoner et al.,1995), canine distemper (Cook et al.,1995) and the Epstein-Barr virus (Bray et al.,1992). However, with each positive result, there is a subsequent negative result, (Godec et al.,1992;Cosby et al.,1989;Johnson,1994). There are also other human demyelinating diseases which are caused by (or at least consistently associated with) a known virus. Examples of this are tropical spastic paraparesis, a disease characterized by weakness and spascticity. In the past, this disorder has been confused with MS (Rice 1992). Tropical spastic paraparesis is associated with the HTLV-I retrovirus. Other CNS demyelinating disorders include 86 subacute sclerosing pancephalitis and acute disseminated encephalomyelopathies both caused by the measles virus and progressive multifocal leukoencephalopathy caused by the papovavirus (Sarchielli et al.> 1993). 1.8. A Maternal Effect and M S A potential maternal effect contributing to multiple sclerosis has never been tested before, yet the literature often suggests its presence. These effects include the intrauterine environment, early rearing environment (e.g. breastfeeding) and also mitochondrial inheritance and possible genomic imprinting. For example, James (1982) cited a DZ twin concordance rate of 15% and compared it to a full-sib risk of 1%. As the rates are significantly different, James (1982) suggested that a causative agent, acting during pregnancy, was responsible for the increased risk to dizygotic co-twins. Drachman et al. (1976) also suggested a maternal effect based upon their investigation of H L A determinants in 8 multiplex families. They failed to observe an association of H L A - A antigens with MS but, given previous evidence for an association, they suggested that the H L A antigen may be indirectly associated with M S via a maternal effect, i.e. H L A - A may affect a mothers immune response, thereby allowing the transplacental passage of viral agents. More recently, McCombe et al. (1990) reported a family with 4 generations of MS and other putative autoimmune disorders. A l l but one of the affected individuals was related to the M S index case through the maternal side of the family. The researchers suggested that H L A and other factors influenced predisposition to autoimmunity in these maternally related individuals. Ho et al. (1982) in their detailed 87 segregation and linkage analysis suggested that MS susceptibility was caused by a dominant gene linked to HLA plus another determinant (which may include the family environment, possible maternal effects, or additional unlinked loci). Not only do maternal effects encompass the intrauterine environment, but also the early environment of the infant. Some researchers have hypothesized that the lack of breastfeeding and excessive consumption of cow's milk were important factors in developing MS (Agranoff et al.,1974;Christensen,1975;Pisacane et al.,1994). Warren (1982) stated that susceptibility was influenced by the absence of Vitamin A and selenium in the first six months of an infants life. Nevertheless, this evidence for a maternal effect remains speculative, circumstantial and has yet to be directly tested. 88 Material and Methods 2.1. Canadian MS Clinic System A nation-wide system of multiple sclerosis clinics are participating in the Canadian Collaborative Project on the Genetic Susceptibility to Multiple Sclerosis (CCPGSMS). This system is made up thirteen hospital-based clinics plus the medical practice of a regional M S specialist in Newfoundland. The clinics have an estimated 16,000 active patients diagnosed with clinically definite or probable multiple sclerosis according to recognised criteria (Poser et al.,1983; Schumacher et al.,1965). It has been shown that the patient population of these clinics is representative of the Canadian M S population (Sadovnick et al.,1993;Weinshenker et al.,1989). The directors of these clinics are aware of the goals and study design of the C C P G S M S project and have all agreed to take part in this national collaboration. The principal investigators are Dr. A D . Sadovnick (Vancouver) and Dr .G.C. Fibers (London). Dr. N.J. Risch (Stanford) is the co-investigator. The study is funded by the Multiple Sclerosis Society of Canada Scientific Research Foundation and the Multiple Sclerosis Society of Canada. The clinics and their directors are as follows; 89 Table 2.1.The Canadian Collaborative Study Groun Clinic Abbreviation Collaborators Vancouver Van S.Hashimoto, D.Paty, J.Oger, A.D. Sadovnick Calgary Cal L.Metz, R.Bell Edmonton Edm K.Warren, S.Warren Saskatoon Sas W.Hader Winnipeg Win T.Auty, A.Nath London Lon D.Bulman, G. Rice, G. Ebers Toronto Tor T.Gray, P. O'Connell Ottawa Ott R.Nelson, M . Freedman Kingston Kin D.Brunet Hamilton Ham R.Paulseth Montreal M N I G.Francis (Montreal Neurological Institute) Montreal N O D P.Duquette (Hopital Notre-Dame) Halifax Hal TJ.Murray, V.Bahn Newfoundland Nfl W.Prvse-PhilliDS Bold lettering refers to the CCPGSMS's two principal investigators 2.2. Diagnosis A n 'active' patient is defined as an individual who is followed-up every 6 months to 2 years by a regional clinic neurologist specializing in the differential diagnosis and treatment of multiple sclerosis. Diagnosis of clinically definite or clinically probable M S are based upon the criteria of Schumacher et al.( 1965) and Poser et al.(1983). The Schumacher criteria (1965) for diagnosis of M S is as follows; 1) Demonstration of lesions that reflect primarily white matter dysfunction disseminated in time and space in a patient between the ages of 10 and 55 years. 2) Objective abnormalities upon physical examination. 3) At least two clear cut episodes of functionally significant worsening, each lasting over 24 hours and separated by one month. 90 Or a slow progressive development of the same pattern evolving for at least six months. 4)A competent, experienced physician must be satisfied that no other disease explains the symptoms and findings, (e.g. supratentorial and spinal tumours, or Arnold-Chiari malformation) In 1983 Poser et al., further defined the diagnosis of M S to account for the better laboratory tools available for diagnosis (e.g. MRI, C S F techniques) and to define the differences between "definite" and "probable" multiple sclerosis. The definitions are as follows; 1) Clinically Definite Two attacks with clinical evidence of two separate lesions in time and space. Or two attacks with clinical evidence of one and paraclinical evidence of another separated in both time and space. 2)Lab-Supported Definite Two attacks with a single, clinical, and/or paraclinical lesion along with oligoclonal banding. Or one attack with clinical and/or paraclinical evidence for dissemination in space and oligoclonal banding. 3) Clinically Probable Two attacks and clinical evidence of one lesion. 4)Lab-Supported Probable Two attacks and evidence of oliclonal bands and/or changes in synthesis of immunoglobulins. Poser criteria also broadened the age of onset range for M S to 10-59 years of age (Poser et al 1983). M S has however been shown to have an onset prior to age 10 (Duquette et al.1987) and after the age of 59 (Noseworthy et al.,1983;Hooge et al.,1993). These strict diagnostic guidelines have been applied to the M S "active" clinic population. Only those meeting the above criteria were included in the initial screen. Additional clinical 91 documentation confirming the definite/probable diagnosis of each index case was obtained from each clinic. A sample of the clinical documentation is given in Appendix A. 23. Screening The 16,000 "active", clinically definite/probable M S patients were initially screened by telephone interview. The interviewers were the research nurses/coordinators or trained personnel at the respective regional clinics. The overall data collection for the C C P G S M S was coordinated by the Vancouver Clinic site under the direct supervision of Dr. A .D . Sadovnick. The purpose of this initial screen was to identify patients who qualify for "eligible for study" status. The six categories of "eligible for study" for the C C P G S M S were; 1 )Index case has/had a twin 2) Index case has/had at least one half-sibling 3) Index case was adopted and/or had adopted sibs and/or had adopted children 4) Index case has/had a family history of multiple sclerosis 5)Index case has/had a spouse with multiple sclerosis 6) Index case was non-Caucasian Figure 6 provides a flow diagram of the screening process (adapted from Sadovnick et al. 1996). If it was (i) not possible to contact a patient, or (ii) they refused to participate, or (iii) they had died, the patients were classified as "unable to complete the interview". Patients who were contacted were categorized into two groups (i) those that had a completed initial screen, but did not meet any eligibility requirements, i.e. the "interviewed, but ineligible" category and (ii) those who that completed an initial interview and qualified for a substudy category. A copy of the initial screen is given in the Appendix B. 92 If the patient qualified for one or more of the above categories, a detailed questionnaire was administered by a site research nurse/coordinator or equivalent. A copy of this detailed questionnaire is given in Appendix C. Upon further questioning, a small number of "eligible" patients would fail to qualify for study, (e.g. i f a half-sib was actually a step-sib). These patients would be placed with the other interviewed patients that did not meet eligibility requirements from the initial screen. During follow-up, a patient could refuse to answer the questions or the interviewer could be unable to re-contact the patient. These patients would be placed in the "unable to complete the interview" category. Patients who qualified for study and completed biological packages were identified for the genetic epidemiology and molecular studies and thus for this thesis project. For this project, information from the adopted, half-sib, and twin packages were entered into a database, (Dbase IV). Two databases were created; the first was the "barebones" database created for this thesis. The second was a more detailed database used for all aspects of the CCPGSMS. Analysis of the thesis database was then performed for the adopted, half-sib, and twin investigations, (the ethnicity, multiplex, and conjugal studies were not part of this thesis project). For all three investigations (Adopted, Half-sib, and Twin) the index cases had to be Caucasian and meet the criteria for their specific study. Index cases qimlifying for the Non-Caucasian study were automatically excluded from the analysis. Index cases were also excluded if they could not provide information regarding sex, age, and affected status of the relatives of interest (biological or non-biological). 93 The index case is any clinically definite/probable M S patient through whom the family is ascertained, i.e. the proband or consultant. A family with multiply affected members may therefore be ascertained more than once, if the affected individuals were ascertained independently. Any affected individual with M S ascertained as a result of another affected family member was not considered an index case. Figure 6. Overview of Methodology (Adapted from Sadovnick et al.,1996) Active Canadian MS Clinic Caseload I Initial Screen Administered by Telephone Interview Interviewed Eligible Not Interviewed-& Ineligible refusal to participate or 1 could not be contacted Detailed "Bibpack" Questionnaire Administered by Telephone Interview i I Interviewed Eligible for Study & Ineligible qualify for twin, adopted, half-sib studies I Biological and Family Data Entry into Database I Analyses 94 2.4. Confirmation of Affected Relatives If a relative (biological, adopted/adoptive) was identified by the index case as "affected" with M S in the biological questionnaire, the relative was assessed at one of the fourteen regional MS clinics whenever possible. If this was not feasible (e.g. due to geographic location, death), the appropriate medical/autopsy records were obtained from a hospital and/or physician and reviewed by an MS clinic neurologist experienced in the differential diagnosis of MS. A relative was counted as "affected" only i f he/she met the criteria for probable or definite MS (Schumacher et al. 1965; Poser et al. 1983). Possible cases were not included in the numerator data. An example of the documentation we required (completed by a regional neurologist) is given in Appendix A. I was not responsible for the verification/confirmation of the reportedly affected relatives. 2.5. Adoption Study The main objective of the MS adoption study was to determine i f the familial aggregation of M S is due to genetics, to the sharing of a common familial environment or to an interaction of these two variables. To qualify for "adoptee" status, the adoption must occur before one year of age. This maximised the environmental sharing between the adoptee and the non-biological first degree relative(s). This also minimized the environmental sharing between the adoptee and the biological family. Just as an adoption that occurred after the age of one would fail to minimise the environmental sharing with the biological family, an adoption by a relative would fail to minimise the genetic sharing between the adoptee and adopting family. , an Therefore, adoptions could only occur between non-related individuals. For example,; individual who was adopted by his or her aunt was not included in the analysis. This investigation used a variation of the adoptee relative method (see Section 1.3.). In this method, the risk to biological relatives of adopted index cases is usually compared to the risk for biological relatives of unaffected adoptees. However, the resources necessary for this type of investigation are unavailable in Canada. To have a valid comparison group, a representative population had to be utilised. The comparison performed was between the number of affected adoptive/adopting (non-biological) relatives of the index case versus the expected number of affecteds derived from a representative population. This representative population was taken from the U.B.C. Multiple Sclerosis Genetic Database created by Dr. Sadovnick and her population studies group. Kaplan-Meier lifetime age specific curves (Kaplan-Meier, 1958) were calculated for first degree biological relatives of 815 consecutively ascertained, non-related, Caucasian MS patients attending the U.B.C. MS Clinic (Sadovnick et al.,1988). Each patient was interviewed and detailed family information was recorded. Lifetime age-corrected curves were calculated and applied to each category of non-biological relatives in our study. However, before the expected number of affected individuals was calculated, the lifetime risk had to be adjusted for any "remaining risk" for unaffected relatives still at risk (or who had died while still at risk) in the non-biological group. This is an important adjustment, especially when we consider that the older adopting parents (Tienari et al.,1994) will have a lesser lifetime risk to MS compared to the younger biological parents. The expected number of affected individuals was then estimated by multiplying the adjusted risk, p, by the number 96 of the adoptive/adopting (non-biological) relatives. These numbers were summed across relative (parent, sib, child) categories, providing the total expected number of affected individuals for all first degree relatives. This was compared to the observed number of affected individuals in the adoptive sample using the Poisson distribution. The Poisson distribution was used as p is relatively small and the sample size is large (Zar,1984). A rationale for this study is given in Table 2.2. To clarify, the index case did not necessarily have to be adopted to be included. He/she only had to have a non-biologically related first degree relative. For example an M S patient would be included i f he/she had an adopted sibling even though the index case was not adopted or, alternatively, if the index case was adopted and the sibling was the biological child of the adopting parents. For clarity I will use the term "adoptive" when referring to these siblings, or "adopting" when referring to the non-biologically related parents and "adopted" when referring to the non-biologically related children. The total sample of non-biologically related first degree relatives will be referred to as "adoptive". The risk of M S in the biological relatives was also investigated. This was limited to the recall of the M S patients. Biological relatives were not contacted specifically for this study due to ethical reasons. Absolutely no pressure was placed on the adopted index case to contact her/his biological relatives. This sample of biological relatives could therefore certainly not be considered unbiased. 97 Table 22 Rationale for the Adopted Study "Relationship" to If Familial Aggregation is due If Familial Aggregation the Index Case to Shared Familial Environment (Hp) is due to Genetics (H A ) Adopting Parents ~3% Adoptive Sibs -3% Adopted Children -3% 0.2% 0.2% 0.2% Where 0.2% is the population lifetime risk for M S (Sadovnick et al.,1995) and -3% is the risk to first degree family members (Sadovnick et al.,1988). 2.6. Half-Sibling Study Design The aims of the half-sib study were to (i) further determine the role of genetic factors) in the familial aggregation of multiple sclerosis, (ii) look for possible maternal effects, and (iii) detect a potential "transmissible agent". To detect a genetic component, the "relatives reared together" design was used (Tierney et al.,1994). This was chosen for two reasons: (1) the lack of an appropriate control group for the "relatives reared apart" design (Tierney, 1994) and (2) the evidence to date that that the environmental component to M S is constant among relative types (Sadovnick et al.,1992). To demonstrate a genetic effect, maximum likelihood risks were estimated for half-sibs and full-sibs of the index case (See Section 2.9). Risk estimates were then compared using the log likelihood ratio test (See Section 2.10). If the risks are significantly different then a genetic effect would be supported. If the risks are not different, then a shared familial environmental effect would be supported (Table 2.3). 98 To test for the presence of maternal effects, maximum likelihood risks were estimated for maternal and paternal half-sibs. A significant difference between the risks would imply a maternal effect while no significant difference would support an absence of maternal effects operating in determining MS risk (Table 2.4). This maternal effect was also tested by the logistic regression (See Section 2.11). The half-sib study was also used to test for the presence of a "within" family component contributing to risk. This was done in two ways. The first method was a simple comparison between the age-adjusted risk estimate for half-sibs who lived with the index case with the age-adjusted risk estimate for half-sibs who had not lived with the index case (Table 2.5). The second method accounted for variation in the "amount of time" the index case may have spent with the half-sib. These analyses were done by the applied logistic regression (SERC,1993). For example, a half-sib who lived with the index case may have lived with the index case for 10 years or only 1 year, and half-sibs who did not live with the index case may have never met the index case or they may have visited the index case every week. This variation is not accounted for in the comparison between half-sibs who have lived with the index case and those who have not lived with the index case. The amount of time a half-sib spent with the index case was ordered and separated into four equal groups based on sample size. The logistic regression "fit" the independent variable (i.e. time spent together) with outcome of M S (See Section 2.11). The statistical package, Egret, was used for this regression analysis (SERC,1993). 99 Table 2.3. Rational for Detecting a Genetic Effect using Half-sibs Hypothesis Expectation If Genetic (HA)** Half-sib* Risk < Full-sib Risk IfEnvironmental(Ho)** Half-sib* Risk = Full-sib Risk *-half-sibs that have lived with the index case **-There is an assumption that the environmental sharing between relatives is constant (i.e. the half-sibs and full-sibs share the environment with the index case to the same extent Table 2.4. Rationale for Detecting a Maternal Effect using Half-sibs Hypothesis Expectation Maternal Effects Paternal Half-sib Risk < Maternal Half-sib Risk contribute to Risk (HA) No Maternal Effects (En) Paternal Half-sib Risk = Maternal Half-sib Risk Table 2.5. Rationale for Detecting if Shared Environment Contibutes to Risk of MS Hypothesis Expectation If the Shared Environment Half-sibs Raised Apart < Half-sibs Raised Together contributes to risk (HA) If the Shared Environment Half-sibs Raised Apart = Half-sibs Raised Together does not contributes to risk (Hp) 2.7. Twin Study Design The purpose of the twin study was to (1) confirm the findings of previous population-based studies (Sadovnick et al. 1992; Ebers et al. 1986) and to (2) examine the possibility of genetic heterogeneity. 100 The zygosity of the twins was determined by self-identification, followed by DNA fingerprinting techniques done by Dr. Ebers in London, Ontario. Proband-wise and pair-wise age-corrected risk estimates for the relatives were derived using the maximum likelihood approach (See Section 2.9). Monozygotic, dizygotic and full-sib risks were estimated. Comparisons were performed using the log likelihood ratio test (See Section 2.10). A comparison between M Z and DZ maximum likelihood risks can determine if a genetic component exists for multiple sclerosis. The comparison between the full-sib and DZ twin risks will determine if a shared familial environmental component exists for multiple sclerosis. Results are given with 95% confidence intervals calculated as follows p ± 1.96Vpq/n (Armitage,1971). The proportions of multiplex families were also investigated to test for genetic heterogeneity/genetic loading. The proportions of concordant and discordant twins with multiply affected family members in addition to the index case and the affected co-twin were compared using the Z test. Maximum likelihood risk estimates were also calculated for the sibs of discordant and concordant M Z and DZ twins. The rationale of the analyses was that i f concordant twins share more liability genes their first degree relatives should have a greater risk of developing MS compared to the relatives of discordant twin pairs. Comparisons were tested with the log likelihood ratio test. 2.8. Objectives The overall objective of this thesis was to determine the role of genetics in the familial aggregation of MS. The specific aims included: 101 1) Compare MS risks for biological and adoptive relatives of M S index cases to determine whether familial aggregation of MS is due to genetic sharing, common familial environment or an interaction of these two factors. 2) Compare MS risks for full-sibs and half-sibs to determine whether the familial aggregation of MS reflects the degree of genetic sharing. 3)Compare MS risks for half-sibs raised apart and those raised together to determine the role of the shared family environment in the familial aggregation of MS. 4) Comapre MS risks for maternal half-sibs and paternal half-sibs to determine whether there is a maternal effect in MS. 5) To conduct a large population-based twin study to verify the findings of others that there is evidence for more than one gene in MS susceptibility. 2.9. Maximum Likelihood Risk Estimation If a sample to be used for a risk calculation is comprised of individuals with a younger average age relative to the onset age of the disorder, then a crude risk (#affected/total) would most likely under-estimate the true risk. Therefore, to accurately estimate the risk, one must adjust the denominator to account for individuals still "at risk", i.e. those individuals who may still become affected as they get older. This age-correction can be done in many ways and is dependent upon the data available to the researcher. The best method for the current study sample is the maximum likelihood approach. This method is used when the age-of-onset is unknown for most of the affected relatives (Sadovnick et al. 1996), as is the situation for the adopted, half-sib and twin data. This method assumes an age-of-onset distribution for the affected relatives based upon the 102 observed age-of-onset distribution of the index cases. This maximum likelihood calculation requires information about (1) a confirmed diagnosis of the index case, (2) the age-of-onset of the index case, (3) the current age of the relative or the age at death, (4) the "affected" status of the relative, and (5) confirmation of affected status, (if the relative is affected). Relatives with unknown ages, (and health status) are excluded from the analysis. Also excluded from the numerator is any "initially" identified affected relative with an unconfirmed diagnosis. The maximum likelihood approach estimates the probability that an individual is susceptible to MS, "p". This assumes that if an individual is susceptible, (and lives long enough), he/she will develop multiple sclerosis. The probability that an individual is not susceptible is "1-p". The probability that an individual will be affected by age y is given by F(y) and this is the underlying age-of-onset distribution of MS. If we let F(y;)=w •» where w,-is the proportion of affected individuals in a sample who had onset prior to age y„ then in a sample of n individuals, the probability that the ith individual (of age j / ) is observed to have MS is given by pw,-. The probability that an individual of the same age is not observed to be affected is given by l-pw ;. The likelihood for the full sample of individuals is therefore: L = II (pwi)" (l-pw,) 1 '" , (Risch, 1983) i=l where Xj is the affected status of the ith individual. If the individual is affected Xj=l. If the individual is unaffected xf=0. The log likelihood is given by, n n n log L = log p £ Xj + X XiIogWi+ S (1- Xj) log (l-pw f) (Risch,1983). i=l i=l i=l 103 To obtain the maximum likelihood estimate, p, the log likelihood is differentiated with respect to p, and then equated to zero: n n 81ogL = l(2 : x i ) - £ ( l - x j ) w j = 0 (Risch,1983) 5p p 1 = 1 1 = 1 l - p W i This equation can then be solved for p by iteration (Rao, 1973). The variance of p is derived from the information matrix (Rao,1973), I(p). n .2, I(p) = J . E(R) + 2 w,2fl-Eftfl P2 1 - 1 (1-pwi)2 (Risch,1983) Where R = Ex,-. I 2.10. Log Likelihood Ratio Test In studying the risk of MS, it is of interest to compare the estimated risks between different relative groups (e.g. full-sibs versus half-sibs). The log likelihood ratio test is used for this: A A A A LR = logL1(p,) + logL 2(p 2)-IogL 1(p 1 + 2)-IogL 2(pi + 2) (Risch,1983) A A A where p1? p2, and p 1 + 2 are the risk estimates of the first, second, and combined samples, respectively. Lj and Lq are the likelihoods of the respective samples. When this statistic is multiplied by two it approximates the X 2 distribution with one degree of freedom (Zar,1984). 2.10. Kaplan Meier Lifetime Risks The Kaplan-Meier estimation (Kaplan and Meier, 1958), or the product-limit method is often used to (i) estimate the survivor distribution in a study population, (ii) to compare 104 graphically the survivorship in two groups, or (iii) to study the effect of an intervention in a clinical trial. Two important assumptions of the Kaplan-Meier risk calculation are that (1) there are no cohort effects, (time the individual entered the study has no effect on survival), and (2) individuals who are lost to follow-up are not different from the study population, (random censoring) (Armitage,1971). The Kaplan-Meier method is non-parametric and makes no assumptions regarding the survivor distribution. The empirical survivor function (St) is a step function which reflects the observed data. The survival function (St) is given by, m S(tm) = n nrdj (Kaplan and Meier,1958) where the data set contains N relatives with M ordered and distinct onset times denoted as (ti<t2<^...tni), dj is the number of affected individuals at time tj and n} is the total number of individuals who are still at risk just prior to tj. The fraction (nj - dj)/nj is therefore the proportion of relatives who are not affected at tj. The estimated variance (cr2) for the survivor function is given by Greenwood's formula (Kaplan and Meier,1958): m °-2=S2(tm) 2 dj r l nj(nj-dj) (Kaplan and Meier,1958) 2.11. Applied Logistic Regression Applied logistic regression uses the binomial distribution to estimate the probability of observing a response (e.g. a dichotomous response such as " M S " or "no MS") given a 105 group of associated, independent variables (or covariates). What makes the logistic regression useful is its flexibility in modelling data and its biologically meanmgful interpretation (Hosmer et al. 1989). The specific form of the logistic regression, (for the case which has a single independent variable), is given as, n(x) = Efy) = e p o + p i x (x) l + e p 0 + p l x (Hosmer etal, 1989) 7t(x) is the conditional mean value of y given x. The independent variable (the covariate) is given by x, while y is the value for the outcome of interest (i.e. MS), "e" is the inverse of the natural logarithm of 1 (i.e. 2.71828...). p 0and Pi are parameters that represent the slope (PJ) and the Y intercept (P0) of the best fit regression model. The logit transformation is applied to this equation so that the model is linear, continuous, and may range between -oo to +oo. Upon transformation, TI(X) is given by g(x). g(x) = ln7rtx} = p 0 + pix 1 -TI(X) (Kleinbaum et al.,1982) again, 7i(x) is the conditional mean of y given x and Pi and p 0 are the slope and y intercept respectively. To fit the regression model, a likelihood function is required. The estimates of the parameters that "best fit" the model will maximise the likelihood function. 106 The likelihood function, 1(B), is given by, n 1(B) = II 7t(X if [ 1 - 7c(Xi)] ^ (Kleinbaum et al.,1982) i=l Expressed as a log likelihood, this expression becomes. In 1(P) = i{yt InMxO] + (1 - yd In [1 - a f t ) ] } 1 1 (Kleinbaum et al.,1982) where X; and y,- are the values of the covariate and outcome variables for the ith observation, respectively. To maximise 1(B), the equation is differentiated with respect to p 0 and Pi and set to 0. These likelihood equations are then used to solve for p 0 and pY To assess the significance of the model, the observed values of the response variable are compared to the predicted values of the regression model. In other words, the test determines whether the observed values "fit" the predicted model which incorporates the independent variable(s) or do the observed values "fit" a model which does not incorporate any covariates. The comparison is based on the deviance of a regression model which is defined as: D = -21n likelihood of the model (with or without the variable) likelihood of the saturated model (Hosmer et al.,1989) where D is termed the deviance. The saturated model is the model where each data observation is a parameter. To test between a model with the variable versus a model without the variable, the deviance for both models are estimated and then subtracted from each other: G = D (without the variable of interest) - D (with the variable of interest) 107 This can also be expressed as follows: n A A G = { £ [yj Info) + (1 - y i) In(l - *,)] - [nt In(n,) + n 0 In(n0) - n In(n)]} (Hosmer etal., 1989) A where 7tj is the maximum likelihood estimate of 7i(Xj), n t = £ y f , n0 =£(1- yi), and n is the sample size. G is the log likelihood statistic and follows a X 2 distribution, and the degree of freedom is the difference in the number of variables between the two models. The information matrix is obtained from the second partial derivatives of the log likelihood equations. The variance is obtained from the inverse of the information matrix, Z(p)=r1(P)-The applied logistic regression was performed using the Egret statistical package (SERC,1993). 108 Results For the analyses in this thesis, it was necessary to cut off the entry of new data from the Canadian centers on May 1,1996. For this reason, some of the results may not be in complete accordance with the results in the resultant publications which may include data received by the Vancouver center after May, 1996. 3.1. General Results By the thesis cut-off date, 14,990 active M S patients had been screened as part of the Canadian Collaborative Project on the Genetic Susceptibility to Multiple Sclerosis (Table 3.1) and detailed biopackages were completed for 3394 of these or 23%. There were 624 index cases initially identified as qualifying for the "adoption" study; of these, the detailed biological package was completed for 582 (93%). There were 936 individuals who were initially identified for the "half-sib" study. Table 3.1 states only 909 because 27 of the "half-sib eligible" index cases also qualified for the "adopted" study. Completed information was available for 871 of the 936 initially identified half-sibs, 93%. Two-hundred and twenty-seven individuals were identified as having a twin, and of these, there was detailed information on 200 individuals, 88%. Again, Table 3.1. presents lower numbers, 202 and 186 respectively, because individuals may also have qualified for the "adopted" and/or "half-sib" studies as well as the twin study. 109 Table 3.1. General Results Category Initially Screened Screened, but not eligible 8077 Unable to complete screen 3172 Eligible for Adopted Study 624 _,o^ Eligible for Half-sib Study 909a (936)b 858a(871)b (minus those that qualified for adopted study) Eligible for Twin Study 202a (227)b 186a(200)b (minus those that qualified for adopted and half-sib studies) Other Eligibles 2010 (minus adopted/half-sib/twin studies) Total Screened 14990 N/A N/A 582 1768 3394 a = the number of index cases uniquely eligible for this category b = the number of index cases eligible for this category who may have been included in other eligibility categories 3.2 The Adopted Study Results 3.2.1. A Description Table 3.2 gives the results of the initial screen for the MS index cases who qualified for the "adopted" study. The initial screen identified 624 individuals that had an adopted child and/or an adoptive sibling and/or adopting parents. Complete information was available for 582 of these patients identified by the cut-off date of May,1996, (93.3%). The ratio of female to male index cases is 2.4 to 1.0. Table 3.2. Individuals Qualifying for the Adopted Study Total Eligible for Adopted Study =624 Total Males =182 Total Females = 442 Total Completed Biopackages = 582 (93.3%) Total Males =169 Total Females = 413 n o Two hundred and sixteen of the 582 index cases with completed biopackages were themselves adopted, 37%. Information was available for 424 of their adopting parents, 252 of their adoptive siblings, and 12 of their adopted children. Three hundred and sixty-six index cases, who were themselves not adopted, but had adopted children and/or adoptive sibling(s). From this group of index cases there were 110 adoptive siblings and 387 adopted children, (Table 3.3). Table 33. Adopted Eligibility Totals Index Case Adopted Index Case not Adopted Total # Adopting 424 Parents # Adopted/ 252 Adoptive Sibs # Adopted 12 Children N / A 110 387 424 362 399 Total 688 . 497 1185 The number of females, 576, and males, 609, did not differ significantly in this sample; X2=0.86,0.25<p<0.50. 3.2.2. The Adopted Study Analysis The Kaplan-Meier lifetime risks for biological parents, sibs and children of MS patients are given in Table 3.4. These risks are 2.0% for parents, 3.6% for sibs, and 2.7% for children. i l l Table 3.4. Kaplan-Meier Lifetime Risks for Biological Relatives Biological Relative #Affected N Age-Adjusted Risk ± S.E.(%) Parents 30 1581 2.0*0.35 Siblings 53 2138 3.6*0.50 Children 9 1336 2.7*1.09 Using this "biological relative" dataset as a representative sample, it was possible to calculate the expected number of affected individuals per relative group in the adoptive sample (Table 3.5). For example, if the risk to an adopting parent is the same as the risk to a biological parent (2.0%) then we would expect 8.5 affected individuals in a sample of 424 adoptive parents. Corrections are necessary because of parental age. The average ages of the adopting parents is generally older than the biological parents (Susser,1992). In our study, the average ages of the biological and adopting parents, at the time of birth of the adoptee, were 28 years and 33 years respectively. Therefore, for each adopting parent, the expected risk had to be corrected for his/her "remaining risk" for M S . For example, i f an adopting parent was 40 years old their remaining risk would be the lifetime risk (2.0%) minus the risk passed by at age 40. This remaining risk (if any) is then subtracted for each individual. After doing this correction for each adopting parent in the sample, the expected lifetime risk decreased from 2.0% to 1.95%, yielding an expected 8.3 individuals to be affected in the sample of 424 adopting parents (instead of 8.5). There was only one adopting parent with definite MS. The Poisson probability of observing one affected adopting parent, given a probability of 1.95% was 0.0023, this was significant. 112 O f the 362 adoptive siblings, we expected to observe 11.22 affected individuals, (also corrected for the remaining risk). There were no adoptive siblings with multiple sclerosis observed. The adjusted probability was 3.1% for full-sibs, and given a sample size of362, the Poisson probability of observing 0 affected individuals was 1.4 X 10~5. O f the 399 adopted children of M S patients, we expected after age corrections to observe 5.68 children affected. There were no observed adopted children with M S . The Poisson probability for this group was 0.0034. When all the "expecteds" were summed over all categories, we expected to observe 25.2 M S cases in the sample of 1185. We observed only one. The Poisson probability of observing only one affected individual in a sample of 1185 was 2.98 X 10"10. This was highly significant, (Table 3.5). These results argue strongly against a shared familial environmental component (i.e. transmissible agent) contributing significantly to the familial aggregation of MS. If the familial aggregation had been a result of the shared environment we would have expected to observe the same proportion of affected "non-genetically" related individuals in the adoptive sample as the "genetically" related representative sample. 113 Table 3.5. Expected Versus O h ^ e d Number nf Active F i r s f n < y r ^ Relatives with Multiple Sclera™ Non-Biological Relatives Parents (N=424) Siblings (N=362) Children (N=399) Total (N=1185) Age Adjusted Expected Observed 8.30 11.22 5.68 25.2 1 0 0 1 Poisson Probability 0.0023 0.000014 0.0034 2.98 X 1 0 1 0 3.2.3. The Biological Relatives of Adopted Index Cases As previously stated, information on biological first degree relatives of adopted individuals was biased. The information in this section is anecdotal and should not be considered to be population-based or representative in any way. O f the 216 adopted index cases, 36 had information on their biological parent(s) and 13 had information on their biological siblings. They thus provided information on 75 biological first degree relatives (Table 3.6). O f these, there were two affected (though unconfirmed) individuals with M S . This provided a maximum likelihood risk of 3.06% (S.E. 2.13%). Table 3.6. Number of Biological Relatives of Adopted Index Cases Males Females Total Informants Biological Parents Biological Siblings 21 8 33 13 54 21 36 13 Total 29 46 75 39 114 3.3. The Half-sib Study 33.1. A Description The results of the initial screen identified 1105 index cases who had/have a half-sibling. One hundred and twelve index cases were unable to provide any information on their half-sibs (i.e. current age, health status, where they lived) and were excluded. A further 57 index cases were not Caucasian and were also excluded. This left 936 index cases initially identified as having a half-sib(s) and thus eligible for the half-sib study. Eight hundred seventy-one of these 936 index cases (93%) had completed biopackage information by the cut-off date of May 1,1996. There were 446 index cases with only a maternal half-sib(s) and 327 index cases with only a paternal half-sib(s) and 98 index cases with both maternal and paternal half-sibs (Table 3.7). Table 3.7. Index Cases Eligible for Half-sib Study Total Index Cases (with complete Biopackages) 871 (93%) Sex Males 229 Females 642 Ratio (fan) 2.8:1 Half-sib Maternal 446 Paternal 327 Both 98 The study consisted of 1913 half-sibs of 871 index cases. They included 963 males and 950 females (Table 3.8). Nine hundred and fifty-five half-sibs had lived with the index case and 958 had never lived with the index case as a family unit. An excess of maternal 115 half-sibs were included (1077 maternal half-sibs compared to 836 paternal half-sibs; X 2 = 32.2, p<0.001. This excess of maternal half-sibs included in the study probably reflects the increased likelihood of familiarity with maternal half-sibs compared to paternal half-sibs (Tierney etal., 1994). Twenty half-sibs of index cases had confirmed MS. The number of affecteds in Table 3.8 is given in brackets (). The "lived together" category are for those half-sibs who lived with the index case for at least one year. The "never lived together" category are for those half-sibs who never lived in the same household as the index case. However, they may or may not have visited the index case before the index case was 21 years old. Table 3.8. Half-sib Summary I Male 963(7) Total 1913(20) 4 Pat Mat 419(3) 544(4) I Female 950(13) Pat Mat Total 417(5) 533(8) 1913(20) Lived Together Never Lived Together 131(0) 340(3) 137(3) 347(6) 955(12) 288(3) 204m 280f2, 186(2^ 958(8) () = # with MS 116 3.3.2. Maximum Likelihood Risks for Half-sibs The maximum likelihood risk estimate for half-sibs is given in Table 3.9. The crude risk for a half-sib of an index case was 20/1913, or 1.05%. The age-adjusted risk estimate was 1.40%. The age-adjusted risk for male half-sibs was 1.03% while for female half-sibs it was 1.78%. Table 3.9. Age-Adjusted Risks for Half-sibs Relation #Affected Size Estimate% S.E. 7) Male 1/2 Sibs 7 963 1.0269 0.3864 8) Female 1/2 Sibs 13 95,0 1.7814 0.4901 9) Total 1/2 Sibs 20 1913 1.4026 0.3117 The risks to male and female half-sibs were not significantly different, G=T.8107, 0.10<p<0.25. The risk to female half-sibs being 1.78% and the risk to male half-sibs being 1.02%). As a note the sex ratio of the affected half-sibs was 1.9:1 females to males (13:7), which is approximately 2:1. The non-significance is likely due to the small numerator data. Tables 3.10 and 3.11. show the maximum likelihood risk estimate for half-sibs of male and female index cases. The risk to a half-sib of an affected male index case was 1.61% and not significantly different than the risk to a half-sib of a female index case (1.32%); G=0.1547,0.50<p<0.90. 117 Relation #Aflected Size F.stimate% S.F, 1) Maternal 1/2 Sibs 4 2) Maternal 1/2 Sisters 4 3) Maternal 1/2 Brothers 0 4) Paternal 1/2 Sibs 2 5) Paternal 1/2 Sisters 2 6) Paternal 1/2 Brothers 0 7) Male 1/2 Sibs 0 8) Female 1/2 Sibs 6 9) Total 1/2 Sibs 6 261 1.9555 0.9691 125 4.0085 1.9677 136 — — 216 1.1916 0.8381 111 2.2512 1.5758 105 — — 241 236 3.1802 1.2796 477 1.6111 0.6530 Table 3.11. Age-Adjusted Risk Estimates for Half-sibs of Female Index Cases Relation #Affected Size Estimate% S.E. 1) Maternal 1/2 Sibs 8 816 1.3125 0.4612 2) Maternal 1/2 Sisters 4 408 1.2616 0.6272 3) Maternal 1/2 Brothers 4 408 1.4164 0.7035 4) Paternal 1/2 Sibs 6 620 1.3434 0.5454 5) Paternal 1/2 Sisters 3 306 1.3387 0.7685 6) Paternal 1/2 Brothers 3 314 1.3730 0.7884 7) Male 1/2 Sibs 7 722 1.3975 0.5250 8) Female 1/2 Sibs 7 714 1.2936 0.4861 9) Total 1/2 Sibs 14 1436 1.3289 0.3531 118 3.3.3. The Maximum Likelihood Risks for Full-sibs O f the 871 index cases with half-sibs, 553 index cases had at least one full-sib for a total of 1465 full-sibs of whom 43 had definite, confirmed M S . The age-adjusted risk to these full-sibs was 3.68% (Table 3.12). When this estimate was compared to the risk to half-sibs (1.40%), the two risks are significantly different; G=T4.124, p<0.001. This supports a genetic component to M S . The risks to male full-sibs (1.99%) was significantly different than the risk to female full-sibs (5.27%); G=10.1064, 0.001<p<0.005. The sex ratio of the affected siblings was 2.9:1.0 females to males in this sample. Table 3.12. Age-Adjusted Risk Estimates for Full-sibs Index Case Relation #Affected Size Estimate% S E l )Male Brother 4 197 2.6041 1.2855 Sister 10 204 5.9875 1.8419 Total 14 401 4.3430 1.1375 2) Female Brother 7 520 1.7601 0.6603 Sister 22 544 4.9995 1.0435 Total 29 1064 3.4311 0.6280 3) Sibling Brother 11 717 1.9953 0.5963 Sister 32 748 5.2715 0.9107 Total 43 1465 3.6831 0.5527 119 3.3.4. The Risk to Half-sibs who Lived Together with Full-sibs The age-adjusted risk for half-sibs who lived together was 1.62% (Table 3.13). Living together was defined as follows: a half-sib must have lived in the same house as the index case for at least one year before the index case was 21 years of age. This risk (1.62%) was significantly lower than the risk to full-sibs, (3.68%), who have also lived in the same household as the index case; G=7.4576,0.005<p<0.01. These data again demonstrate the importance of genetics in M S etiology. Table 3.13. Age Adjusted Risk Estimates for Half-sibs (who lived together) Index Case Relation #Affected Size Estimate% S.E. l)Male Female Female 6 136 5.2994 2.1113 Male 0 126 Total 6 262 2.8258 1.1389 Female 3 348 1.0991 0.6319 Male 3 345 1.1948 0.6860 Total 6 693 1.1332 0.4604 Female 9 484 2.3311 0.7693 Male 3 471 0.8604 0.4948 Total 12 955 1.6178 0.4637 120 3.3.5. The Risk to Half-sibs who have Lived Together with Half-sibs who have Not Lived Together The age-adjusted risk for half-sibs who have never lived together is 1.17% (Table 3.14). This category included those half-sibs who visited the index case often, very little, or had never met the index case before the index case was 21 years of age. This risk (1.17%) is not significantly different than the risk to half-sibs who have lived together (1.62%); G=0.5213, 0.25<p<0.50. This shows that the shared environmental component does not contribute significantly to risk. It also justifies comparing the full-sib risk to the total half-sib risk (those half-sibs who did live with the index case plus those half-sibs that did not live with the index case). Table 3.14. Risk Estimates for Half-sibs (who have never lived together) Index Case Relation # Affected Size Estimate% S.E. 1) Female Female 4 366 1.4915 0.7403 Male 4 377 1.6011 0.7953 Total 8 743 1.5266 0.5360 2) Male Female 0 100 Male 0 115 — Total 0 215 3)Male& Female 4 466 Female Male 4 492 1.1639 0.5786 1.2011 0.5976 1 ° ^ ! 8 958 1.1692 0 4119 3.3.6. A Maternal Effect and MS Crude risks for maternal and paternal half-sibs were 1.11% (12/1077) and 0.96% (8/836) respectively. The age-adjusted risk for maternal half-sibs was 1.47% and the age-adjusted risk for paternal half-sibs was 1.31% (Table 3.15). 121 Table 3.15. Maximum Likelihood Risks for Mafr>rnal a n d Paternal HaifUih, Effected .^e Esrinmtett SP 1) Maternal 1/2 Sibs 12 2) Maternal 1/2 Sisters 8 3) Maternal 1/2 Brothers 4 4) Paternal 1/2 Sibs 8 5) Paternal 1/2 Sisters 5 6) Paternal 1/2 Brothers 3 1077 1.4726 533 1.9106 544 1.0346 836 1.3077 417 1.6056 419 1.0143 0.4222 0.6696 0.5148 0.4598 0.7130 0.5832 was no When the risks to maternal and paternal half-sibs were compared, there significant difference; (G=0.0249,0.75<p<0.90) thus providing evidence that a maternal effect (e.g. mitochondrial inheritance, genomic imprinting, an intrauterine environmental effect, breast feeding, etc.) does not contribute significantly to MS risk. It could be suggested that i f we included (or had complete information on) the paternal half-sibs excluded due to informativeness, a significant difference could be observed. In other words, those excluded half-sibs may not have been representative of the paternal half-sib population (and perhaps were all unaffected). Considering the of the paternal half-sib sample, (and that there was a supposedly affected paternal half-sib in the excluded group), it seems unlikely that a significant difference would have been observed i f the excluded half-sibs were used in the analysis. If we speculate that there were approximately 225 half-sibs excluded due to informativity, and we assume they were all over the age at risk (i.e. 60 years of age) and that none have M S (conservative) and add these to the known paternal half-sib sample, the size maximum 122 likelihood risk decreases to 0.9714% (S.E.0.3420%). This is still not significantly different from the maternal half-sib risk of 1.47% (Table 3.15);G=0.8748, 0.25<p<0. 50. 3.3.7. The Applied Logistic Regression To quantify the "amount of time" the half-sib and index case spent together before the age of 21, each index case was asked to estimate the time, in terms of years and months he/she lived with his/her half-sib(s) or the number of times they may have visited each other per year (before the index case was 21 years of age). The data were grouped into 4 approximately equal categories based on sample size. It should be noted that this information was difficult to group; often, only best estimates were possible. This information was then used in an applied logistic regression. The independent variables used in the regression were the amount of time the half-sib spent with the index case (duration), the type of half-sibship (i.e. maternal or paternal), the sex of the half-sib and the age of the half-sib. The half-sibs were grouped into 3 categories based on age: (i) <20 years, (ii) 20-39 years and (iii) >40 years. The dependent variable was risk of MS. Table 3.16 Results of the Logistic Regression d.f Model Independent Variables Deviance DO Type, Duration, Age group no convergence Sex of the half-sib DI Type, Duration, 218.428 1907 Sex of the half-sib D2 Type, Duration 220.193 1908 D3 Type, Sex of half-sib 220.174 1910 D4 Type 222.085 1911 D5 Sex of half-sib 220.313 1911 D6 Age Group 218.206 1910 D7 Duration 220.193 1909 D8 Intercept 222.218 1912 123 Table 3.17 Comparison of Models Covariate of Interest MnHek Likelihood Ratio d.f. p Value Type of half-sib D5-D3 (mat. or pat.) D8-D4 Duration D3-D1 (length of time spent D4-D2 with the index case) D8-D7 Sex of the Half-sib D4-D3 D2-D1 D8-D5 Age of the Half-sib D7-D6 0.139 0.133 1.746 1.892 2.025 1.911 1.765 1.905 4.012 3 3 3 0.50<p<0.75 0.50<p<0.75 0.50<p<0.75 0.50<p<0.75 0.50<p<0.75 0.10<p<0.25 0.10<p<0.25 0.10<p<0.25 0.10<p<0,25 The results of the logistic regression (Table 3.16,3.17) confirm the absence of maternal effects (0.50<p<0.75). They also confirm the observation that the shared environment does not contribute significantly to risk (0.50<p<0.75). However, sex of the half-sib could not be used to predict M S outcome (0.10<p<0.25). Nevertheless it is known that the female to male ratio of M S approaches 2:l(Sadovnick et al.,1992). The log likelihood ratio test could also not detect a significant difference in male and female maximum likelihood risks; G=1.8107,0.10<p<0.25 (Table 3.9). This was most likely due to the small numerator data, i.e. only 7 males and 13 females were affected from 1913 observations. Age of the half-sib was also not significant. This also may have been due to the lack of numerator data. When the half-sibs were separated into decade groups the likelihood ratio statistic was still non-significant. 124 ic or was 3.4. Twin Study 3.4.1. A Description Two hundred and twenty-seven of the 14,990 index cases reported a dizygotic monozygotic co-twin (Table 3.18). Of these 227, complete data needed for analyses available on 200 index cases by the thesis deadline of May 1,1996. The twinning rate in North America and Europe is approximately 1/80 births (Hrubec et al.,1986). The 227 twins from a population of approximately 15,000 M S patients, does not differ significantly from the expected 187.5, (Z=1.914,0.05<p<0.10). Table 3.18 provides a summary of the results of the twin screen. Of the 200 completed twin biological packages, there were 117 dizygotic twin pairs, 80 monozygotic twin pairs, and 3 triplet births. The 3 triplet births produced 6 dizygotic twin pairs (relative to the index case). The ratio of monozygotic to dizygotic twin pairs is 1.0 to 1.5. This is not significantly different than the 1.0 to 2.0 ratio expected in the Caucasian population; X2=3.12,0.05<p<0.10. Eighteen of the 80 monozygotic twin pairs and 5 of the 123 dizygotic twin pairs were concordant for MS. 125 Table 3.18. Description of the Twin S n w n Total Eligible Index Cases 227 Male Index 73 Female Index 154 DZ Twins 134 M Z Twins 89 Triplets 4 MZ/DZ Ratio 1:1.5 Total Completed Biopackages 200 (88.1%) Male Index 66 Female Index 134 DZTwins 117 M Z Twins 80 Triplets 3* MZ/DZ Ratio 1:1.5 * the 3 completed triplet biopaks represent 6 DZ twin pairs The monozygotic co-twins included 52 females and 28 males for a female to male ratio of 1.8 to 1.0 (Table 3.19). The dizygotic co-twins consisted of 83 females and 40 males for a ratio of 2.0 to 1.0 with 97 male and 106 female DZ co-twins respectively. 126 Table 3.19. Description of the Co-twins MZPairs 80 Index-Twin Male-male 28 Female-female 52 DZ Pairs 123 Index-Twin Male-male 23 Male-female 17 Female-male 46 Female-female 37 Number of Twins Male Twins 97 Female Twins 106 Twins 203 M Z Twins 80 DZ Twins 123(117 from DZ + 6 from triplets) 3.4.2. Risks to MZ, DZ Co-twins, and Full-sibs of Index Cases Table 3.20. summarises the age-corrected proband-wise risk estimates for the co-twins of the index cases. These risks are stratified by sex and type of twin pair. There were 18 affected M Z co-twins for a crude risk of22.50% (18/80) and a maximum likelihood risk estimate of28.25%. The age-adjusted risks for male M Z co-twins and female M Z co-twins were 27.37% and 28.42% respectively. There were 5 affected dizygotic co-twins for a crude risk of 4.06% and an adjusted risk of 5.61%. The risk to the DZ co-twins of male index cases is 3.36%, while the risk to DZ co-twins of female index cases is 6.74%. Pair-wise risk 127 estimates were also calculated. The pair-wise maximum likelihood risk to D Z twin pairs is 4.48%, 4/122 (S.E. 2.20), and the pair-wise risk to the monozygotic twin pairs is 23.15%, 13/75 (S.E. 5.8202). The risk to M Z co-twins, 28.25%, was significantly different than the risk to dizygotic co-twins, 5.61%; G = 17.0711; p « 0 . 0 0 1 . This confirms the results of previous twin investigations (Ebers et al.,1986;Sadovnick et al.,1992) and reaffirms the importance of genetics in M S etiology. The 95% confidence interval for the M Z twin risk was between 18.36%—38.14%. The D Z twin risk 95% confidence interval was between 1.54%—9.68%. The risk to M Z co-twins was also significantly different than the age-adjusted risk for like-sexed D Z co-twins, 7.27% (Table 3.20); G =9.5834, p<0.005. Table 3.20. Proband-wise Risk Estimates for Co-twins Sex of Index Case Sex of Co-twin N #Affeeted Co-twins R i s k r % ) S F 1)MZ male female male female 28 52 6 12 27.3747 28.4219 9.6676 7.2531 Total 80 18 28.2475 5.8418 2)DZ male male Total male female 23 17 40 1 0 1 6.0484 3.3651 5.8745 3.3106 female female Total male female 46 37 83 2 2 4 5.8425 8.0385 6.7410 4.0395 5.4976 3.2795 like-sexed pairs 60 3 7.2654 4.0631 Total 123 5 5.6109 2.4511 128 Table 3.21 presents the risks for the full-sibs of dizygotic and monozygotic twin pairs. Five hundred and fifty-four full-sibs were identified, of which 11 were definitely affected with M S , (crude risk 1.99% (11/554)). The maximum likelihood risk estimate is 2.49% for these full-sibs. This risk estimate is not significantly different than the risk to the dizygotic co-twins (5.61%), G = 1.9772, 0.10<p<0.50. It is also not significantly different than the pair-wise D Z maximum likelihood risk estimate of 4.48%, G=1.2635; 0.10<p<0.50. This comparison was more appropriate given the sampling scheme of the full-sibs (i.e. a full-sib pair could not be multiply ascertained for the twin investigation). Table 3.21. Risk Estimates for the Full-Sihs of Twin Index CMU* Twin Non-twin Male Female M a l e & Females Sibling N #Affected Risk(%) S.E. Brother 96 3 — — V ,i 3.7394 2.1232 Sister 96 2 2.4623 1.7204 Sibling 192 5 3.1038 1.3682 Brother 193 3 2.0446 1.1687 Sister 169 3 2.2268 1.2759 Sibling 362 6 2.1359 0.8643 Brother 289 6 2.6441 1.0662 Sister 265 5 2.3154 1.0259 Sibling 554 11 2.4889 0.7423 129 Table 3.22 and 3.23 show age-adjusted risk estimates for the full-sibs of monozygotic and dizygotic sibships respectively. Table 3.22. Risk Estimates for Full-sibs from MZ Sibships Only Index Case Relation #Affected N Risk S F Male Brother Sister Sibling 0 1 1 38 35 73 3.7929 1.7610 O . J J , . 3.7203 1.7454 Female Brother Sister Sibling 3 1 4 73 50 123 5.7131 2.4768 4.3100 3.2061 2.4459 2.1090 Male & Female Brother Female Sibling 3 2 5 111 85 196 3.6061 2.9964 3.3424 2.0454 2.0868 1.4700 Table3.23. Risk Estimates for Full-sibs from DZ Sibships Only Index Case Relation #Affected N Risk S F Male Brother Sister Sibling 3 1 4 . 58 61 119 6.0475 1.8237 3.8334 . J . L . . 3.3974 1.8083 1.8836 Female Brother Sister Sibling 0 2 2 120 119 239 2.1198 1.0632 1.4907 0.7498 Male& Female Brother Sister Sibling 3 3 6 178 180 358 2.0874 2.0109 2.0524 1.1941 1.1533 0.8313 130 3.4.3. Genetic Heterogeneity The "family" risk of concordant and discordant twin pairs is given in Table 3.24 Of the 23 concordant twin pairs, 7 (30.4%) had at least one other family member affected with MS. There were 180 discordant twin pair families, 29 (16.11%) had a 1st, 2nd, or 3rd degree family member with MS. Though there was a trend for the concordant twin pairs to have more affected relatives than discordant twin pairs, the "family" risks were not significantly different, Z = 1.694, p=0.091. A more appropriate comparison however is between the discordant and concordant DZ twin pair families (as M Z concordant twin pairs are genetically identical). The proportion of concordant DZ twin pair families with additional affected family members (2/5) was not significantly different than the proportion of discordant DZ twin pair families with additionally affected family members (23/118),. Z=1.117,p=0.131. The maximum likelihood M S risks for the full-sibs of the concordant and discordant twin pairs were 4.04% (S.E.2.79%), 2/63, and 2.26% (S.E.0.74%), 9/494, respectively. These two risks were not significantly different, G =0.4873 ;0.25<p<0.50. Again, a more appropriate comparison would be between the risk to the non-twin sibs of discordant and concordant DZ twin pairs. However, of the 16 non-twin sibs of concordant DZ twins there were 0 affected with MS. The maximum likelihood risk to the non-twin sibs of discordant DZ twin pairs was 2.20% (S.E.0.89), 6/331. 131 Table 3.24. Evidence for "Genetic T^adin f f Concordant Pairs "Family" Risk Tvoe # Pairs 1st 2nd Concordant M Z Twins Concordant D Z Twins Total 18 5 3/18 0/5 0/18 1/5 3rd 2/18 1/5 Total 5/18(27.7%) 2/5 (40.0%) Discordant Pairs "Family" Risk Im # Pairs 1st 2nd 3rd Total Discordant M Z Twins Discordant D Z Twins Total 62 118 3 / 6 2 1/62 2/62 6/62(9.7%) 9/118 7/118 11/118 W> 12/180 8/180 13/180 23/118(19.5%) 29/180(16.11%) The total proportions were not significantly different, p=0.091. 132 ean Discussion Multiple sclerosis is a complex disorder with both non-genetic (environmental) and genetic factors involved in its pathogenesis. The non-genetic component to M S etiology has been demonstrated by the effect of distance from the equator on population prevalence in homogeneous populations (Hammond et al.,1988), migration studies (D 1967;Elian et al.,1990) and the finding that approximately 70% of M Z twins remain discordant for M S (Sadovnick et al., 1992). There is also an enormous amount of reported research that has attempted to isolate a (retrovirus in MS patients (reviewed in Souberbielle et al.,1995). This (retro)virus has been hypothesized to be the transmissible agent responsible for the observed familial aggregation of MS (Schapira et al.,1963) and the supposed epidemics observed in the Faroe Islands (Kurtzke et al.,1975). Kurtzke et al. (1993) believes this transmissible agent is necessary for MS to occur, while any genetic component to susceptibility is not necessary. However, the findings of the adopted and half-sib investigations demonstrate that the familial aggregation of M S is due primarily to the shared genetic make-up of family members rather than to their shared familial environment. The risk to half-sibs who did not live with the index case (1.17%), was non-significantly different than the risk to half-sibs who lived with the index case (1.62%), (Tables 3.13,3.14). Additionally, the adoptive first degree relatives of the index case (who shared the familial environment) had a significantly different risk than the genetically-related first degree family members of a representative population (Table 3.5). This data provide strong evidence that the 133 environmental (non-genetic) component to M S etiology is not a "within" family transmissible agent. As a result, future investigations involving the environmental component to M S should focus more on population-based, ubiquitous agents to which the entire population is equally exposed, (e.g. climate and diet). The importance of this ubiquitous environmental factor(s) is best demonstrated by the latitude effect observed in Australia (Hammond et al.,1988), New Zealand (Skegg et al.,1987), and Japan (Kuroiwa et al.,1983). Despite the relatively homogeneous populations, the population prevalence of M S increases as the distance from the equator increases. The broad-based nature of this environmental effect is also supported by migration studies. If an individual from a region with a "high" population prevalence migrates to a region with a "low" population prevalence before age 15 years, he/she will adopt the risk of the new "low" risk environment. However, i f the individual migrates after the age of 15, he/she will retain the "high" risk of the region of origin (Detals et al.,1978:Kurtzke 1985). Migration studies also stress the importance of an environmental factor acting during early adolescence (Poser, 1992). One theory is that the disorder is "acquired" during childhood and that puberty acts as a protective effect (Poser 1992). I prefer a hypothesis suggesting that the time to puberty influences the development and future functioning of the immune system. Thymic involution (or atrophy) begins during puberty and has been shown to have an effect on T cell differentiation pathways (Kay, 1979;Hirokawa et al.,1992). Therefore, it may be hypothesized that any population-based environmental factor(s) influencing the onset of puberty may, indirectly, influence 134 the development of the immune response. An example of an environmental factor affecting the onset of puberty is climate, i.e. the onset of menarche is earlier in hotter climates (Saar et al., 1988). If a delay in puberty onset alters the development/regulation of T cells, an individual may be more susceptible to autoimmune disorders later in life. It would be interesting to investigate the risks to relatives of MS patients, taking into account the ages at which they lived in "high" or "low" risk regions and their age at puberty (i.e. menarche in females). The CCPGSMS provides the sample size and methodology to perform such a large epidemiological investigation and the research will be undertaken as part of the second phase of the CCPGSMS. Evidence for the genetic component to M S has been demonstrated by many genetic epidemiological and genetic studies which include the familial aggregation of the disorder (Sadovnick et al.,1988;Robertson et al.,1996), the MZ/DZ twin concordance rates (Ebers et al.,1986;Sadovnick et al.,1992;Mumford et al.,1994) and the consistent association with H L A haplotypes (Hillert et al.,1993;Hauser et al.,1989). The twin study portion of this thesis is the largest available twin data set to date. The twin study has confirmed the operation of genetics in determining M S susceptibility. The sample of 200 twins provided a M Z concordance rate estimate of 28.2% ± 5.8% and a D Z concordance rate of 5.6% + 2.5% (Table 3.20). The two risks were significantly different from one another, while the risk to the non-twin full-sibs of index cases (2.5%) was not significantly different than the DZ concordance rate (Table 3.21). The operation of genetics was also demonstrated by the observed difference in the risk to full-sibs (3.6% ± 0.5%), and to half-sibs (1.4% ± 0.3%) of index cases (Tables 3.9,3.12). Also, 135 the observation of only one affected adoptive family member in a population of 1185 individuals strongly reflects what one would expect by chance given the Canadian MS prevalence of 0.1 %; p=0.32 (Sweeney et al., 1986). The mode of inheritance for this genetic component to MS susceptibility does not fit a simple mendelian pattern (Sadovnick et al.,1991). The similarity in risk observed between the parents and the sibs of index cases and skewed the sex ratio (Sadovnick et al.,1988;Robertson et al.,1996) rules out the possibility of an autosomal recessive mode of inheritance. An autosomal dominant mode of inheritance with reduced penetrance (i.e. less than 10%) neither explains the observed MZ/DZ risk ratio nor the skewed sex ratio which is well documented in MS (Acheson,1977). A sex-linked recessive mode of inheritance is also not compatible with the observed data because of the preponderance of females. An X-linked dominant mode of inheritance cannot explain the similarity in risk between the paternal and maternal half-sibs (Table 3.15). Taken together, the exclusion of single gene Mendelian inheritance patterns, the skewed sex ratio, the high MZ concordance relative to the DZ concordance, the differential parent-child concordance (based on sex), and the relatively low recurrence risks, suggests a multifactorial mode of inheritance. A multifactorial mode of inheritance implies that there are many variables (genetic and non-genetic) which determine the liability (or susceptibility) of an individual to MS. Expression of MS would require that enough of these liability factors were present in order to attain or surpass a hypothetical "threshold level". Once this "threshold level" is attained, onset of MS could occur by exposure to a possible non-genetic (environmental) triggering event(s). 136 The half-sib study provides insight into the nature Of this complex inheritance pattern. The full-sib risk (3.68%) is more than twice the risk to half-sibs (1.40%). The ratio is therefore 2.63 ± 1.57 (3.68/1.40). Though there is large standard error, this decrease in risk is greater than the decrease in genetic sharing (i.e. greater than half) between the full-sibs (who share 50% of their genetic material) and half-sibs (who share 25% of their genetic material). Therefore, (given that an autosomal recessive mode of inheritance does not explain the similarity in risk between first degree relatives nor the skewed sex ratio) a model with more than one locus operating in MS susceptibility is probable. The rationale for comparing the full-sibs risk to the half-sib risk is similar to the rationale for comparing the M Z and DZ concordance rates with one important exception. The ascertainment bias which is often associated with the use of the twin study design is avoided by using the population-based recurrence risks. Even in our population-based twin study sample there was a slight (though insignificant) excess of M Z twins (Table 3.18). The observation that M S risk to the full-sibs of concordant twin pairs (4.0%) is greater than the risk to the full-sibs of discordant twin pairs (2.3%) is also what would be expected i f there was more than one liability gene contributing to M S i.e. that families of concordant twin pairs are more genetically "loaded" with the susceptibility genes compared to the families of discordant twin pairs. However, the comparison was non-significant, probably due to the small sample size of the sibs of the concordant twins (Table 3.24). Sadovnick et al. (1996) recently demonstrated that the siblings of index cases with an affected parent have a significantly greater risk for M S compared to the 137 is i n siblings of index cases with an unaffected parent (Sadovnick et al.,1996). This agreement with the hypothesis that there are many liability genes involved in multiple sclerosis. The maximum likelihood risks calculated for this thesis can be used to "estimate" the number of the genes involved according to Risch's (1990) method. Using the M Z , full-sib and half-sib recurrence risks, it is possible to predict recurrence risk ratios (recurrence risk/population incidence) for different modes of inheritance (Appendix D). Easily excluded are the single locus, additive, and genetic heterogeneity models (Appendix D-Model A). The equations developed by Risch (1990) predict that i f any of these models were to be operating, the A,R-1 should decrease by a factor of two for each degree of relatedness. Also easily excluded is the polygenic model of infinite loci (Appendix B-Model B). The "best" fit models are for those that included 4-9 epistatically acting loci (each contributing equally to risk) as well as models that included 1-5 epistatically acting loci plus with a polygenic component (Appendix D). Ebers et al. (1996) performed a genomic screen as part of the CCPGSMS. The investigators excluded 88% of the genome containing a locus contributing a A,Sib>1.8. It is therefore unlikely that the models of 1-2 loci with a polygenic component are operating in M S susceptibility. In each of these models the 1-2 loci would have to contribute a X l o c u s>2.5 (Appendix D); this is unlikely considering the exclusion data to date. The models of 7-9 genes contributing epistatically to risk would require that all the susceptibility alleles be extremely common in the population. For example, in the 9 loci model the allele frequencies of the susceptibility alleles would have to be over 46% 138 (given the M S prevalence of 0.001). Taken together, the best conclusion would be that there is more than one gene involved in M S susceptibility and that these susceptibility alleles are interacting epistatically (in order for the XR-l to decrease by a factor greater than 2). No doubt the H L A locus, and perhaps the potential susceptibility allele located on chromosome 5p (Kuokkanen et al., 1996 and Ebers et al., 1996), are two of these interacting loci contributing to risk. The next step would be to further map the potential susceptibility alleles indicated by the CCPGSMS genomic screen. This would be followed by the identification and characterization of the genes involved. How these genes interact with one another and with the environment, and their roles in the pathogenesis of M S is clearly the end point of the molecular investigation. By knowing the steps in M S pathogenesis it may be possible to devise new strategies or effective therapies for the treatment of this disorder. Monozygotic twins share their genes and their environment. Why then is there such a reduced penetrance? For example, the M Z discordance rate in our sample is 71.8%. This suggests that stochastic events are a factor in determining susceptibility. These random/chance events would be expected to influence the development/functioning of the central nervous system and immune system in a potentially susceptible individual. The high discordance could also reflect, to some degree, the variable expression of M S as some individuals, who are clinically assyptomatic, have brain lesions identified by MRI (Lynch et al.,1991) or identified incidentally at autopsy (Gilbert and Sadler, 1983). 139 Considering the nature of M S (a disorder involving the immune system and central nervous system), it is probable that the gene products and the environmental factors contributing to susceptibility also affect the development of these systems. A few target areas that may have a potential effect in determining susceptibility may include the regulation and development of T cells, the expression of specific T cell receptors and M H C alleles, the regulation of the antigen presentation cells, the permeability of the blood brain barrier, the control of the inflammatory response, and the effect of repair mechanisms (Sadovnick et al.,1994). An example may be the observed skewed sex distribution of MS. Estrogen (higher in females) elevates the regulatory hormones of lymphocyte development and T and B cell function (Grossman 1989). Conversely, in diabetes research Nod mice, which have undergone androgen therapy (i.e. testosterone), demonstrate a reduced autoimmune reaction. The mechanism was thought to be due to the hormonal alteration of the immune cells (Fox, 1992). It is probably the hormonal differences in males and females, with their subsequent differential effect on the immune system, that may explain the altered sex distribution to this putative autoimmune disorder. A l l three variables, (i.e. genetic, environmental, and chance) would interact via these potential target regions to determine a susceptible individual. Clinical expression could then be determined by genetic background or additional environmental effects: These susceptibility factors set the stage for the clinical onset of MS. In an individual with enough of these liability factors to attain a "threshold" level, an appropriate trigger would cause disease. This trigger could perhaps be a superantigen as demonstrated by an experimental encephalomyelitis (EAE) mouse model (Brocke et 140 al.,1993). In the investigator's E A E mice, which were in a state of remission, an exacerbation (i.e. a demyelinating attack) could be induced i f the mice were exposed to the superantigen staphyloccal enterotoxin B. Another potential trigger could be molecular mimics. These mimics, recognized by T cells, would activate an autoimmune reaction against the myelin basic protein of the CNS. Recently, Wucherpfenig et al. (1995) demonstrated, in vitro, that a T cell response could be activated in the T cell lines of M S patients i f they were exposed to either the herpes simplex, the Epstein-Barr, the adenovirus type 12 and the influenza type A viruses. Additionally, the bacterium pseudomonas aeruginosa could elicit a T cell response. A l l these agents have a common motif corresponding to an M B P epitope. This suggests that the range of environmental factors able to act as a molecular mimic of myelin basic protein is potentially very high. In vivo, i f these environmental factors were able to activate T cells and i f these cells were able to penetrate the blood brain barrier of a susceptible host, an autoimmune reaction may take place. This could explain the failure of case control studies to consistently identify a single viral species. A study that encompasses many M B P epitope containing environmental factors as the "exposure" group may yield more promising results for identifying a trigger to MS in vivo. However, given the potential for such an array of common triggers (to which we may all be exposed), the key to developing a potential therapy should be focused on the steps to susceptibility. The absence of maternal effects is an integral assumption of the adopted and twin studies. If these effects were indeed present, the results of the twin and adopted studies could have resulted from an environmental sharing effect and not genetics. The 141 increased M Z concordance could have resulted from differences in the pre-natal environmental sharing between M Z and DZ twins (James 1982). Also, an adopted individual who was not exposed to the critical factor during pregnancy or early infancy, may not develop M S even though they grew up in the same household as the index case. The inability to observe a difference between the maternal half-sib risk (1.47%) and the paternal half-sib risk (1.31%), and given the large sample sizes, indicates that maternal effects may not contribute significantly to M S risk (Table 3.15). This effectively rules out breast feeding, and the intrauterine environment as environmental risk factors. It also rules out mitochondrial inheritance and a major contribution by an X-linked locus (maternal half-brothers had approximately equal risk as the paternal half-brothers, Table 3.15). A l l aspects of this thesis are conducive to counselling and should complement the existing literature (Sadovnick et al.,1986;Sadovnick et al.,1988;Robertson et al.,1996). Informing parents that there is probably not a significant environmental factor that they may "pass" on to their child or other family members may ease a patient's guilt or even a family member's fear of MS. It is also important to stress that there are potentially many genes involved in MS susceptibility. There is no single allele that can be inherited which will contribute to the majority of familial risk, but rather a set of genes which interact with one another to determine susceptibility. Lastly, the absence of a significant maternal effect that can be transmitted from affected mother to child will benefit the counselling of women who are pregnant or considering having a child. The influence of the population-based environmental factors as well as the importance of stochastic events 142 should also be used to inform the patient regarding the many factors involved in determining susceptibility. The Canadian Collaborative Project on the Genetic Susceptibility to Multiple Sclerosis (CCPGSMS) is ongoing and provides a unique opportunity to investigate the etiology of multiple sclerosis. The study combines the tools of both genetic epidemiology (of which this thesis is a part) and molecular genetics to accomplish its goal of understanding the pathogenesis of multiple sclerosis. One of the strengths of the epidemiological approach is that the M S study population is derived from 14 regional clinics that have a patient pool representative of the Canadian M S population (Weinshenker et al.,1987;Sadovnick et al.,1996). This minimizes the ascertainment biases inherent in the previous M S genetic epidemiological investigations (e.g.Mackay et al., 1966;Williams et al.,1980). The CCPGSMS is also a nationwide study and can therefore generate a sufficiently large sample size from which significant results from both molecular and epidemiological studies can be attained (Sadovnick et al.,1996;Ebers et al.,1996a; Ebers et al.,1996b). Another unique aspect of the study is that the collaborative team is composed of neurologists from across Canada, who specialize in the diagnosis and treatment of MS. Together, they provide an opportunity to utilize the knowledge and ideas of a large number of individuals who are specialists in the field of M S research. 143 Conclusion This work clearly demonstrates that the well recognized familial aggregation of M S is due to the common genetic material shared by family members. Support for this was derived from three observations (1) the observation of only one case of MS in a sample of 1185 adoptive individuals; strongly reflecting the population prevalence and not familial risk, (2) the similarity between the risk to half-sibs who have lived with the index case versus those who have not lived with the index case, and lastly, (3) the significant difference between M Z and D Z twin risks and the similarity between full-sib and D Z twin risks These results have several important implications. The first is that researchers are justified in pursuing a molecular search for disease genes. The equations of Risch (1990) rule out the possibility of a polygenic mode of inheritance with infinite loci (Appendix D). This mode of inheritance could be considered the "worst" scenario i f researchers have set out to identify susceptibility genes. Another other "worst" situation, would be i f the familial aggregation of MS was due primarily to the shared environment and not to genetics. Given that we have demonstrated that the relatively high recurrence risk ratio for sibs (kSibs=3'7) is due to genetics and not to non-genetic determinants, it may be possible, (given enough affected relative pairs), to identify susceptibility alleles involved in MS risk. 144 A second implication is that the search for the environmental (non-genetic) component(s) to multiple sclerosis must shift from a transmissible "within" family (e.g.viruses) to more ubiquitous, population-based environmental exposures (e.g. climate, diet). The third implication involves maternal effects. 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New Jersey. 167 Appendix A - Clinical Documentation \ 168 MS Clinic: Site Coordinator: Patient Name/ID: Neurologist:_ W H E Q 1 Clinical Course (at most recent assessment) A Relapsing-remitting B Relapsing progressive (Date R-P ) C Chronic progressive D Benign Date of most recent assessment WBAK9 Extended Kurtzke Score Date of most recent assessment Kurtzke score at most recent assessment Current Status at Most Recent Assessment Relapse Progressive Stable Date of most recent assessment Other Diseases Onset Date A A C Q 1 Disposition Investigations Not Done Date Performed CT Scan M R I VEP SEP LP Blood immune function WHEF# Lab-Supported MS (circle) yes no 169 Appendix B- Initial Summary Screen 170 MS Cl i n i c : Site Coordinator: Patient Name/I.D.: Informant: Canadian Collaborative Project: Screen Questionnaire SUMMARY SHEET FAMILY HISTORY OF: (Lastname) Given Name(s) For Females: Maiden Name P rev ious Surname(s) ID Number: Sex: Male Female - „ . / / P l ace of B i r t h : . Date o f B i r t h : / —I _ ( Y r . ) (Month) (Day) M a r i t a l S t a tus ( c i r c l e ) : M S Sep W D CL Address : ' T e l e p h o n e _ ^ _ (Work) (Home) v ' E l i g i b l e for Study YES NO (Circle a l l eligible categories): (1) TWIN - MZ (2) TWIN - DZ (3) ADOPTED (4) MAT. HALF-SIB (5) PAT. HALF-SIB (6) PARENT-CHILD (7) SIB PAIR (8) MAT. FIRST COUSIN (9) PAT. FIRST COUSIN (10) AUNT/UNCLE-NIECE/NEPHEW (11) CONJUGAL (12) "LOADED" FAMILY - 3 or more with MS (13) ETHNIC GROUP OF INTEREST Date of I n t e r v i e w I n t e r v i e w Completed: YES NO I f NO, g i v e reason ( c i r c l e r ea son) : Could not con t ac t d e s p i t e 5 t r i e s Deceased Refused p a r t i c i p a t i o n ( exp l a in ) Unable t o communicate Other ( e x p l a i n ) Form 1 171 Appendix C- Detailed Biological Ouesrinnna.v,, 172 MS Clinic: Site CoordTnator: Patient Name/ID:_ Informant t Canadian Collaborative Project: Data Questionnaire BIOLOGICAL PARENTS ADMINISTER TO ALL INDEX CASES Are either of your parents biological relatives (parent, sibling, aunt, uncle, cousin, etc.) of your biological mother and/or father? Yes No If Yes, Specify Exact Relationship: FATHER First and Last (Maiden) Name: Year of Birth (Best Estimate) Age at Present ( i f alive) Age & Cause of Death ( i f dead) MOTHER Multiple Sclerosis Optic Neuritis Undiagnosed Neurological Disorder Crohn's Disease Ulcerative C o l i t i s Rheumatoid ArthritiB Juvenile Diabetes Comments: YES NO UNSURE YES NO UNSURE DETAILS Y Y N N ?? ?? Y Y N N ?? ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Form 3-1 173 MS Cl i n i c : Site Coordinator: Patient Name/IDr_ Informant t ETHNICITY OF BIOLOGICAL PARENTS (IF Screen Indicates Non-European and from an "Interest" Group) A. Mother of Index Case: Place of Birth: (Town/Province/State/Country) Primary Language(s) Spoken at Home as a Child: Deceased: YES NO If NO, Current Residence: (Town/Province/State/Country) A . f i l Maternal Grandmother's {Grandmother of Index c « n « ) P - a m ^ i y ' Place of Birth: (Town/Province/State/Country) Deceased: YES NO If NO, Current Residence: (Town/Province/State/Country) A . f i i ) Maternal Grandfather's {Grandfather of Index Case) Family: Place of Birth: Deceased: YES If NO, Current Residence: (Town/ Province/ St ate / Country) NO (Town/Province/State/Country) Form 3-2 174 MS Cl i n i c : Site Coordinator: Patient Name/ID:J Informant: B. Father of Index Case: Place of Birth: (Town/Province/State/Country) Primary Language(s) Spoken at Home as a Child: Deceased: YES NO If NO, Current Residence: (Town/Province/State/Country) A.(i) Paternal Grandmother's (Grandmother of Index Case) Family: Place of Birth: (Town/Province/State/Country) Deceased: YES NO If NO, Current Residence: (Town/Province/State/Country) Paternal grandfather's (Grandfather of Index Case) Family! Place of Birth: , , — • — (Town/Province/State/Country) Deceased: YES NO If NO, Current Residence: . . . . ^ — r - - — — (Town/Province/State/Country) 175 Form 3-3 MS C l i n i c : S i t e Coordinator: Patient Name/ID:J Informant: B. Father of Index Case: Place of B i r t h : (Town/Province/State/Country) Primary Language(s) Spoken at Home as a C h i l d : Deceased: YES NO I f NO, Current Residence: (Town/Province/State/Country) A.(i) Paternal Grandmother's {Grandmother of Index Case) Family: Place of B i r t h : (Town/Province/State/Country) Deceased: YES NO I f NO, Current Res idence: (Town/Province/State/Country) A . f i i ) Paternal Grandfather's (Grandfather of Index Case) Familvt Place of B i r t h : (Town/Province/State/Country) Deceased: YES NO I f NO, Current Res idence: (Town/Province/State/Country) Form 3-3 176 MS Cli n i c : Site Coordinator: Patient Name/ID:_ Informant: ~ Canadian Collaborative Project: Data Questionnaire BIOLOGICAL CBTLDREH ADMINISTER TO ALL INDEX CASES Do you have any biological children? Yes No If YES, complete the following. Include a l l liveborn children of the index case, including any who died at a young age. OLDEST NEXT OLDEST NEXT OLDEST / l /2 #3 Sex (circle one) Male Female Male Female Male Female Firs t and Last (Maiden) Name: Year of Birth (Best Estimate): Age at Present (if alive): Age & Cause of Death ( i f dead): City/Country of Residence: YES NO UNSURE YES NO UNSURE ~ s NO UNS1 Multiple Sclerosis Y N ?? Y N ?? Y N ?? Optic Neuritis Y N ?? Y N ?? Y N ?? Undiagnosed Neurological Disorder Y N ?7 Y N ?? Y N ?? Crohn's Disease Y N ?? Y N ?? Y N ?? Ulcerative C o l i t i s Y N ?? Y N ?? Y N ?? Rheumatoid Ar t h r i t i s Y N ?? Y N ?? Y N ?? Juvenile Diabetes Y N ?? Y N ?? Y N ?? Comments: Form 4-1 177 NS Clinic: Site Coordinator t _ Patient Name/ZDt_] Informant: Canadian Collaborative Projectt Data Questionnaire BIOLOGICAL BROTHERS AND SISTERS ADMINISTER TO ALL INDEX CASES BIOLOGICAL FULL (SAME MOTHER AND FATHER) BROTHERS AND SISTERS Include a l l brothers and sisters of the index case, including any who died at a young age. Sex (circle one) First and Last (Maiden) Name: Year of Birth (Best Estimate): Age at Present (if alive): Age & Cause of Death (if dead): OLDEST #1 Male Female NEXT OLDEST #2 Male Female NEXT OLDEST #3 Male Female City/Country of Residence: Multiple Sclerosis Optic Neuritis Undiagnosed Neurological Disorder Crohn's Disease Ulcerative Colitis Rheumatoid Arthritis Juvenile Diabetes Comments: YES NO UNSURE YES NO UNSURE YES NO UNSURE Y Y N N ?? ?? Y Y N N ?? ?? Y Y N N ?? ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Form 5-1 178 MS Clinic: Site Coordinator: Patiant Name/ID:_ Informant: Canadian Collaborative Project: Data Questionnaire HIBCB8 / NEPHEWS (INCLUDING THE CHILDREN OF INDEX CASE'S BIOLOGICAL AND ADOPTED SIBLINGS) ADMINISTER TO ALL INDEX CASES OLDEST NEXT OLDEST #1 #2 Sex (circle one) Male Female Male Female Name of Parent Who Is/Has Sib of Index Case: First and Last (Maiden) Name: Year of Birth (Best Estimate): Age at Present (if alive): Age & Cause of Death (if dead): City/Country of Residence: YES NO UNSURE YES NO UNSURE YES NO UNS1 Multiple Sclerosis Y N ?? Y N ?? Y N ?? Optic Neuritis Y N ?? Y N ?? Y N ?? Undiagnosed Neurological Disorder Y N ?? Y N ?? Y N ?? Crohn's Disease Y N ?? Y N ?? Y N ?? Ulcerative Colitis Y N ?? Y N ?? Y N ?? Rheumatoid Arthritis Y N ?? Y N ?? Y N ?? Juvenile Diabetes Y N ?? Y N ?? Y N ?? Comments: NEXT OLDEST #3 Male Female Form 6-1 179 MS Clinic: Site Coordinator: Patient Name/ZD>_ Informant: ~~ Canadian Collaborative Project: Data Questionnaire MATERNAL AUNTS AND UNCLES ADMINISTER TO ALL INDEX CASES Sex (circle one) Firat and Last (Maiden) Name: Year of Birth (Best Estimate) Age at Present (if alive): Age & Cause of Death (if dead): OLDEST #1 Male Female NEXT OLDEST Male Female NEXT OLDEST #3 Male Female City/Country of Residence: Multiple Sclerosis Optic Neuritis Undiagnosed Neurological Disorder Crohn's Disease Ulcerative Colitis Rheumatoid Arthritis Juvenile Diabetes Comments: YES NO UNSURE YES NO UNSURE YES NO UNSURE Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Form 7-1 180 MS Clinic: Site Coordinator: Patient Mane/ZD:_ Informant: ~ Canadian Collaborative Project: Data Questionnaire MATERNAL FIRST COUSINS ADMINISTER TO ALL. INDEX CASES To estimate "time spent together in childhood", uBe the following guidelines when asking the questions indicated below: 1. Cohabitation - # years and Approximate ages of index case and cousin 2. >1 Year - Saw each other more often than once a year: (a) daily (b) weekly (c) monthly (d) less than monthly but several times a year 3. 1 Year - Annual visit 4. <1 Year - Occasional Visits 5. Never met Question/Cousin: Did the Cousin and Index Case Ever Live Together? YES NO If YES, Age(s) of Index Case When They Lived Together? If NO, How Often Were They Together Until Age 21 ? Form 8-1 181 MS Clinic: Sit* Coordinator: Patient Name/ID:J Informant: ALL MATERNAL FIRST COUSINS Sex (circle one) Name of Parent Who Is/Was Sib of Index Case's Mother: First and Last (Maiden) Name: Year of Birth (Best Estimate): Age at Present (if alive): Age & Cause of Death (if dead): OLDEST / l Male Female NEXT OLDEST Male Female NEXT OLDEST #3 Male Female City/Country of Residence: Did the Cousin and Index Case Ever Live Together? If YES, Age(s) of Index Case When They Lived Together? If NO, How Often Were They Together Until Age 21 ? Multiple Sclerosis Optic Neuritis Undiagnosed Neurological Disorder Crohn's Disease Ulcerative Colitis Rheumatoid Arthritis Juvenile Diabetes Comments: YES NO YES NO YES NO YES NO UNSURE YES NO UNSURE YES NO UNSURE Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Form 8-2 182 MS Clinic: Site Coordinator : Patient Name/IDi_ Informant: ~ Canadian Collaborative Project: Data Questionnaire PATERNAL AUNTS / UNCLES ADMINISTER TO ALL INDEX CASES Sex (circle one) First and Last (Maiden) Name: Year of Birth (Best Estimate): Age at Present (if alive): Age & Cause of Death (if dead): OLDEST #1 Male Female NEXT OLDEST *2 Male Female NEXT OLDEST Male Female City/Country of Residence: Multiple Sclerosis Optic Neuritis Undiagnosed Neurological Disorder Crohn's Disease Ulcerative Colitis Rheumatoid Arthritis Juvenile Diabetes Comments: YES NO UNSURE YES NO UNSURE YES NO UNSURE Y N ?? Y N ?? Y N ?? * N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Form 9-1 183 MS Clinici Site Coordinator: Patient Name/XDt Informant: ~ Canadian Collaborative Project: Data Questionnaire PATERNAL FIRST CPUSIKS ADMINISTER TO ALL INDEX CASES To estimate "time spent together in childhood", use the following guidelines when asking the questions indicated below: 1. Cohabitation - # years and Approximate ages of index case and cousin 2. >1 Year - Saw each other more often than once a year: (a) daily (b) weekly (c) monthly (d) less than monthly but several times a year 3. 1 Year - Annual visit 4. <1 Year - Occasional ViBits 5. Never met Question/Cousin: Did the Cousin and Index Case Ever Live Together? YES NO If YES, Age(s) of Index Case When They Lived Together? If NO, How Often Were They Together Until Age 21 ? Form 10-1 184 MS Clinic: Site Coordinator: Patient Name/ID:_ Informant: ~ ALL PATERNAL FIRST COUSINS Sex (circle one) Name of Parent Who Is/Was Sib of Index Case's Father: First and Last (Maiden) Name: Year of Birth (Best Estimate): Age at Present (if alive): Age & Cause of Death (if dead): OLDEST #1 Male Female NEXT OLDEST *2 Male Female NEXT OLDEST #3 Male Female City/Country of Residence: Did the Cousin and Index Case Ever Live Together? If YES, Age(s) of Index Case When They Lived Together? If NO, How Often Were They Together Until Age 21 ? Multiple Sclerosis Optic Neuritis Undiagnosed Neurological Disorder Crohn's Disease Ulcerative Colitis Rheumatoid Arthritis Juvenile Diabetes Comments: YES NO YES NO YES NO YES NO UNSURE NO UNSURE YES NO UNSURE Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Form 10-2 185 MS Clinict Site Coordinator: Patient Name/ID:_ Informant : — Canadian Collaborative Project: Data Questionnaire ADOPTED PARENTS OF INDEX CASE ADMINISTER ONLY IF INDEX CASE IS ADOPTED BY NON-BIOLOGICAL RELATIVES DURING THE FIRST YEAR OF LIFE Are either of your adopted parents biological relatives (parent, sibling, aunt, uncle, cousin, etc.) of your biological mother and/or father? Yes No If Yes, Specify Exact Relationship: ADOPTED FATHER First and Last (Maiden) Name: Year of Birth (Best Estimate) Age at Present (if alive) Age & Cause of Death (if dead) ' ADOPTED MOTHER Multiple Sclerosis Optic Neuritis Undiagnosed Neurological Disorder Crohn's Disease Ulcerative Colitis Rheumatoid Arthritis Juvenile Diabetes Comments: YES NO UNSURE YES NO UNSURE DETAILS Y Y N N ?? ?? Y Y N N ?? ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Form 11 186 MS Clinici Site Coordinatort Patient Name/ID:_ Informantt — Canadian Collaborative Project: Data Questionnaire ADOPTED SIBS ADMINISTER ONLY IF INDEX CASE HAS SIBLINGS WHO ARE NOT BIOLOGICAL RELATIVES AND WHO WERE ADOPTED WITHIN THE FIRST YEAR OF LIFE ADOPTED BROTHERS AND SISTERS Include a l l adopted brothers and sisters of the index case, including any who died at a young age. Are your adopted brothers and sisters the biological children of your adopted parents? Yes No Some are and some are not Name those who are not the biological children of your adopted parents: Complete the following for a l l adopted siblings: OLDEST NEXT OLDEST #1 *2 Sex (circle one) Male Female Male Female First and Last (Maiden) Name: Year of Birth (Best Estimate): Age at Present (if alive): NEXT OLDEST Male Female Age at Adoption: Age 6 Cause of Death (if dead) Multiple Sclerosis Optic Neuritis Undiagnosed Neurological Disorder Crohn's Disease Ulcerative Colitis Rheumatoid Arthritis Juvenile Diabetes Comments: YES NO UNSURE YES NO UNSURE YES NO UNSURE Y Y Y N N ?? ?? Y Y N N ?? ?? N N ?? ?? Y N ?? Y N ?? Y N ?? Y N ?? y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Form 12-1 187 MS C l i n i c : S i t e Coordinator: Patient Name/ID:_ Informant: ~ Canadian Collaborative Project: Data Questionnaire ADOPTED CHUIDREH ADMINISTER ONLY I P INDEX CASE ADOPTED A t*tm.n Do you have any adopted children? Yes No If YES, complete the following. Include a l l liveborn children of the index case, including any who died at a young age. OLDEST NEXT OLDEST NEXT OLDEST #1 #2 #3 Sex (circle one) Male Female Male Female Male Female First and Last (Maiden) Name: ^ Year of Birth • (Best Estimate): ^ Age at Adoption: Age at Present (if alive): Age & Cause of Death (if dead): City/Country of Residence: Multiple Sclerosis Optic Neuritis Undiagnosed Neurological Disorder Crohn'8 Disease Ulcerative Colitis Rheumatoid Arthritis Juvenile Diabetes Comments: YES NO UNSURE YES NO UNSURE YES NO UNSURE Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Form 13-1 188 MS Cli n i c : Site Coordinator: Patient Name/ID: Informant: ~ Canadian Collaborative Project: Data Questionnaire MATERNAL HALF SIBS ADMINISTER ONLY IF INDEX CASE HAS HAD MATERNAL HALF SIBS To estimate "time spent together in childhood", use the following guidelines when asking the questions indicated below: 1. Cohabitation - t years and Approximate ages of index case and cousin 2. >1 Year - Saw each other more often than once a year: (a) daily (b) weekly (c) monthly (d) less than monthly but several times a year 3. 1 Year - Annual v i s i t 4. <1 Year - Occasional V i s i t s 5. Never met Question/Half-sib: Did the Half-Sib and Index Case Ever Live Together? YES NO If YES, Age(s) of Index Case When They Lived Together? • If NO, How Often Were — They Together Until Age 21 ? Form 14-1 189 MS Clinic: ' Site Coordinators Patient Name/ID: Informant: ~ Canadian Collaborative Project: Data Questionnaire PATERNAL HMJ SIBS ADMINISTER ONLY IP INDEX CASE HAS/BAD PATERNAL HALF SIBS To estimate "time spent together in childhood", use the following guidelines when asking the questions indicated below: 1. Cohabitation - # years and Approximate ages of index case and cousin 2. >1 Year - Saw each other more often than once a year: (a) daily (b) weekly (c) monthly (d) less than monthly but several times a year 3. 1 Year - Annual v i s i t 4. <1 Year - Occasional V i s i t s 5. Meyer met Question/Half-sib: Did the Half-Sib and Index Case Ever Live Together? YES NO If YES, Age(s) of Index Case When They Lived Together? If NO, How Often Were They Together Until Age 21 ? Form 15-1 190 MS C l i n i c : S i t e Coordinator: Patient Name/ID: Informant: — PATERNAL HALF (SAME FATHER) BROTHERS AND SISTERS S12-d'_. Ty^nfage" b r ° t h e r S ^ 8 i B t 6 r 8 ° f t h e i n d ~ « Y Sex (circle one) First and Last (Maiden) Name: Year of Birth (Best Estimate): Age at Present ( i f alive): Age & Cause of Death ( i f dead): OLDEST *1 Male Female NEXT OLDEST *2 Male Female NEXT OLDEST #3 Male Female City/Country of Residences Did Half-Sib Ever Live with Index Case If YES, Indicate Approx. Ages of Index Case When They Lived Together If NO, Indicate Approx. How Often They Spent Time Together Before Age 21 YES NO YES NO YES NO Multiple Sclerosis Optic Neuritis Undiagnosed Neurological Disorder Crohn's Disease Ulcerative C o l i t i s Rheumatoid A r t h r i t i s Juvenile Diabetes Comments: 2I§—£2—UNSURE YES NO U N S T f R - YES NO U N S T T P W Y Y N N ?? ?? Y Y N N ?? ?? Y Y N N ?? ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Y N ?? Form 15-2 191 Appendix D-Recurrence Risk Ratios for Different Modes of Inheritance 192 Appendix D-Recurrence Risk Ratios for Different Modes of Inheritance (Risch 1QQO) Assuming; 1) A lifetime risk of 0.2% for M S (Sadovnick et al.,1994) 2) M Z twin risk of 28.2% (S.E.5.84) 3) Full-sib risk o f 3.7% (S.E.0.55) Estimated from this thesis 4) Half-sib risk of 1.4% (S.E. 0.31) 5) No dominance variation (risk to sibs is non-significantly different than risk to parents) Model J t M 7 Z Score Observed 141.0 18.5 7.0 n/a A 36.0 18.5 9.8 -6.81 B 342.0 18.5 4.3 -6.35 C 57.0 18.5 4.3 -4.32 D 78.9 18.5 6.1 -1.62 E 98.2 18.5 5.6 -0.94 F 115.4 18.5 5.3 -0.66* G 130.6 18.5 5.1 -0.59* H 144.2 18.5 5.0 -0.62* I 156.2 18.5 4.9 -0.72* J 166.9 18.5 4.8 -0.86* K 176.6 18.5 4.8 -1.01 L 312.5 18.5 4.4 -5.07 M 259.2 18.5 4.7 -3.03 N 198.6 18.5 4.9 -1.27 O 140.5 18.5 5.5 -0.34* P 183.2 18.5 4.7 -1.17 Q 102.6 18.5 6.2 -0.61* R 142.5 18.5 5.3 -0.43* S 172.1 18.5 4.9 -0.83* T 103.3 18.5 5.8 -0.71* U 130.9 18.5 5.3 -0.48* V 153.2 18.5 5.0 -0.64* W 171.6 18.5 4.9 -0.87* X 130.7 18.5 5.3 ' -0.50* Y 149.4 18.5 5.1 -0.58* Z 165.5 18.5 4.9 -0.81* (*) highlights the models with the relative "best" fits to the observed data 193 Model A fits the Single Locus/ Additive/ and Genetic Heterogeneity models. Xmz — 2(Xsib - 0.5) Xhalf=0.5(Asib+1) Model B is a model with Infinite Loci with each locus contributing very little to overall risk. Xmz = Xsib2 Mial f=^s ib 0 5 Model C-Kare models of multiple loci contributing multiplicatively to risk. Xmz = 2 n ( l s i b 1 / n -0 .5 ) n Mialf=0.5 n (?isib 1 / n + l ) n n=2,3,4....10,(i.e.#ofloci) Model C involves 2 loci each contributing equally to risk (e.g. Xa=Xb=43). Model D involves 3 loci. Model E involves 4 loci. Model F involves 5 loci. Model G involves 6 loci. Model H involves 7 loci. Model I involves 8 loci. Model J involves 9 loci. Model K involves 10 loci. Models L-Z involve an N epistatic loci component and a polygenic component where each locus contributes relatively little to overall risk and there is an infinite number of loci. N Epistatic Loci + Polygenes Xmz = 2" (Xa U n - 0.5f (Xb2) Jlhalf = 0.5" (Xa V n + l ) n ( Xb05) A.sib - (Xa)*( Xb) Model L involves a single locus contributing Xa = 1.5 and the polygenic component contributing Xb = 12.5. Model M involves a single locus contributing Xa = 2.1 and the polygenic component contributing Xb = 9.0. Model N involves a single locus contributing Xa = 2.85 and the polygenic component contributing A.b = 6.5. Model O involves a single locus contributing Xa = 4.3 and the polygenic component contributing A.b = 4.3. Model P involves a two loci contributing Xa = 4.3 and the polygenic component contributing Xb = 4.3. 194 Model Q involves 1 locus contributing Xa = 6.2 and the polygenic component contributing Xb = 3.0. Model R involves 2 loci contributing Xa = 6.2 and the polygenic component contributing Xb = 3.0. Model S involves 3 loci contributing Xa = 6.2 and the polygenic component contributing A.b = 3.0. Model T involves 2 loci contributing Xa = 9.25 and the polygenic component contributing A,b=2.0. Model U involves 3 loci contributing Xa = 9.25 and the polygenic component contributing Xb=2.0. Model V involves 4 loci contributing Xa = 9.25 and the polygenic component contributing A,b=2.0. Model W involves 5 loci contributing Xa = 9.25 and the polygenic component contributing A,b=2.0. Model X involves 4 loci contributing Xa = 12.5 and the polygenic component contributing Xb=l.5. Model Y involves 5 loci contributing Xa = 12.5 and the polygenic component contributing A,b=1.5. Model Z involves 6 loci contributing Xa = 12.5 and the polygenic component contributing A,b=l.5. The Z score is the log 1 0 likelihood of observing the recurrence risk in the monozygotic twins the sibs, and the half-sibs of index cases. It is calculated by assuming a Poisson distribution of exactly x affected relatives of type R observed in the sample of n relatives of the index case with the proportion, XR multiplied by the population prevalence (K) expected. This is given by, 3 Z = Z log 1 0 P (x:n. X» * K) R - M ^ f { x . ^ x / n ) (Farrall and Holder, AmJ.Hum.Genet. 50: 270-277, 1992). As such, a Z score of 0 corresponds to a "perfect" fit. A Z score of-1 corresponds to a fit 1 OX worse than the "perfect" fit. A Z score of-2 corresponds to a fit 100X worse than the perfect" fit. 195
Thesis/Dissertation
eng
Medical Genetics
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Graduate
Family studies of multiple sclerosis
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