臺灣博碩士論文加值系統

階層架構資料(hierarchical data)在複雜性疾病(complex disease)研究中非常常見；例如，家族研究(pedigree study)或配對病例對照研究(matched case-control study)都是階層架構資料。這一類的資料有一個特點，那就是資料的變異來源並非單一，除了樣本個人之間的變異，還有不同群體之間的差異；此時，使用階層模式(hierarchical model)來分析能適度分別這些不同來源的變異。本研究利用階層模式來處理幾種不同類型的階層架構資料：﹝一﹞研究樣本有些來自不同地區，有些來自相同地區；﹝二﹞配對病例對照研究；﹝三﹞重複測量的家族研究。
有些階層架構資料來自於探討環境因子對健康影響的研究，因此，除了個人層次因子的影響，還必須考慮團體層次因子的影響(contextual effect)。個人層次的因子有個人特徵，如性別、年齡、社經地位，及個人生活習慣等；團體層次的影響則指大環境的某些暴露量對個人的健康所造成的影響。這些團體層次的因子對個人健康所造成的影響，可能是直接作用，也可能在個人層次因子與健康的相關上扮演修飾因子(modifier)的角色。本論文第一個研究即著重於探討團體層次的暴露﹝居住地區的公共休閒設施﹞與代謝症候群(metabolic syndrome)的相關。研究樣本為14658名參加健康檢查的桃園地區居民。探討的團體層次環境因子則為公共休閒設施，例如公園或是學校操場；依其在各鄉鎮的密度，從低到高分為四個類別。分析的方法則使用MLwiN software version 2.0進行多層次邏輯斯迴歸分析(multilevel logistic regression models)。結果發現，在考慮了個人層次解釋變項的影響後，團體層次環境因子可以進一步解釋7.2%代謝症候群的變異。比起公共休閒設施最低的地區，其他三種不同設施密度的地區，其符合代謝症候群診斷的勝算比及95%信賴區間分別﹝從低到高﹞為0.87 (0.71-1.07), 0.87 (0.68-1.12),及 0.78 (0.61-0.99)，且趨勢檢定達統計顯著。此研究顯示，住在公共休閒設施較高的地區，符合代謝症候群診斷的風險會較低。
除了本論文第一個部分所探討的個人層次與團體層次的環境因子之外，本論文進一步在第二個部份探討不同層次因子之間的交互作用，例如基因與團體層次因子之間的交互作用、以及對疾病的影響。這種檢定交互作用項的顯著與否通常必須倚賴大樣本的統計假設來進行概似函數檢定(likelihood ratio test, LRT)；然而，該方法在樣本數不夠多、觀察值具群聚性(clustered data)、或模式中參數量高的情形下，並不適用，也較不容易偵測出交互作用的存在。因此，本論文在這一部份中利用貝氏階層混合效應模式(Bayesian hierarchical mixed-effects model)，針對配對病例對照研究的資料，來分析基因的作用是否會因為團體層次的暴露狀態不同而有所差異，並且以馬可夫鏈蒙地卡羅(Markov chain Monte Carlo, MCMC)方法所得的事後樣本(posterior sample)來進行統計推論。結果發現，rs1801282對代謝症候群的影響，會被地區的運動設施密度高低所修飾。此研究所使用的貝氏階層混合效應模式對於探討跨層次的基因環境交互作用可提供量性的評估。
本論文第三個部份則考慮當基因因子測量變數本身也具誤差時，例如拷貝數變異(copy number variation)，如何評估其與精神分裂症的相關。由於現有實驗方法在測量拷貝數變異時會有誤差，因此常需針對同一個人的同一點位置進行多次測量。本研究收集了607個家族的資料，包括來自全台灣手足皆患有精神分裂症的病患及其一等親家屬。因此，分析時必須同時考慮是否罹患精神分裂症的反應變數在家族之內的相關性、以及重複測量拷貝數變異所產生的相關性。本研究以貝氏階層混合效應模式(Bayesian hierarchical mixed-effects model)，先將連續型的拷貝數變異測量轉換為整數的拷貝數，再將不同點的拷貝數變異集合起來，探討其與疾病的相關與否。當把三個點的拷貝數變異集合起來，其基因效應的事後樣本有80%大於零，表示如果有這三個點的任一變異，比較容易有精神分裂症。這個模式對於找出稀有的致病基因有所幫助。

In association studies of complex diseases, data are usually collected in a hierarchical way. Examples are matched case-control or family-based studies. Such nesting data contain multiple sources of variability including similarity within clustered observed values and between subjects in the same category of certain variable. These sources of variation can be explained properly in a hierarchical model. In this thesis, three different types of nesting data are considered: (1) individuals from the same area share similar environmental effect, (2) the group-level environmental effect depends on the individual-level genetic factor, and (3) genetic covariates are measured repeatedly for a family-based study.
The first focus of this thesis is the environmental influence measured at the individual level, such as by personal characteristics and lifestyle choices, and at the group-level (called contextual variables), such as by community factors and degree of local area development. The contextual influence on individual health status may be direct effect, or it can modify the influence of individual-level factors. The first aim of this thesis is to examine the relationship between contextual variable, the availability of public facilities for habitual physical activity in the community, and metabolic syndrome in northern Taiwan, one of the most densely populated countries in the world. Subjects consisted of 14658 participants aged 40 years or older from 10 districts of Taoyuan County in a health check-up program in 2004–2005. Public facility for habitual physical activity included school campuses and parks. Multilevel logistic regression models were created to examine the effect on metabolic syndrome at both the individual and the contextual level using MLwiN software version 2.0. The addition of the contextual variable to the model that included individual characteristics led to a further reduction of 7.2% in the variance. Using the facility density level I as the reference, the odds ratios (95% confidence interval) of metabolic syndrome for levels II, III, and IV were 0.87 (0.71-1.07), 0.87 (0.68-1.12), and 0.78 (0.61-0.99), respectively, with the trend test reaching significance. Greater availability of free facilities for habitual physical activity in a district was associated with a lower risk of metabolic syndrome among its residents.
The second of the thesis is to explore the cross-level gene-environment interaction. To examine the interaction between genes and contextual factors, traditional approaches usually adopt asymptotic tests to assess whether the interaction exists. Such tests are limited as they cannot evaluate or quantify the strength of difference in genetic effects across different clusters or categories of the contextual variable. A Bayesian hierarchical mixed-effects model can examine whether the genetic effect is modified by residential environment in a matched case-control metabolic syndrome study. The group-level contextual covariate, availability of exercise facilities, contains four categories, from low to high. Based on posterior samples from Markov chain Monte Carlo (MCMC) methods, the interaction between this group-level environmental condition and the individual-level genetic effect is evaluated by differences in allelic effects under various contextual categories. The Bayesian analysis indicates that the effect of rs1801282 on metabolic syndrome development is modified by the contextual environmental factor such that, even with the same genotype, living in a residential area with low availability of exercise facility may result in higher risk. This Bayesian hierarchical mixed-effects model provides a quantitative assessment for the cross-level interaction between genes and contextual variables.
The third part of this thesis focused on the association between copy number variation (CNV), which was measured repeatedly, and schizophrenia. The study sample consisted of 607 families of schizophrenia individuals and their first-degree relatives. Here the correlation arises not only between family members but also within repeated CNV measurements. This Bayesian hierarchical model assigns integers in the probabilistic sense to the repeated-measured quantitatively copy numbers, and is able to test the association simultaneously between all variants under study. When collapsing all three CNVs, the posterior probability of risk was 80%, indicating a higher risk of schizophrenia. This model is useful for scientists who consider CNV for novel target genes identification.

Chapter 1: Thesis Background 1
Chapter 2: Availability of District Facility for Habitual Physical Activity and Metabolic Syndrome in Northern Taiwan: A Multilevel Analysis
2.1. Background 4
2.2. Materials and Methods 6
2.3. Results 12
2.4. Discussion 14
Chapter 3: Inference of cross-level interaction between genes and contextual factors in a matched case-control metabolic syndrome study: a Bayesian approach
3.1. Background 25
3.2. Materials and Methods 28
3.3. Results 32
3.4. Discussion 36
Chapter 4: Bayesian hierarchical model for association with rare variants
4.1. Background 47
4.2. Materials and Methods 49
4.3. Results 52
4.4. Further Applications in Family Studies with Different Distributions for Phenotype and CNV 53
4.5. Discussion 55
Chapter 5: Conclusion and Discussion 61
Reference 63
Appendix I: Complete Specification of the Bayesian Model 81
Appendix II: Wang S-H, Hsiao P-C, Hsiao CK, Liu P-H, Chien K-L, Lin S-R, Lee W-C, Chen WJ* (2011): Multilevel analysis of habitual physical activity and metabolic syndrome in northern Taiwan. Preventive Medicine: (doi:10.1016/j.ypmed.2011.04.017). 82

1.Raudenbush SW, Bryk AS. Hierarchical Linear Models. London: Sage Publications; 2002.
2.Snijders TAB, Bisker RJ. Multilevel Analsis. London: Sage Publications; 1999.
3.Blakely TA, Woodward AJ. Ecological effects in multi-level studies. J Epidemiol Community Health. 2000;54(5):367-74.
4.Greenland S. Ecologic versus individual-level sources of bias in ecologic estimates of contextual health effects. Int J Epidemiol. 2001;30(6):1343-50.
5.Cameron AJ, Shaw JE, Zimmet PZ. The metabolic syndrome: prevalence in worldwide populations. Endocrinology & Metabolism Clinics of North America. 2004;33(2):351-75.
6.Yoon KH, Lee JH, Kim JW, Cho JH, Choi YH, Ko SH, et al. Epidemic obesity and type 2 diabetes in Asia. Lancet. 2006;368(9548):1681-8.
7.Chen WJ, Liu PH, Ho YY, Chien K-L, Lo M-T, Shih W-L, et al. Sibling recurrence risk ratio analysis of the metabolic syndrome and its components over time. BMC Genet. 2003;4(Suppl 1):S33.
8.Poulsen P, Vaag A, Kyvik K, Beck-Nielsen H. Genetic versus environmental etiology of the metabolic syndrome among male and female twins. Diabetologia. 2001;44:537-43.
9.Diez-Roux AV, Link BG, Northridge ME. A multilevel analysis of income inequality and cardiovascular disease risk factors. Social Science & Medicine. 2000;50(5):673-87.
10.Merlo J, Asplund K, Lynch J, Rastam L, Dobson A, World Health Organization MP. Population effects on individual systolic blood pressure: a multilevel analysis of the World Health Organization MONICA Project. Am J Epidemiol. 2004;159(12):1168-79.
11.Robert SA, Reither EN. A multilevel analysis of race, community disadvantage, and body mass index among adults in the US. Soc Sci Med. 2004;59(12):2421-34.
12.Ross NA, Tremblay S, Khan S, Crouse D, Tremblay M, Berthelot J-M. Body mass index in urban Canada: neighborhood and metropolitan area effects. Am J Public Health. 2007;97(3):500-8.
13.Frank LD, Schmid TL, Sallis JF, Chapman J, Saelens BE. Linking objectively measured physical activity with objectively measured urban form: findings from SMARTRAQ. Am J Prev Med. 2005;28(2 Suppl 2):117-25.
14.Humpel N, Owen N, Leslie E. Environmental factors associated with adults'' participation in physical activity: a review. Am J Prev Med. 2002;22(3):188-99.
15.Saelens BE, Sallis JF, Frank LD. Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures. Ann Behav Med. 2003;25(2):80-91.
16.Ewing R, Schmid T, Killingsworth R, Zlot A, Raudenbush S. Relationship between urban sprawl and physical activity, obesity, and morbidity. Am J Health Promot. 2003;18(1):47-57.
17.Frank LD, Andresen MA, Schmid TL. Obesity relationships with community design, physical activity, and time spent in cars. Am J Prev Med. 2004;27(2):87-96.
18.Frank LD, Saelens BE, Powell KE, Chapman JE. Stepping towards causation: do built environments or neighborhood and travel preferences explain physical activity, driving, and obesity? Soc Sci Med. 2007;65(9):1898-914.
19.Lopez R. Urban sprawl and risk for being overweight or obese. Am J Public Health. 2004;94(9):1574-9.
20.Sturm R, Cohen DA. Suburban sprawl and physical and mental health. Public Health. 2004;118(7):488-96.
21.Diez Roux AV, Evenson KR, McGinn AP, Brown DG, Moore L, Brines S, et al. Availability of recreational resources and physical activity in adults. Am J Public Health. 2007;97(3):493-9.
22.Kaczynski AT, Henderson KA. Environmental correlates of physical activity: a review of evidence about parks and recreation. Leisure Sci. 2007;29:315-54.
23.Shephard RJ. Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med. 2003;37(3):197-206; discussion
24.Westerterp KR. Assessment of physical activity: a critical appraisal. Eur J Appl Physiol. 2009;105(6):823-8.
25.Howley ET. Type of activity: resistance, aerobic and leisure versus occupational physical activity. Med Sci Sports Exerc. 2001;33(6 Suppl):S364-9; discussion S419-20.
26.Fund UNP. State of world population 2007: Unleashing the potential of urban growth. Marshall A, editor. New York: United Nations Population Fund; 2007.
27.Chang CJ, Lu FH, Yang YC, Wu J-S, Wu T-J, Chen M-S, et al. Epidemiologic study of type 2 diabetes in Taiwan. Diabetes Research & Clinical Practice. 2000;50(Supplement 2):S49-S59.
28.Lu FH, Yang YC, Wu JS, Wu C-H, Chang C-J. A population-based study of the prevalence and associated factors of diabetes mellitus in southern Taiwan. Diabetic Medicine. 1998;15(7):564-72.
29.Huang KC. Obesity and its related diseases in Taiwan. Obesity Reviews. 2008;9 Suppl 1:32-4.
30.Patel A, Huang KC, Janus ED, Gill T, Neal B, Suriyawongpaisal P, et al. Is a single definition of the metabolic syndrome appropriate?--A comparative study of the USA and Asia. Atherosclerosis. 2006;184(1):225-32.
31.Eriksson C, Rosenlund M, Pershagen G, Hilding A, Ostenson C-G, Bluhm G. Aircraft noise and incidence of hypertension. Epidemiology. 2007;18(6):716-21.
32.Franssen EAM, van Wiechen CMAG, Nagelkerke NJD, Lebret E. Aircraft noise around a large international airport and its impact on general health and medication use. Occupational & Environmental Medicine. 2004;61(5):405-13.
33.Jarup L, Babisch W, Houthuijs D, Pershagen G, Katsouyanni K, Cadum E, et al. Hypertension and exposure to noise near airports: the HYENA study. Environmental Health Perspectives. 2008;116(3):329-33.
34.Rhee MY, Kim HY, Roh SC, Kim H-J, Kwon H-J. The effects of chronic exposure to aircraft noise on the prevalence of hypertension. Hypertension Research. 2008;31(4):641-7.
35.Rosenlund M, Berglind N, Pershagen G, Jarup L, Bluhm G. Increased prevalence of hypertension in a population exposed to aircraft noise. Occupational & Environmental Medicine. 2001;58(12):769-73.
36.Chu NS. Effects of Betel chewing on the central and autonomic nervous systems. J Biomed Sci. 2001;8(3):229-36.
37.Chien KL, Lee BC, Hsu HC, Lin H-J, Chen M-F, Lee Y-T. Prevalence, agreement and classification of various metabolic syndrome criteria among ethnic Chinese: A report on the hospital-based health diagnosis of the adult population. Atherosclerosis. 2008;196(2):764-71.
38.County Government of Taoyuan. The Statistical Abstract of Taoyuan Prefecture 2004: County Government of Taoyuan, Tawiwan; 2005.
39.Rasbash J, Steele F, Browne W, Prosser B. A User''s Guide to MLwiN: Centre for Multilevel Modelling, Institute of Education, University of London; 2004.
40.Larsen K, Merlo J. Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression. Am J Epidemiol. 2005;161(1):81-8.
41.Merlo J, Chaix B, Ohlsson H, Beckman A, Johnell K, Hjerpe P, et al. A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. J Epidemiol Community Health. 2006;60(4):290-7.
42.Browne WJ. MCMC estimation in MLwiN: Centre for Multilevel Modelling, Institute of Education, University of London; 2004.
43.Gu D, Reynolds K, Wu X, Chen J, Duan X, Reynolds RF, et al. Prevalence of the metabolic syndrome and overweight among adults in China. Lancet. 2005;365(9468):1398-405.
44.Nishimura R, Nakagami T, Tominaga M, Yoshiike N, Tajima N. Prevalence of metabolic syndrome and optimal waist circumference cut-off values in Japan. Diabetes Research & Clinical Practice. 2007;78(1):77-84.
45.Bianchi G, Rossi V, Muscari A, Magalotti D, Zoli M, Pianoro Study G. Physical activity is negatively associated with the metabolic syndrome in the elderly. Qjm. 2008;101(9):713-21.
46.Li CL, Lin JD, Lee SJ, Tseng R-F. Associations between the metabolic syndrome and its components, watching television and physical activity. Public Health. 2007;121(2):83-91.
47.Lin CC, Liu CS, Lai MM, Li C-I, Chen C-C, Chang P-C, et al. Metabolic syndrome in a Taiwanese metropolitan adult population. BMC Public Health. 2007;7:239.
48.Martinez MA, Puig JG, Mora M, Aragon R, O''Dogherty P, Anton JL, et al. Metabolic syndrome: prevalence, associated factors, and C-reactive protein: the MADRIC (MADrid RIesgo Cardiovascular) Study. Metabolism. 2008;57(9):1232-40.
49.Gupta M, Brister S. Is South Asian ethnicity an independent cardiovascular risk factor? Canadian Journal of Cardiology. 2006;22(3):193-7.
50.Guh JY, Chuang LY, Chen HC. Betel-quid use is associated with the risk of the metabolic syndrome in adults. Am J Clin Nutr. 2006;83(6):1313-20.
51.Yen AMF, Chiu YH, Chen LS, Wu HM, Huang CC, Boucher BJ, et al. A population-based study of the association between betel-quid chewing and the metabolic syndrome in men. Am J Clin Nutr. 2006;83(5):1153-60.
52.van Lenthe FJ, Mackenbach JP. Neighbourhood deprivation and overweight: the GLOBE study. Int J Obes Relat Metab Disord. 2002;26(2):234-40.
53.Taylor WC, C. PWS, Jones L, Kraft MK. Environmental justice: obesity, physical activity, and health eating. Journal of Physical Activity & Health. 2006:S30-S54.
54.Qi L, Cho YA. Gene-environment interaction and obesity. Nutr Rev. 2008;66(12):684-94.
55.Stephens JW, Bain SC, Humphries SE. Gene-environment interaction and oxidative stress in cardiovascular disease. Atherosclerosis. 2008;200(2):229-38.
56.Thapar A, Harold G, Rice F, Langley K, O''Donovan M. The contribution of gene-environment interaction to psychopathology. Dev Psychopathol. 2007;19(4):989-1004.
57.Dempfle A, Scherag A, Hein R, Beckmann L, Chang-Claude J, Schafer H. Gene-environment interactions for complex traits: definitions, methodological requirements and challenges. Eur J Hum Genet. 2008;16(10):1164-72.
58.Greenland S. Interactions in epidemiology: relevance, identification, and estimation. Epidemiology. 2009;20(1):14-7.
59.Hunter DJ. Gene-environment interactions in human diseases. Nat Rev Genet. 2005;6(4):287-98.
60.Kraft P, Hunter D. Integrating epidemiology and genetic association: the challenge of gene-environment interaction. Philos Trans R Soc Lond B Biol Sci. 2005;360(1460):1609-16.
61.Rutter M. Biological implications of gene-environment interaction. J Abnorm Child Psychol. 2008;36(7):969-75.
62.Diez Roux AV. A glossary for multilevel analysis. J Epidemiol Community Health. 2002;56(8):588-94.
63.Wang SH, Hsiao PC, Hsiao CK, Liu PH, Chien KL, Lin SR, et al. Multilevel analysis of habitual physical activity and metabolic syndrome in Northern Taiwan. Prev Med. 2011:doi:10.1016/j.ypmed.2011.04.017.
64.Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. 2005;365(9468):1415-28.
65.Zimmet P, Alberti KGMM, Shaw J. Global and societal implications of the diabetes epidemic. Nature. 2001;414(6865):782-7.
66.Chen WJ, Liu P-H, Ho Y-Y, Chien K-L, Lo M-T, Shih W-L, et al. Sibling recurrence risk ratio analysis of the metabolic syndrome and its components over time. BMC Genetics. 2003;4(Suppl 1):S33.
67.Diez Roux AV. Estimating neighborhood health effects: the challenges of causal inference in a complex world. Soc Sci Med. 2004;58(10):1953-60.
68.Hara K, Boutin P, Mori Y, Tobe K, Dina C, Yasuda K, et al. Genetic variation in the gene encoding adiponectin is associated with an increased risk of type 2 diabetes in the Japanese population.[erratum appears in Diabetes 2002 Apr;51(4):1294]. Diabetes. 2002;51(2):536-40.
69.Sutton BS, Weinert S, Langefeld CD, Williams AH, Campbell JK, Saad MF, et al. Genetic analysis of adiponectin and obesity in Hispanic families: the IRAS Family Study. Hum Genet. 2005;117(2-3):107-18.
70.Cardona F, Morcillo S, Gonzalo-Marin M, Garrido-Sanchez L, Macias-Gonzalez M, Tinahones FJ. Pro12Ala sequence variant of the PPARG gene is associated with postprandial hypertriglyceridemia in non-E3/E3 patients with the metabolic syndrome. Clin Chem. 2006;52(10):1920-5.
71.Heid IM, Wagner SA, Gohlke H, Iglseder B, Mueller JC, Cip P, et al. Genetic architecture of the APM1 gene and its influence on adiponectin plasma levels and parameters of the metabolic syndrome in 1,727 healthy Caucasians. Diabetes. 2006;55(2):375-84.
72.Mammes O, Betoulle D, Aubert R, Giraud V, Tuzet S, Petiet A, et al. Novel polymorphisms in the 5'' region of the LEP gene: association with leptin levels and response to low-calorie diet in human obesity.[erratum appears in Diabetes 2000 Sep;49(9):1617]. Diabetes. 1998;47(3):487-9.
73.Altshuler D, Hirschhorn JN, Klannemark M, Lindgren CM, Vohl MC, Nemesh J, et al. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet. 2000;26(1):76-80.
74.Deeb SS, Fajas L, Nemoto M, Pihlajamaki J, Mykkanen L, Kuusisto J, et al. A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nat Genet. 1998;20(3):284-7.
75.Beamer BA, Yen CJ, Andersen RE, Muller D, Elahi D, Cheskin LJ, et al. Association of the Pro12Ala variant in the peroxisome proliferator-activated receptor-gamma2 gene with obesity in two Caucasian populations. Diabetes. 1998;47(11):1806-8.
76.Cole SA, Mitchell BD, Hsueh WC, Pineda P, Beamer BA, Shuldiner AR, et al. The Pro12Ala variant of peroxisome proliferator-activated receptor-gamma2 (PPAR-gamma2) is associated with measures of obesity in Mexican Americans. Int J Obes. 2000;24(4):522-4.
77.Valve R, Sivenius K, Miettinen R, Pihlajamaki J, Rissanen A, Deeb SS, et al. Two polymorphisms in the peroxisome proliferator-activated receptor-gamma gene are associated with severe overweight among obese women. J Clin Endocrinol Metab. 1999;84(10):3708-12.
78.Doney A, Fischer B, Frew D, Cumming A, Flavell DM, World M, et al. Haplotype analysis of the PPARgamma Pro12Ala and C1431T variants reveals opposing associations with body weight. BMC Genet. 2002;3:21.
79.Semple RK, Chatterjee VKK, O''Rahilly S. PPAR gamma and human metabolic disease. J Clin Invest. 2006;116(3):581-9.
80.Gelfand AE, Hills SE, Racine-Poon A, Smith AFM. Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling. J Amer Statistical Assoc. 1990;85:972-85.
81.Spiegelhalter D, Thomas A, Best NG. WinBUGS user manual, version 1.4. Cambridge, United Kingdom: MRC Biostatistics Unit, University of Cambridge; 2003.
82.Adamo KB, Sigal RJ, Williams K, Kenny G, Prud''homme D, Tesson F. Influence of Pro12Ala peroxisome proliferator-activated receptor gamma2 polymorphism on glucose response to exercise training in type 2 diabetes. Diabetologia. 2005;48(8):1503-9.
83.Franks PW, Luan J, Browne PO, Harding AH, O''Rahilly S, Chatterjee VKK, et al. Does peroxisome proliferator-activated receptor gamma genotype (Pro12ala) modify the association of physical activity and dietary fat with fasting insulin level? Metabolism. 2004;53(1):11-6.
84.Kahara T, Takamura T, Hayakawa T, Nagai Y, Yamaguchi H, Katsuki T, et al. PPARgamma gene polymorphism is associated with exercise-mediated changes of insulin resistance in healthy men. Metabolism. 2003;52(2):209-12.
85.Lindi VI, Uusitupa MIJ, Lindstrom J, Louheranta A, Eriksson JG, Valle TT, et al. Association of the Pro12Ala polymorphism in the PPAR-gamma2 gene with 3-year incidence of type 2 diabetes and body weight change in the Finnish Diabetes Prevention Study. Diabetes. 2002;51(8):2581-6.
86.Weiss EP, Kulaputana O, Ghiu IA, Brandauer J, Wohn CR, Phares DA, et al. Endurance training-induced changes in the insulin response to oral glucose are associated with the peroxisome proliferator-activated receptor-gamma2 Pro12Ala genotype in men but not in women. Metabolism. 2005;54(1):97-102.
87.Chakravarthy MV, Booth FW. Eating, exercise, and "thrifty" genotypes: connecting the dots toward an evolutionary understanding of modern chronic diseases. J Appl Physiol. 2004;96(1):3-10.
88.Jiang Y, Wilk JB, Borecki I, Williamson S, DeStefano AL, Xu G, et al. Common variants in the 5'' region of the leptin gene are associated with body mass index in men from the National Heart, Lung, and Blood Institute Family Heart Study. Am J Hum Genet. 2004;75(2):220-30.
89.Browne WJ, Draper D. A comparison of Bayesian and likelihood-based methods for fitting multilevel models. International Society for Bayesian Analysis. 2006:473-514.
90.Diggle PJ, Morris SE, Wakefield JC. Point-source modeling using matched case-control data. Biostatistics. 2000;1:89-105.
91.Liao JG. A Hierarchical Bayesian model for combining multiple 2 × 2 tables using conditional likelihoods. Biometrics. 1999;55:268-72.
92.Ghosh M, Chen M-H. Bayesian inference for matched case-control studies. The Indian Journal of Statistics. 2002;64:107-27.
93.Ghosh M, Chen M-H, Ghosh A, Agresti A. Hierarchical Bayesian analysis of binary matched pairs data. Stat Sinica. 2000;10:647-57.
94.Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y, et al. Detection of large-scale variation in the human genome. Nat Genet. 2004;36(9):949-51.
95.Sebat J, Lakshmi B, Troge J, Alexander J, Young J, Lundin P, et al. Large-scale copy number polymorphism in the human genome. Science. 2004;305(5683):525-8.
96.Sharp AJ, Locke DP, McGrath SD, Cheng Z, Bailey JA, Vallente RU, et al. Segmental duplications and copy-number variation in the human genome. Am J Hum Genet. 2005;77(1):78-88.
97.Tuzun E, Sharp AJ, Bailey JA, Kaul R, Morrison VA, Pertz LM, et al. Fine-scale structural variation of the human genome. Nature Genetics. 2005;37(7):727-32.
98.Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, et al. Global variation in copy number in the human genome. Nature. 2006;444(7118):444-54.
99.Stankiewicz P, Lupski JR. Genome architecture, rearrangements and genomic disorders. Trends Genet. 2002;18(2):74-82.
100.Dhami P, Coffey AJ, Abbs S, Vermeesch JR, Dumanski JP, Woodward KJ, et al. Exon array CGH: detection of copy-number changes at the resolution of individual exons in the human genome. Am J Hum Genet. 2005;76(5):750-62.
101.Bieche I, Olivi M, Champeme MH, Vidaud D, Lidereau R, Vidaud M. Novel approach to quantitative polymerase chain reaction using real-time detection: application to the detection of gene amplification in breast cancer. Int J Cancer. 1998;78(5):661-6.
102.Ponchel F, Toomes C, Bransfield K, Leong FT, Douglas SH, Field SL, et al. Real-time PCR based on SYBR-Green I fluorescence: an alternative to the TaqMan assay for a relative quantification of gene rearrangements, gene amplifications and micro gene deletions. BMC Biotechnol. 2003;3:18.
103.Kosta K, Sabroe I, Goke J, Nibbs RJ, Tsanakas J, Whyte MK, et al. A Bayesian approach to copy-number-polymorphism analysis in nuclear pedigrees. American Journal of Human Genetics. 2007;81(4):808-12.
104.Wang K, Chen Z, Tadesse MG, Glessner J, Grant SFA, Hakonarson H, et al. Modeling genetic inheritance of copy number variations. Nucleic Acids Research. 2008;36(21):e138.
105.Barnes C, Plagnol V, Fitzgerald T, Redon R, Marchini J, Clayton D, et al. A robust statistical method for case-control association testing with copy number variation. Nature Genetics. 2008;40(10):1245-52.
106.International Schizophrenia C. Rare chromosomal deletions and duplications increase risk of schizophrenia. Nature. 2008;455(7210):237-41.
107.Walsh T, McClellan JM, McCarthy SE, Addington AM, Pierce SB, Cooper GM, et al. Rare structural variants disrupt multiple genes in neurodevelopmental pathways in schizophrenia. Science. 2008;320(5875):539-43.
108.Stefansson H, Rujescu D, Cichon S, Pietilainen OP, Ingason A, Steinberg S, et al. Large recurrent microdeletions associated with schizophrenia. Nature. 2008;455(7210):232-6.
109.Iyengar SK, Elston RC. The genetic basis of complex traits: rare variants or "common gene, common disease"? Methods in Molecular Biology. 2007;376:71-84.
110.Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. American Journal of Human Genetics. 2008;83(3):311-21.
111.Madsen BE, Browning SR. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genetics. 2009;5(2):e1000384.
112.Price AL, Kryukov GV, de Bakker PIW, Purcell SM, Staples J, Wei L-J, et al. Pooled association tests for rare variants in exon-resequencing studies.[Erratum appears in Am J Hum Genet. 2010 Jun 11;86(6):982]. American Journal of Human Genetics. 2010;86(6):832-8.
113.King CR, Rathouz PJ, Nicolae DL. An evolutionary framework for association testing in resequencing studies. PLoS Genetics. 2010;6(11):e1001202.
114.Tam GW, Redon R, Carter NP, Grant SG. The role of DNA copy number variation in schizophrenia. Biol Psychiatry. 2009;66(11):1005-12.
115.Ikeda M, Aleksic B, Kirov G, Kinoshita Y, Yamanouchi Y, Kitajima T, et al. Copy number variation in schizophrenia in the Japanese population. Biol Psychiatry. 2010;67(3):283-6.
116.Pritchard JK. Are rare variants responsible for susceptibility to complex diseases? Am J Hum Genet. 2001;69(1):124-37.
117.Bodmer W, Bonilla C. Common and rare variants in multifactorial susceptibility to common diseases. Nature Genetics. 2008;40(6):695-701.
118.Stratton MR, Rahman N. The emerging landscape of breast cancer susceptibility. Nature Genetics. 2008;40(1):17-22.
119.Hwu HG, Faraone SV, Liu CM, Chen WJ, Liu SK, Shieh MH, et al. Taiwan schizophrenia linkage study: the field study. Am J Med Genet B Neuropsychiatr Genet. 2005;134B(1):30-6.
120.Lien. Y-J, Hsiao. P-C, Liu. C-M, Faraone. SV, Tsuang. MT, Hwu. H-G, et al. Genetic Linkage Evidence for Distinct Subtypes of Schizophrenia Characterized by Age at Onset and Neurocognitive Deficits. submitted.