Interpreting meta-analyses of genome-wide association studies

PLoS Genetics

Volumn 8, Issue 3, 2012, Pages

  Han, Buhm ^a   Eskin, Eleazar ^a,b  

^a University of California   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; CLINICAL STUDY; COMPUTER ANALYSIS; CROHN DISEASE; DATA ANALYSIS; GENETIC ASSOCIATION; INFORMATION PROCESSING; NON INSULIN DEPENDENT DIABETES MELLITUS; PROBABILITY; SIMULATION; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL ANALYSIS; GENETICS; HUMAN; META ANALYSIS (TOPIC); METHODOLOGY; SAMPLE SIZE; THEORETICAL MODEL;

CROHN DISEASE; DIABETES MELLITUS, TYPE 2; GENOME-WIDE ASSOCIATION STUDY; HUMANS; META-ANALYSIS AS TOPIC; MODELS, THEORETICAL; RESEARCH DESIGN; SAMPLE SIZE;

EID: 84859246571     PISSN: 15537390     EISSN: 15537404     Source Type: Journal    
DOI: 10.1371/journal.pgen.1002555     Document Type: Article

References (40)

1. Cochran WG, (1954) The combination of estimates from different experiments. Biometrics 10: 101-129.
2. Mantel N, Haenszel W, (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22: 719-48.
3. Fleiss JL, (1993) The statistical basis of meta-analysis. Stat Methods Med Res 2: 121-45.
4. Hardy J, Singleton A, (2009) Genomewide association studies and human disease. New England Journal of Medicine 360: 1759-1768.
5. de Bakker PIW, Ferreira MAR, Jia X, Neale BM, Raychaudhuri S, et al. (2008) Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum Mol Genet 17: R122-8.
6. Cantor RM, Lange K, Sinsheimer JS, (2010) Prioritizing gwas results: A review of statistical methods and recommendations for their application. Am J Hum Genet 86: 6-22.
7. Zeggini E, Ioannidis JPA, (2009) Meta-analysis in genome-wide association studies. Pharmacoge- nomics 10: 191-201.
8. Barrett JC, Clayton DG, Concannon P, Akolkar B, Cooper JD, et al. (2009) Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet 41.
9. Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, et al. (2007) Replication of genome- wide association signals in uk samples reveals risk loci for type 2 diabetes. Science 316: 1336-41.
10. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, et al. (2007) A genome-wide association study of type 2 diabetes in finns detects multiple susceptibility variants. Science 316: 1341-5.
11. Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PIW, et al. (2007) Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316: 1331-6.
12. Scott LJ, Muglia P, Kong XQ, Guan W, Flickinger M, et al. (2009) Genome-wide association and meta-analysis of bipolar disorder in individuals of european ancestry. Proc Natl Acad Sci U S A 106: 7501-6.
13. Franke A, McGovern DPB, Barrett JC, Wang K, Radford-Smith GL, et al. (2010) Genome-wide meta-analysis increases to 71 the number of confirmed crohn's disease susceptibility loci. Nat Genet 42: 1118-25.
14. Stahl EA, Raychaudhuri S, Remmers EF, Xie G, Eyre S, et al. (2010) Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat Genet 42: 508-14.
15. DerSimonian R, Laird N, (1986) Meta-analysis in clinical trials. Control Clin Trials 7: 177-88.
16. Higgins JPT, Thompson SG, (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21: 1539-58.
17. Ioannidis JPA, Patsopoulos NA, Evangelou E, (2007) Uncertainty in heterogeneity estimates in meta-analyses. BMJ 335: 914-6.
18. Field AP, (2003) The problems in using fixed-effects models of meta-analysis on real-world data. Understanding Statistics.
19. Tang H, (2006) Confronting ethnicity-specific disease risk. Nature genetics 38: 13.
20. Barroso I, Luan J, Wheeler E, Whittaker P, Wasson J, et al. (2008) Population-specific risk of type 2 diabetes conferred by hnf4a p2 promoter variants: a lesson for replication studies. Diabetes 57: 3161-5.
21. Kim-Cohen J, Caspi A, Taylor A, Williams B, Newcombe R, et al. (2006) Maoa, maltreatment, and gene-environment interaction predicting children's mental health: new evidence and a meta-analysis. Mol Psychiatry 11: 903-913.
22. Pereira TV, Patsopoulos NA, Salanti G, Ioannidis JPA, (2009) Discovery properties of genome-wide association signals from cumulatively combined data sets. Am J Epidemiol 170: 1197-206.
23. Han B, Eskin E, (2011) Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. American journal of human genetics 88: 586-598.
24. Zaitlen N, Eskin E, (2010) Imputation aware meta-analysis of genome-wide association studies. Genet Epidemiol 34: 537-42.
25. Evangelou E, Maraganore DM, Ioannidis JPA, (2007) Meta-analysis in genome-wide association datasets: strategies and application in parkinson disease. PLoS ONE 2: e196 doi:10.1371/journal.pone.0000196.
26. Ioannidis JPA, (2008) Interpretation of tests of heterogeneity and bias in meta-analysis. J Eval Clin Pract 14: 951-7.
27. Hardy RJ, Thompson SG, (1996) A likelihood approach to meta-analysis with random effects. Statistics in Medicine 15: 619-629.
28. Biggerstaff BJ, Tweedie RL, (1997) Incorporating variability in estimates of heterogeneity in the random effects model in meta-analysis. Stat Med 16: 753-68.
29. Thompson SG, Sharp SJ, (1999) Explaining heterogeneity in meta-analysis: a comparison of methods. Statistics in Medicine 18: 2693-2708.
30. Farrer LA, Cupples LA, Haines JL, Hyman B, Kukull WA, et al. (1997) Effects of age, sex, and ethnicity on the association between apolipoprotein e genotype and alzheimer disease. a meta-analysis. apoe and alzheimer disease meta analysis consortium. JAMA 278: 1349-56.
31. Helgadottir A, Manolescu A, Helgason A, Thorleifsson G, Thorsteinsdottir U, et al. (2006) A variant of the gene encoding leukotriene a4 hydrolase confers ethnicity-specific risk of myocardial infarction. Nat Genet 38: 68-74.
32. Corella D, Ordovas JM, (2005) Single nucleotide polymorphisms that inuence lipid metabolism: Interaction with dietary factors. Annu Rev Nutr 25: 341-90.
33. Kleeberger SR, Peden D, (2005) Gene-environment interactions in asthma and other respiratory diseases. Annu Rev Med 56: 383-400.
34. Caspi A, Moffitt TE, (2006) Gene-environment interactions in psychiatry: joining forces with neu-roscience. Nat Rev Neurosci 7: 583-90.
35. Stephens M, Balding DJ, (2009) Bayesian statistical methods for genetic association studies. Nat Rev Genet 10: 681-90.
36. Marchini J, Howie B, Myers S, McVean G, Donnelly P, (2007) A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39: 906-13.
37. Wasserman L, (2004) All of statistics Springer New York.
38. Han B, Kang HM, Eskin E, (2009) Rapid and accurate multiple testing correction and power estimation for millions of correlated markers. PLoS Genet 5: e1000456 doi:10.1371/journal.pgen.1000456.
39. Wakefield J, (2009) Bayes factors for genome-wide association studies: comparison with p-values. Genet Epidemiol 33: 79-86.
40. Consortium WTCC, (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447: 661-78.