Methods for meta-analyses of genome-wide association studies: Critical assessment of empirical evidence

American Journal of Epidemiology

Volumn 175, Issue 8, 2012, Pages 739-749

  Gögele, Martin ^a   Minelli, Cosetta ^a   Thakkinstian, Ammarin ^b   Yurkiewich, Alex ^c   Pattaro, Cristian ^a   Pramstaller, Peter P ^a   Little, Julian ^c   Attia, John ^d   Thompson, John R ^e  

^a University of Lubeck   (Italy)

^b MAHIDOL UNIVERSITY   (Thailand)

^c UNIVERSITY OF OTTAWA   (Canada)

^d UNIVERSITY OF NEWCASTLE   (Australia)

^e UNIVERSITY OF LEICESTER   (United Kingdom)

Author keywords

Epidemiologic methods; Genome wide association study; Meta analysis; Review; Statistics

Indexed keywords

CRITICAL ANALYSIS; DATA SET; EMPIRICAL ANALYSIS; EPIDEMIOLOGY; GENETIC VARIATION; GENOME; HETEROGENEITY; LITERATURE REVIEW; META-ANALYSIS;

ARTICLE; CONTROLLED STUDY; EMPIRICAL RESEARCH; GENETIC ASSOCIATION; GENETIC HETEROGENEITY; META ANALYSIS (TOPIC); METHODOLOGY;

DATA INTERPRETATION, STATISTICAL; GENOME-WIDE ASSOCIATION STUDY; GUIDELINES AS TOPIC; HUMANS; META-ANALYSIS AS TOPIC; POLYMORPHISM, SINGLE NUCLEOTIDE; RESEARCH REPORT; SAMPLE SIZE;

EID: 84859731474     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwr385     Document Type: Article

References (52)

1. Manolio TA. Genomewide association studies and assessment of the risk of disease. N Engl J Med. 2010;363(2):166-176.
2. Ioannidis JP, Thomas G, Daly MJ. Validating, augmenting and refining genome-wide association signals. Nat Rev Genet. 2009; 10(5):318-329.
3. Zintzaras E, Lau J. Synthesis of genetic association studies for pertinent gene-disease associations requires appropriate methodological and statistical approaches. J Clin Epidemiol. 2008;61(7):634-645. (Pubitemid 351766746)
4. Trikalinos TA, Salanti G, Zintzaras E, et al. Meta-analysis methods. Adv Genet. 2008;60:311-334.
5. Cantor RM, Lange K, Sinsheimer JS. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet. 2010;86(1):6-22.
6. de Bakker PI, Ferreira MA, Jia X, et al. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum Mol Genet. 2008;17(R2):R122-R128.
7. Ioannidis JP. Non-replication and inconsistency in the genomewide association setting. Hum Hered. 2007;64(4):203-213. (Pubitemid 47036983)
8. Moonesinghe R, Khoury MJ, Liu T, et al. Required sample size and nonreplicability thresholds for heterogeneous genetic associations. Proc Natl Acad Sci U S A. 2008;105(2):617-622. (Pubitemid 351171757)
9. Pereira TV, Patsopoulos NA, Salanti G, et al. Discovery properties of genome-wide association signals from cumulatively combined data sets. Am J Epidemiol. 2009;170(10):1197-1206.
10. Thompson JR, Attia J, Minelli C. The meta-analysis of genome-wide association studies. Brief Bioinform. 2011;12(3): 259-269.
11. DeVellis RF. Scale Development: Theory and Applications. 2nd ed. Thousand Oaks, CA: Sage Publications; 2003.
12. Li Y, Willer CJ, Ding J, et al. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010;34(8):816-834.
13. Stahl EA, Raychaudhuri S, Remmers EF, et al. Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat Genet. 2010;42(6):508-514.
14. Price AL, Patterson NJ, Plenge RM, et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904-909. (Pubitemid 44141658)
15. Clayton D. Testing for association on the X chromosome. Biostatistics. 2008;9(4):593-600.
16. Houlston RS, Webb E, Broderick P, et al. Meta-analysis of genome-wide association data identifies four new susceptibility loci for colorectal cancer. Nat Genet. 2008;40(12):1426-1435.
17. Wang Y, Broderick P, Webb E, et al. Common 5p15.33 and 6p21.33 variants influence lung cancer risk. Nat Genet. 2008; 40(12):1407-1409.
18. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26(17):2190-2191.
19. Salonen JT, Uimari P, Aalto JM, et al. Type 2 diabetes whole-genome association study in four populations: the DiaGen consortium. Am J Hum Genet. 2007;81(2):338-345. (Pubitemid 47236080)
20. Sklar P, Smoller JW, Fan J, et al. Whole-genome association study of bipolar disorder. Mol Psychiatry. 2008;13(6):558-569. (Pubitemid 351704939)
21. Debette S, Bis JC, Fornage M, et al. Genome-wide association studies of MRI-defined brain infarcts: meta-analysis from the CHARGE Consortium. Stroke. 2010;41(2):210-217.
22. Ikram MA, Seshadri S, Bis JC, et al. Genomewide association studies of stroke. N Engl J Med. 2009;360(17):1718-1728.
23. Pattaro C, De Grandi A, Vitart V, et al. A meta-analysis of genome-wide data from five European isolates reveals an association of COL22A1, SYT1, and GABRR2 with serum creatinine level. EUROSPAN consortium. BMC Med Genet. 2010;11:41. (doi:10.1186/1471-2350-11-41).
24. Hsu L, Starr JR, Zheng Y, et al. On combining triads and unrelated subjects data in candidate gene studies: an application to data on testicular cancer. Hum Hered. 2009;67(2): 88-103.
25. Anderson CD, Nalls MA, Biffi A, et al. The effect of survival bias on case-control genetic association studies of highly lethal diseases. Circ Cardiovasc Genet. 2011;4(2):188-196.
26. Loos RJ, Lindgren CM, Li S, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet. 2008;40(6):768-775.
27. Barrett JC, Cardon LR. Evaluating coverage of genome-wide association studies. Nat Genet. 2006;38(6):659-662. (Pubitemid 43927307)
28. Li Y, Willer C, Sanna S, et al. Genotype imputation. Annu Rev Genomics Hum Genet. 2009;10:387-406.
29. Pompanon F, Bonin A, Bellemain E, et al. Genotyping errors: causes, consequences and solutions. Nat Rev Genet. 2005; 6(11):847-859.
30. Leek JT, Scharpf RB, Bravo HC, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11(10):733-739.
31. Southam L, Panoutsopoulou K, Rayner NW, et al. The effect of genome-wide association scan quality control on imputation outcome for common variants. Eur J Hum Genet. 2011;19(5): 610-614.
32. Marchini J, Howie B, Myers S, et al. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007;39(7):906-913. (Pubitemid 47014502)
33. Nothnagel M, Ellinghaus D, Schreiber S, et al. A comprehensive evaluation of SNP genotype imputation. Hum Genet. 2009;125(2):163-171.
34. Pei YF, Li J, Zhang L, et al. Analyses and comparison of accuracy of different genotype imputation methods. PLoS One. 2008;3(10):e3551. (doi:10.1371/journal.pone.0003551).
35. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559-575. (Pubitemid 47330214)
36. Clayton D, McKeigue PM. Epidemiological methods for studying genes and environmental factors in complex diseases. Lancet. 2001;358(9290):1356-1360. (Pubitemid 33016504)
37. Thakkinstian A, Thompson J, Minelli C, et al. Choosing between per-genotype, per-allele, and trend approaches for initial detection of gene-disease association. J Appl Stat. 2009;36(6): 633-646.
38. Lettre G, Lange C, Hirschhorn JN. Genetic model testing and statistical power in population-based association studies of quantitative traits. Genet Epidemiol. 2007;31(4):358-362. (Pubitemid 46642100)
39. Pereira TV, Patsopoulos NA, Pereira AC, et al. Strategies for genetic model specification in the screening of genome-wide meta-analysis signals for further replication. Int J Epidemiol. 2011;40(2):457-469.
40. Minelli C, Thompson JR, Abrams KR, et al. The choice of a genetic model in the meta-analysis of molecular association studies. Int J Epidemiol. 2005;34(6):1319-1328. (Pubitemid 41819539)
41. Minelli C, Thompson JR, Abrams KR, et al. Bayesian implementation of a genetic model-free approach to the meta-analysis of genetic association studies. Stat Med. 2005;24(24):3845-3861. (Pubitemid 43115734)
42. Salanti G, Southam L, Altshuler D, et al. Underlying genetic models of inheritance in established type 2 diabetes associations. Am J Epidemiol. 2009;170(5):537-545.
43. Fleiss JL. The statistical basis of meta-analysis. Stat Methods Med Res. 1993;2(2):121-145.
44. Higgins JP, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557-560. (Pubitemid 37088507)
45. Rücker G, Schwarzer G, Carpenter JR, et al. Undue reliance on I2 in assessing heterogeneity may mislead. BMC Med Res Methodol. 2008;8:79. (doi:10.1186/1471-2288-8-79).
46. Ioannidis JP, Patsopoulos NA, Evangelou E. Heterogeneity in meta-analyses of genome-wide association investigations. PLoS One. 2007;2(9):e841. (doi:10.1371/journal.pone.0000841).
47. Thomas DC, Casey G, Conti DV, et al. Methodological issues in multistage genome-wide association studies. Stat Sci. 2009; 24(4):414-429.
48. Bowden J, Dudbridge F. Unbiased estimation of odds ratios: combining genomewide association scans with replication studies. Genet Epidemiol. 2009;33(5):406-418.
49. Goodman SN, Berlin JA. The use of predicted confidence intervals when planning experiments and the misuse of power when interpreting results. Ann Intern Med. 1994;121(3): 200-206. (Pubitemid 24225638)
50. Xiao R, Boehnke M. Quantifying and correcting for the winner's curse in genetic association studies. Genet Epidemiol. 2009;33(5):453-462.
51. Lewis S, Clarke M. Forest plots: trying to see the wood and the trees. BMJ. 2001;322(7300):1479-1480. (Pubitemid 32575420)
52. Little J, Higgins JP, Ioannidis JP, et al. STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement. PLoS Med. 2009;6(2):e22. (doi:10.1371/journal.pmed. 1000022).