Approaches to detect genetic effects that differ between two strata in genome-wide meta-analyses: Recommendations based on a systematic evaluation

PLoS ONE

Volumn 12, Issue 7, 2017, Pages

  Winkler, Thomas W ^a   Justice, Anne E ^b   Cupples, L Adrienne ^c,d   Kronenberg, Florian ^e   Kutalik, Zoltán ^f,g   Heid, Iris M ^a  

^a UNIVERSITY OF REGENSBURG   (Germany)

^b University of North Carolina at Chapel Hill   (United States)

^c BOSTON UNIVERSITY SCHOOL OF PUBLIC HEALTH   (United States)

^d FRAMINGHAM HEART STUDY   (United States)

^e INNSBRUCK MEDICAL UNIVERSITY   (Austria)

^f UNIVERSITY OF LAUSANNE   (Switzerland)

^g SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BODY FAT DISTRIBUTION; CONTROLLED STUDY; FEMALE; FILTRATION; GENOME-WIDE ASSOCIATION STUDY; HUMAN; MALE; META ANALYSIS; PRACTICE GUIDELINE; SEX DIFFERENCE; SIMULATION; UNCERTAINTY; COMPUTER SIMULATION; WAIST HIP RATIO;

COMPUTER SIMULATION; FEMALE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; MALE; WAIST-HIP RATIO;

EID: 85026210668     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0181038     Document Type: Article

References (41)

1. Welter D, MacArthur J, Morales J, Burdett T, Hall P, et al. (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42: D1001–1006. https://doi.org/10.1093/nar/gkt1229 PMID: 24316577
2. Winkler TW, Day FR, Croteau-Chonka DC, Wood AR, Locke AE, et al. (2014) Quality control and conduct of genome-wide association meta-analyses. Nat Protoc 9: 1192–1212. https://doi.org/10.1038/nprot.2014.071 PMID: 24762786
3. Hutter CM, Mechanic LE, Chatterjee N, Kraft P, Gillanders EM, et al. (2013) Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report. Genet Epidemiol 37: 643–657. https://doi.org/10.1002/gepi.21756 PMID: 24123198
4. Pattaro C, Teumer A, Gorski M, Chu AY, Li M, et al. (2016) Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun 7: 10023. https://doi.org/10.1038/ncomms10023 PMID: 26831199
5. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, et al. (2015) New genetic loci link adipose and insulin biology to body fat distribution. Nature 518: 187–196. https://doi.org/10.1038/ nature14132 PMID: 25673412
6. Scott RA, Chu AY, Grarup N, Manning AK, Hivert MF, et al. (2012) No interactions between previously associated 2-hour glucose gene variants and physical activity or BMI on 2-hour glucose levels. Diabetes 61: 1291–1296. https://doi.org/10.2337/db11-0973 PMID: 22415877
7. Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L, et al. (2010) Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 42: 949–960. https://doi.org/10.1038/ng.685 PMID: 20935629
8. Manning AK, Hivert MF, Scott RA, Grimsby JL, Bouatia-Naji N, et al. (2012) A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet 44: 659–669. https://doi.org/10.1038/ng.2274 PMID: 22581228
9. Keller MC (2014) Gene x environment interaction studies have not properly controlled for potential con-founders: the problem and the (simple) solution. Biol Psychiatry 75: 18–24. https://doi.org/10.1016/j.biopsych.2013.09.006 PMID: 24135711
10. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, et al. (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42: 937–948. https://doi.org/10.1038/ng.686 PMID: 20935630
11. Cox DR, Hinkley DV (1979) Theoretical statistics. London New York: Chapman and Hall; distributed in U.S. by Halsted Press. xii, 511 p. p.
12. Randall JC, Winkler TW, Kutalik Z, Berndt SI, Jackson AU, et al. (2013) Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genet 9: e1003500. https://doi.org/10.1371/journal.pgen.1003500 PMID: 23754948
13. Aschard H, Hancock DB, London SJ, Kraft P (2010) Genome-wide meta-analysis of joint tests for genetic and gene-environment interaction effects. Hum Hered 70: 292–300. https://doi.org/10.1159/ 000323318 PMID: 21293137
14. Winkler TW, Justice AE, Graff M, Barata L, Feitosa MF, et al. (2015) The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study. PLoS Genet 11: e1005378. https://doi.org/10.1371/journal.pgen.1005378 PMID: 26426971
15. Taylor KC, Carty CL, Dumitrescu L, Buzkova P, Cole SA, et al. (2013) Investigation of gene-by-sex interactions for lipid traits in diverse populations from the population architecture using genomics and epidemiology study. BMC Genet 14: 33. https://doi.org/10.1186/1471-2156-14-33 PMID: 23634756
16. Willer CJ, Li Y, Abecasis GR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26: 2190–2191. https://doi.org/10.1093/bioinformatics/btq340 PMID: 20616382
17. de Bakker PI, Ferreira MA, 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: R122–128. https://doi.org/10.1093/hmg/ddn288 PMID: 18852200
18. Behrens G, Winkler TW, Gorski M, Leitzmann MF, Heid IM (2011) To stratify or not to stratify: power considerations for population-based genome-wide association studies of quantitative traits. Genet Epidemiol 35: 867–879. https://doi.org/10.1002/gepi.20637 PMID: 22125224
19. Sung YJ, Schwander K, Arnett DK, Kardia SL, Rankinen T, et al. (2014) An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions. Genet Epidemiol 38: 369–378. https://doi.org/10.1002/gepi.21800 PMID: 24719363
20. Johnson RC, Nelson GW, Troyer JL, Lautenberger JA, Kessing BD, et al. (2010) Accounting for multiple comparisons in a genome-wide association study (GWAS). BMC Genomics 11: 724. https://doi.org/10.1186/1471-2164-11-724 PMID: 21176216
21. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, et al. (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42: 348–354. https://doi.org/10.1038/ng.548 PMID: 20208533
22. Lin DY, Sullivan PF (2009) Meta-analysis of genome-wide association studies with overlapping subjects. Am J Hum Genet 85: 862–872. https://doi.org/10.1016/j.ajhg.2009.11.001 PMID: 20004761
23. Gao X, Starmer J, Martin ER (2008) A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol 32: 361–369. https://doi.org/10.1002/gepi.20310 PMID: 18271029
24. Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55: 997–1004. PMID: 11315092
25. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, et al. (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518: 197–206. https://doi.org/10.1038/nature14177PMID: 25673413
26. Hancock DB, Soler Artigas M, Gharib SA, Henry A, Manichaikul A, et al. (2012) Genome-wide joint meta-analysis of SNP and SNP-by-smoking interaction identifies novel loci for pulmonary function. PLoS Genet 8: e1003098. https://doi.org/10.1371/journal.pgen.1003098 PMID: 23284291
27. Schoeps A, Rudolph A, Seibold P, Dunning AM, Milne RL, et al. (2014) Identification of new genetic susceptibility loci for breast cancer through consideration of gene-environment interactions. Genet Epidemiol 38: 84–93. https://doi.org/10.1002/gepi.21771 PMID: 24248812
28. Skol AD, Scott LJ, Abecasis GR, Boehnke M (2006) Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet 38: 209–213. https://doi.org/10.1038/ng1706 PMID: 16415888
29. Yang J, Loos RJ, Powell JE, Medland SE, Speliotes EK, et al. (2012) FTO genotype is associated with phenotypic variability of body mass index. Nature 490: 267–272. https://doi.org/10.1038/nature11401PMID: 22982992
30. Li S, Mukherjee B, Taylor JM, Rice KM, Wen X, et al. (2014) The role of environmental heterogeneity in meta-analysis of gene-environment interactions with quantitative traits. Genet Epidemiol 38: 416–429. https://doi.org/10.1002/gepi.21810 PMID: 24801060
31. Piegorsch WW, Weinberg CR, Taylor JA (1994) Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies. Stat Med 13: 153–162. PMID: 8122051
32. Li D, Conti DV (2009) Detecting gene-environment interactions using a combined case-only and case-control approach. Am J Epidemiol 169: 497–504. https://doi.org/10.1093/aje/kwn339 PMID: 19074774
33. Mukherjee B, Ahn J, Gruber SB, Rennert G, Moreno V, et al. (2008) Tests for gene-environment interaction from case-control data: a novel study of type I error, power and designs. Genet Epidemiol 32: 615–626. https://doi.org/10.1002/gepi.20337 PMID: 18473390
34. Mukherjee B, Ahn J, Gruber SB, Ghosh M, Chatterjee N (2010) Case-control studies of gene-environment interaction: Bayesian design and analysis. Biometrics 66: 934–948. https://doi.org/10.1111/j.1541-0420.2009.01357.x PMID: 19930190
35. Murcray CE, Lewinger JP, Gauderman WJ (2009) Gene-environment interaction in genome-wide association studies. Am J Epidemiol 169: 219–226. https://doi.org/10.1093/aje/kwn353 PMID: 19022827
36. Murcray CE, Lewinger JP, Conti DV, Thomas DC, Gauderman WJ (2011) Sample size requirements to detect gene-environment interactions in genome-wide association studies. Genet Epidemiol 35: 201–210. https://doi.org/10.1002/gepi.20569 PMID: 21308767
37. Hsu L, Jiao S, Dai JY, Hutter C, Peters U, et al. (2012) Powerful cocktail methods for detecting genomewide gene-environment interaction. Genet Epidemiol 36: 183–194. https://doi.org/10.1002/gepi.21610PMID: 22714933
38. Gauderman WJ, Zhang P, Morrison JL, Lewinger JP (2013) Finding novel genes by testing G x E interactions in a genome-wide association study. Genet Epidemiol 37: 603–613. https://doi.org/10.1002/gepi.21748 PMID: 23873611
39. Dai JY, Kooperberg C, Leblanc M, Prentice RL (2012) Two-stage testing procedures with independent filtering for genome-wide gene-environment interaction. Biometrika 99: 929–944. https://doi.org/10.1093/biomet/ass044 PMID: 23843674
40. Gao X, Becker LC, Becker DM, Starmer JD, Province MA (2010) Avoiding the high Bonferroni penalty in genome-wide association studies. Genet Epidemiol 34: 100–105. https://doi.org/10.1002/gepi.20430PMID: 19434714
41. Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate—a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B-Methodological 57: 289–300.