Controlling the rate of GWAS false discoveries

Genetics

Volumn 205, Issue 1, 2017, Pages 61-75

  Brzyski, Damian ^a,b   Peterson, Christine B ^c   Sobczyk, Piotr ^d   Candès, Emmanuel J ^e   Bogdan, Malgorzata ^f   Sabatti, Chiara ^e  

^a JAGIELLONIAN UNIVERSITY   (Poland)

^b INDIANA UNIVERSITY   (United States)

^c UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^d WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

^e STANFORD UNIVERSITY   (United States)

^f UNIVERSITY OF WROC AW   (Poland)

Author keywords

Association studies; FDR; Linkage disequilibrium; Multiple penalized regression

Indexed keywords

CHOLESTEROL; HIGH DENSITY LIPOPROTEIN; LOW DENSITY LIPOPROTEIN; TRIACYLGLYCEROL;

ALLELE; ARTICLE; CHOLESTEROL BLOOD LEVEL; GENE LINKAGE DISEQUILIBRIUM; GENETICS; GENOME-WIDE ASSOCIATION STUDY; HUMAN; PRIORITY JOURNAL; QUANTITATIVE TRAIT LOCUS; SINGLE NUCLEOTIDE POLYMORPHISM; BIOLOGICAL MODEL; COHORT ANALYSIS; FALSE POSITIVE RESULT; GENETIC ASSOCIATION STUDY; GENETIC PREDISPOSITION; GENOMICS; HUMAN GENOME; PREDICTIVE VALUE; PROCEDURES; STATISTICAL MODEL;

COHORT STUDIES; FALSE POSITIVE REACTIONS; GENETIC ASSOCIATION STUDIES; GENETIC PREDISPOSITION TO DISEASE; GENOME, HUMAN; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; HUMANS; LINEAR MODELS; LINKAGE DISEQUILIBRIUM; MODELS, GENETIC; POLYMORPHISM, SINGLE NUCLEOTIDE; PREDICTIVE VALUE OF TESTS;

EID: 85008425474     PISSN: 00166731     EISSN: 19432631     Source Type: Journal    
DOI: 10.1534/genetics.116.193987     Document Type: Article

References (49)

1. Abramovich, F., Y. Benjamini, D. L. Donoho, and I. M. Johnstone, 2006 Adapting to unknown sparsity by controlling the false discovery rate. Ann. Stat. 34: 584-653.
2. Alexander, D. H., and K. Lange, 2011 Stability selection for genomewide association. Genet. Epidemiol. 35: 722-728.
3. Ardlie, K. G., D. S. Deluca, A. V. Segre, T. J. Sullivan, T. R. Young et al., 2015 Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348: 648-660.
4. Benjamini, Y., and M. Bogomolov, 2014 Selective inference on multiple families of hypotheses. J. R. Stat. Soc. Series B Stat. Methodol. 76: 297-318.
5. Benjamini, Y., and R. Heller, 2007 False discovery rates for spatial signals. J. Am. Stat. Assoc. 102: 1272-1281.
6. Benjamini, Y., and Y. Hochberg, 1995 Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57: 289-300.
7. Benjamini, Y., and D. Yekutieli, 2001 The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29: 1165-1188.
8. Benjamini, Y., and D. Yekutieli, 2005a False discovery rateadjusted multiple confidence intervals for selected parameters. J. Am. Stat. Assoc. 100: 71-93.
9. Benjamini, Y., and D. Yekutieli, 2005b Quantitative trait loci analysis using the false discovery rate. Genetics 171: 783-790.
10. Bogdan, M., E. van den Berg, C. Sabatti, W. Su, and E. Candès, 2015 SLOPE—adaptive variable selection via convex optimization. Ann. Appl. Stat. 9: 1103-1140, 26709357.
11. Brzyski, D., A. Gossmann, W. Su, and M. Bogdan, 2016 Group SLOPE-adaptive selection of groups of predictors. ArXiv: 1610.04960.
12. Brzyski, D., C. Peterson, P. Sobczyk, E. J. Candès, M. Bogdan et al., 2016 geneSLOPE: genome-wide association study with SLOPE. R package. Available at: https://CRAN.R-project.org/ package=geneSLOPE.
13. Carbonetto, P., and M. Stephens, 2011 Scalable variational inference for Bayesian variable selection, and its accuracy in genetic association studies. Bayesian Anal. 6: 1-42.
14. Dolejsi, E., B. Bodenstorfer, and F. Frommlet, 2014 Analyzing genome-wide association studies with an FDR controlling modification of the Bayesian information criterion. PLoS One 9: e103322.
15. Fan, J., and R. Li, 2001 Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Stat. Assoc. 96: 1348-1360.
16. Fan, J., and J. Lv, 2008 Sure independence screening for ultrahigh dimensional feature space. J. R. Stat. Soc. B 70: 849-911.
17. Foygel Barber, R., and E. J. Candès, 2016 A knockoff filter for high-dimensional selective inference. ArXiv: 1602.03574.
18. Foygel Barber, R., and A. Ramdas, 2015 The p-filter: multi-layer FDR control for grouped hypotheses. ArXiv: 1512.03397.
19. Frommlet, F., F. Ruhaltinger, P. Twaróg, and M. Bogdan, 2012 Modified versions of Bayesian Information Criterion for genome-wide association studies. Comput. Stat. Data Anal. 56: 1038-1051.
20. Global Lipids Genetics Consortium, 2013 Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45: 1274-1283.
21. Halperin, E., G. Kimmel, and R. Shamir, 2005 Tag SNP selection in genotype data for maximizing SNP prediction accuracy. Bioinformatics 21: i195-i203.
22. He, Q., and D. Y. Lin, 2011 A variable selection method for genome-wide association studies. Bioinformatics 27: 1-8.
23. Hoggart, C. J., J. C. Whittaker, M. D. Iorio, and D. J. Balding, 2008 Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. PLoS Genet. 4: e1000130.
24. Hormozdiari, F., E. Kostem, E. Y. Kang, B. Pasaniuc, and E. Eskin, 2014 Identifying causal variants at loci with multiple signals of association. Genetics 198: 497-508.
25. Kang, H. M., J. H. Sul, S. K. Service, N. A. Zaitlen, S. Kong et al., 2010 Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42: 348-354.
26. Kvale, M. N., S. Hesselson, T. J. Hoffmann, Y. Cao, D. Chan et al., 2015 Genotyping informatics and quality control for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Genetics 200: 1051-1060.
27. Logsdon, B. A., G. E. Hoffman, and J. G. Mezey, 2010 A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis. BMC Bioinformatics 11: 58.
28. Manolio, T. A., F. S. Collins, N. J. Cox, D. B. Goldstein, L. A. Hindorff et al., 2009 Finding the missing heritability of complex diseases. Nature 461: 747-753.
29. Perone Pacifico, M., C. Genovese, I. Verdinelli, and L. Wasserman, 2004 False discovery control for random fields. J. Am. Stat. Assoc. 99: 1002-1014.
30. Peterson, C. B., M. Bogomolov, Y. Benjamini, and C. Sabatti, 2016 Many phenotypes without many false discoveries: error controlling strategies for multitrait association studies. Genet. Epidemiol. 40: 45-56.
31. Price, A. L., N. J. Patterson, R. M. Plenge, M. E. Weinblatt, N. A. Shadick et al., 2006 Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38: 904-909.
32. Purcell, S., B. Neale, K. Todd-Brown, L. Thomas, M. A. Ferreira et al., 2007 PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81: 559-575.
33. Purcell, S.M., N.R. Wray, J. L. Stone, P.M. Visscher, M. C. O’Donovan et al., 2009 Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460: 748-752.
34. Sabatti, C., 2013 Multivariate linear models for GWAS, pp. 188-208 in Advances in Statistical Bioinformatics. Cambridge University Press, Cambridge, United Kingdom.
35. Sabatti, C., S. Service, and N. Freimer, 2003 False discovery rate in linkage and association genome screens for complex disorders. Genetics 164: 829-833.
36. Sabatti, C., S. K. Service, A. L. Hartikainen, A. Pouta, S. Ripatti et al., 2009 Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat. Genet. 41: 35-46.
37. Siegmund, D. O., B. Yakir, and N. Zhang, 2011 The false discovery rate for scan statistics. Biometrika 98: 979-985.
38. Stell, L., and C. Sabatti, 2016 Genetic variant selection: learning across traits and sites. Genetics 202: 439-455.
39. Storey, J. D., and R. Tibshirani, 2003 Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100: 9440-9445.
40. Stringer, S., N. R. Wray, R. S. Kahn, and E. M. Derks, 2011 Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes. PLoS One 6: e27964.
41. Sun, T., and C. Zhang, 2012 Scaled sparse linear regression. Biometrika 99: 879-898.
42. Tibshirani, R., 1994 Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58: 267-288.
43. Welter, D., J. MacArthur, J. Morales, T. Burdett, P. Hall et al., 2014 The NHGRI GWAS Catalog, a curated resource of SNPtrait associations. Nucleic Acids Research 42:AD1001-D1006. Available at: http://www.ebi.ac.uk/gwas/.
44. Wu, J., B. Devlin, S. Ringquist, M. Trucco, and K. Roeder, 2010 Screen and clean: a tool for identifying interactions in genome-wide association studies. Genet. Epidemiol. 34: 275-285.
45. Wu, T. T., Y. F. Chen, T. Hastie, E. Sobel, and K. Lange, 2009 Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 25: 714-721.
46. Yang, J., B. Benyamin, B. P. McEvoy, S. Gordon, A. K. Henders et al., 2010 Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42: 565-569.
47. Yang, J., S. H. Lee, M. E. Goddard, and P. M. Visscher, 2011 GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88: 76-82.
48. Yi, H., P. Breheny, N. Imam, Y. Liu, and I. Hoeschele, 2015 Penalized multimarker vs. single-marker regression methods for genome-wide association studies of quantitative traits. Genetics 199: 205-222.
49. Zhou, H., M. E. Sehl, J. S. Sinsheimer, and K. Lange, 2010 Association screening of common and rare genetic variants by penalized regression. Bioinformatics 26: 2375-2382.