Many Phenotypes Without Many False Discoveries: Error Controlling Strategies for Multitrait Association Studies

Genetic Epidemiology

Volumn 40, Issue 1, 2016, Pages 45-56

  Peterson, Christine B ^a   Bogomolov, Marina ^b   Benjamini, Yoav ^c   Sabatti, Chiara ^a  

^a STANFORD UNIVERSITY   (United States)

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^c TEL AVIV UNIVERSITY   (Israel)

Author keywords

Error control; False discovery rate; Genetic association study; Multiple hypothesis testing; Multiple phenotypes

Indexed keywords

ARABIDOPSIS THALIANA; CONTROLLED STUDY; FLOWER DEVELOPMENT; GENETIC ASSOCIATION; GENETIC VARIABILITY; PHENOTYPE; ARABIDOPSIS; BIOLOGICAL MODEL; COMPUTER SIMULATION; FLOWER; GENETIC ASSOCIATION STUDY; GENETICS; GENOME-WIDE ASSOCIATION STUDY; PHYSIOLOGY; REPRODUCIBILITY; SINGLE NUCLEOTIDE POLYMORPHISM;

ARABIDOPSIS; COMPUTER SIMULATION; FLOWERS; GENETIC ASSOCIATION STUDIES; GENOME-WIDE ASSOCIATION STUDY; MODELS, GENETIC; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; REPRODUCIBILITY OF RESULTS;

EID: 84951746644     PISSN: 07410395     EISSN: 10982272     Source Type: Journal    
DOI: 10.1002/gepi.21942     Document Type: Article

References (40)

1. Atwell S. 2010. Online data for Atwell et al. 2010. https://cynin.gmi.oeaw.ac.at/home/resources/atpolydb/genomic-polymorphism-data-in-arabidopsis-thaliana [Accessed: 2015].
2. Atwell S, Huang YS, Vilhjálmsson BJ, Willems G, Horton M, Li Y, Meng D, Platt A, Tarone AM, Hu TT, and others, . 2010. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465:627-631.
3. Benjamini Y, Bogomolov M. 2014. Selective inference on multiple families of hypotheses. J R Stat Soc B 76:297-318.
4. Benjamini Y, Heller R. 2008. Screening for partial conjunction hypotheses. Biometrics 64:1215-1222.
5. Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B Met 57:289-300.
6. Benjamini Y, Yekutieli D. 2001. The control of the false discovery rate in multiple testing under dependency. Ann Stat 29:1165-1188.
7. Benjamini Y, Yekutieli D. 2005. Quantitative trait loci analysis using the false discovery rate. Genetics 171:783-790.
8. Brem RB, Yvert G, Clinton R, Kruglyak L. 2002. Genetic dissection of transcriptional regulation in budding yeast. Science 296:752-755.
9. Cheung VG, Spielman RS, Ewens KG, Weber TM, Morley M, Burdick JT. 2005. Mapping determinants of human gene expression by regional and genome-wide association. Nature 437:1365-1369.
10. Donoho D, Jin J. 2004. Higher criticism for detecting sparse heterogeneous mixtures. Ann Stat 32:962-994.
11. Efron B. 2008. Simultaneous inference: when should hypothesis testing problems be combined? Ann Appl Stat 2:197-223.
12. Efron B, Tibshirani R, Storey JD, Tusher V. 2001. Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 96:1151-1160.
13. Elston RC, Lange K. 1975. The prior probability of autosomal linkage. Ann Hum Genet 38:341-350.
14. Feingold E, Brown PO, Siegmund D. 1993. Gaussian models for genetic linkage analysis using complete high-resolution maps of identity by descent. Am J Hum Genet 53:234-251.
15. Fisher RA. 1932. Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.
16. Flutre T, Wen X, Pritchard J, Stephens M. 2013. A statistical framework for joint eQTL analysis in multiple tissues. PLoS Genet 9:e1003486. DOI: 10.1371/journal.pgen.1003486
17. Foss EJ, Radulovic D, Shaffer SA, Ruderfer DM, Bedalov A, Goodlett DR, Kruglyak L. 2007. Genetic basis of proteome variation in yeast. Nat Genet 39:1369-1375.
18. Hochberg Y, Hommel G. 2006. Simes' test of multiple hypotheses. In: Kotz S, Read CB, Balakrishnan N, Vidakovic B, editors. Encyclopedia of Statistical Sciences. Wiley, New York, pp. 1-5.
19. Illig T, Gieger C, Zhai G, Römisch-Margl W, Wang-Sattler R, Prehn C, Altmaier E, Kastenmüller G, Kato BS, Mewes H, and others, . 2010. A genome-wide perspective of genetic variation in human metabolism. Nat Genet 42:137-141.
20. Joo JW, Kang EY, Org E, Furlotte N, Parks B, Lusis AJ, Eskin E. 2015. Efficient and accurate multiple-phenotypes regression method for high dimensional data considering population structure. In: Przytycka T, editor. Research in Computational Molecular Biology. Springer, pp. 136-153.
21. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S, Freimer NB, Sabatti C, Eskin E. 2010. Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42:348-354.
22. Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E. 2008. Efficient control of population structure in model organism association mapping. Genetics 178:1709-1723.
23. Keurentjes JJ, Fu J, Ric De Vos CH, Lommen A, Hall RD, Bino RJ, van der Plas LH, Jansen RC, Vreugdenhil D, Koornneef M. 2006. The genetics of plant metabolism. Nat Genet 38:842-849.
24. Korte A, Vilhjálmsson BJ, Segura V, Platt A, Long Q, Nordborg M. 2012. A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat Genet 44:1066-1071.
25. Lander E, Kruglyak L. 1995. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 11:241-247.
26. Million Veteran Program. http://www.research.va.gov/MVP/ [Accessed: 2015].
27. Morton NE. 1955. Sequential tests for the detection of linkage. Am J Hum Genet 7:277-318.
28. O'Reilly PF, Hoggart CJ, Pomyen Y, Calboli FC, Elliott P, Jarvelin M, Coin LJ. 2012. MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PLoS One 7:e34861.
29. Peterson CB, Bogomolov M, Benjamini Y, Sabatti C. 2015. TreeQTL: hierarchical error control for eQTL findings. bioRxiv. doi: http://dx.doi.org/10.1101/021170.
30. Reiner A, Yekutieli D, Benjamini Y. 2003. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19:368-375.
31. Research Program on Genes, Environment, and Health. http://www.dor.kaiser.org/external/DORExternal/rpgeh/ [Accessed: 2015].
32. Sabatti C, Service SK, Hartikainen A, Pouta A, Ripatti S, Brodsky J, Jones CG, Zaitlen NA, Varilo T, Kaakinen M, and others, . 2009. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat Genet 41:35-46.
33. Sarkar SK. 1998. Some probability inequalities for ordered MTP2 random variables: a proof of the Simes conjecture. Ann Stat 26:494-504.
34. Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V, Ruff TG, Milligan SB, Lamb JR, Cavet G, and others, . 2003. Genetics of gene expression surveyed in maize, mouse and man. Nature 422:297-302.
35. Shabalin AA. 2012. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28:1353-1358.
36. Simes RJ. 1986. An improved Bonferroni procedure for multiple tests of significance. Biometrika 73:751-754.
37. Stein JL, Hua X, Lee S, Ho AJ, Leow AD, Toga AW, Saykin AJ, Shen L, Foroud T, Pankratz N, and others, . 2010. Voxelwise genome-wide association study (vGWAS). Neuroimage 53:1160-1174.
38. Williams J, Van Eerdewegh P, Almasy L, Blangero J. 1999. Joint multipoint linkage analysis of multivariate qualitative and quantitative traits. I. Likelihood formulation and simulation results. Am J Hum Genet 65:1134-1147.
39. Wu L, Candille SI, Choi Y, Xie D, Jiang L, Li-Pook-Than J, Tang H, Snyder M. 2013. Variation and genetic control of protein abundance in humans. Nature 499:79-82.
40. Zhou X, Stephens M. 2014. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods 11:407-409.