Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies

1. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, others. Common {SNPs} explain a large proportion of the heritability for human height. Nat Gen. 2010;42: 565–569.

2. Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, et al. Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet. 2011;43: 159–162. doi: 10.1038/ng.746 21217756

3. Frazer KA, Murray SS, Schork NJ, Topol EJ. Human genetic variation and its contribution to complex traits. Nat Rev Genet. 2009;10: 241–251. doi: 10.1038/nrg2554 19293820

4. Visscher PM, Yang J, Goddard ME. A commentary on “common SNPs explain a large proportion of the heritability for human height” by Yang et al. (2010). Twin Res Hum Genet. 2010;13: 517–524. doi: 10.1375/twin.13.6.517 21142928

5. Marchini J, Cardon LR, Phillips MS, Donnelly P. The effects of human population structure on large genetic association studies. Nat Genet. 2004;36: 512–517. 15052271

6. Yang J, Zaitlen N a, Goddard ME, Visscher PM, Price AL. Advantages and pitfalls in the application of mixed-model association methods. Nat Genet. 2014;46: 100–6. doi: 10.1038/ng.2876 24473328

7. Price AL, Zaitlen NA, Reich D, Patterson N. New approaches to population stratification in genome-wide association studies. Nat Rev Genet. 2010;11: 459–463. doi: 10.1038/nrg2813 20548291

8. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38: 904–909. 16862161

9. Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2006;38: 203–208. 16380716

10. Thornsberry JM, Goodman MM, Doebley J, Kresovich S, Nielsen D, Buckler ES. Dwarf8 polymorphisms associate with variation in flowering time. Nat Genet. 2001;28: 286–289. 11431702

11. Larsson SJ, Lipka AE, Buckler ES. Lessons from Dwarf8 on the Strengths and Weaknesses of Structured Association Mapping. PLoS Genet. 2013;9.

12. Reich D, Price AL, Patterson N. Principal component analysis of genetic data. Nature genetics. 2008. pp. 491–492. doi: 10.1038/ng0508-491 18443580

13. McVean G. A genealogical interpretation of principal components analysis. PLoS Genet. 2009;5.

14. Zhao K, Aranzana MJ, Kim S, Lister C, Shindo C, Tang C, et al. An Arabidopsis example of association mapping in structured samples. PLoS Genet. 2007;3: 0071–0082.

15. Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, et al. Efficient control of population structure in model organism association mapping. Genetics. 2008;178: 1709–1723. doi: 10.1534/genetics.107.080101 18385116

16. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S- Y, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010;42: 348–354. doi: 10.1038/ng.548 20208533

17. Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore M a, et al. Mixed linear model approach adapted for genome-wide association studies. Nat Genet. Nature Publishing Group; 2010;42: 355–360.

18. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nature Genetics. 2012. pp. 821–824. doi: 10.1038/ng.2310 22706312

19. Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D. FaST linear mixed models for genome-wide association studies. Nature Methods. 2011. pp. 833–835. doi: 10.1038/nmeth.1681 21892150

20. Svishcheva GR, Axenovich TI, Belonogova NM, van Duijn CM, Aulchenko YS. Rapid variance components–based method for whole-genome association analysis. Nature Genetics. 2012. pp. 1166–1170. doi: 10.1038/ng.2410 22983301

21. Atwell S, Huang YS, Vilhjálmsson BJ, Willems G, Horton M, Li Y, et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature. 2010;465: 627–631. doi: 10.1038/nature08800 20336072

22. Li M, Liu X, Bradbury P, Yu J, Zhang Y- M, Todhunter RJ, et al. Enrichment of statistical power for genome-wide association studies. BMC Biol. 2014;12: 73. doi: 10.1186/s12915-014-0073-5 25322753

23. Listgarten J, Lippert C, Kadie CM, Davidson RI, Eskin E, Heckerman D. Improved linear mixed models for genome-wide association studies. Nature Methods. 2012. pp. 525–526. doi: 10.1038/nmeth.2037 22669648

24. Wang Q, Tian F, Pan Y, Buckler ES, Zhang Z. A super powerful method for genome wide association study. PLoS One. 2014;

25. Segura V, Vilhjálmsson BJ, Platt A, Korte A, Seren Ü, Long Q, et al. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nature Genetics. 2012. pp. 825–830. doi: 10.1038/ng.2314 22706313

26. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91: 4414–4423. doi: 10.3168/jds.2007-0980 18946147

27. Lan Q, Hsiung C a, Matsuo K, Hong Y-C, Seow A, Wang Z, et al. Genome-wide association analysis identifies new lung cancer susceptibility loci in never-smoking women in Asia. Nat Genet. 2012;44: 1330–5. doi: 10.1038/ng.2456 23143601

28. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42.

29. Romay MC, Millard MJ, Glaubitz JC, Peiffer JA, Swarts KL, Casstevens TM, et al. Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol. 2013;14: R55. doi: 10.1186/gb-2013-14-6-r55 23759205

30. Neves HHR, Carvalheiro R, Queiroz S a. A comparison of statistical methods for genomic selection in a mice population. BMC Genet. 2012;13: 100. doi: 10.1186/1471-2156-13-100 23134637

31. Fan B, Onteru SK, Du ZQ, Garrick DJ, Stalder KJ, Rothschild MF. Genome-wide association study identifies loci for body composition and structural soundness traits in pigs. PLoS One. 2011;6.

32. Michaels SD, Amasino RM. FLOWERING LOCUS C encodes a novel MADS domain protein that acts as a repressor of flowering. Plant Cell. 1999;11: 949–956. 10330478

33. Tucker G, Price AL, Berger B. Improving the power of GWAS and avoiding confounding from population stratification with PC-select. Genetics. 2014. pp. 1045–1049.

34. Patterson N, Price AL, Reich D. Population structure and eigenanalysis. PLoS Genet. 2006;2: 2074–2093.

35. Loh P, Tucker G, Bulik-sullivan BK, Vilhj BJ. Efficient Bayesian mixed model analysis increases association power in large cohorts. Nat Genet. 2014;47: 284–290.

36. Bulik-Sullivan B, Loh P- R, Finucane H, Ripke S, Yang J, Psychiatric Genomics Consortium SWG, et al. LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies [Internet]. Nature Genetics. 2015.

37. Widmer C, Lippert C, Weissbrod O, Fusi N, Kadie C, Davidson R, et al. Further improvements to linear mixed models for genome-wide association studies. Sci Rep. 2014;4: 6874. doi: 10.1038/srep06874 25387525

38. Kulikova T, Akhtar R, Aldebert P, Althorpe N, Andersson M, Baldwin A, et al. EMBL Nucleotide Sequence Database in 2006. Nucleic Acids Res. 2007;35.

39. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81: 559–575. 17701901

40. Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: an R library for genome-wide association analysis. Bioinformatics. 2007;23: 1294–1296. 17384015

41. Altshuler D, Lander E, Ambrogio L. A map of human genome variation from population scale sequencing. Nature. 2010;476: 1061–1073.

42. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, et al. GAPIT: genome association and prediction integrated tool. Bioinformatics. 2012;28: 2397–9. 22796960