Bayesian multivariate reanalysis of large genetic studies identifies many new associations

English version

Autoři: Michael C. Turchin ^aff001; Matthew Stephens ^aff001
Působiště autorů: Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America ^aff001; Department of Statistics, The University of Chicago, Chicago, Illinois, United States of America ^aff002
Vyšlo v časopise: Bayesian multivariate reanalysis of large genetic studies identifies many new associations. PLoS Genet 15(10): e32767. doi:10.1371/journal.pgen.1008431
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1008431

Souhrn

Genome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is despite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.

Klíčová slova:

Phenotypes – Alleles – Genome-wide association studies – Blood pressure – Genetic polymorphism – Open data

Zdroje

1. Price AL, Spencer CC, Donnelly P. Progress and promise in understanding the genetic basis of common diseases. Proc Biol Sci. 2015;282(1821):20151684. doi: 10.1098/rspb.2015.1684 26702037

2. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017;101(1):5–22. doi: 10.1016/j.ajhg.2017.06.005 28686856

3. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 2017;45(D1):D896–D901. doi: 10.1093/nar/gkw1133 27899670

4. Jiang C, Zeng ZB. Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics. 1995;140(3):1111–27. 7672582

5. Zhu W, Zhang H. Why Do We Test Multiple Traits in Genetic Association Studies? J Korean Stat Soc. 2009;38(1):1–10. doi: 10.1016/j.jkss.2008.10.006 19655045

6. Shriner D. Moving toward System Genetics through Multiple Trait Analysis in Genome-Wide Association Studies. Front Genet. 2012;3:1. doi: 10.3389/fgene.2012.00001 22303408

7. Yang Q, Wang Y. Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies. J Probab Stat. 2012;2012:652569. doi: 10.1155/2012/652569 24748889

8. Galesloot TE, van Steen K, Kiemeney LA, Janss LL, Vermeulen SH. A comparison of multivariate genome-wide association methods. PLoS One. 2014;9(4):e95923. doi: 10.1371/journal.pone.0095923 24763738

9. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274–1283. doi: 10.1038/ng.2797 24097068

10. Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014;46(11):1173–86. doi: 10.1038/ng.3097 25282103

11. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197–206. doi: 10.1038/nature14177 25673413

12. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Magi R, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518(7538):187–196. doi: 10.1038/nature14132 25673412

13. Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, et al. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell. 2016;167(5):1415–1429 e19. doi: 10.1016/j.cell.2016.10.042 27863252

14. Stephens M. A unified framework for association analysis with multiple related phenotypes. PLoS One. 2013;8(7):e65245. doi: 10.1371/journal.pone.0065245 23861737

15. Pickrell JK, Berisa T, Liu JZ, Segurel L, Tung JY, Hinds DA. Detection and interpretation of shared genetic influences on 42 human traits. Nat Genet. 2016;48(7):709–17. doi: 10.1038/ng.3570 27182965

16. Hormozdiari F, van de Bunt M, Segre AV, Li X, Joo JWJ, Bilow M, et al. Colocalization of GWAS and eQTL Signals Detects Target Genes. Am J Hum Genet. 2016;99(6):1245–1260. doi: 10.1016/j.ajhg.2016.10.003 27866706

17. Wakefield J. Bayes factors for genome-wide association studies: comparison with P-values. Genet Epidemiol. 2009;33(1):79–86. doi: 10.1002/gepi.20359 18642345

18. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466(7307):707–13. doi: 10.1038/nature09270 20686565

19. Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010;467(7317):832–8. doi: 10.1038/nature09410 20881960

20. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42(11):937–48. doi: 10.1038/ng.686 20935630

21. Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L, Steinthorsdottir V, et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet. 2010;42(11):949–60. doi: 10.1038/ng.685 20935629

22. van der Harst P, Zhang W, Mateo Leach I, Rendon A, Verweij N, Sehmi J, et al. Seventy-five genetic loci influencing the human red blood cell. Nature. 2012;492(7429):369–75. doi: 10.1038/nature11677 23222517

23. International Consortium for Blood Pressure Genome-Wide Association S, Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478(7367):103–9. doi: 10.1038/nature10405

24. Wain LV, Verwoert GC, O’Reilly PF, Shi G, Johnson T, Johnson AD, et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat Genet. 2011;43(10):1005–11. doi: 10.1038/ng.922 21909110

25. Zheng HF, Forgetta V, Hsu YH, Estrada K, Rosello-Diez A, Leo PJ, et al. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature. 2015;526(7571):112–7. doi: 10.1038/nature14878 26367794

26. Kottgen A, Pattaro C, Boger CA, Fuchsberger C, Olden M, Glazer NL, et al. New loci associated with kidney function and chronic kidney disease. Nat Genet. 2010;42(5):376–84. doi: 10.1038/ng.568 20383146

27. Boger CA, Chen MH, Tin A, Olden M, Kottgen A, de Boer IH, et al. CUBN is a gene locus for albuminuria. J Am Soc Nephrol. 2011;22(3):555–70. doi: 10.1681/ASN.2010060598 21355061

28. Urbut SM, Wang G, Carbonetto P, Stephens M. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nat Genet. 2019;51(1):187–195. doi: 10.1038/s41588-018-0268-8 30478440

29. Porter HF, O’Reilly PF. Multivariate simulation framework reveals performance of multi-trait GWAS methods. Sci Rep. 2017;7:38837. doi: 10.1038/srep38837 28287610

30. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779. doi: 10.1371/journal.pmed.1001779 25826379

31. GTEx Consortium T. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45(6):580–5. doi: 10.1038/ng.2653

32. Gaffney DJ. Global properties and functional complexity of human gene regulatory variation. PLoS Genet. 2013;9(5):e1003501. doi: 10.1371/journal.pgen.1003501 23737752

33. Pai AA, Pritchard JK, Gilad Y. The genetic and mechanistic basis for variation in gene regulation. PLoS Genet. 2015;11(1):e1004857. doi: 10.1371/journal.pgen.1004857 25569255

34. Birney E, Smith GD, Greally JM. Epigenome-wide Association Studies and the Interpretation of Disease -Omics. PLoS Genet. 2016;12(6):e1006105. doi: 10.1371/journal.pgen.1006105 27336614

35. Stricker SH, Koferle A, Beck S. From profiles to function in epigenomics. Nat Rev Genet. 2017;18(1):51–66. doi: 10.1038/nrg.2016.138 27867193

36. Li YI, van de Geijn B, Raj A, Knowles DA, Petti AA, Golan D, et al. RNA splicing is a primary link between genetic variation and disease. Science. 2016;352(6285):600–4. doi: 10.1126/science.aad9417 27126046

37. Li Y, Kellis M. Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases. Nucleic Acids Res. 2016;44(18):e144. doi: 10.1093/nar/gkw627 27407109

38. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48(5):481–7. doi: 10.1038/ng.3538 27019110

39. Wen X, Pique-Regi R, Luca F. Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization. PLoS Genet. 2017;13(3):e1006646. doi: 10.1371/journal.pgen.1006646 28278150

40. Shi H, Mancuso N, Spendlove S, Pasaniuc B. Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits. Am J Hum Genet. 2017;101(5):737–751. doi: 10.1016/j.ajhg.2017.09.022 29100087

41. Dahl A, Iotchkova V, Baud A, Johansson A, Gyllensten U, Soranzo N, et al. A multiple-phenotype imputation method for genetic studies. Nat Genet. 2016;48(4):466–72. doi: 10.1038/ng.3513 26901065

42. Lewin A, Saadi H, Peters JE, Moreno-Moral A, Lee JC, Smith KG, et al. MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues. Bioinformatics. 2016;32(4):523–32. doi: 10.1093/bioinformatics/btv568 26504141

43. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308–11. doi: 10.1093/nar/29.1.308 11125122

44. International HapMap C. The International HapMap Project. Nature. 2003;426(6968):789–96. doi: 10.1038/nature02168

45. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42(2):105–16. doi: 10.1038/ng.520 20081858

46. Benyamin B, Esko T, Ried JS, Radhakrishnan A, Vermeulen SH, Traglia M, et al. Novel loci affecting iron homeostasis and their effects in individuals at risk for hemochromatosis. Nat Commun. 2014;5:4926. doi: 10.1038/ncomms5926 25352340

47. Barban N, Jansen R, de Vlaming R, Vaez A, Mandemakers JJ, Tropf FC, et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat Genet. 2016;48(12):1462–1472. doi: 10.1038/ng.3698 27798627

48. Hibar DP, Stein JL, Renteria ME, Arias-Vasquez A, Desrivieres S, Jahanshad N, et al. Common genetic variants influence human subcortical brain structures. Nature. 2015;520(7546):224–9. doi: 10.1038/nature14101 25607358

49. Genomes Project C, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. doi: 10.1038/nature15393