Evaluating the Performance of Fine-Mapping Strategies at Common Variant GWAS Loci

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Over the last few years, several approaches for fine-mapping genome-wide association studies (GWAS) loci have been proposed and used to localize potential causal variants. However, the performance of these types of tests is often poorly characterized. In this study, we used extensive simulations to show that statistical fine-mapping can indeed accurately reduce the number of likely causal variants at common GWAS loci. These approaches can be further improved by changes in study design, such as the inclusion of multiple ethnic groups in the study population. Finally, we demonstrate the utility of this type of approach on a recently published genome-wide association study for ankylosing spondylitis, where we could fine-map seven of the twenty-six loci to a number of variants (n = 10) which is tractable for follow-up in a laboratory setting.

Vyšlo v časopise: Evaluating the Performance of Fine-Mapping Strategies at Common Variant GWAS Loci. PLoS Genet 11(9): e32767. doi:10.1371/journal.pgen.1005535
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1005535

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1. Dickson SP, Wang K, Krantz I, Hakonarson H, Goldstein DB (2010) Rare variants create synthetic genome-wide associations. PLoS Biol 8: e1000294. doi: 10.1371/journal.pbio.1000294 20126254

2. Hunt KA, Mistry V, Bockett NA, Ahmad T, Ban M, et al. (2013) Negligible impact of rare autoimmune-locus coding-region variants on missing heritability. Nature 498 : 232–235. doi: 10.1038/nature12170 23698362

3. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV, et al. (2012) Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 44 : 981–990. doi: 10.1038/ng.2383 22885922

4. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491 : 56–65. doi: 10.1038/nature11632 23128226

5. Cortes A, Hadler J, Pointon JP, Robinson PC, Karaderi T, et al. (2013) Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci. Nat Genet 45 : 730–738. doi: 10.1038/ng.2667 23749187

6. Maller JB, McVean G, Byrnes J, Vukcevic D, Palin K, et al. (2012) Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat Genet 44 : 1294–1301. doi: 10.1038/ng.2435 23104008

7. Beecham AH, Patsopoulos NA, Xifara DK, Davis MF, Kemppinen A, et al. (2013) Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet.

8. Rivas MA, Beaudoin M, Gardet A, Stevens C, Sharma Y, et al. (2011) Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nat Genet 43 : 1066–1073. doi: 10.1038/ng.952 21983784

9. Wu Y, Waite LL, Jackson AU, Sheu WH, Buyske S, et al. (2013) Trans-ethnic fine-mapping of lipid loci identifies population-specific signals and allelic heterogeneity that increases the trait variance explained. PLoS Genet 9: e1003379. doi: 10.1371/journal.pgen.1003379 23555291

10. Mahajan A, Go MJ, Zhang W, Below JE, Gaulton KJ, et al. (2014) Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 46 : 234–244. doi: 10.1038/ng.2897 24509480

11. Gong J, Schumacher F, Lim U, Hindorff LA, Haessler J, et al. (2013) Fine Mapping and Identification of BMI Loci in African Americans. Am J Hum Genet 93 : 661–671. doi: 10.1016/j.ajhg.2013.08.012 24094743

12. Wakefield J (2007) A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am J Hum Genet 81 : 208–227. 17668372

13. Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, et al. (2012) The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet 8: e1002793. doi: 10.1371/journal.pgen.1002793 22876189

14. Trynka G, Sandor C, Han B, Xu H, Stranger BE, et al. (2013) Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet 45 : 124–130. doi: 10.1038/ng.2504 23263488

15. Pasquali L, Gaulton KJ, Rodriguez-Segui SA, Mularoni L, Miguel-Escalada I, et al. (2014) Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46 : 136–143. doi: 10.1038/ng.2870 24413736

16. Kichaev G, Yang WY, Lindstrom S, Hormozdiari F, Eskin E, et al. (2014) Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet 10: e1004722. doi: 10.1371/journal.pgen.1004722 25357204

17. Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, et al. (2012) Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491 : 119–124. doi: 10.1038/nature11582 23128233

18. Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, et al. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489 : 57–74. doi: 10.1038/nature11247 22955616

19. Su Z, Marchini J, Donnelly P (2011) HAPGEN2: simulation of multiple disease SNPs. Bioinformatics 27 : 2304–2305. doi: 10.1093/bioinformatics/btr341 21653516

20. Magi R, Morris AP (2010) GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11 : 288. doi: 10.1186/1471-2105-11-288 20509871

21. Cortes A, Brown MA (2011) Promise and pitfalls of the Immunochip. Arthritis Res Ther 13 : 101. doi: 10.1186/ar3204 21345260

22. Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5: e1000529. doi: 10.1371/journal.pgen.1000529 19543373