Estimating effect sizes and expected replication probabilities from GWAS summary statistics

Frontiers in Genetics

Volumn 7, Issue FEB, 2016, Pages

  Holland, Dominic ^a,b   Wang, Yunpeng ^a,b,c,d   Thompson, Wesley K ^a   Schork, Andrew ^a,e   Chen, Chi Hua ^a   Lo, Min Tzu ^a   Witoelar, Aree ^c,d   Werge, Thomas ^f   O'Donovan, Michael ^g   Andreassen, Ole A ^c,d   Dale, Anders M ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF OSLO   (Norway)

^d OSLO UNIVERSITY HOSPITAL   (Norway)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f UNIVERSITY OF COPENHAGEN   (Denmark)

^g CARDIFF UNIVERSITY   (United Kingdom)

Author keywords

Effect size; Gaussian mixture model; GWAS; Heritability; Putamen; Schizophrenia; SNP discovery

Indexed keywords

ARTICLE; CONCEPTUAL FRAMEWORK; GENE FREQUENCY; GENETIC ASSOCIATION; GENOTYPE; PHENOTYPE; PLEIOTROPY; SCHIZOPHRENIA; SCORING SYSTEM; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL ANALYSIS; STATISTICS;

EID: 84962499225     PISSN: None     EISSN: 16648021     Source Type: Journal    
DOI: 10.3389/fgene.2016.00015     Document Type: Article

References (55)

1. 1000 Genomes Project Consortium (2010). A map of human genome variation from population-scale sequencing. Nature 467, 1061-1073. doi: 10.1038/nature09534
2. Andreassen, O. A., Djurovic, S., Thompson, W. K., Schork, A. J., Kendler, K. S., O'Donovan, M. C., et al. (2013a). Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. Am. J. Hum. Genet. 92, 197-209. doi: 10.1016/j.ajhg.2013.01.001
3. Andreassen, O. A., Thompson, W. K., Schork, A. J., Ripke, S., Mattingsdal, M., Kelsoe, J. R., et al. (2013b). Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate. PLoS Genet. 9:e1003455. doi: 10.1371/journal.pgen.1003455
4. Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., et al. (2015). Ld score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291-295. doi: 10.1038/ng.3211
5. Chatterjee, N., Wheeler, B., Sampson, J., Hartge, P., Chanock, S. J., and Park, J.-H. (2013). Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat. Genet. 45, 400-405. doi: 10.1038/ng.2579
6. Devlin, B., and Roeder, K. (1999). Genomic control for association studies. Biometrics 55, 997-1004. doi: 10.1111/j.0006-341X.1999.00997.x
7. Dudbridge, F., and Gusnanto, A. (2008). Estimation of significance thresholds for genomewide association scans. Genet. Epidemiol. 32, 227-234. doi: 10.1002/gepi.20297
8. Efron, B. (2013). Large-scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambridge; New York: Cambridge University Press.
9. Erbe, M., Hayes, B., Matukumalli, L., Goswami, S., Bowman, P., Reich, C., et al. (2012). Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. J. Dairy Sci. 95, 4114-4129. doi: 10.3168/jds.2011-5019
10. Fuller, W. A. (2009). Measurement Error Models, Vol. 305. New York, NY: John Wiley & Sons.
11. Ghosh, A., Zou, F., and Wright, F. A. (2008). Estimating odds ratios in genome scans: an approximate conditional likelihood approach. Am. J. Hum. Genet. 82, 1064-1074. doi: 10.1016/j.ajhg.2008.03.002
12. Goddard, M. E., Wray, N. R., Verbyla, K., and Visscher, P. M. (2009). Estimating effects and making predictions from genome-wide marker data. Stat. Sci. 24, 517-529. doi: 10.1214/09-STS306
13. Greenland, S., and Poole, C. (2013). Living with p values: resurrecting a bayesian perspective on frequentist statistics. Epidemiology 24, 62-68. doi: 10.1097/EDE.0b013e3182785741
14. Hibar, D. P., Stein, J. L., Renteria, M. E., Arias-Vasquez, A., Desrivières, S., Jahanshad, N., et al. (2015). Common genetic variants influence human subcortical brain structures. Nature. 520, 224-229. doi: 10.1038/nature14101
15. Lambert, J.-C., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013). Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for alzheimer's disease. Nat. Genet. 45, 1452-1458. doi: 10.1038/ng.2802
16. Lee, S. H., DeCandia, T. R., Ripke, S., Yang, J., Sullivan, P. F., Goddard, M. E., et al. (2012a). Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat. Genet. 44, 247-250. doi: 10.1038/ng.1108
17. Lee, S. H., Goddard, M. E., Wray, N. R., and Visscher, P. M. (2012b). A better coefficient of determination for genetic profile analysis. Genet. Epidemiol. 36, 214-224. doi: 10.1002/gepi.21614
18. Lee, S. H., Wray, N. R., Goddard, M. E., and Visscher, P. M. (2011). Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294-305. doi: 10.1016/j.ajhg.2011.02.002
19. Lewinger, J. P., Conti, D. V., Baurley, J. W., Triche, T. J., and Thomas, D. C. (2007). Hierarchical bayes prioritization of marker associations from a genome-wide association scan for further investigation. Genet. Epidemiol. 31, 871-882. doi: 10.1002/gepi.20248
20. Lichtenstein, P., Yip, B. H., Björk, C., Pawitan, Y., Cannon, T. D., Sullivan, P. F., et al. (2009). Common genetic influences for schizophrenia and bipolar disorder: a population-based study of 2 million nuclear families. Lancet 373, 234-239. doi: 10.1016/S0140-6736(09)60072-6
21. Malo, N., Libiger, O., and Schork, N. J. (2008). Accommodating linkage disequilibrium in genetic-association analyses via ridge regression. Am. J. Hum. Genet. 82, 375-385. doi: 10.1016/j.ajhg.2007.10.012
22. Meuwissen, T. H. E., Hayes, B. J., and Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819-1829.
23. Palla, L., and Dudbridge, F. (2015). A fast method that uses polygenic scores to estimate the variance explained by genome-wide marker panels and the proportion of variants affecting a trait. Am. J. Hum. Genet. 97, 250-259. doi: 10.1016/j.ajhg.2015.06.005
24. Park, J. H., Gail, M. H., Weinberg, C. R., Carroll, R. J., Chung, C. C., Wang, Z., et al. (2011). Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants. Proc. Natl. Acad. Sci. U.S.A. 108, 18026-18031. doi: 10.1073/pnas.1114759108
25. Park, J.-H., Wacholder, S., Gail, M. H., Peters, U., Jacobs, K. B., Chanock, S. J., et al. (2010). Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat. Genet. 42, 570-575. doi: 10.1038/ng.610
26. Pe'er, I., Yelensky, R., Altshuler, D., and Daly, M. J. (2008). Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol. 32, 381-385. doi: 10.1002/gepi.20303
27. Purcell, S. M., Wray, N. R., Stone, J. L., Visscher, P. M., O'Donovan, M. C., Sullivan, P. F., et al. (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748-752. doi: 10.1038/nature08185
28. Ripke, S., O'Dushlaine, C., Chambert, K., Moran, J. L., Kähler, A. K., Akterin, S., et al. (2013a). Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat. Genet. 45, 1150-1159. doi: 10.1038/ng.2742
29. Ripke, S., Wray, N. R., Lewis, C. M., Hamilton, S. P., Weissman, M. M., Breen, G., et al. (2013b). A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497-511. doi: 10.1038/mp.2012.21
30. Satagopan, J. M., Venkatraman, E., and Begg, C. B. (2004). Two-stage designs for gene-disease association studies with sample size constraints. Biometrics 60, 589-597. doi: 10.1111/j.0006-341X.2004.00207.x
31. Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (2011). Genome-wide association study identifies five new schizophrenia loci. Nat. Genet. 43, 969-976. doi: 10.1038/ng.940
32. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421-427. doi: 10.1038/nature13595
33. Schork, A. J., Thompson, W. K., Pham, P., Torkamani, A., Roddey, J. C., Sullivan, P. F., et al. (2013). All snps are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated snps. PLoS Genet. 9:e1003449. doi: 10.1371/journal.pgen.1003449
34. Schork, N. J. (2002). Power calculations for genetic association studies using estimated probability distributions. Am. J. Hum. Genet. 70, 1480-1489. doi: 10.1086/340788
35. Sklar, P., Ripke, S., Scott, L. J., Andreassen, O. A., Cichon, S., Craddock, N., et al. (2011). Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near odz4. Nat. Genet. 43, 977. doi: 10.1038/ng.943
36. Skol, A. D., Scott, L. J., Abecasis, G. R., and Boehnke, M. (2007). Optimal designs for two-stage genome-wide association studies. Genet. Epidemiol. 31, 776-788. doi: 10.1002/gepi.20240
37. So, H.-C., Li, M., and Sham, P. C. (2011). Uncovering the total heritability explained by all true susceptibility variants in a genome-wide association study. Genet. Epidemiol. 35, 447-456. doi: 10.1002/gepi.20593
38. So, H.-C., Yip, B., and Sham, P. C. (2010). Estimating the total number of susceptibility variants underlying complex diseases from genome-wide association studies. PLoS ONE 5:e13898. doi: 10.1371/journal.pone.0013898
39. Speed, D., and Balding, D. J. (2014). Multiblup: improved snp-based prediction for complex traits. Genome Res. 24, 1550-1557. doi: 10.1101/gr.169375.113
40. Stahl, E. A., Wegmann, D., Trynka, G., Gutierrez-Achury, J., Do, R., Voight, B. F., et al. (2012). Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet. 44, 483-489. doi: 10.1038/ng.2232
41. Sullivan, P. F. (2010). The psychiatric GWAS consortium: big science comes to psychiatry. Neuron 68, 182-186. doi: 10.1016/j.neuron.2010.10.003
42. Sullivan, P. F., Kendler, K. S., and Neale, M. C. (2003). Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch. Gen. Psychiatry 60, 1187-1192. doi: 10.1001/archpsyc.60.12.1187
43. Tenesa, A., and Haley, C. S. (2013). The heritability of human disease: estimation, uses and abuses. Nat. Rev. Genet. 14, 139-149. doi: 10.1038/nrg3377
44. Thomas, D. C., Casey, G., Conti, D. V., Haile, R. W., Lewinger, J. P., and Stram, D. O. (2009). Methodological issues in multistage genome-wide association studies. Stat. Sci. 24, 414. doi: 10.1214/09-STS288
45. Thompson, W. K., Wang, Y., Schork, A. J., Witoelar, A., Zuber, V., Xu, S., et al. (2015). An empirical bayes mixture model for effect size distributions in genome-wide association studies. PLoS Genet. 11:e1005717. doi: 10.1371/journal.pgen.1005717
46. Visscher, P. M., Brown, M. A., McCarthy, M. I., and Yang, J. (2012). Five years of gwas discovery. Am. J. Hum. Genet. 90, 7-24. doi: 10.1016/j.ajhg.2011.11.029
47. Wang, Y., Thompson, W. K., Schork, A. J., Holland, D., Chen, C.-H., Bettella, F., et al. (2016). Leveraging genomic annotations and pleiotropic enrichment for improved replication rates in schizophrenia GWAS. PLoS Genet. 12:e1005803. doi: 10.1371/journal.pgen.1005803
48. Witte, J. S., Visscher, P. M., and Wray, N. R. (2014). The contribution of genetic variants to disease depends on the ruler. Nat. Rev. Genet. 15, 765-776. doi: 10.1038/nrg3786
49. Wray, N. R., and Gottesman, I. I. (2012). Using summary data from the danish national registers to estimate heritabilities for schizophrenia, bipolar disorder, and major depressive disorder. Front. Genet. 3:118. doi: 10.3389/fgene.2012.00118
50. Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., et al. (2010). Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565-569. doi: 10.1038/ng.608
51. Yang, J., Lee, S. H., Goddard, M. E., and Visscher, P. M. (2011a). Gcta: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76-82. doi: 10.1016/j.ajhg.2010.11.011
52. Yang, J., Weedon, M. N., Purcell, S., Lettre, G., Estrada, K., Willer, C. J., et al. (2011b). Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19, 807-812. doi: 10.1038/ejhg.2011.39
53. Zhou, X., Carbonetto, P., and Stephens, M. (2013). Polygenic modeling with bayesian sparse linear mixed models. PLoS Genet. 9:e1003264. doi: 10.1371/journal.pgen.1003264
54. Zhou, X., and Stephens, M. (2014). Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat. Methods 11, 407-409. doi: 10.1038/nmeth.2848
55. Zöllner, S., and Pritchard, J. K. (2007). Overcoming the winner2019s curse: estimating penetrance parameters from case-control data. Am. J. Hum. Genet. 80, 605-615. doi: 10.1086/512821