Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/18833
Title: Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model
Contributor(s): Moser, Gerhard (author); Lee, Sang Hong  (author); Hayes, Ben J (author); Goddard, Michael E (author); Wray, Naomi R (author); Visscher, Peter M (author)
Publication Date: 2015
Open Access: Yes
DOI: 10.1371/journal.pgen.1004969Open Access Link
Handle Link: https://hdl.handle.net/1959.11/18833
Abstract: Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.
Publication Type: Journal Article
Grant Details: NHMRC/APP1080157
Source of Publication: PLoS Genetics, 11(4), p. 1-22
Publisher: Public Library of Science (PLoS)
Place of Publication: United States of America
ISSN: 1553-7404
1553-7390
Field of Research (FOR): 070201 Animal Breeding
080301 Bioinformatics Software
060412 Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
Socio-Economic Objective (SEO): 970106 Expanding Knowledge in the Biological Sciences
970107 Expanding Knowledge in the Agricultural and Veterinary Sciences
970108 Expanding Knowledge in the Information and Computing Sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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