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Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model

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  • Gerhard Moser
  • Sang Hong Lee
  • Ben J Hayes
  • Michael E Goddard
  • Naomi R Wray
  • Peter M Visscher

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.Author Summary: Most genome-wide association studies performed to date have focused on testing individual genetic markers for associations with phenotype. Recently, methods that analyse the joint effects of multiple markers on genetic variation have provided further insights into the genetic basis of complex human traits. In addition, there is increasing interest in using genotype data for genetic risk prediction of disease. Often disparate analytical methods are used for each of these tasks. We propose a flexible novel approach that simultaneously performs identification of susceptibility loci, inference on the genetic architecture and provides polygenic risk prediction in the same statistical model. We illustrate the broad applicability of the approach by considering both simulated and real data. In the analysis of seven common diseases we show large differences in the proportion of genetic variation due to loci with different effect sizes and differences in prediction accuracy between complex traits. These findings are important for future studies and the understanding of the complex genetic architecture of common diseases.

Suggested Citation

  • Gerhard Moser & Sang Hong Lee & Ben J Hayes & Michael E Goddard & Naomi R Wray & Peter M Visscher, 2015. "Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model," PLOS Genetics, Public Library of Science, vol. 11(4), pages 1-22, April.
  • Handle: RePEc:plo:pgen00:1004969
    DOI: 10.1371/journal.pgen.1004969
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    References listed on IDEAS

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    1. Clive J Hoggart & John C Whittaker & Maria De Iorio & David J Balding, 2008. "Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies," PLOS Genetics, Public Library of Science, vol. 4(7), pages 1-8, July.
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    Cited by:

    1. Malka Gorfine & Sonja I Berndt & Jenny Chang-Claude & Michael Hoffmeister & Loic Le Marchand & John Potter & Martha L Slattery & Nir Keret & Ulrike Peters & Li Hsu, 2017. "Heritability Estimation using a Regularized Regression Approach (HERRA): Applicable to continuous, dichotomous or age-at-onset outcome," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-19, August.
    2. Marion Patxot & Daniel Trejo Banos & Athanasios Kousathanas & Etienne J. Orliac & Sven E. Ojavee & Gerhard Moser & Alexander Holloway & Julia Sidorenko & Zoltan Kutalik & Reedik Mägi & Peter M. Vissch, 2021. "Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    3. Cox Lwaka Tamba & Yuan-Li Ni & Yuan-Ming Zhang, 2017. "Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-20, January.
    4. Katharina B Böndel & Susanne A Kraemer & Toby Samuels & Deirdre McClean & Josianne Lachapelle & Rob W Ness & Nick Colegrave & Peter D Keightley, 2019. "Inferring the distribution of fitness effects of spontaneous mutations in Chlamydomonas reinhardtii," PLOS Biology, Public Library of Science, vol. 17(6), pages 1-24, June.
    5. Ganwen Zhang & Jianini Zhao & Jieru Wang & Guo Lin & Lin Li & Fengfei Ban & Meiting Zhu & Yangjun Wen & Jin Zhang, 2024. "An Improved Expectation–Maximization Bayesian Algorithm for GWAS," Mathematics, MDPI, vol. 12(13), pages 1-14, June.
    6. M. S. Clark & J. I. Hoffman & L. S. Peck & L. Bargelloni & D. Gande & C. Havermans & B. Meyer & T. Patarnello & T. Phillips & K. R. Stoof-Leichsenring & D. L. J. Vendrami & A. Beck & G. Collins & M. W, 2023. "Multi-omics for studying and understanding polar life," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    7. Theo Meuwissen & Ben Hayes & Iona MacLeod & Michael Goddard, 2022. "Identification of Genomic Variants Causing Variation in Quantitative Traits: A Review," Agriculture, MDPI, vol. 12(10), pages 1-11, October.
    8. Gao Wang & Abhishek Sarkar & Peter Carbonetto & Matthew Stephens, 2020. "A simple new approach to variable selection in regression, with application to genetic fine mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1273-1300, December.
    9. Ye, Mao & Zhang, Peng & Nie, Lizhen, 2018. "Clustering sparse binary data with hierarchical Bayesian Bernoulli mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 32-49.
    10. Carla Márquez-Luna & Steven Gazal & Po-Ru Loh & Samuel S. Kim & Nicholas Furlotte & Adam Auton & Alkes L. Price, 2021. "Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets," Nature Communications, Nature, vol. 12(1), pages 1-11, December.

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