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Bayesian Partition Models for Identifying Expression Quantitative Trait Loci

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  • Bo Jiang
  • Jun S. Liu

Abstract

Expression quantitative trait loci (eQTLs) are genomic locations associated with changes of expression levels of certain genes. By assaying gene expressions and genetic variations simultaneously on a genome-wide scale, scientists wish to discover genomic loci responsible for expression variations of a set of genes. The task can be viewed as a multivariate regression problem with variable selection on both responses (gene expression) and covariates (genetic variations), including also multi-way interactions among covariates. Instead of learning a predictive model of quantitative trait given combinations of genetic markers, we adopt an inverse modeling perspective to model the distribution of genetic markers conditional on gene expression traits. A particular strength of our method is its ability to detect interactive effects of genetic variations with high power even when their marginal effects are weak, addressing a key weakness of many existing eQTL mapping methods. Furthermore, we introduce a hierarchical model to capture the dependence structure among correlated genes. Through simulation studies and a real data example in yeast, we demonstrate how our Bayesian hierarchical partition model achieves a significantly improved power in detecting eQTLs compared to existing methods. Supplementary materials for this article are available online.

Suggested Citation

  • Bo Jiang & Jun S. Liu, 2015. "Bayesian Partition Models for Identifying Expression Quantitative Trait Loci," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1350-1361, December.
    • Handle: RePEc:taf:jnlasa:v:110:y:2015:i:512:p:1350-1361
    DOI: 10.1080/01621459.2015.1049746
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    2. C. M. Kendziorski & M. Chen & M. Yuan & H. Lan & A. D. Attie, 2006. "Statistical Methods for Expression Quantitative Trait Loci (eQTL) Mapping," Biometrics, The International Biometric Society, vol. 62(1), pages 19-27, March.
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    4. Wei Zhang & Jun Zhu & Eric E Schadt & Jun S Liu, 2010. "A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules," PLOS Computational Biology, Public Library of Science, vol. 6(1), pages 1-10, January.
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