Bayesian Partition Models for Identifying Expression Quantitative Trait Loci

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.

• Bayesian variable selection
• Dirichlet process
• Expression quantitative trait loci
• Hierarchical model
• Interaction detection

Additional information

Notes on contributors

Bo Jiang

Bo Jiang, Department of Statistics, 1 Oxford Street, Harvard University, Cambridge, MA 02138 (E-mail: [email protected]). Jun S. Liu is Professor of Statistics, Department of Statistics, Harvard University, Cambridge, MA 02138 (E-mail: [email protected]). Jun S. Liu was supported in part by NIH grant NIH 5R01MH090948, NSF grants DMS-1120368 and DMS-1007762, and by Shenzhen Special Fund for Strategic Emerging Industry (No. ZD201111080127A). The authors are grateful to the editor, the associate editor, and two reviewers for their insightful and constructive comments that helped to greatly improve the presentation of the article.

Jun S. Liu

Bo Jiang, Department of Statistics, 1 Oxford Street, Harvard University, Cambridge, MA 02138 (E-mail: [email protected]). Jun S. Liu is Professor of Statistics, Department of Statistics, Harvard University, Cambridge, MA 02138 (E-mail: [email protected]). Jun S. Liu was supported in part by NIH grant NIH 5R01MH090948, NSF grants DMS-1120368 and DMS-1007762, and by Shenzhen Special Fund for Strategic Emerging Industry (No. ZD201111080127A). The authors are grateful to the editor, the associate editor, and two reviewers for their insightful and constructive comments that helped to greatly improve the presentation of the article.