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A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules

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  • Wei Zhang
  • Jun Zhu
  • Eric E Schadt
  • Jun S Liu

Abstract

Studies of the relationship between DNA variation and gene expression variation, often referred to as “expression quantitative trait loci (eQTL) mapping”, have been conducted in many species and resulted in many significant findings. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to facilitate the task, in which co-expressed genes mapped to a common set of markers are treated as a module characterized by latent indicator variables. A Markov chain Monte Carlo algorithm is designed to search simultaneously for the module genes and their linked markers. We show by simulations that this method is more powerful for detecting true eQTLs and their target genes than traditional QTL mapping methods. We applied the procedure to a data set consisting of gene expression and genotypes for 112 segregants of S. cerevisiae. Our method identified modules containing genes mapped to previously reported eQTL hot spots, and dissected these large eQTL hot spots into several modules corresponding to possibly different biological functions or primary and secondary responses to regulatory perturbations. In addition, we identified nine modules associated with pairs of eQTLs, of which two have been previously reported. We demonstrated that one of the novel modules containing many daughter-cell expressed genes is regulated by AMN1 and BPH1. In conclusion, the Bayesian partition method which simultaneously considers all traits and all markers is more powerful for detecting both pleiotropic and epistatic effects based on both simulated and empirical data.Author Summary: Genome-wide association studies (GWAS) have yielded several causal genes for many human diseases. However, the mechanisms underlying how DNA variations affect disease phenotypes have not been well understood in many cases. Gene expression is intermediate between DNA and clinical endpoints. Linking DNA variation and gene expression variation, often referred to as “expression quantitative trait loci (eQTL) mapping”, has yielded clues of mechanisms and pathways by which DNA variations impact phenotypes. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to identify genetic interactions and more eQTLs by treating co-expressed genes as a module. Our method provides a tool to study genetic interactions in human disease models.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pcbi00:1000642
    DOI: 10.1371/journal.pcbi.1000642
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    References listed on IDEAS

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    1. Yanqing Chen & Jun Zhu & Pek Yee Lum & Xia Yang & Shirly Pinto & Douglas J. MacNeil & Chunsheng Zhang & John Lamb & Stephen Edwards & Solveig K. Sieberts & Amy Leonardson & Lawrence W. Castellini & Su, 2008. "Variations in DNA elucidate molecular networks that cause disease," Nature, Nature, vol. 452(7186), pages 429-435, March.
    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.
    3. Rachel B. Brem & John D. Storey & Jacqueline Whittle & Leonid Kruglyak, 2005. "Genetic interactions between polymorphisms that affect gene expression in yeast," Nature, Nature, vol. 436(7051), pages 701-703, August.
    4. Michael Morley & Cliona M. Molony & Teresa M. Weber & James L. Devlin & Kathryn G. Ewens & Richard S. Spielman & Vivian G. Cheung, 2004. "Genetic analysis of genome-wide variation in human gene expression," Nature, Nature, vol. 430(7001), pages 743-747, August.
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    1. Lin Yuan & Chang-An Yuan & De-Shuang Huang, 2017. "FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis," Complexity, Hindawi, vol. 2017, pages 1-10, September.
    2. Brown Andrew Anand & Richardson Sylvia & Whittaker John, 2011. "Application of the Lasso to Expression Quantitative Trait Loci Mapping," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-35, March.
    3. 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.
    4. Gang Fang & Majda Haznadar & Wen Wang & Haoyu Yu & Michael Steinbach & Timothy R Church & William S Oetting & Brian Van Ness & Vipin Kumar, 2012. "High-Order SNP Combinations Associated with Complex Diseases: Efficient Discovery, Statistical Power and Functional Interactions," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-15, April.

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