A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules

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A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules

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dc.contributor.author Zhu, Jun
dc.contributor.author Schadt, Eric E.
dc.contributor.author Stormo, Gary D.
dc.contributor.author Zhang, Wei
dc.contributor.author Liu, Jun
dc.date.accessioned 2010-09-29T20:30:05Z
dc.date.issued 2010
dc.identifier.citation Zhang, Wei, Jun Zhu, Eric E. Schadt, Liu, Jun S. Liu, and Gary D. Stormo. 2010. A bayesian partition method for detecting pleiotropic and epistatic eQTL modules. PLoS Computational Biology 6(1): e1000642. en_US
dc.identifier.issn 1553-734X en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4453998
dc.description.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. en_US
dc.description.sponsorship Statistics en_US
dc.language.iso en_US en_US
dc.publisher Public Library of Science en_US
dc.relation.isversionof doi:10.1371/journal.pcbi.1000642 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2797600/pdf/ en_US
dash.license OAP
dc.subject computational biology en_US
dc.subject genetics and genomics en_US
dc.subject genomics en_US
dc.subject polulation genetics en_US
dc.subject systems biology en_US
dc.subject complex traits en_US
dc.subject epigenetics en_US
dc.subject gene expression en_US
dc.title A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal PLoS Computational Biology en_US
dash.depositing.author Liu, Jun
dc.date.available 2010-09-29T20:30:05Z

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  • FAS Scholarly Articles [6948]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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