Please use this identifier to cite or link to this item: https://doi.org/10.1534/1449.full.pdf
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dc.titleBayesian detection of expression quantitative trait
dc.contributor.authorSpots L.H.
dc.contributor.authorBottolo L.
dc.contributor.authorPetretto E.
dc.contributor.authorBlankenberg S.
dc.contributor.authorCambien F.
dc.contributor.authorCook S.A.
dc.contributor.authorTiret L.
dc.contributor.authorRichardson S.
dc.date.accessioned2018-11-29T07:16:25Z
dc.date.available2018-11-29T07:16:25Z
dc.date.issued2011
dc.identifier.citationSpots L.H., Bottolo L., Petretto E., Blankenberg S., Cambien F., Cook S.A., Tiret L., Richardson S. (2011). Bayesian detection of expression quantitative trait. Genetics 189 (4) : 1449-1459. ScholarBank@NUS Repository. https://doi.org/10.1534/1449.full.pdf
dc.identifier.issn166731
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/149267
dc.description.abstractHigh-throughput genomics allows genome-wide quantification of gene expression levels in tissues and cell types and, when combined with sequence variation data, permits the identification of genetic control points of expression (expression QTL or eQTL). Clusters of eQTL influenced by single genetic polymorphisms can inform on hotspots of regulation of pathways and networks, although very few hotspots have been robustly detected, replicated, or experimentally verified. Here we present a novel modeling strategy to estimate the propensity of a genetic marker to influence several expression traits at the same time, based on a hierarchical formulation of related regressions. We implement this hierarchical regression model in a Bayesian framework using a stochastic search algorithm, HESS, that efficiently probes sparse subsets of genetic markers in a high-dimensional data matrix to identify hotspots and to pinpoint the individual genetic effects (eQTL). Simulating complex regulatory scenarios, we demonstrate that our method outperforms current state-of-the-art approaches, in particular when the number of transcripts is large. We also illustrate the applicability of HESS to diverse real-case data sets, in mouse and human genetic settings, and show that it provides new insights into regulatory hotspots that were not detected by conventional methods. The results suggest that the combination of our modeling strategy and algorithmic implementation provides significant advantages for the identification of functional eQTL hotspots, revealing key regulators underlying pathways. � 2011 by the Genetics Society of America.
dc.publisherGenetic Society America
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentDUKE-NUS MEDICAL SCHOOL
dc.description.doi10.1534/1449.full.pdf
dc.description.sourcetitleGenetics
dc.description.volume189
dc.description.issue4
dc.description.page1449-1459
dc.published.statepublished
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