Bayesian detection of expression quantitative trait loci hot spots
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Bayesian detection of expression quantitative trait loci hot spots. / Bottolo, Leonardo; Petretto, Enrico; Blankenberg, Stefan; Cambien, François; Cook, Stuart A; Tiret, Laurence; Richardson, Sylvia.
In: GENETICS, Vol. 189, No. 4, 12.2011, p. 1449-1459.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Bayesian detection of expression quantitative trait loci hot spots
AU - Bottolo, Leonardo
AU - Petretto, Enrico
AU - Blankenberg, Stefan
AU - Cambien, François
AU - Cook, Stuart A
AU - Tiret, Laurence
AU - Richardson, Sylvia
PY - 2011/12
Y1 - 2011/12
N2 - High-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.
AB - High-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.
KW - Bayes Theorem
KW - Gene Expression
KW - Humans
KW - Quantitative Trait Loci
U2 - 10.1534/genetics.111.131425
DO - 10.1534/genetics.111.131425
M3 - SCORING: Journal article
C2 - 21926303
VL - 189
SP - 1449
EP - 1459
JO - GENETICS
JF - GENETICS
SN - 0016-6731
IS - 4
ER -