Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.

Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown subtle environmental perturbations. The...

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Autores principales:	Nicoló Fusi, Oliver Stegle, Neil D Lawrence
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4b6151bc571d47f289677404c95410ea
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spelling	oai:doaj.org-article:4b6151bc571d47f289677404c95410ea2021-11-18T05:51:40ZJoint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.1553-734X1553-735810.1371/journal.pcbi.1002330https://doaj.org/article/4b6151bc571d47f289677404c95410ea2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22241974/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown subtle environmental perturbations. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, this new model can more accurately distinguish true genetic association signals from confounding variation. We applied our model and compared it to existing methods on different datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, our approach not only identifies a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies. A software implementation of PANAMA is freely available online at http://ml.sheffield.ac.uk/qtl/.Nicoló FusiOliver StegleNeil D LawrencePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 1, p e1002330 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Nicoló Fusi Oliver Stegle Neil D Lawrence Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.
description	Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown subtle environmental perturbations. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, this new model can more accurately distinguish true genetic association signals from confounding variation. We applied our model and compared it to existing methods on different datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, our approach not only identifies a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies. A software implementation of PANAMA is freely available online at http://ml.sheffield.ac.uk/qtl/.
format	article
author	Nicoló Fusi Oliver Stegle Neil D Lawrence
author_facet	Nicoló Fusi Oliver Stegle Neil D Lawrence
author_sort	Nicoló Fusi
title	Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.
title_short	Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.
title_full	Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.
title_fullStr	Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.
title_full_unstemmed	Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.
title_sort	joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/4b6151bc571d47f289677404c95410ea
work_keys_str_mv	AT nicolofusi jointmodellingofconfoundingfactorsandprominentgeneticregulatorsprovidesincreasedaccuracyingeneticalgenomicsstudies AT oliverstegle jointmodellingofconfoundingfactorsandprominentgeneticregulatorsprovidesincreasedaccuracyingeneticalgenomicsstudies AT neildlawrence jointmodellingofconfoundingfactorsandprominentgeneticregulatorsprovidesincreasedaccuracyingeneticalgenomicsstudies
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Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.

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