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Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression
Expression quantitative trait loci (eQTL) mapping is a widely used tool to study the genetics of gene expression. Confounding factors and the burden of multiple testing limit the ability to map distal trans eQTLs, which is important to understand downstream genetic effects on genes and pathways. We...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765046/ https://www.ncbi.nlm.nih.gov/pubmed/26911988 http://dx.doi.org/10.1186/s13059-016-0895-2 |
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author | Rakitsch, Barbara Stegle, Oliver |
author_facet | Rakitsch, Barbara Stegle, Oliver |
author_sort | Rakitsch, Barbara |
collection | PubMed |
description | Expression quantitative trait loci (eQTL) mapping is a widely used tool to study the genetics of gene expression. Confounding factors and the burden of multiple testing limit the ability to map distal trans eQTLs, which is important to understand downstream genetic effects on genes and pathways. We propose a two-stage linear mixed model that first learns local directed gene-regulatory networks to then condition on the expression levels of selected genes. We show that this covariate selection approach controls for confounding factors and regulatory context, thereby increasing eQTL detection power and improving the consistency between studies. GNet-LMM is available at: https://github.com/PMBio/GNetLMM. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-016-0895-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4765046 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47650462016-02-25 Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression Rakitsch, Barbara Stegle, Oliver Genome Biol Method Expression quantitative trait loci (eQTL) mapping is a widely used tool to study the genetics of gene expression. Confounding factors and the burden of multiple testing limit the ability to map distal trans eQTLs, which is important to understand downstream genetic effects on genes and pathways. We propose a two-stage linear mixed model that first learns local directed gene-regulatory networks to then condition on the expression levels of selected genes. We show that this covariate selection approach controls for confounding factors and regulatory context, thereby increasing eQTL detection power and improving the consistency between studies. GNet-LMM is available at: https://github.com/PMBio/GNetLMM. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-016-0895-2) contains supplementary material, which is available to authorized users. BioMed Central 2016-02-24 /pmc/articles/PMC4765046/ /pubmed/26911988 http://dx.doi.org/10.1186/s13059-016-0895-2 Text en © Rakitsch and Stegle. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Method Rakitsch, Barbara Stegle, Oliver Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression |
title | Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression |
title_full | Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression |
title_fullStr | Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression |
title_full_unstemmed | Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression |
title_short | Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression |
title_sort | modelling local gene networks increases power to detect trans-acting genetic effects on gene expression |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765046/ https://www.ncbi.nlm.nih.gov/pubmed/26911988 http://dx.doi.org/10.1186/s13059-016-0895-2 |
work_keys_str_mv | AT rakitschbarbara modellinglocalgenenetworksincreasespowertodetecttransactinggeneticeffectsongeneexpression AT stegleoliver modellinglocalgenenetworksincreasespowertodetecttransactinggeneticeffectsongeneexpression |