Cargando…

Fast and robust group-wise eQTL mapping using sparse graphical models

BACKGROUND: Genome-wide expression quantitative trait loci (eQTL) studies have emerged as a powerful tool to understand the genetic basis of gene expression and complex traits. The traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) an...

Descripción completa

Detalles Bibliográficos
Autores principales: Cheng, Wei, Shi, Yu, Zhang, Xiang, Wang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4387667/
https://www.ncbi.nlm.nih.gov/pubmed/25593000
http://dx.doi.org/10.1186/s12859-014-0421-z
_version_ 1782365304625561600
author Cheng, Wei
Shi, Yu
Zhang, Xiang
Wang, Wei
author_facet Cheng, Wei
Shi, Yu
Zhang, Xiang
Wang, Wei
author_sort Cheng, Wei
collection PubMed
description BACKGROUND: Genome-wide expression quantitative trait loci (eQTL) studies have emerged as a powerful tool to understand the genetic basis of gene expression and complex traits. The traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to hidden biological pathways. RESULTS: We introduce a new approach to identify novel group-wise associations between sets of SNPs and sets of genes. Such associations are captured by hidden variables connecting SNPs and genes. Our model is a linear-Gaussian model and uses two types of hidden variables. One captures the set associations between SNPs and genes, and the other captures confounders. We develop an efficient optimization procedure which makes this approach suitable for large scale studies. Extensive experimental evaluations on both simulated and real datasets demonstrate that the proposed methods can effectively capture both individual and group-wise signals that cannot be identified by the state-of-the-art eQTL mapping methods. CONCLUSIONS: Considering group-wise associations significantly improves the accuracy of eQTL mapping, and the successful multi-layer regression model opens a new approach to understand how multiple SNPs interact with each other to jointly affect the expression level of a group of genes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0421-z) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-4387667
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-43876672015-04-08 Fast and robust group-wise eQTL mapping using sparse graphical models Cheng, Wei Shi, Yu Zhang, Xiang Wang, Wei BMC Bioinformatics Research Article BACKGROUND: Genome-wide expression quantitative trait loci (eQTL) studies have emerged as a powerful tool to understand the genetic basis of gene expression and complex traits. The traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to hidden biological pathways. RESULTS: We introduce a new approach to identify novel group-wise associations between sets of SNPs and sets of genes. Such associations are captured by hidden variables connecting SNPs and genes. Our model is a linear-Gaussian model and uses two types of hidden variables. One captures the set associations between SNPs and genes, and the other captures confounders. We develop an efficient optimization procedure which makes this approach suitable for large scale studies. Extensive experimental evaluations on both simulated and real datasets demonstrate that the proposed methods can effectively capture both individual and group-wise signals that cannot be identified by the state-of-the-art eQTL mapping methods. CONCLUSIONS: Considering group-wise associations significantly improves the accuracy of eQTL mapping, and the successful multi-layer regression model opens a new approach to understand how multiple SNPs interact with each other to jointly affect the expression level of a group of genes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0421-z) contains supplementary material, which is available to authorized users. BioMed Central 2015-01-16 /pmc/articles/PMC4387667/ /pubmed/25593000 http://dx.doi.org/10.1186/s12859-014-0421-z Text en © Cheng et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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 Research Article
Cheng, Wei
Shi, Yu
Zhang, Xiang
Wang, Wei
Fast and robust group-wise eQTL mapping using sparse graphical models
title Fast and robust group-wise eQTL mapping using sparse graphical models
title_full Fast and robust group-wise eQTL mapping using sparse graphical models
title_fullStr Fast and robust group-wise eQTL mapping using sparse graphical models
title_full_unstemmed Fast and robust group-wise eQTL mapping using sparse graphical models
title_short Fast and robust group-wise eQTL mapping using sparse graphical models
title_sort fast and robust group-wise eqtl mapping using sparse graphical models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4387667/
https://www.ncbi.nlm.nih.gov/pubmed/25593000
http://dx.doi.org/10.1186/s12859-014-0421-z
work_keys_str_mv AT chengwei fastandrobustgroupwiseeqtlmappingusingsparsegraphicalmodels
AT shiyu fastandrobustgroupwiseeqtlmappingusingsparsegraphicalmodels
AT zhangxiang fastandrobustgroupwiseeqtlmappingusingsparsegraphicalmodels
AT wangwei fastandrobustgroupwiseeqtlmappingusingsparsegraphicalmodels