Exploiting expression patterns across multiple tissues to map expression quantitative trait loci

BMC Bioinformatics

Methodology Article

Janice M. McCarthy¹  Kouros Owzar¹  Andrew S. Allen²  Chaitanya R. Acharya² 

[1] Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Rd, 27708, Durham, USA;Program in Computational Biology and Bioinformatics, Duke University, 101 Science Dr, 27708, Durham, USA;Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Rd, 27708, Durham, USA;

关键词: eQTL mapping;    Multiple tissues;    Score test;    Tissue-specificity;   

DOI  :  10.1186/s12859-016-1123-5

 received in 2016-01-08, accepted in 2016-06-07,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundIn order to better understand complex diseases, it is important to understand how genetic variation in the regulatory regions affects gene expression. Genetic variants found in these regulatory regions have been shown to activate transcription in a tissue-specific manner. Therefore, it is important to map the aforementioned expression quantitative trait loci (eQTL) using a statistically disciplined approach that jointly models all the tissues and makes use of all the information available to maximize the power of eQTL mapping. In this context, we are proposing a score test-based approach where we model tissue-specificity as a random effect and investigate an overall shift in the gene expression combined with tissue-specific effects due to genetic variants.ResultsOur approach has 1) a distinct computational edge, and 2) comparable performance in terms of statistical power over other currently existing joint modeling approaches such as MetaTissue eQTL and eQTL-BMA. Using simulations, we show that our method increases the power to detect eQTLs when compared to a tissue-by-tissue approach and can exceed the performance, in terms of computational speed, of MetaTissue eQTL and eQTL-BMA. We apply our method to two publicly available expression datasets from normal human brains, one comprised of four brain regions from 150 neuropathologically normal samples and another comprised of ten brain regions from 134 neuropathologically normal samples, and show that by using our method and jointly analyzing multiple brain regions, we identify eQTLs within more genes when compared to three often used existing methods.ConclusionsSince we employ a score test-based approach, there is no need for parameter estimation under the alternative hypothesis. As a result, model parameters only have to be estimated once per genome, significantly decreasing computation time. Our method also accommodates the analysis of next- generation sequencing data. As an example, by modeling gene transcripts in an analogous fashion to tissues in our current formulation one would be able to test for both a variant overall effect across all isoforms of a gene as well as transcript-specific effects. We implement our approach within the R package JAGUAR, which is now available at the Comprehensive R Archive Network repository.

【 授权许可】

CC BY   
© The Author(s) 2016

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