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

Publication ,  Journal Article
Acharya, CR; McCarthy, JM; Owzar, K; Allen, AS
Published in: BMC Bioinformatics
June 24, 2016

BACKGROUND: In 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. RESULTS: Our 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. CONCLUSIONS: Since 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.

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Published In

BMC Bioinformatics

DOI

EISSN

1471-2105

Publication Date

June 24, 2016

Volume

17

Start / End Page

257

Location

England

Related Subject Headings

  • Software
  • Regulatory Sequences, Nucleic Acid
  • Regression Analysis
  • Quantitative Trait Loci
  • Organ Specificity
  • Humans
  • Genome-Wide Association Study
  • Genetic Variation
  • Gene Expression Regulation
  • Gene Expression Profiling
 

Citation

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Acharya, C. R., McCarthy, J. M., Owzar, K., & Allen, A. S. (2016). Exploiting expression patterns across multiple tissues to map expression quantitative trait loci. BMC Bioinformatics, 17, 257. https://doi.org/10.1186/s12859-016-1123-5
Acharya, Chaitanya R., Janice M. McCarthy, Kouros Owzar, and Andrew S. Allen. “Exploiting expression patterns across multiple tissues to map expression quantitative trait loci.BMC Bioinformatics 17 (June 24, 2016): 257. https://doi.org/10.1186/s12859-016-1123-5.
Acharya CR, McCarthy JM, Owzar K, Allen AS. Exploiting expression patterns across multiple tissues to map expression quantitative trait loci. BMC Bioinformatics. 2016 Jun 24;17:257.
Acharya, Chaitanya R., et al. “Exploiting expression patterns across multiple tissues to map expression quantitative trait loci.BMC Bioinformatics, vol. 17, June 2016, p. 257. Pubmed, doi:10.1186/s12859-016-1123-5.
Acharya CR, McCarthy JM, Owzar K, Allen AS. Exploiting expression patterns across multiple tissues to map expression quantitative trait loci. BMC Bioinformatics. 2016 Jun 24;17:257.
Journal cover image

Published In

BMC Bioinformatics

DOI

EISSN

1471-2105

Publication Date

June 24, 2016

Volume

17

Start / End Page

257

Location

England

Related Subject Headings

  • Software
  • Regulatory Sequences, Nucleic Acid
  • Regression Analysis
  • Quantitative Trait Loci
  • Organ Specificity
  • Humans
  • Genome-Wide Association Study
  • Genetic Variation
  • Gene Expression Regulation
  • Gene Expression Profiling