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

Chaitanya R Acharya1,2, Janice M McCarthy2, Kouros Owzar2

  • 1Program in Computational Biology and Bioinformatics, Duke University, 101 Science Dr, Durham, 27708, USA.

BMC Bioinformatics
|June 26, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for mapping expression quantitative trait loci (eQTLs) by jointly analyzing multiple tissues. The approach enhances the power to detect genetic variants influencing gene expression and offers significant computational speed advantages.

Keywords:
Multiple tissuesScore testTissue-specificityeQTL mapping

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Understanding complex diseases requires knowledge of how genetic variations in regulatory regions impact gene expression.
  • Tissue-specific genetic variants can activate transcription, necessitating robust methods for mapping expression quantitative trait loci (eQTLs).
  • Current eQTL mapping methods often lack the power to fully leverage information across multiple tissues.

Purpose of the Study:

  • To develop a statistically disciplined approach for joint eQTL mapping across multiple tissues.
  • To model tissue-specificity as a random effect, combining overall gene expression shifts with variant-specific effects.
  • To maximize the power of eQTL detection by utilizing all available information.

Main Methods:

  • A score test-based approach is proposed, modeling tissue-specificity as a random effect.
  • The method jointly models all tissues to investigate genetic variant effects on gene expression.
  • Parameter estimation is performed once per genome, reducing computational burden.

Main Results:

  • The proposed method demonstrates a computational advantage and comparable statistical power to existing joint modeling approaches like MetaTissue eQTL and eQTL-BMA.
  • Simulations show increased power for eQTL detection compared to tissue-by-tissue analyses.
  • Application to human brain expression datasets identified eQTLs in more genes than three existing methods when jointly analyzing multiple brain regions.

Conclusions:

  • The score test-based approach eliminates the need for parameter estimation under the alternative hypothesis, significantly decreasing computation time.
  • The method accommodates next-generation sequencing data and can analyze isoform-specific effects.
  • The approach is implemented in the R package JAGUAR, available on CRAN.