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Joint variable selection and network modeling for detecting eQTLs.

Xuan Cao1, Lili Ding2, Tesfaye B Mersha3

  • 1Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH45221,USA.

Statistical Applications in Genetics and Molecular Biology
|February 21, 2020
PubMed
Summary
This summary is machine-generated.

We introduce JDAG, a new Bayesian method for joint variable selection and gene network estimation. JDAG shows higher sensitivity and specificity in expression quantitative trait loci (eQTL) analysis compared to existing methods.

Keywords:
eQTLjoin analysisnetwork modelingvariable selection

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Traditional univariate methods for eQTL analysis are limited.
  • Joint variable selection and covariance estimation are crucial for complex trait analysis.
  • Bayesian multiplicity adjustment offers an alternative to traditional multiple testing corrections.

Purpose of the Study:

  • To compare three recent statistical methods for joint variable selection and covariance estimation.
  • To introduce and evaluate a novel hierarchical Bayesian method, JDAG.
  • To apply these methods to expression quantitative trait loci (eQTL) and gene network estimation.

Main Methods:

  • Multivariate regression models incorporating phenotype dependence.
  • Bayesian multiplicity adjustment for statistical inference.
  • Comparison of MSSL, SSUR, OBFBF, and the proposed JDAG methods.
  • Application to simulated data and HapMap data for asthma.

Main Results:

  • JDAG demonstrated higher sensitivity and specificity in network identification under sparse settings.
  • JDAG showed computational efficiency in small-to-moderate dimensions.
  • eQTL analysis identified known asthma-related gene regulations (STARD3, IKZF3, PGAP3).

Conclusions:

  • JDAG is a promising method for joint eQTL and gene network estimation.
  • The method excels in identifying regulatory networks with improved accuracy.
  • JDAG's applicability is currently limited to non-high-dimensional data.