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A framework for integrating directed and undirected annotations to build explanatory models of cis-eQTL data

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  • David Lamparter
  • Rajat Bhatnagar
  • Katja Hebestreit
  • T Grant Belgard
  • Alice Zhang
  • Victor Hanson-Smith

Abstract

A longstanding goal of regulatory genetics is to understand how variants in genome sequences lead to changes in gene expression. Here we present a method named Bayesian Annotation Guided eQTL Analysis (BAGEA), a variational Bayes framework to model cis-eQTLs using directed and undirected genomic annotations. We used BAGEA to integrate directed genomic annotations with eQTL summary statistics from tissues of various origins. This analysis revealed epigenetic marks that are relevant for gene expression in different tissues and cell types. We estimated the predictive power of the models that were fitted based on directed genomic annotations. This analysis showed that, depending on the underlying eQTL data used, the directed genomic annotations could predict up to 1.5% of the variance observed in the expression of genes with top nominal eQTL association p-values

Suggested Citation

  • David Lamparter & Rajat Bhatnagar & Katja Hebestreit & T Grant Belgard & Alice Zhang & Victor Hanson-Smith, 2020. "A framework for integrating directed and undirected annotations to build explanatory models of cis-eQTL data," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-27, June.
    • Handle: RePEc:plo:pcbi00:1007770
    DOI: 10.1371/journal.pcbi.1007770
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    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Joseph K. Pickrell & John C. Marioni & Athma A. Pai & Jacob F. Degner & Barbara E. Engelhardt & Everlyne Nkadori & Jean-Baptiste Veyrieras & Matthew Stephens & Yoav Gilad & Jonathan K. Pritchard, 2010. "Understanding mechanisms underlying human gene expression variation with RNA sequencing," Nature, Nature, vol. 464(7289), pages 768-772, April.
    3. Avinash Das & Michael Morley & Christine S. Moravec & W. H. W. Tang & Hakon Hakonarson & Kenneth B. Margulies & Thomas P. Cappola & Shane Jensen & Sridhar Hannenhalli, 2015. "Bayesian integration of genetics and epigenetics detects causal regulatory SNPs underlying expression variability," Nature Communications, Nature, vol. 6(1), pages 1-11, December.
    4. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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