Bayesian integration of genetics and epigenetics detects causal regulatory SNPs underlying expression variability

Avinash Das, Michael Morley, Christine S. Moravec, W. H.W. Tang, Hakon Hakonarson, Kenneth B. Margulies, Thomas P. Cappola, Shane Jensen, Sridhar Hannenhalli, Euan A. Ashley, Jeffrey Brandimarto, Ray Hu, Mingyao Li, Hongzhe Li, Yichuan Liu, Liming Qu, Pablo Sanchez

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

The standard expression quantitative trait loci (eQTL) detects polymorphisms associated with gene expression without revealing causality. We introduce a coupled Bayesian regression approach-eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combination of regulatory single-nucleotide polymorphisms (SNPs) that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance but also predicts gene expression more accurately than other methods. Based on realistic simulated data, we demonstrate that eQTeL accurately detects causal regulatory SNPs, including those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal.

Original languageEnglish (US)
Article number8555
JournalNature Communications
Volume6
DOIs
StatePublished - Oct 12 2015

Fingerprint

polymorphism
nucleotides
Polymorphism
Epigenomics
Single Nucleotide Polymorphism
Nucleotides
gene expression
Gene expression
Gene Expression
Histone Code
proportion
Bayes Theorem
Quantitative Trait Loci
Regulator Genes
Protein Binding
Causality
loci
estimates
determinants
Transcription Factors

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

Cite this

Das, A., Morley, M., Moravec, C. S., Tang, W. H. W., Hakonarson, H., Margulies, K. B., ... Sanchez, P. (2015). Bayesian integration of genetics and epigenetics detects causal regulatory SNPs underlying expression variability. Nature Communications, 6, [8555]. https://doi.org/10.1038/ncomms9555

Bayesian integration of genetics and epigenetics detects causal regulatory SNPs underlying expression variability. / Das, Avinash; Morley, Michael; Moravec, Christine S.; Tang, W. H.W.; Hakonarson, Hakon; Margulies, Kenneth B.; Cappola, Thomas P.; Jensen, Shane; Hannenhalli, Sridhar; Ashley, Euan A.; Brandimarto, Jeffrey; Hu, Ray; Li, Mingyao; Li, Hongzhe; Liu, Yichuan; Qu, Liming; Sanchez, Pablo.

In: Nature Communications, Vol. 6, 8555, 12.10.2015.

Research output: Contribution to journalArticle

Das, A, Morley, M, Moravec, CS, Tang, WHW, Hakonarson, H, Margulies, KB, Cappola, TP, Jensen, S, Hannenhalli, S, Ashley, EA, Brandimarto, J, Hu, R, Li, M, Li, H, Liu, Y, Qu, L & Sanchez, P 2015, 'Bayesian integration of genetics and epigenetics detects causal regulatory SNPs underlying expression variability', Nature Communications, vol. 6, 8555. https://doi.org/10.1038/ncomms9555
Das, Avinash ; Morley, Michael ; Moravec, Christine S. ; Tang, W. H.W. ; Hakonarson, Hakon ; Margulies, Kenneth B. ; Cappola, Thomas P. ; Jensen, Shane ; Hannenhalli, Sridhar ; Ashley, Euan A. ; Brandimarto, Jeffrey ; Hu, Ray ; Li, Mingyao ; Li, Hongzhe ; Liu, Yichuan ; Qu, Liming ; Sanchez, Pablo. / Bayesian integration of genetics and epigenetics detects causal regulatory SNPs underlying expression variability. In: Nature Communications. 2015 ; Vol. 6.
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