Statistics Papers
Document Type
Technical Report
Date of this Version
10-12-2015
Publication Source
Nature Communications
Volume
6
DOI
10.1038/ncomms9555
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.
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Recommended Citation
Das, A., Morley, M., Moravec, C. S., Tang, W., Hakonarson, H., Consortium, M., Margulies, K. B., Cappola, T. P., Jensen, S. T., & Hannenhalli, S. (2015). Bayesian Integration of Genetics and Epigenetics Detects Causal Regulatory SNPs Underlying Expression Variability. Nature Communications, 6 http://dx.doi.org/10.1038/ncomms9555
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Date Posted: 25 October 2018
This document has been peer reviewed.