Mechanistic interpretation of non-coding variants for discovering transcriptional regulators of drug response.

Journal: BMC biology
Published Date:

Abstract

BACKGROUND: Identification of functional non-coding variants and their mechanistic interpretation is a major challenge of modern genomics, especially for precision medicine. Transcription factor (TF) binding profiles and epigenomic landscapes in reference samples allow functional annotation of the genome, but do not provide ready answers regarding the effects of non-coding variants on phenotypes. A promising computational approach is to build models that predict TF-DNA binding from sequence, and use such models to score a variant's impact on TF binding strength. Here, we asked if this mechanistic approach to variant interpretation can be combined with information on genotype-phenotype associations to discover transcription factors regulating phenotypic variation among individuals.

Authors

  • Xiaoman Xie
    Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
  • Casey Hanson
    Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
  • Saurabh Sinha
    Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA sinhas@illinois.edu.