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

BMC Biology
X. XieSaurabh Sinha

Abstract

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. We developed a statistical approach that integrates phenotype, genotype, gene expression, TF ChIP-seq, and Hi-C chromatin interaction data to answer this question. Using drug sensitivity of lymphoblastoid cell lines as the phenotype of interest, we tested if non-coding variants statistically linked to the phenotype are enriched for strong predicted impact on DNA binding strength of a TF and thus identified TFs regulating individual differences in the phenotype. O...Continue Reading

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Citations

Jul 1, 2020·Expert Opinion on Drug Metabolism & Toxicology·Gloria RavegniniSabrina Angelini
May 1, 2020·International Journal of Molecular Sciences·Jihye ParkJu Han Kim
Jun 13, 2020·Mechanisms of Ageing and Development·Samir Kumar Patra
May 18, 2021·Clinical Pharmacology and Therapeutics·Yitian Zhou, Volker M Lauschke
Jul 28, 2021·Nucleic Acids Research·Xiaoman XieSaurabh Sinha

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Datasets Mentioned

BETA
GM12878

Methods Mentioned

BETA
ChIP
ChIP-seq
Hi-C
Assay

Software Mentioned

DREAM
MEME
Sasquatch
R
Delta
gkm
STAP
R package “ drc ”
e1071
PWM

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