A functional strategy to characterize expression Quantitative Trait Loci

Human Genetics
Elena GrassiPaolo Provero

Abstract

The study of genetic variation has been revolutionized by the advent of high-throughput technologies able to determine the complete genomic sequence of thousands of individuals. Understanding the functional relevance of variants is, however, still a difficult task, especially when focusing on non-coding variants. Most of the variants associated with disease by Genome-Wide Association Studies (GWAS) are indeed non-coding, and presumably exert their effects by altering gene regulation. Expression Quantitative Trait Loci (eQTL) studies represent an important step in understanding the functional relevance of regulatory variants. We propose a new strategy to detect and characterize eQTLs, based on the effect of variants on the Total Binding Affinity (TBA) profiles of regulatory regions. Using a large dataset of coupled genome and expression data, we show that TBA-based inference allows the identification of eQTLs not revealed by traditional methods and helps in their interpretation in terms of altered transcription factor binding.

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Citations

Nov 14, 2020·Database : the Journal of Biological Databases and Curation·Davide DalfovoAlessandro Romanel

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Methods Mentioned

BETA
exome sequencing

Software Mentioned

cran package pls
JASPAR
JASPAR Core Vertebrate Collection Mathelier
rider
PreSTIGE
HOCOMOCO
Ensembl
GEUVADIS Data Browser
TBA
Gencode

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