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.

A functional strategy to characterize expression Quantitative Trait Loci

Grassi, Elena;MARIELLA, ELISA;Forneris, Mattia;MAROTTA, FEDERICO;CATAPANO, MARIKA;Molineris, Ivan;Provero, Paolo
Last
2017

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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http://www.springerlink.com/content/100421/
Genomics; Humans; Gene Expression Regulation; Genome-Wide Association Study; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Regulatory Sequences, Nucleic Acid; Transcription, Genetic; Genetics; Genetics (clinical)
Grassi, Elena; Mariella, Elisa; Forneris, Mattia; Marotta, Federico; Catapano, Marika; Molineris, Ivan; Provero, Paolo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1657317
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