From genome to function by studying eQTLs

Harm-Jan Westra, Lude Franke*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

74 Citations (Scopus)

Abstract

Genome-wide association studies (GWASs) have shown a large number of genetic variants to be associated with complex diseases. The identification of the causal variant within an associated locus can sometimes be difficult because of the linkage disequilibrium between the associated variants and because most GWAS loci contain multiple genes, or no genes at all. Expression quantitative trait locus (eQTL) mapping is a method used to determine the effects of genetic variants on gene expression levels. eQTL mapping studies have enabled the prioritization of genetic variants within GWAS loci, and have shown that trait-associated single nucleotide polymorphisms (SNPs) often function in a tissue- or cell type-specific manner, sometimes having downstream effects on completely different chromosomes. Furthermore, recent RNA-sequencing (RNA-seq) studies have shown that a large repertoire of transcripts is available in cells, which are actively regulated by (trait-associated) variants. Future eQTL mapping studies will focus on broadening the range of available tissues and cell types, in order to determine the key tissues and cell types involved in complex traits. Finally, large meta-analyses will be able to pinpoint the causal variants within the trait-associated loci and determine their downstream effects in greater detail. This article is part of a Special Issue entitled: From Genome to Function. (C) 2014 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)1896-1902
Number of pages7
JournalBiochimica et biophysica acta-Molecular basis of disease
Volume1842
Issue number10
DOIs
Publication statusPublished - Oct-2014

Keywords

  • eQTL
  • Gene expression
  • Genetics
  • Micro-array
  • RNA-seq
  • HUMAN GENE-EXPRESSION
  • RED-BLOOD-CELL
  • WIDE ASSOCIATION
  • MULTIPLE COMMON
  • CELIAC-DISEASE
  • ADULT OBESITY
  • TRANSCRIPTOME
  • VARIANTS
  • GENOTYPE
  • TRAITS

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