Genetic effects on gene expression across human tissues

By F. Aguet, Andrew Anand Brown, Stephane E. Castel, Joe R. Davis, Yuan He, Brian Jo, Pejman Mohammadi, YoSon Park, P. Parsana, A. Segrè and 244 more. 2017

3,717 citations recorded by the source corpus.

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

Subfields: Genetics, Molecular Biology

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In Genetics

1. Matthew Stephens
2. Frank Dudbridge
3. Naomi R. Wray
4. Peter M. Visscher
5. Peter Carbonetto
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In Molecular Biology

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Citations in the corpus

• Comprehensive functional genomic resource and integrative model for the human brain (2018, result): “This estimate identified substantially more eQTLs and associated eGenes than previous studies, reflecting our large sample size (8, 11, 21).”
• Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018, positive): “Functional genomic studies often remove such cell type-specific signals, through use of large numbers of expression-derived principle components or surrogate variables as covariates, to remove unwanted sources of variation and maximize detection of cis eQTLs (44).”