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Nat Genet. 2018 Jul;50(7):956-967. doi: 10.1038/s41588-018-0154-4. Epub 2018 Jun 28.

Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation.

Author information

1
Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. egamazon@uchicago.edu.
2
Clare Hall, University of Cambridge, Cambridge, UK. egamazon@uchicago.edu.
3
The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. asegre@broadinstitute.org.
4
Department of Ophthalmology and Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA. asegre@broadinstitute.org.
5
Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
6
Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK.
7
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
8
Computational Sciences, Pfizer Inc, Cambridge, MA, USA.
9
Department of Computer Science, University of California, Los Angeles, CA, USA.
10
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
11
Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
12
Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, Geneva, Switzerland.
13
Swiss Institute of Bioinformatics, Geneva, Switzerland.
14
Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
15
Translational Neurogenomics Group, QIMR Berghofer, Brisbane, Queensland, Australia.
16
The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
17
Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA.
18
Department of Statistics, The University of Chicago, Chicago, IL, USA.
19
Department of Human Genetics, The University of Chicago, Chicago, IL, USA.
20
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
21
Massachusetts General Hospital Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA, USA.

Abstract

We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40-80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU.

PMID:
29955180
PMCID:
PMC6248311
DOI:
10.1038/s41588-018-0154-4
[Indexed for MEDLINE]
Free PMC Article

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