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Nat Genet. 2017 May;49(5):700-707. doi: 10.1038/ng.3840. Epub 2017 Apr 10.

Population- and individual-specific regulatory variation in Sardinia.

Author information

1
Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, Italy.
2
Department of Pathology, Stanford University School of Medicine, Stanford, California, USA.
3
Dipartimento di Scienze Biomediche, Universita di Sassari, Sassari, Italy.
4
Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.
5
Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA.
6
CRS4, Advanced Genomic Computing Technology, Pula, Italy.
7
Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
8
Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland, USA.
9
Department of Human Genetics, University of Chicago, Chicago, Illinois, USA.
10
Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA.
11
Laboratory of Genetics, National Institute on Aging, Baltimore, Maryland, USA.

Abstract

Genetic studies of complex traits have mainly identified associations with noncoding variants. To further determine the contribution of regulatory variation, we combined whole-genome and transcriptome data for 624 individuals from Sardinia to identify common and rare variants that influence gene expression and splicing. We identified 21,183 expression quantitative trait loci (eQTLs) and 6,768 splicing quantitative trait loci (sQTLs), including 619 new QTLs. We identified high-frequency QTLs and found evidence of selection near genes involved in malarial resistance and increased multiple sclerosis risk, reflecting the epidemiological history of Sardinia. Using family relationships, we identified 809 segregating expression outliers (median z score of 2.97), averaging 13.3 genes per individual. Outlier genes were enriched for proximal rare variants, providing a new approach to study large-effect regulatory variants and their relevance to traits. Our results provide insight into the effects of regulatory variants and their relationship to population history and individual genetic risk.

PMID:
28394350
PMCID:
PMC5411016
DOI:
10.1038/ng.3840
[Indexed for MEDLINE]
Free PMC Article

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