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Predicting causal variants affecting expression by using whole-genome sequencing and RNA-seq from multiple human tissues

Lookup NU author(s): Dr Ana Viñuela

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Abstract

© 2017 Nature America, Inc.Genetic association mapping produces statistical links between phenotypes and genomic regions, but identifying causal variants remains difficult. Whole-genome sequencing (WGS) can help by providing complete knowledge of all genetic variants, but it is financially prohibitive for well-powered GWAS studies. We performed mapping of expression quantitative trait loci (eQTLs) with WGS and RNA-seq, and found that lead eQTL variants called with WGS were more likely to be causal. Through simulations, we derived properties of causal variants and used them to develop a method for identifying likely causal SNPs. We estimated that 25-70% of causal variants were located in open-chromatin regions, depending on the tissue and experiment. Finally, we identified a set of high-confidence causal variants and showed that these were more enriched in GWAS associations than other eQTLs. Of those, we found 65 associations with GWAS traits and provide examples in which genes implicated by expression are functionally validated as being relevant for complex traits.


Publication metadata

Author(s): Brown AA, Vinuela A, Delaneau O, Spector TD, Small KS, Dermitzakis ET

Publication type: Article

Publication status: Published

Journal: Nature Genetics

Year: 2017

Volume: 49

Issue: 12

Pages: 1747-1751

Print publication date: 01/12/2017

Online publication date: 23/10/2017

Acceptance date: 27/09/2017

ISSN (print): 1061-4036

ISSN (electronic): 1546-1718

Publisher: Nature Publishing Group

URL: https://doi.org/10.1038/ng.3979

DOI: 10.1038/ng.3979

PubMed id: 29058714


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