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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Andrew Anand Brown, Ana Viñuela, Olivier Delaneau, Tim D Spector, Kerrin S Small, Emmanouil T Dermitzakis

ABSTRACT

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. More... »

PAGES

1747

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    URI

    http://scigraph.springernature.com/pub.10.1038/ng.3979

    DOI

    http://dx.doi.org/10.1038/ng.3979

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1092346581

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29058714


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