GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals.

Abstract:

:Loci discovered by genome-wide association studies predominantly map outside protein-coding genes. The interpretation of the functional consequences of non-coding variants can be greatly enhanced by catalogs of regulatory genomic regions in cell lines and primary tissues. However, robust and readily applicable methods are still lacking by which to systematically evaluate the contribution of these regions to genetic variation implicated in diseases or quantitative traits. Here we propose a novel approach that leverages genome-wide association studies' findings with regulatory or functional annotations to classify features relevant to a phenotype of interest. Within our framework, we account for major sources of confounding not offered by current methods. We further assess enrichment of genome-wide association studies for 19 traits within Encyclopedia of DNA Elements- and Roadmap-derived regulatory regions. We characterize unique enrichment patterns for traits and annotations driving novel biological insights. The method is implemented in standalone software and an R package, to facilitate its application by the research community.

journal_name

Nat Genet

journal_title

Nature genetics

authors

Iotchkova V,Ritchie GRS,Geihs M,Morganella S,Min JL,Walter K,Timpson NJ,UK10K Consortium.,Dunham I,Birney E,Soranzo N

doi

10.1038/s41588-018-0322-6

subject

Has Abstract

pub_date

2019-02-01 00:00:00

pages

343-353

issue

2

eissn

1061-4036

issn

1546-1718

pii

10.1038/s41588-018-0322-6

journal_volume

51

pub_type

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