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

UK10K Consortium, Scott Wilson

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)343-+
Number of pages13
JournalNature Genetics
Volume51
Issue number2
DOIs
Publication statusPublished - Feb 2019

Cite this

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title = "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.",
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GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. / UK10K Consortium ; Wilson, Scott.

In: Nature Genetics, Vol. 51, No. 2, 02.2019, p. 343-+.

Research output: Contribution to journalArticle

TY - JOUR

T1 - GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals

AU - UK10K Consortium

AU - Wilson, Scott

AU - Iotchkova, Valentina

AU - Ritchie, Graham R. S.

AU - Geihs, Matthias

AU - Morganella, Sandro

AU - Min, Josine L.

AU - Walter, Klaudia

AU - Timpson, Nicholas John

AU - Dunham, Ian

AU - Birney, Ewan

AU - Soranzo, Nicole

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AB - 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.

KW - WIDE ASSOCIATION

KW - COMMON VARIANTS

KW - LOCI

KW - INSIGHTS

KW - PATHOPHYSIOLOGY

KW - LANDSCAPE

KW - PROVIDES

KW - GLUCOSE

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