SORA

Advancing, promoting and sharing knowledge of health through excellence in teaching, clinical practice and research into the prevention and treatment of illness

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

Iotchkova, V; Ritchie, GRS; Geihs, M; Morganella, S; Min, JL; Walter, K; Timpson, NJ; UK10K Consortium, ; Dunham, I; Birney, E; et al. Iotchkova, V; Ritchie, GRS; Geihs, M; Morganella, S; Min, JL; Walter, K; Timpson, NJ; UK10K Consortium; Dunham, I; Birney, E; Soranzo, N (2019) GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat Genet, 51 (2). pp. 343-353. ISSN 1546-1718 https://doi.org/10.1038/s41588-018-0322-6
SGUL Authors: Jamshidi, Yalda

[img]
Preview
PDF Accepted Version
Available under License ["licenses_description_publisher" not defined].

Download (1MB) | Preview
[img]
Preview
PDF (Figure 1) Accepted Version
Available under License ["licenses_description_publisher" not defined].

Download (448kB) | Preview
[img]
Preview
PDF (Figure 2) Accepted Version
Available under License ["licenses_description_publisher" not defined].

Download (55kB) | Preview
[img]
Preview
PDF (Figure 3) Accepted Version
Available under License ["licenses_description_publisher" not defined].

Download (2MB) | Preview
[img]
Preview
PDF (Figure 4) Accepted Version
Available under License ["licenses_description_publisher" not defined].

Download (436kB) | Preview
[img]
Preview
PDF (Figure 5) Accepted Version
Available under License ["licenses_description_publisher" not defined].

Download (107kB) | Preview

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.

Item Type: Article
Additional Information: This is a post-peer-review, pre-copyedit version of an article published in Nature Genetics. The final authenticated version is available online at: http://dx.doi.org/10.1038/s41588-018-0322-6
Keywords: Disease, Genome, Genome-Wide Association Study, Genomics, Humans, Molecular Sequence Annotation, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Regulatory Sequences, Nucleic Acid, Software, UK10K Consortium, Humans, Disease, Genomics, Regulatory Sequences, Nucleic Acid, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome, Software, Genome-Wide Association Study, Molecular Sequence Annotation, 11 Medical And Health Sciences, 06 Biological Sciences, Developmental Biology
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Journal or Publication Title: Nat Genet
ISSN: 1546-1718
Language: eng
Dates:
DateEvent
February 2019Published
28 January 2019Published Online
29 November 2018Accepted
Publisher License: Publisher's own licence
Projects:
Project IDFunderFunder ID
MC_UU_12013/2Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
MC_UU_12013/3Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
WT091310Wellcome Trusthttp://dx.doi.org/10.13039/100004440
WT098051Wellcome Trusthttp://dx.doi.org/10.13039/100004440
WT091310Wellcome Trusthttp://dx.doi.org/10.13039/100004440
202802/Z/16/ZWellcome Trusthttp://dx.doi.org/10.13039/100004440
102215/2/13/2Wellcome Trusthttp://dx.doi.org/10.13039/100004440
BRC-1215-20011National Institute for Health Researchhttp://dx.doi.org/10.13039/501100000272
C18281/A19169Cancer Research UKhttp://dx.doi.org/10.13039/501100000289
PubMed ID: 30692680
Web of Science ID: WOS:000457314300021
Go to PubMed abstract
URI: http://sgultest.da.ulcc.ac.uk/id/eprint/111171
Publisher's version: https://doi.org/10.1038/s41588-018-0322-6

Actions (login required)

Edit Item Edit Item