Linking disease associations with regulatory information in the human genome

Handle URI:
http://hdl.handle.net/10754/598722
Title:
Linking disease associations with regulatory information in the human genome
Authors:
Schaub, M. A.; Boyle, A. P.; Kundaje, A.; Batzoglou, S.; Snyder, M.
Abstract:
Genome-wide association studies have been successful in identifying single nucleotide polymorphisms (SNPs) associated with a large number of phenotypes. However, an associated SNP is likely part of a larger region of linkage disequilibrium. This makes it difficult to precisely identify the SNPs that have a biological link with the phenotype. We have systematically investigated the association of multiple types of ENCODE data with disease-associated SNPs and show that there is significant enrichment for functional SNPs among the currently identified associations. This enrichment is strongest when integrating multiple sources of functional information and when highest confidence disease-associated SNPs are used. We propose an approach that integrates multiple types of functional data generated by the ENCODE Consortium to help identify "functional SNPs" that may be associated with the disease phenotype. Our approach generates putative functional annotations for up to 80% of all previously reported associations. We show that for most associations, the functional SNP most strongly supported by experimental evidence is a SNP in linkage disequilibrium with the reported association rather than the reported SNP itself. Our results show that the experimental data sets generated by the ENCODE Consortium can be successfully used to suggest functional hypotheses for variants associated with diseases and other phenotypes.
Citation:
Schaub MA, Boyle AP, Kundaje A, Batzoglou S, Snyder M (2012) Linking disease associations with regulatory information in the human genome. Genome Research 22: 1748–1759. Available: http://dx.doi.org/10.1101/gr.136127.111.
Publisher:
Cold Spring Harbor Laboratory Press
Journal:
Genome Research
Issue Date:
1-Sep-2012
DOI:
10.1101/gr.136127.111
PubMed ID:
22955986
PubMed Central ID:
PMC3431491
Type:
Article
ISSN:
1088-9051
Sponsors:
We thank Ross Hardison, Ewan Birney, Jason Ernst, KonradKarczewski, Manoj Hariharan, and the members of the Batzogloulaboratory for suggestions and comments. We thank the anonymousreviewers for valuable feedback and suggestions. We thankthe ENCODE Consortium, the Office of Population Genomics atthe National Human Genome Research Institute, the HapMapConsortium, and the Genome Bioinformatics Group at the Universityof California–Santa Cruz for generating the data and toolsused in this work. This work was supported in part by the ENCODEConsortium under Grant No. NIH 5U54 HG 004558, by theNational Science Foundation under Grant No. 0640211, fundingfrom the Beta Cell Consortium, and by a King Abdullah Universityof Science and Technology research grant. M.A.S. was supportedin part by a Richard and Naomi Horowitz Stanford GraduateFellowship. A.K. was partially supported by an ENCODE analysissubcontract.
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DC FieldValue Language
dc.contributor.authorSchaub, M. A.en
dc.contributor.authorBoyle, A. P.en
dc.contributor.authorKundaje, A.en
dc.contributor.authorBatzoglou, S.en
dc.contributor.authorSnyder, M.en
dc.date.accessioned2016-02-25T13:35:06Zen
dc.date.available2016-02-25T13:35:06Zen
dc.date.issued2012-09-01en
dc.identifier.citationSchaub MA, Boyle AP, Kundaje A, Batzoglou S, Snyder M (2012) Linking disease associations with regulatory information in the human genome. Genome Research 22: 1748–1759. Available: http://dx.doi.org/10.1101/gr.136127.111.en
dc.identifier.issn1088-9051en
dc.identifier.pmid22955986en
dc.identifier.doi10.1101/gr.136127.111en
dc.identifier.urihttp://hdl.handle.net/10754/598722en
dc.description.abstractGenome-wide association studies have been successful in identifying single nucleotide polymorphisms (SNPs) associated with a large number of phenotypes. However, an associated SNP is likely part of a larger region of linkage disequilibrium. This makes it difficult to precisely identify the SNPs that have a biological link with the phenotype. We have systematically investigated the association of multiple types of ENCODE data with disease-associated SNPs and show that there is significant enrichment for functional SNPs among the currently identified associations. This enrichment is strongest when integrating multiple sources of functional information and when highest confidence disease-associated SNPs are used. We propose an approach that integrates multiple types of functional data generated by the ENCODE Consortium to help identify "functional SNPs" that may be associated with the disease phenotype. Our approach generates putative functional annotations for up to 80% of all previously reported associations. We show that for most associations, the functional SNP most strongly supported by experimental evidence is a SNP in linkage disequilibrium with the reported association rather than the reported SNP itself. Our results show that the experimental data sets generated by the ENCODE Consortium can be successfully used to suggest functional hypotheses for variants associated with diseases and other phenotypes.en
dc.description.sponsorshipWe thank Ross Hardison, Ewan Birney, Jason Ernst, KonradKarczewski, Manoj Hariharan, and the members of the Batzogloulaboratory for suggestions and comments. We thank the anonymousreviewers for valuable feedback and suggestions. We thankthe ENCODE Consortium, the Office of Population Genomics atthe National Human Genome Research Institute, the HapMapConsortium, and the Genome Bioinformatics Group at the Universityof California–Santa Cruz for generating the data and toolsused in this work. This work was supported in part by the ENCODEConsortium under Grant No. NIH 5U54 HG 004558, by theNational Science Foundation under Grant No. 0640211, fundingfrom the Beta Cell Consortium, and by a King Abdullah Universityof Science and Technology research grant. M.A.S. was supportedin part by a Richard and Naomi Horowitz Stanford GraduateFellowship. A.K. was partially supported by an ENCODE analysissubcontract.en
dc.publisherCold Spring Harbor Laboratory Pressen
dc.subject.meshChromosome Mappingen
dc.subject.meshRegulatory Sequences, Nucleic Aciden
dc.subject.meshGenome, Humanen
dc.subject.meshGenome-Wide Association Studyen
dc.subject.meshGenetic Linkageen
dc.titleLinking disease associations with regulatory information in the human genomeen
dc.typeArticleen
dc.identifier.journalGenome Researchen
dc.identifier.pmcidPMC3431491en
dc.contributor.institutionStanford University, Palo Alto, United Statesen
dc.contributor.institutionStanford University School of Medicine, Stanford, United Statesen
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