Functional annotation signatures of disease susceptibility loci improve SNP association analysis.

dc.contributor.author

Iversen, ES

dc.contributor.author

Lipton, G

dc.contributor.author

Clyde, MA

dc.contributor.author

Monteiro, ANA

dc.coverage.spatial

England

dc.date.accessioned

2014-06-04T18:08:31Z

dc.date.issued

2014-05-24

dc.description.abstract

BACKGROUND: Genetic association studies are conducted to discover genetic loci that contribute to an inherited trait, identify the variants behind these associations and ascertain their functional role in determining the phenotype. To date, functional annotations of the genetic variants have rarely played more than an indirect role in assessing evidence for association. Here, we demonstrate how these data can be systematically integrated into an association study's analysis plan. RESULTS: We developed a Bayesian statistical model for the prior probability of phenotype-genotype association that incorporates data from past association studies and publicly available functional annotation data regarding the susceptibility variants under study. The model takes the form of a binary regression of association status on a set of annotation variables whose coefficients were estimated through an analysis of associated SNPs in the GWAS Catalog (GC). The functional predictors examined included measures that have been demonstrated to correlate with the association status of SNPs in the GC and some whose utility in this regard is speculative: summaries of the UCSC Human Genome Browser ENCODE super-track data, dbSNP function class, sequence conservation summaries, proximity to genomic variants in the Database of Genomic Variants and known regulatory elements in the Open Regulatory Annotation database, PolyPhen-2 probabilities and RegulomeDB categories. Because we expected that only a fraction of the annotations would contribute to predicting association, we employed a penalized likelihood method to reduce the impact of non-informative predictors and evaluated the model's ability to predict GC SNPs not used to construct the model. We show that the functional data alone are predictive of a SNP's presence in the GC. Further, using data from a genome-wide study of ovarian cancer, we demonstrate that their use as prior data when testing for association is practical at the genome-wide scale and improves power to detect associations. CONCLUSIONS: We show how diverse functional annotations can be efficiently combined to create 'functional signatures' that predict the a priori odds of a variant's association to a trait and how these signatures can be integrated into a standard genome-wide-scale association analysis, resulting in improved power to detect truly associated variants.

dc.identifier

http://www.ncbi.nlm.nih.gov/pubmed/24886216

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1471-2164-15-398

dc.identifier.eissn

1471-2164

dc.identifier.uri

https://hdl.handle.net/10161/8882

dc.language

eng

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BMC Genomics

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BMC Genomics

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10.1186/1471-2164-15-398

dc.subject

Disease Susceptibility

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Female

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Genetic Association Studies

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Genetic Loci

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Genome-Wide Association Study

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Humans

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Molecular Sequence Annotation

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Ovarian Neoplasms

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Polymorphism, Single Nucleotide

dc.title

Functional annotation signatures of disease susceptibility loci improve SNP association analysis.

dc.type

Journal article

duke.contributor.orcid

Iversen, ES|0000-0002-0066-2763

duke.contributor.orcid

Clyde, MA|0000-0002-3595-1872

pubs.author-url

http://www.ncbi.nlm.nih.gov/pubmed/24886216

pubs.begin-page

398

pubs.organisational-group

Duke

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Duke Cancer Institute

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Institutes and Centers

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School of Medicine

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Statistical Science

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Trinity College of Arts & Sciences

pubs.publication-status

Published online

pubs.volume

15

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