Rights statement: © 2011 Knight et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Using functional annotation for the empirical determination of Bayes Factors for genome-wide association study analysis
AU - Knight, Jo
AU - Barnes, Michael R.
AU - Breen, Gerome
AU - Weale, Michael E.
N1 - © 2011 Knight et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2011/4/27
Y1 - 2011/4/27
N2 - A genome wide association study (GWAS) typically results in a few highly significant 'hits' and a much larger set of suggestive signals ('near-hits'). The latter group are expected to be a mixture of true and false associations. One promising strategy to help separate these is to use functional annotations for prioritisation of variants for follow-up. A key task is to determine which annotations might prove most valuable. We address this question by examining the functional annotations of previously published GWAS hits. We explore three annotation categories: non-synonymous SNPs (nsSNPs), promoter SNPs and cis expression quantitative trait loci (eQTLs) in open chromatin regions. We demonstrate that GWAS hit SNPs are enriched for these three functional categories, and that it would be appropriate to provide a higher weighting for such SNPs when performing Bayesian association analyses. For GWAS studies, our analyses suggest the use of a Bayes Factor of about 4 for cis eQTL SNPs within regions of open chromatin, 3 for nsSNPs and 2 for promoter SNPs.
AB - A genome wide association study (GWAS) typically results in a few highly significant 'hits' and a much larger set of suggestive signals ('near-hits'). The latter group are expected to be a mixture of true and false associations. One promising strategy to help separate these is to use functional annotations for prioritisation of variants for follow-up. A key task is to determine which annotations might prove most valuable. We address this question by examining the functional annotations of previously published GWAS hits. We explore three annotation categories: non-synonymous SNPs (nsSNPs), promoter SNPs and cis expression quantitative trait loci (eQTLs) in open chromatin regions. We demonstrate that GWAS hit SNPs are enriched for these three functional categories, and that it would be appropriate to provide a higher weighting for such SNPs when performing Bayesian association analyses. For GWAS studies, our analyses suggest the use of a Bayes Factor of about 4 for cis eQTL SNPs within regions of open chromatin, 3 for nsSNPs and 2 for promoter SNPs.
KW - Bayes Theorem
KW - Genome-Wide Association Study
KW - Humans
KW - Linkage Disequilibrium
KW - Polymorphism, Single Nucleotide
KW - Promoter Regions, Genetic
KW - Quantitative Trait Loci
U2 - 10.1371/journal.pone.0014808
DO - 10.1371/journal.pone.0014808
M3 - Journal article
C2 - 21556132
VL - 6
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 4
M1 - e14808
ER -