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Multi-ethnic fine-mapping of 14 central adiposity loci.

TitleMulti-ethnic fine-mapping of 14 central adiposity loci.
Publication TypeJournal Article
Year of Publication2014
AuthorsLiu C-T, Buchkovich ML, Winkler TW, Heid IM, Borecki IB, Fox CS, Mohlke KL, North KE
Secondary AuthorsL Cupples A
Corporate AuthorsAfrican Ancestry Anthropometry Genetics Consortium, GIANT consortium
JournalHum Mol Genet
Volume23
Issue17
Pagination4738-44
Date Published2014 Sep 01
ISSN1460-2083
KeywordsAdiposity, Anthropometry, Computational Biology, Ethnic Groups, Genetic Loci, Humans, Molecular Sequence Annotation, Physical Chromosome Mapping, Polymorphism, Single Nucleotide
Abstract

The Genetic Investigation of Anthropometric Traits (GIANT) consortium identified 14 loci in European Ancestry (EA) individuals associated with waist-to-hip ratio (WHR) adjusted for body mass index. These loci are wide and narrowing the signals remains necessary. Twelve of 14 loci identified in GIANT EA samples retained strong associations with WHR in our joint EA/individuals of African Ancestry (AA) analysis (log-Bayes factor >6.1). Trans-ethnic analyses at five loci (TBX15-WARS2, LYPLAL1, ADAMTS9, LY86 and ITPR2-SSPN) substantially narrowed the signals to smaller sets of variants, some of which are in regions that have evidence of regulatory activity. By leveraging varying linkage disequilibrium structures across different populations, single-nucleotide polymorphisms (SNPs) with strong signals and narrower credible sets from trans-ethnic meta-analysis of central obesity provide more precise localizations of potential functional variants and suggest a possible regulatory role. Meta-analysis results for WHR were obtained from 77 167 EA participants from GIANT and 23 564 AA participants from the African Ancestry Anthropometry Genetics Consortium. For fine mapping we interrogated SNPs within ┬▒ 250 kb flanking regions of 14 previously reported index SNPs from loci discovered in EA populations by performing trans-ethnic meta-analysis of results from the EA and AA meta-analyses. We applied a Bayesian approach that leverages allelic heterogeneity across populations to combine meta-analysis results and aids in fine-mapping shared variants at these locations. We annotated variants using information from the ENCODE Consortium and Roadmap Epigenomics Project to prioritize variants for possible functionality.

DOI10.1093/hmg/ddu183
Alternate JournalHum Mol Genet
PubMed ID24760767
PubMed Central IDPMC4119415
Grant ListR21 DA027040 / DA / NIDA NIH HHS / United States
R21DA027040 / DA / NIDA NIH HHS / United States
T32 GM067553 / GM / NIGMS NIH HHS / United States
R01DK8925601 / DK / NIDDK NIH HHS / United States
R01 DK072193 / DK / NIDDK NIH HHS / United States
097117 / / Wellcome Trust / United Kingdom
P20 MD006899 / MD / NIMHD NIH HHS / United States
R01DK072193 / DK / NIDDK NIH HHS / United States
T32GM067553 / GM / NIGMS NIH HHS / United States