Title | Multi-ethnic fine-mapping of 14 central adiposity loci. |
Publication Type | Journal Article |
Year of Publication | 2014 |
Authors | Liu C-T, Buchkovich ML, Winkler TW, Heid IM, Borecki IB, Fox CS, Mohlke KL, North KE |
Secondary Authors | L Cupples A |
Corporate Authors | African Ancestry Anthropometry Genetics Consortium, GIANT consortium |
Journal | Hum Mol Genet |
Volume | 23 |
Issue | 17 |
Pagination | 4738-44 |
Date Published | 2014 Sep 01 |
ISSN | 1460-2083 |
Keywords | Adiposity, 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. |
DOI | 10.1093/hmg/ddu183 |
Alternate Journal | Hum Mol Genet |
PubMed ID | 24760767 |
PubMed Central ID | PMC4119415 |
Grant List | R21 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 |