Citation
Fernández-Rhodes, Lindsay; Gong, Jian; Haessler, Jeffrey; Franceschini, Nora; Graff, Mariaelisa; Nishimura, Katherine K.; Wang, Yujie; Highland, Heather M.; Yoneyama, Sachiko; & Bush, William S., et al. (2017). Trans-Ethnic Fine-Mapping of Genetic Loci for Body Mass Index in the Diverse Ancestral Populations of the Population Architecture Using Genomics and Epidemiology (PAGE) Study Reveals Evidence for Multiple Signals at Established Loci. Human Genetics, 136(6), 771-800. PMCID: PMC5485655Abstract
Most body mass index (BMI) genetic loci have been identified in studies of primarily European ancestries. The effect of these loci in other racial/ethnic groups is less clear. Thus, we aimed to characterize the generalizability of 170 established BMI variants, or their proxies, to diverse US populations and trans-ethnically fine-map 36 BMI loci using a sample of >102,000 adults of African, Hispanic/Latino, Asian, European and American Indian/Alaskan Native descent from the Population Architecture using Genomics and Epidemiology Study. We performed linear regression of the natural log of BMI (18.5-70kg/m2) on the additive single nucleotide polymorphisms (SNPs) at BMI loci on the MetaboChip (Illumina, Inc.), adjusting for age, sex, population stratification, study site or relatedness. We then performed fixed-effect meta-analyses and a Bayesian trans-ethnic meta-analysis to empirically cluster by allele frequency differences. Lastly, we approximated conditional and joint associations to test for the presence of secondary signals. We noted directional consistency with the previously reported risk alleles beyond what would have been expected by chance (binomial p<0.05). Nearly a quarter of the previously described BMI index SNPs and 29 of 36 densely-genotyped BMI loci on the MetaboChip replicated/generalized in trans-ethnic analyses. We observed multiple signals at 9 loci, including the description of seven loci with novel multiple signals. This study supports the generalization of most common genetic loci to diverse ancestral populations and emphasizes the importance of dense multi-ethnic genomic data in refining the functional variation at genetic loci of interest and describing several loci with multiple underlying genetic variants.URL
http://dx.doi.org/10.1007/s00439-017-1787-6Reference Type
Journal ArticleYear Published
2017Journal Title
Human GeneticsAuthor(s)
Fernández-Rhodes, LindsayGong, Jian
Haessler, Jeffrey
Franceschini, Nora
Graff, Mariaelisa
Nishimura, Katherine K.
Wang, Yujie
Highland, Heather M.
Yoneyama, Sachiko
Bush, William S.
Goodloe, Robert
Ritchie, Marylyn D.
Crawford, Dana C.
Gross, Myron D.
Fornage, Myriam
Buzkova, Petra
Tao, Ran
Isasi, Carmen R.
Aviles-Santa, M. Larissa
Daviglus, Martha L.
Mackey, Rachel H.
Houston, Denise K.
Gu, C. Charles
Ehret, Georg B.
Nguyen, Khanh-Dung H.
Lewis, Cora E.
Leppert, Mark F.
Irvin, Marguerite Ryan
Lim, Unhee
Haiman, Christopher A.
Le Marchand, Loic
Schumacher, Fredrick R.
Wilkens, Lynne R.
Lu, Yingchang
Bottinger, Erwin P.
Loos, Ruth J.L.
Sheu, Wayne Huey-Herng
Guo, Xiuqing
Lee, Wen-Jane
Hai, Yang
Hung, Yi-Jen
Absher, Devin M.
Wu, I-Chien
Taylor, Kent D.
Lee, I-Te
Liu, Yeheng
Wang, Tzung-Dau
Quertermous, Thomas
Juang, Jyh-Ming J.
Rotter, Jerome I.
Assimes, Themistocles L.
Hsiung, Chao A.
Chen, Yii-Der Ida
Prentice, Ross L.
Kuller, Lewis H.
Manson, JoAnn E.
Kooperberg, Charles L.
Smokowski, Paul
Robinson, Whitney R.
Gordon-Larsen, Penny
Li, Rongling L.
Hindorff, Lucia A.
Buyske, Steven G.
Matise, Tara C.
Peters, Ulrike
North, Kari E.