Fine Mapping and Identification of BMI Loci in African Americans

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• 作者：Jian Gong ; Fredrick Schumacher ; Unhee Lim ; Lucia?A. Hindorff ; Jeff Haessler ; Steven Buyske ; Christopher?S. Carlson ; Stephanie Rosse ; Petra B??kov¨¢ ; Myriam Fornage ; Myron Gross ; Nathan Pankratz ; James?S. Pankow ; Pamela?J. Schreiner ; Richard Cooper ; Georg Ehret ; C.?Charles Gu ; Denise Houston ; Marguerite?R. Irvin ; Rebecca Jackson ; et al.
• 刊名：The American Journal of Human Genetics
• 出版年：2013
• 出版时间：3 October, 2013
• 年：2013
• 卷：93
• 期：4
• 页码：661-671
• 全文大小：545 K

文摘

Genome-wide association studies (GWASs) primarily performed in European-ancestry (EA) populations have identified numerous loci associated with body mass index (BMI). However, it is still unclear whether these GWAS loci can be generalized to other ethnic groups, such as African Americans (AAs). Furthermore, the putative functional variant or variants in these loci mostly remain under investigation. The overall lower linkage disequilibrium in AA compared to EA populations provides the opportunity to narrow in or fine-map these BMI-related loci. Therefore, we used the Metabochip to densely genotype and evaluate 21 BMI GWAS loci identified in EA studies in 29,151 AAs from the Population Architecture using Genomics and Epidemiology (PAGE) study. Eight of the 21 loci (SEC16B, TMEM18, ETV5, GNPDA2, TFAP2B, BDNF, FTO, and MC4R) were found to be associated with BMI in AAs at 5.8?¡Á 10^?5. Within seven out of these eight loci, we found that, on average, a substantially smaller number of variants was correlated (r² > 0.5) with the most significant SNP in AA than in EA populations (16 versus 55). Conditional analyses revealed GNPDA2 harboring a potential additional independent signal. Moreover, Metabochip-wide discovery analyses revealed two BMI-related loci, BRE (rs116612809, p = 3.6?¡Á 10^?8) and DHX34 (rs4802349, p = 1.2?¡Á 10^?7), which were significant when adjustment was made for the total number of SNPs tested across the chip. These results demonstrate that fine mapping in AAs is a powerful approach for both narrowing in on the underlying causal variants in known loci and discovering BMI-related loci.