Genome-Wide Analysis in Africans Provides Novel Insight into the Genetic Basis of the Metabolic Syndrome. F. Tekola-Ayele, A. P. Doumatey, G. Chen, D. Shriner, A. R. Bentley, J. Zhou, A. Adeyemo, C. N. Rotimi Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD., USA.
The metabolic syndrome (MetS) is a constellation of heritable risk factors that increase the risk of developing several metabolic disorders including type 2 diabetes and cardiovascular diseases. Understanding the genetic basis of MetS is likely to provide insight into the biological pathways and mechanisms shared by the components of the MetS. We conducted a genome-wide association study (GWAS) of MetS in 1,427 West Africans (602 MetS and 825 non-MetS) recruited from Ghana and Nigeria, followed by replication testing and meta-analysis in East Africans recruited from Kenya as participants in the Africa America Diabetes Mellitus study. MetS status was assigned to each sample based on the definition of the National Cholesterol Education Program. A continuous MetS score (cMetS) was assigned to each sample based on the sum of the standardized residuals of the MetS components. Samples were genotyped on the Affymetrix Axiom PANAFR SNP array that contains ~2.2 million SNPs. Imputed dosage data were analyzed using logistic regression model with adjustment for age, sex, and the first three principal components. We found a low-frequency (1.6%) variant near CA10 that confers a strong risk of MetS in the discovery sample (P=3.86x10-8, OR= 6.80). This variant had the same frequency in the 1000 genomes West Africa population samples, but was absent in all other population samples including East Africans. In meta-analysis of the West and East African samples, we found two variants that reduce risk of MetS: an African population-specific, low-frequency (1.7%) variant in CTNNA3 (P=1.63x10-8, OR= 0.35) and a common variant (46.4%) near RALYL (P=7.37x10-9, OR= 0.11). Analyses of the samples in the two extreme 33.3% and 25% tails of the empirical distribution of cMetS identified two variants that reduce risk of MetS: one near KSR2 (P=4.52x10-8, Pmeta=7.82x10-9, OR=0.53, allele frequency=30%), and an African population-specific, low-frequency (4%) variant near MBNL1 (Pmeta=3.51x10-8). In all, this first GWAS of MetS identified five loci that may have pleiotropic effects in several metabolic and cardiovascular diseases. The study also showed variants present only in African populations influencing risk of MetS, suggesting differences in genetic architecture of MetS among human populations, and the significance of studying ancestrally diverse populations to identify novel genetic variants.
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