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Apolipoprotein A5 and Lipoprotein Lipase Interact to Modulate Anthropometric Measures in Hispanics of Caribbean Origin
Authors:Caren E. Smith  Katherine L. Tucker  Chao‐Qiang Lai  Laurence D. Parnell  Yu‐Chi Lee  José M. Ordovás
Affiliation:Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, Massachusetts, USA
Abstract:Apolipoprotein A5 (APOA5) and lipoprotein lipase (LPL) proteins interact functionally to regulate lipid metabolism, and single‐nucleotide polymorphisms (SNPs) for each gene have also been associated independently with obesity risk. Evaluating gene combinations may be more effective than single SNP analyses in identifying genetic risk, but insufficient minor allele frequency (MAF) often limits evaluations of potential epistatic relationships. Populations with multiple ancestral admixtures may provide unique opportunities for evaluating genetic interactions. We examined relationships between LPL m107 (rs1800590) and APOA5 S19W (rs3135506) and lipid and anthropometric measures in Caribbean origin Hispanics (n = 1,019, aged 45–75 years) living in the Boston metropolitan area. Significant interaction terms between LPL m107 and APOA5 S19W were observed for BMI (P = 0.003) and waist circumference (P = 0.019). Higher BMI (P = 0.001), waist (P = 0.011) and hip (P = 0.026) circumference were observed in minor allele (G) carriers for LPL m107 who also carried the APOA5 S19W minor allele (G). Additionally, extreme obesity (BMI ≥ 40 kg/m2) risk was higher (odds ratio = 4.02; 95% confidence interval: 1.81–8.91; global P = 0.008) for minor allele carriers for both SNPs (LPL TG+GG, APOA5 CG+GG) compared to major allele carriers for both SNPs. In summary, we identified significant interactions for APOA5 S19W and LPL m107 for obesity in Caribbean Hispanics. Population‐specific MAFs increase the difficulties of replicating gene–gene interactions, but may support the hypothesis that combinations of frequencies in selected genes could heighten obesity susceptibility in a given population. Analyses of gene–gene interactions may improve understanding of genetically based obesity risk, and underscore the need for further study of groups with multiple ancestral admixtures.
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