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A Genome-Wide Association Study on Obesity and Obesity-Related Traits

Large-scale genome-wide association studies (GWAS) have identified many loci associated with body mass index (BMI), but few studies focused on obesity as a binary trait. Here we report the results of a GWAS and candidate SNP genotyping study of obesity, including extremely obese cases and never over...

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Detalles Bibliográficos
Autores principales: Wang, Kai, Li, Wei-Dong, Zhang, Clarence K., Wang, Zuoheng, Glessner, Joseph T., Grant, Struan F. A., Zhao, Hongyu, Hakonarson, Hakon, Price, R. Arlen
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3084240/
https://www.ncbi.nlm.nih.gov/pubmed/21552555
http://dx.doi.org/10.1371/journal.pone.0018939
Descripción
Sumario:Large-scale genome-wide association studies (GWAS) have identified many loci associated with body mass index (BMI), but few studies focused on obesity as a binary trait. Here we report the results of a GWAS and candidate SNP genotyping study of obesity, including extremely obese cases and never overweight controls as well as families segregating extreme obesity and thinness. We first performed a GWAS on 520 cases (BMI>35 kg/m(2)) and 540 control subjects (BMI<25 kg/m(2)), on measures of obesity and obesity-related traits. We subsequently followed up obesity-associated signals by genotyping the top ∼500 SNPs from GWAS in the combined sample of cases, controls and family members totaling 2,256 individuals. For the binary trait of obesity, we found 16 genome-wide significant signals within the FTO gene (strongest signal at rs17817449, P = 2.5×10(−12)). We next examined obesity-related quantitative traits (such as total body weight, waist circumference and waist to hip ratio), and detected genome-wide significant signals between waist to hip ratio and NRXN3 (rs11624704, P = 2.67×10(−9)), previously associated with body weight and fat distribution. Our study demonstrated how a relatively small sample ascertained through extreme phenotypes can detect genuine associations in a GWAS.