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肥胖及肥胖相关特征的全基因组关联研究。

A genome-wide association study on obesity and obesity-related traits.

机构信息

Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.

出版信息

PLoS One. 2011 Apr 28;6(4):e18939. doi: 10.1371/journal.pone.0018939.

Abstract

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.

摘要

大规模全基因组关联研究(GWAS)已经确定了许多与体重指数(BMI)相关的基因座,但很少有研究关注肥胖作为一种二元特征。在这里,我们报告了一项肥胖的 GWAS 和候选 SNP 基因分型研究的结果,包括极度肥胖的病例和从未超重的对照组,以及分离出极度肥胖和消瘦的家族。我们首先对 520 例病例(BMI>35kg/m(2))和 540 例对照组(BMI<25kg/m(2))进行了 GWAS,以评估肥胖和肥胖相关特征的衡量标准。随后,我们对 2256 名病例、对照组和家族成员的组合样本中的 GWAS 中约 500 个 top SNPs 进行了基因分型,以跟踪肥胖相关信号。对于肥胖的二元特征,我们在 FTO 基因内发现了 16 个全基因组显著信号(最强信号位于 rs17817449,P=2.5×10(-12))。接下来,我们检查了肥胖相关的定量特征(如总体重、腰围和腰臀比),并在腰臀比和 NRXN3 之间检测到了全基因组显著信号(rs11624704,P=2.67×10(-9)),该信号之前与体重和脂肪分布有关。我们的研究表明,通过极端表型确定的相对较小的样本如何在 GWAS 中检测到真正的关联。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ff9/3084240/0c02cabd7a0c/pone.0018939.g001.jpg

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