MRC CAiTE centre, University of Bristol, Bristol, United Kingdom.
PLoS One. 2011;6(9):e24303. doi: 10.1371/journal.pone.0024303. Epub 2011 Sep 15.
Thirty-two common variants associated with body mass index (BMI) have been identified in genome-wide association studies, explaining ∼1.45% of BMI variation in general population cohorts. We performed a genome-wide association study in a sample of young adults enriched for extremely overweight individuals. We aimed to identify new loci associated with BMI and to ascertain whether using an extreme sampling design would identify the variants known to be associated with BMI in general populations.
METHODOLOGY/PRINCIPAL FINDINGS: From two large Danish cohorts we selected all extremely overweight young men and women (n = 2,633), and equal numbers of population-based controls (n = 2,740, drawn randomly from the same populations as the extremes, representing ∼212,000 individuals). We followed up novel (at the time of the study) association signals (p<0.001) from the discovery cohort in a genome-wide study of 5,846 Europeans, before attempting to replicate the most strongly associated 28 SNPs in an independent sample of Danish individuals (n = 20,917) and a population-based cohort of 15-year-old British adolescents (n = 2,418). Our discovery analysis identified SNPs at three loci known to be associated with BMI with genome-wide confidence (P<5×10(-8); FTO, MC4R and FAIM2). We also found strong evidence of association at the known TMEM18, GNPDA2, SEC16B, TFAP2B, SH2B1 and KCTD15 loci (p<0.001), and nominal association (p<0.05) at a further 8 loci known to be associated with BMI. However, meta-analyses of our discovery and replication cohorts identified no novel associations.
Our results indicate that the detectable genetic variation associated with extreme overweight is very similar to that previously found for general BMI. This suggests that population-based study designs with enriched sampling of individuals with the extreme phenotype may be an efficient method for identifying common variants that influence quantitative traits and a valid alternative to genotyping all individuals in large population-based studies, which may require tens of thousands of subjects to achieve similar power.
在全基因组关联研究中已经确定了 32 个与体重指数（BMI）相关的常见变异，这些变异可以解释一般人群队列中 BMI 变化的约 1.45%。我们对一个超重个体富集的年轻成人样本进行了全基因组关联研究。我们的目的是确定与 BMI 相关的新基因座，并确定采用极端抽样设计是否会识别出与一般人群 BMI 相关的已知变异。
方法/主要发现：我们从两个大型丹麦队列中选择了所有超重的年轻男性和女性（n=2633），并选择了数量相等的基于人群的对照（n=2740，从极端人群中随机抽取，代表约 212000 人）。我们在一个由 5846 名欧洲人组成的全基因组研究中对发现队列中的新（在研究时）关联信号（p<0.001）进行了随访，然后试图在丹麦个体的独立样本（n=20917）和一个基于人群的 15 岁英国青少年队列（n=2418）中复制最强相关的 28 个 SNP。我们的发现分析确定了三个已知与 BMI 相关的基因座的 SNP 具有全基因组置信度（P<5×10(-8)；FTO、MC4R 和 FAIM2）。我们还发现了已知 TMEM18、GNPDA2、SEC16B、TFAP2B、SH2B1 和 KCTD15 基因座的强烈关联证据（p<0.001），以及另外 8 个已知与 BMI 相关的基因座的名义关联（p<0.05）。然而，我们的发现和复制队列的荟萃分析没有发现新的关联。
我们的结果表明，与极度超重相关的可检测遗传变异与以前发现的一般 BMI 非常相似。这表明，基于人群的研究设计，对具有极端表型的个体进行富集采样，可能是一种有效的方法，可以识别影响定量特征的常见变异，并且是对在大型基于人群的研究中对所有个体进行基因分型的有效替代方法，后者可能需要数千名受试者才能达到类似的效果。
