肥胖是许多疾病的主要危险因素,影响全球超过6亿人。全基因组关联研究(GWAS)已经确定了数百种影响体重指数(BMI)的遗传变异,评估肥胖风险的常用指标。大多数变体是非编码的,可能通过调节附近的基因起作用。这里,我们应用多种计算方法,在536个先前报道的GWAS鉴定的BMI相关位点中,对每个可能的因果基因进行优先排序.我们进行了基于汇总数据的孟德尔随机化(SMR),FINEMAP,DEPICT,岩浆,全转录组关联研究(TWASs),突变显著性截止(MSC),多基因优先评分(PoPS),和最近的基因策略。每种方法的结果都是根据它们在识别已知与肥胖有关的基因方面的成功进行加权的。根据置信度得分(最小值:0;最大值:28)对所有优先基因进行排序。我们在264个基因座中鉴定出292个高得分基因(≥11个),包括已知在体重调节中起作用的基因(例如,DGKI,ANKRD26,MC4R,LEPR,BDNF,GIPR,AKT3、KAT8、MTOR)和与合并症相关的基因(例如,FGFR1,ISL1,TFAP2B,PARK2、TCF7L2、GSK3B)。对于大多数得分高的基因来说,然而,我们发现在肥胖中起作用的证据有限或没有,包括得分最高的基因BPTF。许多得分最高的基因似乎通过体重的神经元调节起作用,而其他人影响外周途径,包括昼夜节律,胰岛素分泌,以及葡萄糖和碳水化合物稳态。这些可能的因果基因的表征可以增加我们对潜在生物学的理解,并提供开发减肥疗法的途径。
Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of genetic variants that influence body mass index (BMI), a commonly used metric to assess obesity risk. Most variants are non-coding and likely act through regulating genes nearby. Here, we apply multiple computational methods to prioritize the likely causal gene(s) within each of the 536 previously reported GWAS-identified BMI-associated loci. We performed summary-data-based Mendelian randomization (SMR), FINEMAP, DEPICT, MAGMA, transcriptome-wide association studies (TWASs), mutation significance cutoff (MSC), polygenic priority score (PoPS), and the nearest gene strategy. Results of each method were weighted based on their success in identifying genes known to be implicated in obesity, ranking all prioritized genes according to a confidence score (minimum: 0; max: 28). We identified 292 high-scoring genes (≥11) in 264 loci, including genes known to play a role in body weight regulation (e.g., DGKI, ANKRD26, MC4R, LEPR, BDNF, GIPR, AKT3, KAT8, MTOR) and genes related to comorbidities (e.g., FGFR1, ISL1, TFAP2B, PARK2, TCF7L2, GSK3B). For most of the high-scoring genes, however, we found limited or no evidence for a role in obesity, including the top-scoring gene BPTF. Many of the top-scoring genes seem to act through a neuronal regulation of body weight, whereas others affect peripheral pathways, including circadian rhythm, insulin secretion, and glucose and carbohydrate homeostasis. The characterization of these likely causal genes can increase our understanding of the underlying biology and offer avenues to develop therapeutics for weight loss.