Study of coding variants in human obesity and their functional characterization using human iPSC-derived cellular models

全基因组关联研究已经确定了100种常见的与体重相关的变异 体重指数(BMI)和肥胖风险。对邻近基因的途径和组织表达分析提供了强有力的 中枢神经系统(CNS)在体重调节中的作用的证据。然而，这些GWA 变异的影响很小，是常见的、非编码的、内含子或基因间的，不改变蛋白质的功能和 通常不是真正的致病变种。然而，了解致病基因/变异对 在体重调节生物学中将GWAs基因座转化为新的洞察力。 因此，显然需要更有效的基因发现策略来确定病因。 基因/变种，这反过来将促进将基因发现转化为新的生物学。因此，我们 建议筛查外显子组中与BMI相关的罕见(MAF&lt；1%)编码单核苷酸变体(SNV) 和肥胖风险，使用来自100万人的数据，目的是加快因果SNV(AIM)的准确定位 1)。我们随后将根据它们对蛋白质功能的影响来确定已识别的编码SNV的优先顺序 以及在极度肥胖的情况下增加营养(目标2)。排名靠前的编码SNV将在功能上 在人类诱导多能干细胞(HiPSC)来源的细胞模型中具有特征(目标3)。 具体地说，在目标1中，我们将应用和定制针对大规模队列进行优化以执行的方法 第一个100万规模的外显子组广泛关联研究利用了来自两个外显子组芯片的基因数据 国际合作；大型财团(N&GT；525,000)和英国生物库(N~500,000)。我们有 &gt；90%的统计能力，可识别MAF低至0.02%、具有临床相关效果的编码SNV 尺码(身高1.7米(5英尺7英寸)的人，体重为6公斤/等位基因(13.2磅))。 在目标2中，我们开发了一个分析流水线来确定来自目标1的已识别编码SNV的优先级。该流水线 将注释SNV，量化它们对基因功能的影响，并识别组织 受影响最大的地区。我们将检查SNV是否在极度肥胖的病例中得到丰富 独特的研究设计，包括一项不协调的家庭研究和一项长期极端肥胖的纵向研究。 一组10-15个按优先级排序的编码SNV将在目标3中进行功能描述。我们将打击 分别使用CRISPR/Cas9将SNV编码为hiPSC，并将它们区分为相关的细胞类型(例如 神经元、脂肪细胞、β细胞、肠道细胞、肝细胞)。然后我们将调查SNV对细胞的影响 和分子肥胖相关的表型，以阐明潜在的生物学。 我们在识别和功能表征肥胖的罕见编码SNV方面具有独特的地位。是这样的 SNV有望不成比例地增加我们对肥胖生物学的理解，并可能导致 到预防和治疗肥胖症的新的、更精确的战略，这个领域鲜有人看到 过去30年的创新。

Genome-wide association studies (GWAS) have identified >100 common variants associated with body mass index (BMI) and obesity risk. Pathway and tissue expression analyses of nearby genes have provided strong evidence for a role of the central nervous system (CNS) in body weight regulation. However, these GWAS variants have small effects, are common, non-coding, intronic or intergenic, do not alter protein function and are typically not the true disease-causing variants. Yet, knowing the causal gene/variant is critical for the translation of a GWAS locus into new insights in the biology of body weight regulation. Thus, there is a clear need for more effective gene-discovery strategies to identify causal genes/variants, which in turn will facilitate the translation of gene discoveries into new biology. Therefore, we propose to screen the exome for rare (MAF<1%) coding single nucleotide variants (SNVs) associated with BMI and obesity risk using data from 1 million people, with the aim to expedite the pinpointing of causal SNVs (Aim 1). We will subsequently prioritize the identified coding SNVs based on their implications on protein function and enrichment in extremely obese cases (Aim 2). The top-ranked coding SNVs will be functionally characterized in human induced pluripotent stem cell (hiPSC)-derived cellular models (Aim 3). Specifically, in Aim 1, we will apply and customize methods optimized for mega-scale cohorts to perform the first 1 million-scale exome-wide association study leveraging exome-chip genotype data from two international collaborations; the GIANT consortium (N>525,000) and the UKBiobank (N~500,000). We have >90% statistical power to identify coding SNVs with a MAF as low as 0.02% that have clinically relevant effect sizes (>6 kg/allele (>13.2 lbs) for a 1.7m (5ft 7in) tall person). In Aim 2, we develop an analytical pipeline to prioritize the identified coding SNVs from Aim 1. The pipeline will annotate SNVs, quantify their intolerance with regard to impact on gene function, and identify the tissues that are affected the most. We will examine whether SNVs are enriched in extremely obese cases using unique study designs, including a discordant family study and a longitudinal study of longtime extreme obesity. A set of 10-15 prioritized coding SNVs will be functionally characterized in Aim 3. We will knock the respective coding SNV into hiPSC using CRISPR/Cas9 and differentiate these into the relevant cell type (e.g. neurons, adipocytes, beta cells, gut cells, hepatocytes). We will then investigate the impact of SNV on cellular and molecular obesity-relevant phenotypes to elucidate underlying biology. We are uniquely positioned to identify and functionally characterize rare coding SNVs for obesity. Such SNVs have the promise to disproportionally increase our understanding of the biology of obesity and may lead to new and more precise strategies for prevention and treatment of obesity, a field that has seen little innovation in the past 30 years.