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

使用人类 iPSC 衍生的细胞模型研究人类肥胖的编码变异及其功能表征

• 批准号: 9977170
• 负责人: Ruth JF Loos
• 金额: $ 55.37万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-17 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9977170
• 关键词: Address; Adipocytes; Affect; Alleles; Beta Cell; Biological; Biology; Body Weight; Body mass index; Brain-Derived Neurotrophic Factor; CRISPR/Cas technology; Candidate Disease Gene; Case Study; Cell model; Cells; Cellular Morphology; Code; Collaborations; Complex; Custom; DNA-Protein Interaction; Data; Disease; Eating Behavior; Family; Family Study; Food; Genes; Genetic; Genome; Genotype; Hepatocyte; Human; Hypothalamic structure; Individual; Insulin Resistance; International; Investigation; Knowledge; Lead; Leptin; Longitudinal Studies; Longitudinal cohort; Meta-Analysis; Metabolic Pathway; Methods; Molecular; Morbid Obesity; Mutation; Neuraxis; Neurons; Neuropeptides; Obesity; Pathway interactions; Peripheral; Persons; Phenotype; Play; Positioning Attribute; Predisposition; Prevention strategy; Proteins; Regulation; Research Design; Role; SH2B gene; Sampling; Single Nucleotide Polymorphism; Tissues; Translations; Twin Studies; Untranslated RNA; Variant; base; biobank; causal variant; cell type; clinically relevant; cohort; exome; exome sequencing; gene discovery; gene function; genetic variant; genome wide association study; hedonic; induced pluripotent stem cell; innovation; insight; insulin signaling; lipid biosynthesis; neuronal circuitry; obesity genetics; obesity risk; obesity treatment; protein function; statistics; synaptogenesis; targeted sequencing; trait; transcriptome sequencing; treatment strategy

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.

全基因组关联研究（GWAS）已经确定了超过100种与体重相关的常见变异 指数（BMI）和肥胖风险。对附近基因的途径和组织表达分析提供了强有力的证据。 中枢神经系统（CNS）在体重调节中的作用的证据。然而，这些GWAS 变体具有小的影响，是常见的、非编码的、内含子的或基因间的，不改变蛋白质功能， 通常不是真正的致病变种然而，了解致病基因/变异对于 将GWAS基因座翻译成体重调节生物学的新见解。 因此，显然需要更有效的基因发现策略来确定因果关系， 基因/变异，这反过来又将促进基因发现转化为新的生物学。所以我们 建议筛选与BMI相关的罕见（MAF<1%）编码单核苷酸变异（SNV）的外显子组 和肥胖风险，目的是加快因果SNV的精确定位（Aim 1）。随后，我们将根据其对蛋白质功能的影响，对已鉴定的编码SNV进行优先排序 在极度肥胖的病例中进行富集（目标2）。排名靠前的编码SNV将在功能上 在人诱导多能干细胞（hiPSC）衍生的细胞模型中表征（Aim 3）。 具体来说，在目标1中，我们将应用和定制针对大型队列优化的方法， 第一个100万规模的外显子组全关联研究利用了来自两个国家的外显子组芯片基因型数据， 国际合作; GIANT联盟（N> 525，000）和英国生物库（N~ 500，000）。我们有 >90%的统计功效，以识别MAF低至0.02%的编码SNV，具有临床相关效应 大小（对于1.7米（5英尺7英寸）高的人，>6 kg/等位基因（>13.2磅））。 在目标2中，我们开发了一个分析管道，以优先考虑目标1中识别的编码SNV。管道 将注释SNV，量化它们对基因功能影响的不耐受性，并识别组织 受影响最大的地区我们将研究SNV是否在极度肥胖病例中富集， 独特的研究设计，包括不一致的家庭研究和长期极端肥胖的纵向研究。 一组10-15个优先编码SNV将在目标3中进行功能表征。我们会敲 使用CRISPR/Cas9将各自的编码SNV转化为hiPSC，并将它们分化为相关的细胞类型（例如 神经元、脂肪细胞、β细胞、肠细胞、肝细胞）。然后，我们将研究SNV对细胞的影响。 和分子肥胖相关表型来阐明潜在的生物学。 我们处于独特的地位，以确定和功能特性的罕见编码SNV的肥胖。等 SNV有希望增加我们对肥胖生物学的理解，并可能导致 新的和更精确的预防和治疗肥胖的策略， 30年来的创新。