High Throughput Functional Dissection Of Adiposity GWAS Loci Using Model Systems

使用模型系统对肥胖 GWAS 位点进行高通量功能解剖

• 批准号: 10396596
• 负责人: Thomas John Baranski
• 金额: $ 67.29万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10396596
• 关键词: Adipose tissue; Automobile Driving; Big Data; Biological; Biological Models; Biology; Brain; Candidate Disease Gene; Clinical; Cohort Studies; Consumption; Databases; Dissection; Drosophila genus; Drosophila melanogaster; Ethnic Origin; Evaluation; Fatty acid glycerol esters; Gene Expression Regulation; Genes; Genetic; Genome; Genome Scan; Genomic Segment; Genotype-Tissue Expression Project; Grant; Human; Human Genetics; Individual; Insecta; Lead; Light; Maps; Meta-Analysis; Methods; Methylation; Mus; Obesity; Obesity Epidemic; Orthologous Gene; Pathway interactions; Pattern; Phenotype; Prevention approach; Prevention strategy; Procedures; Publishing; RNA Interference; Research Personnel; Research Subjects; Resources; Risk; Sampling; Scanning; Science; Scientist; System; Time; Tissues; Trans-Omics for Precision Medicine; Uncertainty; Untranslated RNA; Variant; Whole Blood; Work; causal variant; cohort; deep sequencing; experimental study; fly; gene interaction; genetic variant; genome wide association study; knock-down; lipid metabolism; multiple omics; novel; novel therapeutics; obesity genetics; obesity risk; overexpression; promoter; risk variant; success; virtual

We believe that the field of human obesity genetics has stalled. Massive Genome-Wide Association studies of hundreds of thousands of subjects succeeded in identifying over a thousand novel “statistical loci” that unquestionably tag increased obesity risk variants. Because these loci are almost all in regions that were not on anyone’s candidate gene list, the promise has been that these novel loci point to new biology that could lead to new therapies and prevention strategies. But exactly what these loci do and how they do it continues to remain a mystery for almost all loci. Functional scientists have faced challenges following up on these findings, because of a “perfect storm” of difficulties. LD patterns in the genome complicate efforts to fine map and statistically dissect driver from passenger variants, even after deep sequencing of the regions in multiple ethnicities, so the exact causal variants are rarely known. Even if they were known, the effect sizes of these variants are each clinically trivial despite their overwhelming statistical significance. Worst of all, it is estimated that over 90% appear to be tagging non-coding regions. We believe a huge rate-limiting step to exploiting these new discoveries continues to be moving from “statistical loci” to the gene(s) through which they are acting. Many researchers use a default annotation of the “nearest gene” to the statistical locus as a starting point, but if our published experiments (Baranski et al., 2018) are generalizable, this may be wrong about half the time. Regulome annotation in reference databases is emerging as a critical resource (e.g. ENCODE and GTEX), but it is still early days. The non-coding genome is huge, and much of the annotation is either lacking, insufficient, or not specific enough to allow definitive mapping from locus to gene with these resources alone. Large cohort studies and consortia such as TOPMed have begun conducting various Omics scans in the same individuals in which locus discoveries were made, thus providing the potential to shed light on the underlying mechanisms behind the statistical loci, and in particular, suggest which genes, might be critical. But almost all of these large human efforts are of practical necessity limited to whole blood and tissues of convenience, and much less has been possible in the presumed tissues of action. By contrast, the most important tissues for obesity, like the brain, can be accessed and manipulated in model systems to shed light on mechanisms. Likely every such locus will have a different biological explanation, but pursuing functional experiments for each locus one at a time is challenging, time consuming and expensive. What is needed is a high throughput strategy, that will interrogate many loci simultaneously. We propose to use our successful, published high throughput Drosophila system to efficiently screen for many fat storage genes among the set of human candidates that have fly orthologs (recognizing that this is a “low hanging fruit” approach that will not work for every locus), and then validate in the mouse those that are conserved from human to insect.

我们认为人类肥胖遗传学领域已经停滞不前。大规模全基因组关联研究 成千上万的受试者成功地确定了一千多个新的“统计位点”， 毫无疑问地标记了肥胖风险增加的变体。因为这些基因座几乎都位于 在任何人的候选基因名单上，这些新的基因座都有可能指向新的生物学， 新疗法和预防策略。但这些基因座的作用和作用方式 对几乎所有的地点来说都是个谜。功能科学家在跟进这些发现时面临着挑战， 因为一场困难的“完美风暴”。基因组中的LD模式使精细图谱的工作复杂化， 从统计学上分析驾驶员与乘客的变异，即使在多个区域的深度测序之后， 种族，所以确切的因果变异很少知道。即使它们是已知的， 尽管变体具有压倒性的统计学显著性，但它们各自在临床上是微不足道的。最糟糕的是，据估计 超过90%的基因都在标记非编码区我们认为，利用这些技术的一个巨大的限速步骤 新的发现继续从“统计基因座”转移到它们发挥作用的基因。许多 研究人员使用与统计位点“最近基因”的默认注释作为起点，但如果我们的 公开的实验（Baranski等人，2018）是可概括的，这可能是错误的大约一半的时间。 参考数据库中的调节组注释正在成为一种关键资源（例如ENCODE和GTEX），但 现在还为时尚早。非编码基因组是巨大的，许多注释要么是缺乏的，不足的， 或者不够特异，无法仅使用这些资源进行从基因座到基因的明确映射。大型队列 研究和财团，如TOPMed已经开始在同一个人进行各种组学扫描， 发现了哪些位点，从而提供了阐明潜在机制的可能性。 在统计学位点背后，特别是，暗示哪些基因可能是关键的。但几乎所有这些大型 人类的努力实际上必须限于全血和方便的组织， 在假定的作用组织中是可能的。相比之下，最重要的肥胖组织，如 大脑，可以在模型系统中访问和操纵，以揭示机制。可能每一个这样的 基因座会有不同的生物学解释，但对每个基因座进行功能实验， 时间是具有挑战性的、耗时的和昂贵的。所需要的是高通量策略，其将 同时询问多个位点。我们建议使用我们成功的，已发表的高通量果蝇 该系统可以在一组有苍蝇的人类候选人中有效筛选许多脂肪储存基因 直系同源物（认识到这是一种“低挂水果”的方法，不会对每个位点都起作用），然后 在小鼠中验证那些从人类到昆虫的保守基因。