Integrating diabetes pathophysiology from genotype to phenotype in whole genome sequence association studies of glycemic traits

 描述（由申请人提供）：“在血糖性状的全基因组序列关联研究中，从基因型到表型整合糖尿病病理生理学。“2型糖尿病（T2 D）和血糖特征，如空腹血糖（FG）和空腹胰岛素（FI）水平受环境，生理和遗传因素的影响。全基因组关联研究（GWAS）显示，非编码基因组包含与T2 D、FG和FI相关的大多数常见等位基因。该项目的目标是彻底探索与胰岛素信号（IS）和葡萄糖稳态（GH）功能相关的非编码遗传变异。这些研究的动机是最近在IS和GH途径中发现的与FI和FG相关的罕见编码变体。在该项目中，提出了提高全基因组序列关联研究（WGSAS）效率的方法，以发现罕见的非编码血糖性状关联。探索的一个假设是，WGSAS的功效可以通过以下方式增加：（a）优先考虑与IS和GH基因功能相关的基因组区域，以及（B）使用性状转换和协变量调整来测试关联，增加表型同质性将导致更大的遗传效应。该计划将通过以下具体目标实现这些目标。目标1：为了优先考虑目标区域，将注释应用于具有从参与IS和GH的代谢组织中鉴定的非编码功能元件的基因组，并检查所有功能元件以及与IS和GH途径中的基因相关的元件中的血糖性状关联的富集。目标二：为了解决表型异质性，将使用来自单基因疾病的见解以及胰岛素抵抗、肥胖症、环境暴露、代谢健康和T2 D之间的表型和遗传联系来推导精确的表型模型。目标3：为了发现与血糖性状相关的新的、罕见的非编码等位基因，将开发一个统计框架，该框架整合了来自目标1的靶区域与来自目标2的衍生表型，并将该框架应用于T2 D-GENES、GoT 2D和CHARGE联合体中的数据集。这些目标整合了从基因型到表型的糖尿病病理生理学，反映了精心计划的培训计划，并将导致研究独立性和R 01提案，用于对目标3中发现的非编码等位基因变体进行功能性随访，并通过目标1和目标2通知细胞类型和分子表型测定。主要研究者Alisa K，Manning博士，是一个早期的职业统计遗传学家，专注于血糖性状，并有开发遗传关联研究统计方法的经验。为了发展成为一名独立的研究人员，她需要在调控基因组学和葡萄糖调节生理学方面进行额外的培训。如果成功，这将为治疗或预防2型糖尿病提供新的药物靶点。此外，这些综合方法和结果将用于阐明T2 D的遗传结构， 基因组中的等位基因，从而更好地了解T2 D病因。

 DESCRIPTION (provided by applicant):"Integrating diabetes pathophysiology from genotype to phenotype in whole genome sequence association studies of glycemic traits." Type 2 diabetes (T2D) and glycemic traits such as fasting glucose (FG) and fasting insulin (FI) levels are influenced by environmental, physiological and genetic factors. Genome-wide association studies (GWAS) show that the non-coding genome harbors most of the common alleles associated with T2D, FG and FI. The goal of this project is to thoroughly explore non-coding genetic variation functionally linked to insulin signaling (IS) and glucose homeostasis (GH). These studies are motivated by recent rare coding variants associated with FI and FG that have been found in IS and GH pathways. In this project, methods are proposed that increase the efficiency of whole genome sequence association studies (WGSAS) to discover rare non-coding glycemic trait associations. A hypothesis explored is that power of WGSAS can be increased by: (a) prioritizing regions of the genome functionally linked to IS and GH genes, and (b) testing associations using trait transformations and covariate adjustments that increase phenotype homogeneity will result in larger genetic effects. This program will accomplish these goals with the following Specific Aims. Aim 1: To prioritize target regions, annotations will be applied to th genome with non-coding functional elements identified from metabolic tissues involved in IS and GH and examine enrichment of glycemic trait associations in all the functional elements and in elements linked to genes in IS and GH pathways. Aim 2: To address phenotype heterogeneity, refined phenotype models will be derived using both insight from monogenic disorders and phenotypic and genetic links between insulin resistance, adiposity, environmental exposures, metabolic health, and T2D. Aim 3: To find new, rare non-coding alleles associated with glycemic traits, a statistical framework will be developed that integrates the target regions from Aim 1 wit the derived phenotypes from Aim 2 and apply this framework to the data sets in the T2D-GENES, GoT2D and CHARGE consortia. These aims integrate diabetes pathophysiology from genotype to phenotype, reflect a carefully planned training program, and will lead to research independence and R01 proposals for functional follow-up of the non-coding alleles variants discovered in Aim 3 with cell types and molecular phenotype assays informed by Aim 1 and Aim 2. The Principle Investigator, Dr. Alisa K, Manning, Ph.D., is an early career statistical geneticit with an established focus on glycemic traits and experience developing statistical methods for genetic association studies. To develop into an independent researcher, she requires additional training in regulatory genomics and the physiology of glucose regulation. If successful, this could provide new drug targets to treat or prevent type 2 diabetes. Furthermore, these integrative methods and results will be used to illuminate the genetic architecture of T2D for rare alleles across the genome, leading to a better understanding of T2D etiology.