Low-Pass Sequencing and High-Density SNP Genotyping for Type 2 Diabetes

2 型糖尿病的低通测序和高密度 SNP 基因分型

• 批准号: 7943106
• 负责人: David Altshuler
• 金额: $ 891.23万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2013-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7943106
• 关键词: Address; Alleles; American; Blood Pressure; Collaborations; Communities; Complex; Computer Simulation; DNA Resequencing; Data; Diagnosis; Disease; Economics; Etiology; Fatty acid glycerol esters; Frequencies; Functional disorder; Funding Mechanisms; Gene Frequency; Gene Mutation; General Population; Generations; Genes; Genetic; Genetic Research; Genetic Variation; Genetic screening method; Genome; Genomics; Genotype; Glucose; Goals; Health Care Costs; Height; Heritability; Human; Human Genetics; Individual; Institutes; Insulin; International; Investments; Joints; Large-Scale Sequencing; Lipids; Magic; Maps; Meta-Analysis; Metabolic; Metabolic Diseases; Methods; Michigan; Morbidity - disease rate; Mutation; Non-Insulin-Dependent Diabetes Mellitus; Penetrance; Pharmaceutical Preparations; Phenotype; Population; Predisposition; Prevention; Prevention approach; Relative (related person); Research; Research Infrastructure; Research Personnel; Risk; Role; Running; SNP genotyping; Sampling; Signal Transduction; Trust; Uncertainty; Universities; Validation; Variant; Work; base; case control; cost effectiveness; data sharing; density; design; diabetes control; diabetes mellitus genetics; diabetes risk; empowered; experience; genetic linkage analysis; genome sequencing; genome wide association study; genome-wide; genome-wide linkage; human disease; improved; innovation; insight; mortality; multidisciplinary; next generation; novel; public health relevance; tool; trait

DESCRIPTION (provided by applicant): This application proposes large-scale sequencing and genotyping in type 2 diabetes (T2D) case-control samples, addressing one of the major questions in human genetics: how and to what extent can insights into disease etiology be advanced by studying low frequency variants using next-generation sequencing platforms. First, building on our successful deployment of genome-wide association (GWA) analyses to identify novel common T2D-susceptibility variants, and our leading roles in the 1000 Genomes Project, we will address the strategic issues relevant to design of the next wave of large-scale human genetics projects. Specifically, despite great progress mapping common variants for common diseases including T2D, the vast majority of heritability remains unexplained. Our project compares three strategies that represent near-term approaches to the challenges of discovering and more fully characterizing genes for T2D and other diseases - in particular, by querying lower-frequency causal alleles (such as those found in IL23R, NOD2, IFIH1, and PCSK9). The three strategies are: (1) imputation and in silico association analysis using existing GWA data and data from 1000 Genomes Project; (2) design and deployment of a next-generation high-density SNP array (~5M SNPs); and (3) low-pass (~4x) whole-genome sequencing. Each strategy will be implemented in 3,000 T2D case-control samples from the DGI, FUSION, and WTCCC GWA sets, with extension (through imputation) to ~54,000 samples (T2D and controls) available from the DIAGRAM Consortium. We will evaluate each strategy with regard to completeness of variant discovery, genotype accuracy, and cost-effectiveness, providing guidance to other researchers in the field. The reference genotype data generated will, via imputation, empower GWA analysis of less common variants in a wide variety of diseases and traits. Second, we will use the data generated to identify rare variants influencing T2D and related quantitative traits (QTs), both genome-wide (to find novel loci) and in established regions (to fine-map causal variants and identify new susceptibility alleles). By sequencing cases and controls enriched for extreme phenotypes, we will increase power to discover low-frequency alleles that were poorly-captured in prior GWA studies, and alleles that are rare in the general population but common in cases. We will analyze related QTs in collaboration with the relevant international consortia, providing a broad set of insights into metabolic diseases. This project will leverage information from the 1000 Genomes Project to provide critical tools (genotyping, resequencing, and imputation) for next-generation genetic studies of human traits, and facilitate identification of disease mutations. Application to T2D and related QT's will provide new insights into the pathophysiology of T2D, suggest new targets for therapy, and improve predictive genetic testing to identify individuals at risk. PUBLIC HEALTH RELEVANCE: This proposal is relevant to several key objectives of the Grand Opportunity call. The research described represents groundbreaking, innovative, high impact research with the potential to accelerate genetic research by a wide range of investigators. The work is multi-disciplinary and integrates the activities of outstanding researchers at the Broad Institute, University of Michigan, and (through a proposed joint-funding mechanism with the Wellcome Trust) University of Oxford, and the Wellcome Trust Case Control Consortium. The Aims have the potential to uncover a significant fraction of the as-yet unaccounted for heritability in T2D, by identifying less common alleles of larger effect as well as indels and copy number variants that were not well captured by previous GWA studies. The genes and mutations identified as influencing T2D and metabolic diseases have the potential to inform breakthrough strategies to develop drugs to treat T2D, for genetic tests to stratify risk, and to enable more targeted approaches to prevention and treatment in the population.

描述（由申请人提供）：本申请提出了2型糖尿病（T2 D）病例对照样本的大规模测序和基因分型，解决了人类遗传学中的一个主要问题：如何以及在多大程度上可以通过使用下一代测序平台研究低频变异来深入了解疾病病因。 首先，在我们成功部署全基因组关联（GWA）分析以识别新的常见T2 D易感性变体的基础上，以及我们在1000个基因组计划中的领导作用，我们将解决与下一波大规模人类遗传学项目设计相关的战略问题。具体来说，尽管在绘制包括T2 D在内的常见疾病的常见变异方面取得了很大进展，但绝大多数遗传性仍然无法解释。我们的项目比较了三种策略，这些策略代表了发现和更全面地表征T2 D和其他疾病基因的挑战的近期方法-特别是通过查询较低频率的致病等位基因（例如在IL 23 R，NOD 2，IFIH 1和PCSK 9中发现的等位基因）。这三项战略是：（1）使用现有GWA数据和1000基因组计划数据进行插补和计算机关联分析;（2）设计和部署下一代高密度SNP阵列（~ 5 M SNP）;和（3）低通（~4x）全基因组测序。每种策略将在来自DGI、FUSION和WTCCC GWA集的3，000份T2 D病例对照样本中实施，并扩展（通过插补）至DIAGRAM联盟提供的约54，000份样本（T2 D和对照）。我们将从变异发现的完整性、基因型准确性和成本效益方面评估每种策略，为该领域的其他研究人员提供指导。通过插补，生成的参考基因型数据将使GWA能够分析各种疾病和性状中不太常见的变异。 其次，我们将使用生成的数据来识别影响T2 D和相关数量性状（QT）的罕见变异，包括全基因组（寻找新的基因座）和已建立的区域（精细定位因果变异并识别新的易感等位基因）。通过对极端表型富集的病例和对照进行测序，我们将增加发现在先前的GWA研究中捕获不足的低频等位基因以及在一般人群中罕见但在病例中常见的等位基因的能力。我们将与相关的国际财团合作分析相关的QT，为代谢性疾病提供广泛的见解。 该项目将利用来自1000个基因组计划的信息，为下一代人类性状的遗传研究提供关键工具（基因分型，重测序和插补），并促进疾病突变的识别。应用于T2 D和相关QT将为T2 D的病理生理学提供新的见解，提出新的治疗靶点，并改善预测性基因检测以识别风险个体。 公共卫生相关性：该提案与“大机会”呼吁的几个关键目标相关。所描述的研究代表了开创性，创新性，高影响力的研究，有可能加速广泛的研究人员的遗传研究。这项工作是多学科的，整合了密歇根大学布罗德研究所和牛津大学（通过拟议的与威康信托基金的联合资助机制）以及威康信托基金病例控制联盟的杰出研究人员的活动。这些目标有可能通过鉴定具有较大效应的不太常见的等位基因以及先前GWA研究未很好捕获的插入缺失和拷贝数变异，揭示T2 D中尚未解释的遗传力的显著部分。被确定为影响T2 D和代谢疾病的基因和突变有可能为开发治疗T2 D的药物提供突破性策略，用于基因检测以分层风险，并使人群中的预防和治疗方法更具针对性。