权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Design and Analysis of Human Gene Mapping Studies

人类基因图谱研究的设计与分析

基本信息

批准号：
10418763
负责人：
MICHAEL L BOEHNKE
金额：
$ 48.93万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-07 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10418763
关键词：
Address Base Sequence Biological Biology Bipolar Disorder Catalogs Chromosome Mapping Complex Genetic Trait Computer Simulation Computer software Computing Methodologies Consensus DNA DNA Sequence Data Data Set Development Disease Equilibrium Etiology Frequencies Funding Gene Frequency Genes Genetic Genetic Variation Genetic study Genome Genotype Genotype-Tissue Expression Project Goals Health Human Human Gene Mapping Human Genetics Human Genome Human Genome Project Human Resources Lead Linkage Disequilibrium Methods Michigan Minor Non-Insulin-Dependent Diabetes Mellitus Population Problem Sets Problem Solving Production Quality Control RNA Research Research Design Research Personnel Resources Sampling Schizophrenia Sequence Analysis Software Tools Statistical Methods Structure Test Result Testing Time Trans-Omics for Precision Medicine Variant base causal variant computer data analysis computerized tools cost cost effective data resource design disease classification disorder prevention disorder risk epigenomics exome sequencing experimental study genetic testing genetic variant genome sequencing genome wide association study human disease improved insight method development novel novel therapeutics open source risk prediction risk stratification success targeted treatment tool trait transcriptome sequencing web services

项目摘要

The Human Genome Project and follow-on projects such as 1000 Genomes, GTEx, ENCODE, and TOPMed provide powerful resources to identify genes that influence human health and disease and variability in disease-related quantitative traits (QTs). Along with these resources have come increasingly efficient tools to genotype, sequence, and annotate the genome, and to support computation across these data. These resources and tools will be critical as we continue to explore the genetic basis of human disease and disease-related QTs. In this proposal, we describe statistical and computational problems that arise in human gene mapping, with a particular focus on sequence analysis, genotype imputation, and quality control. We describe statistical methods to address these problems and software tools and web services to facilitate their use. We will test resulting methods, tools, and web services via computer simulation and analysis of data from complex trait genetics studies in which we are involved. Specifically, we will: (1) develop tools to detect and estimate DNA sample contamination that are agnostic to genetic ancestry; (2) develop a test for Hardy-Weinberg equilibrium of sequence-based or imputed genotypes in the presence of population structure and robust to sample contamination; (3) enable more accurate variant filtering and genotype calling from DNA sequence data in the presence of population structure and/or sample contamination; (4) develop methods to detect sample contamination in RNA- and epigenomic sequence data; (5) extend the Michigan Imputation Server (MIS) to increase power of a sequence-based association studies by supporting use of external controls from existing sequence data resources, augmenting an existing imputation reference panel with the investigator's sequenced samples, and checking for contamination; and (6) document, distribute, and support efficient software tools to support these methods. Under separate funding, we will apply the resulting methods to help understand the genetic basis of type 2 diabetes and related QTs, and of schizophrenia and bipolar disorder. Success in these aims will enable more rapid identification of variants that predispose to human disease and account for variability in disease-related QTs, and has the potential to lead to new insights into basic biology and disease etiology, identify novel therapies, improve targeting of therapies, assist in disease classification, and support more accurate disease risk prediction. The modest cost of statistical and computational methods development, and the impact of these methods across many studies, makes our proposed research highly cost effective.

人类基因组计划和后续计划，如1000基因组、GTEX、ENCODE和TOPMed 提供强大的资源，以确定影响人类健康和疾病的基因以及疾病相关数量性状(QTS)。伴随着这些资源，出现了越来越有效的工具来对基因组进行基因分型、排序和注释，并支持跨这些数据的计算。这些资源随着我们继续探索人类疾病和与疾病相关的QT的遗传基础，工具将是至关重要的。在这项建议中，我们描述了人类基因图谱中出现的统计和计算问题，重点放在序列分析、基因定位和质量控制上。我们描述了统计学解决这些问题的方法以及促进其使用的软件工具和网络服务。我们将测试通过计算机模拟和分析来自复杂特征的数据而得到的方法、工具和网络服务我们所参与的遗传学研究。具体来说，我们会： (1)开发工具来检测和评估与遗传祖先无关的DNA样本污染； (2)建立了基于序列的或推定的基因类型的Hardy-Weinberg平衡检验种群结构和对样品污染的稳健性； (3)在存在的情况下，能够从DNA序列数据中更准确地进行变异筛选和基因分型种群结构和/或样本污染； (4)开发检测RNA和表观基因组序列数据中样本污染的方法； (5)扩展密歇根分配服务器(MIS)，通过以下方式提高基于序列的关联研究的能力支持使用来自现有序列数据资源的外部控制，增强了现有的归责使用调查员的排序样本的参照板，并检查是否有污染；以及 (6)记录、分发和支持支持这些方法的高效软件工具。在单独的资助下，我们将应用由此产生的方法来帮助理解2型的遗传基础。糖尿病和相关的QTS，以及精神分裂症和双相情感障碍。这些目标的成功将使人们能够更快地识别出易患人类疾病的变异。并解释了与疾病相关的QT的可变性，并有可能导致对基础生物学和疾病病因学，确定新的治疗方法，提高治疗的针对性，协助疾病分类，并支持更准确的疾病风险预测。统计和分析的适度成本计算方法的发展，以及这些方法在许多研究中的影响，使得我们的建议的研究具有极高的成本效益。