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.

人类基因组项目和后续项目，例如1000个基因组，GTEX，编码和顶部提供强大的资源来识别影响人类健康和疾病的基因以及变异性与疾病相关的定量性状（QTS）。与这些资源一起，越来越有效的工具基因型，序列和注释基因组，并支持这些数据的计算。这些资源当我们继续探索人类疾病和与疾病相关的QT的遗传基础时，工具将是至关重要的。在此提案中，我们描述了人类基因映射中出现的统计和计算问题，特别关注序列分析，基因型推出和质量控制。我们描述统计解决这些问题，软件工具和Web服务以促进其使用的方法。我们将测试通过计算机仿真和复杂性状的数据分析产生的方法，工具和Web服务我们参与其中的遗传学研究。具体来说，我们将：（1）开发工具来检测和估计对遗传血统不可知的DNA样品污染；（2）在存在的情况下制定基于序列或估算的基因型的Hardy-Weinberg平衡测试种群结构和稳健的样品污染；（3）在存在的情况下，启用更准确的变体滤波和从DNA序列数据调用的基因型种群结构和/或样品污染；（4）开发方法来检测RNA和表观基因组序列数据中样品污染的方法；（5）扩展密歇根州插补服务器（MIS）以通过支持从现有序列数据资源中使用外部控件，增加现有的插补带有研究者测序样品的参考面板，并检查是否污染；和（6）文档，分发和支持有效的软件工具以支持这些方法。在单独的资金下，我们将采用最终的方法来帮助了解2型的遗传基础糖尿病和相关QT，精神分裂症和躁郁症。这些目标的成功将使人们能够更快地识别易受人类疾病的变体并说明与疾病相关QT的变异性，并有可能导致新见解生物学和疾病病因，识别新疗法，改善疗法的靶向，有助于疾病分类并支持更准确的疾病风险预测。统计和计算方法的开发以及这些方法对许多研究的影响，使我们拟议的研究高度成本效益。