Analyzing the behavior and interpreting the results of gene based tests of rare variant association

分析罕见变异关联的行为并解释基于基因的测试结果

• 批准号: 9813293
• 负责人: Nathan L Tintle
• 金额: $ 35.27万
• 依托单位: DORDT COLLEGE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-20 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9813293
• 关键词: Academic Research Enhancement Awards; Address; Area; Arts; Award; Behavior; Biological Markers; Categories; Complex; Computer software; Counseling; Data; Data Quality; Data Set; Disease; Electronic Health Record; Environment; Etiology; Eye; Fatty Acids; Foundations; Genetic; Genetic Markers; Genetic Research; Genetic Variation; Genetic screening method; Genome; Genotype; Goals; Human Genome; Hypertension; Individual; International; Knowledge; Light; Linear Models; Linear Regressions; Measurement; Methods; Mind; Performance; Phenotype; Privacy; Publications; Recording of previous events; Research; Research Personnel; Role; Source; Statistical Methods; Students; Technology; Testing; Uncertainty; United States National Institutes of Health; Variant; Work; biobank; clinically relevant; college; cost; design; experience; genetic architecture; genetic association; genetic variant; human disease; human genome sequencing; improved; journal article; next generation sequencing; novel; performance tests; phenotypic data; programs; rare variant; repository; statistics; undergraduate student

The technological and computational breakthroughs in the years since the sequencing of the human genome have provided an unprecedented opportunity to understand the etiology of complex human diseases. Notably, the diminishing cost of next-generation sequencing means that it is now possible for researchers to obtain complete genome sequence information on many thousands of individuals, with widespread access to that data via large repositories of electronic health records (EHRs; e.g,. biobanks). However, major statistical questions remain about biobank-era analysis strategies in order to study the contribution of genetic variation to common diseases. In particular, foundational statistical questions exist in the areas of: (a) a recognition of the need to minimize computational complexity and respect data privacy concerns, (b) random and non-random missing data, (c) data uncertainty and errors (both phenotypic and genotypic), and (d) the role of multi-marker (variant-set) tests, which aggregate evidence from many individual variants into a single test statistic. Research by our group as part of our existing award (R15-HG0006915 (2011-present)) began by developing a framework for evaluating the performance of existing variant-set tests. We then utilized this framework to provide a clear understanding of test performance in a variety of circumstances, developed novel robust and powerful tests, evaluated method performance in light of genotype uncertainty, developed methods to characterize underlying genetic architecture and demonstrated the utility of these methods to understand the genetics of fatty acids and high blood pressure. Recently, we proposed a novel method for utilizing summary statistics from simple one variant – one phenotype linear models to draw inferences about complex phenotypes (in this case, the linear combination of phenotypes) as a first step to provide computationally efficient, biobank era-ready statistical methods for assessing genotype-phenotype association. Moving forward, our research will generalize this initial method to be applicable to any complex phenotype. Additionally, we will continue to build on a strong history of exploration of uncertainty, by considering the impact of random and non-random errors and uncertainty on genotype-phenotype association tests in the biobank era, and extension of these methods to multi-marker test settings. Methods we develop will be tested on both simulated and real data via the CHARGE consortium. Additionally, the work we will perform addresses the three main goals of NIH’s R15 program: (a) to conduct meritorious research that will (b) strengthen the research environment of the liberal arts college where the research will be conducted, while (c) exposing undergraduate students to statistical genetics research. With this last goal in mind, the fourth aim of our proposal is to provide research experiences to undergraduate students when conducting aims 1, 2 and 3.

自人类基因组测序以来，技术和计算方面的突破