The pursuit of genetic causal mechanisms

追求遗传因果机制

• 批准号: 10321012
• 负责人: CHIARA SABATTI
• 金额: $ 42万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10321012
• 关键词: Algorithms; All of Us Research Program; Attention; Awareness; Biological; Characteristics; Chromatin; Cohort Studies; Complex; Computer software; Counseling; DNA Resequencing; DNA Sequence Alteration; Data; Data Analyses; Data Set; Dependence; Development; Disease; Drug Targeting; Environment; Environmental Exposure; Ethnic Origin; Evaluation; Family; Gene Frequency; Gene Mutation; Generations; Genes; Genetic; Genetic Determinism; Genetic Polymorphism; Genetic Predisposition to Disease; Genetic Risk; Genetic Variation; Genome; Genomics; Genotype; Heterogeneity; Individual; Linear Models; Link; Linkage Disequilibrium; Literature; Measures; Mediating; Medical; Medical Records; Methodology; Methods; Modeling; Nature; Noise; Pathway interactions; Patients; Performance; Phenotype; Population; Positioning Attribute; Prevention; Probability; Research; Research Personnel; Research Project Grants; Resolution; Resources; Risk; Risk Assessment; Role; Sample Size; Sampling; Scientist; Side; Solid; Specificity; Statistical Data Interpretation; Statistical Methods; Structure; Testing; Time; Training; Variant; Veterans; base; causal variant; computer science; drug development; flexibility; genetic architecture; genetic variant; genome sequencing; genome wide association study; genome-wide; genomic locus; graduate student; human disease; improved; insight; interest; large datasets; machine learning algorithm; non-genetic; novel; novel strategies; personalized medicine; polygenic risk score; prediction algorithm; programs; rare variant; sex; software development; statistics; tool; trait; virtual; whole genome

Project Summary Recent years have witnessed the development of large research projects that involve genotyping hundreds of thousands of individuals, on which we have available detailed medical records. Examples include the All of us research project, the Million Veteran Program, and the UKBiobank resource. Often, whole-genome sequencing data is also available for a substantial fraction of the individuals. These large samples, with their precise genotypic and phenotypic information, give us the opportunity to bring our understanding of the relations between genetic variation and traits of medical interest to the next level. While the initial small sample sizes available for genome wide association studies (GWAS) motivated analyses that were approximative in nature, we are now in the position to probe more closely the genetic causal mechanisms underlying medically relevant phenotypes. We can aspire to distinguish variants that have causal effects from those that are associated because of linkage disequilibrium or population structure. Indeed, we need to pay even greater attention to the implications of hidden confounders: even small effects become significant when sample sizes are large enough. Increasing the resolution with which we can describe causal mechanisms will result in the identification of clearer targets for drug development. It will also improve the precision of personalized risk evaluations based on genotypes: if we can construct risk scores using variants that are truly causal, their performance will remain solid across ethnicities and environmental exposures. To zoom in on genetic variants with causal effects, this project will leverage a set of new statistical methodologies that the investigators have recently introduced. These new approaches are remarkably flexible, in that they do not rely on specific assumptions of how phenotypes are linked to genetic variants. Indeed, they allow researchers to capitalize on powerful machine learning algorithms and, crucially, equip their results with precise replicability guarantees. We have assembled a diverse and complementary team, including experts in statistical genomics, methodological statistics and computer science, with a strong record both of software development and genetic data analysis. A postdoctoral scholar and two graduate students will contribute to the research program, and the interdisciplinary training they will acquire in statistics, computation and genetics will add another substantial benefit.

项目摘要 近年来，我们见证了大型研究项目的发展， 对成千上万的人进行基因分型，我们有详细的医疗记录。 例子包括我们所有人的研究项目，百万退伍军人计划和英国生物银行 resource.通常，全基因组测序数据也可用于大部分的基因组。 个体这些大样本及其精确的基因型和表型信息， 我们有机会把我们的理解之间的关系遗传变异和 医学兴趣的特征提升到一个新的水平。 虽然全基因组关联研究（GWAS）最初的小样本量 有动机的分析是近似的性质，我们现在可以探索更多 密切关注医学相关表型的遗传因果机制。我们可以渴望 区分具有因果效应的变异和因连锁而相关的变异 人口结构不平衡。事实上，我们需要更加重视 隐藏混杂因素的影响：当样本量 足够大。 提高我们描述因果机制的分辨率将导致 为药物开发确定更明确的目标。它还将提高 基于基因型的个性化风险评估：如果我们可以使用变异构建风险评分 真正的因果关系，他们的表现将保持坚实的种族和环境 暴露。 为了放大具有因果效应的遗传变异，该项目将利用一系列新的 这是研究人员最近采用的统计方法。这些新方法 是非常灵活的，因为他们不依赖于特定的假设， 与基因变异有关。事实上，它们使研究人员能够利用强大的机器 学习算法，并且，至关重要的是，使其结果具有精确的可复制性保证。 我们组建了一支多元化且互补的团队，其中包括统计专家 基因组学、方法统计学和计算机科学，在软件方面都有很好的记录。 发展和基因数据分析。一名博士后学者和两名研究生将 有助于研究计划，以及他们将获得的跨学科培训 统计学、计算和遗传学将增加另一个实质性的好处。

项目成果

Second-order group knockoffs with applications to GWAS.

• 发表时间: 2023-10
• 期刊: ArXiv
• 作者: Benjamin Chu;Jiaqi Gu;Zhaomeng Chen;Tim Morrison;E. Candès;Zihuai He;C. Sabatti