Population genetic modeling of genetic variation for complex traits and diseases

复杂性状和疾病遗传变异的群体遗传模型

• 批准号: 10714605
• 负责人: Jeremy Jackson Berg
• 金额: $ 39.26万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714605
• 关键词: Accounting; Biology; Complex; Data; Data Set; Development; Diagnosis; Disease; Event; Evolution; Exhibits; Genetic; Genetic Models; Genetic Variation; Genomic Segment; Heritability; Human; Human Genetics; Learning; Measurement; Methods; Modeling; Modernization; Mutation; Pattern; Phenotype; Population; Population Genetics; Process; Research; Resources; Risk; Role; Shapes; Site; Statistical Methods; Stratification; Variant; Work; biobank; disorder risk; exome; genetic architecture; genetic association; genome sequencing; genome-wide; improved; mathematical model; non-genetic; pressure; tool; trait

Project Summary Many phenotypes, as well as the risk of developing many diseases, are genetically complex, and involve contributions from both genetic and non-genetic factors. Work in human genetics over the past two decades have shown that this variation is the result of contributions from a very large number of sites, on the order of thousands or tens of thousands. This presents challenges for both the measurement and interpretation of genetic association studies, as real genetic effects can be difficult to distinguish from the effects of confounding biases. On the other hand, biobank scale resources represent a tremendous opportunity to learn about both the biology of complex traits, and the evolutionary forces that have shaped modern patterns of variation. My group will develop statistical methods to overcome several current challenges in the study of genetically complex traits by apply tools from population, quantitative and statistical genetics. First, we will develop tools to diagnose and correct for ancestry stratification biases in polygenic scores. Even subtle stratification biases compound across loci to cause problems with polygenic predictions, so methods of carefully accounting for these biases are needed. Second, we will study the role of mutational pressure in maintaining complex disease and shaping its genetic architecture. The increasingly availability of exome and genome-wide sequencing association datasets make estimating the strength of mutational pressure toward increased disease risk increasingly feasible. New theoretical development will be needed to make and interpret these measurements. Third, we will develop models to study how mutation and selection jointly shape the distribution of heritability for complex traits across genomic regions with different functions. Current methods confound these two effects, so there is an opportunity for principled population genetic modeling to provide clarity on the biology of complex traits. Finally, we will develop improved methods for coalescent inference in population genetics. Recent breakthroughs in coalescent inference have begun to reshape our ability to learn about evolutionary events from genome sequencing data. However, these methods exhibit clear accuracy-scalability tradeoffs, suggesting that a thoughtful approach to inference is needed if the benefits of these methods are to be fully realized. My group will develop methods for accurately estimating coalescent times from sequencing data.

项目摘要 许多表型以及发展许多疾病的风险在遗传上是复杂的， 涉及遗传和非遗传因素的贡献。在人类遗传学方面的工作 过去的二十年表明，这种变化是由一个非常大的 数量级为数千或数万的站点。这对以下方面提出了挑战： 遗传关联研究的测量和解释，作为真实的遗传效应， 很难与混杂偏差的影响区分开来。另一方面，生物银行 规模资源代表了一个巨大的机会，了解生物学的复杂， 性状，以及塑造现代变异模式的进化力量。我的团队将 开发统计方法，以克服遗传学研究中的几个当前挑战 通过应用群体遗传学、数量遗传学和统计遗传学的工具来研究复杂性状。 首先，我们将开发工具来诊断和纠正多基因遗传学中的祖先分层偏差。 成绩.即使是微妙的分层偏见化合物在基因座造成的问题，多基因 预测，因此需要仔细考虑这些偏差的方法。二是 研究突变压力在维持复杂疾病和塑造其遗传 架构外显子组和全基因组测序关联的日益可用性 数据集使估计突变压力的强度增加疾病风险 越来越可行。需要新的理论发展来制定和解释这些 测量.第三，我们将开发模型来研究突变和选择如何共同塑造 复杂性状的遗传力在不同功能基因组区域的分布。 目前的方法混淆了这两种效应，因此有机会进行有原则的人口普查。 遗传建模，以提供复杂性状的生物学上的清晰度。最后，我们将开发 群体遗传学中的联合推理的改进方法。最近的突破， 联合推理已经开始重塑我们从进化论中学习进化事件的能力。 基因组测序数据。然而，这些方法表现出明显的准确性-可扩展性权衡， 这表明，如果这些方法的好处是， 要完全实现。我的小组将开发出精确估算聚结时间的方法 from sequencing测序data数据.