Population genetic modeling of genetic variation for complex traits and diseases

项目摘要 许多表型以及患上许多疾病的风险都是遗传复杂的，而且 涉及遗传和非遗传因素的贡献。在人类遗传学方面的工作 过去二十年的变化表明，这种差异是由一个非常大的 网站的数量，在数千或数万的数量级上。这给以下方面带来了挑战 遗传关联研究的测量和解释，作为真实的遗传效应 可能很难从混淆的偏见的影响中区分出来。另一方面，生物库 规模资源代表着一个巨大的机会来学习复杂的 特征，以及形成现代变异模式的进化力量。我的团队会 发展统计方法以克服目前遗传学研究中的几个挑战 通过应用群体遗传学、数量遗传学和统计遗传学的工具，研究复杂的性状。 首先，我们将开发工具来诊断和纠正多基因中的祖先分层偏差 得分。即使是细微的分层偏差也会在多个基因座上产生复合效应，从而导致多基因问题 预测，因此需要仔细计算这些偏差的方法。第二，我们将 研究突变压力在维持复杂疾病及其基因形成中的作用 建筑。外显子组和全基因组测序关联的日益可用性 数据集使得对突变压力的强度进行估计，从而增加疾病风险 越来越可行。需要新的理论发展来做出和解释这些 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具有不同功能的基因组区域的分布。 目前的方法混淆了这两种影响，所以有原则的人口有机会 基因建模，为复杂性状的生物学提供清晰的信息。最后，我们将发展 改进的群体遗传学联合推理方法。最近在以下方面的突破 联合推理已经开始重塑我们了解进化事件的能力 基因组测序数据。然而，这些方法表现出明显的准确性和可伸缩性折衷， 这表明如果这些方法的好处是 才能完全实现。我的团队将开发准确估计合并时间的方法 从测序数据中。

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.