Dissecting the genetics and evolution of complex traits using whole-genome genealogies

项目摘要 Wei实验室开发了群体遗传学中准确和可扩展的推理方法， 统计遗传学在接下来的几年里，我们将专注于了解进化， 复杂性状的遗传基础。拥有数十万人类的大型生物库数据集 基因组和成千上万的表型测量提供了前所未有的 了解复杂表型的机会。与此同时，这些海量数据集 需要更可扩展和无偏的计算方法。我的实验室最近开发了一种新的 算法和数据结构，以提高标准计算的可伸缩性， 基因型矩阵，包括遗传力分量和连锁的计算 不平衡分数我们的RSHE方法比当前最先进的方法快10- 100倍 该方法允许对生物库大小的全基因组测序数据进行遗传性分析。进一步 方法学的改进需要基因型-表型的新概念 关系。传统的统计遗传学直接使用基因型矩阵，忽略了 遗传多态性通过基因谱系被组织成可解释的图结构。 全基因组系谱现在可以很容易地推断使用祖先重组图 (ARG)推理软件研究ARG上的谱系-表型关系可以确定 因果突变，减少多次测试，提高算法效率， 有进化意义的模型。我们正在开发一个可扩展的算法， 协会的研究，并将证明其优势，即使在面对的不确定性，在ARG 重建我们还将开发ARG上的精细映射方法来研究同质 混合人口。利用我们的RSHE代码，我们将实现一个可扩展的方法， 估计遗传力从ARG，并将这种新方法应用于英国生物库， 理解为什么在无关个体中估计的遗传力低于从系谱中估计的遗传力 分析。在此基础上，我们将实施一种新的模型参数化来研究 复杂的性状进化。目前的多基因适应性论文都不可避免地假设 GWAS显著的SNPs可被视为致病变异， 可以忽略表型间的水平。我们提出的方法将提供第一个严格的 一个考虑到这些因素的框架。总之，本提案将制定方法， 将ARG完全融入统计遗传学，以更好地理解和概念化 表型-系谱关系。它将为该领域提供更具可扩展性的计算工具 以响应快速增长的生物医学数据， 人类疾病遗传学和表型进化的发现。

Project Summary The Wei Lab develops accurate and scalable inference methods in population genetics and statistical genetics. In the next few years, we will focus on understanding the evolution and genetic basis of complex traits. Large biobank datasets with hundreds of thousands of human genomes and tens of thousands of phenotypic measurements provide unprecedented opportunities to understand complex phenotypes. At the same time, these massive data sets demand more scalable and unbiased computational methods. My lab recently developed new algorithms and data structures to improve the scalability of standard computations involving genotype matrices, including the calculation of heritability components and linkage disequilibrium scores. Our RSHE method runs 10-100x faster than the current state-of-the-art method to allow heritability analysis on biobank-size whole-genome sequencing data. Further methodological improvements will require new conceptualizations of the genotype-phenotype relationships. Conventional statistical genetics uses genotype matrices directly, ignoring that genetic polymorphisms are organized by gene genealogy into an interpretable graph structure. Whole-genome genealogies can now be readily inferred using ancestral recombination graph (ARG) inference software. Studying genealogy-phenotype relationships on ARGs could pinpoint causal mutations, reduce multiple testing, promote algorithm efficiency, and integrate evolutionarily meaningful models. We are developing a scalable algorithm for ARG-wide association studies and will demonstrate its advantages even in the face of uncertainty in ARG reconstruction. We will also develop fine-mapping methods on ARGs to study homogeneous and admixed populations. Leveraging our RSHE code, we will implement a scalable method for estimating heritability from ARGs and will apply this new method to the UK biobank to understand why heritability estimated in unrelated individuals is lower than that from pedigree analyses. Building upon this, we will implement a novel model parameterization to study complex trait evolution using ARGs. Current polygenic adaptation papers all inevitably assume that GWAS significant SNPs can be treated as causal variants and that different polygenicity levels across phenotypes can be ignored. Our proposed method will provide the first rigorous framework that takes these factors into account. In summary, this proposal will develop methods to fully integrate ARGs into statistical genetics to better understand and conceptualize phenotype-genealogy relationships. It will provide more scalable computational tools for the field in response to the rapidly growing biomedical data and enable novel and more calibrated discoveries for human disease genetics and phenotypic evolution.