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

使用全基因组谱系剖析复杂性状的遗传学和进化

• 批准号: 10714153
• 负责人: Xinzhu Wei
• 金额: $ 37.98万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-19 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714153
• 关键词: Algorithms; Calibration; Code; Complex; Computer software; Computing Methodologies; Data; Data Set; Evolution; Gene Structure; Genealogy; Genetic; Genetic Diseases; Genetic Polymorphism; Genetic Recombination; Genotype; Graph; Heritability; Human Genome; Individual; Linkage Disequilibrium; Maps; Measurement; Methodology; Methods; Modeling; Mutation; Paper; Phenotype; Population; Population Genetics; Running; Structure; Testing; Time; Uncertainty; biobank; causal variant; computerized tools; genetic pedigree; genome sequencing; genome wide association study; human disease; improved; novel; reconstruction; response; trait; whole genome

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.

项目摘要 魏实验室开发了精确和可扩展的群体遗传学和 统计遗传学。在接下来的几年里，我们将专注于了解 复杂性状的遗传基础。包含数十万人的大型生物库数据集 基因组和数以万计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 需要更具伸缩性和不偏不倚的计算方法。我的实验室最近开发出了新的 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方法的运行速度比当前最先进的方法快10-100倍 允许对生物库大小的全基因组测序数据进行遗传力分析的方法。进一步 方法学的改进将需要对基因-表型进行新的概念化。 两性关系。传统的统计遗传学直接使用了基因型矩阵，而忽略了 遗传多态由基因谱系组织成可解释的图形结构。 全基因组谱系现在可以很容易地使用祖先重组图来推断 (Arg)推理软件。研究ARGs的系谱-表型关系可以准确地确定 因果突变，减少多次测试，提升算法效率，整合 对进化有意义的模型。我们正在为ARG范围内开发一种可扩展的算法 协会研究并将在ARG面临不确定性的情况下展示其优势 重建。我们还将开发ARG上的精细映射方法来研究同质性 和混合种群。利用我们的RSHE代码，我们将为 从ARGS估计遗传力，并将这种新方法应用于英国生物库 理解为什么无亲缘关系个体的遗传力估计低于家系估计遗传力 分析。在此基础上，我们将实现一种新的模型参数化来研究 使用ARGS进行复杂的性状进化。目前的多基因适应论文都不可避免地假设 Gwas显著SNPs可被视为因果变异，且不同的多基因 表型的水平可以忽略不计。我们提议的方法将提供第一个严格的 考虑到这些因素的框架。总而言之，这项提案将制定方法 将ARGS完全整合到统计遗传学中，以更好地理解和概念化 表型-家谱关系。它将为现场提供更具伸缩性的计算工具 响应快速增长的生物医学数据并使新的和更精确的 人类疾病遗传学和表型进化的发现。