Population genetics for large-scale sequencing studies of diverse populations

总结 基于人群的研究确定了影响复杂人类疾病的遗传变异， 在研究设计、质量控制和基因型等重要任务中， 归责大规模疾病基因分型和测序研究的急剧增长产生了新的 挑战，无论是建模潜在的生殖群体遗传过程，产生证据， 数据集中的疾病关联，并进行统计分析，以揭示疾病变异。这些 这些挑战放大了基于群体遗传学的方法的潜力，以最大限度地提高 正在进行的调查。由于迄今为止的研究往往集中在欧洲血统的人口， 关键是新方法为更大的种群多样性提供了工具。该项目建立在生产力 在前两个资助期内，利用人类群体遗传学研究， 设计、分析和解释疾病的基因组研究。它利用了 人类遗传学群体遗传现象是造成隐性遗传基因纯合定位的原因。 风险变异，以及最近的认识，积累的运行纯合性（ROH），因此， 在纯合形式中，多个影响小的隐性有害变体可导致疾病风险。 特别是对于大规模基因分型和低覆盖率测序研究，其中罕见的隐性变异 很难分析，这个项目使用ROH的群体遗传学来加强发现。项目 扩展到小人群中罕见疾病的背景之外，建立在ROH和 影响小的隐性有害变异体的积累导致远交后代的复杂疾病风险 群体，包括混合群体。(1)我们将建立相互作用的人口效应模型- 基因对ROH的影响这样的模型将使研究人员有可能将ROH模式归因于效应 近亲繁殖，人口规模的历史，混合，并对有害的隐性变异的选择。(2)我们 将开发强大的新测试来衡量ROH对复杂疾病风险的影响。这些测试将使用 群体遗传模型，包括遗传结构和基因组参数的特征，以评估 如果ROH和疾病之间的关联反映了隐性疾病变异的可能存在。(3)我们将 区分生殖系和体细胞获得的纯合性，利用中间数据中的信号 类型和基因型分布，以识别假阳性ROH， 检测染色体改变。(4)我们将全面评估ROH对医疗的影响 在多种疾病研究中，使用密歇根基因组学计划和英国生物银行来测试和告知我们的 方法。ROH与疾病之间关联性检验的应用将有助于全表型的 关联研究，以确定ROH变量具有有意义的预测联系的特征， 表型。为了便于使用我们的方法，我们将生产，测试和分发新的用户友好的软件。

Summary Population-based studies identifying the genetic variants that affect complex human diseases have relied heavily on population-genetic principles in important tasks such as study design, quality control, and genotype imputation. The dramatic growth of large-scale genotyping and sequencing studies of disease generates new challenges both for modeling the underlying generative population-genetic processes that give rise to evidence of disease association in data sets and for performing statistical analysis to uncover disease variants. These challenges magnify the potential for approaches grounded in population genetics to maximize the return from ongoing investigations. Because studies thus far have often focused on populations of European descent, it is critical that new methods provide tools for a greater diversity of populations. This project builds on productive efforts in two previous funding periods, capitalizing on the study of human population genetics to enhance the design, analysis, and interpretation of genomic studies of disease. It exploits the fundamental principle of human genetics that population-genetic phenomena are responsible for homozygous placement of recessive risk variants, and the recent recognition that accumulations of runs of homozygosity (ROH), and hence, of multiple recessive deleterious variants of small effect in homozygous form, can contribute to disease risk. Particularly for large-scale genotyping and low-coverage sequencing studies, in which rare recessive variants are difficult to analyze, this project uses the population genetics of ROH to enhance discovery. The project expands beyond the setting of rare diseases in small populations, building on observations that ROH and accumulations of recessive deleterious variants of small effect contribute to complex disease risk in outbred groups, including admixed populations. (1) We will construct models of the effects of interacting population- genetic forces on ROH. Such models will make it possible for researchers to attribute ROH patterns to effects of inbreeding, population size history, admixture, and selection against deleterious recessive variants. (2) We will develop powerful new tests that measure effects of ROH on complex disease risk. These tests will employ population-genetic models that incorporate features of genetic architecture and genomic parameters to assess if associations between ROH and disease reflect the likely presence of recessive disease variants. (3) We will differentiate between germline and somatically acquired homozygosity, leveraging signals in intermediate data types and genotype distributions from population genetics, to identify false-positive ROH and to refine detection of chromosomal alterations. (4) We will comprehensively evaluate the impact of ROH on medical traits in multiple disease studies, using the Michigan Genomics Initiative and UK Biobank to test and inform our approaches. The application of association testing between ROH and disease will contribute a phenome-wide association study to identify traits for which ROH variables possess meaningful predictive connections to phenotypes. To facilitate use of our methods, we will produce, test, and distribute new user-friendly software.