Population genetics to improve homozygosity mapping and mapping in admixed groups

群体遗传学改善混合群体的纯合性作图和作图

• 批准号: 8003715
• 负责人: Amy Lynne Williams
• 金额: $ 4.56万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8003715
• 关键词: Admixture; American; Autistic Disorder; Chromosome Mapping; Collection; Complex; DNA Resequencing; Data; Data Set; Disease; European; Event; Genes; Genetic; Genetic Techniques; Haplotypes; Hereditary Disease; High Prevalence; Hispanics; Individual; Latino; Maps; Medical Genetics; Methods; Modeling; Native Americans; Non-Insulin-Dependent Diabetes Mellitus; Population; Population Genetics; Sampling; Scheme; Statistical Methods; Techniques; Technology; Variant; Work; base; direct application; experience; genome sequencing; genome wide association study; improved; insight; novel; public health relevance; tool

DESCRIPTION (provided by applicant): Population genetics is a crucial tool for facilitating medical genetics and disease gene mapping studies. This proposal outlines two novel population genetic techniques that each facilitate disease gene mapping in different ways. First, it describes a probabilistic technique for detecting the proportion of homozygosity an individual is likely to have based on SNP array data. This is useful for prioritizing disease case sample individuals for whole genome sequencing and subsequent homozygosity mapping to identify recessive disease genes. Such a tool is useful for large outbred populations such as European Americans for which homozygous regions are likely to be short and therefore cannot be unambiguously detected using existing techniques for SNP array data. I will apply this homozygosity prioritization scheme to a dataset of individuals with autism to identify individuals that will be most informative to resequence and will perform homozygosity mapping on the sequence data to locate recessive disease genes. The second aim is to develop methods to permit combined admixture mapping and genome-wide association (GWA) of Latinos, a population for which current admixture mapping methods fail. Latino admixture mapping is challenging because suitable reference haplotypes for their Native American ancestry are lacking, and because existing techniques cannot model their complex three-way admixture. I will explore three methods for performing admixture mapping of Latinos, including using linear combinations of collections of Native American reference haplotypes and utilizing the information about Native American variation present in the Latinos themselves. To show that my approach works in practice, I will apply this tool to study the genetics of type 2 diabetes, a disease with higher prevalence among Latinos. PUBLIC HEALTH RELEVANCE: Insights from population genetics and statistical methods grounded in medical genetics have been very important in facilitating medical genetics and disease gene mapping studies. This pro- posal describes novel population genetic techniques with direct application to disease gene mapping, both in populations that have experienced founder events and in U.S. Latino populations that are admixed. The technology that I develop will be widely applicable to many disease gene mapping studies, but I will focus as proof-of-principle on two diseases: autism and type 2 diabetes.

描述(申请人提供)：群体遗传学是促进医学遗传学和疾病基因图谱研究的重要工具。这项提案概述了两种新的种群遗传技术，每种技术都以不同的方式促进了疾病基因图谱的绘制。首先，它描述了一种基于SNP阵列数据检测个体可能具有的纯合子比例的概率技术。这对于为全基因组测序和随后的纯合性作图确定隐性疾病基因的疾病病例样本个体的优先顺序是有用的。这样的工具对于像欧洲美国人这样的大的异交群体是有用的，对于这些群体来说，纯合子区域可能很短，因此不能使用现有的SNP阵列数据技术明确地检测到。我将把这种纯合子优先排序方案应用于自闭症患者的数据集，以确定对重测序最有信息量的个人，并将在序列数据上执行纯合子映射，以定位隐性疾病基因。第二个目标是开发方法，允许混合作图和拉丁裔全基因组关联(GWA)的组合，目前的混合作图方法对这一群体无效。拉丁裔混杂作图具有挑战性，因为他们的美洲原住民祖先缺乏合适的参考单倍型，而且现有技术无法模拟他们复杂的三向混杂。我将探索三种方法来进行拉丁裔的混合作图，包括使用美洲原住民参考单倍型集合的线性组合和利用拉丁裔本身存在的关于美洲原住民变异的信息。为了证明我的方法在实践中是有效的，我将使用这个工具来研究2型糖尿病的遗传学，这是一种在拉丁裔中发病率更高的疾病。 公共卫生相关性：来自群体遗传学和以医学遗传学为基础的统计方法的见解在促进医学遗传学和疾病基因图谱研究方面非常重要。这篇正文描述了新的群体遗传学技术，直接应用于疾病基因图谱，既包括经历过创始人事件的群体，也包括混杂在一起的美国拉丁裔群体。我开发的技术将广泛适用于许多疾病的基因图谱研究，但作为原则证明，我将重点关注两种疾病：自闭症和2型糖尿病。