Genealogical-based inference in statistical genetics

统计遗传学中基于谱系的推断

• 批准号: 435822-2013
• 负责人: Burkett, Kelly
• 金额: $ 1.09万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635913
• 关键词: Genealogical; based; inference; statistical; genetics

The variation observed today in the human genome is a result of genetic processes, such as mutation, acting over time on the DNA of our ancestors. The 'gene genealogy' for a sample of genes from unrelated individuals is a tree describing the historical ancestral genetic events and relationships giving rise to this variation. However, since the time scale is on the order of tens of thousands of years, the true gene genealogy cannot be known. I am interested in using information from genetic markers - variable DNA sites - to infer genealogical relationships between unrelated individuals. My research, which draws on statistics and population genetics, involves the development and dissemination of statistical approaches incorporating these ancestral trees. I have recently developed an approach to probabilistically sample genealogical trees that are likely to have given rise to a sample of genetic data. The central goal of my research program is to increase the utility and application of the genealogy sampling approach that I developed. This is accomplished with two long-term objectives. The first objective of my research program is to make computational improvements to the sampling approach. The first improvement will use statistical techniques to speed up the computational time of the sampler. The second improvement will extend the sampler to handle missing genotype data. Additional power to discover genetic risk factors is of great interest to human geneticists. The second objective of my research program is to develop a new genealogy-based method for finding genetic risk factors, and in particular rare variants. I will develop and evaluate statistics that relate similarity in disease state to genetic relatedness, as measured by the genealogical tree. The sampling approach that I have developed will be used to handle the uncertainty of the genealogy and I will compare my approach to existing approaches that ignore the uncertainty.

今天在人类基因组中观察到的变异是遗传过程的结果，例如突变，随着时间的推移作用于我们祖先的DNA。来自无关个体的基因样本的“基因谱系”是描述历史祖先遗传事件和引起这种变异的关系的树。然而，由于时间尺度是在数万年的数量级上，因此无法知道真正的基因谱系。我感兴趣的是利用遗传标记（可变DNA位点）的信息来推断无关个体之间的系谱关系。我的研究，这利用统计学和人口遗传学，涉及到发展和传播的统计方法，将这些祖先树。我最近开发了一种方法，可以对可能产生遗传数据样本的系谱树进行概率采样。我的研究计划的中心目标是增加我开发的家谱抽样方法的实用性和应用。实现这一目标有两个长期目标。我的研究计划的第一个目标是对抽样方法进行计算改进。第一个改进将使用统计技术来加快采样器的计算时间。第二个改进将扩展采样器以处理缺失的基因型数据。人类遗传学家对发现遗传风险因素的额外能力非常感兴趣。我的研究计划的第二个目标是开发一种新的基于系谱的方法来发现遗传风险因素，特别是罕见的变异。我将开发和评估统计数据，将疾病状态的相似性与遗传相关性联系起来，如家谱树所测量的那样。我开发的抽样方法将用于处理家谱的不确定性，我将比较我的方法与忽略不确定性的现有方法。