Ancestry-based statistical methods for genetic studies

用于遗传研究的基于祖先的统计方法

• 批准号: RGPIN-2019-06051
• 负责人: Burkett, Kelly
• 金额: $ 1.31万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759202
• 关键词: Ancestry; based; statistical; methods; genetic

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' of a sample of genes from unrelated individuals is an ancestral tree that captures the history of these genetic events. If the gene genealogy was known, it could provide information about the location of mutations that contribute to observed characteristics. However, the true gene genealogy cannot be known since the time scale is on the order of tens of thousands of years. I am interested in how genetic data can be used to reconstruct this ancestral history and how the genealogy can be incorporated in statistical methods to relate observed characteristics with genetic variants. The over-arching vision of my research program - which draws on both statistical theory and population genetics - is to develop, test and implement innovative statistical methods for gene mapping that use the gene genealogy to model the genetic similarity between individuals. This is accomplished with two long-term objectives. The first objective of my research program is to propose and test a flexible `gene mapping' statistic that can handle different types of response variables, variable frequencies of the causal genetic variant in the population, and that includes non-genetic variables in the modelling. The second objective is to design a model and algorithm to probabilistically sample gene genealogies that are compatible with genetic data observed on families. This ambitious objective represents a merging of two types of trees in genetic data: family trees and gene genealogies. The central goal of this research program is to increase the utility and availability of tree-based gene mapping for researchers in human and population genetics. All techniques will be implemented in freely-available open source software for researchers in Canada and abroad. The statistical tools that I develop can be used by researchers in other disciplines, such as in human health and medical genetics. This research program will provide high-quality training opportunities for undergraduate, graduate and postdoctoral HQP. All HQP will acquire advanced statistical and computational skills, which will prepare them for their future careers as professional statisticians in government, industry or in an academic setting.

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。来自不相关个体的基因样本的“基因谱系”是一个祖先树，它捕捉了这些遗传事件的历史。如果基因谱系是已知的，它可以提供有关突变位置的信息，这些突变有助于观察到的特征。然而，真正的基因谱系无法得知，因为时间尺度是数万年的顺序。 我感兴趣的是遗传数据如何被用来重建这一祖先的历史，以及如何将家谱纳入统计方法，将观察到的特征与遗传变异联系起来。我的研究计划的总体愿景-它借鉴了统计理论和群体遗传学-是开发，测试和实施创新的统计方法，用于基因作图，使用基因系谱来模拟个体之间的遗传相似性。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提出和测试一个灵活的“基因图谱”统计，可以处理不同类型的响应变量，人口中的因果遗传变异的可变频率，并包括非遗传变量的建模。第二个目标是设计一个模型和算法，以概率抽样基因系谱，是与家庭中观察到的遗传数据兼容。这个雄心勃勃的目标代表了遗传数据中两种类型的树的合并：家谱和基因谱系。 这项研究计划的中心目标是提高人类和群体遗传学研究人员基于树的基因图谱的实用性和可用性。所有技术都将在加拿大和国外的研究人员免费获得的开源软件中实施。我开发的统计工具可以被其他学科的研究人员使用，例如人类健康和医学遗传学。该研究计划将为本科生，研究生和博士后HQP提供高质量的培训机会。所有HQP将获得先进的统计和计算技能，这将为他们未来的职业生涯做好准备，在政府，工业或学术环境中的专业统计人员。