III-CXT: Population Stratification Methods

为了了解复杂疾病背后的遗传因素，进行了疾病关联研究，收集了病例和对照，并比较了它们的DNA变异(SNP)。疾病关联性研究中日益增长的一个主要担忧是，种群亚结构可能会带来虚假的发现，特别是在最近的技术进步中，成千上万的人在整个基因组上进行了基因分型。特别是，如果病例和对照来自不同种族构成的人群，两组SNP变异之间的差异可能是由于人口结构而不是疾病造成的。该项目的主要目标是为不同情况下的人口分层方法开发有效和准确的工具，并将这些工具整合到病例对照研究中。现有的方法要么太慢，要么不能准确地预测种群的子结构，而且缺乏严格的分析来证明它们的正确性。新算法将考虑种群是种群集合的情况，即个体可能具有混合血统的种群，并使用单倍型相关性来改进算法。这一拟议项目的影响源于这样一个事实，即目前在关联研究中报告的许多结果是虚假的，因为人群亚结构，导致对导致疾病的生物学机制的错误理解。该项目开发的算法将有助于避免这种虚假的结果，从而提高我们对人类生物学和疾病的理解。该项目涉及一名研究生和六名暑期学生的培训。该项目的协作性质将使学生接触到医学和遗传学世界，同时，它将提高他们设计和实现复杂算法问题的能力。该项目开发的软件将与现有的公开可用的网络服务器HAP相结合，该服务器由私人投资机构开发，已被世界各地的遗传学家使用了9000多个。

In order to understand the genetic factors underlying complex diseases, disease association studies are performed, where cases and controls are collected and their DNA variants (SNPs) are compared. One of the main growing concerns in disease association studies is that population substructure may raise spurious discoveries, especially with the recent advances in technology where thousands of individuals are genotyped over the whole genome. In particular, if the cases and controls are collected from populations with different ethnic composition, the differences between the SNP variations in the two groups may be due to the population structure and not due to the disease. The main goal of this project is to develop efficient and accurate tools for population stratification methods, under different scenarios, and to integrate those tools in case control studies. The existing methods are either too slow or do not accurately predict the population substructure, and they lack rigorous analysis that proves their correctness. The new algorithms will consider cases in which the population is a collection of populations, populations in which individuals may have a mixed ancestry and use haplotype correlations to improve the algorithms. The impact of this proposed project stems from the fact that many current results reported in association studies are spurious due to population substructure, resulting in an incorrect understanding of the biological mechanisms causing a disease. The algorithms developed in this project will help to avoid such spurious results, thus improving our understanding of human biology and disease. The project involves the training of a graduate student and six summer students. The collaborative nature of the project will expose the students to the medical and genetics worlds, and at the same, it will improve their abilities to design and implement complex algorithmic problems. The software developed in this project will be integrated with the existing publicly available webserver HAP, which was developed by the PIs and has been used more than 9000 by geneticists worldwide.