Methods for Human Genetic Mapping

项目摘要/摘要 常见、复杂的疾病加在一起占美国医疗保健负担的很大一部分，而且 对这些特征的遗传分析仍然是生物医学研究人员面临的主要挑战之一。高科技的研究进展 吞吐量技术导致了大规模遗传序列信息和其他相关信息的可用性增加 生物数据集。如果开发出可靠的、强大的统计和计算方法和工具来分析这些 数据，那么在识别和表征复合体的遗传成分方面就可以取得更多的进展 精神错乱。这反过来有可能(1)导致对这种疾病的生物学更好的理解，(2)澄清 环境风险因素的作用，这些因素可以成为具有成本效益的治疗和预防战略的目标；以及 (3)促进个性化医疗服务的改善。该项目的目标是开发健壮、强大的 特性-关联数据分析方法，将在全光谱研究中对各种复杂的特性有用 设计，包括群体结构温和的无关样本、相关个体样本和个体样本 来自混血或创始人群体。该项目的具体目标是(1)更强大的协会的发展 二元性状的方法，包括多个表型和多个遗传变异的联合分析；(2)快速、稳健 在广泛的关联研究中评估显著性fi的方法，包括检测稀疏性和 弱关联信号；以及(3)在关联分析中分析遗传交互作用的方法，针对一个基因组 或者一对相互作用的基因组。拟议的方法纳入了相关的协变量，允许确定，并且 说明样本中个体的群体结构和亲缘关系。总之，从以下方面获得的见解 提出的方法及其在当前遗传问题上的应用将推动进一步的发现和创造 对复杂性状的遗传学有更好的理解。 1

Project Summary/Abstract Common, complex diseases together account for a large portion of the health care burden in the United States, and genetic analysis of these traits remains one of the major challenges facing biomedical researchers. Advances in high- throughput technologies have led to increasing availability of large-scale genetic sequence information and other related biological data sets. If robust, powerful statistical and computational methods and tools are developed to analyze these data, then additional progress can be made on identifying and characterizing the genetic components of complex disorders. This, in turn, has the potential to (1) lead to better understanding of the biology of such disorders, (2) clarify the role of environmental risk factors, which could be targets of cost-effective treatment and prevention strategies, and (3) lead to improvements in personalized medical care. The goal of the project is development of robust, powerful trait-association data analysis methods that will be useful for a wide variety of complex traits in a full spectrum of study designs, including unrelated samples with mild population structure, samples of related individuals, and individuals from admixed or founder populations. Specific aims of the project are development of (1) more powerful association methods for binary traits, including joint analysis of multiple phenotypes and multiple genetic variants; (2) fast, robust methods for assessing significance in a wide variety of association studies, including methods to detect sparse and weak association signals; and (3) methods to analyze genetic interaction in an association analysis, for one genome or a pair of interacting genomes. The proposed methods incorporate relevant covariates, allow ascertainment, and account for population structure and relatedness of individuals in the sample. Together, the insights attained from the proposed methods and their application to current genetic questions will drive further discoveries into and create greater understanding of the genetics of complex traits. 1