Methods for Human Genetic Mapping

描述(由申请人提供)：拟议研究的长期目标是开发统计方法，用于绘制和遗传分析人类复杂特征，这些特征占美国医疗保健负担的主要部分。复杂的性状是家族性的，但没有简单的传递模式，很可能是多种遗传和环境因素作用和相互作用的结果。随着高密度单核苷酸多态信息的获得，有可能使用基于关联的作图方法来识别相关的遗传变异，以及阐明环境风险因素的作用。病例对照关联试验在可适应的研究设计方面比TDT类型的关联试验更具多样性。多数 病例对照关联作图方法是为无关个体的样本设计的，但包含两个或更多受影响个体的家庭仍然是进行遗传关联研究的强大资源，因为在复杂的遗传模型下，与没有受影响亲属的受影响个体相比，有受影响亲属的受影响个体比没有受影响亲属的受影响个体更容易获得致病等位基因。因此，应该期望这些个体对病例对照关联研究的力量做出不成比例的贡献。需要强大、强大的关联映射方法，这些方法将在全面的研究设计中有用，一方面是与无关个人的兄弟姐妹关系的简单组合，另一方面是具有复杂近亲系谱的孤立的创始人群体。关联测试的另一个关键方面是检测和解释可能导致假阳性结果的假设的违反，包括(1)人口分层和(2)可能导致人为病例对照差异的实验人工制品。我们的具体目标包括开发(1)包含任意相关个体的样本中的关联映射的方法，包括稳健和强大的二进制和定量方法 性状，允许单倍型效应、协变量信息、多个基因座的效应以及可能的 它们之间的相互作用，其中这些方法广泛适用于广泛的研究设计；(2)关联作图方法，通过主成分分析同时调整样本中可能存在的群体分层，并允许样本中相关的个体；(3)在关联测试的背景下测试标记基因类型的信息缺失，以便检测由于实验人工制品而可能产生的假阳性关联结果。

DESCRIPTION (provided by applicant): The long-term objective of the proposed research is development of statistical methods for mapping and genetic analysis of human complex traits, which account for a major portion of the health care burden in the United States. Complex traits are familial, but do not have simple patterns of transmission and are likely to result from the actions and interactions of multiple genetic and environmental factors. With the availability of high-density single-nucleotide polymorphism information, there is the potential to use association-based mapping methods to identify relevant genetic variants, as well as to clarify the role of environmental risk factors. Case-control association tests are more versatile in terms of the study designs they can accommodate than are TDT-type association tests. Most case-control association mapping methods are designed for samples of unrelated individuals, but families containing two or more affected individuals remain a powerful resource for genetic association studies, because under complex genetic models, affected individuals with affected relatives are enriched for disease-predisposing alleles, compared to affected individuals without affected relatives. Thus, these individuals should be expected to contribute disproportionately to the power of a case-control association study. Robust, powerful association mapping methods are needed that will be useful in a full spectrum of study designs, from simple combinations of sibships with unrelated individuals on the one hand, to isolated founder populations with complex inbred pedigrees, on the other hand. Another critical aspect of association testing is the ability to detect and account for violation of assumptions that may cause false positive results, including (1) population stratification and (2) experimental artifacts that may cause artificial case-control differences. Our specific aims include methods development for (1) association mapping in samples that contain arbitrarily related individuals, including robust and powerful methods for both binary and quantitative traits, allowing for haplotype effects, covariate information, effects of multiple loci, and possible interactions among these, where these methods are broadly applicable across a wide range of study designs; (2) association mapping methods that simultaneously adjust for the possible presence of population stratification in the sample by principal components analysis and allow for related individuals in the sample; (3) tests for informative missingness of marker genotypes in the context of association testing, in order to detect possible false positive association results that are due to experimental artifacts.