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用于人类复杂性状的绘图和遗传分析的统计方法，这些性状占美国医疗保健负担的主要部分。复杂性状是家族性的，但没有简单的传播模式，可能是由多种遗传和环境因素的作用和相互作用造成的。随着高密度单核苷酸多态性信息的可用性，有可能使用基于关联的映射方法来识别相关的遗传变异，以及澄清环境风险因素的作用。病例对照关联检验在研究设计方面比TDT型关联检验更通用。最 病例-对照关联作图方法是为无关个体的样本设计的，但包含两个或更多个受影响个体的家庭仍然是遗传关联研究的有力资源，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，具有受影响亲属的受影响个体富集了疾病易感等位基因。因此，这些个体应该被期望对病例对照关联研究的力量做出不成比例的贡献。强大的，强大的关联映射方法是必要的，这将是有用的，在一个全方位的研究设计，从简单的组合的同胞关系与无关的个人，一方面，孤立的创始人人口与复杂的近交系，另一方面。关联检验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违反的能力，包括（1）人群分层和（2）可能导致人为病例对照差异的实验伪影。我们的具体目标包括方法开发（1）包含任意相关个体的样本中的关联映射，包括二元和定量的稳健和强大的方法 性状，允许单倍型效应，协变量信息，多个基因座的影响，以及可能的 这些方法之间的相互作用，其中这些方法广泛适用于广泛的研究设计;（2）关联映射方法，同时通过主成分分析调整样本中可能存在的群体分层，并考虑样本中的相关个体;（3）在关联测试的背景下，测试标记基因型的信息缺失，以便检测由于实验伪像而导致的可能的假阳性关联结果。

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.