A UNIFIED ASSOCIATION ANALYSIS APPROACH FOR FAMILY AND UNRELATED SAMPLES

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 有两种常见的设计用于复杂疾病的关联映射：病例对照设计和基于家庭的设计。病例对照样本比包含相同数量的受影响和未受影响的人的基于家庭的样本更能检测遗传效应，尽管可能需要额外的标记来控制虚假关联。当家庭和无关的样本，统计分析往往是在家庭和无关的样本分别进行，对前者的父母信息的条件，从而导致权力降低。在这份报告中，我们提出了一个统一的方法，可以将家庭和病例对照样本，并提供额外的标志物是可用的，在同一时间纠正人口分层。我们应用标记矩阵的主成分来调整人口分层的影响。这种统一的方法使得不需要对家庭数据进行条件分析，并且比对无关和家庭样本的单独分析或通过结合通常单独分析的结果进行的荟萃分析更强大。这一特性在各种模拟模型和经验数据中都得到了证明。所提出的方法可以同样适用于质量和数量性状的分析。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. There are two common designs for association mapping of complex diseases: case-control and family-based designs. A case-control sample is more powerful to detect genetic effects than a family-based sample that contains the same numbers of affected and unaffected persons, although additional markers may be required to control for spurious association. When family and unrelated samples are available, statistical analyses are often performed in the family and unrelated samples separately, conditioning on parental information for the former, thus resulting in reduced power. In this report, we propose a unified approach that can incorporate both family and case-control samples and, provided the additional markers are available, at the same time corrects for population stratification. We apply the principal components of a marker matrix to adjust for the effect of population stratification. This unified approach makes it unnecessary to perform a conditional analysis of the family data and is more powerful than the separate analyses of unrelated and family samples, or a meta-analysis performed by combining the results of the usual separate analyses. This property is demonstrated in both a variety of simulation models and empirical data. The proposed approach can be equally applied to the analysis of both qualitative and quantitative traits.