A UNIFIED ASSOCIATION ANALYSIS APPROACH FOR FAMILY AND UNRELATED SAMPLES

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 复杂疾病的关联图谱有两种常见的设计：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患者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。当有家庭和无关样本时，通常分别在家庭和无关样本中进行统计分析，以前者的父母信息为条件，从而导致权力减少。在这份报告中，我们提出了一种统一的方法，既可以纳入家庭样本，也可以包括病例对照样本，并且在提供额外标记的情况下，同时纠正人口分层。我们应用标记矩阵的主成分来调整种群分层的影响。这种统一的方法不需要对家庭数据进行条件分析，而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。该方法同样适用于质量性状和数量性状的分析。

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.