A UNIFIED ASSOCIATION ANALYSIS APPROACH FOR FAMILY AND UNRELATED SAMPLES

这个子项目是许多研究子项目中的一个 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来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 随着分子流行病学向全基因组关联研究和复杂建模发展的趋势，对大样本量的需求继续增加，以检测小的影响，并允许在模型中估计许多参数。遗憾的是，大多数关联分析方法仅限于基于家族或病例对照的设计，导致缺乏对多项研究数据的综合。传播不平衡型方法检测连锁不平衡的家庭数据被开发作为一种有效的方法，防止检测的关联，由于人口分层。然而，由于这些方法以父母的基因型为条件，它们排除了对家庭和病例对照数据的联合分析，尽管病例对照数据的方法可能无法防止人口分层，也不允许家庭相关性。我们在这里提出了一个扩展的连续性状，同时测试，如果必要的控制，人口分层的家庭为基础的关联分析方法。我们进一步扩展这种方法来分析二元性状（因此家庭和病例对照数据在一起），并准确地估计遗传效应的人口，即使使用一个确定的家庭样本。最后，我们提出了这种二进制扩展的功率为家庭和联合家庭和病例对照数据，并证明了关联参数和方差分量在确定的家庭样本的准确性。

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. With the trend in molecular epidemiology towards both genome-wide association studies and complex modelling, the need for large sample sizes to detect small effects and to allow for the estimation of many parameters within a model continues to increase. Unfortunately, most methods of association analysis have been restricted to either a family-based or a case-control design, resulting in the lack of synthesis of data from multiple studies. Transmission disequilibrium-type methods for detecting linkage disequilibrium from family data were developed as an effective way of preventing the detection of association due to population stratification. Because these methods condition on parental genotype, however, they have precluded the joint analysis of family and case-control data, although methods for case-control data may not protect against population stratification and do not allow for familial correlations. We present here an extension of a family-based association analysis method for continuous traits that will simultaneously test for, and if necessary control for, population stratification. We further extend this method to analyse binary traits (and therefore family and case-control data together) and accurately to estimate genetic effects in the population, even when using an ascertained family sample. Finally, we present the power of this binary extension for both family-only and joint family and case-control data, and demonstrate the accuracy of the association parameter and variance components in an ascertained family sample.