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。 随着分子流行病学朝着全基因组关联研究和复杂建模的方向发展，对大样本规模的需求不断增加，以检测小的影响并允许估计模型中的许多参数。不幸的是，大多数关联分析方法仅限于基于家庭的设计或病例对照设计，导致缺乏综合多项研究的数据。从家系数据中检测连锁不平衡的传递不平衡类型方法被开发出来，作为防止由于人口分层而检测到关联的有效方法。然而，由于这些方法以双亲基因为条件，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不能防止人群分层，也不允许有家族相关性。我们在这里提出了一种基于家庭的连续性状关联分析方法的扩展，该方法将同时测试并在必要时控制种群分层。我们进一步将这种方法扩展到分析二元性状(因此也分析了家系和病例对照数据)，并准确地估计了群体中的遗传效应，即使使用已确定的家系样本。最后，我们给出了这种二进制扩展对于纯家族和联合家族和病例对照数据的威力，并在已确定的家族样本中证明了关联参数和方差分量的准确性。

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.