A UNIFIED ASSOCIATION ANALYSIS APPROACH FOR FAMILY AND UNRELATED SAMPLES

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 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，对大样本量来检测微小影响并允许估计模型内许多参数的需求不断增加。不幸的是，大多数关联分析方法仅限于基于家庭或病例对照设计，导致缺乏对多项研究数据的综合。开发了用于从家庭数据中检测连锁不平衡的传递不平衡型方法，作为防止由于人口分层而检测到关联的有效方法。然而，由于这些方法以父母基因型为条件，因此它们排除了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群分层并且不允许家族相关性。我们在这里提出了基于家族的连续性状关联分析方法的扩展，该方法将同时测试人口分层，并在必要时控制人口分层。我们进一步扩展这种方法来分析二元性状（因此将家庭和病例对照数据放在一起），并准确地估计人群中的遗传效应，即使在使用确定的家庭样本时也是如此。最后，我们展示了这种二元扩展对于仅家庭数据、联合家庭数据和病例对照数据的威力，并证明了确定的家庭样本中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.