Shannon W D, Province M A, Rao D C
Division of General Medical Sciences, Washington University School of Medicine, St. Louis, Missouri 63110, USA.
Genet Epidemiol. 2001 Apr;20(3):293-306. doi: 10.1002/gepi.1.
We propose a new splitting rule for recursively partitioning sibpair data into relatively more homogeneous subgroups. This strategy is designed to identify subgroups of sibpairs such that within-subgroup analyses result in increased power to detect linkage using Haseman-Elston regression. We assume that the subgroups can be defined by patterns of non-genetic binary covariates measured on each sibpair. The data we consider consists of the squared difference of a quantitative trait measurement on each sibpair, estimates of identity-by-descent (IBD) values at each genetic marker, and binary covariate data describing characteristics of the sibpair (e.g., race, sex, family history of disease). To test the efficacy of this method in linkage analysis, we performed two simulation experiments. In the first, we simulated a mixture consisting of 66.6% of the sibpairs with no linkage and 33.3% of the sibpairs with genetic linkage to one marker. The two groups were distinguished by the value of a single binary covariate. We also simulated one unlinked marker and one random covariate to include as noise in the data. In the second experiment, we simulated a mixture consisting of 55% of the sibpairs with no genetic linkage, 22.5% of the sibpairs with genetic linkage to one marker, and 22.5% of the sibpairs with linkage to a different marker. Each subgroup was defined by a distinct pattern of two binary covariates. We also simulated one unlinked marker and two random covariates to include as noise in the data. Our simulation studies found that we can significantly increase the overall power to detect linkage by fitting Haseman-Elston regression models to homogeneous subgroups with only a small increase in the false-positive rate. Second, the splitting rule can correctly identify important covariates and linked markers. Third, recursive partitioning of sibpair data using this splitting rule can correctly identify sibpair subgroups. These results indicate that partitioning sibpairs into homogeneous subgroups is feasible and significantly increases the power to detect linkage, thus demonstrating the practical utility and potential this new methodology holds.
我们提出了一种新的分割规则，用于将同胞对数据递归地划分为相对更同质的亚组。该策略旨在识别同胞对的亚组，使得亚组内分析在使用哈斯曼 - 埃尔斯顿回归检测连锁时能提高功效。我们假设亚组可由在每个同胞对上测量的非遗传二元协变量模式来定义。我们所考虑的数据包括每个同胞对定量性状测量的平方差、每个遗传标记处的同源系数（IBD）值估计，以及描述同胞对特征的二元协变量数据（例如，种族、性别、疾病家族史）。为了测试该方法在连锁分析中的功效，我们进行了两个模拟实验。在第一个实验中，我们模拟了一个混合物，其中66.6%的同胞对无连锁，33.3%的同胞对与一个标记存在遗传连锁。这两组通过单个二元协变量的值来区分。我们还模拟了一个无连锁的标记和一个随机协变量作为数据中的噪声。在第二个实验中，我们模拟了一个混合物，其中55%的同胞对无遗传连锁，22.5%的同胞对与一个标记存在遗传连锁，22.5%的同胞对与另一个不同的标记存在连锁。每个亚组由两个二元协变量的不同模式定义。我们还模拟了一个无连锁的标记和两个随机协变量作为数据中的噪声。我们的模拟研究发现，通过将哈斯曼 - 埃尔斯顿回归模型应用于同质亚组，我们可以显著提高检测连锁的总体功效，同时假阳性率仅小幅增加。其次，分割规则能够正确识别重要的协变量和连锁标记。第三，使用此分割规则对同胞对数据进行递归划分能够正确识别同胞对亚组。这些结果表明，将同胞对划分为同质亚组是可行的，并且显著提高了检测连锁的功效，从而证明了这种新方法的实际效用和潜力。
