Zhong Xiaoyun, Li Hongzhe
Departments of Statistics and Medicine, University of California, Davis, CA 95616-8500, USA.
Biostatistics. 2004 Apr;5(2):307-27. doi: 10.1093/biostatistics/5.2.307.
Nuclear families with multiple affected sibs are often collected for genetic linkage analysis of complex diseases. Once linkage evidence is established, dense markers are often typed in the linked region for genetic association analysis based on linkage disequilibrium (LD). Detection of association in the presence of linkage localizes disease genes more accurately than the methods that rely on linkage alone. However, test of association due to LD in the linked region needs to account for dependency of the allele transmissions to different sibs within a family. In this paper, we define a joint model for genetic linkage and association and derive the corresponding joint survival function of age of onset for the sibs within a sibship. The joint survival function is a function of both the inheritance vector and the genotypes at the candidate marker locus. Based on this joint survival function, we derive score tests for genetic association. The proposed methods utilize the phenotype data of all the sibs and have the advantages of family-based designs which can avoid the potential spurious association caused by population admixture. In addition, the methods can account for variable age of onset or age at censoring and possible covariate effects, and therefore provide important tools for modelling disease heterogeneity. Simulation studies and application to the data sets from the 12th Genetic Analysis Workshop indicate that the proposed methods have correct type 1 error rates and increased power over other existing methods for testing allelic association.
患有复杂疾病的多个同胞的核心家庭常被收集用于复杂疾病的基因连锁分析。一旦建立了连锁证据，通常会在连锁区域对密集标记进行分型，以基于连锁不平衡（LD）进行基因关联分析。在存在连锁的情况下检测关联比仅依赖连锁的方法能更准确地定位疾病基因。然而，由于连锁区域中的LD进行关联检验需要考虑一个家庭中不同同胞等位基因传递的依赖性。在本文中，我们定义了一个基因连锁和关联的联合模型，并推导了同胞组内同胞发病年龄的相应联合生存函数。联合生存函数是遗传向量和候选标记位点基因型的函数。基于这个联合生存函数，我们推导了基因关联的得分检验。所提出的方法利用了所有同胞的表型数据，具有基于家庭设计的优点，可避免由群体混杂引起的潜在虚假关联。此外，这些方法可以考虑发病年龄或截尾年龄的变异性以及可能的协变量效应，因此为疾病异质性建模提供了重要工具。模拟研究以及对第12届遗传分析研讨会数据集的应用表明，所提出的方法具有正确的I型错误率，并且在检验等位基因关联方面比其他现有方法具有更高的效能。
