Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan.
PLoS One. 2011;6(7):e21890. doi: 10.1371/journal.pone.0021890. Epub 2011 Jul 15.
Haplotype association studies based on family genotype data can provide more biological information than single marker association studies. Difficulties arise, however, in the inference of haplotype phase determination and in haplotype transmission/non-transmission status. Incorporation of the uncertainty associated with haplotype inference into regression models requires special care. This task can get even more complicated when the genetic region contains a large number of haplotypes. To avoid the curse of dimensionality, we employ a clustering algorithm based on the evolutionary relationship among haplotypes and retain for regression analysis only the ancestral core haplotypes identified by it. To integrate the three sources of variation, phase ambiguity, transmission status and ancestral uncertainty, we propose an uncertainty-coding matrix which combines these three types of variability simultaneously. Next we evaluate haplotype risk with the use of such a matrix in a Bayesian conditional logistic regression model. Simulation studies and one application, a schizophrenia multiplex family study, are presented and the results are compared with those from other family based analysis tools such as FBAT. Our proposed method (Bayesian regression using uncertainty-coding matrix, BRUCM) is shown to perform better and the implementation in R is freely available.
基于家系基因型数据的单体型关联研究可以提供比单一标记关联研究更多的生物学信息。然而，在单体型相位确定和单体型传递/非传递状态的推断中会出现困难。将与单体型推断相关的不确定性纳入回归模型需要特别注意。当遗传区域包含大量单体型时，这项任务可能会变得更加复杂。为了避免维度灾难，我们采用了一种基于单体型进化关系的聚类算法，并仅保留通过该算法确定的祖先核心单体型进行回归分析。为了整合三种变异来源，即相位模糊性、传递状态和祖先不确定性，我们提出了一种不确定性编码矩阵，该矩阵可以同时结合这三种类型的变异性。接下来，我们使用这种矩阵在贝叶斯条件逻辑回归模型中评估单体型风险。我们进行了模拟研究和一个应用，即精神分裂症多重家系研究，并将结果与其他基于家系的分析工具（如 FBAT）的结果进行了比较。我们提出的方法（使用不确定性编码矩阵的贝叶斯回归，BRUCM）表现更好，其在 R 中的实现是免费提供的。
