Wang Kai, Huang Jian
Division of Statistical Genetics, Department of Biostatistics, University of Iowa, Iowa City, Iowa 52242, USA.
Genet Epidemiol. 2002 Nov;23(4):398-412. doi: 10.1002/gepi.10203.
In the linakge analysis of quantitative traits, an additive model that assumes no dominance effect is often adopted. Intuitively, when the no-dominance-effect assumption does not hold, such a practice does not make efficient use of the data, and its power to detect linkage can be improved. Here we introduce a score statistic for detecting quantitative trait loci when the dominance effect is not neglible or the dominance effect is a concern. This statistic is derived from a normal likelihood function for sibships of arbitrary size. In the derivation, the inherent genetic constraints on model parameters are fully taken into consideration. This score statistic is asymptotically equivalent to the corresponding likelihood ratio statistic, but it is much easier to compute. The asymptotic distribution of this statistic is derived, which is a mixture of chi(0) (2), chi(1) (2), and chi(2) (2). Weights for distribution components are functions of the informativeness of the marker data. The type I error rate and the power of the proposed statistic in finite sample are evaluated via simulations.
在数量性状的连锁分析中,常采用假定无显性效应的加性模型。直观地讲,当无显性效应这一假设不成立时,这种做法无法有效利用数据,其检测连锁的效能也可得到提升。在此,我们引入一种用于检测数量性状基因座的得分统计量,当显性效应不可忽略或显性效应值得关注时使用。该统计量源自任意大小同胞对的正态似然函数。在推导过程中,充分考虑了模型参数的内在遗传约束。此得分统计量渐近等同于相应的似然比统计量,但计算起来要容易得多。推导得出了该统计量的渐近分布,它是χ(0)(2)、χ(1)(2)和χ(2)(2)的混合分布。分布成分的权重是标记数据信息量的函数。通过模拟评估了所提统计量在有限样本中的I型错误率和效能。