Whittemore A S
Department of Health Research and Policy, Stanford University School of Medicine, CA, USA.
Am J Hum Genet. 1996 Sep;59(3):704-16.
Several different methods for linkage analysis are shown to arise from a single likelihood function L for the observed allele-sharing data at multiple markers in a chromosomal region. These include classical parametric lod score methods, nonparametric or "model-free" affected pedigree-member (APM) methods, and the Gaussian process method. Setting the methods in the context of the likelihood function L clarifies their underlying assumptions. A test statistic derived from L, the efficient score statistic, is introduced. It is asymptotically equivalent to the lod score, but it can be easier to compute when the penetrances and frequencies of alleles of the trait gene are not known. APM test statistics and the Gaussian lod score are shown to be special cases of efficient score statistics. This unified framework facilitates exploration of a range of models for the effects of a putative trait-predisposing gene, and it facilitates sensitivity analyses to examine the consequences of model misspecification.
针对染色体区域多个标记处观察到的等位基因共享数据，有几种不同的连锁分析方法均源自单一似然函数L。这些方法包括经典的参数化对数优势分数方法、非参数或“无模型”的患病家系成员（APM）方法以及高斯过程方法。将这些方法置于似然函数L的背景下，可阐明其潜在假设。引入了从L导出的检验统计量——有效得分统计量。它在渐近意义上等同于对数优势分数，但当性状基因的外显率和等位基因频率未知时，计算起来可能更容易。APM检验统计量和高斯对数优势分数被证明是有效得分统计量的特殊情况。这个统一的框架有助于探索一系列关于假定的性状易感性基因效应的模型，并且便于进行敏感性分析以检验模型错误设定的后果。
