Sinha Ritwik, Gray-McGuire Courtney
Case Western Reserve University, Cleveland, Ohio 44106-7281, USA.
Hum Hered. 2008;65(2):66-76. doi: 10.1159/000108938. Epub 2007 Sep 26.
One of the first tools for performing linkage analysis, Haseman-Elston regression (HE), has been successfully used to identify linkages to several disease traits. A recent explosion in extensions of HE leaves one faced with the task of choosing a flavor of HE best suited for a given situation. This paper puts this dilemma into perspective and proposes a modification to HE for highly ascertained samples (BLUP-PM).
Using data simulated for a range of models, we evaluated type I error and power of several dependent variables in HE, including the novel BLUP-PM.
When analyzing a continuous trait, even in highly ascertained samples, type I error is stable and approximately nominal across dependent variables. When analyzing binary traits in highly ascertained samples, type I error is elevated and unstable for all except BLUP-PM. Regardless of trait type, the optimally weighted HE regression and BLUP-PM have the greatest power.
Ascertained samples do not always reflect the population from which they are drawn and therefore choice of dependent variable in HE becomes increasingly important. Our results do not reveal a single, universal choice, but offer criteria by which to choose and demonstrate BLUP-PM performs well in most situations.
作为进行连锁分析的首批工具之一，哈斯曼 - 埃尔斯顿回归（HE）已成功用于识别与多种疾病性状的连锁关系。最近，HE的扩展方法激增，这使得人们面临着为特定情况选择最适合的HE方法的任务。本文阐述了这一困境，并针对高度确诊的样本提出了一种对HE的改进方法（BLUP - PM）。
我们使用针对一系列模型模拟的数据，评估了HE中几个因变量的I型错误和检验效能，包括新的BLUP - PM。
在分析连续性状时，即使是在高度确诊的样本中，I型错误在各因变量间也是稳定的且近似于名义水平。在高度确诊的样本中分析二元性状时，除BLUP - PM外，所有因变量的I型错误都会升高且不稳定。无论性状类型如何，最优加权的HE回归和BLUP - PM检验效能最高。
确诊样本并不总是反映其来源人群的特征，因此在HE中选择因变量变得越来越重要。我们的结果并未揭示单一的通用选择，但提供了选择标准，并证明BLUP - PM在大多数情况下表现良好。
