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Haseman Elston regression in ascertained samples: Importance of dependent variable and mean correction factor selection

机译：确定样本中的Haseman Elston回归：因变量和均值校正因子选择的重要性

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Objective: 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 (BLUPPM). Methods: Using data simulated for a range of models, we evaluated type I error and power of several dependent variables in HE, including the novel BLUPPM. Results: 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. Conclusions: 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. Copyright (C) 2007 S. Karger AG, Basel.
机译：目的：进行连锁分析的首批工具之一是Haseman-Elston回归（HE），已成功用于识别与多种疾病特征的连锁。最近在HE扩展领域的爆炸式增长使人们面临着选择最适合给定情况的HE口味的任务。本文将这一难题视为现实，并针对高度确定的样本（BLUPPM）提出了对HE的修改。方法：使用为一系列模型模拟的数据，我们评估了I型错误和HE中几个因变量的功效，包括新型BLUPPM。结果：在分析连续性状时，即使在高度确定的样本中，I型误差也是稳定的，并且在因变量之间近似标称。当分析高度确定的样本中的二进制特征时，除BLUP-PM以外的所有样本的I型误差均升高且不稳定。无论性状类型如何，最优加权HE回归和BLUP-PM具有最大功效。结论：确定的样本并不总是能反映出样本的总体，因此选择HE中的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，而是提供了选择标准并证明BLUP-PM在大多数情况下表现良好的标准。版权所有（C）2007 S.Karger AG，巴塞尔。

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