Schork N J
Department of Medicine, University of Michigan, Ann Arbor 48109-0500.
Am J Hum Genet. 1993 Dec;53(6):1306-19.
Goldgar introduced a novel marker-based method for partitioning the variation of a quantitative trait into specific chromosomal regions. Unlike traditional linkage mapping methods, Goldgar's method does not require the estimation of statistical quantities characterizing each locus thought to influence the trait under scrutiny (e.g., allele frequencies, penetrances, etc.). Goldgar's method is thus more flexible and less model dependent than many traditional marker-based genetic analysis techniques. Unfortunately, however, many of the properties of Goldgar's method have not been investigated. In this paper, the utility of an extended version of Goldgar's approach is studied in settings in which sibships are taken as the sampling unit of interest. The extensions discussed resolve around the incorporation of a wider variety of effects and factors into Goldgar's basic model. Analytic studies pertaining to power, sample-size requirements, and estimation procedures for the proposed extended version of Goldgar's method are described. Hypothesis-testing strategies are also discussed. The results of the analytic studies indicate that, although an extended sib-pair version of Goldgar's variance-partitioning approach to modeling the chromosomal determinants of a quantitative trait will be useful only for traits with high heritabilities or when fine-scale genetic maps can be employed. Goldgar's technique as a whole has promise, as it can be made relatively robust statistically, refined through some simple and intuitive extensions, and can be easily adapted to work with more complex sampling units. Further extensions of Goldgar's methods are proposed, and areas in need of additional research are discussed.
戈尔加（Goldgar）提出了一种基于标记的新方法，用于将数量性状的变异划分为特定的染色体区域。与传统的连锁图谱绘制方法不同，戈尔加的方法不需要估计表征每个被认为影响所研究性状的基因座的统计量（例如等位基因频率、外显率等）。因此，与许多传统的基于标记的遗传分析技术相比，戈尔加的方法更加灵活，对模型的依赖性更小。然而，不幸的是，戈尔加方法的许多特性尚未得到研究。在本文中，我们研究了戈尔加方法扩展版本在以同胞组作为感兴趣的抽样单位的情况下的效用。所讨论的扩展主要围绕将更广泛的效应和因素纳入戈尔加的基本模型。描述了与所提出的戈尔加方法扩展版本的功效、样本量要求和估计程序相关的分析研究。还讨论了假设检验策略。分析研究结果表明，虽然戈尔加用于对数量性状的染色体决定因素进行建模的方差划分方法的扩展同胞对版本仅对具有高遗传力的性状或在可以使用精细遗传图谱时才有用。戈尔加的技术总体上具有前景，因为它在统计上可以变得相对稳健，可以通过一些简单直观的扩展进行完善，并且可以很容易地适应与更复杂的抽样单位一起使用。本文还提出了戈尔加方法的进一步扩展，并讨论了需要进一步研究的领域。
