Schork N, Schork M A
Department of Medicine, School of Public Health, University of Michigan, Ann Arbor 48109.
Am J Hum Genet. 1989 Nov;45(5):803-13.
Aspects of the statistical modeling and assessment of hypotheses concerning quantitative traits in genetics research are discussed. It is suggested that a traditional approach to such modeling and hypothesis testing, whereby competing models are "nested" in an effort to simplify their probabilistic assessment, can be complimented by an alternative statistical paradigm - the separate-families-of-hypotheses approach to segregation analysis. Two bootstrap-based methods are described that allow testing of any two, possibly non-nested, parametric genetic hypotheses. These procedures utilize a strategy in which the unknown distribution of a likelihood ratio-based test statistic is simulated, thereby allowing the estimation of critical values for the test statistic. Though the focus of this paper concerns quantitative traits, the strategies described can be applied to qualitative traits as well. The conceptual advantages and computational ease of these strategies are discussed, and their significance levels and power are examined through Monte Carlo experimentation. It is concluded that the separate-families-of-hypotheses approach, when carried out with the methods described in this paper, not only possesses some favorable statistical properties but also is well suited for genetic segregation analysis.
讨论了遗传学研究中关于数量性状的统计建模和假设评估的各个方面。有人提出，对于此类建模和假设检验的传统方法，即通过将竞争模型“嵌套”以简化其概率评估，可以通过另一种统计范式——用于分离分析的独立假设族方法来补充。描述了两种基于自助法的方法，它们允许对任何两个可能非嵌套的参数遗传假设进行检验。这些程序采用一种策略，其中基于似然比的检验统计量的未知分布被模拟，从而允许估计检验统计量的临界值。尽管本文的重点是数量性状，但所描述的策略也可应用于质量性状。讨论了这些策略的概念优势和计算简便性，并通过蒙特卡罗实验检验了它们的显著性水平和功效。得出的结论是，当使用本文所述方法进行独立假设族方法时，不仅具有一些良好的统计特性，而且非常适合遗传分离分析。
