Abel L, Alcais A, Mallet A
INSERM U.436 Mathematical and Statistical Modeling in Biology and Medicine, Hôpital Pitié-Salpêtrière, Paris, France.
Genet Epidemiol. 1998;15(4):371-90. doi: 10.1002/(SICI)1098-2272(1998)15:4<371::AID-GEPI4>3.0.CO;2-5.
Family samples collected for sib-pair linkage studies usually include some sibships with more than two affecteds (multiplex sibships). Several methods have been proposed to take into account these multiplex sibships, and four of them are discussed in this work. Two methods, which are the most widely used, are based on the number of alleles shared by the sib-pairs constitutive of the multiplex sibship, with the first using the total number of these shared alleles ("all possible pairs" method) and the second considering a weighted number of these alleles (weighted method). The two other approaches considered the sibship as a whole, with in particular a likelihood method based on a binomial distribution of parental alleles among affected offspring. We theoretically show that, in the analysis of sibships with two affecteds, this likelihood method is expected to be more powerful than the classical mean test when a common asymptotic type I error is used. The variation of the sibship informativeness (assessed by the proportion of heterozygous parents) according to the number of affected sibs is investigated under various genetic models. Simulations under the null hypothesis of no linkage indicate that the "all possible pairs" is anticonservative, especially for type I errors < or = 0.001, whereas the weighted method generally provides satisfactory results. The likelihood method shows very consistent results in terms of type I errors, whatever the sample size, and provides power levels similar to those of the other methods. This binomial likelihood approach, which accounts in a natural way for multiplex sibships and provides a simple likelihood-ratio test for linkage involving a single parameter, appears to be a quite interesting alternative to analyze sib-pair studies.
为同胞对连锁研究收集的家系样本通常包括一些有两个以上患病个体的同胞组(多重同胞组)。已经提出了几种方法来考虑这些多重同胞组,本文讨论其中四种。两种最常用的方法基于构成多重同胞组的同胞对共享的等位基因数量,第一种使用这些共享等位基因的总数(“所有可能对”方法),第二种考虑这些等位基因的加权数量(加权方法)。另外两种方法将同胞组作为一个整体考虑,特别是一种基于患病后代中亲代等位基因二项分布的似然方法。我们从理论上表明,在分析有两个患病个体的同胞组时,当使用常见的渐近I型错误时,这种似然方法预计比经典均值检验更有效。在各种遗传模型下,研究了同胞组信息性(通过杂合亲本的比例评估)随患病同胞数量的变化。在无连锁的零假设下进行的模拟表明,“所有可能对”方法是反保守的,特别是对于I型错误≤0.001时,而加权方法通常能提供令人满意的结果。无论样本大小如何,似然方法在I型错误方面显示出非常一致的结果,并且提供与其他方法相似的检验效能水平。这种二项似然方法以自然的方式考虑了多重同胞组,并为涉及单个参数的连锁提供了简单的似然比检验,似乎是分析同胞对研究的一个非常有趣的替代方法。