Comparison of four sib-pair linkage methods for analyzing sibships with more than two affecteds: interest of the binomial maximum likelihood approach.
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.
Abel
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INSERM U.436 Mathematical and Statistical Modeling in Biology and Medicine, Hôpital Pitié-Salpêtrière, Paris, France. abel@biomath.jussieu.fr
Alcais
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Mallet
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D000328:Adult; D002648:Child; D003198:Computer Simulation; D005190:Family; D008040:Genetic Linkage; D005826:Genetics, Medical; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical