The Maximum-Likelihood-Binomial method revisited: a robust approach for model-free linkage analysis of quantitative traits in large sibships

Abstract

Model-free linkage analysis methods, based on identity-by-descent allele sharing, are commonly used for complex trait analysis. The Maximum-Likelihood-Binomial (MLB) approach, which is based on the hypothesis that parental alleles are binomially distributed among affected sibs, is particularly popular. An extension of this method to quantitative traits (QT) has been proposed (MLB-QTL), based on the introduction of a latent binary variable capturing information about the linkage between the QT and the marker. Interestingly, the MLB-QTL method does not require the decomposition of sibships into constituent sibpairs and requires no prior assumption about the distribution of the QT. We propose a new formulation of the MLB method for quantitative traits (nMLB-QTL) that explicitly takes advantage of the independence of paternal and maternal allele transmission under the null hypothesis of no linkage. Simulation studies under H₀ showed that the nMLB-QTL method generated very consistent type I errors. Furthermore, simulations under the alternative hypothesis showed that the nMLB-QTL method was slightly, but systematically more powerful than the MLB-QTL method, whatever the genetic model, residual correlation, ascertainment strategy and sibship size considered. Finally, the power of the nMLB-QTL method is illustrated by a chromosome-wide linkage scan for a quantitative endophenotype of leprosy infection. Overall, the nMLB-QTL method is a robust, powerful, and flexible approach for detecting linkage with quantitative phenotypes, particularly in studies of non Gaussian phenotypes in large sibships.