Comparison between some approximate maximum-likelihood methods for quantitative trait locus detection in progeny test designs

 The power and efficiency of parameter estimation of four approximate maximum-likelihood segregation-analysis methods for QTL detection were numerically compared using Monte Carlo simulation. The approximations were designed to avoid the long computation required by exact maximum-likelihood segregation analysis for populations composed of large, independent half-sib families, as found in forest-tree and animal-breeding programs. The methods were compared both when information from a marker closely linked to the QTL was available and when it was not. Three of the approximations were from the literature: the Modal-Estimation method initially developed by Le Roy et al., an approximate Regressive Model from Demenais and Bonney, and the Within-Sire method used by Boichard et al. The fourth method was derived from this Within-Sire method by ignoring between-male-parent information and segregation within families due to the alleles inherited from the female parents. The relative advantages of the criteria are compared for various hypotheses concerning the characteristics of the QTL and the size of the population. No one method was clearly superior over all situations studied. The fourth, and simplest, method, however, performed sufficiently well when marker data were available, particularly in terms of power, for it to provide a tool for rapid preliminary screening of data from QTL mapping studies.