Maximum likelihood analysis of quantitative trait loci under selective genotyping

Selective genotyping is a cost-saving strategy in mapping quantitative trai t loci (QTLs). When the proportion of individuals selected for genotyping i s low, the majority of the individuals are not genotyped, but their phenoty pic values, if available, are still included in the data analysis to correc t the bias in parameter estimation. These ungenotyped individuals do not co ntribute much information about linkage analysis and their inclusion can su bstantially increase the computational burden. For multiple trait analysis, ungenotyped individuals may not have a full array of phenotypic measuremen ts. In this case, unbiased estimation of QTL effects using current methods seems to be impossible. In this study, we develop a maximum likelihood meth od of QTL mapping under selective genotyping using only the phenotypic valu es of genotyped individuals. Compared with the full data analysis (using al l phenotypic values), the proposed method performs well. We derive an expec tation-maximization (EM) algorithm that appears to be a simple modification of the existing EM algorithm for standard interval mapping. The new method can be readily incorporated into a standard QTL mapping software, e.g. MAP MAKER. A general recommendation is that whenever full data analysis is poss ible, the full maximum likelihood analysis should be performed. If it is im possible to analyse the full data, e.g. sample sizes are too large, phenoty pic values of ungenotyped individuals are missing or composite interval map ping is to be performed, the proposed method can be applied.