A hierarchical statistical model for estimating population properties of quantitative genes

BACKGROUND: Earlier methods for detecting major genes responsible for a quantitative trait rely critically upon a well-structured pedigree in which the segregation pattern of genes exactly follow Mendelian inheritance laws. However, for many outcrossing species, such pedigrees are not available and genes also display population properties. RESULTS: In this paper, a hierarchical statistical model is proposed to monitor the existence of a major gene based on its segregation and transmission across two successive generations. The model is implemented with an EM algorithm to provide maximum likelihood estimates for genetic parameters of the major locus. This new method is successfully applied to identify an additive gene having a large effect on stem height growth of aspen trees. The estimates of population genetic parameters for this major gene can be generalized to the original breeding population from which the parents were sampled. A simulation study is presented to evaluate finite sample properties of the model. CONCLUSIONS: A hierarchical model was derived for detecting major genes affecting a quantitative trait based on progeny tests of outcrossing species. The new model takes into account the population genetic properties of genes and is expected to enhance the accuracy, precision and power of gene detection.