A hierarchical statistical model for estimating population properties of quantitative genes

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.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.The identification of individual genes governing phenotypic variation is crucial to understand the mechanistic basis of quantitative inheritance and ultimately provide information about the design of optimal breeding strategies in both plants and animals. Quantitative genetics based on new statistical and computational technologies has advanced to the point at which individual genes can be detected and mapped on chromosomes [1,2]. The underpinning for the detection and mapping of genes is founded on their particular segregation pattern. For a pedigree derived from inbred lines, the segregation pattern of a gene can be exactly predicted. In this situation, quantitative genetic theory can lay a sufficient foundation for gene detection and, thus, the properties of a detected gene can be adequately described by its effect and genomic location. However, for outcrossing populations, such as forest trees, in which inbred lines are not available, genes also display population genetic properties [3]. Hence, the foundation fo