Method to represent the distribution of QTL additive and dominance effects associated with quantitative traits in computer simulation.

BACKGROUND: Computer simulation is a resource which can be employed to identify optimal breeding strategies to effectively and efficiently achieve specific goals in developing improved cultivars. In some instances, it is crucial to assess in silico the options as well as the impact of various crossing schemes and breeding approaches on performance for traits of interest such as grain yield. For this, a means by which gene effects can be represented in the genome model is critical.

RESULTS: To address this need, we devised a method to represent the genomic distribution of additive and dominance gene effects associated with quantitative traits. The method, based on meta-analysis of previously-estimated QTL effects following Bennewitz and Meuwissen (J Anim Breed Genet 127:171-9, 2010), utilizes a modified Dirichlet process Gaussian mixture model (DPGMM) to fit the number of mixture components and estimate parameters (i.e. mean and variance) of the genomic distribution. The method was demonstrated using several maize QTL data sets to provide estimates of additive and dominance effects for grain yield and other quantitative traits for application in maize genome simulations.

CONCLUSIONS: The DPGMM method offers an alternative to the over-simplified infinitesimal model in computer simulation as a means to better represent the genetic architecture of quantitative traits, which likely involve some large effects in addition to many small effects. Furthermore, it confers an advantage over other methods in that the number of mixture model components need not be known a priori. In addition, the method is robust with use of large-scale, multi-allelic data sets or with meta-analyses of smaller QTL data sets which may be derived from bi-parental populations in precisely estimating distribution parameters. Thus, the method has high utility in representing the genetic architecture of quantitative traits in computer simulation.