Abstract
BackgroundComputer 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.ResultsTo 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0906-z) contains supplementary material, which is available to authorized users.
Highlights
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
We devised a method to represent the genomic distribution of additive and dominance gene effects associated with quantitative traits, which utilizes a modified Dirichlet process Gaussian mixture model (DPGMM) [7] to fit the number of mixture components and estimate parameters
We modeled the distribution of additive quantitative trait locus/loci (QTL) effects and dominance coefficients using mixtures of normal distributions, namely GMM [11]
Summary
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. Recovery of the target line (i.e. recurrent parent) is estimated by the average proportion of genetic material carried through the backcrossing process and this estimate implicitly assumes that the many genes for key quantitative traits like grain yield are dispersed uniformly across the genome, each contributing only small effect. By including a more realistic representation of gene effects in the genome model to assess backcross breeding strategies, the means to most rapidly and effectively recover the germplasm per se, but the important genes contributing to performance of the variety or hybrid targeted for conversion, can be considered in evaluating strategies and approaches. Capitalizing on the large number of QTL studies, Bennewitz and Meuwissen [1] conducted a meta-analysis of published QTL mapping data across traits to infer the distribution of additive QTL effects as well as dominance coefficients, fitting a Gaussian mixture model (GMM). The merit of employing GMM is its flexibility with different combinations of mixtures of normals leading to different shapes of the distribution
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