ArticleBMC bioinformatics2016
Method to represent the distribution of QTL additive and dominance effects associated with quantitative traits in computer simulation.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture.Genetics, selection, evolution : GSE · 2022Article
- A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression.Scientific reports · 2021Article
- Predicting the purebred-crossbred genetic correlation from the genetic variance components in the parental lines.Genetics, selection, evolution : GSE · 2021Article
- The Impact of Non-additive Effects on the Genetic Correlation Between Populations.G3 (Bethesda, Md.) · 2020Article
- Seed protein content and its relationships with agronomic traits in pigeonpea is controlled by both main and epistatic effects QTLs.Scientific reports · 2020Article
- Optimized breeding strategies for multiple trait integration: III. Parameters for success in version testing.Molecular breeding : new strategies in plant improvementArticle
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2 authors.
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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.
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