A novel selection model based on multivariate methods and arbitrary genetic parameters: a case study on tomato families

Abstract

Background: Selection is one of the essential skills whereby breeders reduce the population size and increase the chance of success. Various selection methods with special applications have been developed. Superior genotypes are assessed according to interesting traits, including univariate, multivariate, phenotypic, genotypic, etc. Methods: Mathematical calculation of the traits' importance based on the genetic makeup of investigated population (average degree of dominance/additive involved in the action of genes) and arbitrary genetic parameters is functional. In this paper, a general model for multivariate selection has been presented whereby the selection can be made for (a) more than one interesting trait, (b) the trait(s) with complex inheritance, (c) finding superior genotypes from among a large-scale population, (d) finding superior genotypes in segregating generations and (f) finding tolerant genotypes to stresses. This model is developed based on biometric concepts in four steps. MATLAB script is provided for the model, and users can easily apply that to identify the most suitable genotypes after data collection according to the breeding purposes. Results: The main features of this model are simplicity, precision, repeatability, and speed (improving several traits simultaneously). All the steps and the analysis of the results are explained step by step in a case study.