A simulation study to evaluate optimal strategies for selection on a quantitative trait using major gene information

dc.contributor.advisorMcMillan, Ian
dc.contributor.advisorDekkers, J.C.M.
dc.contributor.authorMalek, Massoud
dc.degree.departmentDepartment of Animal and Poultry Scienceen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.description.abstractPrevious studies have shown that Marker-Assisted Selection (MAS) based on a known major gene and genotypic selection can increase response to selection in the short term but reduce longer-term response to selection. The criterion for genotypic selection is:$$I = g + h\sp2(P - G)$$where g is the breeding value for the major gene and $h\sp2(P - G)$ is the animal's polygenic EBV based on phenotype (p) adjusted for major genotype (G). Recently, Dekkers and van Arendonk (1998) developed methods to optimize the use of a known major gene in selection to maximize response over a planning horizon. The objective of this study was to use stochastic simulation to evaluate the optimal strategies developed by Dekkers and van Arendonk (1998) under a model in which genetic variance declined as a result of selection (Bulmer effect). A population with discrete generations, fixed size and equal selection among males and females was considered. Resulting strategies maximized cumulative response to selection for a pre-specified planning horizon. Results show that optimal strategies that are derived under a model with constant genetic variance may not result in greater responses to selection for all situations. Further improvements in optimal strategies are under development.en_US
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectMarker-Assisted Selectionen_US
dc.subjectOptimal strategiesen_US
dc.subjectQuantitative traiten_US
dc.subjectMajor geneen_US
dc.subjectStochastic simulationen_US
dc.titleA simulation study to evaluate optimal strategies for selection on a quantitative trait using major gene informationen_US


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