title: The Predicted Cross Value for Genetic Introgression of Multiple Alleles.
abstract: We consider the plant genetic improvement challenge of introgressing multiple alleles from a homozygous donor to a recipient. First, we frame the project as an algorithmic process that can be mathematically formulated. We then introduce a novel metric for selecting breeding parents that we refer to as the predicted cross value (PCV). Unlike estimated breeding values, which represent predictions of general combining ability, the PCV predicts specific combining ability. The PCV takes estimates of recombination frequencies as an input vector and calculates the probability that a pair of parents will produce a gamete with desirable alleles at all specified loci. We compared the PCV approach with existing estimated-breeding-value approaches in two simulation experiments, in which 7 and 20 desirable alleles were to be introgressed from a donor line into a recipient line. Results suggest that the PCV is more efficient and effective for multi-allelic trait introgression. We also discuss how operations research can be used for other crop genetic improvement projects and suggest several future research directions.
authors.affiliation: Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, Iowa 50011.
authors.forename: Ye
authors.initials: Y
authors.lastname: Han
authors.affiliation: Department of Agronomy, Iowa State University, Ames, Iowa 50011.
authors.forename: John N
authors.initials: JN
authors.lastname: Cameron
authors.affiliation: Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, Iowa 50011 lzwang@iastate.edu.
authors.forename: Lizhi
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Agronomy, Iowa State University, Ames, Iowa 50011.
authors.forename: William D
authors.initials: WD
authors.lastname: Beavis
mesh_terms: D000465:Algorithms; D000483:Alleles; D018556:Crops, Agricultural / Q000235:genetics*; D056426:Genetic Loci; D006824:Hybridization, Genetic; D008957:Models, Genetic; D000069600:Plant Breeding / Q000379:methods*; D011995:Recombination, Genetic; D012641:Selection, Genetic
keywords: gene stacking; operations research; parental selection; predicted cross value; trait introgression