The Predicted Cross Value for Genetic Introgression of Multiple Alleles.
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.
['Han|Ye|Y|', 'Cameron|John N|JN|', 'Wang|Lizhi|L|', 'Beavis|William D|WD|']
D000465:Algorithms D000483:Alleles D018556:Crops, Agricultural D056426:Genetic Loci D006824:Hybridization, Genetic D008957:Models, Genetic D000069600:Plant Breeding D011995:Recombination, Genetic D012641:Selection, Genetic
predicted cross value trait introgression operations research gene stacking parental selection