I present here three studies on genomewide selection, a marker based selection procedure with the potential to accelerate genetic gain while decreasing costs. For the first study, I looked at factors that have been previously derived by other researchers as determining the accuracy of genomewide selection: training population size (N), trait heritability (h2), and effective number of loci or chromosome segments underlying the trait (Me). My objective was to determine if prediction accuracy is equal across traits if N, h2 and marker number (NM) are kept constant. Cross validations indicated that the traits predicted most accurately did not always stay the same across changes to h2, N, and NM. For the second study, I investigated the use of marker imputation to reduce costs by genotyping the training population with many markers (MTotal), genotyping the validation population with fewer markers (MLow), and predicting the genotypes at the MTotal – MLow markers in the validation population. My objective was to determine if genomewide prediction with imputed markers can be as accurate as genomewide prediction with non-imputed markers in inbred collections. With imputation, many combinations of MTotal and MLow led to prediction accuracy that was as high as the accuracy with MTotal non-imputed markers. For the third study, I used a semidwarf corn (Zea mays L.) line that could potentially be grown in new areas of production or in alternative crop rotations. My objectives were to determine: (i) if genomewide selection is useful for the rapid improvement of an adapted × exotic cross; and (ii) if 4 cycles of genomewide selection are more effective than phenotypic backcrossing to the BC4 for a trait with major genes. Genomewide selection from Cycle 1 until Cycle 5 either maintained or improved upon the gains from phenotypic selection achieved in Cycle 1. Compared with phenotypic backcrossing, genomewide selection led to better mean performance and a higher proportion of exotic germplasm introgressed. To my knowledge, this is the first empirical study on genomewide selection to improve an exotic × adapted cross.
University of Minnesota Ph.D. dissertation. December 2012. Major: Applied Plant Sciences. Advisor: Rex Bernardo. 1 computer file (PDF); viii, 86 pages, appendix p. 79-86.
Combs, Emily Elizabeth.
Genomewide selection: prediction accuracy, marker Imputation, and introgression of semidwarf corn germplasm.
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