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a Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Cir., St. Paul, MN 55108
b Dep. of Agronomy, Kansas State Univ., 2004 Throckmorton Hall, Manhattan, KS 66506
* Corresponding author (bernardo{at}umn.edu).
The availability of cheap and abundant molecular markers in maize (Zea mays L.) has allowed breeders to ask how molecular markers may best be used to achieve breeding progress, without conditioning the question on how breeding has traditionally been done. Genomewide selection refers to marker-based selection without first identifying a subset of markers with significant effects. Our objectives were to assess the response due to genomewide selection compared with marker-assisted recurrent selection (MARS) and to determine the extent to which phenotyping can be minimized and genotyping maximized in genomewide selection. We simulated genomewide selection by evaluating doubled haploids for testcross performance in Cycle 0, followed by two cycles of selection based on markers. Individuals were genotyped for NM markers, and breeding values associated with each of the NM markers were predicted and were all used in genomewide selection. We found that across different numbers of quantitative trait loci (20, 40, and 100) and levels of heritability, the response to genomewide selection was 18 to 43% larger than the response to MARS. Responses to selection were maintained when the number of doubled haploids phenotyped and genotyped in Cycle 0 was reduced and the number of plants genotyped in Cycles 1 and 2 was increased. Such schemes that minimize phenotyping and maximize genotyping would be feasible only if the cost per marker data point is reduced to about 2 cents. The convenient but incorrect assumption of equal marker variances led to only a minimal loss in the response to genomewide selection. We conclude that genomewide selection, as a brute-force and black-box procedure that exploits cheap and abundant molecular markers, is superior to MARS in maize.
Abbreviations: BLUP, best linear unbiased predictions • MARS, marker-assisted recurrent selection • QTL, quantitative trait loci • SNP, single nucleotide polymorphism.
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