Marker Imputation Before Genomewide Selection in Biparental Maize Populations

Marker imputation can be used to increase the number of markers in genomewide selection. Our objectives were to determine (i) if marker imputation increases the response to selection (R) and prediction accuracy (r_MP) among the progeny of two maize (Zea mays L.) parental inbreds (A and B); (ii) the number of imputed single nucleotide polymorphism (SNP) markers needed to reach a plateau in r_MP for grain yield, moisture, and test weight; and (iii) the lowest number of assayed SNP markers that can be used for imputation without a significant decrease in r_MP. The progeny of 27 biparental crosses between A and B (A/B) were assayed with 49 to 100 SNP markers, and imputation was conducted to increase the number of markers to 2911. For each A/B test population, the training population in the general combining ability (GCA) model consisted of 4 to 26 maize crosses with A and B as one of the parents, whereas the training population in the A/B model was the A/B population itself. Marker imputation made the GCA model as good as or better than the A/B model in terms of R and r_MP for all traits. The r_MP values did not increase significantly beyond 500 imputed markers for grain yield and beyond 1000 imputed markers for moisture and test weight. We recommend that maize breeders assay an elite biparental cross with only around 50 polymorphic SNP markers, increase marker coverage to around 1000 markers by imputation, and use the GCA model with imputed markers for genomewide selection within the cross.