Genomic prediction in doubled haploid maize (Zea mays) populations under water stress at flowering and well‐watered conditions using high‐density single‐nucleotide polymorphisms

Abstract

AbstractThe most important applications of genomic selection (GS) in maize breeding is to predict and identify the best untested lines from biparental populations, when the training and validation sets are derived from the same cross. A total of 300 hybrids were generated using doubled haploid lines crossed to single known tester. The test hybrids and checks were evaluated for drought tolerance, grain yield and yield attributes under well‐watered (WW) and water stress at flowering (WSF) conditions during rabi 2018 at Hyderabad and Aurangabad locations. The study was further deep dived and practiced GS using 3352 single‐nucleotide polymorphism (SNP) markers. An extension of the genomic best linear unbiassed predictor that incorporates genotype × environment (GE) interaction was used to predict genotypic values; cross‐validation methods were applied to quantify prediction accuracy. Our results showed that the highest cross‐validation prediction accuracy for grain yield was 0.47 under WSF condition in TPS3, whereas under WW condition, prediction accuracy was 0.44 in TPS2, which is statistically on par with WSF condition. Among the secondary traits, the peak GS accuracies recorded for the traits anthesis silking interval (0.52) and ears per plant (0.48) under WSF. Under both the water regimes, anthesis silking interval and plant height recorded higher prediction accuracy when compared with grain yield. Hence, GS could be practiced for anthesis silking interval and ears per plant under stress condition in maize. Further while optimizing the population size, it was revealed from the study that increasing size of the population increases GS accuracy and TPS2 considered as optimum size of population for GS prediction.