Genome-wide association analysis for nine agronomic traits in maize under well-watered and water-stressed conditions.

Drought can cause severe reduction in maize production, and strongly threatens crop yields. To dissect this complex trait and identify superior alleles, 350 tropical and subtropical maize inbred lines were genotyped using a 1536-SNP array developed from drought-related genes and an array of 56,110 random SNPs. The inbred lines were crossed with a common tester, CML312, and the testcrosses were phenotyped for nine traits under well-watered and water-stressed conditions in seven environments. Using genome-wide association mapping with correction for population structure, 42 associated SNPs (P ≤ 2.25 × 10(-6) 0.1/N) were identified, located in 33 genes for 126 trait × environment × treatment combinations. Of these genes, three were co-localized to drought-related QTL regions. Gene GRMZM2G125777 was strongly associated with ear relative position, hundred kernel weight and timing of male and female flowering, and encodes NAC domain-containing protein 2, a transcription factor expressed in different tissues. These results provide some good information for understanding the genetic basis for drought tolerance and further studies on identified candidate genes should illuminate mechanisms of drought tolerance and provide tools for designing drought-tolerant maize cultivars tailored to different environmental scenarios.

Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments.

Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F(2)-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F(2)-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.