Expression analysis of phosphate induced genes in contrasting maize genotypes for phosphorus use efficiency.

Phosphorus is an essential nutrient for plant growth and development. The ability of plants to acquire phosphate (Pi) from the rhizosphere soil is critical in the Brazilian Cerrado characterized by acidic soil. The induction of Pi transporters is one of the earliest molecular responses to Pi deficiency in plants. In this study, we characterize the transcriptional regulation of six (ZmPT1 to ZmPT6) high-affinity Pi transporters genes in four Pi-efficient and four Pi-inefficient maize (Zea mays) genotypes. The expression analysis indicated that Pi-starvation induced the transcription of all ZmPT genes tested. The abundance of transcripts was inversely related to Pi concentration in nutrient solution and was observed as early as five days following the Pi deprivation. The Pi-starved plants replenished with 250 µM Pi for four to five days resulted in ZmPT suppression, indicating the Pi role in gene expression. The tissue-specific expression analysis revealed the abundance of ZmPT transcripts in roots and shoots. The six maize Pi transporters were primarily detected in the upper and middle root portions and barely expressed in root tips. The expression profiles of the six ZmPTs phosphate transporters between and among Pi-efficient and Pi-inefficient genotypes showed an absence of significant differences in the expression pattern of the ZmPTs among Pi-efficient and Pi-inefficient genotypes. The results suggested that Pi acquisition efficiency is a complex trait determined by quantitative loci in maize.

Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data.

KEY MESSAGE: Weighted outperformed unweighted genomic prediction using an unbalanced dataset representative of a commercial breeding program. Moreover, the use of the two cycles preceding predictions as training set achieved optimal prediction ability. Predicting the performance of untested single-cross hybrids through genomic prediction (GP) is highly desirable to increase genetic gain. Here, we evaluate the predictive ability (PA) of novel genomic strategies to predict single-cross maize hybrids using an unbalanced historical dataset of a tropical breeding program. Field data comprised 949 single-cross hybrids evaluated from 2006 to 2013, representing eight breeding cycles. Hybrid genotypes were inferred based on their parents' genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GP analyses were fitted using genomic best linear unbiased prediction via a stage-wise approach, considering two distinct cross-validation schemes. Results highlight the importance of taking into account the uncertainty regarding the adjusted means at each step of a stage-wise analysis, due to the highly unbalanced data structure and the expected heterogeneity of variances across years and locations of a commercial breeding program. Further, an increase in the size of the training set was not always advantageous even in the same breeding program. The use of the two cycles preceding predictions achieved optimal PA of untested single-cross hybrids in a forward prediction scenario, which could be used to replace the first step of field screening. Finally, in addition to the practical and theoretical results applied to maize hybrid breeding programs, the stage-wise analysis performed in this study may be applied to any crop historical unbalanced data.