Genetic analysis of popping quality traits and development of superior quality popcorn hybrids for a sustainable popcorn breeding program in India.

BACKGROUND: Popcorn is the most popular specialty maize and it makes a significant contribution to the Indian and global economies. Despite perfect exploration of heterosis in field corn, progress in popcorn breeding remains constrained due to its narrow genetic base, leading to a significant dependence on imports. In this study, 15 landrace- and population-derived inbreds from temperate and tropical germplasm were crossed with five testers, which are the parents of superior popcorn hybrids, in a line × tester mating design. RESULTS: Significant variation was observed in popping quality and agronomic traits among crosses evaluated across three locations representing diverse maize-based agro-climatic zones in India. Additive genetic variance governed the traits related to popping quality, whereas dominance variance was responsible for the agronomic traits. In addition to significant heterosis specific to certain traits, we identified promising crosses that exhibited superior performance in both popping quality and grain yield (GY). The genotype + genotype × environment (GGE) biplot methodology identified PMI-PC-104 and PMI-PC-101 as the best discriminating testers for popping quality traits and Dpcl-15-90 for GY. Lines PMI-PC-205, PMI-PC-207, and PMI-PC-209 were the best general combiners for popping quality traits and GY. The heterotic groups identified based on GGE-biplots and the magnitude, direction and stability of combining ability effects would serve in the development of competitive popcorn hybrids for a sustainable popcorn market. CONCLUSION: Using the additive nature of popping quality traits and the dominant nature of GY, recurrent intrapopulation selection can be employed to derive desirable popping quality inbreds with high GY for genetic enhancement. Desirable popping quality alleles from novel germplasm can thus be combined with high-yielding domestic elite inbreds to establish a sustainable popcorn breeding program. © 2024 Society of Chemical Industry.

Analysis of nutritional composition in opaque2- and crtRB1-based single- and double-biofortified super sweet corn.

Sweet corn has emerged as a favorite food item worldwide owing to its kernel sweetness. However, traditional sweet corn cultivars are poor in provitamin-A (proA) and essential amino acids, viz., lysine and tryptophan. So far, no sweet corn hybrid with high nutritional qualities has been commercialized elsewhere. Here, we analyzed accumulation of provitamin-A (proA), lysine, and tryptophan in a set of mutant versions of (i) crtRB1-, (ii) o2-, and (iii) crtRB1 + o2-based sweet corn inbreds and hybrids with (iv) traditional sweet corn (wild-type: O2 + CrtRB1). The crtRB1- and crtRB1 + o2-based genotypes possessed significantly higher proA (17.31 ppm) over traditional sweet corn (2.83 ppm), while o2- and crtRB1 + o2-based genotypes possessed significantly higher lysine (0.345%) and tryptophan (0.080%) over traditional sweet corn (lysine 0.169%, tryptophan 0.036%). Late sowing favored high kernel lysine, proA, and green cob yield among hybrids. Sweetness (17.87%) among the improved inbreds and hybrids was comparable to the original sweetcorn genotypes (17.84%). Among the four genotypic classes, crtRB1 + o2-based improved genotypes showed stronger association among traits over genotypes with o2 and crtRB1 genes alone. Significant association was observed among (i) proA and BC (r = 0.99), (ii) proA and BCX (r = 0.93), (iii) lysine and tryptophan (r = 0.99), and (iv) green cob yield with fodder yield (r = 0.73) in sweet corn hybrids. The study demonstrated that combining crtRB1 and o2 genes did not pose any negative impact on nutritional, yield, and agronomic performance. Sweet corn with crtRB1 + o2 assumes significance for alleviating malnutrition through sustainable and cost-effective approach.

Prediction of matrilineal specific patatin-like protein governing in-vivo maternal haploid induction in maize using support vector machine and di-peptide composition.

The mutant matrilineal (mtl) gene encoding patatin-like phospholipase activity is involved in in-vivo maternal haploid induction in maize. Doubling of chromosomes in haploids by colchicine treatment leads to complete fixation of inbreds in just one generation compared to 6-7 generations of selfing. Thus, knowledge of patatin-like proteins in other crops assumes great significance for in-vivo haploid induction. So far, no online tool is available that can classify unknown proteins into patatin-like proteins. Here, we aimed to optimize a machine learning-based algorithm to predict the patatin-like phospholipase activity of unknown proteins. Four different kernels [radial basis function (RBF), sigmoid, polynomial, and linear] were used for building support vector machine (SVM) classifiers using six different sequence-based compositional features (AAC, DPC, GDPC, CTDC, CTDT, and GAAC). A total of 1170 protein sequences including both patatin-like (585 sequences) from various monocots, dicots, and microbes; and non-patatin-like proteins (585 sequences) from different subspecies of Zea mays were analyzed. RBF and polynomial kernels were quite promising in the prediction of patatin-like proteins. Among six sequence-based compositional features, di-peptide composition attained > 90% prediction accuracies using RBF and polynomial kernels. Using mutual information, most explaining dipeptides that contributed the highest to the prediction process were identified. The knowledge generated in this study can be utilized in other crops prior to the initiation of any experiment. The developed SVM model opened a new paradigm for scientists working in in-vivo haploid induction in commercial crops. This is the first report of machine learning of the identification of proteins with patatin-like activity.