Understanding genetic diversity in drought-adaptive hybrid parental lines in pearl millet.

Information on genetic diversity and population structure is helpful to strategize enhancing the genetic base of hybrid parental lines in breeding programs. The present study determined the population structure and genetic diversity of 109 pearl millet hybrid parental lines, known for their better adaptation and performance in drought-prone environments, using 16,472 single nucleotide polymorphic (SNP) markers generated from GBS (genotyping-by-sequencing) platforms. The SNPs were distributed uniformly across the pearl millet genome and showed considerable genetic diversity (0.337), expected heterozygosity (0.334), and observed heterozygosity (0.031). Most of the pairs of lines (78.36%) had Identity-by-State (IBS) based genetic distances of more than 0.3, indicating a significant amount of genetic diversity among the parental lines. Bayesian model-based population stratification, neighbor-joining phylogenetic analysis, and principal coordinate analysis (PCoA) differentiated all hybrid parental lines into two clear-cut major groups, one each for seed parents (B-lines) and pollinators (R-lines). Majority of parental lines sharing common parentages were found grouped in the same cluster. Analysis of molecular variance (AMOVA) revealed 7% of the variation among subpopulations, and 93% of the variation was attributable to within sub-populations. Chromosome 3 had the highest number of LD regions. Genomic LD decay distance was 0.69 Mb and varied across the different chromosomes. Genetic diversity based on 11 agro-morphological and grain quality traits also suggested that the majority of the B- and R-lines were grouped into two major clusters with few overlaps. In addition, the combined analysis of phenotypic and genotypic data showed similarities in the population grouping patterns. The present study revealed the uniqueness of most of the inbred lines, which can be a valuable source of new alleles and help breeders to utilize these inbred lines for the development of hybrids in drought-prone environments.

Corrigendum: Grain nutrients variability in pigeonpea genebank collection and its potential for promoting nutritional security in dryland ecologies.

[This corrects the article DOI: 10.3389/fpls.2022.934296.].

Grain Nutrients Variability in Pigeonpea Genebank Collection and Its Potential for Promoting Nutritional Security in Dryland Ecologies.

Pigeonpea, a climate-resilient legume, is nutritionally rich and of great value in Asia, Africa, and Caribbean regions to alleviate malnutrition. Assessing the grain nutrient variability in genebank collections can identify potential sources for biofortification. This study aimed to assess the genetic variability for grain nutrients in a set of 600 pigeonpea germplasms conserved at the RS Paroda Genebank, ICRISAT, India. The field trials conducted during the 2019 and 2020 rainy seasons in augmented design with four checks revealed significant differences among genotypes for all the agronomic traits and grain nutrients studied. The germplasm had a wider variation for agronomic traits like days to 50% flowering (67-166 days), days to maturity (112-213 days), 100-seed weight (1.69-22.17 g), and grain yield per plant (16.54-57.93 g). A good variability was observed for grain nutrients, namely, protein (23.35-29.50%), P (0.36-0.50%), K (1.43-1.63%), Ca (1,042.36-2,099.76 mg/kg), Mg (1,311.01-1,865.65 mg/kg), Fe (29.23-40.98 mg/kg), Zn (24.14-35.68 mg/kg), Mn (8.56-14.01 mg/kg), and Cu (7.72-14.20 mg/kg). The germplasm from the Asian region varied widely for grain nutrients, and the ones from African region had high nutrient density. The significant genotype × environment interaction for most of the grain nutrients (except for P, K, and Ca) indicated the sensitivity of nutrient accumulation to the environment. Days to 50% flowering and days to maturity had significant negative correlation with most of the grain nutrients, while grain yield per plant had significant positive correlation with protein and magnesium, which can benefit simultaneous improvement of agronomic traits with grain nutrients. Clustering of germplasms based on Ward.D2 clustering algorithm revealed the co-clustering of germplasm from different regions. The identified top 10 nutrient-specific and 15 multi-nutrient dense landraces can serve as promising sources for the development of biofortified lines in a superior agronomic background with a broad genetic base to fit the drylands. Furthermore, the large phenotypic data generated in this study can serve as a raw material for conducting SNP/haplotype-based GWAS to identify genetic variants that can accelerate genetic gains in grain nutrient improvement.