Mapping Quantitative Trait Loci for Carotenoid Concentration in Three F2 Populations of Chickpea.

CORE IDEAS: Quantitative trait locus (QTL) analyses for carotenoids in chickpea were completed for three F2 populations. A moderate number of QTLs and candidate genes associated with carotenoid concentration in chickpea seeds were identified. Green cotyledon color is positively associated with provitamin A carotenoids. Three F2 populations derived from crosses between cultivars with green and yellow cotyledon colors were used to identify quantitative trait loci (QTLs) associated with carotenoid components in chickpea (Cicer arietinum L.) seeds developed by the Crop Development Centre (CDC). Carotenoids including violaxanthin, lutein, zeaxanthin, ß-cryptoxanthin, and ß-carotene were assessed in the F2:3 seeds via high-performance liquid chromatography (HPLC). In the 'CDC Jade' × 'CDC Frontier' population, 1068 bin markers derived from the 50K Axiom CicerSNP array were mapped onto eight linkage groups (LGs). Eight QTLs, including two each for ß-carotene and zeaxanthin and one each for total carotenoids, ß-cryptoxanthin, ß-carotene, and violaxanthin were identified in this population. In the 'CDC Cory' × 'CDC Jade' population, 694 bin markers were mapped onto eight LGs and one partial LG. Quantitative trait loci for ß-cryptoxanthin, ß-carotene, violaxanthin, lutein, and total carotenoids were identified on LG8. A map with eight LGs was developed from 581 bin markers in the third population derived from the 'ICC4475' × 'CDC Jade' cross. One QTL for ß-carotene and four QTLs, one each for ß-cryptoxanthin, ß-carotene, lutein, and total carotenoids, were identified in this population. The highest phenotypic variation explained by the QTLs was for ß-carotene, which ranged from 58 to 70% in all three populations. A major gene for cotyledon color was mapped on LG8 in each population. A significant positive correlation between cotyledon color and carotenoid concentration was observed. Potential candidate genes associated with carotenoid components were obtained and their locations on the chickpea genome are presented.

Relationship of Parental Genetic Distance with Heterosis and Specific Combining Ability in Sesame (Sesamum indicum L.) Based on Phenotypic and Molecular Marker Analysis.

The genetic distance analysis for selection of suitable parents has been established and effectively used in many crops; however, there is dearth of conclusive report of relationship of genetic distance analysis with heterosis in sesame. In the present study, an attempt was made to estimate the associations of genetic distances using SSR (GDSSR), seed-storage protein profiling (GDSDS) and agro-morphological traits (GDMOR) with hybrid performance. Seven parents were selected from 60 exotic and Indian genotypes based on genetic distance from clustering pattern based on SSR, seed-storage protein, morphological traits and per se performance. For combining ability analysis, 7 parents and 21 crosses generated from 7 × 7 half diallel evaluated at two environments in a replicated field trial during pre-kharif season of 2013. Compared with the average parents yield (12.57 g plant-1), eight hybrids had a significant (P < 0.01) yield advantage across environments, with averages of 26.94 and 29.99% for better-parent heterosis (BPH) and mid-parent heterosis (MPH), respectively, across environments. Highly significant positive correlation was observed between specific combining ability (SCA) and per se performance (0.97), while positive non-significant correlation of BPH with GDSSR (0.048), and non-significant negative correlations with GDMOR (- 0.01) and GDSDS (- 0.256) were observed. The linear regressions of SCA on MPH, BPH and per se performance of F1s were significant with R2 value of 0.88, 0.84 and 0.95 respectively. The present findings revealed a weak association of GDSSR with F1's performance; however, SCA has appeared as an important factor in the determination of heterosis and per se performance of the hybrids. The present findings also indicated that parental divergence in the intermediate group would likely produce high heterotic crosses in sesame.

Current Status and Future Prospects of Next-Generation Data Management and Analytical Decision Support Tools for Enhancing Genetic Gains in Crops.

Agricultural disciplines are becoming data intensive and the agricultural research data generation technologies are becoming sophisticated and high throughput. On the one hand, high-throughput genotyping is generating petabytes of data; on the other hand, high-throughput phenotyping platforms are also generating data of similar magnitude. Under modern integrated crop breeding, scientists are working together by integrating genomic and phenomic data sets of huge data volumes on a routine basis. To manage such huge research data sets and use them appropriately in decision making, Data Management Analysis & Decision Support Tools (DMASTs) are a prerequisite. DMASTs are required for a range of operations including generating the correct breeding experiments, maintaining pedigrees, managing phenotypic data, storing and retrieving high-throughput genotypic data, performing analytics, including trial analysis, spatial adjustments, identifications of MTAs, predicting Genomic Breeding Values (GEBVs), and various selection indices. DMASTs are also a prerequisite for understanding trait dynamics, gene action, interactions, biology, GxE, and various other factors contributing to crop improvement programs by integrating data generated from various science streams. These tools have simplified scientists' lives and empowered them in terms of data storage, data retrieval, data analytics, data visualization, and sharing with other researchers and collaborators. This chapter focuses on availability, uses, and gaps in present-day DMASTs. Graphical Abstract.

Morphological and genetic diversity assessment of sesame (Sesamum indicum L.) accessions differing in origin.

Sesame is an important ancient oilseed crop of high medicinal value. In the present study, 37 characters including both quantitative and qualitative traits of sixty genotypes were characterized following IPGRI morphological descriptors for sesame. Multivariate analysis was computed to distinguish the varieties into different groups. Though thirty six microsatellite markers including genomic and Est-SSR markers were initially selected, but, finally, the accessions were genotyped by eight polymorphic primers. Altogether, 27 alleles were detected among the 60 genotypes, with an average of 3.37 alleles per locus. The number of alleles ranged from 2 to 6 alleles. From data of microsatellite markers, dissimilarity coefficients between varieties were computed following Jaccard's coefficient method. Principal co-ordinate analysis was used to represent the varieties in bi-directional space. Dendrogram was constructed using NJ method based on dissimilarity matrix. Cluster analysis based on morphological and molecular marker classified sesame genotypes into two major groups. Mantel test showed an insignificant correlation between phenotypic and molecular marker information. The genotypes belonging to the same geographical area did not always occupy the same cluster. The results confirmed that both genetic and phenotypic diversity in a combined way could efficiently evaluate the variation present in different sesame accessions in any breeding program.