Optimization of a breeding program for increased genetic gain requires quality assurance (QA) and quality control (QC) at key phases of the breeding process. One vital phase in a breeding program that requires QC and QA is the choice of parents and successful hybridizations to combine parental attributes and create variations. The objective of this study was to determine parental diversity and confirm hybridity of cowpea F1 progenies using KASP (Kompetitive Allele-Specific PCR)-based single nucleotide polymorphism (SNP) markers. A total of 1,436 F1 plants were derived from crossing 220 cowpea breeding lines and landraces to 2 elite sister lines IT99K-573-1-1 and IT99K-573-2-1 as male parents, constituting 225 cross combinations. The progenies and the parents were genotyped with 17 QC SNP markers via high-throughput KASP genotyping assay. The QC markers differentiated the parents with mean efficiency of 37.90% and a range of 3.4–82.8%, revealing unique fingerprints of the parents. Neighbor-Joining cladogram divided the 222 parents into 3 clusters. Genetic distances between parents ranged from 0 to 3.74 with a mean of 2.41. Principal component analysis (PCA) depicted a considerable overlap between parents and F1 progenies with more scatters among parents than the F1s. The differentiation among parents and F1s was best contributed to by 82% of the markers. As expected, parents and F1s showed a significant contrast in proportion of heterozygous individuals, with mean values of 0.02 and 0.32, respectively. KASP markers detected true hybridity with 100% success rate in 72% of the populations. Overall, 79% of the putative F1 plants were true hybrids, 14% were selfed plants, and 7% were undetermined due to missing data and lack of marker polymorphism between parents. The study demonstrated an effective application of KASP-based SNP assay in fingerprinting, confirmation of hybridity, and early detection of false F1 plants. The results further uncovered the need to deploy markers as a QC step in a breeding program.
The objective of this study was to determine genetic potentials in eight sets of cowpea lines for grain yield (GY), hundred seed weight (HSDWT) and days to 50% flowering (DT50FL). A total of 614 F6 genotypes constituting the sets, grouped by maturity, were evaluated across two locations in Northern Nigeria, in an alpha lattice design, two replications each. Data were recorded on GY, HSDWT and DT50FL.Variance components, genotypic coefficient of variation (GCV), and genetic advance (GA) were used to decode the magnitude of genetic variance within and among sets. Genetic usefulness (Up) which depends on mean and variance to score the genetic merits in historically bi-parental populations was applied to groups of breeding lines with mixed parentage. Principal component analysis (PCA) was used to depict contribution of traits to observed variations. GY and DT50FL explained the variance within and between sets respectively. Genotypes were significantly different, although genotype-by-location and set-by-location interaction effects were also prominent. Genetic variance (δ2G) and GCV were high for GY in Prelim2 (δ2G = 45,897; GCV = 19.58%), HSDWT in Prelim11 (δ2G = 7.137; GCV = 17.07%) and DT50F in Prelim5 (δ2G = 4.54; GCV = 4.4%). Heritability varied among sets for GY (H = 0.21 to 0.57), HSDWT (H = 0.76 to 0.93) and DT50FL (H = 0.20 to 0.81). GA and percentage GA (GAPM) were high for GY in Prelim2 (GAPM = 24.59%; GA = 269.05Kg/ha), HSDWT in Prelim11 (GAPM = 28.54%; GA = 4.47 g), and DT50F in Prelim10 (GAPM = 6.49%; GA = 3.01 days). These sets also registered high values of genetic usefulness, suggesting potential application in non-full sib populations. These approaches can be used during preliminary performance tests to reinforce decisions in extracting promising lines and choose among defined groups of lines.
This study uses socioeconomic and demographic data to demonstrate the value of a novel multidimensional approach to healthcare accessibility. The optimum location for healthcare facilities in relation to demand areas was determined using location-allocation models and local multiscale geographically weighted regression (MGWR) to explore spatially non-stationary relationships. The result shows that the potential accessibility of a community to primary healthcare depends on the geographic and socioeconomic characteristics of various places. The results of this study may be used to inform policy planning and decision-making for increasing accessibility to healthcare services, particularly in rural areas for achieving the Sustainable Development Goals (SDGs).
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