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Ssemakula, M. O.

Publications and source records attributed to Ssemakula, M. O..

2 recordsLinked to original sources
bioRxiv2025

Pathogenic and genetic diversity of Sclerotium rolfsii, the causal agent of Southern blight of common bean in Uganda

Sclerotium rolfsi Sacc. is a soil born fungus that causes southern blight to many crops in the tropical and subtropical regions. In 2018, Southern blight was reported to be the most prevalent bean root rot in Uganda. Earlier studies ascertained the morphological and pathogenic diversity of S. rolfsii, but limited understanding of its genetic diversity exists. Knowledge of S. rolfsii genetic diversity is a critical resource in the development of common bean varieties with durable resistance to the pathogen. A total of 188 S. rolfsii strains were collected from seven agro-ecological zones of Uganda in 2013, 2020 and 2021, and characterized for pathogenecity, growth rate, the number and colour of sclerotia produced and colony texture. The genetic diversity of the 188 strains was assessed using single nucleotide polymorphisms (SNPs) obtained from whole genome sequencing. The growth rate of the strains ranged between 1.1 to 3.6 cm/day while the number of sclerotia produced ranged between 0 to 543 per strain. The strains had fluffy, fibrous and compact colony texture. The sclerotia were either brown or dark brown. The analyzed strains were pathogenic on common bean and caused a disease severity indices between 10.1% and 93.3%. Average polymorphic information content across all chromosomes was 0.322 with a minimum of 0.145 and maximum of 0.525. Average observed heterozygosity across all the chromosomes was 0.782. Population structure analysis identified five genetically distinct groups. The results of analysis of molecular variance revealed that 0.39% of the variation was between population while 11.98% was between samples within population and 87.62% between samples across populations. The average genetic distances between the isolates from different agro-ecological zones was 0.038 while the average similarity coefficient was 0.968 There were significant differences in the morphological traits and pathogenicity across the different genetic clusters. This high levels of genetic diversity within strains needs to be taken into consideration while selecting strains for screening breeding lines. Key word: Pathogenicity, genetic diversity, Characterisation, Southern blight, common bean

microbiology↗
bioRxiv2024

Genomic prediction by modelling genotype by environment interaction for yield in groundnut

Multi-environment trials (METs) are routinely conducted in plant breeding to capture the genotype-by-environment (GxE) interaction effects. We assessed the potential of using genomic prediction (GP) in four groundnut traits (pod yield[PY], shelling percentage[SP], seed weight[SW] and seed weight of 100 seeds[SW100]) observed in four environments. Three prediction models (M1: Environment + Line, M2: Environment + Line + Genomic, and M3: Environment + Line + Genomic + Genomic x Environment) were evaluated under four cross-validation (CV) schemes that simulate realistic problems that breeders face at different stages of their programs. CV2 predicts lines tested in sparse multi-location trials; CV1 predicts newly developed lines; CV0 predicts tested lines in unobserved environments by leaving one environment out; CV00 predicts untested lines in unobserved environments. Under CV2, the average predictive ability (PA) range was 0.6-0.68 (PY), -0.26-0.05 (SP), 0.5-0.59 (SW) and 0.79-0.86 (SW100). For CV1, the PA range was -0.12-0.51 (PY), -0.08-0.35 (SP), -0.10-0.47 (SW) and -0.05-0.47 (SW100). In CV0, the PA range was 0.27-0.35 (PY), -0.18--0.17 (SP), 0.24-0.32 (SW) and 0.59-0.66 (SW100). In CV00, the PA range was 0.01-0.23 (PY), -0.03-0.07 (SP), 0.01-0.19 (SW) and -0.02-0.38 (SW100). In all the four CV schemes, including marker data improved the PA. Incorporating GxE in M3 model improved PA in CV2 and CV1 schemes and also reduced the residual and environment variances. The high PA in CV2 imply that sparse testing could be implemented. The low PA recorded in CV00 shows that it is difficult to predict untested lines in new environments.

genomics↗