Browsing by Author "Thomas Lapaka Odong"

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    Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding
    (Frontiers in Plant Science, 2022-06-14) Ivan Chapu; David Kalule Okello; Robert C. Ongom Okello; Thomas Lapaka Odong; Sayantan Sarka; Maria Balota
    Late leaf spot (LLS), caused by Nothopassalora personata (Berk. & M.A Curt.), and groundnut rosette disease (GRD), [caused by groundnut rosette virus (GRV)], represent the most important biotic constraints to groundnut production in Uganda. Application of visual scores in selection for disease resistance presents a challenge especially when breeding experiments are large because it is resource-intensive, subjective, and error- prone. High-throughput phenotyping (HTP) can alleviate these constraints. The objective of this study is to determine if HTP derived indices can replace visual scores in a groundnut breeding program in Uganda. Fifty genotypes were planted under rain-fed conditions at two locations, Nakabango (GRD hotspot) and NaSARRI (LLS hotspot). Three handheld sensors (RGB camera, GreenSeeker, and Thermal camera) were used to collect HTP data on the dates visual scores were taken. Pearson correlation was made between the indices and visual scores, and logistic models for predicting visual scores were developed. Normalized difference vegetation index (NDVI) (r = –0.89) and red-green-blue (RGB) color space indices CSI (r = 0.76), v∗ (r = –0.80), and b∗ (r = –0.75) were highly correlated with LLS visual scores. NDVI (r = –0.72), v∗ (r = –0.71), b∗ (r = –0.64), and GA (r = –0.67) were best related to the GRD visual symptoms. Heritability estimates indicated NDVI, green area (GA), greener area (GGA), a∗, and hue angle having the highest heritability (H2 > 0.75). Logistic models developed using these indices were 68% accurate for LLS and 45% accurate for GRD. The accuracy of the models improved to 91 and 84% when the nearest score method was used for LLS and GRD, respectively. Results presented in this study indicated that use of handheld remote sensing tools can improve screening for GRD and LLS resistance, and the best associated indices can be used for indirect selection for resistance and improve genetic gain in groundnut breeding.
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    Genetic diversity and population structure among Ugandan shea tree (Vitellaria paradoxa subsp. nilotica) accessions based on DarTSeq markers
    (Crop Science, 2023-06-11) Juventine Boaz Odoi; Emmanuel Amponsah Adjei; Prasad Hendre; Judith Ssali Nantongo; Alfred Adibo Ozimati; Arfang Badji; Grace Nakabonge; Richard Edema; Thomas Lapaka Odong
    Molecular markers such as single nucleotide polymorphisms (SNPs) and SilicoDArT are important in dissecting the genetic diversity of a population at DNA level. The two marker types were analyzed using the same genotyping platform. Although the two marker types were analyzed using the same genotyping platform, they were filtered using a different marker stringency. We determined the genetic diversity of 623 half-sibs of shea tree (Vitellaria paradoxa a C. F. Gaertn. nilotica) assembled from five geographical locations (Katakwi, Otuke, Amuru, Moyo, and Arua) in Uganda’s shea parklands. A total of 27,063 Diversity Arrays Technology (DArT) SNP and 9018 SilicoDArT markers were used to assess genetic diversity and population structure. The populations showed a low genetic diversity (Gst = 0.21), very low population differentiation (FST = 0.02), low-to-moderate linkage disequilibrium (r = 0.2), and Hardy–Weinberg Equilibrium (HWE = 0.1982). The study further revealed a higher genetic diversity within population (0.26) than among the population (0.21). A high level of heterozygosity was observed within individuals (0.26) and markers (0.32) revealing a high non-random association of alleles at different loci that offer opportunities to design association studies and allele transfer in marker-assisted selection in the population. The markers varied in their polymorphic information content values (SilicoDArT = 0.11) and (SNPs = 0.15) on genetic diversity. This study reveals the importance of genetic diversity and population structure analysis using high-density DArT-Seq SilicoDaRT and SNP makers in the conservation and breeding of shea tree in Uganda.