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  • Author or Editor: M. Mcharo x
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Iron and zinc are micronutrients essential to the human diet but are in deficient supply to many in the tropics. Fortifying the micronutrient content of staple crops like sweetpotato [Ipomoea batatas (L.) Lam.] would go far in alleviating this intractable problem. This article presents estimates of broad-sense heritability for iron and zinc content in sweetpotato roots using a technique based on full-sibling families. Among individual genotypes, iron and zinc concentration varied by a fourfold and sixfold difference, respectively, whereas dry matter concentration showed a threefold variation. Family mean estimates varied significantly for the three traits. High broad-sense heritability for iron (H = 0.74), zinc (H = 0.82), and dry matter concentration (H = 0.93) were obtained among full-sibling families. These results suggest that traditional breeding strategies like mass selection could improve the micronutritional value of sweetpotato and that true sweetpotato seed, which has no international phytosanitary restrictions on transfer, can be used to quickly estimate heritability.

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Molecular markers linked to resistance to sweetpotato chlorotic stunt closterovirus [SPCSV (genus Crinivirus, family Closteroviridae)] and sweetpotato feathery mottle virus [SPFMV (genus Potyvirus, family Potyviridae)] were selected using quantitative trait loci (QTL) analysis, discriminant analysis and logistic regression. Eighty-seven F1 sweetpotato [Ipomoea batatas (L.) Lam.] genotypes from a cross of `Tanzania' and `Wagabolige' landraces were used to generate DNA marker profiles for this study. Forty-five of the clones were resistant to SPCSV while 37 were resistant to SPFMV. A combination of 232 amplified fragment length polymorphism (AFLP) markers and 37 random amplified polymorphic DNA (RAPD) markers obtained were analyzed to determine the most informative markers. All three statistical procedures revealed that AFLP marker e41m33.a contributed the greatest variation in SPCSV resistance and RAPD marker S13.1130 accounted for most of the variation in SPFMV resistance. The power of discriminant and logistic analyses is that you do not need a parent-progeny population. An evaluation of these two models indicated a classification and prediction accuracy rates of 96% with as few as four markers in a model. Both multivariate techniques identified one important discriminatory marker (e44m41.j) for SPCSV and two markers (e41m37.a and e44m36.d) for SPFMV that were not identified by QTL analysis.

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