Associating Molecular Markers with Phenotypes in Sweetpotatoes and Liriopogons Using Multivariate Statistical Modeling
Two horticultural crops, the ornamental liriopogon and the sweetpotato [Ipomoea batatas (L.) Lam.], were analyzed for morphological, quantitative and molecular marker variation using Amplified Fragment Length Polymorphism (AFLP) and various multivariate statistical techniques. Ornamental cultivars in genera Liriope and Ophiopogon were analyzed for relatedness using AFLP marker data and statistical clustering methods. Marker data did not substantiate the separation of these two genera. Greater than 95 % of the total genetic variability present was attributed to within group variation (P < 0.05). Trait-linked molecular markers were identified using Quantitative trait loci (QTL) analysis, logistic regression and discriminant analysis in the studies involving sweetpotato. The traits studied included dry matter content, virus disease resistance, root-knot nematode resistance, sugar content and ?-carotene content. Analysis of molecular variance found significant (P <0.001) differences between two phenotypic groups from unrelated genotypes for dry matter data. Using 14 markers selected through discriminant analysis the phenotypic grouping was validated with a zero error rate. Eighty-seven F1 sweetpotato genotypes from a cross of Tanzania and Wagabolige landraces were used to generate AFLP and random amplified polymorphic DNA (RAPD) marker profiles for this study. One AFLP marker linked to sweetpotato chlorotic stunt closterovirus resistance and one RAPD marker linked to sweetpotato feathery mottle virus resistance previously identified by traditional mapping strategies were selected plus new markers. Two diverse F1 populations of sweetpotato were used to identify and select markers suitable for identification of plants possessing a resistant reaction to southern root-knot nematode race 3 [Meloidogyne incognita (Kofoid and White) Chitwood]. Results for plant nematode resistance indicated a binomial distribution among the genotypes for population 1 and a normal distribution for population 2. A comparison of the power of discriminant analysis models for southern root-knot nematode resistance class prediction achieved 88% classification efficiencies. An F1 population of 73 clones consisting of parents and half-sibs was grouped into 2 phenotypic classes based on their sugar and ß-carotene content. Logistic regression and discriminant analysis selected meaningful markers that had significant associations with each of the traits. These results validated discriminant analysis and logistic regression as meaningful trait-linked marker selection methods.
Advisor:Don R. LaBonte; Russell L. Chapman; James H. Oard; David H. Picha; Gerald Myers
School:Louisiana State University in Shreveport
School Location:USA - Louisiana
Source Type:Master's Thesis
Date of Publication:03/16/2005