Selection of molecular markers associated with resistance to Fusarium wilt disease in chickpea (Cicer arietinum L.) using multivariate statistical techniques

作者：Haji Allahverdipoor Kaveh; Bahramnejad Bahman^*; Amini Jahanshir

来源：Australian Journal of Crop Science, 2011, 5(13): 1801-1809.

• Discriminant analysis
• Fusarium oxysporum f.sp ciceris
• ISSR
• Logistic regression
• Molecular markers
• Multiple regression analysis (MRA)
• Multivariate statistical techniques

Fusarium wilt is a destructive and widespread disease of chickpea caused by Fusarium oxysporum f.sp ciceris. A total of 40 unrelated genotypes of chickpea were classified into two distinct phenotypic groups as resistant or susceptible to F. oxysporum f.sp ciceris. Genotype selection was based on disease severity in chickpea following inoculation. Inter-simple-sequence-repeat (ISSR) marker profiles were generated for each individual and used in association studies to identify markers suitable for classifying the two pre-defined phenotypic classes. Nine ISSR primers were screened and optimized for detecting genetic diversity. From these primer combinations a total of 44 polymorphic clear bands out of a total of 61 (72.1%) were generated. Two multivariate statistical methods, Discriminant analysis and logistic regression were used to select informative markers, and to develop models that would classify the two phenotypic groups. Both discriminant analysis and logistic regression in P value equal at 0.03 selected three markers, UBC-864(400bp), UBC-811(1250bp) and UBC-811(650bp) that achieved 80% correct classification genotypes into phenotypic groups. Logistic regression and discriminant analysis achieved 100% correct classification of tolerant and susceptible phenotypes with 13 and 28 markers, respectively. To achieve higher correct classification rate using more informative marker, logistic regression were more efficient than discriminant analysis. Stepwise multiple regression analysis selected the same two markers, UBC-864(400bp) and UBC-8111(250bp) which selected with discriminant analysis and logistic regression methods. Our results suggest that these informative markers can be used to efficiently select for disease resistant individuals in a breeding population.

• 出版日期2011-12