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Analysis of the population structure 2015 Puccinia graminis f. sp tritici (Pgt) in Kenya using simple sequence repeats markers

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Research Paper 01/11/2019
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Analysis of the population structure 2015 Puccinia graminis f. sp tritici (Pgt) in Kenya using simple sequence repeats markers

OE Okello, OJ Ochuodho, R Wanyera, S Bhavani, LJ Szabo
Int. J. Agron. Agri. Res.15( 5), 1-7, November 2019.
Certificate: IJAAR 2019 [Generate Certificate]
Key: Pgt SRR markersPopulation structureSSR-MLGsWheat stem rust Ug99

Abstract

N Wheat (Triticum astevium) production in Kenya has been severely affected by stem rust Ug99 and its related race groups. The consequence of not controlling this disease is steep decline in the crop production thus creating food insecurity to over 70% of the small-scale holder farmers who depend on it as a source of food as well as income. The causative agent of the disease Puccinia graminis f. sp tritici (Pgt) has been studied by researchers globally because of its rapid evolution of races within lineage overcoming existing resistant genes. Understanding the population structure will highlight the predominant race(s) as well as their geographical distribution. This information is required to enable breeding for resistant wheat varieties. The objective of this study was to characterize the population structure of Puccinia graminis f. sp tritici population in 2015. Using 10pgt Simple Sequence Repeats (SSR) markers 104 single uredenial-pustule samples were analysed. Minimum spanning network pattern was composed of five Simple Sequence Repeats multi-locus genotypes (SSR-MLGs) that were organized around three nodes based on samples chosen from wheat growing fields with the reference isolates; races TTKSK and TKTTF. In addition to this, non-parametric DAPC analysis showed the presence of single population made up of two predominant races from clade I (Ug99 race group) and clade IV-B (race TKTTF/TTTTF). Analysis of molecular variance (AMOVA) according to Bayesian Information Criterion showed clustering was majorly based within populations (0.576%) rather than among clusters (0.441%).

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