Genetic footprint of population diversity and genetic structure of Venturia inaequalis infecting apple (Malus × domestica Borkh.)

• Published in: 3 Biotech Vol. 13; no. 8; p. 273
• Main Authors: Dar, Mohammad Saleem, Ahmad, Mushtaq, Yetoo, Nakeeb-Un-Nisa, Bhatt, Bhagyshree, Bhat, Suhail Nazir, Altaf, Heena, Rafiqee, Sumira, Nabi, Asha, Mohiddin, F. A., Gaafar, Abdel-Rhman Z., Mansoor, Sheikh, Shah, Mehraj D., Mushtaq, Muntazir
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2023; Springer Nature B.V
• Subjects: Agriculture; Bioinformatics; Biomaterials; Biotechnology; Cancer Research; Chemistry; Chemistry and Materials Science; Clustering; Differentiation; Gene flow; Genetic analysis; Genetic distance; Genetic diversity; Genetic markers; Genetic structure; Heterozygosity; Machine learning; Malus domestica; Microsatellites; Orchards; Original; Original Article; Population genetics; Populations; Principal components analysis; Stem Cells; Unsupervised learning; Variance analysis; Venturia inaequalis; Genetic structure; Apple; Population diversity; Venturia inaequalis
• ISSN: 2190-572X; 2190-5738
• DOI: 10.1007/s13205-023-03672-2

Apple scab instigated by Venturia inaequalis impels remarkable losses to apple fruit production. In an effort to comprehend the key mechanisms of evolutionary potential defining V. inaequalis population, 132 isolates of V. inaequalis from five commercial apple orchards were collected and assayed using 14 microsatellite markers. The average diversity was observed within the individuals of populations based on the Shannon-Wieners index (I) and observed heterozygosity (Ho) was average but considerably lower than expected heterozygosity (He). The genetic differentiation based on FST values was revealed as an average measure of divergence between populations and had varying proportions of gene flow and migration among themselves. Analysis of Molecular Variance (AMOVA) revealed that variance (94%) was dispersed across individuals with a significant (6%) variation between populations from different regions. To examine host specialization within the V . inaequalis population, the assignment approach based on K-means of clustering (an unsupervised machine learning approach), revealed that the clustering method supported three clusters at ( K  = 3) and three major clusters were also observed in Principle Component Analysis (PCA). Additionally, Nei’s genetic distance values, pairwise estimates of genetic differentiation, dendrogram using the neighbor-joining and PCoA revealed the random distribution of V . Inaequalis isolates that depicted a high proportion of genotypic diversity within populations and population genetic structure.