Characterization of Simple Sequence Repeat (SSR) Markers Mined in Whole Grape Genomes

Genetic Mapping and Characterization of the Clubroot Resistance Gene BraPb8.3 in Brassica rapa

Clubroot, a significant soil-borne disease, severely impacts the productivity of cruciferous crops. The identification and development of clubroot resistance (CR) genes are crucial for mitigating this disease. This study investigated the genetic inheritance of clubroot resistance within an F2 progeny derived from the cross of a resistant parent, designated "377", and a susceptible parent, designated "12A". Notably, "377" exhibited robust resistance to the "KEL-23" strain of Plasmodiophora brassicae, the causative agent of clubroot. Genetic analyses suggested that the observed resistance is controlled by a single dominant gene. Through Bulked Segregant Analysis sequencing (BSA-seq) and preliminary gene mapping, we localized the CR gene locus, designated as BraPb8.3, to a 1.30 Mb genomic segment on chromosome A08, flanked by the markers "333" and "sau332-1". Further fine mapping precisely narrowed down the position of BraPb8.3 to a 173.8 kb region between the markers "srt8-65" and "srt8-25", where we identified 22 genes, including Bra020861 with a TIR-NBS-LRR domain and Bra020876 with an LRR domain. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analyses confirmed that both Bra020861 and Bra020876 exhibit increased expression levels in the resistant parent "377" following inoculation with P. brassicae, thereby underscoring their potential as key genes implicated in BraPb8.3-mediated clubroot resistance. This study not only identifies molecular markers associated with BraPb8.3 but also enriches the genetic resources available for breeding programs aimed at enhancing resistance to clubroot.

Integration of machine learning models with microsatellite markers: New avenue in world grapevine germplasm characterization

Development of efficient analytical techniques is required for effective interpretation of biological data to take novel hypotheses and finding the critical predictive patterns. Machine Learning algorithms provide a novel opportunity for development of low-cost and practical solutions in biology. In this study, we proposed a new integrated analytical approach using supervised machine learning algorithms and microsatellites data of worldwide vitis populations. A total of 1378 wild (V. vinifera spp. sylvestris) and cultivated (V. vinifera spp. sativa) accessions of grapevine were investigated using 20 microsatellite markers. Data cleaning, feature selection, and supervised machine learning classification models vis, Naive Bayes, Support Vector Machine (SVM) and Tree Induction methods were implied to find most indicative and diagnostic alleles to represent wild/cultivated and originated geography of each population. Our combined approaches showed microsatellite markers with the highest differentiating capacity and proved efficiency for our pipeline of classification and prediction of vitis accessions. Moreover, our study proposed the best combination of markers for better distinguishing of populations, which can be exploited in future germplasm conservation and breeding programs.