ArticleThe plant genome2026
Genetic insights into bacterial wilt resistance using genomic prediction and association mapping in blueberry.
Article in The plant genome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Genetic insights into bacterial wilt resistance using genomic prediction and association mapping in blueberry.The plant genome · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bacterial wilt, caused by Ralstonia spp., poses a major threat to blueberry (Vaccinium corymbosum) production due to its persistence and rapid spread through soil and infected stock, highlighting the need for genetic insights to guide breeding strategies. This study investigated the genetic basis of bacterial wilt resistance in blueberry using a genome-wide association study (GWAS) across two populations comprising 401 advanced selections from the University of Florida Blueberry Breeding and Genomics Program. A high-throughput screening assay was developed to evaluate southern highbush blueberry responses to bacterial wilt based on leaf wilting severity and stem necrosis. Capture sequencing identified 38,379 single-nucleotide polymorphisms. Moderate narrow-sense heritability estimates were observed for leaf severity (0.26) and stem necrosis (0.20), and GWAS identified five small-effect quantitative trait loci on chromosomes 1, 2, 5, and 11, each explaining 4.0%-7.4% of the phenotypic variance. Candidate gene analysis revealed putative pentatricopeptide repeat (PPR), serine/threonine protein kinase, and MYB-related proteins for leaf severity, and Mlo genes and polysaccharide biosynthesis genes for stem necrosis. Genomic selection (GS) analyses demonstrated potential for improving bacterial wilt resistance, with the GS de novo GWAS approach achieving the highest predictive ability by leveraging two key markers on chromosomes 1 and 11. These results elucidate the genetic architecture of bacterial wilt resistance in blueberries and provide resources for molecular breeding strategies to enhance resistance and ensure sustainable production.
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