ArticleThe plant genome2025
Development of a 101.6K liquid-phased probe for GWAS and genomic selection in pine wilt disease-resistance breeding in Masson pine.
Article in The plant genome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Integrating GWAS-guided markers preselection with genomic selection enhances prediction of pulpwood-related traits in slash pine (Pinus elliottii Englem.).BMC plant biology · 2026Article
- A Three-Tier In Vitro Strategy for Accelerated Pine Breeding and Resistance Research Against Pine Wilt Disease.Plants (Basel, Switzerland) · 2026Article
- Decoding hybrid origins and genetic architecture of leaf traits variation inHorticulture research · 2025Article
- Development of a 101.6K liquid-phased probe for GWAS and genomic selection in pine wilt disease-resistance breeding in Masson pine.The plant genome · 2025Article
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8 authors.
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Abstract
Masson pine (Pinus massoniana Lamb.), indigenous to southern China, faces serious threats from pine wilt disease (PWD). Several natural genotypes have survived PWD outbreaks. Conducting genetic breeding with these resistant genotypes holds promise for enhancing resistance to PWD in Masson pine at its source. We conducted a genome-wide association study (GWAS) and genomic selection (GS) on 1013 Masson pine seedlings from 72 half-sib families to advance disease-resistance breeding. A set of efficient 101.6K liquid-phased probes was developed for single-nucleotide polymorphisms (SNPs) genotyping through target sequencing. PWD inoculation experiments were then performed to obtain phenotypic data for these populations. Our analysis reveals that the targeted sequencing data successfully divided the experimental population into three subpopulations consistent with the provenance, verifying the reliability of the liquid-phased probe. A total of 548 SNPs were considerably associated with disease-resistance traits using four GWAS algorithms. Among them, 283 were located on or linked to 169 genes, including common plant disease resistance-related protein families such as NBS-LRR and AP2/ERF. The DNNGP (deep neural network-based method for genomic prediction) model demonstrated superior performance in GS, achieving a maximum predictive accuracy of 0.71. The accuracy of disease resistance predictions reached 90% for the top 20% of the testing population ordered by resistance genomic estimated breeding value. This study establishes a foundational framework for advancing research on disease-resistant genes in P. massoniana and offers preliminary evidence supporting the feasibility of utilizing GS for the early identification of disease-resistant individuals.
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