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The plant genome · 2026

Association mapping and genome prediction reveal a complex genetic architecture for resistance to black and frosty pod rot in cacao (Theobroma cacao L.) populations.

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Jacquotte Elisa, Holzwarth James A, Mota Ana Zotta, Leandro-Muñoz Mariela E, Rhoné Bénédicte, Todd Evelyn T, Lepelley Maud, Bellanger Laurence, Descombes Patrick, Arigoni Fabrizio, Fillodeau Audrey, Beche Eduardo

Аннотация

Cacao (Theobroma cacao L.) production is severely constrained by black pod rot (BPR) and frosty pod rot (FPR), two major diseases that cause substantial yield losses across the tropics. Breeding for disease resistance remains challenging due to long generation intervals and the complex genetic control of disease responses. Here, we used genome-wide association studies (GWASs) and genomic prediction (GP) to dissect the genetic basis of BPR and FPR resistance in an interrelated multi-parent F1 panel comprising 495 individuals derived from eight bi-parental crosses. Whole-genome sequencing of 495 F1 cacao plants yielded 103,479 high-quality single-nucleotide polymorphisms used for association mapping. GWAS identified 45 quantitative trait loci (QTLs) associated with BPR or FPR resistance, including a shared hotspot on chromosome 7 involved in both diseases. Disease resistance loci exhibited small individual effects (0%-8% variance explained), consistent with a highly polygenic architecture. Genomic prediction showed encouraging predictive abilities ranging from 0.36 to 0.64 for FPR and BPR. Multi-trait GP models that incorporated secondary traits increased predictive ability between 1.3- and 2-folds for frosty pod rot traits. Marker subset and training population analyses demonstrated that predictive ability plateaued at ∼1000 markers and ∼450 training individuals, respectively, with little improvements on accuracy after that. Our results highlight the polygenic nature of resistance to BPR and FPR and demonstrate the strong potential of incorporating secondary traits into a multi-trait analyses for increasing accuracies for primary traits.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты