Genomic prediction of resistance to Phytophthora megakarya in cacao from a sparse host-pathogen screen
- 1University of Florida
Abstract
Black pod rot caused by Phytophthora megakarya is a major disease impacting cacao (Theobroma cacao L.) production in West Africa. Resistance is influenced by genetic variation in both host and pathogen populations. While genomic prediction approaches have been widely applied in plant breeding, most studies incorporate only host genomic information. The value of explicitly modeling pathogen genomic relatedness in quantitative disease prediction remains largely unknown. We genotyped 947 clones from the International Cacao Collection at CATIE using a 10K-SNP panel and characterized 67 P. megakarya isolates by sequencing 3,505 virulence-associated genes and 2,774 SNP loci. Cacao clones were extensively admixed, while isolates formed three geographically structured subpopulations. Mixed linear models were fitted to lesion diameters on detached pods of 350 genotyped clones inoculated with 58 isolates in 30 bioassays, covering 5% of possible clone-by-isolate combinations. Differences among isolates explained a larger share of lesion-size variation than differences among clones (0.34 and 0.27 of phenotypic variance, respectively). After accounting for broad pathogen subpopulation structure, incorporating within-subpopulation genomic relatedness among isolates improved neither model fit nor prediction, whereas incorporating genomic relationships among cacao clones improved model fit and enabled prediction of phenotypically untested clones with a predictive ability of 0.41. Clone rankings were stable across pathogen subpopulations (genomic correlation 0.86 {+/-} 0.06), whereas clone-by-isolate interactions accounted for little phenotypic variation and were not resolved with confidence in the sparse design. An exploratory ancestry analysis associated several Upper Amazon ancestry components with smaller predicted lesions. These are the first genomic predictions of resistance to P. megakarya in cacao. In this strongly structured host-pathogen system, genomic relationships among hosts carried most of the predictable signal beyond broad pathogen subpopulation effects, and the predictions provide a starting point for prioritizing screening and selection efforts.
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BibTeX
@misc{schlathoelter2026genomic,
title = {{Genomic prediction of resistance to Phytophthora megakarya in cacao from a sparse host-pathogen screen}},
author = {I. Schlathoelter and A. J. Gitto and A. L. Arciniegas and M. Herrera Corzo and O. O. Kolawole and E. M. Goss and D. Livingstone and A. Firl and D. R. Henderson and D. R. Drost and D. Rodezno and J.-P. Marelli and J. T. Brawner},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.03.764016},
url = {https://www.biorxiv.org/content/10.64898/2026.11.03.764016v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Genomic prediction of resistance to Phytophthora megakarya in cacao from a sparse host-pathogen screen
AU - I. Schlathoelter
AU - A. J. Gitto
AU - A. L. Arciniegas
AU - M. Herrera Corzo
AU - O. O. Kolawole
AU - E. M. Goss
AU - D. Livingstone
AU - A. Firl
AU - D. R. Henderson
AU - D. R. Drost
AU - D. Rodezno
AU - J.-P. Marelli
AU - J. T. Brawner
PY - 2026
DA - 2026/11/03
PB - bioRxiv
DO - 10.64898/2026.11.03.764016
UR - https://www.biorxiv.org/content/10.64898/2026.11.03.764016v1
AB - Black pod rot caused by Phytophthora megakarya is a major disease impacting cacao (Theobroma cacao L.) production in West Africa. Resistance is influenced by genetic variation in both host and pathogen populations. While genomic prediction approaches have been widely applied in plant breeding, most studies incorporate only host genomic information. The value of explicitly modeling pathogen genomic relatedness in quantitative disease prediction remains largely unknown. We genotyped 947 clones from the International Cacao Collection at CATIE using a 10K-SNP panel and characterized 67 P. megakarya isolates by sequencing 3,505 virulence-associated genes and 2,774 SNP loci. Cacao clones were extensively admixed, while isolates formed three geographically structured subpopulations. Mixed linear models were fitted to lesion diameters on detached pods of 350 genotyped clones inoculated with 58 isolates in 30 bioassays, covering 5% of possible clone-by-isolate combinations. Differences among isolates explained a larger share of lesion-size variation than differences among clones (0.34 and 0.27 of phenotypic variance, respectively). After accounting for broad pathogen subpopulation structure, incorporating within-subpopulation genomic relatedness among isolates improved neither model fit nor prediction, whereas incorporating genomic relationships among cacao clones improved model fit and enabled prediction of phenotypically untested clones with a predictive ability of 0.41. Clone rankings were stable across pathogen subpopulations (genomic correlation 0.86 {+/-} 0.06), whereas clone-by-isolate interactions accounted for little phenotypic variation and were not resolved with confidence in the sparse design. An exploratory ancestry analysis associated several Upper Amazon ancestry components with smaller predicted lesions. These are the first genomic predictions of resistance to P. megakarya in cacao. In this strongly structured host-pathogen system, genomic relationships among hosts carried most of the predictable signal beyond broad pathogen subpopulation effects, and the predictions provide a starting point for prioritizing screening and selection efforts.
ER -