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Accepted ManuscriptCC BY V4.0, Open Access
Journal article
Strategic global data integration to improve genomic prediction accuracy in trees breeding programs facing resource limitations, a case study in mango
Horticulture research, Accepted
2026
Abstract
Genomic prediction (GP) in mango breeding faces challenges due to the species’ complex biology, long cycles, and limited reference populations. To accelerate genetic improvement, this study integrated data from diverse global populations to increase the reference population size. It included three mango collections reserved in Australia (225), USA (161), and China (224), totalling 610 individuals. Fruit weight (FW) and total soluble solids (TSS) were measured in multiple datasets, while several other traits were measured in specific datasets. We evaluated genetic diversity, performed genome-wide association studies (GWAS), and assessed GP accuracy using standard, genotype-by-environment (GxE), and multi-trait models, both within and across collections. Findings revealed a highly admixed genetic structure, with faster linkage disequilibrium (LD) decay in the Chinese collection, indicating higher genetic diversity. Data integration significantly enhanced GWAS power, identifying 19 quantitative trait loci (QTL) for FW and 9 for TSS. GxE models consistently achieved higher or comparable prediction accuracies for FW and TSS compared to the non-GxE models, especially when combining Australian and USA collections. This was not the case when predicting into or from the Chinese collection, mostly due to differences in the phenotyping protocol. While single-trait models performed comparably to multi-trait models in predicting new individuals (Coss-Validation: CV1), multi-trait models significantly improved prediction accuracy in scenarios with incomplete phenotypic records (CV2). This study demonstrates that strategic global data integration significantly enhances GWAS power and GP accuracy in mango. This collaborative approach is crucial for developing more efficient and accelerated breeding programs for mango and other perennial trees.
Details
- Title
- Strategic global data integration to improve genomic prediction accuracy in trees breeding programs facing resource limitations, a case study in mango
- Authors/Creators
- Abdulqader Jighly - AgriBioNorman Munyengwa - Agriculture and FoodReem Joukhadar - AgriBioVanika Garg - Murdoch UniversityNatalie Dillon - New South Wales Department of Primary IndustriesRhys G R Copeland - Murdoch UniversityJugpreet Singh - Southwest Florida ResearchSukhwinder Singh - Agricultural Research ServiceChristopher I Cazzonelli - Western Sydney UniversityPenghao Wang - Murdoch UniversityPeter Prentis - The University of QueenslandCraig Hardner - Agriculture and FoodRajeev K Varshney - Murdoch University
- Publication Details
- Horticulture research, Accepted
- Identifiers
- 991005850886307891
- Copyright
- © The Author(s) 2026
- Murdoch Affiliation
- Centre for Crop and Food Innovation; State Agricultural Biotechnology Centre; Food Futures Institute
- Language
- English
- Resource Type
- Journal article
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