Mango (Mangifera indica L.) is one of the world's important tropical fruit crops, but improving its yield, fruit quality, and resilience remains slow. Trees often require several years before fruiting, while full breeding cycles may take well over a decade. Large orchards are costly to maintain, and measuring fruit traits is labor-intensive. These barriers limit the size of reference populations needed for genomic prediction (GP), especially when traits are affected by environment and genotype-by-environment interaction (GxE). Smaller, less representative datasets reduce prediction accuracy and slow genetic gain. Due to these challenges, there is a need to conduct in-depth research on globally integrated genomic and phenotypic resources for mango breeding.
Researchers from Murdoch University, AgriSapiens PTY LTD, The University of Queensland, Queensland Department of Primary Industries, University of Florida IFAS, United States Department of Agriculture Agricultural Research Service, Western Sydney University, and Queensland University of Technology published (DOI: 10.1093/hr/uhag004) the study on January 6, 2026, in Horticulture Research. The study integrated mango collections from Australia, the United States, and China to evaluate genetic diversity, genome-wide association study (GWAS) power, and the performance of GP models for fruit-related traits.
The study brought together 610 mango accessions, including 225 from Australia, 161 from the United States, and 224 from China. After single nucleotide polymorphism (SNP) filtering, more than 12.5 million high-quality SNPs were used for downstream analyses. Genetic diversity analysis showed a highly admixed population structure, while the Chinese collection displayed faster linkage disequilibrium (LD) decay, suggesting higher underlying genetic diversity. This diversity could be valuable for broadening breeding pools.
The integrated dataset substantially improved GWAS power, identifying 19 quantitative trait loci (QTLs) for fruit weight (FW) and 9 QTLs for total soluble solids (TSS), a key indicator of sweetness and fruit quality. For prediction, GxE models achieved higher or comparable accuracy for FW and TSS, particularly when Australian and United States collections were combined. However, prediction involving the Chinese collection was weaker, largely because FW was measured using a different phenotyping protocol. Multitrait (MT) and multitrait with interaction (MxT) models were especially useful when records were incomplete, with average prediction accuracy increasing from 0.55 in single-trait analysis to 0.66 with MT models and 0.72 with MxT models under cross-validation 2 (CV2).
The authors said the findings show that mango breeding does not have to depend only on the size of a single orchard or national collection. By connecting compatible datasets, breeding programs can build stronger reference populations, find trait-linked genomic regions with greater confidence, and make earlier selection decisions. They said the work also sends a practical message: international data sharing is most powerful when phenotyping methods are aligned, because inconsistent trait measurement can weaken prediction even when valuable genetic diversity is available.
The study provides a practical framework for mango improvement under real-world resource constraints. For breeders, integrated genomic resources could help reduce the time and cost required to identify superior parents and select promising seedlings. For growers and consumers, this may support future cultivars with better yield, fruit quality, and environmental resilience. The approach may also be useful for other perennial fruit and tree crops facing long generation times and expensive phenotyping. Future progress will depend on standardized international trait measurement, larger shared datasets, advanced GP models, and high-throughput tools such as image analysis, near-infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, and multiomics platforms.
###
References
DOI
10.1093/hr/uhag004
Original Source URL
https://doi.org/10.1093/hr/uhag004
Funding information
The financial support from Hort Innovation and contributions from the Australian government for supporting the research project ‘Genetics for Next Generation Orchards (AS23003) (AS21006)’.
About Horticulture Research
Horticulture Research is an open access journal of Nanjing Agricultural University and ranked number one in the Horticulture category of the Journal Citation Reports ™ from Clarivate, 2023. The journal is committed to publishing original research articles, reviews, perspectives, comments, correspondence articles and letters to the editor related to all major horticultural plants and disciplines, including biotechnology, breeding, cellular and molecular biology, evolution, genetics, inter-species interactions, physiology, and the origination and domestication of crops.