Strategic global data integration to improve genomic prediction accuracy in tree breeding programs facing resource...

Area of Science:

• Plant genetics
• Agricultural science
• Genomics

Background:

• Genomic prediction (GP) in mango breeding is hindered by complex genetics, long breeding cycles, and small reference populations.
• Accelerating genetic gain requires overcoming these limitations through enhanced data integration and analytical models.

Purpose of the Study:

• To increase reference population size by integrating diverse global mango collections.
• To evaluate the impact of data integration on genome-wide association studies (GWAS) and GP accuracy.
• To compare different GP models (standard, GxE, multitrait) across diverse collections.

Main Methods:

• Integrated genomic and phenotypic data from Australian, US, and Chinese mango collections (610 individuals).
• Performed genetic diversity analysis, GWAS, and assessed GP accuracy using various models (standard, GxE, multitrait).
• Evaluated model performance within and across collections using cross-validation (CV1 and CV2).

Main Results:

• Data integration revealed admixed genetic structure and enhanced GWAS power, identifying 19 QTLs for fruit weight and 9 for total soluble solids.
• Genotype-by-environment (GxE) models improved prediction accuracy, particularly when combining Australian and US data.
• Multitrait models significantly enhanced prediction accuracy with incomplete phenotypic data (CV2).

Conclusions:

• Strategic integration of global mango data significantly improves GWAS power and genomic prediction accuracy.
• A collaborative, data-integrated approach is vital for efficient mango breeding programs.
• Findings provide a framework for accelerating genetic improvement in mango and other perennial crops.