ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Plant Breeding
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1664012
This article is part of the Research TopicAdvancing Plant Breeding Through Pangenomics and Multi-Omics Integration: Toward AI-Driven Predictive Models for Crop ImprovementView all articles
Increased Genomic Predictive Ability in Mango Using GWAS-Preselected Variants and Fixed-Effect SNPs
Provisionally accepted- 1The University of Queensland, Brisbane, Australia
- 2Other
- 3Queensland Department of Primary Industries, Brisbane, Australia
- 4Stellenbosch University, Stellenbosch, South Africa
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Genomic selection (GS) using whole-genome sequencing (WGS) data has potential to improve breeding value accuracy in fruit trees, but previous studies have reported limited gains compared to high-density marker sets. Incorporating preselected variants identified through genome-wide association studies (GWAS) is a promising strategy to enhance the predictive power of WGS data. We investigated whether incorporating GWAS-preselected variants and fixed-effect markers into genomic best linear unbiased prediction (GBLUP) models improves predictive ability for fruit blush color (FBC), average fruit weight (AFW), fruit firmness (FF), and trunk circumference (TC) in mango (Mangifera indica L.). The study used 225 gene pool accessions from the Queensland Department of Primary Industries in Australia, with phenotypes collected between 1999 and 2024. Predictive ability was assessed using models that ignored or accounted for population structure using fixed principal components. Accounting for population structure reduced predictive ability across all traits, suggesting that initial predictive abilities may have been partly driven by genetic differences between subpopulations. GWAS-preselected variants improved predictive abilities compared to using all WGS data, especially when population structure was accounted for in both parental and 5-fold cross-validation. Gains under parental validation reached 0.28 for AFW (from 0.30 to 0.58) and 0.06 for FBC (from 0.44 to 0.50). In 5-fold cross validation, gains were up to 0.16 for AFW (from 0.32 to 0.48) and 0.10 for FBC (from 0.35 to 0.45). This suggests that prioritizing markers that better capture relationships at causal loci can improve genomic predictive ability. Fixed-effect SNPs improved predictive ability of WGS data, particularly for FBC, with increases of up to 0.18 (from 0.44 to 0.62). The combination of GWAS-preselected variants and fixed-effect markers yielded the highest improvements in predictive ability for FBC and TC. GWAS identified 5 trait-associated SNPs for FBC, 11 for AFW, and 8 for TC. These results demonstrate that leveraging GWAS-preselected variants and fixed-effect SNPs improves predictive ability, potentially enhancing breeding efficiency in fruit trees.
Keywords: Genomic prediction, Mango, GWAS-preselected variants, genome-wideassociation studies, population structure
Received: 11 Jul 2025; Accepted: 23 Sep 2025.
Copyright: © 2025 Munyengwa, Wilkinson, Ortiz-Barrientos, Dillon, Webb, Ali, Bally, Myburg and Hardner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Norman Munyengwa, n.munyengwa@uq.edu.au
Craig Hardner, craig.hardner@uq.edu.au
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