Files and links (1)
Open Access CC BY V4.0
Journal article
Machine learning to predict genotypes and genotype-environment interaction associated with complex traits for genomic selection
Plant phenomics, Vol.8(3), 100224
2026
Abstract
Genomic selection (GS) can accelerate crop breeding and enhance selection efficiency. However, accurately predicting genomic estimated breeding values (GEBVs) for complex traits and applying GS in diverse environments remains challenging. To address these issues, we developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions. This method offers precise predictions of phenotypic performance for complex traits, identifies haplotypes associated with desirable phenotypes, and enables prediction of optimal haplotypes tailored to specific environments. We evaluated the approach using a dataset of 855 barley lines, with phenotypic data for grain yield and flowering time collected across multiple environments. The model incorporated 30,543 SNPs, nine soil parameters, and six daily environmental variables, achieving high prediction accuracies, with correlation coefficients of 0.93 for flowering time and 0.82 for grain yield. Our method identified 10 haplotype blocks significantly associated with flowering time and 13 blocks with grain yield, collectively accounting for over 90% of the total genetic variance. Additionally, we predicted the phenotypic effects of each haplotype and identified elite varieties carrying the most favourable haplotypes for crossing design and selection. The method also allows prediction of untested genotype × environment combinations, enabling selection of optimal genotypes for targeted environments. To facilitate its application, we developed a web-based interface (accessible at [https://penghaowang.shinyapps.io/shinygui/]), which enables breeders to identify optimal haplotypes and the varieties that carry them, streamlining the process of haplotype-based, environment-informed breeding. We note that the reverse prediction framework is currently applied on a single-trait basis and does not resolve multi-trait trade-offs such as between flowering time and yield, which remains a topic for future extensions.
Details
- Title
- Machine learning to predict genotypes and genotype-environment interaction associated with complex traits for genomic selection
- Authors/Creators
- Penghao Wang - Murdoch University, School of Medical, Molecular and Forensic SciencesXiao-Qi Zhang - Murdoch University, Centre for Crop and Food InnovationViet Dang - Murdoch UniversityMeixue Zhou - University of TasmaniaSergey Shabala - University of TasmaniaTianhua He - Murdoch University, Centre for Crop and Food InnovationChengdao Li - Murdoch University
- Publication Details
- Plant phenomics, Vol.8(3), 100224
- Publisher
- Elsevier B.V.
- Number of pages
- 15
- Identifiers
- 991005884932907891
- Copyright
- © 2026 The Authors.
- Murdoch Affiliation
- Centre for Crop and Food Innovation; School of Medical, Molecular and Forensic Sciences
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: SDGs in the Output
Metrics
1 Record Views