Marker pre-selection as a strategy to enhance genomic prediction with machine learning: Exploring the influence of trait-specific genomic structures
Abstract
This study focused on incorporating dimensionality reduction based on marker significance to better harness the potential of machine learning for genomic prediction in different trait-genomic structures. The aim was to show that outcomes achieved with reduced data would improve predictive accuracy ( ) and precision (root-mean-square error: RMSE) while reducing computational time. Distinct subsets of markers, in simulated data, were chosen by prioritizing importance via the Bagging technique. Predictive modelling was subsequently conducted using both Bagging and the diverse architectures of a Multilayer Perceptron (MLP) neural network. This study was carried out with six traits of an F2 simulated population (derived from contrasting homozygotes) with 1,000 individuals. Three traits had three different heritabilities (0.4, 0.6, and 0.8) and were controlled by a set of 40 quantitative trait loci (QTLs). Additionally, four QTLs with more pronounced heritability effects (set at unity) were introduced in three other traits while preserving the same genetic control structure as the earlier traits. In our investigation, as the number of markers increased, both techniques gradually increased training time; however, the time needed for computation notably extended beyond the threshold of 100 markers for Bagging. In comparison to the MLP model, the Bagging model generally obtained better accuracy (higher ) and precision (lower RMSE) values regardless of heritability and added QTLs. Most importantly, results highlight that for traits subject to robust genetic control of additional QTLs, MLP networks experienced a decline in prediction performance from a few markers (~10). In contrast, Bagging kept constant or subtly improved predication performance. Finally, the dimensionality reduction procedure effectively improves genomic prediction, and Bagging captures complex genetic control structures for prediction better than MLP networks.
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References
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Copyright (c) 2025 Wagner Faria Barbosa, Antônio Carlos da Silva Júnior, Ithalo Coelho de Sousa, Francyse Edite de Oliveira Chagas de Moraes, Michele Jorge Silva Siqueira, Leonardo Lopes Bhering, Moysés Nascimento, Cosme Damião Cruz (Autor)
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