Evidence map›Paper›PMID 42265584›Full record

ArticleBMC genomics2026

TKOA-GPNN: a model framework for genotype-to-phenotype prediction in Duroc pigs.

Jiali Zhou, Zuhong Liu, Kaiyue Liu, Bing Deng, Jianghui Wen

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Article in BMC genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Jiali ZhouWuhan University of Technology, Wuhan, 430070, People's Republic of China.
Zuhong LiuWuhan Academy of Agricultural Sciences, Wuhan, 430208, People's Republic of China.
Kaiyue LiuWuhan Academy of Agricultural Sciences, Wuhan, 430208, People's Republic of China.
Bing Deng *Wuhan Academy of Agricultural Sciences, Wuhan, 430208, People's Republic of China. dengbing@wuhanagri.com.
Jianghui Wen *Wuhan University of Technology, Wuhan, 430070, People's Republic of China. jhwen@whut.edu.cn.

Funding

Natural Science Foundation of Wuhan (2026040301020089) (2026040301020089)
6 · The paper itself

Abstract

contextGenomic prediction has been established as a powerful tool in pig breeding, which typically entails the analysis of large-scale genomic datasets. Neural network models, renowned for their robust pattern recognition capabilities, have been successfully applied to extract complex genetic patterns from such high-dimensional genomic data.

objectiveBecause growth traits are critical economic indicators in pig breeding, their accurate prediction is essential for genetic improvement. However, the underlying genetic architecture of these traits is highly complex, regulated by numerous genetic loci. Given that not all loci exert significant effects on growth traits and traditional neural networks face challenges in capturing genome-wide genetic interactions, there is an urgent need for a more efficient and accurate prediction framework.

methodsTo address this challenge, we propose a novel model, TKOA-GPNN, which comprises two components: a two-stage genomic feature selection module and a genomic prediction neural network. First, symmetric uncertainty correlation analysis is employed to reduce the feature space. Subsequently, a kepler optimization algorithm integrated with tent mapping is applied to select trait-associated loci. The selected features are then fed into the neural network (GPNN) to conduct genomic prediction. RESULTS AND

conclusionsExperimental results demonstrate that TKOA-GPNN outperforms 14 existing models with superior accuracy and efficiency. Tests based on offspring data shows favorable performance with genotyping array data, while further validation using whole-genome sequencing data confirmed the robustness of the proposed model. These results suggest that TKOA-GPNN is applicable to genomic data of different types and quality levels. SIGNIFICANCE: Overall, TKOA-GPNN improves both prediction accuracy and computational efficiency, providing an effective solution for intelligent breeding and genetic improvement in pigs breeding.

Indexed as

Genetic Association StudiesGenomicsGenotypeModels, GeneticNeural Networks, ComputerAlgorithmsAnimalsBreedingPhenotypePrediction AlgorithmsQuantitative Trait LociSwineGenomic feature selectionGenomic predictionGrowth traitsPig breedingTKOA-GPNN model

Identifiers

PMID42265584
PMCPMC13474873

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.