ArticleScientific reports2022
Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 36 citations in OpenAlex.
- Genomic prediction vs. gene-based crop models: a case study on rice trait prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Evolutionary structure constrains genomic prediction accuracy more than model complexity in mango (Mangifera indica L.).G3 (Bethesda, Md.) · 2026Article
- ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome-wide polymorphisms.The plant genome · 2026Article
- Characterization of Genetic Diversity and Genomic Prediction of Secondary Metabolites in Pea Genetic Resources.Plants (Basel, Switzerland) · 2026Article
- Article
- Normalized cumulative gain as an alternative evaluation measure for genomic selection models.Genetics, selection, evolution : GSE · 2025Article
- A Deep Autoencoder Compression-Based Genomic Prediction Method for Whole-Genome Sequencing Data.Biology · 2025Article
- Genetic Variation and Genome-Enabled Prediction of White Lupin Frost Resistance in Different Reference Populations.International journal of molecular sciences · 2025Article
- Genetic variation and genome-enabled selection of white lupin for key seed quality traits.BMC genomics · 2025Article
- Towards smart agriculture: AI-driven prediction of key genes for revolutionizing crop breeding.Planta · 2025Review
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Genomic prediction with kinship-based multiple kernel learning produces hypothesis on the underlying inheritance mechanisms of phenotypic traits.Genome biology · 2025Article
- Article
- Explainable artificial intelligence for genotype-to-phenotype prediction in plant breeding: a case study with a dataset from an almond germplasm collection.Frontiers in plant science · 2024Article
- Genomic prediction for rust resistance in pea.Frontiers in plant science · 2024Article
- Genetic Parameter and Hyper-Parameter Estimation Underlie Nitrogen Use Efficiency in Bread Wheat.International journal of molecular sciences · 2023Article
- Dual spin max pooling convolutional neural network for solar cell crack detection.Scientific reports · 2023Article
- Multimodal deep learning methods enhance genomic prediction of wheat breeding.G3 (Bethesda, Md.) · 2023Article
- White Lupin Adaptation to Moderately Calcareous Soils: Phenotypic Variation and Genome-Enabled Prediction.Plants (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Deep learning is impacting many fields of data science with often spectacular results. However, its application to whole-genome predictions in plant and animal science or in human biology has been rather limited, with mostly underwhelming results. While most works focus on exploring alternative network architectures, in this study we propose an innovative representation of marker genotype data and tested it against the GBLUP (Genomic BLUP) benchmark with linear and nonlinear phenotypes. From publicly available cattle SNP genotype data, different types of genomic kinship matrices are stacked together in a 3D pile from where 2D grayscale slices are extracted and fed to a deep convolutional neural network (DNN). We simulated nine phenotype scenarios with combinations of additivity, dominance and epistasis, and compared the DNN to GBLUP-A (computed using only the additive kinship matrix) and GBLUP-optim (additive, dominance, and epistasis kinship matrices, as needed). Results varied depending on the accuracy metric employed, with DNN performing better in terms of root mean squared error (1-12% lower than GBLUP-A; 1-9% lower than GBLUP-optim) but worse in terms of Pearson's correlation (0.505 for DNN compared to 0.672 and 0.669 of GBLUP-A and GBLUP-optim for fully additive case; 0.274 for DNN, 0.279 for GBLUP-A, and 0.477 for GBLUP-optim for fully dominant case). The proposed approach offers a basis to explore further the application of DNN to tabular data in whole-genome predictions.
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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 Socratic graph.