ArticleGenetics2022
Extend mixed models to multilayer neural networks for genomic prediction including intermediate omics data.
Article in Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Structured multimodal deep learning improves genomic prediction in future environments.Genetics · 2026Article
- Prediction of disease resilience of pigs using multi-omics data.Journal of animal science and biotechnology · 2026Article
- Interpretable multi-omics machine learning reveals drought-driven shifts in plant-microbe interactions.Environmental microbiome · 2026Article
- Integration of proxy intermediate omics traits into a nonlinear two-step model for accurate phenotypic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- FSBLUP: a novel strategy of fusion similarity matrix construction via optimally integrating intermediate omics data to enhance genomic prediction.Genome biology · 2026Article
- Reciprocal BLUP: A Predictability-Guided Multi-Omics Framework for Plant Phenotype Prediction.Plants (Basel, Switzerland) · 2025Article
- Integrating Longitudinal Metabolite Profiles Improves Trait Prediction in Pigs in a Trait- and Timepoint-Dependent Manner.bioRxiv : the preprint server for biology · 2025Article
- Improving genomic prediction accuracy for methane emission and feed efficiency in sheep: integrating rumen microbial PCA with host genomic variation using neural network GBLUP (NN-GBLUP).Genetics, selection, evolution : GSE · 2025Article
- GCRP: Integrated Global Chicken Reference Panel from 11,951 Chicken Genomes.Genomics, proteomics & bioinformatics · 2025Article
- Optimizing Cost-Effective gene expression phenotyping approaches in cattle using 3' mRNA sequencing.BMC genomics · 2025Article
- Comparison of genomic prediction accuracies in dairy cattle lactation traits using five classes of functional variants versus generic SNP.Genetics, selection, evolution : GSE · 2025Article
- DeepAnnotation: A novel interpretable deep learning-based genomic selection model that integrates comprehensive functional annotations.GigaScience · 2025Article
- A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding.Genetics · 2024Article
- Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework.Journal of animal science and biotechnology · 2024Article
- Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle.Genetics, selection, evolution : GSE · 2024Article
- Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches.Frontiers in plant science · 2024Article
- Genetic analysis of the blood transcriptome of young healthy pigs to improve disease resilience.Genetics, selection, evolution : GSE · 2023Article
- Interpreting single-step genomic evaluation as a neural network of three layers: pedigree, genotypes, and phenotypes.Genetics, selection, evolution : GSE · 2023Article
- Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley.Genetics, selection, evolution : GSE · 2023Article
- Combining host and rumen metagenome profiling for selection in sheep: prediction of methane, feed efficiency, production, and health traits.Genetics, selection, evolution : GSE · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the growing amount and diversity of intermediate omics data complementary to genomics (e.g. DNA methylation, gene expression, and protein abundance), there is a need to develop methods to incorporate intermediate omics data into conventional genomic evaluation. The omics data help decode the multiple layers of regulation from genotypes to phenotypes, thus forms a connected multilayer network naturally. We developed a new method named NN-MM to model the multiple layers of regulation from genotypes to intermediate omics features, then to phenotypes, by extending conventional linear mixed models ("MM") to multilayer artificial neural networks ("NN"). NN-MM incorporates intermediate omics features by adding middle layers between genotypes and phenotypes. Linear mixed models (e.g. pedigree-based BLUP, GBLUP, Bayesian Alphabet, single-step GBLUP, or single-step Bayesian Alphabet) can be used to sample marker effects or genetic values on intermediate omics features, and activation functions in neural networks are used to capture the nonlinear relationships between intermediate omics features and phenotypes. NN-MM had significantly better prediction performance than the recently proposed single-step approach for genomic prediction with intermediate omics data. Compared to the single-step approach, NN-MM can handle various patterns of missing omics measures and allows nonlinear relationships between intermediate omics features and phenotypes. NN-MM has been implemented in an open-source package called "JWAS".
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Registered trials
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.