ArticleG3 (Bethesda, Md.)2015
Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix.
Article in G3 (Bethesda, Md.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
- Gsformer: a dual-architecture deep learning framework with CNN-self-attention and sparse-attention for genomic selection.Genetics, selection, evolution : GSE · 2026Article
- Enhancing genomic prediction accuracy in Huaxi cattle through integration of transcriptomic data and a self-attention-based SNP selection strategy.BMC bioinformatics · 2026Article
- GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.Briefings in bioinformatics · 2026Article
- ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.Genetics, selection, evolution : GSE · 2026Article
- Comparative evaluation of SNP-weighted, Bayesian, and machine learning models for genomic prediction in Holstein cattle.BMC genomics · 2025Article
- Genome Selection for Fleece Traits in Inner Mongolia Cashmere Goats Based on GWAS Prior Marker Information.Animals : an open access journal from MDPI · 2025Article
- AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Innovations in Cattle Breeding Technology: Prospects in the Era of Gene Editing.Animals : an open access journal from MDPI · 2025Review
- ExAutoGP: Enhancing Genomic Prediction Stability and Interpretability with Automated Machine Learning and SHAP.Animals : an open access journal from MDPI · 2025Article
- Mutual information-based best linear unbiased prediction for enhanced genomic prediction accuracy.Journal of animal science · 2025Article
- Impact of different genomic relationship matrix construction methods on the accuracy of genomic prediction in different species.Frontiers in genetics · 2025Article
- KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.Briefings in bioinformatics · 2024Article
- Genomic prediction of male fertility in Brown Swiss cattle.JDS communications · 2024Article
- Genomic Selection for Weaning Weight in Alpine Merino Sheep Based on GWAS Prior Marker Information.Animals : an open access journal from MDPI · 2024Article
- Article
- MPH: fast REML for large-scale genome partitioning of quantitative genetic variation.Bioinformatics (Oxford, England) · 2024Article
- Ensemble learning for integrative prediction of genetic values with genomic variants.BMC bioinformatics · 2024Article
- Large-scale genomic and transcriptomic analyses elucidate the genetic basis of high meat yield in chickens.Journal of advanced research · 2024Article
- SLEMM: million-scale genomic predictions with window-based SNP weighting.Bioinformatics (Oxford, England) · 2023Article
- Article
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8 authors.
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Abstract
Obtaining accurate predictions of unobserved genetic or phenotypic values for complex traits in animal, plant, and human populations is possible through whole-genome prediction (WGP), a combined analysis of genotypic and phenotypic data. Because the underlying genetic architecture of the trait of interest is an important factor affecting model selection, we propose a new strategy, termed BLUP|GA (BLUP-given genetic architecture), which can use genetic architecture information within the dataset at hand rather than from public sources. This is achieved by using a trait-specific covariance matrix ( T: ), which is a weighted sum of a genetic architecture part ( S: matrix) and the realized relationship matrix ( G: ). The algorithm of BLUP|GA (BLUP-given genetic architecture) is provided and illustrated with real and simulated datasets. Predictive ability of BLUP|GA was validated with three model traits in a dairy cattle dataset and 11 traits in three public datasets with a variety of genetic architectures and compared with GBLUP and other approaches. Results show that BLUP|GA outperformed GBLUP in 20 of 21 scenarios in the dairy cattle dataset and outperformed GBLUP, BayesA, and BayesB in 12 of 13 traits in the analyzed public datasets. Further analyses showed that the difference of accuracies for BLUP|GA and GBLUP significantly correlate with the distance between the T: and G: matrices. The new strategy applied in BLUP|GA is a favorable and flexible alternative to the standard GBLUP model, allowing to account for the genetic architecture of the quantitative trait under consideration when necessary. This feature is mainly due to the increased similarity between the trait-specific relationship matrix ( T: matrix) and the genetic relationship matrix at unobserved causal loci. Applying BLUP|GA in WGP would ease the burden of model selection.
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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.