ArticleGenetics2013
Genomic BLUP decoded: a look into the black box of genomic prediction.
Article in Genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 192 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
192 citing papers in PubMed.
- Impact of QTL number, heritability and reference population size on the benefit of machine learning models over GBLUP in genomic prediction.Genetics, selection, evolution : GSE · 2026Article
- Local Enrichment of Multi-SNP Markers Improves Genomic Prediction from Low-Density Genotyping in Pacific White Shrimp.International journal of molecular sciences · 2026Article
- OptimGS: a dual integrative genomic prediction framework for improving cold stress tolerance in wheat.Briefings in bioinformatics · 2026Article
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- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- Physiological determinants of heterosis in hybrid rice (Frontiers in plant science · 2026Article
- Application of omics technologies to identify reproductive phenotypes in the context of assisted reproductive technologies for cattle.Animal reproduction · 2026Article
- Rapid cycling genomic selection in maize landraces: a step toward closing the yield gap.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- The effect of haplotype size on genomic selection accuracy and epistasis: An empirical study in rice.The plant genome · 2025Article
- Sequence-based chromatin activity modeling and regulatory impact prediction of genetic variants in farmed animals using deep learning.NAR genomics and bioinformatics · 2025Article
- Advancements in Animal Breeding: From Mendelian Genetics to Machine Learning.International journal of molecular sciences · 2025Review
- Leveraging genetic resources and genomic prediction to enhance flavonol content in cranberry fruit.The plant genome · 2025Article
- Breaking down data silos across companies to train genome-wide predictions: A feasibility study in wheat.Plant biotechnology journal · 2025Article
- Practical Considerations When Using Mendelian Sampling Variances for Selection Decisions in Genomic Selection Programs.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2025Article
- Environmental data provide marginal benefit for predicting climate adaptation.PLoS genetics · 2025Article
- Article
- Genomic prediction with kinship-based multiple kernel learning produces hypothesis on the underlying inheritance mechanisms of phenotypic traits.Genome biology · 2025Article
- Erosion of estimated genomic breeding values with generations is due to long distance associations between markers and QTL.Genetics, selection, evolution : GSE · 2025Article
- Rapid cycling genomic selection in maize landraces.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
132 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Genomic best linear unbiased prediction (BLUP) is a statistical method that uses relationships between individuals calculated from single-nucleotide polymorphisms (SNPs) to capture relationships at quantitative trait loci (QTL). We show that genomic BLUP exploits not only linkage disequilibrium (LD) and additive-genetic relationships, but also cosegregation to capture relationships at QTL. Simulations were used to study the contributions of those types of information to accuracy of genomic estimated breeding values (GEBVs), their persistence over generations without retraining, and their effect on the correlation of GEBVs within families. We show that accuracy of GEBVs based on additive-genetic relationships can decline with increasing training data size and speculate that modeling polygenic effects via pedigree relationships jointly with genomic breeding values using Bayesian methods may prevent that decline. Cosegregation information from half sibs contributes little to accuracy of GEBVs in current dairy cattle breeding schemes but from full sibs it contributes considerably to accuracy within family in corn breeding. Cosegregation information also declines with increasing training data size, and its persistence over generations is lower than that of LD, suggesting the need to model LD and cosegregation explicitly. The correlation between GEBVs within families depends largely on additive-genetic relationship information, which is determined by the effective number of SNPs and training data size. As genomic BLUP cannot capture short-range LD information well, we recommend Bayesian methods with t-distributed priors.
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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 OpenQuestion graph.