ArticleHeredity2018
Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits.
Article in Heredity, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Reversible jumps for the joint modeling of the dimension of pseudo-functional models in chromosomal windows in genome-wide selection.BMC genomics · 2026Article
- Evolutionary structure constrains genomic prediction accuracy more than model complexity in mango (Mangifera indica L.).G3 (Bethesda, Md.) · 2026Article
- GAPIT version 4: integration of GWAS into genomic prediction.Molecular biology and evolution · 2026Article
- Identification of quantitative trait loci qPL6 for petiole length in soybean.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Decades' progress and prospects on maize functional genomics and molecular breeding.Science China. Life sciences · 2025Review
- Leveraging the multi-trait donor Pusa Srijan for biofortification and yield improvement in bread wheat.Scientific reports · 2025Article
- Evaluation of genomic breeding values and accuracy for carcass traits in Korean Hanwoo cows using whole-genome SNP chip panels.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Article
- Identification of MAPK10 as a Candidate Gene for High Milk Production in Water Buffaloes Through a Genome-Wide Association Study.Animals : an open access journal from MDPI · 2025Article
- Toward a general framework for AI-enabled prediction in crop improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genomic prediction with NetGP based on gene network and multi-omics data in plants.Plant biotechnology journal · 2025Article
- Optimizing the selection of quantitative traits in plant breeding using simulation.Frontiers in plant science · 2025Review
- Genome-wide association and selection studies for pod dehiscence resistance in the USDA soybean germplasm collection.PloS one · 2025Article
- Salinity stress tolerance prediction for biomass-related traits in maize (Zea mays L.) using genome-wide markers.The plant genome · 2023Article
- Genomic prediction of tocochromanols in exotic-derived maize.The plant genome · 2023Article
- Application of machine learning to explore the genomic prediction accuracy of fall dormancy in autotetraploid alfalfa.Horticulture research · 2023Article
- Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks.PloS one · 2023Article
- Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.Heredity · 2022Article
- Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding.Plant methods · 2022Article
- Population-tailored mock genome enables genomic studies in species without a reference genome.Molecular genetics and genomics : MGG · 2022Article
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9 authors.
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Abstract
Improvement of statistical methods is crucial for realizing the potential of increasingly dense genetic markers. Bayesian methods treat all markers as random effects, exhibit an advantage on dense markers, and offer the flexibility of using different priors. In contrast, genomic best linear unbiased prediction (gBLUP) is superior in computing speed, but only superior in prediction accuracy for extremely complex traits. Currently, the existing variety in the BLUP method is insufficient for adapting to new sequencing technologies and traits with different genetic architectures. In this study, we found two ways to change the kinship derivation in the BLUP method that improve prediction accuracy while maintaining the computational advantage. First, using the settlement under progressively exclusive relationship (SUPER) algorithm, we substituted all available markers with estimated quantitative trait nucleotides (QTNs) to derive kinship. Second, we compressed individuals into groups based on kinship, and then used the groups as random effects instead of individuals. The two methods were named as SUPER BLUP (sBLUP) and compressed BLUP (cBLUP). Analyses on both simulated and real data demonstrated that these two methods offer flexibility for evaluating a variety of traits, covering a broadened realm of genetic architectures. For traits controlled by small numbers of genes, sBLUP outperforms Bayesian LASSO (least absolute shrinkage and selection operator). For traits with low heritability, cBLUP outperforms both gBLUP and Bayesian LASSO methods. We implemented these new BLUP alphabet series methods in an R package, Genome Association and Prediction Integrated Tool (GAPIT), available at http://zzlab.net/GAPIT .
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