ArticleGenetics, selection, evolution : GSE2016
Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species.
Article in Genetics, selection, evolution : GSE, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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50 citing papers in PubMed.
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- Decreasing the computing time of approximated reliabilities of genomic estimated breeding values in the single-step genomic best linear unbiased predictor using different core sizes for the algorithm for proven and young.JDS communications · 2026Article
- Opportunities and computational challenges in large-scale whole-genome sequencing data analysis.Journal of animal science · 2026Review
- Estimation of heritabilities and genetic correlations by time slices using predictivity in large genomic models.Genetics · 2025Article
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- Development of a prototype genetic evaluation for teat and udder score in American Angus cattle.Journal of animal science · 2025Article
- Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.Frontiers in genetics · 2025Article
- Efficient large-scale genomic prediction in approximate genome-based kernel model.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Approximation of reliabilities for random-regression single-step genomic best linear unbiased predictor models.JDS communications · 2024Article
- Potential negative effects of genomic selection.Journal of animal science · 2024Article
- Comparing algorithms to approximate accuracies for single-step genomic best linear unbiased predictor.Journal of animal science · 2024Article
- A dimensionality-reduction genomic prediction method without direct inverse of the genomic relationship matrix for large genomic data.Plant cell reports · 2023Article
- Potential of low-density genotype imputation for cost-efficient genomic selection for resistance to Flavobacterium columnare in rainbow trout (Oncorhynchus mykiss).Genetics, selection, evolution : GSE · 2023Article
- Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs.Genetics, selection, evolution : GSE · 2023Article
- Dimensionality of genomic information and its impact on genome-wide associations and variant selection for genomic prediction: a simulation study.Genetics, selection, evolution : GSE · 2023Article
- Boundaries for genotype, phenotype, and pedigree truncation in genomic evaluations in pigs.Journal of animal science · 2023Article
- Efficient ways to combine data from broiler and layer chickens to account for sequential genomic selection.Journal of animal science · 2023Article
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4 authors.
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Abstract
backgroundA genomic relationship matrix (GRM) can be inverted efficiently with the Algorithm for Proven and Young (APY) through recursion on a small number of core animals. The number of core animals is theoretically linked to effective population size (N
methodsDatasets included phenotypes, pedigrees, and genotypes for populations of Holstein, Jersey, and Angus cattle, pigs, and broiler chickens. The number of genotyped animals varied from 15,000 for broiler chickens to 77,000 for Holsteins, and the number of single-nucleotide polymorphisms used for genomic prediction varied from 37,000 to 61,000. Eigenvalue decomposition of the GRM for each population determined numbers of largest eigenvalues corresponding to 90, 95, 98, and 99% of variation.
resultsThe number of eigenvalues corresponding to 90% (98%) of variation was 4527 (14,026) for Holstein, 3325 (11,500) for Jersey, 3654 (10,605) for Angus, 1239 (4103) for pig, and 1655 (4171) for broiler chicken. Each trait in each species was analyzed using the APY inverse of the GRM with randomly selected core animals, and their number was equal to the number of largest eigenvalues. Realized accuracies peaked with the number of core animals corresponding to 98% of variation for Holstein and Jersey and closer to 99% for other breed/species. N
conclusionsEigenvalue profiles of GRM for common species are similar to those in simulation studies although they are affected by number of genotyped animals and genotyping quality. For all investigated species, the APY required less than 15,000 core animals. Realized accuracies were equal or greater with the APY inverse than with regular inversion. Eigenvalue analysis of GRM can provide a realistic estimate of N
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