ArticleFrontiers in genetics2023
An effective hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation.
Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Methods to detect selection history in a population under ongoing directional selection.Genetics · 2026Article
- Evaluating Adjusted ssGBLUP Models for Genomic Prediction and Matrix Compatibility in South African Holstein Cattle.Animals : an open access journal from MDPI · 2026Article
- Genomic Evaluation in Nellore Cattle for Reproductive Traits: Multiple Ways to Account for Missing Pedigrees.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2026Article
- Preliminary Evaluation of Blending, Tuning, and Scaling Parameters in ssGBLUP for Genomic Prediction Accuracy in South African Holstein Cattle.Animals : an open access journal from MDPI · 2025Article
- Genomic Prediction of Milk Fat Percentage Among Crossbred Cattle in the Indian Subcontinent.Animals : an open access journal from MDPI · 2025Article
- Design of risk prediction model for esophageal cancer based on machine learning approach.Heliyon · 2024Article
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6 authors.
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Abstract
The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter <1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor,
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