ArticleHeredity2017
Incorporating the single-step strategy into a random regression model to enhance genomic prediction of longitudinal traits.
Article in Heredity, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Accuracy of single-step genomic selection for egg production in yellow-feathered broilers using a random regression model.Poultry science · 2026Article
- Approximation of reliabilities for random-regression single-step genomic best linear unbiased predictor models.JDS communications · 2024Article
- Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population.Animals : an open access journal from MDPI · 2023Article
- Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation.Scientific reports · 2021Article
- Genetic analysis of egg production traits in turkeys (Meleagris gallopavo) using a single-step genomic random regression model.Genetics, selection, evolution : GSE · 2021Article
- A Random Regression Model Based on a Single-Step Method for Improving the Genomic Prediction Accuracy of Residual Feed Intake in Pigs.Frontiers in genetics · 2021Article
- Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops.Frontiers in plant science · 2020Review
- Article
- PIBLUP: High-Performance Software for Large-Scale Genetic Evaluation of Animals and Plants.Frontiers in genetics · 2018Article
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Authors and funding
5 authors.
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Abstract
In prediction of genomic values, the single-step method has been demonstrated to outperform multi-step methods. In statistical analyses of longitudinal traits, the random regression test-day model (RR-TDM) has clear advantages over other models. Our goal in this study was to evaluate the performance of a model that integrates both single-step and RR-TDM prediction methods, called the single-step random regression test-day model (SS RR-TDM), in comparison with the pedigree-based RR-TDM and genomic best linear unbiased prediction (GBLUP) model. We performed extensive simulations to exploit the potential advantages of SS RR-TDM over the other two models under various scenarios with different levels of heritability, number of quantitative trait loci, as well as selection scheme. SS RR-TDM was found to achieve the highest accuracy and unbiasedness under all scenarios, exhibiting robust prediction ability in longitudinal trait analyses. Moreover, SS RR-TDM showed better persistency of accuracy over generations than the GBLUP model. In addition, we also found that the SS RR-TDM had advantages over RR-TDM and GBLUP in terms of its being a real data set of humans contributed by the Genetic Analysis Workshop 18. The findings of our study demonstrated the feasibility and advantages of SS RR-TDM, thus enhancing the strategies for genomic prediction of longitudinal traits in the future.
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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.