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Data from: Incorporating single-step strategy into random regression model to enhance genomic prediction of longitudinal trait

Citation

Kang, Huimin et al. (2016), Data from: Incorporating single-step strategy into random regression model to enhance genomic prediction of longitudinal trait, Dryad, Dataset, https://doi.org/10.5061/dryad.8df69

Abstract

In prediction of genomic values, single-step method has been demonstrated to outperform multi-step methods. In statistical analyses of longitudinal traits, random regression test-day model (RR-TDM) has clear advantages over other models. Our goal in this study was to evaluate the performance of the model integrating both single-step and RR-TDM prediction methods, called 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 potential advantages of SS RR-TDM over the other two models under various scenarios with different level of heritability, the number of QTL as well as the 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 GBLUP model. In addition, we also found that the SS RR-TDM had advantages over RR-TDM and GBLUP in terms of a real dataset of human contributed by the GAW18 workshop. The findings in our study firstly proved the feasibility and advantages of the SS RR-TDM, and further enhanced strategies for the genomic prediction of longitudinal traits in the future.

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