An effective hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation
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(Published version)
Date
2023
Authors
Neshat, M.
Lee, S.
Momin, M.M.
Truong, B.
van der Werf, J.H.J.
Lee, S.H.
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Journal article
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Frontiers in Genetics, 2023; 14(1104906):1-12
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Mehdi Neshat, Soohyun Lee, Md. Moksedul Momin, Buu Truong, Julius H. J. van der Werf, and S. Hong Lee
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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, α, which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase prediction accuracy, in addition to blending and tuning processes, when using HBLUP.
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Data source: Supplementary information, https://doi.org/10.3389/fgene.2023.1104906
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© 2023 Neshat, Lee, Momin, Truong, van der Werf and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.