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Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle
BACKGROUND: Size of reference population is a crucial factor affecting the accuracy of prediction of the genomic estimated breeding value (GEBV). There are few studies in beef cattle that have compared accuracies achieved using real data to that achieved with simulated data and deterministic predict...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8572443/ https://www.ncbi.nlm.nih.gov/pubmed/34742249 http://dx.doi.org/10.1186/s12864-021-08121-z |
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author | Takeda, Masayuki Inoue, Keiichi Oyama, Hidemi Uchiyama, Katsuo Yoshinari, Kanako Sasago, Nanae Kojima, Takatoshi Kashima, Masashi Suzuki, Hiromi Kamata, Takehiro Kumagai, Masahiro Takasugi, Wataru Aonuma, Tatsuya Soma, Yuusuke Konno, Sachi Saito, Takaaki Ishida, Mana Muraki, Eiji Inoue, Yoshinobu Takayama, Megumi Nariai, Shota Hideshima, Ryoya Nakamura, Ryoichi Nishikawa, Sayuri Kobayashi, Hiroshi Shibata, Eri Yamamoto, Koji Yoshimura, Kenichi Matsuda, Hironori Inoue, Tetsuro Fujita, Atsumi Terayama, Shohei Inoue, Kazuya Morita, Sayuri Nakashima, Ryotaro Suezawa, Ryohei Hanamure, Takeshi Zoda, Atsushi Uemoto, Yoshinobu |
author_facet | Takeda, Masayuki Inoue, Keiichi Oyama, Hidemi Uchiyama, Katsuo Yoshinari, Kanako Sasago, Nanae Kojima, Takatoshi Kashima, Masashi Suzuki, Hiromi Kamata, Takehiro Kumagai, Masahiro Takasugi, Wataru Aonuma, Tatsuya Soma, Yuusuke Konno, Sachi Saito, Takaaki Ishida, Mana Muraki, Eiji Inoue, Yoshinobu Takayama, Megumi Nariai, Shota Hideshima, Ryoya Nakamura, Ryoichi Nishikawa, Sayuri Kobayashi, Hiroshi Shibata, Eri Yamamoto, Koji Yoshimura, Kenichi Matsuda, Hironori Inoue, Tetsuro Fujita, Atsumi Terayama, Shohei Inoue, Kazuya Morita, Sayuri Nakashima, Ryotaro Suezawa, Ryohei Hanamure, Takeshi Zoda, Atsushi Uemoto, Yoshinobu |
author_sort | Takeda, Masayuki |
collection | PubMed |
description | BACKGROUND: Size of reference population is a crucial factor affecting the accuracy of prediction of the genomic estimated breeding value (GEBV). There are few studies in beef cattle that have compared accuracies achieved using real data to that achieved with simulated data and deterministic predictions. Thus, extent to which traits of interest affect accuracy of genomic prediction in Japanese Black cattle remains obscure. This study aimed to explore the size of reference population for expected accuracy of genomic prediction for simulated and carcass traits in Japanese Black cattle using a large amount of samples. RESULTS: A simulation analysis showed that heritability and size of reference population substantially impacted the accuracy of GEBV, whereas the number of quantitative trait loci did not. The estimated numbers of independent chromosome segments (M(e)) and the related weighting factor (w) derived from simulation results and a maximum likelihood (ML) approach were 1900–3900 and 1, respectively. The expected accuracy for trait with heritability of 0.1–0.5 fitted well with empirical values when the reference population comprised > 5000 animals. The heritability for carcass traits was estimated to be 0.29–0.41 and the accuracy of GEBVs was relatively consistent with simulation results. When the reference population comprised 7000–11,000 animals, the accuracy of GEBV for carcass traits can range 0.73–0.79, which is comparable to estimated breeding value obtained in the progeny test. CONCLUSION: Our simulation analysis demonstrated that the expected accuracy of GEBV for a polygenic trait with low-to-moderate heritability could be practical in Japanese Black cattle population. For carcass traits, a total of 7000–11,000 animals can be a sufficient size of reference population for genomic prediction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-08121-z. |
format | Online Article Text |
id | pubmed-8572443 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85724432021-11-08 Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle Takeda, Masayuki Inoue, Keiichi Oyama, Hidemi Uchiyama, Katsuo Yoshinari, Kanako Sasago, Nanae Kojima, Takatoshi Kashima, Masashi Suzuki, Hiromi Kamata, Takehiro Kumagai, Masahiro Takasugi, Wataru Aonuma, Tatsuya Soma, Yuusuke Konno, Sachi Saito, Takaaki Ishida, Mana Muraki, Eiji Inoue, Yoshinobu Takayama, Megumi Nariai, Shota Hideshima, Ryoya Nakamura, Ryoichi Nishikawa, Sayuri Kobayashi, Hiroshi Shibata, Eri Yamamoto, Koji Yoshimura, Kenichi Matsuda, Hironori Inoue, Tetsuro Fujita, Atsumi Terayama, Shohei Inoue, Kazuya Morita, Sayuri Nakashima, Ryotaro Suezawa, Ryohei Hanamure, Takeshi Zoda, Atsushi Uemoto, Yoshinobu BMC Genomics Research BACKGROUND: Size of reference population is a crucial factor affecting the accuracy of prediction of the genomic estimated breeding value (GEBV). There are few studies in beef cattle that have compared accuracies achieved using real data to that achieved with simulated data and deterministic predictions. Thus, extent to which traits of interest affect accuracy of genomic prediction in Japanese Black cattle remains obscure. This study aimed to explore the size of reference population for expected accuracy of genomic prediction for simulated and carcass traits in Japanese Black cattle using a large amount of samples. RESULTS: A simulation analysis showed that heritability and size of reference population substantially impacted the accuracy of GEBV, whereas the number of quantitative trait loci did not. The estimated numbers of independent chromosome segments (M(e)) and the related weighting factor (w) derived from simulation results and a maximum likelihood (ML) approach were 1900–3900 and 1, respectively. The expected accuracy for trait with heritability of 0.1–0.5 fitted well with empirical values when the reference population comprised > 5000 animals. The heritability for carcass traits was estimated to be 0.29–0.41 and the accuracy of GEBVs was relatively consistent with simulation results. When the reference population comprised 7000–11,000 animals, the accuracy of GEBV for carcass traits can range 0.73–0.79, which is comparable to estimated breeding value obtained in the progeny test. CONCLUSION: Our simulation analysis demonstrated that the expected accuracy of GEBV for a polygenic trait with low-to-moderate heritability could be practical in Japanese Black cattle population. For carcass traits, a total of 7000–11,000 animals can be a sufficient size of reference population for genomic prediction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-08121-z. BioMed Central 2021-11-06 /pmc/articles/PMC8572443/ /pubmed/34742249 http://dx.doi.org/10.1186/s12864-021-08121-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Takeda, Masayuki Inoue, Keiichi Oyama, Hidemi Uchiyama, Katsuo Yoshinari, Kanako Sasago, Nanae Kojima, Takatoshi Kashima, Masashi Suzuki, Hiromi Kamata, Takehiro Kumagai, Masahiro Takasugi, Wataru Aonuma, Tatsuya Soma, Yuusuke Konno, Sachi Saito, Takaaki Ishida, Mana Muraki, Eiji Inoue, Yoshinobu Takayama, Megumi Nariai, Shota Hideshima, Ryoya Nakamura, Ryoichi Nishikawa, Sayuri Kobayashi, Hiroshi Shibata, Eri Yamamoto, Koji Yoshimura, Kenichi Matsuda, Hironori Inoue, Tetsuro Fujita, Atsumi Terayama, Shohei Inoue, Kazuya Morita, Sayuri Nakashima, Ryotaro Suezawa, Ryohei Hanamure, Takeshi Zoda, Atsushi Uemoto, Yoshinobu Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle |
title | Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle |
title_full | Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle |
title_fullStr | Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle |
title_full_unstemmed | Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle |
title_short | Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle |
title_sort | exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in japanese black cattle |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8572443/ https://www.ncbi.nlm.nih.gov/pubmed/34742249 http://dx.doi.org/10.1186/s12864-021-08121-z |
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