The theory on and software simulating large-scale genomic data for genotype-by-environment interactions

BACKGROUND: With the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches...

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Autores principales: Li, Xiujin, Song, Hailiang, Zhang, Zhe, Huang, Yunmao, Zhang, Qin, Ding, Xiangdong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8647494/
https://www.ncbi.nlm.nih.gov/pubmed/34865618
http://dx.doi.org/10.1186/s12864-021-08191-z
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author Li, Xiujin
Song, Hailiang
Zhang, Zhe
Huang, Yunmao
Zhang, Qin
Ding, Xiangdong
author_facet Li, Xiujin
Song, Hailiang
Zhang, Zhe
Huang, Yunmao
Zhang, Qin
Ding, Xiangdong
author_sort Li, Xiujin
collection PubMed
description BACKGROUND: With the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches. RESULTS: We proposed a theory to simulate large-scale genomic data on genotype-by-environment interactions and added this new function to our developed tool GPOPSIM. Additionally, a simulated threshold trait with large-scale genomic data was also added. The validation of the simulated data indicated that GPOSPIM2.0 is an efficient tool for mimicking the phenotypic data of quantitative traits, threshold traits, and genetically correlated traits with large-scale genomic data while taking genotype-by-environment interactions into account. CONCLUSIONS: This tool is useful for assessing genotype-by-environment interactions and threshold traits methods.
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spelling pubmed-86474942021-12-07 The theory on and software simulating large-scale genomic data for genotype-by-environment interactions Li, Xiujin Song, Hailiang Zhang, Zhe Huang, Yunmao Zhang, Qin Ding, Xiangdong BMC Genomics Software BACKGROUND: With the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches. RESULTS: We proposed a theory to simulate large-scale genomic data on genotype-by-environment interactions and added this new function to our developed tool GPOPSIM. Additionally, a simulated threshold trait with large-scale genomic data was also added. The validation of the simulated data indicated that GPOSPIM2.0 is an efficient tool for mimicking the phenotypic data of quantitative traits, threshold traits, and genetically correlated traits with large-scale genomic data while taking genotype-by-environment interactions into account. CONCLUSIONS: This tool is useful for assessing genotype-by-environment interactions and threshold traits methods. BioMed Central 2021-12-05 /pmc/articles/PMC8647494/ /pubmed/34865618 http://dx.doi.org/10.1186/s12864-021-08191-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 Software
Li, Xiujin
Song, Hailiang
Zhang, Zhe
Huang, Yunmao
Zhang, Qin
Ding, Xiangdong
The theory on and software simulating large-scale genomic data for genotype-by-environment interactions
title The theory on and software simulating large-scale genomic data for genotype-by-environment interactions
title_full The theory on and software simulating large-scale genomic data for genotype-by-environment interactions
title_fullStr The theory on and software simulating large-scale genomic data for genotype-by-environment interactions
title_full_unstemmed The theory on and software simulating large-scale genomic data for genotype-by-environment interactions
title_short The theory on and software simulating large-scale genomic data for genotype-by-environment interactions
title_sort theory on and software simulating large-scale genomic data for genotype-by-environment interactions
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8647494/
https://www.ncbi.nlm.nih.gov/pubmed/34865618
http://dx.doi.org/10.1186/s12864-021-08191-z
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