Cargando…
Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits
Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in or...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383196/ https://www.ncbi.nlm.nih.gov/pubmed/37514272 http://dx.doi.org/10.3390/plants12142659 |
_version_ | 1785080848055271424 |
---|---|
author | Yoosefzadeh-Najafabadi, Mohsen Torabi, Sepideh Tulpan, Dan Rajcan, Istvan Eskandari, Milad |
author_facet | Yoosefzadeh-Najafabadi, Mohsen Torabi, Sepideh Tulpan, Dan Rajcan, Istvan Eskandari, Milad |
author_sort | Yoosefzadeh-Najafabadi, Mohsen |
collection | PubMed |
description | Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research. |
format | Online Article Text |
id | pubmed-10383196 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103831962023-07-30 Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits Yoosefzadeh-Najafabadi, Mohsen Torabi, Sepideh Tulpan, Dan Rajcan, Istvan Eskandari, Milad Plants (Basel) Article Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research. MDPI 2023-07-16 /pmc/articles/PMC10383196/ /pubmed/37514272 http://dx.doi.org/10.3390/plants12142659 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yoosefzadeh-Najafabadi, Mohsen Torabi, Sepideh Tulpan, Dan Rajcan, Istvan Eskandari, Milad Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits |
title | Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits |
title_full | Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits |
title_fullStr | Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits |
title_full_unstemmed | Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits |
title_short | Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits |
title_sort | application of svr-mediated gwas for identification of durable genetic regions associated with soybean seed quality traits |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383196/ https://www.ncbi.nlm.nih.gov/pubmed/37514272 http://dx.doi.org/10.3390/plants12142659 |
work_keys_str_mv | AT yoosefzadehnajafabadimohsen applicationofsvrmediatedgwasforidentificationofdurablegeneticregionsassociatedwithsoybeanseedqualitytraits AT torabisepideh applicationofsvrmediatedgwasforidentificationofdurablegeneticregionsassociatedwithsoybeanseedqualitytraits AT tulpandan applicationofsvrmediatedgwasforidentificationofdurablegeneticregionsassociatedwithsoybeanseedqualitytraits AT rajcanistvan applicationofsvrmediatedgwasforidentificationofdurablegeneticregionsassociatedwithsoybeanseedqualitytraits AT eskandarimilad applicationofsvrmediatedgwasforidentificationofdurablegeneticregionsassociatedwithsoybeanseedqualitytraits |