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SNP-SNP Interaction Analysis on Soybean Oil Content under Multi-Environments

Soybean oil content is one of main quality traits. In this study, we used the multifactor dimensionality reduction (MDR) method and a soybean high-density genetic map including 5,308 markers to identify stable single nucleotide polymorphism (SNP)—SNP interactions controlling oil content in soybean a...

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Autores principales: Chen, Qingshan, Mao, Xinrui, Zhang, Zhanguo, Zhu, Rongsheng, Yin, Zhengong, Leng, Yue, Yu, Hongxiao, Jia, Huiying, Jiang, Shanshan, Ni, Zhongqiu, Jiang, Hongwei, Han, Xue, Liu, Chunyan, Hu, Zhenbang, Wu, Xiaoxia, Hu, Guohua, Xin, Dawei, Qi, Zhaoming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036806/
https://www.ncbi.nlm.nih.gov/pubmed/27668866
http://dx.doi.org/10.1371/journal.pone.0163692
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author Chen, Qingshan
Mao, Xinrui
Zhang, Zhanguo
Zhu, Rongsheng
Yin, Zhengong
Leng, Yue
Yu, Hongxiao
Jia, Huiying
Jiang, Shanshan
Ni, Zhongqiu
Jiang, Hongwei
Han, Xue
Liu, Chunyan
Hu, Zhenbang
Wu, Xiaoxia
Hu, Guohua
Xin, Dawei
Qi, Zhaoming
author_facet Chen, Qingshan
Mao, Xinrui
Zhang, Zhanguo
Zhu, Rongsheng
Yin, Zhengong
Leng, Yue
Yu, Hongxiao
Jia, Huiying
Jiang, Shanshan
Ni, Zhongqiu
Jiang, Hongwei
Han, Xue
Liu, Chunyan
Hu, Zhenbang
Wu, Xiaoxia
Hu, Guohua
Xin, Dawei
Qi, Zhaoming
author_sort Chen, Qingshan
collection PubMed
description Soybean oil content is one of main quality traits. In this study, we used the multifactor dimensionality reduction (MDR) method and a soybean high-density genetic map including 5,308 markers to identify stable single nucleotide polymorphism (SNP)—SNP interactions controlling oil content in soybean across 23 environments. In total, 36,442,756 SNP-SNP interaction pairs were detected, 1865 of all interaction pairs associated with soybean oil content were identified under multiple environments by the Bonferroni correction with p <3.55×10(−11). Two and 1863 SNP-SNP interaction pairs detected stable across 12 and 11 environments, respectively, which account around 50% of total environments. Epistasis values and contribution rates of stable interaction (the SNP interaction pairs were detected in more than 2 environments) pairs were detected by the two way ANOVA test, the available interaction pairs were ranged 0.01 to 0.89 and from 0.01 to 0.85, respectively. Some of one side of the interaction pairs were identified with previously research as a major QTL without epistasis effects. The results of this study provide insights into the genetic architecture of soybean oil content and can serve as a basis for marker-assisted selection breeding.
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spelling pubmed-50368062016-10-27 SNP-SNP Interaction Analysis on Soybean Oil Content under Multi-Environments Chen, Qingshan Mao, Xinrui Zhang, Zhanguo Zhu, Rongsheng Yin, Zhengong Leng, Yue Yu, Hongxiao Jia, Huiying Jiang, Shanshan Ni, Zhongqiu Jiang, Hongwei Han, Xue Liu, Chunyan Hu, Zhenbang Wu, Xiaoxia Hu, Guohua Xin, Dawei Qi, Zhaoming PLoS One Research Article Soybean oil content is one of main quality traits. In this study, we used the multifactor dimensionality reduction (MDR) method and a soybean high-density genetic map including 5,308 markers to identify stable single nucleotide polymorphism (SNP)—SNP interactions controlling oil content in soybean across 23 environments. In total, 36,442,756 SNP-SNP interaction pairs were detected, 1865 of all interaction pairs associated with soybean oil content were identified under multiple environments by the Bonferroni correction with p <3.55×10(−11). Two and 1863 SNP-SNP interaction pairs detected stable across 12 and 11 environments, respectively, which account around 50% of total environments. Epistasis values and contribution rates of stable interaction (the SNP interaction pairs were detected in more than 2 environments) pairs were detected by the two way ANOVA test, the available interaction pairs were ranged 0.01 to 0.89 and from 0.01 to 0.85, respectively. Some of one side of the interaction pairs were identified with previously research as a major QTL without epistasis effects. The results of this study provide insights into the genetic architecture of soybean oil content and can serve as a basis for marker-assisted selection breeding. Public Library of Science 2016-09-26 /pmc/articles/PMC5036806/ /pubmed/27668866 http://dx.doi.org/10.1371/journal.pone.0163692 Text en © 2016 Chen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Chen, Qingshan
Mao, Xinrui
Zhang, Zhanguo
Zhu, Rongsheng
Yin, Zhengong
Leng, Yue
Yu, Hongxiao
Jia, Huiying
Jiang, Shanshan
Ni, Zhongqiu
Jiang, Hongwei
Han, Xue
Liu, Chunyan
Hu, Zhenbang
Wu, Xiaoxia
Hu, Guohua
Xin, Dawei
Qi, Zhaoming
SNP-SNP Interaction Analysis on Soybean Oil Content under Multi-Environments
title SNP-SNP Interaction Analysis on Soybean Oil Content under Multi-Environments
title_full SNP-SNP Interaction Analysis on Soybean Oil Content under Multi-Environments
title_fullStr SNP-SNP Interaction Analysis on Soybean Oil Content under Multi-Environments
title_full_unstemmed SNP-SNP Interaction Analysis on Soybean Oil Content under Multi-Environments
title_short SNP-SNP Interaction Analysis on Soybean Oil Content under Multi-Environments
title_sort snp-snp interaction analysis on soybean oil content under multi-environments
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036806/
https://www.ncbi.nlm.nih.gov/pubmed/27668866
http://dx.doi.org/10.1371/journal.pone.0163692
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