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作物学报 ›› 2022, Vol. 48 ›› Issue (6): 1416-1424.doi: 10.3724/SP.J.1006.2022.14088

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

数量性状主基因+多基因混合遗传分析R软件包SEA v2.0

王靖天**(), 张亚雯**, 杜应雯**, 任文龙, 李宏福, 孙文献, 葛超, 章元明*()   

  1. 华中农业大学植物科学技术学院, 湖北武汉 430070
  • 收稿日期:2021-05-15 接受日期:2021-09-10 出版日期:2022-06-12 网络出版日期:2021-10-18
  • 通讯作者: 章元明
  • 作者简介:王靖天, E-mail: 1390032282@qq.com第一联系人：

    ** 同等贡献

  • 基金资助:
    国家自然科学基金项目(32070557)

SEA v2.0: an R software package for mixed major genes plus polygenes inheritance analysis of quantitative traits

WANG Jing-Tian**(), ZHANG Ya-Wen**, DU Ying-Wen**, REN Wen-Long, LI Hong-Fu, SUN Wen-Xian, GE Chao, ZHANG Yuan-Ming*()   

  1. College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
  • Received:2021-05-15 Accepted:2021-09-10 Published:2022-06-12 Published online:2021-10-18
  • Contact: ZHANG Yuan-Ming
  • About author:First author contact:

    ** Contributed equally to this work

  • Supported by:
    National Natural Science Foundation of China(32070557)

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摘要：

利用双亲分离群体数量性状表型值可鉴定其主基因+多基因混合遗传模型, 为数量性状遗传基础和作物育种提供参考信息。为全面总结数量性状分离分析的研究成果、添加软件包新功能和矫正以前版本的缺陷, 在R Studio-1.4.1103平台和R编程语言框架下, 开发了具有交互式图形用户界面的R软件包SEA v2.0。该软件可分析14种双亲分离群体类型, 每种群体类型均有数据导入、数据分析、后验概率计算和分布曲线绘制4个模块。为节省计算时间, 用doParallel包并行计算、data.table包读写数据和MASS包估计分布参数。用KScorrect、kolmim和shiny包简化程序。只要用户上传*.csv格式数据文件并设置相关参数, 可快速显示计算结果。通过大豆结荚习性数据分析和Monte Carlo模拟研究, 证实了SEA v2.0软件包的有效性。软件包可从https://cran.r-project.org/web/packages/SEA/index.html下载。

关键词: 双亲分离群体, 数量性状, 主基因+多基因混合模型, R软件包, SEA

Abstract:

The phenotypic values for quantitative trait from bi-parental segregation populations can be used to identify its mixed major genes plus polygenes inheritance model, which provides important information for the genetic basis of quantitative traits and crop breeding. To comprehensively summarize the research results of methodological advances, add the new functions of the software and correct its shortcomings in previous versions, an R software package SEA v2.0 with interactive graphical user interface is developed under R studio-1.4.1103 platform and R environment. In this software, there were 14 types of bi-parental segregation populations, and each type included four modules: data input, data analysis, posterior probability calculation, and distribution curve drawing. To save running time, doParallel was used to conduct parallel computing, data.table was used to quickly read and write datasets, and MASS was used to estimate the parameters in component distributions. KScorrect, kolmim, and shiny packages were used to simplify the programs. As long as users uploaded the data file with *.csv format and set the related parameters, the results could be quickly displayed. The software was validated by real data analysis of soybean podding habit and Monte Carlo simulation studies, and can be downloaded from https://cran.r-project.org/web/packages/SEA/index.html.

Key words: bi-parental segregation population, quantitative trait, mixed major genes plus polygenes inheritance model, R software, SEA

表1

SEA v2.0软件的14种群体组合及其缩写"

群体类型
Population type		 缩写
Abbreviation		 文件列名
Column name		 参考文献
Reference		 群体类型
Population type		 缩写
Abbreviation		 文件列名
Column name		 参考文献
Reference
F2 SEA-F2 F2 [12, 13] P1, F1, P2, F2:3 SEA-G4F3 P1, F1, P2, F23 [16]
F2:3 SEA-F3 F23 [14] P1, P2, DH SEA-G3DH P1, P2, DH [15]
DH/RIL SEA-DH DH [15] P1, F1, P2 B1, B2 SEA-G5BC P1, F1, P2, B1, B2 [17]
BIL SEA-BIL BIL [11] P1, F1, P2, B1:2, B2:2 SEA-G5BCF P1, F1, P2, B12, B22 [17]
B1, B2 SEA-BC B1, B2 [13] P1, F1, P2, F2, F2:3 SEA-G5 P1, F1, P2, F2, F23 [18, 19]
B1:2, B2:2 SEA-BCF B12, B22 [11] P1, F1, P2, F2, B1, B2 SEA-G6 P1, F1, P2, F2, B1, B2 [20, 21]
P1, F1, P2, F2 SEA-G4F2 P1, F1, P2, F2 [16] P1, F1, P2, F2:3, B1:2, B2:2 SEA-G6F P1, F1, P2, F23, B12, B22 [22]

表2

SEA v2.0软件的遗传模型与模型代号"

类别
Type		 主基因
Major genes		 多基因
Polygenes		 模型代号 Model code
主基因
Only major genes		 主基因+多基因
Major gene & polygenes
多基因 加性-显性-上位性ADE PG-ADI
Polygenes 加性-显性 AD PG-AD
1对主基因 加性-显性 AD 加性-显性-上位性ADE 1MG-AD MX1-AD-ADI
One MG 加性-显性 AD 加性-显性 AD 1MG-AD MX1-AD-AD
加性 A 加性-显性 AD 1MG-A MX1-A-AD
加性 A 加性-上位性 AE 1MG-A MX1-A-AI
加性 A 加性 A 1MG-A MX1-A-A
完全显性 CD 加性-显性 AD 1MG-EAD MX1-EAD-AD
负向完全显性 NCD 加性-显性 AD 1MG-NCD MX1-NCD-AD
2对主基因 加性-显性-上位性 ADE 加性-显性-上位性 ADE 2MG-ADI MX2-ADI-ADI
Two MGs 加性-显性-上位性 ADE 加性-显性 AD 2MG-ADI MX2-ADI-AD
加性-显性 AD 加性-显性 AD 2MG-AD MX2-AD-AD
加性 A 加性-显性 AD 2MG-A MX2-A-AD
加性 A 加性 A 2MG-A MX2-A-A
加性-上位性 AE 加性-上位性 AE 2MG-AI MX2-AI-AI
加性-上位性 AE 加性 A 2MG-AI MX2-AI-A
等加性 EA 加性-显性 AD 2MG-EA MX2-EA-AD
等加性 EA 加性 A 2MG-EA MX2-EA-A
2对主基因 显性上位 DE1 加性 A 2MG-ED MX2-ED-A
Two MGs 隐性上位 RE 加性 A 2MG-ER MX2-ER-A
累加作用 CE1 加性 A 2MG-AE MX2-AE-A
互补作用 CE2 加性 A 2MG-CE MX2-CE-A
重叠作用 DE2 加性 A 2MG-DE MX2-DE-A
抑制作用 IE 加性 A 2MG-IE MX2-IE-A
完全显性 CD 加性-显性 AD 2MG-CD MX2-CD-AD
等显性 ED 加性-显性 AD 2MG-EAD MX2-EAD-AD
3对主基因 加性-上位性 AE 加性-上位性 AE 3MG-AI MX3-AI-AI
Three MGs 加性-上位性 AE 加性 A 3MG-AI MX3-AI-A
加性 A 加性 A 3MG-A MX3-A-A
等加性 EA 加性 A 3MG-CEA MX3-CEA-A
部分等加性 PEA 加性 A 3MG-PEA MX3-PEA-A
4对主基因 加性-上位性 AE 4MG-AI
Four MGs 等加性 EA 4MG-CEA
部分等加性1 PEA1 4MG-EEA
部分等加性2 PEA2 4MG-EEEA

图1

软件包SEA v2.0的使用"

图2

主基因+多基因混合遗传分析输出结果"

表3

P1、F1、P2、B1、B2和F2群体数量性状分离分析的Monte Carlo模拟研究结果"

模型
Model		 统计量
Statistic		 平均数
Mean (m)		 加性效应1
Additive (da)		 加性效应2
Additive (db)		 显性效应1
Dominant (ha)		 显性效应2
Dominant (hb)		 误差方差
Residual variance		 主基因遗传方差
MG variance		 B1主基因遗传率
MG heritability in B1 (%)		 功效
Power (%)
1MG-AD 参数真值 TVP 100.00 10.00 3.68 10.00 10.00 50.00 92.00
估计值±标准差
Estimates ± SD		 99.97±0.17 9.97±0.09 3.69±0.20 9.96±0.58 10.02±1.31 49.99±3.77
平均差 AD 0.14 0.07 0.15 0.48 1.04 3.02
2MG-ADI 参数真值 TVP 10.00 7.07 3.16 14.14 6.32 10.00 15.00 60.00 90.00
估计值±标准差
Estimates ± SD		 101.59±1.77 7.90±1.23 2.25±1.24 11.05±1.83 6.45±1.82 9.64±0.80 12.34±1.29 56.07±3.41
平均差 AD 1.76 1.08 1.11 3.09 1.63 0.71 2.71 4.36
PG-ADI 参数真值 TVP 100.00 10.00 100.00
估计值±标准差
Estimates ± SD		 100.32±0.28 9.59±1.34
平均差 AD 0.35 1.18
MX1-AD-ADI 参数真值 TVP 100.00 10.00 17.07 10.00 12.50 50.00 99.00
估计值±标准差
Estimates ± SD		 115.24±0.36 10.05±0.15 17.06±0.26 9.47±0.76 12.31±1.41 54.40±4.20
平均差 AD 15.24 0.13 0.21 0.73 1.13 5.25
MX2-ADI-ADI 参数真值 TVP 100.00 7.07 3.16 14.14 6.32 10.00 15.00 60.00 79.00
估计值±标准差
Estimates ± SD		 102.06±2.41 7.06±1.10 7.06±1.10 8.79±2.88 4.06±2.99 9.82±1.14 13.04±3.78 47.27±13.16
平均差 AD 2.51 0.87 3.90 5.41 2.93 0.91 3.29 14.01
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