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Acta Agronomica Sinica ›› 2022, Vol. 48 ›› Issue (6): 1416-1424.doi: 10.3724/SP.J.1006.2022.14088

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

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 Online:2022-06-12 Published:2021-10-18
  • Contact: ZHANG Yuan-Ming E-mail:1390032282@qq.com;soyzhang@mail.hzau.edu.cn
  • About author:First author contact:

    ** Contributed equally to this work

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

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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

Table 1

14 population types and their abbreviations in software SEA v2.0"

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]

Table 2

Genetic models and their codes in software SEA v2.0"

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

Fig. 1

Application of software package SEA v2.0"

Fig. 2

Output results of mixed major-genes plus polygenes inheritance analysis"

Table 3

Monte Carlo simulation results of segregation analysis for quantitative traits in P1, F1, P2, B1, B2, and F2"

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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