关键词:不结球白菜; 维生素C; 主基因+多基因; 遗传模型分析 Major Gene Plus Polygene Inheritance of Vitamin C Content in Non-heading Chinese Cabbage LIN Ting-Ting, WANG Jian-Jun, WANG Li, CHEN Xuan, HOU Xi-Lin, LI Ying* State Key Laboratory of Corp Genetics and Germplasm Enhancement / College of Horticulture of Nanjing Agricultural University, Nanjing 210095, China Fund: AbstractThe mixed major gene plus polygene inheritance model was used to investigate the inheritance law of vitamin C in non-heading Chinese cabbage (Brassica campestris ssp.chinensis Makino) in six generations (P1, P2, F1, B1, B2, and F2) derived from Wutacai line (with high vitamin C content) × Erqing line (with low vitamin C content). The results showed that the trait of vitamin C was controlled by one additive major gene plus additive-dominant polygene in the joint analysis of six generations. The result in 2011 showed that the additive effect was 13.15. Heritabilities of major genes in B1, B2, and F2 populations were 54.38%, 38.58%, and 18.69%, respectively, while those of polygenes in the three populations were 24.69%, 36.92%, and 40.7%, respectively. The result in 2013 showed that the additive effect was 6.04. Heritabilities of major genes in B1, B2, and F2 populations were 1.88%, 6.41%, and 45.04%, respectively, while those of polygenes in B1, B2, and F2 populations were 39.67%, 16.57%, and 16.91%, respectively. The results from two years indicated that environmental factors could play roles in inheritance of vitamin C in non-heading Chinese cabbage. Thus, the marker assisted selection method could be used to screen high vitamin C cultivars of non-heading Chinese cabbage. In addition, environmental conditions should also be considered in the breeding process.
Keyword:Non-heading Chinese cabbage; Vitamin C; Major gene plus polygene; Inheritance model Show Figures Show Figures
表2 各遗传模型的极大对数似然值和AIC值 Table 2 Estimation of max-likelihood-value and AIC value of different genetic models
模型 Model
2011
2013
模型 Model
2011
2013
MLV
AIC
MLV
AIC
MLV
AIC
MLV
AIC
A-1
-1512.29
3032.58
-1673.58
3355.15
D
-1484.77
2993.53
-1643.46
3310.93
A-2
-1519.35
3044.70
-1673.74
3353.48
D-1
-1484.60
2987.21
-1642.26
3302.52
A-3
-1512.87
3031.74
-1684.64
3375.29
D-2
-1484.60
2985.21
-1642.26
3300.52
A-4
-1535.11
3076.22
-1671.37
3348.73
D-3
-1486.71
2989.41
-1642.36
3300.72
B-1
-1493.93
3007.87
-1640.71
3301.42
D-4
-1484.79
2985.59
-1647.43
3310.87
B-2
-1502.46
3016.92
-1671.15
3354.29
E
-1483.01
3002.01
-1639.10
3314.21
B-3
-1555.51
3119.02
-1692.82
3393.63
E-1
-1483.58
2997.15
-1638.23
3306.47
B-4
-1519.57
3045.15
-1675.81
3357.63
E-2
-1499.99
3021.97
-1672.22
3366.45
B-5
-1512.71
3033.41
-1684.64
3377.27
E-3
-1491.71
3001.43
-1648.88
3315.75
B-6
-1512.71
3031.41
-1685.19
3376.39
E-4
-1502.32
3020.64
-1672.25
3360.49
C
-1485.24
2990.49
-1644.71
3309.42
E-5
-1502.42
3022.83
-1672.25
3362.50
C-1
-1502.44
3018.87
-1672.12
3358.24
E-6
-1503.10
3022.20
-1674.44
3364.89
MLV: maximum likelihood method; AIC: Akaike information criterion.MLV: 极大对数似然值; AIC: AIC值。
表2 各遗传模型的极大对数似然值和AIC值 Table 2 Estimation of max-likelihood-value and AIC value of different genetic models
表3 Table 3 表3(Table 3)
表3 备选模型的适合性检验 Table 3 Fitness test of candidate genetic models
年份 Year
模型 Model
群体 Population
统计量Statistic
U12
U22
U32
nW2
Dn
2011
D-2
P1
0.463(0.4962)
0.084(0.7722)
2.183(0.1395)
0.1321
0.2287(>0.05)
F1
0.937(0.3330)
0.359(0.5489)
1.828(0.1763)
0.2068
0.2532(>0.05)
P2
0.028(0.8671)
0.332(0.5645)
2.745(0.0976)
0.0989
0.1689(>0.05)
B1
1.782(0.1819)
1.670(0.1963)
0.000(0.9986)
0.1733
0.1057(>0.05)
B2
0.013(0.9084)
0.013(0.9097)
0.000(0.9933)
0.0305
0.0576(>0.05)
F2
0.057(0.8119)
0.225(0.6350)
0.955(0.3285)
0.0610
0.0484(>0.05)
D-4
P1
0.469(0.4934)
0.086(0.7692)
2.188(0.1391)
0.1327
0.2292(>0.05)
F1
0.887(0.3464)
0.333(0.5639)
1.792(0.1807)
0.2017
0.2510(>0.05)
P2
0.015(0.9012)
0.286(0.5926)
2.754(0.0970)
0.0977
0.1654(>0.05)
B1
1.808(0.1788)
1.693(0.1932)
0.000(0.9976)
0.1757
0.1061(>0.05)
B2
0.001(0.9710)
0.000(0.9846)
0.047(0.8275)
0.0286
0.0556(>0.05)
F2
0.056(0.8128)
0.224(0.6361)
0.952(0.3293)
0.0609
0.0485(>0.05)
2013
D-2
P1
1.203(0.2727)
2.656(0.1031)
5.157(0.0231)*
0.2433
0.2568(>0.05)
F1
2.558(0.1097)
1.649(0.1992)
1.120(0.2898)
0.3743
0.2358(>0.05)
P2
0.031(0.8592)
0.047(0.8277)
2.426(0.1194)
0.0961
0.1554(>0.05)
B1
1.968(0.1607)
2.119(0.1455)
0.152(0.6965)
0.2457
0.1465(>0.05)
B2
0.001(0.9693)
0.011(0.9176)
0.070(0.7909)
0.0413
0.0701(>0.05)
F2
0.274(0.6010)
0.113(0.7363)
0.461(0.4973)
0.0658
0.0504(>0.05)
D-3
P1
1.206(0.2722)
2.660(0.1029)
5.159(0.0231)*
0.2435
0.2568(>0.05)
F1
2.534(0.1114)
1.627(0.2022)
1.131(0.2875)
0.3719
0.2353(>0.05)
P2
0.028(0.8668)
0.051(0.8207)
2.422(0.1196)
0.0959
0.1548(>0.05)
B1
1.977(0.1597)
2.128(0.1446)
0.151(0.6972)
0.2465
0.1467(>0.05)
B2
0.001(0.9766)
0.009(0.9256)
0.067(0.7951)
0.0414
0.0704(>0.05)
F2
0.274(0.6009)
0.113(0.7363)
0.461(0.4971)
0.0658
0.0504(>0.05)
U12, U22, U32: statistic of Uniformity test;nW2: statistic of Smirnov test; Dn: statistic of Kolmogorov;* indicates significance at P < 0.05. U12、 U22、 U32为均匀性检验统计量;nW2为Smirnov检验统计量; Dn为Kolmogorov检验统计量;*表示0.05的显著水平。
表3 备选模型的适合性检验 Table 3 Fitness test of candidate genetic models
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