2.1 Selection of SSR primers and polymorphisms in the amplified products
A total of 126 pairs of candidate SSR primers were selected by using 8 samples of DNA from ICR-CAAS14 and ‘Xinluzao6’ as templates (Fig. 1), and 71 pairs of SSR primers that gave good polymorphisms, clear amplified bands, and excellent stability were chosen. Except for the unknown chromosome information for 17 pairs of primers, the other primers covered all cotton chromosomes except for Chr. 04, Chr. 08, Chr. 16, and Chr. 26.
A total of 142 effective allelic variants were detected by amplifying DNA from the 79 accessions with the selected 71 pairs of SSR primers. The average number of alleles detected for each SSR primer pair was 2.01, with a range of 1-6. The effective allele numbers ranged from 1.2256 to 10.4502 with an average of 3.4379. The polymorphism information content (PIC) ranged from 0.1841 to 0.9043, with an average of 0.6494. The PIC of the primer pair MON_CGR5565 had the highest value of 0.9043, and the primer pair NAU4044 had the second highest PIC value of 0.8822, whereas the primer pair NAU3181 had the lowest PIC of 0.1841. Genetic diversity (H') ranged from 1.2256 to 10.4502 (Table 2).
Table 2. SSR marker loci, chromosomal locations, and SSR-PCR polymorphism data
SN
|
SRR locus
|
Chromosome
|
PIC
|
Genetic diversity (H')
|
Effective Number
of Alleles (Ne)
|
1
|
NAU4073
|
Chr.01
|
0.8168
|
5.7030
|
5.4582
|
2
|
NAU2457
|
Chr.01
|
0.2577
|
1.5311
|
1.3471
|
3
|
NAU2083
|
Chr.01
|
0.4356
|
1.8725
|
1.7717
|
4
|
NAU4044
|
Chr.01
|
0.8822
|
9.1008
|
8.4884
|
5
|
MON_COT064
|
Chr.02
|
0.4980
|
1.9960
|
1.9920
|
6
|
NAU1190
|
Chr.03
|
0.7956
|
5.3171
|
4.8933
|
7
|
MON_CGR6528
|
Chr.03
|
0.7423
|
3.9381
|
3.8800
|
8
|
MON_CGR6683
|
Chr.03
|
0.4576
|
1.9158
|
1.8437
|
9
|
NAU1269
|
Chr.05
|
0.7314
|
3.8504
|
3.7234
|
10
|
MON_CGR5732
|
Chr.05
|
0.7307
|
3.8447
|
3.7138
|
11
|
NAU1225
|
Chr.05
|
0.7307
|
3.8447
|
3.7138
|
12
|
MON_DC40122
|
Chr.05
|
0.6884
|
3.5157
|
3.2096
|
13
|
NAU1221
|
Chr.05
|
0.7307
|
3.8447
|
3.7138
|
14
|
MON_CGR5651
|
Chr.06
|
0.6811
|
3.4515
|
3.1354
|
15
|
MON_DPL0702
|
Chr.06
|
0.7349
|
3.8787
|
3.7721
|
16
|
MON_COT002
|
Chr.06
|
0.7266
|
3.8116
|
3.6572
|
17
|
BNL1694
|
Chr.07
|
0.7442
|
3.9534
|
3.9092
|
18
|
MUSS095
|
Chr.07
|
0.7431
|
3.9447
|
3.8921
|
19
|
MON_DC30218
|
Chr.07
|
0.5000
|
2.0000
|
2.0000
|
20
|
NAU3859
|
Chr.09
|
0.4980
|
1.9960
|
1.9920
|
21
|
DPL0431
|
Chr.10
|
0.6873
|
3.4966
|
3.1981
|
22
|
NAU3784
|
Chr.11
|
0.7071
|
3.6561
|
3.4141
|
23
|
MON_CER0098
|
Chr.11
|
0.7250
|
3.8002
|
3.6363
|
24
|
NAU3563
|
Chr.11
|
0.8579
|
7.4448
|
7.0361
|
25
|
NAU2671
|
Chr.12
|
0.6978
|
3.5853
|
3.3095
|
26
|
MON_DPL0491
|
Chr.12
|
0.2397
|
1.4972
|
1.3153
|
27
|
NAU3991
|
Chr.13
|
0.6801
|
3.4455
|
3.1264
|
28
|
MON_COT009
|
Chr.13
|
0.6886
|
3.5149
|
3.2116
|
29
|
BNL1421
|
Chr.13
|
0.7107
|
3.6867
|
3.4567
|
30
|
NAU3308
|
Chr.14
|
0.6721
|
3.3728
|
3.0500
|
31
|
GH304
|
Chr.15
|
0.7085
|
3.6704
|
3.4305
|
32
|
MUSS440
|
Chr.15
|
0.8784
|
8.9176
|
8.2218
|
33
|
NAU2343
|
Chr.15
|
0.7354
|
3.8824
|
3.7790
|
34
|
BNL2646
|
Chr.15
|
0.6360
|
3.1112
|
2.7475
|
35
|
NAU2742
|
Chr.17
|
0.6635
|
3.3170
|
2.9714
|
36
|
MON_DPL0308
|
Chr.18
|
0.5896
|
2.7536
|
2.4365
|
37
|
NAU3011
|
Chr.18
|
0.4980
|
1.9960
|
1.9920
|
38
|
NAU5262
|
Chr.18
|
0.7407
|
3.9252
|
3.8560
|
39
|
MON_CGR6151
|
Chr.19
|
0.4768
|
1.9539
|
1.9115
|
40
|
NAU1187
|
Chr.19
|
0.7307
|
3.8447
|
3.7138
|
41
|
NAU1042
|
Chr.19
|
0.7307
|
3.8447
|
3.7138
|
42
|
MON_CGR5590
|
Chr.19
|
0.7193
|
3.7542
|
3.5631
|
43
|
TMB1791
|
Chr.19
|
0.7387
|
3.9101
|
3.8277
|
44
|
MON_CGR6439
|
Chr.20
|
0.2604
|
1.5362
|
1.3520
|
45
|
DPL0442
|
Chr.20
|
0.7317
|
3.8542
|
3.7271
|
46
|
MON_SHIN1421
|
Chr.20
|
0.7418
|
3.9344
|
3.8728
|
47
|
MON_CGR5565
|
Chr.20
|
0.9043
|
11.0904
|
10.4502
|
48
|
BNL1551
|
Chr.21
|
0.7006
|
3.6046
|
3.3401
|
49
|
GH222
|
Chr.22
|
0.8094
|
5.5713
|
5.2460
|
50
|
MON_CGR6410
|
Chr.22
|
0.7378
|
3.9022
|
3.8136
|
51
|
CIR253
|
Chr.22
|
0.6158
|
2.9431
|
2.6031
|
52
|
MUSS139
|
Chr.23
|
0.7426
|
3.9408
|
3.8848
|
53
|
MON_CGR5202
|
Chr.24
|
0.3230
|
1.6552
|
1.4772
|
54
|
MON_CGR6932
|
Chr.25
|
0.7423
|
3.9381
|
3.8800
|
55
|
CRI151
|
|
0.7312
|
3.8485
|
3.7200
|
56
|
MON_CGR6389
|
|
0.7430
|
3.9442
|
3.8913
|
57
|
NAU3181
|
|
0.1841
|
1.3919
|
1.2256
|
58
|
MON_C2-0118
|
|
0.7397
|
3.9179
|
3.8418
|
59
|
CRI002
|
|
0.6296
|
3.0356
|
2.7000
|
60
|
MUCS375
|
|
0.3519
|
1.7103
|
1.5429
|
61
|
MON_CGR6784
|
|
0.6384
|
3.1230
|
2.7658
|
62
|
CIR096
|
|
0.7210
|
3.7659
|
3.5837
|
63
|
NAU3254
|
|
0.8289
|
6.4664
|
5.8436
|
64
|
MON_DPL0133
|
|
0.5479
|
2.4396
|
2.2119
|
65
|
MON_CER0168
|
|
0.7397
|
3.9179
|
3.8421
|
66
|
MUSB0175
|
|
0.4734
|
1.9470
|
1.8989
|
67
|
MON_DPL0906
|
|
0.4999
|
1.9998
|
1.9997
|
68
|
GH111
|
|
0.4734
|
1.9470
|
1.8989
|
69
|
GH112
|
|
0.4647
|
1.9297
|
1.8680
|
70
|
MON_DC40266
|
|
0.7037
|
3.6271
|
3.3744
|
71
|
MON_DC40286
|
|
0.6888
|
3.5120
|
3.2131
|
2.2 DNA fingerprinting analysis of the 79 cotton accessions
Fingerprinting analysis of the 79 early-maturing upland cotton accessions was performed using 71 pairs of SSR primers. We found that nine accessions had characteristic bands for which only one primer pair was needed to distinguish each accession from the others. Among them, ICR-CAAS64 had two characteristic primers, ‘Xinluzao20’, ‘Xinluzao25’, ‘Jiumian9’, ‘Liaomian5’, ‘Liaomian17’, ‘Liaomian19’, ‘Lumianyan28’, and ‘Jinmian23’ each had one characteristic primer (Table 3). The primer pair NAU4044 was able to uniquely identify four varieties including ‘Xinluzao25’, ‘Jiumian9’, ‘Lumianyan28’, and ‘Liaomian 5’. Primer pair NAU3254 could distinguish three varieties, ‘Xinluzao20’, ‘Liaomian17’, and ‘Liaomian19’. These results indicated that these two primer pairs had abundant polymorphism, strong discrimination power, and numerous characteristic bands, and could be used as preferred markers in the identification of fingerprints.
Table 3. Cotton accessions identified with specific SSR primer pairs
Cultivar
|
Specific primer
|
Cultivar
|
Specific primer
|
ICR-CAAS64
|
NAU1190,MUSS440
|
Xinluzao20
|
NAU3254
|
Liaomian17
|
NAU3254
|
Liaomian19
|
NAU3254
|
Xinluzao25
|
NAU4044
|
Jiumian9
|
NAU4044
|
Liaomian5
|
NAU4044
|
Lumianyan28
|
NAU4044
|
Jinmian23
|
NAU4073
|
|
|
A total of 55 of the 79 cotton accessions could be identified by three pairs of primers, NAU4044, MUSS440, and MON_CGR5565, which were selected from the 71 pairs of core primers with high PIC values, strong discriminative power, clear bands on the gels, and high reproducibility. Seventy-two varieties could be identified by adding another primer pair, GH222. By adding primer pairs NAU1190 and BNL1694, all 79 of the early-maturing upland cotton varieties could be completely distinguished from one another (Table 4).
Table 4. Fingerprinting data for the 79 early-maturing upland cotton accessions
SN
|
Cultivar
|
NAU
4044
|
MUSS
440
|
MON_CGR
5565
|
GH
222
|
NAU
1190
|
BNL
1694
|
1
|
ICR-CAAS10
|
10001
|
00101
|
001100
|
001
|
101
|
10
|
2
|
ICR-CAAS14
|
10001
|
11101
|
001100
|
010
|
101
|
01
|
3
|
ICR-CAAS16
|
10001
|
11011
|
001100
|
001
|
101
|
01
|
4
|
ICR-CAAS18
|
10010
|
00101
|
001100
|
001
|
011
|
10
|
5
|
ICR-CAAS24
|
00101
|
00101
|
101101
|
101
|
101
|
11
|
6
|
ICR-CAAS20
|
10010
|
00101
|
001100
|
100
|
011
|
10
|
7
|
ICR-CAAS26
|
10101
|
00101
|
110001
|
001
|
011
|
01
|
8
|
ICR-CAAS27
|
10001
|
11101
|
001100
|
110
|
111
|
10
|
9
|
ICR-CAAS35
|
11000
|
11000
|
010001
|
001
|
101
|
10
|
10
|
ICR-CAAS36
|
10001
|
11101
|
001100
|
101
|
101
|
10
|
11
|
ICR-CAAS37
|
00100
|
11000
|
001010
|
010
|
101
|
01
|
12
|
ICR-CAAS42
|
00100
|
00101
|
100001
|
100
|
011
|
10
|
13
|
ICR-CAAS50
|
10001
|
11000
|
100001
|
100
|
101
|
10
|
14
|
ICR-CAAS58
|
10100
|
00101
|
100001
|
100
|
011
|
10
|
15
|
ICR-CAAS64
|
00100
|
00000
|
100001
|
100
|
000
|
10
|
16
|
Xinluzao1
|
11000
|
00101
|
100001
|
100
|
101
|
01
|
17
|
Xinluzao3
|
10101
|
00101
|
101101
|
010
|
101
|
01
|
18
|
Xinluzao4
|
10001
|
00011
|
100001
|
100
|
010
|
01
|
19
|
Xinluzao6
|
10010
|
11000
|
001100
|
001
|
101
|
01
|
20
|
Xinluzao7
|
10001
|
11000
|
100001
|
010
|
111
|
01
|
21
|
Xinluzao9
|
10010
|
00101
|
100001
|
001
|
101
|
01
|
22
|
Xinluzao10
|
01010
|
11101
|
100001
|
001
|
011
|
01
|
23
|
Xinluzao11
|
10101
|
11101
|
001110
|
001
|
111
|
11
|
24
|
Xinluzao12
|
11101
|
00101
|
001110
|
010
|
101
|
11
|
25
|
Jiumian2
|
10100
|
00101
|
001100
|
101
|
101
|
01
|
26
|
Xinluzao13
|
10001
|
11000
|
010001
|
001
|
101
|
10
|
27
|
Xinluzao20
|
11101
|
01111
|
001100
|
110
|
101
|
11
|
28
|
Xinluzao22
|
11000
|
00101
|
001010
|
100
|
101
|
10
|
29
|
Xinluzao23
|
10100
|
00101
|
111111
|
001
|
101
|
01
|
30
|
Xinluzao25
|
11011
|
11011
|
001110
|
101
|
101
|
01
|
31
|
Xinluzao26
|
00011
|
11011
|
001110
|
001
|
110
|
11
|
32
|
Xinluzao27
|
10001
|
11000
|
001100
|
001
|
011
|
10
|
33
|
Xinluzao30
|
10100
|
00101
|
001100
|
100
|
101
|
01
|
34
|
Xinluzao31
|
10010
|
00101
|
001100
|
001
|
101
|
01
|
35
|
Xinluzao32
|
01010
|
11000
|
100001
|
001
|
011
|
10
|
36
|
Xinluzao34
|
01000
|
00011
|
100001
|
001
|
101
|
10
|
37
|
Xinluzao35
|
00101
|
00101
|
111111
|
001
|
111
|
11
|
38
|
Xinluzao36
|
10010
|
11000
|
100001
|
010
|
011
|
01
|
39
|
Xinluzao37
|
00101
|
01111
|
001110
|
001
|
101
|
01
|
40
|
Jiumian3
|
00100
|
00101
|
001100
|
101
|
101
|
01
|
41
|
Xinluzao39
|
01000
|
00101
|
010001
|
001
|
011
|
10
|
42
|
Jiumian8
|
00100
|
11000
|
001100
|
001
|
101
|
01
|
43
|
Jiumian9
|
10011
|
00101
|
101101
|
100
|
101
|
01
|
44
|
Heishanmian1
|
10001
|
11000
|
001010
|
001
|
011
|
10
|
45
|
Liaomian5
|
00001
|
00101
|
100001
|
010
|
011
|
01
|
46
|
Liaomian6
|
00100
|
00101
|
001010
|
010
|
101
|
01
|
47
|
Liaomian7
|
00100
|
00101
|
001100
|
100
|
101
|
01
|
48
|
Liaomian9
|
10101
|
00101
|
001100
|
011
|
101
|
11
|
49
|
Liaomian10
|
11101
|
00101
|
001100
|
010
|
101
|
10
|
50
|
Liaojinmian3
|
10100
|
11000
|
001010
|
001
|
101
|
01
|
51
|
Liaomian12
|
00100
|
00101
|
001100
|
011
|
101
|
01
|
52
|
Liaomian15
|
10100
|
00011
|
001100
|
001
|
-
|
10
|
53
|
Liaomian16
|
10100
|
00101
|
-
|
010
|
101
|
10
|
54
|
Liaomian17
|
00100
|
01111
|
001100
|
001
|
101
|
01
|
55
|
Liaomian18
|
10100
|
00011
|
001100
|
010
|
100
|
10
|
56
|
Liaomian19
|
00100
|
01111
|
101101
|
010
|
010
|
10
|
57
|
Jinzhong200
|
10001
|
00101
|
100001
|
010
|
101
|
10
|
58
|
Lumian1
|
10001
|
01111
|
111111
|
001
|
110
|
10
|
59
|
Sumian1
|
00100
|
00011
|
001010
|
010
|
101
|
10
|
60
|
Yumian3
|
10100
|
00101
|
111111
|
011
|
011
|
11
|
61
|
Yumian5
|
11000
|
00101
|
001100
|
001
|
101
|
10
|
62
|
Lumian10
|
10010
|
00101
|
110001
|
001
|
101
|
01
|
63
|
Yumian7
|
10100
|
00101
|
001010
|
100
|
101
|
10
|
64
|
Yumian9
|
10001
|
00101
|
001100
|
001
|
101
|
01
|
65
|
Yumian10
|
11100
|
00101
|
010001
|
001
|
011
|
01
|
66
|
Sumian10
|
10101
|
00011
|
001100
|
011
|
010
|
01
|
67
|
Sumian11
|
11100
|
00101
|
110001
|
001
|
101
|
11
|
68
|
Jinmian23
|
11111
|
11101
|
111111
|
101
|
101
|
10
|
69
|
Jinmian26
|
11111
|
11101
|
101101
|
101
|
101
|
01
|
70
|
Jinmian28
|
10100
|
11101
|
101101
|
011
|
101
|
01
|
71
|
Jinmian34
|
10100
|
00101
|
100001
|
001
|
101
|
01
|
72
|
Jinmian36
|
11000
|
00011
|
010001
|
010
|
010
|
01
|
73
|
Jinmian44
|
00100
|
00011
|
100001
|
001
|
010
|
01
|
74
|
Lumianyan27
|
10100
|
00011
|
-
|
001
|
010
|
01
|
75
|
Lumianyan28
|
01100
|
01111
|
110001
|
010
|
010
|
01
|
76
|
Kings improved1
|
10001
|
00101
|
001100
|
001
|
011
|
10
|
77
|
Foster cotton6243
|
10100
|
00101
|
001100
|
001
|
101
|
10
|
78
|
Soviet Union91-357
|
00011
|
00101
|
001100
|
011
|
110
|
10
|
79
|
Soviet Union10633
|
10100
|
00011
|
001100
|
001
|
100
|
01
|
2.3 Genetic diversity analysis
Similarity coefficients between varieties were calculated using NTSYS-pc 2.11 and Microsoft Excel software. The results showed that the genetic similarity coefficients among the 79 early-maturing upland cotton accessions ranged from 0.3310 (‘Jinmian36’ and ‘Xinluzao25’) to 0.8705 (‘Liaomian15’ and ‘Liaomian18’), with an average of 0.5861, indicating that ‘Jinmian36’ and’ Xinluzao25’ have the highest genetic diversity, while ‘Liaomian15’ and ‘Liaomian18’ have the lowest. The similarity coefficients between varieties that were <0.4 (large genetic difference) accounted for 1.1%, 11.9% at 0.4-0.5, 44.1% at 0.5-0.6, 35.2% at 0.6-0.7, and 7.1% at 0.7-0.8. The similarity coefficients >0.8 accounted for 0.5% (Fig. 2). This demonstrates that the genetic relationships are relatively close among the 79 early-maturing upland cotton accessions, but that some genetic diversity is still present.
We performed genetic diversity analysis of the 79 early-maturing upland cotton accessions from six regions that included the China Cotton Institute, YRB (Henan, Shanxi, Shandong, Jiangsu), the Northwest Inland Cotton Region (Xinjiang, Gansu), the Liaohe River Basin, the United States, and the former Soviet Union. By comparison, the accessions from the former Soviet Union had the smallest average genetic similarity coefficients of the six regions, which increased in the order of the YRB cotton area, the United States, the China Cotton Institute, the Liaohe River Basin Early Maturing Cotton Area, and the Northwest Inland Cotton Area, indicating that there is ample genetic diversity in cotton resources imported from abroad. At the same time, the genetic diversity of accessions from the YRB cotton region is relatively high, which may be related to the complex geographical diversity of the YRB cotton region and the dispersion of breeders in Henan, Shanxi, Shandong, Jiangsu, and other provinces.
We found that the genetic similarity coefficients of accessions from the six regions were between 0.5575 and 0.6143, and the highest similarity coefficients were found between accessions from China and the USA. This indicates that early-maturing upland cotton varieties selected by ICR-CAAS have close genetic relationships to selections from the USA. In general Chinese cotton germplasm is more frequently exchanged for resources from the USA compared with other regions. The lowest genetic similarity coefficient in the early-maturing cotton areas is between YRB and the Liaohe River Basin, with a value of 0.5575, and the second lowest is between YRB and the Northwest Inland Cotton Area, with a value of 0.5636 (Table 5). The underlying reason for this may be that the YRB cotton-growing area has a better climate with warmer conditions, resulting in more varieties of early-maturing upland cotton and larger differences between varieties than the early-maturing cotton areas of the Liaohe River Basin and the Northwest Inland Cotton Area.
Comparisons of the genetic similarity coefficients between domestic and foreign early-maturing upland cotton varieties showed that except for the Northwestern Inland Cotton Area, the genetic similarity coefficients between cotton varieties from the ICR-CAAS, YRB, and the Liaohe River Basin are higher. This suggests that early-maturing upland cotton grown in the ICR-CAAS, YRB, and Liaohe River Basin in the early-maturing cotton area contains more American germplasm. Because of the introduction and utilization of early-maturing upland cotton in China, the majority of early maturity genetic resources came from American gold-colored cotton. The genetic similarity coefficients between accessions from the Northwestern Inland Cotton Region and the former Soviet Union is relatively high. This may be because the Northwest Inland Cotton Region is adjacent to the former Soviet Union, so it is easier to introduce germplasm resources into China from there.
Table 5. Genetic diversity of cotton cultivars from the six cotton-growing regions in China
Region
|
Genetic similarity coefficient
|
CAAS
|
YRB
|
the Northwest Inland Region
|
the Liaohe River Basin
|
the United States
|
the former Soviet Union
|
CAAS
|
Max
|
0.8058
|
0.7817
|
0.8169
|
0.8451
|
0.7447
|
0.6691
|
Min
|
0.3630
|
0.3643
|
0.3704
|
0.3582
|
0.4789
|
0.4296
|
Mean
|
0.6032
|
0.5796
|
0.5943
|
0.5824
|
0.6143
|
0.5672
|
YRB
|
Max
|
|
0.7899
|
0.8028
|
0.7606
|
0.7042
|
0.7042
|
Min
|
|
0.4085
|
0.3310
|
0.3475
|
0.3521
|
0.4366
|
Mean
|
|
0.5802
|
0.5636
|
0.5575
|
0.5911
|
0.5661
|
the Northwest Inland Region
|
Max
|
|
|
0.8239
|
0.8310
|
0.7324
|
0.7254
|
Min
|
|
|
0.4225
|
0.3582
|
0.4296
|
0.4296
|
Mean
|
|
|
0.6221
|
0.5936
|
0.5799
|
0.5894
|
the Liaohe River Basin
|
Max
|
|
|
|
0.8705
|
0.7465
|
0.6812
|
Min
|
|
|
|
0.3944
|
0.4718
|
0.4014
|
Mean
|
|
|
|
0.6134
|
0.5783
|
0.5684
|
the United States
|
Max
|
|
|
|
|
0.5845
|
0.6549
|
Min
|
|
|
|
|
0.5845
|
0.5357
|
Mean
|
|
|
|
|
0.5845
|
0.6027
|
the former Soviet Union
|
Max
|
|
|
|
|
|
0.5286
|
Min
|
|
|
|
|
|
0.5286
|
Mean
|
|
|
|
|
|
0.5286
|
The results of our study show that the average genetic similarity coefficient of bred and certified varieties of early-maturing upland cotton varieties from different areas in China showed a low-high-low pattern of variation as determined by genetic diversity analysis. ‘Jinzhong200’, ‘Xinluzao1’, ‘Lumian1’, ‘Heishanmian1’, and ‘Liaomia5’, which were selected prior to the 1980s, had the lowest genetic similarity coefficient of 0.5704. The nine varieties ‘ICR-CAAS10’, ‘ICR-CAAS14’, ‘Xinluzao3’, ‘Liaomian6’, ‘Liaomian7, ‘Liaomian9’, ‘Sumian1’, ‘Yumian3’, and ‘Yumian5’, which were selected in the 1980s, have the highest average genetic similarity coefficient of 0.6306. The 29 varieties selected in the 1990s, which include ‘ICR-CAAS16’, ‘ICR-CAAS18’, ‘Xinluzao4’, ‘Liaomian10’, ‘Lumian10’, ‘Yumian7’, ‘Sumian10’, and ‘Jinmian23’, have an average genetic similarity coefficient of 0.5993. The average similarity coefficient of 32 varieties including ‘ICR-CAAS42’, ‘ICR-CAAS50’, ‘Xinluzao13’, ‘Jiumian2’, ‘Liaomian17’, ‘Jinmian34’, and ‘Lumianyan27’ selected after 2000 is 0.5791.
The average genetic similarity coefficients of early-maturing upland cotton varieties in China have shown a low-high-low pattern over time (Fig. 3). This may be because before the 1980s, domestic early-maturing upland cotton breeding was mainly carried out by introducing different early-maturing varieties from abroad and systematically using them in breeding. Since the early 1980s, cotton production and the cotton spinning industry have developed rapidly. Due to economic reform and the opening up of the country, transportation is more convenient, and the exchange of germplasm resources between breeding units has become frequent. In particular, a number of outstanding varieties (lines) such as ‘Heishanmian1’ and ‘ICR-CAAS10’ stand out from the competition and are used by other breeders as donor parents. This has resulted in closer genetic relationships between the varieties selected at this time, with higher genetic similarity coefficients and less genetic difference. In the 1990s, the difficulties of domestic distant hybridization were continuously overcome, and breeders consciously chose parental materials with complex genetic backgrounds for cross-breeding, which resulted in a significant reduction in the genetic similarity coefficients of cotton varieties and increased the genetic difference. After 2000, the use of modern breeding technologies (transgenics and molecular marker-assisted breeding) not only accelerated the cotton breeding process, but also broadened the source of available cotton genes, resulting in further reductions in the genetic similarity coefficients among new varieties of early-maturing upland cotton in China20.
Based on the Jaccard similarity coefficient, 79 early-maturing upland cotton varieties were grouped using a hierarchical clustering method (UPGMA) (Fig. 4). The results showed that at a genetic similarity coefficient of 0.87, all 79 early-maturing upland cotton varieties were completely separated. At a similarity coefficient of 0.57, the 79 main varieties could be divided into five categories or classes. Class I contains 42 varieties, including seven varieties from the China Cotton Institute, 20 varieties from the Northwest Inland Cotton Area, nine varieties from the special early-maturing cotton area of the Liaohe River Basin, four varieties from the YRB cotton area, one variety from the US, and one variety from the former Soviet Union. Class II contains 27 varieties, including six varieties from the China Cotton Institute, six varieties from the Northwest Inland Cotton Area, two varieties from the special early-maturing cotton area of the Liaohe River Basin, 11 varieties from the YRB cotton area, and one variety each from the United States and the former Soviet Union. Class III has only varieties, both of which are from the Northwestern Inland Cotton Area; Class IV includes a single variety from YRB; and Class V contains seven varieties, including two from Zhongmian, two varieties from the Liaohe Basin early-maturing cotton area, and three varieties from the YRB cotton area. Among these five classes, most of the selected varieties of cotton grown in China clustered in Class I and Class II, accounting for 46.7% and 40.0% respectively. Most of the varieties from the Northwestern Inland cotton area are in Class I, accounting for 71.4%; most of varieties from the Liaohe Basin are concentrated in Class I, accounting for 69.2%; and most of the cotton varieties from YRB are concentrated in Class II, accounting for 57.9%. This shows that the clustering results reflect certain geographical distribution characteristics, and the genetic differences of the cultivars from the same area are relatively small, which is why they cluster together.