3.1. Phenotypic variation of the association panel
We analyzed the phenotypes of 12 traits in the association panel across all three environments under NCE conditions and across all four environments under SCE conditions. The frequency distributions of root-related traits were continuous and normally distributed in both NCE and SCE (Fig.S1). Differences in culture conditions affect root traits. For example, the RDW, RFW, TRL, RS, RV, and RN under SCE were higher than NCE, whereas RD was lower. Under NCE, the range of variation for RDW was the largest, from 2.43 mg to 9.10 mg. The RDW of Jinmai 72, Chang 7016 and Tai 113 were more sensitive to the change in culture conditions. Under SCE, the variation range of TRL was the largest, from 58.19cm to 156.81cm. The TRL of Jinmai 65, Jinmai 68 and Baitumai were more sensitive to the change in culture conditions, indicating varieties were greatly affected by environment. The relative variation range of RD was the smallest under the two conditions, from 0.44 cm to 0.50 cm under NCE and from 0.37 cm to 0.53 cm under SCE. The RD variation of Jinmai 16, Jinchun 15, and Jinmai 102 between NC and SC was less, indicating that these varieties were less affected by changes in culture conditions.
The phenotype was analyzed according to the type of variety. Under NCE, the CV of root traits ranged from 2.29% to 19.45% for irrigated cultivars, from 2.07% to 17.54% for dryland cultivars, and from 1.73% to 18.48% for landraces. Under SCE, the CV of root traits ranged from 5.94% to 14.60% for irrigated cultivars, from 3.04% to 13.23% for dryland cultivars, and from 2.58% to 14.21% for landraces (Table.S1). In general, the root phenotype of the seedling stage was various and had a wide range of genetic variation. Among the variety types, phenotypic variation was the largest in irrigated cultivars, less in dryland cultivars, and lowest in landraces.
Different culture conditions led to different phenotypes of root traits at the seedling stage. The root biomass (RDW and RFW), TRL, RS, RV, and RN were significantly lower under NCE than under SCE, while RD was significantly higher than under SCE (Table.1). Compared with SCE, the variation in root traits was more abundant under NCE. The CV of root traits at the seedling stage in NCE was between 2.25 % and 20.80 %, among which the CV of RDW was the greatest and the CV of RD was the least (Table.1). Under SCE, the CV of root traits at seedling stage was between 5.61% and 15.03 %, among which the CV of SDW was the greatest and the CV of RDW was the least (Table.1). ANOVA showed that there was no significant difference in MRL between the two culture conditions, while the differences between NCE and SCE for other traits were significant (P<0.05).
Fig.1. Phenotypes for root traits at the seedling stage in the association panel. a and b represent the root phenotype under NCE and SCE, respectively. c and d represent the root phenotypic differences of cultivars under NCE / SCE. Among them, c represents the number of lateral roots, TRL, and number of root tips of Jinmai 65 were significantly higher at SCE than NCE. d represents that no significant difference in root traits between NCE and SCE in Jinmai 102. e represents the correlation analysis between seedling traits and adult agronomic traits
NCE, nutrient solution culture experiment; SCE, soil culture experiment
MRL, maximum root length; RFW, root fresh weight; RS, root surface-area; RV, root volume
Table.1. A Statistical analysis of 12 root-related traits at the seedling stage under NCE and SCE conditions
Traits
|
NCE
|
SCE
|
P
|
Range
|
Mean
|
CV %
|
Range
|
Mean
|
CV %
|
SDW
|
mg
|
13.41–15.98
|
14.31±0.39
|
2.75
|
9.33–22.57
|
15.50±2.33
|
15.03
|
**
|
RDW
|
mg
|
2.43–9.10
|
4.51±0.94
|
20.80
|
9.85–16.83
|
10.93±0.61
|
5.61
|
**
|
DW
|
mg
|
16.22–23.34
|
18.82±1.24
|
6.56
|
18.59–45.68
|
26.43±3.07
|
11.60
|
*
|
SFW
|
mg
|
142.88–172.78
|
153.97±4.59
|
2.98
|
71.20–170.10
|
110.32±15.31
|
13.88
|
**
|
RFW
|
mg
|
50.68–71.38
|
58.98±4.13
|
7.01
|
76.50–152.89
|
113.95±13.25
|
11.63
|
**
|
FW
|
mg
|
196.49–244.77
|
212.91±8.02
|
3.77
|
148.71–313.17
|
224.27±27.21
|
12.13
|
*
|
MRL
|
cm
|
13.65–19.59
|
16.29±1.07
|
6.58
|
13.72–18.36
|
16.20±0.94
|
5.81
|
NS
|
TRL
|
cm
|
33.43–58.78
|
43.77±4.43
|
10.12
|
58.19–156.81
|
104.10±14.95
|
14.36
|
*
|
RS
|
cm2
|
6.90–10.07
|
8.28±0.53
|
6.40
|
7.53–16.46
|
12.21±1.55
|
12.69
|
**
|
RV
|
cm3
|
0.09–0.13
|
0.11±0.01
|
4.84
|
0.10–0.22
|
0.15±0.02
|
14.45
|
**
|
RD
|
cm
|
0.44–0.5
|
0.46±0.01
|
2.25
|
0.37–0.53
|
0.43±0.03
|
6.69
|
**
|
RN
|
|
2.68–5.14
|
5.10±0.15
|
2.87
|
3.37–6.34
|
5.16±0.45
|
8.64
|
**
|
SDW, shoot dry weight; RDW, root dry weight; DW, plant dry weight; SFW, shoot fresh weight; RFW, root fresh weight; FW, plant fresh weight; MRL, maximum root length; TRL, total root length; RS, root surface-area; RV, root volume; RD, root diameter; RN, root number
CV, coefficient of variation
**, significant difference at P < 0.01; *, significant difference at P < 0.05
NCE, nutrient solution culture experiment; SCE, soil culture experiment
3.2. Relationship between seedling root traits and agronomic traits in the association panel
Root traits at the seedling stage often reflect root morphology and distribution at the adult stage. The correlation between 12 root-related traits under SCE at the seedling stage and 13 agronomic traits at the adult stage was analyzed. RDW, RFW, RS, RV, SDW, SFW, DW, and FW were significantly correlated with more than 10 agronomic traits (Fig. S2a). Under the NCE condition at the seedling stage, RFW, RDW, RS, and RV were significantly correlated with agronomic traits, while SDW, DW, SFW, and FW were not significantly correlated with most of agronomic traits (Fig. S2b). On this basis, the correlation between the same traits under SCE and NCE conditions at the seedling stage was compared, and RFW, RDW, RS, and RV were significantly correlated under the two culture conditions (Fig. S2c). This result indicated that the method of hydroponic identification of RFW, RDW, RS, and RV at the seedling stage could replace the method of soil culture to further study the relationship with agronomic traits, while SDW, DW, SFW, and FW were more accurately assessed under soil culture conditions.
To study the relationship between root traits at the seedling stage and agronomic traits at the adult stage of different types of varieties, correlation analyses were performed between root-related traits at the seedling stage and agronomic traits of irrigated cultivars and dryland cultivars under SCE conditions. There were significant correlations between root traits at the seedling stage and PH, TGW, GL, GW, and GT at the adult stage in dryland cultivars, but the correlation was lower in irrigated cultivars (Table S2). A comparison of root phenotypes of irrigated cultivars and dryland cultivars showed that the MRL of dryland cultivars at the seedling stage was slightly longer than that of irrigated cultivars (Table S1). Therefore, agronomic traits such as PH, TKW, GL, GW, and GT were compared between irrigated cultivars and dryland cultivars. The PH, TKW and GW of dryland cultivars were significantly higher than those of irrigated cultivars, which indicated that the longer root length of dryland cultivars may have promoted more biomass accumulation and yield formation under the same growing conditions.
3.3. Association analysis of association panel with SNP markers
Association analysis between 12 root-related traits and SNP markers was performed using the MLM model. Under NCE conditions, a total of 29 stable loci distributed on 11 chromosomes were identified, which explained 5.08% to 23.25% of the phenotypic variation. Thirteen of these loci have been reported to be associated with root architecture (Fig.S3,Table.2). The significance level of QDw.sxau-1B.2 ranged from 9.02 to 11.90 in all three environments, with a R2 of 16.65% to 23.25%. Furthermore, QDw.sxau-2A.1 was stably detected in all three environments, with R2 ranging from 5.98% to 9.02%(Fig.S3,Table.2).
Under SCE conditions, GWAS identified 23 loci for 12 seedling traits in different environments on 11 chromosomes, including chromosomes 1A, 1B and 2B, which explained 5.56% - 17.02% of the phenotypic variation. Five of these loci have been reported previously to be associated with root architecture. The QRdw.sxau-2D.2 had the highest phenotypic variation rate, with a R2 of up to 17.02% (Fig.S3,Table.2).
Table.2. Characteristics of significant loci related to root traits for wheat under two culture conditions.
Traits
|
Loci Name
|
Culture system
|
Chr.
|
Position (Mb)
|
-log10(P)
|
R2 (%)
|
References
|
SDW
|
QSdw.sxau-2A.1
|
NCE
|
2A
|
305.69-305.69
|
3.17-3.39
|
5.80-6.01
|
|
|
QSdw.sxau-2A.2
|
NCE
|
2A
|
747.08-747.08
|
3.23-3.72
|
5.99-6.24
|
Li et al. 2020
|
|
QSdw.sxau-2B
|
SCE
|
2B
|
135.69-135.69
|
3.56-4.00
|
7.03-8.56
|
|
|
QSdw.sxau-3D
|
NCE
|
3D
|
602.07-602.07
|
3.88-4.02
|
6.62-7.59
|
Salarpour et al. 2020
|
|
QSdw.sxau-6A
|
NCE
|
6A
|
597.62-597.62
|
3.44-3.53
|
5.75-6.56
|
Yang et al. 2021b Ayalew et al. 2017 Li et al. 2020
|
RDW
|
QRdw.sxau-2A
|
NCE
|
2A
|
191.20-194.09
|
3.18-3.66
|
5.40-6.41
|
|
|
QRdw.sxau-2D.1
|
SCE
|
2D
|
579.39-585.48
|
3.92-7.51
|
8.40-16.75
|
|
|
QRdw.sxau-2D.2
|
SCE
|
2D
|
586.95-593.12
|
4.11-7.62
|
8.82-17.02
|
|
|
QRdw.sxau-5D.1
|
SCE
|
5D
|
359.10-359.20
|
4.91-6.18
|
10.62-13.57
|
|
|
QRdw.sxau-6A
|
SCE/NCE
|
6A
|
596.50-602.16
|
3.43-6.57
|
5.99-14.49
|
Yang et al. 2021b Ayalew et al. 2017 Li et al. 2020
|
|
QRdw.sxau-6B
|
NCE
|
6B
|
512.21-512.21
|
3.51-4.28
|
5.98-8.00
|
|
DW
|
QDw.sxau-1B.1
|
NCE
|
1B
|
110.64-110.64
|
5.54-7.01
|
9.96-12.89
|
|
|
QDw.sxau-1B.2
|
NCE
|
1B
|
135.81-135.81
|
9.02-11.90
|
16.65-23.25
|
|
|
QDw.sxau-1D
|
NCE
|
1D
|
35.79-43.72
|
3.72-5.02
|
7.24-16.34
|
Yang et al. 2021a
|
|
QDw.sxau-2A.1
|
NCE
|
2A
|
305.69-305.69
|
3.16-5.10
|
5.98-9.02
|
|
|
QDw.sxau-2A.2
|
NCE
|
2A
|
327.21-327.21
|
3.89-5.89
|
6.66-10.49
|
|
|
QDw.sxau-2B
|
NCE
|
2B
|
771.81-771.81
|
4.94-6.96
|
9.15-13.01
|
|
|
QDw.sxau-2D
|
SCE
|
2D
|
584.52-589.19
|
3.56-6.50
|
6.94-14.34
|
|
|
QDw.sxau-4B
|
NCE
|
4B
|
526.96-526.96
|
6.20-6.94
|
11.47-12.44
|
|
|
QDw.sxau-5D
|
NCE
|
5D
|
450.20-450.20
|
5.92-6.63
|
10.54-11.71
|
|
|
QDw.sxau-6A
|
SCE/NCE
|
6A
|
596.50-602.16
|
3.58-6.19
|
5.56-13.60
|
Yang et al. 2021b Ayalew et al. 2017 Li et al. 2020
|
|
QDw.sxau-6B
|
SCE
|
6B
|
714.44-714.44
|
3.02-3.66
|
5.94-8.08
|
|
SFW
|
QSfw.sxau-1B
|
NCE
|
1B
|
514.94-514.94
|
3.35-3.77
|
6.18-6.35
|
Huang et al. 2020
|
|
QSfw.sxau-2A
|
NCE
|
2A
|
305.69-305.69
|
3.44-3.71
|
5.92-7.02
|
|
|
QSfw.sxau-2B
|
SCE
|
2B
|
135.69-135.69
|
3.03-3.69
|
6.36-7.76
|
|
|
QSfw.sxau-3D
|
NCE
|
3D
|
602.07.602.07
|
3.08-3.21
|
5.49-5.73
|
Salarpour et al. 2020
|
|
QSfw.sxau-5A
|
SCE
|
5A
|
100.59-109.92
|
3.08-3.35
|
5.90-6.42
|
|
|
QSfw.sxau-6A
|
NCE
|
6A
|
609.31-609.31
|
3.05-4.05
|
5.80-7.52
|
Yang et al. 2021b Ayalew et al. 2017 Li et al. 2020
|
|
QSfw.sxau-7B
|
SCE
|
7B
|
169.59-169.59
|
3.23-3.54
|
6.20-7.14
|
|
RFW
|
QRfw.sxau-5B
|
NCE
|
5B
|
138.30-138.30
|
3.07-3.19
|
5.04-5.93
|
|
FW
|
QFw.sxau-2A
|
NCE
|
2A
|
305.69-305.69
|
3.00-3.76
|
5.08-7.13
|
|
|
QFw.sxau-2B
|
SCE
|
2B
|
135.69-135.69
|
3.13-3.43
|
6.66-7.37
|
|
|
QFw.sxau-3D
|
NCE
|
3D
|
602.07-602.07
|
3.28-3.39
|
5.69-6.13
|
Salarpour et al. 2020
|
|
QFw.sxau-5A
|
SCE
|
5A
|
382.13-382.14
|
3.02-4.10
|
5.81-8.42
|
|
|
QFw.sxau-6A
|
NCE
|
6A
|
609.31-609.31
|
3.23-3.93
|
5.31-7.31
|
Yang et al. 2021b Ayalew et al. 2017 Li et al. 2020
|
MRL
|
QMrl.sxau-1B
|
SCE
|
1B
|
38.01-38.01
|
3.16-3,36
|
7.76-8.43
|
|
|
QMrl.sxau-4B
|
NCE
|
4B
|
37.70-37.70
|
3.11-3.15
|
5.25-5.91
|
Zheng et al. 2019
|
|
QMrl.sxau-7A
|
SCE
|
7A
|
709.98-709.98
|
3.21-5.35
|
8.83-11.62
|
Yang et al. 2021b
|
RV
|
QRv.sxau-2A
|
NCE
|
2A
|
55.93-55.93
|
3.52-3.69
|
5.43-6.47
|
Yang et al. 2021a
|
|
QRv.sxau-3B
|
SCE
|
3B
|
318.27-318.27
|
3.11-3.34
|
6.45-7.04
|
|
RD
|
QRd.sxau-1A
|
SCE
|
1A
|
592.35-592.35
|
3.31-5.22
|
6.55-10.26
|
Li et al. 2020
Salarpour et al. 2020
Yang et al. 2021a
|
|
QRd.sxau-1B.1
|
SCE
|
1B
|
408.45-408.45
|
3.10-3.51
|
6.42-7.35
|
|
|
QRd.sxau-1B.2
|
SCE/NCE
|
1B
|
492.38-492.75
|
3.01-3.29
|
6.04-6.16
|
|
|
QRd.sxau-4A
|
NCE
|
4A
|
450.89-450.89
|
3.08-4.80
|
5.25-8.39
|
|
|
QRd.sxau-5A
|
SCE
|
5A
|
502.69-502.69
|
3.42-3.83
|
7.17-7.40
|
|
|
QRd.sxau-6A
|
NCE
|
6A
|
546.08-546.08
|
3.20-3.31
|
5.44-5.68
|
|
RN
|
QRn.sxau-1A
|
SCE
|
1A
|
367.05-367.05
|
3.10-3.20
|
6.34-6.41
|
|
|
QRn.sxau-5A.1
|
SCE
|
5A
|
394.56-394.56
|
3.45-3.84
|
7.13-7.74
|
Ayalew et al. 2017
|
|
QRn.sxau-5A.2
|
SCE
|
5A
|
684.73-684.73
|
3.00-3.59
|
6.70-7.21
|
|
SDW, shoot dry weight; RDW, root dry weight; DW, plant dry weight; SFW, shoot fresh weight; RFW, root fresh weight; FW, plant fresh weight; MRL, maximum root length; RV, root volume; RD, root diameter; RN, root number
NCE, nutrient solution culture experiment; SCE, soil culture experiment
3.4. Distribution and association analysis of SCVs in the association panel
ONPM # 7, a powerful tool for studying structural variation diversity and genetic effects, is composed of 12 sequences, and was used to identify PAVs/CNVs alleles at 202 SCVs loci across all 21 wheat chromosomes (Zhao et al. 2023). A total of 74 polymorphic SCVs were identified by FISH in the association panel, which were located on 17 chromosomes, including 1A, 1B and 1D (Zhao et al. 2022). In the DH population, 13 polymorphic SCVs located on 11 chromosomes were identified by FISH. In the association panel, GWAS identified 9 and 26 SCVs under NCE and SCE conditions, respectively (Table S3). These significant SCVs were distributed on 12 chromosomes, explaining 4.48% - 11.76% of the phenotypic variation. Thus, SCVs have important effects on root architecture.
Under NCE conditions, three SCVs were detected in three datasets. The highest R2 value (8.13%) was found for the association between Mg1B-1 and RFW, which was confirmed in all three environments. Mg1B-1 and Mr1B-3 had significant effects on RFW, FW, TRL, RV, and RS , with R2 ranging from 5.74% to 9.07%. In addition, Mg2B-9 was significantly associated with DW, Mg1B-1 and Mr1B-3 were significantly associated with RS, and Mr1B-3 was significantly associated with TRL in all three environments, with R2 ranging from 4.55% to 9.07%(Fig.3, Table S3).
Under SCE conditions, twelve SCVs were detected in three or more datasets. The highest R2 ranging from 10.86% to 11.76% were found for the association between Mr5B-11 and RDW. Mr1B-3 was significantly associated with 10 traits simultaneously, including FW, TRL, RS, RV and RN, explaining 5.77% - 9.86% of phenotypic variation.
In addition, several SCVs were stably detected in three or more environments, including Mg1D-2 associated with DW, Mr1A-1 associated with MRL, Mg2B-12 associated with RFW, Mr1D-5 associated with RV, Mg1D-2, Mg2B-6, Mg6A-10, Mr6A-2 and Mg6A-9 associated with SDW, and Mr3A-3 and Mr3A-4 associated with SFW (Fig.3, Table S3).
Fig.2. Identification of SCVs based on karyotype after ONPM # 7 FISH. a Blue indicates chromosomes of Chinese Spring counterstained with DAPI. b Green indicates BSCL135-1, BSCL135-2, and (GAA)10 modified with FAM. c Red indicates oligonucleotides pAs1-1, pAs1-3, pAs1-4, pAs1-6, pSc119.2–1, AFA-3, AFA-4, Grass-5S-1, and Grass-5S-2 modified with TAMRA. d Karyotype of Chinese Spring via merging of panels a, b, and c. e SCVs significantly associated with root traits.
3.5. Linkage analysis of SNPs and SCVs analysis in the DH population
To comprehensively and systematically verify the results of GWAS in the association panel, QTL mapping was performed on 201 lines using the DH population. Twenty-one stable QTL were detected by linkage analysis of 12 seedling root traits (Table S4). Five QTL were found for MRL, explaining 5.11 % -11.03 % of the phenotypic variance. The phenotypic variation explanation rate of QMrl.sxau-6B.1 was the highest. QTrl.sxau-6B.1 and QTrl.sxau-6B.2 explained 11.34-12.34 % of the phenotypic variation for TRL. QRv.sxau-5A was detected for RV, explaining up to 15.01 % of the phenotypic variation. QRd.sxau-1B.3 and QRd.sxau-4D explained 8.51%–17.51% of the phenotypic variance for RD. In addition, seven QTL, including QRfw.sxau-6B.2, QMrl.sxau-6B.1, and QRd.sxau-4D, have been reported to be associated with root structure (Table S4). The R2 of six QTL were higher than 10 %, including QMrl.sxau-6B.1, QRv.sxau-5A and QRd.sxau-4D, were identified as major and stable QTL in multiple environments (Table S4). Among these QTL, QRv.sxau-5A, QTrl.sxau-6B.1, QTrl.sxau-6B.2 and QMrl.sxau-6B.1 had additive effects on root-related traits such as maximum root length, total root length, root surface area and root biomass (Fig. S4).
The SCV of polymorphic chromosomes from the DH population with positive impact on root-related traits were PAV.2A.1+2 , PAV.2A.1, PAV.2A.2, 1BL/1RS, CNV.4B, and PAV.6B. Each SCV was associated with one or more root traits during the seedling stage. PAV.2A.1+2 was significantly associated with SDW, DW, MRL, RDW, and RS. PAV.2A.1 was significantly associated with RDW, MRL, TRL, and RS. PAV.2A.2 was significantly associated with SDW, DW, MRL, and RV. 1BL/1RS was significantly associated with SDW, DW, MRL, TRL, and RD. CNV.4B was significantly associated with TRL and SDW. PAV.6B was significantly associated with SDW, SFW, FW, MRL, and TRL. Thus, SCV had a significant effect on root-related traits.
3.6. Co-location analysis of multiple methods
3.6.1 Co-localization analysis of SCE and NCE in association panel
Three stably loci, QRdw.sxau-6A, QRd.sxau-1B.2, and QDw.sxau-6A, were detected in both the SCE and NCE conditions based on SNP marker association analysis. QRdw.sxau-6A was composed of four significant MTAs, which were in the 596.50-602.16 Mb physical interval on chromosome 6A, with the R2 values ranging from 5.99 to 14.49%. QDw.sxau-6A was also co-located in the same genetic interval with a R2 value range of 5.56%-13.60% for four MTAs (Fig.3, Table.2). This genetic interval was also reported to be significantly correlated with root length. In addition, QRd.sxau-1B.2 was located at 492.38-492.75Mb on chromosome 1B, explaining 6.04%-6.16% of phenotypic variation, and has not been reported previously.
Mr1B-3, Mr3A-3 and Mr3A-4 were identified under both NCE and SCE conditions based on SCV association analysis, explaining 4.74%–9.07% of the phenotypic variation (Table S3). Mr1B-3 was located on chromosome 1B at 69.79 Mb and had effects on RFW, RS, RV, TRL, and FW. In addition, Mr3A-3 at 566.23Mb and Mr3A-4 at 685.43Mb on chromosome 3A explained 4.74-5.38% of the phenotypic variation,which were significantly associated with TRL in four datasets. These results showed that SCVs were positively associated with root-related traits.
Fig.3. Identification of for DW and RD with multiple methods. a represents the co-localization map of DW, from the outermost to the innermost circle: association analysis with SNPs under NCE conditions (A), SCE conditions (B), linkage analysis of a DH population with SNPs (C), and association analysis of SCV (D); b represents the co-localization map of RD, from the outermost to the innermost circle: association analysis with SNPs under NCE conditions (E), association analysis of SNPs under SCE conditions (F-G), and association analysis with SCV (H)
DW, plant dry weight; RD, root diameter
3.6.2 Co-localization analysis of SCV and SNP in association panel
A total of fourteen MTAs were co-located via SNP and SCV marker association analysis (Table S5). Under NCE conditions, eight MTAs were detected with R2 ranging 4.64% to 7.74% for SNP-MTAs and 4.62% to 6.04% for SCV-MTAs. Under SCE conditions, a total of six MTAs were co-located via SNPs and SCVs, with R2 ranging 6.59% to 14.49% for SNP-MTAs and 5.68% to 6.82% for SCV-MTAs. Among these MTAs, Mr5A-6 and QRd.sxau-5A (5A _ 502688179) were significantly associated with RD, explaining the highest R2 of root traits (Fig.3, Table.S5). 6A_597623440 (Mg6A-9) had the same physical location as the peak markers of QSdw.sxau.6A, QRdw.sxau.6A, and QDw.sxau.6A (Fig.3). These loci were stable in multiple environments, indicating that 6A_597623440 was a stable locus associated with the seedling root traits, and suggesting that related genes regulating root architecture may exist within this loci interval on chromosome 6A.
The locus QDw.sxau-6A was stably detected in both NCE and SCE, explaining 5.56%-13.60% of the phenotypic variation. Mg6A-9 was significantly and stably associated with DW in SCE, explaining 6.03-6.35 % of the phenotypic variation. According to the Zheng et al. (2022) analysis of LD attenuation distance, QDw.sxau-6A and Mg6A-9 were on the same locus (Fig.4a-c). In the same genetic interval, Mr6A-11 and Mg6A-10 also were significantly associated with root traits, indicating that this interval on chromosome 6A is an important region for regulating root structure. The locus for the peak SNP marker 6A_597623440 in QDw.sxau-6A had positive effects on root traits. The favored allele for this locus had significant effects on the root traits RDW, MRL, RS, TRL, RV, and RN. Varieties with favorable alleles showed increases in RDW, MRL, RS, TRL, RV, and RN (Fig.4d), indicating that QDw.sxau-6A had significant effects on root traits.
Fig.4. Analysis of QDW.saw-6A. a Idiogram of Mg6A-9 identified by Oligo-FISH. b Physical map for QDW.saw-6A. c Manhattan plot of QDW.saw-6A identified by GWAS. d Effect of QDW.saw-6A on root related traits.
RDW, root dry weight; MRL, maximum root length; RS, root surface-area; TRL, total root length; RV, root volume; RN, root number
3.6.3 Co-localization analysis of association panel and DH population
To more precisely locate loci related to root traits, the co-localization results of the association panel and the DH population were analyzed. Among the QTL obtained by linkage analysis, 10 QTL were also identified via association analysis (Table S6). Five QTL were stably detected in multiple datasets of the DH population, explaining 4.59 % to 17.51 % of the phenotypic variation. QRd.sxau-4D was detected in all datasets of the DH population with the highest R2 value. This locus has been reported as significantly correlated with root-shoot ratio (Yang et al. 2021a). The physical location of QMrl.sxau-3A in the DH population was 0.3 Mb from the locus Mr3A-4 related to the TRL in the association panel, which explained R2 of 5.11 % (Table S6). In addition, QDw.sxau-1D was stably detected by multiple datasets in both the association panel and the DH population (Fig.5a-b). QDw.sxau-1D explained 5.17-5.92 % of the phenotypic variation for DW in the DH population and 7.24-16.34 % in the association panel. This locus also has been reported to be significantly related to the root diameter (Yang et al. 2021a). The favorable alleles of QDw.sxau-1D had significant effects on root traits RDW, RFW, DW and RV (Fig.5c).
Fig.5. Analysis of QDW.saw-1D. a Manhattan plot of DW with markers located on chromosome 1D. b Physical map for QDW.saw-1D. c Genetic linkage map for QDW.saw-1D. d Effect of QDW.saw-1D on root-related traits.
RDW, root dry weight; DW, plant dry weight; RFW, root fresh weight; RV, root volume
3.7. Effects of loci in association panel
The effect of loci on root traits was analyzed in the association panel according to the type of allelic variation. For phenotypic traits and SNPs, variation at the nine loci differed in their effects on root traits (Fig.S5). Of these, 1B _ 110637082, 2A _ 327206091 and 6A _ 597623440 had the strongest effects (Fig.S5). The effects of 1A_592348186, 2D_584522338, 5D_450204045 and 5D_359103858 were smaller. Among these loci, the proportions of favored alleles for loci 1A_592348186 were more than 90%, whereas the proportion for loci 5D_450204045 and 5D_359103858 were less than 10%. The association analysis between phenotypic traits and SCV showed that 11 loci varied significantly in their effects on root traits (Fig.S6). Mr1D-5, Mr3A-3, and Mr3A-4 significantly affected more than nine root traits, while Mg1D-2 had lesser effect. The proportions of favorable alleles for loci Mr1A-1 and Mr2B-12 were more than 80%, whereas Mg1D-2, Mr3A-3, and Mr3A-4 were less than 10%.
Molecular marker-assisted selection requires markers with strong effects. At present, only a single type of marker such as SNP or SSR are used to study additive effects, and there is no report describing the combined effects of cytological markers and SNP markers. In the present study, the additive effects on root traits of loci were analyzed by combining SNPs and SCVs. Markers with significant effects on all root traits were selected for analysis and additive effects were analyzed based on peak markers. Four SNPs and five SCVs had additive effects on root traits, including 1B_110637082, 2A_327206091, 5D_359103858, 6A_597623440, Mr5B-11, Mr1D-5, Mr2B-12, Mg2B-6 and Mr3A-4. The average values of root traits increased as the number of positive alleles increased (Fig. 6). This result confirms the usefulness of these markers for selecting wheat cultivars for improved root systems.
Fig.6. Effects of increasing numbers of favorable alleles for stable loci on root traits using SNPs and SCV in the association panel
RFW, root fresh weight; RS, root surface-area; RV, root volume; TRL, total root length
3.8. Identification of candidate genes for the SNP and SCV co-localized locus QDw.sxau-6A
Combined analyses of the two types of markers indicated that some of the overlapping segments were close to candidate genes. Overall, 439 high-confidence genes from the functional interval were annotated. Gene annotation and orthologous gene analysis indicate that TraesCS6A02G372300, TraesCS6A02G382900 and TraesCS6A02G365100 are likely involved in either root development or lateral root formation. Gene annotation for TraesCS6A02G372300 indicated an association with lateral root development and maintenance of root viability, with homologous genes RID2 (AT5G57280) (Konishi et al. 2003) in Arabidopsis and Os02g0804300 in rice. TraesCS6A02G382900 was associated with regulation of root development and nutrient reservoir activity, and it was homologous to Os01g0284500 in rice. The function of TraesCS6A02G365100 was annotated as related to lateral root formation and growth regulation. It was homologous to RLF (AT5G09680 ) in Arabidopsis and Os07g0232200 in rice. RLF has been shown to control early cell division in the initiation of lateral roots formation (Ikeyama et al. 2010). The expression levels of genes within the functional interval were analyzed. There was high variation between the expression patterns in different tissues. TraesCS6A02G414300 (G35) and TraesCS6A02G399500 (G15) were specifically expressed only in roots, and TraesCS6A02G356900 (G33) was specifically expressed in embryonic and adventitious roots. TraesCS6A02G382900 (G6) and TraesCS6A02G403000 (G40) showed high expression levels in root, suggesting that both are candidate genes for QDw.sxau-6A (Fig.7).
Fig.7. a The Manhattan plot for QDw.sxau-6A; b Linkage disequilibrium heatmap of candidate region on chromosome 6A; c Transcriptome data for candidate region genes in different tissues. A, radicle; B, roots; C, axillary roots; D,shoots; E, coleoptile; F, stem; G, flag leaf; H, leaf; I, pistil; J, stamen; K, spike; L, spikelets; M, grain; N, endosperm; O, aleurone layer