Bibliographic search for wheat drought QTLs
In the present study we used 34 QTL (Table 1) mapping results related to wheat drought tolerance to conduct a meta-QTL analysis. Each of these study elaborates distinct mapping population, collectively identifying a total of 1291 QTLs. Theses QTLs were allocated on 21 chromosome belonging to seven homoleoogous groups or to three sub- genomes(A, B, and D). Each homoeologous group of individual chromosomes observed significance difference in the number of QTLs. The characterization of the QTL on the map is most likely to the position of the markers, confidence interval, LOD score and PEV value.
High resolution wheat consensus genetic map
In order to combine quantitative genetic studies of wheat traits with different sources under drought stress, we established a high resolution wheat genetic consensus map by using BioMercator V4.2 software showing markers position in the genetic maps. Following this strategy, 34 genetic maps consisting of 28821 markers and 1291 QTLs were combined.
Genomic distribution of individual wheat QTLs
In the past, many studies have demonstrated genetic control of different agronomic wheat traits involved in genomic regions located in seven wheat chromosome groups. In the current study, we used data from 34 published QTL results related to wheat drought resistance, and identified several consensus genomic regions for drought resistance. A total of 1291 individual major QTL (Fig. 1) were reported for 21 linkage groups of agronomic traits in wheat drought stress.
Through a careful survey, only 211 QTLs were kept and were selected for further analysis, with the other remaining QTLs being discarded for they did not show any common flanking markers on consensus map. Out of these 211 QTLs, the maximum numbers of QTL were distributed on chromosomes 4A (39), 5B (39) and 7B (35), and the remaining QTLs distributed on chromosomes 1D, 3B 4D, and 6B respectively (Fig. 2).
Table 3 Detailed summary of MQTLs in the present study
MQTLs
|
Position (cM)
|
Flanking markers
|
LOD averaged
|
No. of traits
|
No. of QTLs
|
Traits involved
|
1.1 (1D)
|
65.51 -66.52
|
wsnp_Ra_c1020_2062200 - Tdurum_contig11756_458
|
11.53
|
2
|
2
|
LW, LT
|
1.2 (1D)
|
65.51 -66.52
|
tplb0061f07_1411 - Tdurum_contig11756_458
|
11.53
|
2
|
2
|
LW, LT
|
1.3 (1D)
|
65.51-66. 58
|
tplb0061f07_1411- Tdurum_contig11756_458
|
19.56
|
1
|
1
|
LW
|
1.4 (1D)
|
119.57- 119.66
|
Xbarc229 - Xbarc162
|
7.33
|
3
|
3
|
PH,TKW, RN
|
1.5 (1D)
|
119.59-119.68
|
Xbarc162 - Xwmc93b
|
3.46
|
3
|
3
|
RN, GFD, D-value
|
1.6 (1D)
|
119.59-119.68
|
Xbarc162 - Xwmc93b
|
7.1
|
4
|
4
|
PH, TKW, KWS,RFW
|
1.7 (1D)
|
139.4 -142.72
|
BS00094562_51 - Excalibur_c23906_303
|
6.1
|
2
|
2
|
DTA, TRW
|
1.8 (1D)
|
141.53 -144.82
|
BobWhite_c14141_197 - Tdurum_contig75731_537
|
3.6
|
2
|
2
|
DTM, BWDW
|
1.9 (1D)
|
224.95 -291.53
|
Kukri_rep_c106578_178 - Excalibur_c981_294
|
22.9
|
1
|
1
|
PH
|
3.1 (3B)
|
152.76-168.64
|
Xwmc0044 - Xwmc245
|
24.89
|
2
|
2
|
PH, LW
|
3.2 (3B)
|
154.27 - 168.5
|
Xwmc245 - Xwmc105
|
24.89
|
2
|
2
|
PH, LW
|
3.3 (3B)
|
346.28 -346.68
|
Xbarc0075 - Xbarc206
|
5.35
|
3
|
3
|
TKW, TRL, TRW
|
3.4 (3B)
|
348.26-350.46
|
Xbarc206 - Kukri_rep_c104399_156
|
4.97
|
6
|
6
|
GY, TRL, RL, CHL, TKW, ABA
|
3.5 (3B)
|
357.59-358.45
|
Kukri_c2227_583 - RFL_Contig4792_379
|
4.1
|
3
|
3
|
PH, SRN SPAD
|
3.6 (3B)
|
406.68 -409.04
|
RAC875_C66953_100 - Tdurum_contig10458_460
|
6.85
|
2
|
2
|
SL, SPAD
|
3.7 (3B)
|
406.68-409.04
|
Kukri_c42669_583 - Tdurum_contig10458_460
|
6.85
|
2
|
2
|
SL, SPAD
|
4.1 (4A)
|
144.43-144.97
|
wsnp_JD_c38619_27992279 - Kukri_c25832_687
|
3.5
|
3
|
3
|
TGW. RSDWR, GVW
|
4.2 (4A)
|
168.63-169.46
|
BS00001921_51 - Excalibur_c23921_738
|
4.54
|
6
|
6
|
GY, RSA PRA, CHL ,KWS, RDW
|
4.3 (4A)
|
180.28-180.63
|
wsnp_Ra_c4400_7986499 - Excalibur_rep_c101560_2243
|
4.95
|
5
|
5
|
RSA, PRA, HI, CL, GS
|
4.4 (4A)
|
185.01-185.59
|
BobWhite_c33799_145 - Excalibur_c1904_2824
|
4.43
|
3
|
3
|
VIG, HI, GS
|
4.5 (4A)
|
207.87-208.03
|
Xwms0742 - Xwms0855
|
3.4
|
3
|
3
|
PH, RN, RL
|
4.1 (4D)
|
21.64 - 22.5
|
AX-94618548 - AX-95257633
|
3.77
|
2
|
2
|
GP, SDW
|
4.2 (4D)
|
21.64 - 28.43
|
AX-94618548 - Xcfa285
|
3.77
|
2
|
2
|
GP, SDW
|
4.3 (4D)
|
28.43 - 31.43
|
Xcfa285 - Xwmc285
|
3.6
|
3
|
3
|
GP, SDW, RSDWR
|
4.4 (4D)
|
51.11 - 55.14
|
Xpsp3101.1 - X302.2
|
3.71
|
4
|
4
|
LWUE, RL, GS, RSDWR
|
4.5 (4D)
|
59.41 - 59.78
|
Xbarc0288 - Xwmc331
|
6.37
|
8
|
8
|
SPAD, LWUE, RL, PH, SCR,TWT GS, HI
|
4.6 (4D)
|
59.48 - 59.86
|
Xwmc331 - Xwmc399
|
3.54
|
7
|
7
|
GS, RSDWR, RL, SPAD, KPSM, SCR,KWT
|
4.7 (4D)
|
59.48 - 59.86
|
Xwmc331 - wPt-0431
|
6.37
|
8
|
8
|
SPAD, LWUE, RL, PH, SCR,TWT GS, HI
|
4.8 (4D)
|
125.68 - 137.16
|
wPt-0377 - Xgwm265
|
4.33
|
3
|
3
|
PH, CHL, RN
|
5.1 (5B)
|
63.9 - 64.89
|
BobWhite_rep_c54139_273 - Excalibur_c3730_546
|
4.29
|
6
|
6
|
PH, CL, GS, SDW, NDVI, GY
|
5.2 (5B)
|
81.95 - 82.3
|
tplb0033f11_1381 - Kukri_c83977_279
|
4.3
|
11
|
10
|
RL, WSC, TSS, VIG, TKW, DTM, FLW, CHL,KW,GY
|
5.3 (5B)
|
94.17 - 94.62
|
Excalibur_c7968_2218 - BS00009335_51
|
6
|
13
|
13
|
SPAD, LWUE, CHL,MRL, RSA, ABA, HI,PRA,RN, TKW, GFD, GY, TRL
|
5.4 (5B)
|
178.25 - 178.74
|
RAC875_c278_1801 - TA002682-0717
|
13.35
|
4
|
4
|
NDVI, PH ,LW, CL
|
6.1 (6B)
|
86.72 - 89.61
|
Tdurum_contig29162_378 - Tdurum_contig50121_249
|
14.8
|
3
|
3
|
PH, CL, RL
|
6.2 (6B)
|
104.8 - 106.34
|
BobWhite_c9330_499 - BS00074183_51
|
10.04
|
6
|
6
|
TKW, CL, PH, RL, GY, SH
|
6.3 (6B)
|
104.8 - 106.6
|
BS00074183_51 - GENE-0221_721
|
10.04
|
6
|
6
|
TKW, CL, PH, RL, GY, SH
|
6.4 (6B)
|
112.11 - 113.99
|
Xwmc486 - Xbarc76
|
4.84
|
4
|
4
|
TKW,KNS, SH,GY
|
6.5 (6B)
|
129.57 - 130.24
|
wPt-9990 - Xbarc0045
|
6.19
|
10
|
10
|
KNS, PH, LWUE, LW, GY, DTM, CHL,CL, RDW, DTA
|
6.6 (6B)
|
129.6 - 130.26
|
Xbarc0045 - Xgwn70
|
5.66
|
9
|
9
|
ABA, FSS, SSS, CHL, GP, DM, PH, GY, TKW
|
6.7 (6B)
|
130.32 - 130.98
|
Xgwm70 - Xwmc786
|
5.88
|
12
|
12
|
KNS, PH, LWUE, LW, GY, DTM, CHL,CL, RDW, DTA, LR, RDW
|
6.8 (6B)
|
182.60 - 185.82
|
BS00011795_51 - RAC875_C17011_373
|
3.06
|
3
|
3
|
SPAD, SR, LR
|
6.9 (6B)
|
185.82 - 210.84
|
RAC875_C17011_373 - Xwmc106
|
3.06
|
3
|
3
|
SPAD, SR, LR
|
7.1 (7B)
|
71.84 - 75.28
|
Excalibur_c3423_1170 - GENE-4833_102
|
5.31
|
5
|
5
|
PH, DTH, GVW,GY, DPM
|
7.2 (7B)
|
113.98 - 114.36
|
Xpsp3033 - BobWhite_c14812_828
|
7.39
|
11
|
11
|
DTH, CHL, PH, RN,GVW, GY, MRL, DTA,TKW, GFR,GN
|
7.3 (7B)
|
114.61 - 115.33
|
BobWhite_c14812_828 - Tdurum_contig68347_605
|
8.84
|
9
|
9
|
DTH,CHL,MRL,PH, GS,RN, GN, GY, GFR
|
7.4 (7B)
|
141.4 - 141.67
|
Kukri_c9405_379 - Excalibur_c13444_235
|
7.06
|
4
|
4
|
FRW, FSW,RL, SPAD
|
7.5 (7B)
|
141.67 - 142.06
|
Excalibur_c13444_235 - BS00089942_51
|
7.06
|
4
|
4
|
FRW, FSW,RL, SPAD
|
7.6 (7B)
|
154.02 - 155.1
|
BobWhite_c8890_279 - tplb0062h23_1362
|
3.27
|
2
|
2
|
SL, SDW
|
7.7 (7B)
|
154.02 - 155.1
|
BobWhite_c33540_69 - Excalibur_c6871_217
|
3.27
|
2
|
2
|
SL, SDW
|
Abbreviations: Drought tolerance evaluation (D-value), Grain yield (GY), Shoot length (SL), Root length (RL), Days to heading (DTH), Grain number (GN), Chlorophyll content (CHL), Root number (RN), Coleoptile length (CL) , Maximum root length (MRL), Seedling height (SH), Seedling dry weight (SDW), Leaf chlorophyll content (SPAD), Seedlig recovery (SR), Leaf rolling (LR), Abscisic acid (ABA), Cell membrane stability (CAM), Days of maturity (DPM), Grain filling rate (GFR), Drought susceptibilty index (DSI), grain volume weight (GVW), fresh root weight (FRW), fresh shoot weight (FSW), plant height (PH), total root weight (TRW), Above wax seminal dry weight (AWDW), Below wax seminal dry weight (BWDW), Kernal weight per spike (KWS), thousand kernal weight (TKW), spikelet compactness (SCN), intercellular CO2 concentration (Ci), transpiration rate (E), Sterile spikelet number per spike (SSS), Kernal per spike (KNS), Spike number per plant (SNP), Leaf water use efficiency (LWUE), Dry matter (DM), Fertile spikelets number (FSS), Days to anthesis (DTA), Grain filling durtion (GFD), Days to maturity (DTM), Germination percentage (GP), Thousand grain weight (TGW).
QTL projection and meta-QTL analysis
In this study, only 211 out of 1291 QTLs projected on consensus map, the remaining QTLs could not be projected due to their lower PEV value or wider confidence interval. The projection resulted in the identification 49 MQTLs on chromosomes 1D, 4A, 4D, 5B, 6B, and 7B.
Fig. 10 MQTLs distribution on per chromosomes
For chromosome 1D, 17 QTLs and nine MQTL regions are identified, with MQTL 1.6 containing numerous numbers of QTLs, potentially governing important traits indicating that these traits are associated with MQTLs. Traits within MQTL 1.6 encompass PH, TKW, BWDW, KWS and RFW, while traits within MQTL 1.5 involves traits like PH, TKW and RN. A minimal QTL presence is noted on MQTL 1.3, and MQTL 1.9 housing a single “LW and PH” trait QTL. Genotypes Chuan35050, Shannong483, Kukri, and Excalibur exhibit MQTL 1.6, while genotypes, Cranbrook, Halberd, Kukri, and Excalibur, exhibit MQTL 1.5.
For chromosome 3B, 38 QTLs and 7 MQTLs regions are identified with MQTL 3.4 containing numerous numbers of QTLs. Traits within MQTL 3.4 encompass such as GY, TRL, RL, CHL, TKW, and ABA, while traits within MQTL 3.3 are involved such as TKW, TRL, TRW. The genotypes, Veery, Yecora_Rojo, Luohan2, Weimai8, Yannong19, and Weimai exhibit MQTL 3.4, while MQTL 3.3 exhibit Luohan2, Weimai8, Yannong19, Weimai, Chuan35050, and Shannong483. QTL presence on the genomic region MQTL 3.1 and MQTL 3.2 are minimal numbers of QTL.
Chromosome 4A contains the highest number (39) of QTLs focusing on MQTL regions. Five MQTL regions are identified on chromosome 4A. MQTL 4.2 containing numerous numbers of QTLs within which encompass traits like GY, RSA, PRA, CHL, KWS, and RDW, the presence of multiple traits suggests the potential pleiotropic effects or genetic linkage within MQTL 4.2. MQTL 4.1 involves traits like TGW, RSDWR and GVW. A minimal QTL presence is noted on MQTL 4.4 suggesting that fewer genetic factors within this region may contribute to the associated traits with MQTLs. The specific wheat genotypes, Chuan35050, Shannong483, CO940610, and PI596297 (Platte) are associated with MQTL 3.2, while MQTL 3.4 exhibit Reeder, Alban, W7984, and Opata.
For chromosome 4D, 15 QTLs and nine MQTL regions are identified, with MQTL 4. 5 containing numerous numbers of QTLs, potentially governing important traits indicating that these traits are associated with MQTLs. Traits within MQTL 4.5 encompass SPAD, LWUE, RL, PH, SCR,TWT GS, and HI, and MQTL 4.6 involves traits like GS, RSDWR, RL, SPAD, KPSM, SCR, and KWT. A minimal QTL presence is noted on MQTL 4.1, and MQTL 4.2 housing a “GP and SDW” trait QTLs.
Chromosome 5B contains the equally highest number (39) of QTLs as chromosome 4A focusing on MQTL regions. Four MQTL regions are identified on chromosome 5B, with MQTL 5.3 containing numerous numbers of QTLs. Traits within MQTL 5.3 encompass traits such as SPAD, LWUE, CHL, MRL, RSA, ABA, HI, PRA, RN, TKW, GFD, GY, and TRL, while MQTL 5.2 involves traits like RL, WSC, TSS, VIG, TKW, DTM, FLW, CHL, KW, and GY. The presence of multiple traits suggests the potential pleiotropic effects or genetic linkage within MQTL 5.2 and 5.3. A minimal QTL presence is noted on MQTL 5.4 suggesting that fewer genetic factors within this region may contribute to the associated traits with MQTLs.
For chromosome 6B, 29 QTLs and nine MQTL regions are identified, with MQTL 6.7 containing numerous numbers of QTLs, potentially governing important traits indicating that these traits are associated with MQTLs. Traits within MQTL 6.7 encompass KNS, PH, LWUE, LW, GY, DTM, CHL, CL, RDW, DTA, LR, and RDW. MQTL 6.5 involves traits like KNS, PH, LWUE, LW, GY, DTM, CHL, CL, RDW, and DTA. A minimal QTL presence is noted on MQTL 6.1, and MQTL 6.9 housing a “PH, CL and RL” trait QTLs.
For chromosome 7B, 35 QTLs and seven MQTL regions are identified, with MQTL 7.2 containing numerous numbers of QTLs, potentially governing important traits indicating that these traits are associated with MQTLs. Traits within MQTL 7.2 encompass DTH, CHL, PH, RN, GVW, GY, MRL, DTA, TKW, GFR, and GN. MQTL 7.3 involves traits like DTH, CHL, MRL, PH, GS, RN, GN, GY, and GFR. A minimal QTL presence is noted on MQTL 7.6, and MQTL 7.7 housing a “SL and SDW” trait QTLs.
Table 4 High confidence occurring candidate genes in genomic region of wheat MQTLs identified in the present study
MQTL regions
|
Chr
|
Pop
|
QTLs
|
CI(left)
|
CI(right)
|
CI interval
(cM)
|
Traits
|
Markers
|
Total Genes
|
CGs
|
MQTL1.1
|
1D
|
2
|
2
|
65.51
|
66.52
|
1.01
|
LW, LT
|
632
|
162
|
TaFBA1, TaRPK1, TaPLDα, and TaSnRK2.4
|
MQTL 1.2
|
1D
|
4
|
2
|
65.51
|
66.52
|
1.01
|
LW, LT
|
994
|
304
|
TaABC1
|
MQTL 1.3
|
1D
|
1
|
1
|
65.51
|
66.58
|
1.07
|
LW
|
212
|
304
|
TaLEA3
|
MQTL 1.4
|
1D
|
2
|
3
|
119.57
|
119.66
|
0.09
|
PH,TKW, RN
|
141
|
3035
|
TaPYL5
|
MQTL 1.5
|
1D
|
2
|
3
|
119.59
|
119.68
|
0.09
|
RN, GFD, D-value
|
432
|
131
|
TaSnRK2.8
|
MQTL 1.6
|
1D
|
3
|
4
|
119.59
|
119.68
|
0.09
|
PH, TKW, KWS,RFW
|
1806
|
131
|
NA
|
MQTL 1.7
|
1D
|
2
|
2
|
139.40
|
142.72
|
3.32
|
DTA, TRW
|
926
|
1326
|
TaCIPK23, TaABC1K, TaCIPK27, TaPUB2, TaPIMP1, TaMYB33, TaMYBsm3, TaLhc2, TaC5-MTases, TaABF2, TaERF87, TaAKS1, TaP5CS1, and TaP5CR1
|
MQTL 1.8
|
1D
|
1
|
2
|
141.53
|
144.82
|
3.29
|
DTM, BWDW
|
300
|
1606
|
TaNF-YA10-1, TaCIPK19, TaWRKY2, and TaPP2C158
|
MQTL 1.9
|
1D
|
1
|
1
|
224.95
|
291.53
|
66.58
|
PH
|
571
|
46
|
TaSnRK2, TabZIP2
|
MQTL 2.1
|
3B
|
2
|
2
|
152.76
|
168.64
|
15.88
|
PH, LW
|
1766
|
90
|
TaLRRK
|
MQTL 2.2
|
3B
|
2
|
2
|
152.27
|
168.50
|
16.23
|
PH, LW
|
1265
|
2080
|
NA
|
MQTL 2.3
|
3B
|
2
|
3
|
246.28
|
248.68
|
2.4
|
TKW, TRL, TRW
|
278
|
142
|
NA
|
MQTL 2.4
|
3B
|
4
|
6
|
348.26
|
350.46
|
2.2
|
GY, TRL, CHL, TKW, ABA
|
306
|
2255
|
NA
|
MQTL 2.5
|
3B
|
3
|
3
|
357.59
|
358.45
|
0.86
|
PH, SRN SPAD
|
111
|
45
|
NA
|
MQTL 2.6
|
3B
|
2
|
2
|
406.68
|
409.04
|
2.36
|
SL, SPAD
|
87
|
77
|
NA
|
MQTL 2.7
|
3B
|
2
|
2
|
406.68
|
409.04
|
2.36
|
SL, SPAD
|
79
|
73
|
NA
|
MQTL 3.1
|
4A
|
2
|
3
|
144.43
|
144.97
|
0.54
|
TGW. RSDWR, GVW
|
567
|
387
|
TaRPK1,TaSUTs, TaSUT1_4A
|
MQTL 3.2
|
4A
|
4
|
6
|
168.63
|
169.46
|
0.83
|
GY, RSA, PRA, CHL, ,KWS, RDW
|
213
|
56
|
TaPP2C158
TaCYP450s
|
MQTL 3.3
|
4A
|
4
|
5
|
180.28
|
180.63
|
0.35
|
RSA, PRA, HI, CL, GS
|
91
|
96
|
NA
|
MQTL 3.4
|
4A
|
2
|
3
|
185.01
|
185.59
|
0.58
|
VIG, HI, GS
|
2132
|
6059
|
TaMSRA4.1
|
MQTL 3.5
|
4A
|
2
|
3
|
207.87
|
208.03
|
0.16
|
PH, RN, RL
|
76
|
71
|
NA
|
MQTL 4.1
|
4D
|
1
|
2
|
21.64
|
22.50
|
0.86
|
GP, SDW
|
105
|
620
|
TaSWEETs
|
MQTL 4.2
|
4D
|
1
|
2
|
21.64
|
28.43
|
6.79
|
GP, SDW
|
312
|
1175
|
TaWRKY10
|
MQTL 4.3
|
4D
|
2
|
3
|
28.43
|
31.43
|
3
|
GP, SDW, RSDWR
|
109
|
304
|
NA
|
MQTL 4.4
|
4D
|
3
|
4
|
51.11
|
55.14
|
4.03
|
LWUE, RL, GS, RSDWR
|
214
|
213
|
NA
|
MQTL 4.5
|
4D
|
5
|
8
|
59.41
|
59.78
|
0.37
|
SPAD, LWUE, RL, PH, SCR,TWT,GS, HI
|
745
|
1987
|
TabHLH49
|
MQTL 4.6
|
4D
|
6
|
7
|
59.48
|
59.86
|
0.38
|
GS, RSDWR, RL, SPAD,KPSM, SCR, KWT
|
124
|
523
|
TaGAPC1, TaWRKY40, TaWRKY 28, TaWRKY 47, and TaWRKY 33
|
MQTL 4.7
|
4D
|
6
|
8
|
59.48
|
59.86
|
0.38
|
SPAD, LWUE, RL, PH, SCR,TWT GS, HI
|
55
|
9
|
TaGAPC2 and TaWRKY33
|
MQTL 4.8
|
4D
|
2
|
3
|
125.68
|
137.16
|
11.48
|
PH, CHL, RN
|
67
|
36
|
NA
|
MQTL 5.1
|
5B
|
5
|
6
|
63.90
|
64.89
|
0.99
|
PH, CL, GS, SDW, NDVI, GY
|
87
|
66
|
NA
|
MQTL 5.2
|
5B
|
6
|
10
|
81.95
|
82.30
|
0.35
|
RL, WSC, TSS, VIG, TKW, DTM, FLW, CHL,KW,GY
|
2809
|
5065
|
TaZFPs
TaXTHs
|
MQTL 5.3
|
5B
|
6
|
13
|
94.17
|
94.62
|
0.45
|
SPAD, LWUE, CHL,MRL, RSA, ABA, HI,PRA,RN, TKW, GFD, GY, TRL
|
123
|
151
|
TaFBK
|
MQTL 5.4
|
5B
|
3
|
4
|
178.25
|
178.74
|
0.49
|
NDVI, PH ,LW, CL
|
154
|
161
|
NA
|
MQTL 6.1
|
6B
|
2
|
3
|
86.72
|
89.61
|
2.89
|
PH, CL, RL
|
123
|
287
|
TaEXPA2
|
MQTL 6.2
|
6B
|
4
|
6
|
104.80
|
106.34
|
1.54
|
TKW, CL, PH, RL, GY, SH
|
126
|
143
|
TaH2A.7
|
MQTL 6.3
|
6B
|
4
|
6
|
104.80
|
106.63
|
1.83
|
TKW, CL, PH, RL, GY, SH
|
234
|
746
|
TaMPK3, TaPYL4
|
MQTL 6.4
|
6B
|
3
|
4
|
112.11
|
113.99
|
1.88
|
TKW,KNS, SH,GY
|
46
|
26
|
NA
|
MQTL 6.5
|
6B
|
6
|
10
|
129.57
|
130.24
|
0.67
|
KNS, PH, LWUE, LW, GY, DTM, CHL,CL, RDW, DTA
|
101
|
245
|
NA
|
MQTL 6.6
|
6B
|
6
|
9
|
129.60
|
130.26
|
0.66
|
ABA, FSS, SSS, CHL, GP, DM, PH, GY, TKW
|
99
|
111
|
TaGATA62, TaGATA73
|
MQTL 6.7
|
6B
|
7
|
12
|
130.32
|
130.98
|
0.66
|
KNS, PH, LWUE, LW, GY, DTM, CHL,CL, RDW, DTA, LR, RDW,
|
302
|
304
|
TaSTP
|
MQTL 6.8
|
6B
|
2
|
3
|
182.60
|
185.82
|
3.22
|
SPAD, SR, LR
|
1398
|
539
|
TaPUB3
|
MQTL 6.9
|
6B
|
2
|
3
|
185.82
|
210.84
|
25.02
|
SPAD, SR, LR
|
1501
|
458
|
NA
|
MQTL 7.1
|
7B
|
4
|
5
|
71.84
|
75.28
|
3.44
|
PH, DTH, GVW,GY, DPM
|
166
|
148
|
TaWRKY31
|
MQTL 7.2
|
7B
|
8
|
11
|
113.98
|
114.36
|
0.38
|
DTH, CHL, PH, RN,GVW, GY, MRL, DTA,TKW, GFR,GN
|
1620
|
3576
|
TaVAMP727, TabZIP174, TaAAP3, TaATLa2 and TaATLb13
|
MQTL 7.3
|
7B
|
6
|
9
|
114.61
|
115.33
|
0.72
|
DTH,CHL,MRL,PH, GS,RN, GN, GY, GFR
|
231
|
111
|
NA
|
MQTL 7.4
|
7B
|
3
|
4
|
141.40
|
141.67
|
0.27
|
FRW, FSW,RL, SPAD
|
105
|
209
|
NA
|
MQTL 7.5
|
7B
|
3
|
4
|
141.67
|
142.06
|
0.39
|
FRW, FSW,RL, SPAD
|
66
|
301
|
TaNAC69-1
|
MQTL 7.6
|
7B
|
2
|
2
|
154.02
|
155.10
|
1.08
|
SL, SDW
|
24
|
11
|
TaSAP5
|
MQTL 7.7
|
7B
|
2
|
2
|
154.02
|
155.10
|
1.08
|
SL, SDW
|
2345
|
2902
|
TaSPL6 and TaLTP4
|
Putative candidate genes associated with MQTLs region
In the present study, a total of 66 putative candidate genes were elucidated within MQTL regions, and there existed known genes co-localized within wheat MQTLs, such as, TaRPK1-1D (MQTL 1.1), TaCIPK27-1D, TaMYB33-1D (MQTL 1.6), TaLRRK-3B (MQTL 3.1), TaSUT1_4A (MQTL 4.1), TabHLH49-4D (MQTL 4.5), TaZFPs-5B (MQTL 5.2), TaMPK3-6B (MQTL 6.3), TaVAMP727-7B (MQTL 7.2), and TaSAP5-7B (MQTL 7.6). For these 66 putative candidate genes, encoding a variety of proteins like F-box domain containing protein, NBS-LRR-like resistance protein, protein kinase, putative, o-methyltransferase, phospholipase D, late embryogenesis abundant protein, F-box protein, kinase family protein, E3 ubiquitin-protein ligase, MYB transcription factor, chlorophyll a/b binding protein, methyltransferase, ethylene-responsive transcription factor, heat shock transcription factor, nuclear factor Y subunit C, calcineurin B-like protein, WRKY transcription factor, leucine-rich repeat receptor-like protein kinase family protein, receptor kinase 1, sucrose transporter, protein phosphatase 2C (PP2C), cytochrome P450, heme oxygenase 1 protein, sugar transporter family protein, dehydrin, glyceraldehyde-3-phosphate dehydrogenase, zinc finger family protein, xyloglucan endotransglucosylase/hydrolase, F-box/kelch-repeat protein, expansin protein, histone H2A, mitogen-activated protein kinase kinase 1, GATA transcription factor, autophagy-related protein 2 (ATGs), BZIP transcription factor, amino acid transporter family protein, elongation factor, squamosal promoter binding-like protein, and lipid transfer protein (in the Table S2).
Drought stress responsive genes
Although there is an overlap in the expression pattern of certain stress-responsive genes, specific genes exhibited altered expression patterns in response to control, drought_1h, and drought_6h conditions, indicating the involvement of distinct expression in response to different stresses. To further examine the expression levels of the stress-related genes identified in our study, we performed heatmap (Fig. 17). The up regulated genes displayed higher expression under both normal and drought stress conditions, including TraesCS1D02G363500, TraesCS1D02G369200, TraesCS1D02G369300, TraesCS4D02G185400, TraesCS7B02G362600, and TraesCS7B02G159400. Conversely, the remaining genes exhibited down-regulated expression under both normal and drought stress conditions. Certain candidate gene, such as TraesCS1D02G363500, TraesCS1D02G369200, TraesCS1D02G369300 and TraesCS7B02G159400, displayed down regulated expression levels under control and drought_1h conditions but up-regulated expression levels under drought_6h conditions.
The heatmap scale indicated the expression patterns of the most promising genes across various stress condition between two cultivars Ataya85 and Zubkove (resistant and susceptible) stress treatment apply on root, leaf and grain tissues. Although there is an overlap in the expression pattern of certain stress-responsive genes, specific genes exhibited altered expression patterns in response to control and drought stress conditions, indicating the involvement of distinct expression in response to different stresses. To further examine the expression levels of the stress-related genes identified in our study, we performed heatmap (Fig. 18). The up regulated genes displayed higher expression under both normal and drought stress conditions, including TraesCS1D02G367100, TraesCS1D02G369200, TraesCS1D02G369300, TraesCS1D02G450400, TraesCS4A02G459600, TraesCS4D02G185400, TraesCS7B02G119000, and TraesCS7B02G362600. Conversely, the remaining genes exhibited down-regulated expression under both normal and drought stress conditions. Certain candidate gene, such as TraesCS1D02G369200, TraesCS1D02G369300, TraesCS7B02G119000, and TraesCS7B02G362600, displayed up-regulated expression root tissue under various stress condition between two cultivars Ataya85 and Zubkove (resistant and susceptible). TraesCS4D02G185400 genes exhibited up-regulated expression on grain yield under susceptible cultivar under both normal and drought stress conditions.
In brief summary, the present study of meta-QTL analysis in wheat under drought tolerance clearly shows that, MQTLs are associated with high confidence of interval that are confirmed through with relevant genes and associated with numerous single independent analysis and markers derived to be considered in future breeding program scheme.