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. 2021 Mar 25;11:6942. doi: 10.1038/s41598-021-86259-2

Meta-QTL and ortho-MQTL analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions

Bahman Khahani 1, Elahe Tavakol 1,✉, Vahid Shariati 2,✉, Laura Rossini 3
PMCID: PMC7994909  PMID: 33767323

Abstract

Meta-QTL (MQTL) analysis is a robust approach for genetic dissection of complex quantitative traits. Rice varieties adapted to non-flooded cultivation are highly desirable in breeding programs due to the water deficit global problem. In order to identify stable QTLs for major agronomic traits under water deficit conditions, we performed a comprehensive MQTL analysis on 563 QTLs from 67 rice populations published from 2001 to 2019. Yield and yield-related traits including grain weight, heading date, plant height, tiller number as well as root architecture-related traits including root dry weight, root length, root number, root thickness, the ratio of deep rooting and plant water content under water deficit condition were investigated. A total of 61 stable MQTLs over different genetic backgrounds and environments were identified. The average confidence interval of MQTLs was considerably refined compared to the initial QTLs, resulted in the identification of some well-known functionally characterized genes and several putative novel CGs for investigated traits. Ortho-MQTL mining based on genomic collinearity between rice and maize allowed identification of five ortho-MQTLs between these two cereals. The results can help breeders to improve yield under water deficit conditions.

Subject terms: Genetics, Plant sciences

Introduction

Rice is the world’s most important staple food and it is an excellent model crop for plant genetic studies1. Considering climate change scenarios and increasing water deficits, rice breeding programs have invested significant efforts into producing new rice varieties suitable for growing under reduced water inputs2–4. Tolerance to water deficit is a highly complex trait controlled by quantitative trait loci (QTLs). QTL mapping based on bi-parental populations is strongly influenced by the choice of marker sets, parents, population size, population types and environments5–9 hampering the transfer of QTLs and associated markers across different breeding programs. A powerful approach to circumvent this issue is Meta-analysis of QTLs (MQTL), which compiles QTL data from independent studies, locations, years and genetic backgrounds in order to detect stable and reliable QTLs10–12. An additional benefit of this approach is the reduction of confidence intervals (CIs) in the MQTLs leading to improved genetic resolution for marker-assisted selection (MAS) and identification of candidate genes (CGs). Together, MQTL analysis may increase selection accuracy and efficiency, thus enhancing genetic gains in plants breeding programs5,9,13–15. Several MQTL studies for drought stress have been conducted in cereals such as wheat16, maize8,17, and barley5,18. While a recent rice MQTL study considered various traits under unstressed conditions19, relatively few reports address water deficit conditions in rice: MQTL studies by Swamy et al. and Trijatmiko et al. focused on yield integrating data from 15 and 13 experiments20,21, respectively, and Khowaja et al. and Yang et al. reported some MQTLs for plant height and heading date based on QTLs published until 2009 and 2011, respectively22,23.

In the current study, we conducted a comprehensive genome-wide meta-analysis on QTLs reported in the last two decades controlling yield and yield-related traits in rice under water deficit conditions including Yield (YLD), grain weight (GW), heading date (HD), plant height (PH), tiller number (TN) as well as some drought tolerance criteria. Moreover, considering the key role of root architecture in plant responses to water deficit, different root related traits including root dry weight (RDW), root length (RL), root thickness (RT), roots number (RN) and rate of deep rooting (RDR) were subjected to MQTL analyses. We further scanned refined intervals of resulting stable QTLs for CGs related to the aforementioned traits. Additionally, to evaluate transferability of information to other cereals, ortho-MQTLs were investigated based on genomic collinearity between rice and maize24. Results will be applicable to improve selection for yield potential, stability and performance under water deficit conditions in cereal breeding programs.

Results and discussion

Distribution of yield and yield-related QTLs under water deficit conditions on the rice genome

In order to discover consensus genomic regions associated with YLD, PH, TN, HD, GW, RDW, RL, RT, RN and RDR and some drought tolerance-related traits including drought response index (DRI), relative water content (RWC), canopy temperature (CT), leaf rolling (LR), leaf drying (LD) under water deficit conditions in rice, we compiled a total of 563 QTLs derived from 67 QTL populations (57 studies) reported from 2001 to 2019 (Table 1; Fig. 1A). The number of QTLs for each trait and their distribution on 12 rice chromosomes are shown in Fig. 1A,B. The chromosome 1 harbored the highest number of QTLs for all studied traits with 84 initial QTLs followed by chromosome 3 (62 QTLs) and chromosome 4 (58 QTLs). Whereas chromosome 10 harbored the lowest number of QTLs with 23 QTLs (Fig. 1B). The distribution of QTLs on different rice chromosomes with the highest number of QTLs on chromosomes 1 and 3 was similar to previous reports14,19,20. The number of QTLs on each chromosome exhibited a positive correlation (r = 0.73) with the length of chromosome.

Table 1.

Summary of QTL studies used in the QTL meta-analysis for YLD, GW, HD, PH, TN, RWC, CT, LR, LD, DRI, RDW, RL, RN, RT, and DT traits in rice under water deficit condition.

Ref no. Number of QTL population(s) Parents of population Population type Population size No. of markers Map density (cM) Marker type Trait(s) References
1 1 Caiapo × IRGC105491 BC 300 718 2.49 SSR, RFLP HD, PH, GW 99
2 1 IR58821 × IR52561 RIL 183 178 5.29 RFLP, AFLP RT 100
3 1 Bala × Aucena RIL 205 6969 0.20 RFLP, AFLP, SSR RDW, RT, RL, RN 101
4 1 CT9993 × IR62266 DH 220 399 5.49 RFLP, AFLP, SSR YLD, HD, PH, RWC, CT, LR, LD 102
5 1 IAC65 × Co39 RIL 125 115 10.20 RFLP RL, RT 103
6 1 Zhenshan 97 × Minghui 63 RIL 241 208 8.05 SSR, RFLP YLD, GW 104
7 1 Milyang23 × Akihikari RIL 191 182 6.56 RFLP TN 105
8 1 IR1552 × Azucena RIL 96 117 11.01 RFLP, AFLP, SSR RL, RN 106
9 1 Zhenshan 97 × Minghui 63 RIL 241 208 8.05 SSR, RFLP PH, TN, HD 107
10 1 CT9993 × IR62266 DH 220 182 4.19 RFLP, AFLP, SSR YLD, HD, PH 108
11 1 Yuefu × IRAT109 DH 116 4662 0.23 SSR, RFLP RT, RN, RL, RDW 109
12 1 ZenShan 97B × IRAT109 RIL 187 339 2.99 SSR YLD, GW 2
13 1 ZenShan 97 × IRAT109 RIL 180 683 2.45 SSR HD, DRI, LR, LD 67
14 1 Bala × Azucena RIL 177 592 0.58 RFLP, AFLP PH, HD, LD 110
15 1 Akihikari × IRAT109 BC 106 2506 0.23 SSR RDW, RL 111
16 1 IR58821 × IR 52,561 RIL 148 231 5.43 RFLP, AFLP YLD, GW, PH, HD, CT, LR, LD 68
17 1 IR64 × Azucena BC 323 944 0.27 SSR, RFLP RL, RT 112
18 1 ZenShan 97B × IRAT109 RIL 182 6969 0.20 SSR RDR, RL 113
19 1 Otomemochi × Yumenohatamochi RIL 98 2187 0.25 SSR RDW, RN, RL 114
20 1 Taichung 189 × Milyang 23 F2 100 718 2.49 SSR YLD, GW, PH 115
21 1 CT9993 × IR62266 DH 220 154 5.14 AFLP YLD, HD 116
22 1 Vandana × Way Rarem F2 436 112 12.37 SSR YLD, PH, HD 117
23 1 IRAT109 × Yuefu RIL 120 1541 0.21 SSR RT 118
24 1 Yuefu × IRAT109 RIL 120 6969 0.25 SSR RT, RN, RL 119
25 1 CT9993 × IR62266 DH 220 207 4.96 RFLP, AFLP YLD, HD, PH, LD, RWC 69
26 1 Kinandang Patong × IR64 F2 117 1694 0.20 SSR, STS RT 120
27 1 Zhenshan 97 × IRAT109 RIL 180 344 2.69 SSR PH 121
28 1 IR64 × Azucena DH 96 110 9.50 RFLP, SSR RL 122
29 1 Norungan × IR64 RIL 380 126 7.61 SSR YLD, GW, PH, TN, LR, RWC 31
30 1 IR20 × Nootripathu RIL 250 24 14.90 SSR, RAPD, EST PH, TN, CT, LD, LR 123
31 2 Yuefu × IRAT109 BC 430 4475 0.23 SSR RT 124
Yuefu × IL255 F2 304 7 2.95 SSR RT
32 1 CT9993 × IR62266 DH 135 399 5.49 SSR, AFLP, RFLP YLD, HD, GW, PH, TN, DRI 3
33 1 IR64 × INRC10192 RIL 140 14 11.20 SSR RDW 125
34 1 IR64 × Kinandang Patong RIL 117 406 0.33 SSR, STS RDR 126
35 1 CT9993 × IR20 BC 234 577 0.24 SSR RT 127
36 1 Teqing × Binam BC 77 718 2.49 SSR YLD, GW, PH 128
37 1 OM1490 × WAB880 BC 229 133 11.06 SSR YLD, HD, PH 129
38 2 HKR47 × MAS26 F2 94 74 11.70 SSR YLD, PH, TN 4
MASARB25 × Pusa Basmati 1460 F2 100 33 13.26 SSR YLD, PH, TN
39 3 Kinandang Patong × ARC5955 F2 138 1307 0.23 SNP, SSR RDR 130
Kinandang Patong × Pinulupot1 F2 134 577 0.24 SNP, SSR RDR
Kinandang Patong × Tupa729 F2 133 1259 0.22 SNP, SSR RDR
40 1 IR64 × Dro1-NIL BC 4560 406 0.33 SSR RDR 131
41 2 Tarom Molaei × Teqing BC 85 718 2.49 SSR YLD, GW 132
Tarom Molaei × IR64 BC 72 718 2.49 SSR YLD, GW
42 1 IR77298 × Sabitri BC 294 68 3.39 SSR YLD, HD 38
43 1 IR55419 × TDK1 BC 365 418 0.68 SSR YLD, HD, PH 133
44 1 Xiaobaijingzi × Kongyu 131 RIL 220 73 12.89 SSR YLD, PH 134
45 3 Kinandang Patong × Momiroman F2 123 3129 0.20 SNP, SSR RDR 135
Kinandang Patong × Yumeaoba F2 128 4749 0.22 SNP, SSR RDR
Kinandang Patong × Tachisugata F2 121 2923 0.23 SNP, SSR RDR
46 1 Yuefu × IRAT109 F2 2013 5 5.48 SSR RT, RL 136
47 1 Zhenshan 97B × IRAT109 RIL 180 3129 0.22 SNP RDR 137
48 3 IR20 × Nootripathu RIL 397 51 16.79 SSR PH, RWC, CT, LR 32
IR20 × Nootripathu RIL 340 51 16.79 SSR YLD, HD, GW, PH, TN
IR20 × Nootripathu RIL 330 51 16.79 SSR YLD, PH
49 1 Nipponbare × Kasalath F2 155 934 0.38 SSR, RFLP, AFLP RN 138
50 1 Kinandang Patong × IR64 F2 121 1220 0.21 SSR, SNP RDR 139
51 1 KaliAus × AUS276 BC 276 6969 0.20 SNP RDW, RL 140
52 1 IR64 × Dular RIL 490 1892 0.24 SSR RL, RDW, RN, RDR 141
53 1 N-22 × Cocodrie RIL 183 2670 0.25 SNP RL, RDW 142
54 1 Cocodrie × Vandana F2 187 136 7.75 SNP YLD 56
55 2 D123 × Shennong265 BC 178 40 12.24 SSR GW, PH, HD 39
D123 × Shennong265 BC 314 29 19.04 SSR YLD, GW, PH, TN
56 1 IR55419 × Super Basmati F2 418 1702 0.25 SSR RDW, RL 143
57 1 M-203 × M-206 RIL 241 2474 0.23 SNP RL, RDW 144
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BC backcross, DH double haploids, RIL recombinant inbred lines, YLD yield, GW grain weight, PH plant height, HD heading date, TN tiller number, RWC relative water content, CT canopy temperature, LR leaf rolling, LD leaf drying, DRI drought response index, RDW root dry weight, RL root length, RN root number, RT root thickness, RDR ratio of deep rooting.

Figure 1.

Figure 1

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The number and distribution of QTLs and MQTLs. (A) The number of initial QTLs used in the MQTL analysis for YLD, HD, PH, GW, TN, DT, RT, RL, RDR, RDW and RN. (B) The distribution of QTLs and MQTLs on the twelve chromosomes of rice shown in dark blue and purple, respectively. (C) The number of MQTLs for different traits on each chromosome of rice.

Among the studied traits, PH, RT and YLD had the highest number of QTLs with 89, 71 and 63 QTLs, respectively (Fig. 1A). The highest number of QTLs for PH was located on chromosome 1 with 24 QTLs, whereas chromosome 3 with 16 and 12 QTLs had the highest number of QTLs for HD and YLD, respectively. The QTLs for TN were mainly situated on chromosome 6 and QTLs for GW were evenly distributed on all chromosomes. For RWC, DRI, CT, LD and LR traits related to drought tolerance, 56 QTLs were distributed all over 12 chromosomes in rice with the highest number of QTLs on chromosome 2.

Detected MQTLs and their distribution on the rice genome

A total of 527 QTLs out of the 563 initial QTLs (93%) were successfully projected on the reference map (Table 2). Consequently, chromosome 1 had the highest (83) and chromosome 5 and 10 had the lowest (21) number of projected QTLs. The meta-analysis greatly summarized the total number of projected QTLs from 527 to 61 MQTLs (11.5%; Fig. 1B,C) supported by at least two QTLs deduced from different populations and considerably reduced the respective confidence intervals (CI) in comparison to the initial QTLs (Table 3). Therefore, MQTL analysis can efficiently confine the number of QTLs and narrow down the genomic regions controlling different traits19.

Table 2.

The number of initial QTLs on the 12 chromosomes of rice for YLD, GW, HD, PH, TN, DT, RT, RL, RDR, RDW and RN traits under water deficit condition used for MQTL analysis after integrating into the reference map.

Chromosome PH RT RL YLD HD RDR DT RDW TN RN GW Total
1 24 11 4 7 2 3 7 7 7 7 3 83
2 10 4 2 5 2 9 8 1 3 2 3 49
3 5 3 4 12 14 4 5 2 2 5 4 60
4 9 12 8 6 1 11 2 1 1 3 2 56
5 2 2 3 0 2 0 1 3 2 2 4 21
6 9 7 1 10 5 3 3 2 7 3 1 51
7 4 2 1 1 2 5 3 3 2 2 2 27
8 7 6 3 5 5 2 6 4 4 1 3 46
9 3 12 13 0 6 4 6 4 2 2 0 52
10 2 3 1 4 3 5 1 0 2 0 0 21
11 3 4 3 3 3 3 4 6 2 2 4 37
12 5 3 3 5 4 0 1 1 0 1 2 25
Total 83 69 46 58 49 49 47 34 34 30 28 527
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YLD yield, GW grain weight, PH plant height, HD heading date, TN tiller number, DT drought tolerance, RDW root dry weight, RL root length, RN root number, RT root thickness, RDR ratio of deep rooting.

Table 3.

Summary of the detected MQTLs for YLD, GW, HD, PH, TN, DT, RT, RL, RDR, RDW and RN traits in rice under water deficit condition.

Trait Chr MQTL Flanking markers Position on the consensus reference map (cM) Confidence interval (cM) Genomic position on the rice genome (Mb) Number of initial QTLs Number of studies/populations Phenotypic variance range (%) Number of genes underlying the MQTL interval
GW 2 MQTL-GW1 RG102-R418 118.97 1.84 27.48–28.94 2 2/2 5.9–10 170
5 MQTL-GW2 C61983S-RM3419 34.86 2.54 4.33–5.28 4 4/4 8.63–14.2 76
7 MQTL-GW3 C1467-R10022S 83.43 19.85 20.73–25.43 2 1/2 6.95–8.82 597
8 MQTL-GW4 S3680-RM3689 79.81 0.96 18.25–19.33 2 2/2 4.15–10 78
12 MTQL-GW5 R10851S-RM7376 79.84 19.14 19.87–23.44 2 2/2 8–21.9 257
HD 3 MQTL-HD1 C60980S-RM6496 43.96 3.92 8.80–10.14 5 2/2 10.2–22.3 194
3 MQTL-HD2 S1764-RM6881 80.74 2.93 15.94–16.87 3 2/2 9.3–10.6 70
5 MQTL-HD3 RM305-RM2357 92.66 33.1 20.94–26.85 2 2/2 8.5–17.15 750
9 MQTL-HD4 S781-G1047 44.29 0.3 1.21–4.70 2 2/2 9.97–15.8 181
9 MQTL-HD5 R1751-S2074 94.7 1.71 14.36–15.07 2 2/2 7.03–23.8 73
10 MQTL-HD6 RM4455-C1369 32.9 22.85 11.66–17.15 3 2/2 3.54–8.06 456
12 MQTL-HD7 C53024S-RM1337 51.07 0.43 10.60–11.93 4 3/3 5–21.84 66
PH 1 MQTL-PH1 RM8066-RM3627 54.37 3.01 9.56–10.30 3 2/2 5.22–11.48 75
1 MQTL-PH2 R530-RM3324 129.7 3.07 30.50–31.71 2 2/2 10–22.7 182
1 MQTL-PH3 RM6387-RM3285 137.42 0.12 32.54–33.04 3 2/3 9.9–27.5 62
2 MQTL-PH4 S14115-G1340 45.05 6.29 8.72–10.42 2 2/2 5.83–12.3 130
2 MQTL-PH5 RM208-RM498 140.44 0.03 35.13–35.39 5 2/2 2.9–13.9 46
3 MQTL-PH6 C52104S-E1419S 92.38 1.27 23.13–23.88 2 2/2 4.62–6.06 58
4 MQTL-PH7 RG329-RM3836 106.79 1.28 30.85–31.62 6 4/4 2.26–14.4 115
7 MQTL-PH8 RM3718-R1788 49.37 3.68 7.95–15.20 3 2/2 4.33–4.44 338
8 MQTL-PH9 RM7049-E60162S 92.7 4.15 20.81–21.76 2 2/2 10–28.2 92
12 MQTL-PH10 S10904-C53024S 49.59 1.41 7.98–10.60 4 3/3 4.94–13.11 120
TN 5 MQTL-TN1 C1268-S10569 80 5 20.15–20.80 2 2/2 4.19–14.7 76
6 MQTL-TN2 C1032-RM8258 14.54 2.76 3.16–4.73 2 2/2 9.39–10 230
YLD 1 MQTL-YLD1 RM1152-G1372 127 0.6 30.09–30.49 3 2/2 5–14.57 70
2 MQTL-YLD2 RM5706-L107 111.56 3.6 26.47–27.59 2 2/2 10–43.2 142
3 MQTL-YLD3 C51151S-RM3525 131.47 9.37 28.56–30.38 4 2/2 6.35–15 224
4 MQTL-YLD4 R2737-RG329 100.94 7.3 29.15–30.85 2 2/2 1.31–15.8 229
6 MQTL-YLD5 RM5531-R10069S 54.48 7.05 7.17–10.46 2 2/2 6.7–12.18 183
8 MQTL-YLD6 RM2344-RZ143 16.62 5.96 0.07–1.52 2 2/2 3.24–8.5 198
10 MQTL-YLD7 R1261-C63979S 16.65 0.4 8.85–9.92 2 2/2 9.5–11.4 53
11 MQTL-YLD8 RM6085-S20163S 28.03 12.3 3.04–5.37 2 2/2 8.5–15.5 219
12 MQTL-YLD9 E30009S-R3276S 46.69 6.68 6.98–10.43 3 2/2 13.89–30 178
12 MQTL-YLD10 S10043S-S826 58.47 3.9 15.32–17.56 2 2/2 9.27–22.3 66
DT 1 MQTL-DT1 RM7318-C10728S 113.16 0.41 26.14–26.88 4 3/3 9.25–250.8 89
4 MQTL-DT2 C12216S- E61747S 41.52 2.5 18.39–18.44 2 2/2 7.7–10.19 24
8 MQTL-DT3 RM7356-S11114 93.44 0.54 21.28–21.47 2 2/2 10–10.1 21
11 MQTL-DT4 E20817-E3558S 74 2.71 16.81–17.89 2 2/2 19.05–19.8 74
RT 1 MQTL-RT1 C409-RM7566 111.11 11.93 24.93–27.76 2 2/2 8.7–10.1 308
1 MQTL-RT2 E50125S-RM5759 150.68 6.05 37.23–39.02 2 2/2 7.2–21 291
4 MQTL-RT3 C1087-C377 70.17 7.01 21.98–23.99 2 2/2 7.7–20.6 294
5 MQTL-RT4 RM3381-RM5948 62.56 16.45 9.58–18.97 2 2/2 5–7.4 546
6 MQTL-RT5 RM8112-RM584 13.32 6.98 2.17–3.41 2 2/2 2–10.8 214
9 MQTL-RT6 RM3787-C482 123.47 3.01 20.04–21.05 4 2/2 10.9–14.6 155
RL 4 MQTL-RL1 RM6992-RM6909 105.97 6.41 30.76–32.06 2 2/2 8.45–11.86 194
9 MQTL-RL2 C2985-C397 81.42 3.07 11.79–12.28 6 3/3 9.11–11 47
9 MQTL-RL3 S4677S-RM6839 92.16 3.33 13.62–14.56 4 4/4 8.2–32.5 91
9 MQTL-RL4 C12357S-RM6643 132.34 0.58 21.52–21.70 2 2/2 12.9–13.4 35
RDR 2 MQTL-RDR1 R418-RM6424 124.07 2.14 28.94–29.62 4 2/3 9.3–19.9 88
4 MQTL-RDR2 RM5320-R2737 91.65 8.52 28.01–29.15 4 3/3 10–56.6 133
9 MQTL-RDR3 RM5526-RM7038 78.11 4.22 7.31–11.80 2 2/2 7.99–10 220
RDW 1 MQTL-RDW1 E50125S-RM6593 148.37 0.95 37.23–38.02 3 3/3 7.6–26.8 134
5 MQTL-RDW2 E417S-RM3631 104.43 3.42 24.11–25.83 2 2/2 10–12.2 214
8 MQTL-RDW3 RM8266-RM8256 53.06 12.21 3.98–7.78 2 2/2 3.3–7.9 294
8 MQTL-RDW4 S11102-RM8043 103.72 1.56 22.87–23.57 2 2/2 4.4–16 72
9 MQTL-RDW5 RM3909-C11503S 120.57 1.19 19.53–19.88 2 2/2 4.31–13.1 51
11 MQTL-RDW6 S2137-C61883S 57.03 8.53 8.29–10.13 2 2/2 6.14–14 94
11 MQTL-RDW7 RM7240-RM6688 119.95 0.25 27.02–27.54 4 3/3 2.2–11.1 46
RN 1 MQTL-RN1 RM2772-C808 104.4 3.38 24.08–25.63 2 2/2 12–22.8 182
5 MQTL-RN2 RM5401-RM2457 100.8 16.25 22.28–26.87 2 2/2 5.1–10 602
9 MQTL-RN3 RM3808-C482 124.14 2.2 20.54–21.05 2 2/2 8.6–11.6 75
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YLD yield, GW grain weight, PH plant height, HD heading date, TN Tiller number, DT drought tolerance, RDW root dry weight, RL root length, RN root number, RT root thickness, RDR ratio of deep rooting, Chr chromosome.

The number of MQTLs per chromosome ranged from two (chromosome 10) to nine (chromosomes 1 and 9) with an average of 5.08 MQTLs (Fig. 2; Table 3; Additional file 1). Chromosomes 1 and 9 with nine MQTLs and chromosomes 10 and 7 with two MQTLs had the highest and lowest number of MQTLs, respectively (Table 3; Additional file 1). There was a low correlation between the number of initial QTLs and the final number of MQTLs on each chromosome (r = 0.58).

Figure 2.

Figure 2

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Heatmap of MQTLs for YLD, HD, PH, GW, TN, DT, RT, RL, RDR, RDW and RN presented on the rice genome in Mb. The gene density of each chromosome is indicated on the right chromosome.

Out of the total number of 61 MQTLs, we detected 10 MQTLs for YLD, five MQTLs for GW, seven MQTLs for HD, 10 MQTLs for PH, two MQTLs for TN, four MQTLs for DT, seven MQTLs for RDW, six MQTLs for RT, four MQTLs for RL, three MQTLs for RN and three MQTLs for RDR. These MQTLs were stable across different environments and genetic backgrounds. MQTL-PH7 and MQTL-RL2 involving the highest number of initial QTLs (6) were considered as the most stable QTLs (Table 3; Additional file 1). Among the identified MQTLs, four MQTLs for HD (MQTL-HD3, MQTL-HD4, MQTL-HD5 and MQTL-HD6) and three MQTLs for YLD (MQTL-YLD2, MQTL-YLD7 and MQTL-YLD10) overlapped with previously reported MQTLs under drought conditions in rice20,23. To the best of our knowledge this is the first MQTL analysis for GW, TN and DT in rice.

A total of 10 MQTLs were detected in the same chromosomal regions with similar yield and yield-related traits under well-water condition in rice19. This indicates the same loci might control aforementioned traits under both water deficit and well-water conditions (Additional file 2). They include five MQTLs for PH (MQTL-PH2, PH4, PH7, PH8 and PH9) on chromosomes 1, 2, 4, 7 and 8, two MQTLs for GW (MQTL-GW4 and GW5) on chromosomes 8 and 12, two MQTLs for HD (MQTL-HD1 and HD3) on chromosomes 3 and 5 and one MQTL for YLD (MQTL-YLD3) on chromosome 3 (Fig. 3; Additional file 2).

Figure 3.

Figure 3

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The distribution pattern of (A) functionally characterized genes on rice chromosomes, (B) gene density on rice chromosomes, (C) MQTLs under water deficit condition for different traits indicated in the color scale on the right side, (D) MQTLs under normal condition for different traits indicated in the color scale on the right side, (E) QTLs density, (F) SNP density shown in white to dark blue scale for the lowest to the highest density, (G) Structural variants (SV) density shown in white to dark red scale for the lowest to the highest density, (H) recombination density shown in white to dark red scale for the lowest to the highest density, (I) rice duplicated regions and rice syntenic regions with maize in light blue, (J) maize chromosomes with orthologous MQTLs with rice, (K) gene density on the maize chromosomes and (L) ortho-MQTLs between rice and maize. The outermost circle represents the rice genome in Mb.

The MQTL analysis considerably narrowed the CI allowing for exploration of a reduced number of candidate genes (CGs) for the investigated traits. The average CI was reduced from 15.57 cM in the initial QTLs to 5.48 cM in the MQTL with 65% of MQTLs having CI < 5 cM (Table 3). In 10 MQTLs, MQTL-GW4, HD4, HD7, PH3, PH5, YLD1, YLD7, DT1, DT3, RL4, RDW1 and RDW7, the CI was reduced to < 1 cM (Table 3). Therefore, MQTL analysis can significantly raise the accuracy of identification of CGs. All the annotated genes located within the CI of each MQTL and the most promising CGs based on their reported function in previous studies are reported in Additional file 3. Some functionally characterized genes such as GRAIN SIZE 2 (GS2), GRAIN WEIGHT 7 (GW7), Early heading date 1 (Ehd1), DWARF 10 (d10) and Grain number, plant height, heading date7 (Ghd7), OsPIN3t, OsSAUR45, and WEG1 were located within MQTL-GW1, GW3, HD6, PH2, PH8, RT1, RL4, RDW5, respectively, and OsAIR1 located at MQTL-RN2 and RDW2, and OsMGT1 located at MQTL-RT2 and RDW1, that are assumed to control the aforementioned traits. Putative novels CGs for each trait are discussed below. In addition, the positions of MQTLs on the rice genome were compared with the gene density, and densities of SNPs, structural variants (SV), recombination and functional variants, and the reported selective sweep regions25 (Fig. 3). Most of the detected MQTLs were located in sub-telomeric regions where generally the gene, SNP, SV and recombination densities are higher (Figs. 2, 3). This is consistent with previous results in barley, maize and rice15,19,26. The regions with high SV frequency could play an effective role in stress response27. A total of 13 MQTLs (MQTL-YLD3, YLD6, GW2, GW5, HD5, TN2, DT2, RT3, RT4, RL3, RL4, RDR3 and RDW6) were co-located with selective sweep regions reported by Huang et al. These MQTLs are likely effective for selection towards drought adaptation during rice breeding and domestication processes25. Five of these MQTLs including MQTL-YLD3, TN2, GW5, RT4 and RDW6 were also co-located with the position of reported functional variants25.

The investigation of collinear regions within the rice genome resulted in identification of five duplicated regions containing MQTLs for the same traits. MQTL-YLD2 and MQTL-YLD4 on chromosomes 2 and 4, and MQTL-YLD8 and MQTL-YLD9 on chromosomes 11 and 12 for yield, MQTL-RWD1 and MQTL-RWD2 on chromosomes 1 and 5, MQTL-RWD4 and MQTL-RWD5 on chromosomes 8 and 9, and MQTL-RN1 and MQTL-RN2 on chromosomes 1 and 5 for root-related traits are co-located at rice genome duplicated regions (Fig. 3). Duplicated genomic regions derived from common ancestors might contain paralogous genes with similar functions that can be considered as promising CGs controlling the trait28. Consequently, we carefully surveyed these regions for detecting possible paralogous CGs in the duplicated regions. In MQTL-RN2, we note the OsABIL3 or PP2C50 gene which has a key role in root architecture and response to drought stress by affecting ABA signaling: overexpression of this gene was reported to lead to the ABA insensitivity along with stomatal density and root architecture29. The paralogous gene Os01g0618200 encoding PP2C07 is also present at the duplicated region on chromosome 1 with MQTL-RN1 for the same root number trait. Moreover, at MQTL-YLD4 interval on chromosome 4, we detected GRAS23 that contributes to drought response in rice30, with paralogues HAM1 and HAM2 colocalizing with the duplicated genome on chromosome 2 with MQTL-YLD2 for the yield under drought stress.

MQTLs and CGs for grain weight

GW is one of the most important components of YLD in rice1,31 and it critically limits YLD during late season drought stress3,32,33. A total of five MQTLs were identified for GW (Table 3). MQTL-GW2 on chromosome 5 is the most stable MQTL for GW with the highest number of initial QTLs from four independent studies. Among the identified MQTLs for GW, MQTL-GW4 and GW5 on chromosomes 8 and 12 were located at the same region of GW MQTLs under well-water conditions19. Therefore, the same genes might control GW under both conditions. MQTL-GW5 and GW2 were co-located with selective sweep regions and functional variants were reported on the former. These MQTLs could be effective for selection towards drought adaptation25. The same source of favorable allele from ‘Tarom Molaei’ parent derived from two independent populations was detected in QTLs located at MQTL-GW3 (Additional file 4).

Some well-known genes controlling GW such as GS234 and GW735 were located within MQTL-GW1 and MQTL-GW3, respectively, suggesting that these genes may play the same role under water deficit conditions. The list of functionally characterized and novel CGs within each MQTL interval are reported (Additional file 3). For instance, the genomic region spanning MQTL-GW3 contains the CYP78A1336 and DEP231 genes that are reported to control grain size and YLD in rice. MQTL-GW4 on chromosome 8 and MQTL-GW5 on chromosome 12 contain Os08g0390000 encoding brassinosteroid receptor kinase (BRI1)31 and the OsVIL237 genes, respectively, which regulate grain size in rice.

MQTLs and CGs for heading date

It is well known that HD is highly correlated with YLD38 and drought adaptation39,40. We detected seven MQTLs for HD under water deficit conditions including two MQTLs on chromosomes 3 and 9 and one MQTL each on chromosomes 5, 10 and 12 (Table 3). MQTL-HD1 on chromosome 3 had the highest number of supporting QTLs with five QTLs from two independent studies (Table 3).

Among annotated genes within MQTL-HD1, MQTL-HD3 and MQTL-HD6 intervals, OsCCT1141, HBF142 and Ehd138,43,44, respectively, were identified as potential candidates for HD under water deficit conditions. OsCCT11 is considered as a positive regulator of heading date since RNAi-mediated downregulation of this gene delays HD41, while HBF1 is considered as a negative regulator of HD since mutation promotes flowering42. Among genes within the MQTL-HD3 interval, basic region/leucine zipper motif (bZIP), FT-like and circadian clock genes are promising candidates43,45,46. Another CG at this MQTL is OsHAPL1, known to prevent flowering under long-day conditions47. OsTrx148 and OsCCT3141 are potential candidates for MQTL-HD4. In MQTL-HD6, the BRD2 gene49 and the Ehd2 and Ehd1 genes38,43,45 are reported to modify flowering time in rice.

MQTLs and CGs for plant height

Since the Green Revolution, PH has been considered as a major target for YLD improvement50 and it also contributes to drought tolerance40. Among the studied traits, PH and YLD had the highest number of MQTLs; we identified 10 MQTLs for PH including three MQTLs on chromosome 1, two MQTLs on chromosome 2 and one MQTL each on chromosomes 3, 4, 7, 8 and 12. The MQTL-PH7 on chromosome 4 had the largest number of initial QTLs with six QTLs from four independent studies followed by MQTL-PH10 on chromosome 12 with three QTLs reported from three independent studies. These MQTLs are the most stable QTLs for PH under water deficit conditions.

The d1051 and Ghd752 genes are reported to regulate plant height in rice, and they are positioned within MQTL-PH2 and MQTL-PH8 genomic regions, respectively. MQTL-PH4 contains EP3/LP gene, whose mutant shows increased panicle size and PH in rice53. Conversely, mutations in OsKS2 and NAL154,55 at MQTL-PH7 and OsSEC3A56 at MQTL-PH6 decrease PH in rice. In MQTL-PH10, we detected Os12g0271600 that encodes BRI1 and the mutant alleles could act as a dwarfism gene50.

MQTLs and CGs for yield

The maintenance of YLD under drought condition is the ultimate goal in cereal breeding57,58. We identified 10 MQTLs for YLD consisting of two MQTLs on chromosome 12 and one MQTL each on chromosomes 1, 2, 3, 4, 6, 8, 10 and 11 (Table 3). Among them MQTL-YLD3 on chromosome 3 overlapped with a YLD MQTL identified under well-water conditions19. Therefore, the same genes might control YLD under both mentioned conditions at this position.

We detected some genes which affect the photosynthetic rate including Roc5 at MQTL-YLD259, UCL8 at MQTL-YLD360 and OsPTR6 at MQTL-YLD461 that might indirectly contribute to the final YLD. OsALMT7 is located at the MQTL-YLD2 interval with pleiotropic effects on YLD and panicle size62. TAC3 might indirectly regulate YLD through changing HD and tiller angle in rice at MQTL-YLD363. The most likely CGs at MQTL-YLD5 are Os06g0274500 which encodes SERK-like gene and BRI1-associated receptor kinase 1 (BAK1) that affects grain size and number in rice64. The OsNAC5 gene on MQTL-YLD8 is known to have a positive effect on YLD under drought condition65.

MQTLs and CGs for number of tillers

The number of fertile tillers is a major contributor to YLD and its alteration during drought stress can result in drought adaptation4,66–68. Tillering is a complex process and highly affected by environmental conditions66. We detected only two MQTLs on chromosomes 5 and 6 which were associated with TN (Table 3). In MQTL-TN2, we identified OsAID1 as a gene associated to TN regulation in rice69.

MQTLs and CGs for drought tolerance

Plant water content is highly affected by water deficit conditions and in turn can contribute to drought tolerance. Plant water content can be measured by different criteria including RWC, CT, LR, LD and DRI3,32,33,70,71. We identified four MQTLs for DT on chromosomes 1, 4, 8 and 11. MQTL-DT1 on chromosome 1 is the most stable MQTL related to rice water content under water deficit conditions with the highest number of initial QTLs (4) from three independent studies. Within the MQTL-DT1 and MQTL-DT4 intervals, we detected several CGs including OsBZ872 and Os6PGDH273 that contribute to abiotic stresses tolerance, respectively.

MQTLs and CGs for root architecture

Root architecture develops through dynamic processes that effectively contribute to water deficit adaptation allowing water and nutrient uptake from deep soil74,75. We studied five major traits related to root architecture including RDW, RL, RN, RT and RDR under water deficit conditions and identified 23 MQTLs including seven MQTLs for RDW, four MQTLs for RL, three MQTLs for RN, six MQTLs for RT and three MQTLs for RDR (Table 3). MQTL-RL2 had the highest number of initial QTLs (six QTLs from three independent studies) and it was considered as the most stable QTL for root architecture (Table 3). Interestingly, MQTL-YLD4 for YLD under water deficit conditions on chromosome 4 overlapped with MQTL-RDR2 and RL1 (Fig. 2).

Overlapping MQTLs for different root architecture traits included MQTL-RDW1 and MQTL-RT2 on chromosome 1, MQTL-RDW2 and MQTL-RN2 on chromosome 5 and MQTL-RN1 and MQTL-RT1 on chromosome 1, suggesting the possible existence of genes with pleiotropic effects on these traits. For example, the genomic region spanning MQTL-RN2 and MQTL-RDW2 harbors OsAIR1, a gene affecting root architecture and contributing to drought tolerance76. In the overlap region between MQTL-RT2 and MQTL-RDW1, noteworthy is OsMGT1 which was shown to affect root architecture during salinity stress77. MQTL-RT1 contains OsPIN3t78 and OsFBK179 genes that are reported to control root architecture under water deficit conditions. The SMOS1 gene within MQTL-RT4 determines root meristem size80, and the cZOGT381 gene within MQTL-RDR2 regulates root architecture. Additionally, AMTR1 (MQTL-RN2) affects root architecture under drought stress82.

The same source of favorable allele from ‘IRAT109’ parent derived from two independent populations was identified in QTLs located at MQTL-RL3 (Additional file 4). The FC1 gene83 on MQTL-RL1 controls root growth and might contribute to drought tolerance under water deficit conditions. Within MQTL-RL4, we detected a cluster of small auxin-up RNA (SAUR) genes. Over-expression of OsSAUR45 regulates root length and other related root traits84.

For RDW, we detected two co-located MQTLs including MQTL-RDW1 and RDW2 co-locating with OsMGT1 and OsAIR1genes, respectively. Additionally, WEG1 at MQTL-RDW5 is a novel gene that regulates root related traits85 and may keep the same role under water deficit conditions.

Ortho-MQTL mining

To investigate ortho-MQTLs for yield and yield-related traits under water deficit conditions between rice and maize as the two most important cereals with generally high water demand, the syntenic regions of all detected rice MQTLs in this study were compared with published maize MQTLs17,86,87. Comparative genomic analyses provide a valuable approach to transfer information across species and identify conserved genes19. Through synteny analysis between rice and maize, we uncovered 5 ortho-MQTLs including 4 ortho-MQTLs for YLD on chromosomes 2, 3, 4 and 8 and 1 ortho-MQTL for PH on chromosome 4 (Table 4; Fig. 3). The genes located at these syntenic regions were further investigated (Additional file 5; Fig. 4).

Table 4.

Ortho-MQTLs in rice and maize based on the syntenic analyses.

Ortho-MQTL Rice MQTL Rice chr. no. (genomic position in Mb) Maize original MQTL name Maize chr. no. (genomic position in Mb) Maize MQTL reference
Ortho-MQTL-PH7 MQTL-PH7 4 (30.86–31.61) mQTL_PEH_10 10 (142.39–143.64) 85
MQTL-YLD2 2 (26.47–27.59) mQTL_GY_5 5 (196.85–199.69) 84
Ortho-MQTL-YLD2 MQTL5.7 5 (199.95–200.82) 16
Ortho-MQTL-YLD3 MQTL-YLD3 3 (29.58–30.36) mQTL_GY_1b 1 (276.04–279.33) 84
Ortho-MQTL-YLD4 MQTL-YLD4 4 (29.15–30.82) mQTL_GY_10b 10 (135.81–142.38) 84
Ortho-MQTL-YLD6 MQTL-YLD6 8 (0.26–0.71) MQTL6.1 6 (1.58–3.65) 16
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Figure 4.

Figure 4

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Comparative maps of ortho-MQTLs between rice and maize. (A) ortho-MQTL-PH7, (B) ortho-MQTL-YLD2, (C) ortho-MQTL-YLD3, (D) ortho-MQTL-YLD4 and (E) ortho-MQTL-YLD6. The chromosome number, genomic position in Mb and the original name of MQTLs are indicated. The orthologous genes in rice and maize are indicated in green color with the corresponding rice gene name.

MQTL-PH7 and MQTL-YLD4 on chromosome 4 of rice were co-linear with mQTL_PEH_10 and mQTL_GY_10b on chromosome 10 in maize, respectively (Table 4). Three rice MQTLs including MQTL-YLD2, YLD3 and YLD6 on chromosomes 2, 3 and 8, respectively, were situated in syntenic regions of maize yield MQTLs on chromosome 5, 1 and 6, respectively (Table 4; Fig. 3).

The orthologous genes located at these ortho-MQTLs in both rice and maize are shown in the Additional file 5 and Fig. 4. The rice genomic region subtending MQTL-PH7 harbors the NAL1 gene as a regulator of PH55: we identified the ortholog of this gene (Zm00001d026296) in the maize ortho-MQTL. In the syntenic region of rice MQTL-YLD2 on chromosome 5 of maize, there were two MQTLs (mQTL_GY_586, MQTL5.717) containing the orthologs of OsALMT7 and SID1 genes (Zm00001d017571 and Zm00001d017560), known to affect YLD in rice62,88. The orthologous gene of TAC3 in maize (Zm00001d033857) in the syntenic region of MQTL-YLD3 in maize (mQTL_GY_1b) regulates tiller angle that might affect YLD under water deficit conditions63. In the syntenic region of rice MQTL-YLD6, there was a MQTL (MQTL6.117) on chromosome 6 of maize. This rice MQTL contains Ehd3 gene regulating flowering and consequently YLD38 and its orthologous (Zm00001d035008) was detected in its ortho-MQTL in maize, likely to have similar functions. This approach provided better understanding of genes controlling investigated traits under water deficit conditions with similar evolutionary history and conserved function between these cereals. These results can benefits breeders by tracing CGs and using marker-assisted selection in breeding programs of cereals under water deficit conditions.

Conclusions

Through MQTL analysis this study provides an overview of genomic regions controlling YLD, yield-related traits, root architecture and plant water content including GW, HD, PH, TN, RDW, RL, RT, RN, RDR and DT under water deficit conditions in rice. This approach is useful in overcoming some limitations of single QTL mapping studies on different genetic backgrounds and environments and greatly facilitates the identification of CGs and robust flanking markers for MAS in breeding programs. The results offer a framework for future genetic studies of yield under drought conditions, e.g. through fine mapping, positional cloning, producing chromosome substitution lines, as well as validation of CGs by genome editing using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and similar approaches. This study also demonstrates the value of ortho-MQTL mining among evolutionarily close crop species for identification of genomic regions and CGs controlling complex quantitative traits.

Materials and methods

QTL studies used for MQTL analysis

An exhaustive bibliographic review was carried out on rice QTLs related to yield and yield-related traits under water deficit conditions published from 2001 to 2019. All QTL studies except those lacking proper genetic map information or QTL-related information were used in the MQTL analysis. Consequently, a total of 563 QTLs for YLD, PH, TN, HD, GW, RDW, RL, RT, RN, RDR and traits related to water content of plant under water deficit conditions including DRI, RWC, CT, LR and LD from 67 biparental rice populationsextracted from 57 studies, including all the five major subpopulations of rice—Indica, tropical japonica, temperate japonica, aus, aromatic and also one wild species O. rufipogon IRGC 105491 and landraces, were implemented for the MQTL analysis (Table 1). The size of mapping populations varied from 72 to 4560 progenies of various types including 7 DH, 17 F2, 15 BC and 28 RIL populations phenotyped in different locations and years (Table 1). Moreover, 56 QTLs related to water content of plant under water deficit conditions including DRI, RWC, CT, LR, LD were subjected to MQTL analysis and resulting MQTLs were reported as drought tolerance (DT). Detailed information on the used QTLs including parents, population type and size, number of markers, map density and evaluated traits are reported in Table 1.

Projection of QTLs on the reference map

A rice reference map of Wu et al. which is the most comprehensive available genetic map integrated from six identified and saturated maps in rice was chosen based on its high marker density and inclusion of different marker types including SSR, RFLP and AFLP markers. It consists of 6969 markers with an average distance of 0.25 cM between markers, and the average chromosome length is 147.65 cM for a total length of 1771.8 cM89. In order to incorporate SNP markers of those initial QTLs with SNP markers (Table 1) into the reference map, we applied our previous approach19 in which the genomic position of SNP markers on the rice genome were determined and in consequence the closest markers based on the physical position were used to project them on the reference map.

QTL position, CI, proportion of phenotypic variance (R2), log of odds ratio (LOD score), additive effects and favorable alleles were compiled for each QTL in the 67 populations (Additional file 4). In order to calculate 95% CI for QTLs, we used the following formulas: CI = 530/(N × R2) for BC and F2 lines, CI = 287/(N × R2) for DH lines and CI = 163/(N × R2) for RLLs lines90,91, where N is the population size and R2 is the proportion of phenotypic variance of the QTL. MQTL analysis was carried out using BioMercator V4.211,92.

Meta-QTL analysis and ortho-MQTL mining

The MQTL analysis was conducted on integrated and re-positioned QTLs on the reference map using BioMercator V4.211,12,92. The best model of MQTLs was chosen according to the prevalent value among Akaike Information Criterion (AIC), corrected Akaike Information Criterion (AICc and AIC3), Bayesian Information Criterion (BIC) and Average Weight of Evidence (AWE) criteria. Therefore, the consensus QTL from the best model was reported as a “real” QTL/MQTL12,92. Considering the known correlations among RWC, CT, LR, LD and DRI3,32,33,70, the respective QTLs were analyzed together as one trait named as DT in BioMercator V4.2.12,92. Mapchart V.2.32 software93 was used to show the MQTLs and related QTLs on the reference map.

The distribution of MQTLs on the rice genome (IRGSP-1.0) compared to the position of centromeric and telomeric regions and the gene density along each chromosome were surveyed and shown by heatmap using pheatmap and R94,95. Centromere position, gene density, SNP and structural variations (SV) and recombination rate density of each chromosome, as well as rice genome duplications were retrieved from EnsemblPlants (https://plants.ensembl.org/index.html) database. Additionally, the position of identified MQTLs were compared with selective sweep regions and functional variants in coding regions with strong alteration in allele frequency between cultivated and wild rice reported by Huang et al.25. The distribution of aforementioned factors, number of MQTL under water deficit conditions and number of MQTLs under well-water conditions19 over the rice genome were shown by using Circos96.

To detect ortho-MQTLs between rice and maize, syntenic regions between the two species were identified by using EnsemblPlants database97. MQTLs identified for yield and yield-related traits under drought conditions in maize17,86,87 were compared with MQTLs detected for similar traits in our study.

Identification of candidate genes

CGs related to YLD, PH, TN, HD, GW, RDW, RL, RT, RN, RDR and DT traits located in the CI of each detected MQTL were investigated on the rice genome (IRGSP-1.0) using EnsemblPlants and Gramene (http://archive.gramene.org/qtl/). In case of flanking markers without genomic positions, the closest markers were applied for detecting the genomic coordinates of MQTL. Gene annotations within MQTL genomic regions were carefully explored by EnsemblPlants (https://plants.ensembl.org/index.html) and FunRiceGenes (https://funricegenes.github.io/)98 databases.

Supplementary Information

Supplementary Information 1. (5.9MB, docx)
Supplementary Information 2. (11.3KB, xlsx)
Supplementary Information 3. (643.3KB, xlsx)
Supplementary Information 4. (55.9KB, xlsx)
Supplementary Information 5. (39.1KB, xlsx)

Acknowledgements

This work has been supported by the Shiraz University, Iran.

Abbreviations

AIC

Akaike information content

AICs

AIC correction

AIC3

AIC 3 candidate models

AWE

Average weight of evidence

BIC

Bayesian information criterion

CGs

Candidate genes

CT

Canopy temperature

CI

Confidence interval

DRI

Drought response index

DT

Drought tolerance

GW

Grain weight

HD

Heading date

LOD

The log of odds ratio

LR

Leaf rolling

LD

Leaf drying

MQTL

Meta-QTL

MAS

Marker-assisted selection

PH

Plant height

QTL

Quantitative trait loci

R2

The proportion of phenotype variance

RWC

Relative water content

RDW

Root dry weight

RL

Root length

RT

Root thickness

RN

Roots number

RDR

Rate of deep rooting

TN

Tiller number

YLD

Yield

Author contributions

B.K. performed the initial analyses and wrote the draft of manuscript, E.T. conceived and designed the project and complemented the analyses and writing of the manuscript, V.S. enriched the analyses and provided critical advices on the project and the manuscript, L.R. provided critical advices on the manuscript. All authors have read and approved the final manuscript.

Funding

This work has been supported by the Center for International Scientific Studies and collaboration (CISSC), Ministry of Science, Research and Technology, Iran and Shiraz University, Iran.

Data availability

The relevant data and additional information are available in the supplementary files and also from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Elahe Tavakol, Email: elahetavackol@gmail.com.

Vahid Shariati, Email: vshariati@nigeb.ac.ir.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-021-86259-2.

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