Identification of quantitative trait loci (QTLs) controlling yield and yield-related traits in rice was performed in the F2 mapping population derived from parental rice genotypes DHMAS and K343. A total of 30 QTLs governing nine different traits were identified using the composite interval mapping (CIM) method. Four QTLs were mapped for number of tillers per plant on chromosomes 1 (2 QTLs), 2 and 3; three QTLs for panicle number per plant on chromosomes 1 (2 QTLs) and 3; four QTLs for plant height on chromosomes 2, 4, 5 and 6; one QTL for spikelet density on chromosome 5; four QTLs for spikelet fertility percentage (SFP) on chromosomes 2, 3 and 5 (2 QTLs); two QTLs for grain length on chromosomes 1 and 8; three QTLs for grain width on chromosomes1, 3 and 8; three QTLs for 1000-grain weight (TGW) on chromosomes 1, 4 and 8 and six QTLs for yield per plant (YPP) on chromosomes 2 (3 QTLs), 4, 6 and 8. Most of the QTLs were detected on chromosome 2, so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety. Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection (MAS) breeding. Further, the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.
Rice (
Yield is a highly complex trait having a high level of environmental effect and is also governed by many small-effect genes [
The F2 population has been utilized in several studies to uncover QTLs with additive and dominant effects across the whole rice genome, as well as to examine the genetic component of rice yield and its components. EP3, APO1, DEP2/EP2, DEP3, SRS3, and GIF1 are a few of the genes that have been successfully recognized and isolated by exploiting F2 populations. In addition, several genes regulating important traits have been cloned through the QTL mapping. Recent reports signify that 20 QTLs that significantly influence paddy grain yield and its other elements, have been successfully replicated with NIL-F2 generation, and 14 additional grain yield QTLs have been confirmed in NILs [
Considering the global significance of rice yield, the current research was conducted to identify QTLs for yield and yield-related traits. Total rice yield is majorly determined by various traits such as plant height (PH), number of tillers per plant (TPP), panicle number per plant (PPP), spikelet fertility percentage (SFP), spikelet density (SD), grain width (GW), grain length (GL), yield per plant (YPP) and 1000-grain weight (TGW). In the F2 population derived from the two parental lines, K343 and DHMAS, 30 QTLs were discovered. Furthermore, SSR markers were also used to create a linkage map for all yield-related traits. The molecular markers linked with definitive QTL which are identified for different traits in the present study can be useful for MAS. Chromosomal regions carrying important QTLs identified during the current investigation can be enriched with more markers for high-resolution mapping and reducing genetic background noise. Information about molecular markers tightly linked to the QTLs that control yield traits should greatly accelerate future rice breeding through the rapid generation of improved lines with desired trait improvement in any elite background.
Over a two-year period (2015–16 and 2016–17), the current study was conducted at the Research Farm and the Molecular Laboratory of Rice, School of Biotechnology, SKUAST-Jammu, India.
An F2 population was created by crossing several rice lines K343 and DHMAS. K343 (a major non-basmati rice cultivar having medium height plant with medium/short & bold grains) is a popular rice cultivar and a high yielding variety of hill region of J&K. It usually takes 100 to 130 days to mature, with an average yield of 5.5–6.0 t/ha. The second parent was DHMAS, a long-grain variety developed at IRRI and CSK HPKV Palampur (HP) using doubled haploid marker-assisted selection (MAS). DHMAS usually takes 120 to 140 days to reach full maturity, because DHMAS is tall, it lodges in heavy soils, resulting in a poor yield. Hence, the cross of these contrasting traits was focused upon and used as a potent technique for detecting novel QTLs for this diverse cultivar combination. At Experimental Farm, parental lines and F2 progenies were sown and transplanted in an augmented design-I (un-replicated design) during
All the observations were taken from a single plant in each row of the plot, which was tagged assigning a specific number to them. The variation of the yield and its component characters were evaluated using a one-way ANOVA, which revealed significance at the 5% level of significance. Pearson’s correlation analysis with SPSS was used to obtain the Pearson’s correlation coefficient (PCC) between the attributes (version 20). The parents and F2 mapping population were examined, and data for phonological, morphological, yield and its component traits were recorded for various variables as shown in
SL. No. | Traits | Mean ± SD | Range | CD value | CV value | ||
---|---|---|---|---|---|---|---|
Max. | Min. | Test | Check | Value | |||
1. | Plant Height (cm) | 27.93 ± 2.79 | 36.00 | 24.00 | 8.11 | 3.82 | 9.99 |
2. | Days to Flowering | 83.90 ± 7.08 | 91.00 | 65.00 | 8.85 | 4.17 | 8.43 |
3. | No. of Tillers/Plant | 19.20 ± 2.64 | 28.00 | 15.00 | 1.44 | 0.68 | 13.73 |
4. | Duration of Grain Filling | 111.83 ± 6.94 | 126.00 | 94.00 | 16.30 | 7.68 | 6.20 |
5. | Panicle No./Plant | 19.18 ± 2.62 | 28.00 | 15.00 | 1.44 | 0.68 | 13.66 |
6. | Days to Maturity | 126.61 ± 10.78 | 140.90 | 100 | 3.32 | 1.56 | 8.51 |
7. | Spikelet Fertility Percentage | 86.80 ± 7.82 | 100.00 | 56.47 | 12.29 | 5.79 | 9.01 |
8. | Spikelet Density | 5.07 ± 0.83 | 8.18 | 3.61 | 1.03 | 0.48 | 16.42 |
9. | Grain Width (mm) | 2.35 ± 0.15 | 2.70 | 1.97 | 0.092 | 0.043 | 6.62 |
10. | Grain Length (mm) | 4.77 ± 0.48 | 7.08 | 3.92 | 0.52 | 0.24 | 10.05 |
11. | Yield/Plant (g) | 37.57 ± 5.90 | 50.12 | 29.07 | 0.90 | 0.42 | 15.70 |
12. | 1000 Grain Weight | 25.07 ± 2.12 | 31.71 | 20.11 | 1.09 | 0.51 | 8.48 |
To establish the heritable potency of the relevant genes as well as the influence of the environment on them, genetic variability components such as heritability in the broad sense (H2), phenotypic and genotypic coefficients of variation (PCV and GCV), and genetic advance (GA) were calculated.
To evaluate the genotype of the F2 population, the modified Doyle and Doyle, method [
A set of 450 SSRs covering the entire rice genome were selected. Double distilled, autoclaved and deionized water was used to dilute primers at a concentration of 10 pmol. To study polymorphism among F2 population genotypes generated from K343 and DHMAS combination, 96 well thermal-cycler was used to carry out PCR amplification subjected to the thermal profile. The thermo-cycler was configured with a 5-min. denaturation stage, followed by 35 cycles of denaturation (at 94°C for 30 s), annealing (at 55°C for 30 s), and extension (at 72°C for 30 s). The last extension was done for 7 min at 72°C, after that, the PCR products were kept at 4°C. However, the identical reaction mixture lacking genomic DNA was used for each reaction, as a negative control. The SSR markers which showed parental polymorphism were used for genotyping. Based on polymorphism, out of 450 SSR markers, 53 markers were utilized for the PCR amplification of the genomic DNA of the 233 F2 mapping population (
S. No. | Marker | Sequence | Tm | Product size |
---|---|---|---|---|
1. | RM216 | 5’ GCATGGCCGATGGTAAAG 3’ | ||
3’ TGTATAAAACCACACGGCCA 5’ | 55 | 146 | ||
2. | RM13838 | 5’ CCCAACTGCTAGGTTTCTGATCC 3’ | ||
3’ ACTGTGTTACTGTGTGCCGTTGC 5’ | 55 | 129 | ||
3. | RM262 | 5’ CATTCCGTCTCGGCTCAACT 3’ | ||
3’ CAGAGCAAGGTGGCTTGC 5’ | 55 | 154 | ||
4. | RM5 | 5’ TGCAACTTCTAGCTGCTCGA 3’ | ||
3’ GCATCCGATCTTGATGGG 5’ | 55 | 113 | ||
5. | RM528 | 5’ GGCATCCAATTTTACCCCTC 3’ | ||
3’ CCGTAGGTTAAAATGGGGAC 5’ | 55 | 232 | ||
6. | RM6832 | 5’ GTTGTAAATGCCTGAGTGC 3’ | ||
3’ AAAGAGCTAAACCGCTAGG 5’ | 55 | 182 | ||
7. | RM223 | 5’ GAGTGAGCTTGGGCTGAAAC 3’ | ||
3’ GAAGGCAAGTCTTGGCACTG5’ | 55 | 165 | ||
8. | RM4A | 5’ TTGACGAGGTCAGCACTGAC 3’ | ||
3’ AGGGTGTATCCGACTCATCG 5’ | 55 | 159 | ||
9. | RM7492 | 5’ AGATGGTTGCCAAGAGCATG 3’ | ||
3’ GTCACGTGGCGATTTAGGAG 5’ | 55 | 145 | ||
10. | RM517 | 5’ GGCTTACTGGCTTCGATTTG 3’ | ||
3’ CGTCTCCTTTGGTTAGTGCC 5’ | 55 | 266 | ||
11. | RM580 | 5’ GATGAACTCGAATTTGCATCC 3’ | ||
3’ CACTCCCATGTTTGGCTCC 5’ | 55 | 221 | ||
12. | RM5699 | 5’ ATCGTTTCGCATATGTTT 3’ | ||
3’ ATCGGTAAAAGATGAGCC 5’ | 55 | 167 | ||
13. | RM447 | 5’ CCCTTGTGCTGTCTCCTCTC 3’ | ||
3’ ACGGGCTTCTTCTCCTTCTC 5’ | 55 | 111 | ||
14. | RM471 | 5’ ACGCACAAGCAGATGATGAG 3’ | ||
3’ GGGAGAAGACGAATGTTTGC 5’ | 55 | 106 | ||
15. | RM202 | 5’ CAGATTGGAGATGAAGTCCTCC 3’ | ||
3’ CCAGCAAGCATGTCAATGTA 5’ | 55 | 189 | ||
16. | RM413 | 5’ GGCGATTCTTGGATGAAGAG 3’ | ||
3’ TCCCCACCAATCTTGTCTTC 5’ | 55 | 79 | ||
17. | RM169 | 5’ TGGCTGGCTCCGTGGGTAGCTG 3’ | ||
3’ TCCCGTTGCCGTTCATCCCTCC 5’ | 55 | 169 | ||
18. | RM80 | 5’ TTGAAGGCGCTGAAGGAG 3’ | ||
3’ CATCAACCTCGTCTTCACCG 5’ | 55 | 142 | ||
19. | RM101 | 5’ GTGAATGGTCAAGTGACTTAGGTGGC 3’ | ||
3’ ACACAACATGTTCCCTCCCATGC 5’ | 55 | 324 | ||
20. | RM13840 | 5’ CGGTCTTTAGTAATGGTGCTTTGC 3’ | ||
3’ GAGGCAGGTGTTTGTCGTCTAGC 5’ | 55 | 195 | ||
21. | RM25003 | 5’ GATTGATCCGAGAGACAAATCC 3’ | ||
3’ TCGATCAATAGTAGCAGCAGTAGG 5’ | 55 | 115 | ||
22. | RM3295 | 5’ TCGTGTCATGCGATCGAC 3’ | ||
3’ GCTTCGACTCGACCAAGATC 5’ | 55 | 92 | ||
23. | RM7 | 5’ TTCGCCATGAAGTCTCTCG 3’ | ||
3’ CCTCCCATCATTTCGTTGTT 5’ | 55 | 180 | ||
24. | RM208 | 5’ TCTGCAAGCCTTGTCTGATG 3’ | ||
3’ TAAGTCGATCATTGTGTGGACC 5’ | 55 | 173 | ||
25. | RM7102 | 5’ TTGAGAGCGTTTTTAGGATG 3’ | ||
3’ TCGGTTTACTTGGTTACTCG 5’ | 55 | 169 | ||
26. | RM149 | 5’ GCTGACCAACGAACCTAGGCCG 3’ | ||
3’ GTTGGAAGCCTTTCCTCGTAACACG 5’ | 55 | 233 | ||
27. | RM240 | 5’ CCTTAATGGGTAGTGTGCAC 3’ | ||
3’ TGTAACCATTCCTTCCATCC 5’ | 55 | 132 | ||
28. | RM1370 | 5’ AAACGAGAACCAACCGACAC 3’ | ||
3’ GGAGGGAGGAATGGGTACAC 5’ | 55 | 173 | ||
29. | RM3874 | 5’ TGGGTGATCTTAGTTTGGCC 3’ | ||
3’ AATGTGCCTGCACATGTCAC 5’ | 55 | 206 | ||
30. | RM232 | 5’ CCGGTATCCTTCGATATTGC 3’ | ||
3’ CCGACTTTTCCTCCTGACG 5’ | 55 | 158 | ||
31. | RM28048 | 5’ TTCAGCCGATCCATTCAATTCC 3’ | ||
3’ GCTATTGGCCGGAAAGTAGTTAGC 5’ | 55 | 93 | ||
32. | RM3 | 5’ ACACTGTAGCGGCCACTG 3’ | ||
3’ CCTCCACTGCTCCACATCTT 5’ | 55 | 145 | ||
33. | RM220 | 5’ GGAAGGTAACTGTTTCCAAC 3’ | ||
3’ GAAATGCTTCCCACATGTCT 5’ | 55 | 127 | ||
34. | RM110 | 5’ TCGAAGCCATCCACCAACGAAG 3’ | ||
3’ TCCGTACGCCGACGAGGTCGAG 5’ | 55 | 156 | ||
35. | RM204 | 5’ GTGACTGACTTGGTCATAGGG 3’ | ||
3’ GCTAGCCATGCTCTCGTACC 5’ | 55 | 169 | ||
36. | RM324 | 5’ CTGATTCCACACACTTGTGC 3’ | ||
3’ GATTCCACGTCAGGATCTTC 5’ | 55 | 175 | ||
37. | RM1211 | 5’ TACAGTGGCGAAAGGAATAC 3’ | ||
3’ CCATCACGCATGTTAGTTAG 5’ | 55 | 213 | ||
38. | RM218 | 5’ TGGTCAAACCAAGGTCCTTC 3’ | ||
3’ GACATACATTCTACCCCCGG 5’ | 55 | 148 | ||
39. | RM242 | 5’ GGCCAACGTGTGTATGTCTC 3’ | ||
3’ TATATGCCAAGACGGATGGG 5’ | 55 | 225 | ||
40. | RM167 | 5’ GATCCAGCGTGAGGAACACGT 3’ | ||
3’ AGTCCGACCACAAGGTGCGTTGTC 5’ | 55 | 128 | ||
41. | RM219 | 5’ CGTCGGATGATGTAAAGCCT 3’ | ||
3’ CATATCGGCATTCGCCTG 5’ | 55 | 202 | ||
42. | RM144 | 5’ TGCCCTGGCGCAAATTTGATCC 3’ | ||
3’GCTAGAGGAGATCAGATGGTAGTGCATG 5’ | 55 | 237 | ||
43. | RM225 | 5’ TGCCCATATGGTCTGGATG 3’ | ||
3’ GAAAGTGGATCAGGAAGGC 5’ | 55 | 140 | ||
44. | RM227 | 5’ ACCTTTCGTCATAAAGACGAG 3’ | ||
3’ GATTGGAGAGAAAAGAAGCC 5’ | 57 | 106 | ||
45. | RM15838 | 5’ CGATGTCATTCGGTAGAAACAAGC 3’ | ||
3’ CCTAGTCAAGGCATGGTCAATCC 5’ | 57 | 262 | ||
46. | RM3524 | 5’ CGGAGCTGGTCTAGCCATC 3’ | ||
3’ GTCTCCGTCTTCCTCACTCG 5’ | 57 | 129 | ||
47. | RM1282 | 5’ AAGCATGACAGCTGCAAGAC 3’ | ||
3’ GGGGATGAAGGGTAATTTCG 5’ | 58 | 157 | ||
48. | RM7300 | 5’ TCCGTATCCTAGTCGCGATC 3’ | ||
3’ CGCCGTCATGACTCATACTC 5’ | 58 | 102 | ||
49. | RM168 | 3’ TGCTGCTTGCCTGCTTCCTTT 3’ | ||
5’ GAAACGAATCAATCCACGGC 5’ | 58 | 116 | ||
50. | RM231 | 5’ CCAGATTATTTCCTGAGGTC 3’ | ||
3’ CACTTGCATAGTTCTGCATTG 5’ | 58 | 182 | ||
51. | RM545 | 5’ CAATGGCAGAGACCCAAAAG 3’ | ||
3’ CTGGCATGTAACGACAGTGG 5’ | 58 | 226 | ||
52. | RM1178 | 5’ CAGTGGGCGAGCATAGGAG 3’ | ||
3’ ATCCTTTTCTCCCTCTCTCG 5’ | 58 | 112 | ||
53. | RM315 | 5’ GAGGTACTTCCTCCGTTTCAC 3’ | ||
3’ AGTCAGCTCACTGTGCAGTG 5’ | 58 | 133 |
MAPMAKER/Exp v. 3.0-based program was used to compute the genetic linkage order and genetic distances of the 53 marker loci [
QTLs associated to yield and its component traits were detected and identified using Statistical software viz., QTLCARTOGRAPHER version 2.5 [
Both K343 and DMAS have been genotyped using SSR molecular markers. The alleles present at two different positions in both the parental genotypes were scored as polymorphic.
In the present study, Composite interval mapping (CIM) using genotypic data, phenotypic and genetic linkage map recognized total of 30 QTLs for nine yield and yield contributing traits. There were 30 QTLs for yield and yield-related traits identified, with chromosomal number, marker-interval of peak LOD, additive effect, peak LOD value and the phenotypic variation explained (R2) (
Trait | Chromosome | No. of QTLs | QTL | LOD value | Marker interval | Position (cM) | Additive effect | PVE % |
---|---|---|---|---|---|---|---|---|
(R2) | ||||||||
Number of tillers per plant (TPP) | 1 | 2 | 2.67 | RM13838 | 30.5 | −0.58 | 12 | |
3 | RM13838 | 36.5 | −0.57 | 14 | ||||
2 | 1 | 2.5 | RM5699-RM240 | 123 | 0.12 | 57.2 | ||
3 | 1 | 3.5 | RM6832 | 12 | −1.36 | 11 | ||
Panicle number per plant (PPP) | 1 | 2 | 2.6 | RM13838 | 30.5 | −0.59 | 11.8 | |
3 | RM13838 | 36.3 | −0.58 | 14 | ||||
3 | 1 | 3.38 | RM6832 | 10 | −1.48 | 11.3 | ||
Plant height (PH) | 2 | 1 | 3.5 | RM5699-RM240 | 125 | 2.83 | 55.3 | |
4 | 1 | 7.9 | RM471-RM3524 | 44 | 666 | 65.2 | ||
5 | 1 | 2.5 | RM413 | 28.1 | −4.16 | 7 | ||
6 | 1 | 3.1 | RM204-RM225 | 22 | −7.11 | 36.7 | ||
Spikelet density (SD) | 5 | 1 | 2.5 | RM413 | 12.1 | −0.27 | 4.4 | |
Spikelet fertility percentage (SFP) | 2 | 1 | 3.8 | RM5699-RM240 | 129 | −10.45 | 45.8 | |
3 | 1 | 2.8 | RM6832 | 2 | 2.21 | 5 | ||
5 | 2 | 3.7 | RM413 | 10.6 | 1.88 | 15.2 | ||
4.5 | RM413 | 20.1 | 2.39 | 16 | ||||
Grain length (GL) | 1 | 1 | 25.2 | RM13838 | 40.3 | −0.29 | 47.7 | |
8 | 1 | 3 | RM447 | 6 | 0.16 | 5.9 | ||
Grain width (GW) | 1 | 1 | 2.6 | RM13838 | 41.9 | 0.028 | 3 | |
3 | 1 | 4 | RM6832 | 26.9 | 0.06 | 5.8 | ||
8 | 1 | 5.2 | RM447-RM80 | 26 | 0.8 | 21 | ||
1000-Grain weight (TGW) | 1 | 1 | 10.2 | RM13838 | 41.9 | −0.98 | 16.8 | |
4 | 1 | 5.8 | RM471-RM3524 | 28 | 2.7 | 37 | ||
8 | 1 | 3.5 | RM447-RM80 | 26 | 0.8 | 11 | ||
Yield per plant (YPP) | 2 | 3 | 2.5 | RM262-RM3874 | 74.5 | −1.9 | 4.5 | |
4.9 | RM5699-RM240 | 123 | 1.37 | 69 | ||||
3.8 | RM5699-RM240 | 139 | 0.83 | 65 | ||||
4 | 1 | 7.2 | RM471-RM3524 | 52 | 7.19 | 67 | ||
6 | 1 | 3 | RM204-RM225 | 26 | −2 | 43 | ||
8 | 1 | 3 | RM447-RM80 | 26 | −0.73 | 55 |
There were a total of four QTLs discovered for the number of tillers per plant, out of which 2 QTLs were detected at chromosome 1, with a flanking marker RM13838 on linkage map with 30.5 cM position named as
Three QTLs were identified for panicle number per plant out of which 2 QTLs were located at chromosome 1, at a flanking marker RM13838 on linkage map with 30.5 cM position named
Four QTLs were detected for plant height. One QTL named
On chromosome 5, a substantial QTL qSD5 was discovered at 12.1 cM with a flanking marker RM413, and LOD value for this QTL was 2.5 and explained the phenotypic variability by 4.4%. The peak is shown in
Four QTLs namely
Two QTLs were identified for grain length at 40.3 and 6.0 cM distance at chromosomes 1 and 8, respectively. QTLs,
In the case of grain width, three QTLs were discovered. QTL namely
Three QTLs namely
A total of six QTLs namely
QTL analysis is based on the idea of identifying a relationship between phenotype, marker genotype, and population type, suggesting that the marker locus employed to partition the mapping population is connected to a QTL regulating the trait [
Out of the 30 QTLs found in this study, four significant QTLs for the number of tillers per plant were identified as phenotypic variation explained (PVE%) for all QTLs, i.e.,
Although heritability for spikelet density was observed to be 0.68, still detection of minor QTLs may be due to the set of markers employed or the phenotypic appearance of such traits may be affected by both environmental effects and pleiotropic effects of genes for non-target traits. Spikelet fertility is another important trait. In the present investigation, three major & one minor QTLs namely
Grain length and grain width are the important constituents of plant yield and are controlled by quantitative trait loci (QTLs) [
Three major QTLs namely
Maximum numbers of QTLs were detected for the trait yield per plant; similar reports for yield were shown in a similar study [
The variation explained by individual QTLs ranged from a low of 3.0% to a high of 69.0%, with the majority of them explaining the range of 10%–20% of the variation. One QTL of plant height shows a close linkage with one QTL of yield per plant & one QTL of 1000-grain weight as they were detected on the same chromosome number 4 and with 44, 28 and, 52 cM distance, respectively, and all these three QTLs were detected between the same marker-interval RM471-RM3524. Most of the QTLs were detected on chromosome 2, so further studies on chromosome 2 could help unlock some new chapters of QTL for this set of parents. The molecular markers linked with definitive QTL identified for different traits in the current research can be beneficial for MAS. The chromosomal regions carrying important QTL identified during the present study can be enriched with more markers by high-resolution mapping. Evidence about molecular markers tightly linked to QTL that govern these traits may accelerate crop improvement strategies in rice.
Any scientific advancement that focuses on increasing rice yields will have a significant impact on global food and nutrition security. Given the rapid growth of the global population, the global population is anticipated to reach nine billion by the middle of this century. Rice grain yield should be increased by 70%–100%, relative to the current levels, to feed the increasing global population [
The authors gratefully acknowledge King Saud University in Riyadh, Saudi Arabia, for sponsoring their Researchers Supporting Project (RSP-2021/298). The authors are grateful to SKUAST-Jammu, School of Biotechnology for allowing them to conduct the study.