Research Journal of Biotechnology Vol. 9(9) September (2014) Res. J. Biotech

Transcription

1 Marker-Trait Association for Fertility Restoration in Hybrids of Rice (Oryza sativa L.) under Aerobic Conditions Reddy Kamalnath K. R. 1, Kaki Nagendra 1, Varma Mohan Kumar C. 1,2, Patil Kalmeshwar Gouda 1,3, Payasi Devendra 1,4, Anantha M. S. 1,5, Shashidhar H. E. 1,6 * and Shenoy Vinay 1 1. Barwale Foundation, Himayathnagar, Hyderabad, Andhra Pradesh, INDIA 2. Bayer Crop Science, SINGAPORE 3. Monsanto India Ltd., Bengaluru, INDIA 4. JNKV, Regional Agricultural Research Station, Sagar, Madhya Pradesh, INDIA 5. Central Rainfed Upland Rice Research Station (CRRI), Hazaribagh, Jharkhand, INDIA 6. Dept. of Biotechnology, University of Agricultural Sciences, GKVK, Bengaluru, INDIA *heshashidhar@rediffmail.com Abstract A study was conducted to validate earlier reported SSR markers closely associated with fertility restoration (Rf gene) of WA-CMS lines in rice. Thirteen SSR markers across three chromosomes (chr.1, 10 and 12) were evaluated among 21 drought tolerant restorer lines. These lines were crossed to five different CMS lines to obtain 105 F 1 s. Single Marker Analysis was performed to determine the association between marker and fertility restoration in hybrids. Out of thirteen markers, RM6100 marker linked to Rf4 gene on chromosome 10 has shown significant association under both aerobic and wetland conditions in two consecutive Kharif (2010 and 2011) seasons, followed by other markers viz. RM171 linked to Rf4 gene RM258 linked to Rf4 and Rf6 genes all on chromosome 10. These markers will help facilitate marker assisted selection for identification of restorer lines in CMS-WA system. Molecular diversity analysis for twenty one genotypes along with known restorers using thirteen markers was carried out and the dendrogram revealed four clusters. Cluster-II and Cluster-IV consisted of single genotypes and Cluster-I consisted of 7 genotypes and Cluster-III consisted of 14 genotypes. Combined ANOVA for spikelet fertility revealed significant variation for season, moisture regime and genotypes. Keywords: ANOVA, Aerobic Condition, Wetland Condition, Cytoplasmic Male Sterility (CMS), Fertility restoration, Simple Sequence Repeat markers, R 2 value, Validation. Introduction Rice (Oryza sativa L.) is the staple food of large segment of Asian and half of the world s population. Over 17 million ha of Asia s irrigated rice may experience physical water scarcity and 22 million ha may experience economic water scarcity by About 28 % of the world s rice is grown in rainfed lowland and 13% in upland ecosystem which frequently faces severe water deficit due to uneven rainfall pattern 2. Thus, drought is a major constraint in addition to depletion of ground water for rice production under rainfed condition 3. The genetic correlation (r g ) between yield under moisture stress and non-stress is usually low but positive 4 indicating that it is possible to develop cultivars combining drought tolerance with high yield potential under favourable moisture levels. Aerobic rice is grown in well-drained, non puddled and non saturated soils without ponded water 5. For rice to be a successful crop under aerobic condition, it should tolerate intermittent water deficits and high soil impedance 6. Drought tolerant rice varieties have been developed by UAS Bangalore namely MAS 946-1, MAS 26 by using marker assisted selection and ARB6 (Anagha) by using conventional breeding methods that are suitable for aerated soil environments (non-puddled rice fields) 7. Facing the challenges of population growth and water shortage, to resolve this problem, there is a need to focus on increasing the yield of rice and other food grain crops by means of developing high yielding drought tolerant varieties and hybrids that are the need of hour to meet the future requirements. In rice, fertility restoration genes and their inheritance in WA CMS system have been extensively studied. Different Rf genes are reported in the past on different chromosomal locations ranging from Rf1 to Rf17. Among these, Rf1 to Rf6 genes have been commonly reported in many studies. Most of the researchers reported two genes controlling this trait in a given mapping populations. One or two dominant restorer alleles (Rf3 and/or Rf4) are usually suggested to be responsible for the fertility restoration of WA-CMS 8. The use of molecular markers has enabled to determine the chromosomal locations of the Rf genes for the various CMS restoration systems. Rf1 and Rf2 genes were mapped on chromosomes 10 and 2 by trisomic analysis 9, 10. Rf1 located on chromosome 10 was found to restore cytoplasmic male sterility in BT cytoplasm 9. Ichikawa et al 11 also mapped this Rf gene using RAPD/RFLP markers. Zhang et al 12 mapped Rf 3 gene using RFLP markers on chromosome 1 for CMS -WA system. Yao et al 13 identified two Rf genes viz. Rf3 and Rf4, for the WA-CMS system on chromosome 46

2 1 and chromosome 10 respectively. Bharaj et al 14 located two restorer genes viz. Rf4 and Rf6 on chromosome 6 and chromosome 7 respectively. HL type fertility restoration gene Rf6 (synonym Rf5) was mapped on chromosome 10, 7.8 cm from RM Bazrkar et al 16 identified Rf7 on chromosome 12 linked to RM7003 at a genetic distance of 13.3 cm. Ngangkham et al 17 fine mapped Rf4 locus in Basmati restorer line PRR78 with interval of 0.8 cm. Rf3 and Rf4 loci have been identified in different donors using rice microsatellite SSR markers 16, Earlier studies also found Rf3 and Rf4 loci on chromosome 1 and 10 respectively which appear to be consistent in restoring the fertility. Balaji Suresh et al 21 fine mapped Rf3 and Rf4 loci using SSR markers. The SSR RM6100 linked to Rf4 gene on chromosome 10 and RM10313 linked to Rf3 gene on chromosome 1, showed 85 and 81 percentage efficiency in identifying the restorer lines respectively 22. A significant advance in the practical utilization of molecular markers was the development of SSR markers which are also referred as microsatellite markers 23. These markers are highly polymorphic in nature and easy to detect. PCR amplified products are usually resolved by polyacrylamide gel electrophoresis followed by silver staining. SSR markers closely linked to Rf genes are used for markers assisted selection (MAS) in hybrid rice breeding program by reducing time and workload for identifying potential restorers 16. Keeping above points in view, present study was conducted to study the restoration behaviour of drought tolerant advanced breeding lines and Barwale Foundation germplasm lines for validation of SSR markers linked to fertility restoration (Rf) genes. Material and Methods In earlier studies a set of 70 drought tolerant lines [received from Drought Breeding Network program (IRRI) and Barwale Foundation germplasm] were test crossed to two CMS lines and 21 drought tolerant restorer lines were identified. These 21 lines were used in the present study (Table 1). All the lines were grown in the field of Maharajpet farm, Barwale Foundation, Hyderabad, India. The experimental study was conducted at Barwale Foundation Research Farm, Hyderabad located at the latitude of N and longitude of E and an altitude of 536 meters above mean sea level. The soil of the experimental conditions was vertisol/clay loam having ph more than 7. Phenotyping: To estimate the fertility restoration ability of these 21 drought tolerant restorer lines, they were test crossed with the five different CMS lines i.e. IR58025A, IR68888A, IR68897A, IR75596A and IR79156A. The experimental hybrids (105F 1 s) were evaluated using randomized complete block design (RCBD) with two replications under two different conditions i.e. irrigated (well watered) and aerobic conditions in two consecutive seasons (Kharif 2010 and 2011). Under irrigated condition, 21 days old seedlings were transplanted at a spacing of 20 x 20 cm and one plant per hill whereas under aerobic condition, sowing was done using dry direct seeding method at a spacing of 30 x15 cm. For calculating spikelet fertility percentage, six panicles were covered with selfing covers during flowering stage and at harvesting stage the panicles were collected from each entry and average number of filled grains and unfilled grains per panicle were counted and expressed as per cent. The percentage spikelet fertility was calculated by considering the number of filled grains out of the total no. of spikelets per panicle. Genotyping: The genomic DNA was extracted from the young seedlings of four weeks old by using Dellaporta method 24. PCR reaction was carried out in total of 15 µl reaction volume consisting of 5 µl of 1 X PCR buffer (10 mm Tris-Cl; 50 mm KCl), 1 µl of 2.5 mm dntps, 5 picomoles of each 1 µl forward and 1 µl reverse primer, 0.5µl (1U/µl) of Taq polymerase enzyme (Merck Specialties Pvt. Ltd.) and 2 µl of DNA template (50 ng/µl concentration), 4.5 µl of double distilled sterile water. PCR was carried out in Eppendorf EP master with the following cyclic conditions, initial denaturation of 95 0 C for 5 min., followed by denaturation at 94 0 C for 15 sec., primer annealing at 55 0 C for 45 seconds and primer extension 72 0 C for 45 sec for 35 cycles followed by the final extension of 72 0 C for 5 min. The amplified products along with the 100 bp ladder marker were resolved on 4% polyacrylamide gel by electrophoresis, followed by silver staining procedure. The genotypic dataset was generated based on the PCR amplification product size scored as different alleles based on the base pair (bp) size of the amplified products. The SSR markers for the study were selected based on the centimorgan (cm) distance between the gene and the marker (Table 2). Molecular markers reported by the earlier findings by using SSR (Simple Sequence Repeats) related to five fertility restorer (Rf) genes of WA-Cytoplasmic male sterile lines were used in the present study (Table 3). Cluster analysis of molecular data: The genotypic data generated from thirteen closely linked Rf gene based markers across three chromosomes (1, 10 and 12) were utilized for generating dendrogram explaining the twenty one identified restorer lines along with two commercial checks namely PRR78 and BR DARwin v 5.0 programme 25. Dendrogram was generated by using genetic dissimilarity and neighbor joining procedure 26.Tree construction was performed using the horizontal tree representation mode (Figure 1). The results indicated that four major clusters were formed designated as I, II, III and IV. In cluster I and III, there 47

3 were two sub clusters each and there was no sub clustering in II and IV. Dissimilarity index clearly showed that maximum dissimilarity was found between the and DR15 and also between and KMR3 and minimum dissimilarity was found between DR15 and AR53, KMR3 and DR15, and Combined ANOVA: Spikelet fertility was measured for all hybrids generated by five different CMS lines crossed with 21 restorer lines under aerobic and wetland conditions for two consecutive Kharif seasons of 2010 and Results revealed that there was significant difference between year (Y), moisture regime (M) and genotypes (G) related to spikelet fertility in F 1 s derived from different CMS lines (IR58025A, IR68888A, IR68897A, IR79956A and IR79156A). The interactions Y*M, Y*G, M*G and Y*M*G had a significant difference (Table 5). Results and Discussion Single-marker analysis: Single Marker Association (SMA) was done with t-test and regression analysis using SPSS 16.0 (SPSS Inc.) to find the association between the molecular markers of the twenty one accessions from drought tolerant lines and spikelet fertility in 105 F 1 s derived from five different CMS lines. Evaluation was done under both irrigated and aerobic conditions across two seasons (Kharif 2010 and 2011). Thirteen markers tightly linked with five different Rf genes for WA-CMS were screened. Genotypic data showed the variation in the alleles for different markers. Out of thirteen markers, six markers, namely RM6100, RM171, RM7466, RM7003, RM311 and RM244 showed two alleles each. Three markers, namely RM1108, RM443 and RM1 showed three alleles each. Two markers RM258 and RM228 showed four alleles, RM 490 showed five alleles and RM6737 showed six alleles. The results have shown evidence that the reported Rf gene linked SSR markers were polymorphic in nature. Results are depicted in table 4. a. IR58025A: Single marker analysis revealed that out of 13 Rf gene based markers RM171, RM6100 and RM258 showed significant association with fertility restoration having phenotypic variation of 20.40, and 42.20% respectively, under Kharif 2010 aerobic condition. RM171, RM6100, RM1108, RM7466, RM258, RM6737 showed significant association having phenotypic variation of 54.60, 58.80, 46.30, 32.80, and 68.80% respectively under Kharif 2010 wetland condition. RM171, RM6100, RM258 and RM6737 showed significant association with phenotypic variation of 22.60, 30.60, and respectively under Kharif 2011 aerobic condition. RM171, RM6100, RM7003, RM7466 and RM6737 showed significant association with phenotypic variation of 38.80, 35.70, 20.80, and respectively under Kharif 2011 wetland condition as shown in table 4a. b. IR68888A: Under aerobic condition of Kharif 2010, RM171, RM6100, RM1108, RM7466, RM258 and RM6737 showed significant association with phenotypic variation of 36.60, 58.70, 50.40, 25.10, and 69.40% respectively and during Kharif 2010 wetland condition, single marker analysis showed significant association with phenotypic variation for the following markers- RM171 (35.20%), RM6100 (47.80%), RM1108 (44.30%), RM490 (47.30%), RM7466 (19.90%), RM258 (67.10%) and RM6737 (57.00%) percentage. In Kharif 2011 under aerobic condition RM171 (36.20%), RM6100 (56.50%), RM1108 (53.60%), RM7466 (22.40%), RM258 (51.60%) and RM6737 (67.20%) showed significant association with phenotypic variation, where as in Kharif 2011 under irrigated condition, the markers namely RM171, RM6100, RM1108, RM7466, RM258 and RM6737 showed significant association with phenotypic variation of 25.90, 40.20, 38.80, 17.20, and 66.80% respectively as shown in table 4b. c. IR68897A: With IR68897A under Kharif 2010 aerobic condition the markers, namely RM171, RM6100, RM490, RM311, RM258 and RM244 showed significant association with phenotypic variation of 21.00, 34.30, 43.70, 39.60, and 32.20% respectively but when it comes to irrigated condition of Kharif 2010, Rf gene based markers, namely RM6100 (27.80%), RM490 (54.00%), RM311 (19.50%), RM7466 (40.90%) and RM244 (27.00%) showed significant association with phenotypic variation. Under Kharif 2011 aerobic condition single marker analysis showed that RM171, RM6100, RM1108, RM311, RM258, RM6737 and RM244 showed significant association with phenotypic variation of 23.40, 35.30, 36.30, 38.00, 48.70, and 29.10% respectively where as in Kharif 2011 under wetland condition the markers RM171 (23.40%), RM6100 (30.80%), RM490 (53.70%), RM311 (41.70%), RM7466 (22.60%), RM258 (38.30%) and RM244 (27.80%) showed significant association with phenotypic variation as shown in table 4c. d. IR75596A: The Rf gene based markers, namely RM171, RM6100, RM1108, RM311, RM258, RM6737 and RM244 showed significant association with phenotypic variation of 22.70, 35.20, 31.40, 30.50, 52.80, and 36.20%, respectively under Kharif 2010 aerobic condition where as in Kharif 2010 wetland condition RM171, RM6100, RM1108, RM490, RM311, RM258, RM6737 and RM244 markers showed significant association phenotypic variation of 22.50, 30.50, 35.30, 45.60, 27.80, 45.00, and 38.30% respectively. Under Kharif 2011 aerobic condition RM171, RM6100, RM1108, RM311, RM258, RM6737 and RM244 showed significant phenotypic variation of 27.00, 37.40, 34.20, 30.40, 52.20, and 34.40% respectively. Under Kharif 2011 wetland condition RM171 (27.50%), RM6100 (34.90%), RM1108 (44.30%), RM490 (45.80%), RM258 (45.50%), RM6737 (56.30%) and RM244 (36.60%) showed significant association with phenotypic variation as shown in table 4d. e. IR79156A: Based on single marker analysis the Rf gene 48

4 based markers, namely RM171, RM6100, RM1108, RM7466, RM258, RM6737 and RM244, showed significant association with phenotypic variation of 25.20, 38.30, 37.50, 21.60, 43.20, and % respectively under Kharif 2010 aerobic condition where as in Kharif 2010 under wetland condition RM171, RM6100, RM1108, RM7466, RM258 and RM6737 showed significant association with phenotypic variation of 54.40, 65.10, 50.40, 30.60, and 57.00% respectively. Under Kharif 2011 aerobic condition, the markers, namely RM171, RM6100, RM1108, RM258 and RM6737 showed significant association with phenotypic variation of 22.30, 38.70, 28.80, and 57.80% respectively where as in Kharif 2011 wetland condition RM171, RM6100, RM258 and RM6737 showed significant association with phenotypic variation of 22.60, 44.90, and 61.10%, respectively as shown in table 4e. Comparison of spikelet fertility between aerobic and wetland conditions in two Kharif seasons with different CMS lines a.ir58025a: In Kharif 2010 under aerobic condition, the spikelet fertility was in the range of to 84.89%. Highest spikelet fertility was observed in IR (84.89%), followed by IR79906-B (83.91%) and IR (83.22%) and lowest spikelet fertility was observed in IR B (53.31%). In Kharif 2010 during wetland condition the spikelet fertility ranged from to 87.26%. Highest spikelet fertility was recorded in IR (87.26%) followed by KMR3 (87.21%) and IR (86.75%) and lowest spikelet fertility was observed in IR80408-B-43-3 (76.99%) as shown in figure 2a. Under Kharif 2011 aerobic condition, the spikelet fertility was in the range of to 88.83%. Highest spikelet fertility was observed in IR (88.83%) followed by KMR3 (86.76%) and IR (85.05%). Lowest spikelet fertility was recorded in IR B (48.21%). Under Kharif 2011 wetland condition, the spikelet fertility was in the range of to 90.18%. Highest spikelet fertility was observed in KMR3 (90.18%) followed by IR (89.05%) and IR (88.51%), lowest spikelet fertility was observed in IR79956-B (76.89%) as shown in figure 2b. b.ir68888a: The spikelet fertility in Kharif 2010 under aerobic condition was recorded to be between to 85.24%. Highest spikelet fertility was observed in KMR3 (85.24%) followed by IR84891-B-112-CRA-15-1 (83.58%) and IR (82.81%) and lowest spikelet fertility was observed in IR78937-B-4-B-B-B (46.52%). Under Kharif 2010 wetland condition, the spikelet fertility was in the range of to 89.93%. Highest spikelet fertility was observed in KMR3 (89.93%) followed by IR (88.21%) and IR (87.49%), lowest spikelet fertility was documented in IR79956-B (51.93%) as shown in figure 2c. Under Kharif 2011 aerobic condition, the spikelet fertility was in the range of to 86.70%. Highest spikelet fertility was observed in KMR3 (86.70%) followed by IR84891-B-112-CRA-15-1 (84.83%) and IR (84.26%). Lowest spikelet fertility was observed in IR78937-B-4-B-B-B (42.19%). Under Kharif 2011 wetland condition, the spikelet fertility was in the range of to 88.29%. Highest spikelet fertility was observed in IR84891-B-112-CRA-15-1 (88.29%) followed by KMR3 (87.10%) and IR (85.40%), lowest spikelet fertility was observed in IR78937-B-4-B-B-B (46.31%) as shown in figure 2d. c. IR68897A: The spikelet fertility in Kharif 2010 under aerobic condition was in the range of to 82.82%. Highest spikelet fertility was recorded in IR (82.82%) followed by IR (82.74%) and IR81430-B-B-94 (82.09%). Lowest spikelet fertility was observed in IR B (42.04%). Under Kharif 2010 wetland condition, the spikelet fertility was in the range of to 85.65%. Highest spikelet fertility was observed in IR (85.65%) followed by IR (85.30%) and IR81430-B-B-94 (84.72%) and lowest spikelet fertility was observed in IR B (45.96%) as shown in figure 2e. Under Kharif 2011 aerobic condition, the spikelet fertility was in the range of 44.27% to 85.84%. Highest spikelet fertility was recorded in IR (85.84%), followed by IR81430-B-B-94 (84.21%), IR (83.63%). Lowest spikelet fertility was observed in IR B (44.27%). Under Kharif 2011 wetland condition, the spikelet fertility was in the range of to 89.24%. Highest spikelet fertility was observed in KMR3 (89.24%), followed by IR (87.04%) and IR81430-B-B-94 (86.64%). Lowest spikelet fertility was observed in IR B (49.18%) as shown in figure 2f. d.ir75596a: In Kharif 2010 under aerobic condition, the spikelet fertility ranged from to 82.79%. Highest spikelet fertility was found in IR84891-B-112-CRA-15-1 (82.79%), followed by KMR3 (82.44%) and IR (80.99%) and the lowest spikelet fertility was observed in IR B (44.56%). Under Kharif 2010 wetland condition, the spikelet fertility was in the range of to 87.69%. Highest spikelet fertility was observed in IR79906-B (87.69%) followed by IR84891-B-112- CRA-15-1 (85.26%) and KMR3 (84.45%) and lowest spikelet fertility was recorded in IR B (49.70%) as shown in figure 2g. The spikelet fertility under Kharif 2011 aerobic condition was in the range of to 86.97%. Highest spikelet fertility was observed in KMR3 (86.97%), followed by IR79906-B (83.96%) and IR84891-B-112-CRA (83.03%) and lowest spikelet fertility was recorded in 49

5 IR B (43.98%). Under Kharif 2011 wetland condition, the spikelet fertility was in the range of to 90.40%. Highest spikelet fertility was observed in KMR3 (90.40%) followed by IR84891-B-112-CRA-15-1 (87.25%) and IR (86.60%) and the lowest spikelet fertility was recorded in IR B (53.51%) as shown in figure 2h. e.ir79156a: The spikelet fertility during Kharif 2010 aerobic condition ranged from to 81.03%. Highest spikelet fertility was observed in IR79906-B (81.03%), followed by IR (80.93%) and KMR3 (80.75%). Lowest spikelet fertility was recorded in IR79899-B (53.07%). Under Kharif 2010 wetland condition the spikelet fertility was in the range of to 86.16%. Highest spikelet fertility was observed in IR (86.16%), followed by IR (85.09%) and Minghui63 (84.13%) and lowest spikelet fertility was observed in IR B (76.19%) as shown in figure 2i. Under Kharif 2011 aerobic condition, the spikelet fertility was in the range of to 84.86%. Highest spikelet fertility was observed in Minghui63 (84.86%), followed by KMR3 (84.16%) and IR (82.77%). Lowest spikelet fertility was recorded in IR B (46.22%). Under Kharif 2011 wetland condition the spikelet fertility was in the range of to 88.30%. Highest spikelet fertility was observed in Minghui63 (88.30%), followed by KMR3 (87.87%) and IR81430-B-B- 94 (84.86%) and lowest spikelet fertility was recorded in IR B (77.23%) as shown in figure 2j. Discussion The results showed that the markers RM 171 and RM 6100 were having significant association with spikelet fertility in both the seasons under both the conditions in case of CMS line IR 58025A. The marker RM 258 was also significantly associated with spikelet fertility when tested in 2010 in both the conditions and Kharif 2011 aerobic condition. Hence, these markers could be used for marker assisted selection while working with CMS line IR 58025A. In case of CMS line IR 68888A, the markers RM 171, RM 6100, RM 1108, RM 7466 and RM 258 were having significant association with spikelet fertility in both the seasons and in both the conditions. When IR 78897A was used as line, the markers RM6100, RM311, RM244 had significant association with spikelet fertility in both the Kharif seasons under both aerobic condition and RM171 and RM258 also showed significant association with spikelet fertility in both aerobic and wetland conditions of Kharif 2011 and aerobic condition of Kharif 2010, hence these Rf gene based markers can be utilized for screening restorers when dealing with IR78897A. With IR79956A, the results revealed that RM171, RM6100, RM1108, RM258, RM6737 and RM244 have shown significant association with spikelet fertility in two consecutive Kharif seasons under both aerobic and irrigated conditions. Hence, the above mentioned markers can be utilized for screening the restorers in early breeding stage. In IR79156, the results also revealed that RM6100, RM171, RM258, RM1108, RM6737 showed significant association with spikelet fertility under both aerobic and irrigated conditions of both Kharif seasons, hence, these markers can be utilized for identification of restorers. In the present study, single marker analysis showed that RM6100 linked to Rf4 gene located on chromosome 10 is associated with significant phenotypic variation among the F 1 s evaluated under both aerobic and wetland conditions of two consecutive Kharif seasons, followed by RM171 linked to Rf 4 gene on chromosome 10, RM258 linked to Rf4 and Rf6 genes on chromosome 10 were also having significant association with phenotypic variation. These results are in close confirmation with the previous reports. 27 Sheeba et al 28 reported that RM6100 amplified the Rf4 linked allele in a majority of the restorers with a selection accuracy of 94.87% and RM6100 was observed to be very close to the gene at a distance of 1.2 cm. Revathy et al 22 reported that RM6100 exhibited 80% efficiency in identifying the restorers. The results of spikelet fertility for combined ANOVA related to two consecutive Kharif seasons under aerobic and wetland condition revealed that there is a significant difference between year (Y), moisture regime(m), genotypes(g) and also between the interactions of Y*M, Y*G,Y*M*G. These results are in accordance with the earlier report related to wetland condition only 29. The average spikelet fertility of two Kharif seasons under aerobic and wetland conditions among the 21 genotypes, indicated that KMR3, IR79906-B-192-1, IR84891-B-112- CRA-15-1, IR , IR and Minghui63 were having highest spikelet fertility. In our earlier report, these lines have shown highest spikelet and pollen fertility under irrigated condition 30. Conclusion Identification of restorers in drought tolerant condition could be one of the most challenging priorities prevailing in the current situation. Among the 13 markers linked to different fertility restoration gene on various chromosomes, RM6100 linked to Rf4 gene which showed highly significant association with phenotypic variation followed by RM171 linked to Rf 4 gene on chromosome 10, RM258 linked to Rf4 and Rf6 genes on chromosome 10 can be utilized for identification of restorers. Molecular screening for fertility restoration genes linked to various Rf loci will help in identification of the potential restorers with higher accuracy without going for routine test crossing but as a precaution, results should be checked for confirmation with the test crosses with known CMS lines to derive higher level of heterosis. 50

6 Figure 1: Clustering dendrogram based on fertility restoration gene markers 35-08: IR ; 37-09: IR B-112-CRA-15-1 ; 62-09: IR36; 62-08: IR B ; MG63: Minghui63; 25-08: IR B; 10-08: IR B; 65-08: IR B-4-B-B-B;13-08: IR B; AR40: IR80408-B-43-3; 50-09: IR ; 56-08: IR B ;44-08: IR B; 38-08: IR B ;55-09: IR80461-B-7-1; 40-09: IR81430-B-B-94; AR42: IR80411-B-49-1; DR14: IR79956-B ; DR15: IR79906-B :AR53: IR79906-B ; PRR: PRR78; BR: BR a.IR58025A 2b.IR58025A 2c. IR68888A 2d. IR68888A 51

7 2e. IR68897A 2f. IR68897A 2g. IR79956A 2h. IR79956A 2i. IR79156A 2j. IR79156A Figure 2: Comparison of spikelet fertility for F 1 s generated with different CMS lines between aerobic and wetland conditions in two Kharif seasons 25A: IR58025A; 7A: IR78888A; 9A: IR68897A; 31A: IR79956A; 40A: IR79156A; K10: Kharif 2010; K11: Kharif 2011; AR: Aerobic; WL: Wetland Table 1 List of drought tolerant lines used for validation of Rf gene linked markers S. N. Entry Source Parentage 1 IR IRRI IRRI 132/IR IR84891-B-112-CRA-15-1 IRRI IR /IR IR36 IRRI IR /IR1737//CR IR79899-B IRRI CT /IR KMR3 UASB 6 MINGHUI63 IRRI 7 IR B IRRI IR B-1-2/IR 64 8 IR B IRRI IR B-1-2/IR 64 9 IR78937-B-4-B-B-B IRRI IR B-1/IR IR B IRRI IR B-1-2/IR IR80408-B-43-3 IRRI IR /IR00W IR IRRI KalingaIII/CT IR80013-B IRRI IRRI 132/CT IR B IRRI IR B-1-2/IR IR79956-B IRRI UPL RI 7/IR 36 52

8 16 IR80461-B-7-1 IRRI IR77080-B-34-3/IR00A IR81430-B-B-94 IRRI KalingaIII/IRRI IR80411-B IRRI IR PMI /IRRI IR79956-B IRRI UPL RI 7/IR IR79906-B IRRI IR /IR IR79906-B IRRI IR /IR * IRRI: International Rice Research Institute, Philippines. UAS B: University of Agricultural Sciences, Bengaluru, India. Table 2 List of Rf gene linked markers used for validation S. N. Marker Rf gene Chr. Distance (cm) Reference RM7466 Rf Sattari et al RM490 Rf Sattari et al RM443 Rf Bazrkar et al RM1 Rf Ahmadikhah et al RM171 Rf Agdaca et al. 32 5a RM171 Rf Nematzadeh et al RM6100 Rf Sheeba et al RM1108 Rf Sattari et al RM6737 Rf Ahmadikhah et al RM258 Rf Nematzadeh et al. 20 9a RM258 Rf Bazrkar et al RM228 Rf Jing et al RM311 Rf Ahmadikhah et al RM244 Rf 6(t) Mishra et al RM7003 Rf Bazrkar et al. 16 Table 3 Primer information of the SSR markers used for validation and alleles identified using the markers S.N. Microsate llite ID Forward Sequence(5'-3') Reverse Sequence (5'-3') Chr.no Ann. temp. Repeat motif # alleles detected 1 RM7466 CGGTCTGCCTAG CTTGTCTC ACCGAACACGGA AAAGCC 1 55 (TAGA)6 2 2 RM490 ATCTGCACACTG CAAACACC AGCAAGCAGTGC TTTCAGAG 1 55 (CT) RM443 GATGGTTTTCAT CGGCTACG AGTCCCAGAATG TCGTTTCG 1 55 (GT) RM1 GCGAAAACACA ATGCAAAAA GCGTTGGTTGGA CCTGAC 1 55 (GA) RM171 AACGCGAGGAC ACGTACTTAC ACGAGATACGTA CGCCTTTG (GATG)5 2 6 RM6100 TCCTCTACCAGT ACCGCACC GCTGGATCACAG ATCATTGC (CGA)8 2 7 RM1108 GCTCGCGAATCA ATCCAC CTGGATCCTGGA CAGACGAG (AG) RM6737 CATTGGGGGTGG ATAAAGAG TATCCTCTACTCC CTCGGCC (TAT) RM258 TGCTGTATGTAG CTCGCACC TGGCCTTTAAAGC TGTCGC GA)21(GGA) RM228 CTGGCCATTAGT CCTTGG GCTTGCGGCTCTG CTTAC (CA)6(GA) RM311 TGGTAGTATAGG TACTAAACAT TCCTATACACATA CAAACATAC (GT)3(GTAT )8(GT) RM244 CCGACTGTTCGT CTGCTCTCGGGTG (CT)4(CG)3C 2 53

9 13 RM7003 CCTTATCA AACGT (CT)6 GGCAGACACATA TGCAAATGAACC CAGCTTATAGGC CCTCTAGC (AAAC)6 2 S.N. Table 4 Single-marker analysis showing F value and R 2 values of SSR markers in Hybrids a. IR58025A: Marker Kharif 2010 Kharif 2011 Aerobic Wetland Aerobic Wetland F value R 2 (%) F value R 2 (%) F value R 2 (%) F value R 2 (%) 1 RM * ** * ** RM ** ** ** ** RM ** RM * RM ** * RM * ** * RM ** * * b. IR68888A: Kharif 2010 Kharif 2011 S.N. Marker Aerobic Wetland Aerobic Wetland F value R 2 (%) F value R 2 (%) F value R 2 (%) F value R 2 (%) 1 RM ** * ** * RM ** * ** ** RM ** * ** ** RM * RM * * * RM ** ** ** ** RM ** ** ** ** c. IR68897A: Kharif 2010 Kharif 2011 S.N. Marker Aerobic Wetland Aerobic Wetland F value R 2 (%) F value R 2 (%) F value R 2 (%) F value R 2 (%) 1 RM * * * RM ** ** ** ** RM * RM ** ** ** RM ** ** ** ** RM * * RM ** ** * RM * RM ** * ** ** d. IR75596A: Kharif 2010 Kharif 2011 S.N. Marker Aerobic Wetland Aerobic Wetland F value R 2 (%) F value R 2 (%) F value R 2 (%) F value R 2 (%) 1 RM * * * * RM ** ** ** ** RM * * * ** RM * * RM * * * RM ** * ** ** RM ** * ** * RM ** * ** **

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