| Rice is one of the most important staple crops in the world and a model plant for genetic ananlysis. Drought is one of the most important limiting factors for rice production in many countries.With the decreasing of available water resources and frequent unexpected drought events, drought resistance breeding was particularly important. So, increasing drought resistance was an arduous and long-term task in rice. Drought tolerance is one of the drought resistance mechinisms, which was controlled by minorgene.In previous study, drought resistance study mainly focus on adult stage traits, such as root traits, leaf traits, and physiological and biochemical characteristics, such as osmotic adjustment, abscisic acid(ABA) and other related traits. But drought tolerance was seldom reported in rice seedlings. Using 159 recombinant inbred lines(RILs) derived from a cross of Zhengshan97B(ZS97B)/IRAT109 for this research. Drought stress was simulated by poly ethylene glycol(PEG6000). The traits including plant height, root length, physiology and biochemical character were invsetigated for traditional QTL mapping and QTL-seq methord. Main results of the present are as following:1. Drought tolerance identification for ZS97 B and IRAT109. Both of their plant height and root length are significant different under normol condition and 18% PEG. The plant height and root length of IRAT109 was significant inhibition, but for ZS97 B are less constrained under drought stress. Both root length were significant different under normal conditions, but there was no difference under 18% PEG treatment. Furthermore, both their pysiology and biochemical character, including catalase(CAT), hydrogen peroxide(H2O2) and proline(Pro), were significant different, but ZS97 B has more CAT than IRAT109 after treatment with 18% PEG-6000. Pro content increase rapidly under treatment, and the Pro content in ZS97 B was significant higher than in IRAT109. It is speculated that the osmotic adjudstment ability of ZS97 B has advantage over IRAT109.2. QTL mapping for plant height, root length, leaves rolling in RILs(F9) under contrasting condtion. The related QTLs were detected 17 relatives QTLs on chromosome 1, 2, 3, 4, 5, 7, 10, 11 and 12, respectively. A QTL(q RL-7-2) for root length was identified in the interval RM18-RM478 on chromosome 7 under both normal and stress condition could explain 10.45% and 10.89% of the phenotypic variation, repectively. Two QTLs(q PH-1 and q PH-5-2) for plant height were identified in the interval RM472-RM104 and RM459-RM161 on chromosome 1 and 5, respectively, under both normal and stress condition, these QTLs could explain 34.31% and 30.78%(under normal condition), 17.61% and 9.87%(under 18% PEG-6000 stress) of the phenotypic variation, respectively, In addition, a QTL(q RL-1-3) in the interval RM472-RM104 could explain 9.08% of the root length variation under stress condition. Two QTLs(q LR-1 and q LR-4) were identified in the interval RM302-RM476 B and RM261-RM471 on chromosome 1 and 4, could explain 10.74% and 12.27% of the phenotypic variation, repectively.3. QTL mapping for physiological character in RILs. The total 26 related QTLs were detected on chromosome 1, 2, 3, 4, 6, 8, 9, 10, 11 and 12, respectively. A QTL in the interval RM508-RM435 was detected for AOC and H2O2(q AOC-6 and q H2O2-6) on chromosome 6, this QTL could explain 12.2% and 7.90% of the phenotypic variation under normal condition. Under PEG-6000 stress condition, a QTL in the interval RM216-RMRM31 on chromosome 10 associated with AOC and H2O2(q AOC-10 and q H2O2-10) was detected, accounting 14.14% and 9.13% of the phenotypic variation. A H2O2 and SP(q H2O2-11 and q SP-11-2) associated QTL was detected, at the same codition, in RM206-RMRM144 on chromosome 11 accounting 15.42% and 16.02% of the phenotypic variation, respectively. Meanwhile, a QTL associated with Pro and SP(q Pro-12 and q SP-12)was detected in the interval RM463-RM235 on chromosome 12, accounting 64.25% and 17.97% of the phenotypic variation, especially the QTL assiciated with Pro has the singnificant value. And with the same condition a QTL in the RM220-RM490 on chromosome 1 associated with CAT and soluble protein(SP)(q CAT-1-1 and q SP-1-1) was detected, could explain 11.22% and 9.77% of the phenotypic variation, and a QTL associated with H2O2(q H2O2-1-1) was also detected in the same interval, could explain 22.82% of the phenotypic variation.4. The extreme plants of RILs derived from ZS97B/IRAT109 QTL-seq. Recent studies have indicated that bulked segregant ananlysis(BSA) combined with whole genome resequencing can be a labor intensive, cost-effective and time-consuming excellent way for dissecting quantitative loci for drought tolerance in rice, also named QTL-seq. We employed the QTL-seq to map QTL conferring drought tolerance at seedling stage in rice. An extreme DNA pools derived from 30 extremely sensitive and 30 extremely tolerant individuals were constructed and sequenced with the illumina technology. The preprocessed reads that passed the quanlity control were anligned to the published rice(Nipponbare) refernce genome using version 6.0. A total of about 320 M were obtained, with approximately 154 M and 166 M from extreme drought and extreme senstive pools, repectively. About 92% of the selected reads were mapped to unique position in the reference genome afer trimming and filtering. These uniquely mapped reads covered nearly 98% of genome in both pools, with 29× and 31× in extreme drought and extreme senstive pools, repectively. And, a toal of 2535464 SNPs were obtained. We also obtained 15 candidate regions, including 69 genes with non-synonymous SNP, 74 genes with synonymous SNP and 14 genes with frameshift SNP.5. The results intergrated QTL mapping with QTL-seq. Combining the results of QTL mapping with the results of QTL-seq based on SNP, there were overlaps in the interval RM302-RM476 B of chromosome1, RM261-RM471 and RM255-RM127 of chromosome 4, RM261-RM311 of chromosome 10, mainly relaitive to leaf rlling, Pro and T-SOD. Beside, among 188 candidate genes, there were 16, 14, 1, and 4 candidate genes on the overlaps, respectively. These genes function were mainly relative to desease genes, express protein, slocus receptorkinase and PPR repeat protein genes. |
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