| Waterlogging damage is an important disaster affecting global wheat production. Every year there is about 15-20% of the global wheat planting area blighted by waterlogging damage. Waterlogging generally refers to cause anoxic environment when saturated soil water, causing plant metabolic abnormalities by oxygen deficit, hurting the plant normal growth and development. Waterlogging damage does effect wheat plant development and morphogenesis of plant organs, grain formation, grouting and mature, even seriously cause large area of reduced-production and wheat quality deterioration. Cultivation and useage of waterlogging resistant varieties is an effective way to reduce the effects of wheat waterloging damage, therefore, it will be of great significance to reduce the harm of wheat waterlogging through screening of resistant varieties and dissecting the genetic mechanisms of waterlogging resistance.This study selected 244 wheat varieties as the experimental material from different wheat areas in China, in seedling stage and booting stage by the test of the basin for waterlogging treatment, investigation under the stress of waterlogging plant morphological indicators, to determine the waterlogging resistance of different varieties, at the same time, we choosed 122 genome-wide SSR markers, for detecting genetic diversity of wheat varieties, analysising its population structure and linkage disequilibrium. With the results of waterlogging resistant identification, association analysis was carried out on the experimental material, and excellent allelic variation correlating with resistance was also found, so as to provide reference for wheat breeding of waterlogging tolerance. Main results are as follows:1. In wheat seedling stage we selected leave yellow rate after 4 weeks waterlogging and the plant survival rate after 8 weeks as waterlogging resistance index. And in booting stage, we used main stem leaves rate after waterlogging stress for 10 days, main stem leaves rate after 7 days restore growing, plant height and 1000-grain weight of mature period as the study indicators. Analysis showed that the phenotypic variation of wheat germplasms ranged widely, with large differences between varieties. The test of correlation analysis showed that there was only little correlation coefficient between the 6 phenotypic traits, of which the correlation is relatively high between yellow leaves rate and plant survival rate, main stem leaves rate and plant height.2. There were 453 allelic variation detcecting 103 SSR markers in 244 wheat materials, each locous detected 2～8 allelic variation, with an average allele number of 4.40; polymorphism information content (PIC) ranged form 0.139 to 0.837, the average is 0.559.3. LD analysis showed that a total of 5253 two-two combinated points in 21 linkage group, regardless of the linear combination (the same linkage group), or a non-linear combination of (different linkage groups), it exists a certain degree of LD; but the combination number of D’> 0.5 was very small, only 40 with 0.76% of total sites. There was 13.57% combinated points which obtained statistical probability (P<0.01) suppoting linkage disequilibrium.4. Using Structure2.2 software for structural analysis with 103 SSR markers, the results showed that the optimal subpopulation number (K) of tested population was 3.5. Throuh association analysis using P<0.05 as the significant level, we found that: There were 11 SSR loci associated with yellow leaves rate; 19 loci associated with plant survival; 19 loci ssociated with main stem green leaves(flooded-growing 10 days); 20 loci ssociated with main stem green leaves(recovery-growing 7 days); 11 loci associated with plant height; but we only detected 3 loci correlating with 1000-grain weight.6. Analysising from the chromosome distribution of markers, there was no coorelated-markers detected on chromosome 12, while the other 20 chromosomes detecting associated markers. Chromosome 6 and chromosome 19 had the most coorelated-markers, which detected 9 seperately. The number of coorelated sites ranged from 1 to 6 on the remaining 19 chromosomes.7. Elite allelic variations analysis was also conducted on the 21 stable-associated-markers bellow the significant level 0.01, results showed that:The elite allele with maximum efficiency which was associated with yellow leava rate is wmc494-240 (typical carrier material is Laizhou 95021); and barc20-205 has the minimum efficiency, (typical carrier material is Yumai 18);The elite allele with maximum efficiency which was associated with yellow leava rate is gwml69-210 (typical carrier material is Xiaoyan 22); and wmc311-112 has the minimum efficiency (typical carrier material is Dinghong 201);The elite allele with maximum efficiency which was associated with yellow leava rate is wmc407-134 (typical carrier material is Wanmai 27); and gwm169-170 has the minimum efficiency (typical carrier material is Huaimai 18);The elite allele with maximum efficiency which was associated with yellow leava rate is wmc361-213 (typical carrier material is Ning 894037); and gwm169-210 has the minimum efficiency (typical carrier material is Beinuo 1 hao);The elite allele with maximum efficiency which was associated with yellow leava rate is wmc494-240 (typical carrier material is Jimai 20); and cfd152-247 has the minimum efficiency (typical carrier material is Ning 0311);The elite allele with maximum efficiency which was associated with yellow leava rate is gwm169-210 (typical carrier material is Wanmai 38); and wmc54-137 has the minimum efficiency (typical carrier material is Ning 06-174). |
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