| Peanut is one of the most important oil crops and economic crops all over the world. Early leaf spot (ELS), late leaf spot (LLS), and Tomato spotted wilt virus (TSWV) are the most important diseases in peanut. Among all the control strategies, using host-plant resistance and developing resistant cultivars are the best control mechanism, which has the advantage of being cost effective and eco-friendly. But conventional breeding has been the major force in providing modern cultivars to the farmers. Integration of genomics tools with conventional breeding has provided the possibility for improving peanut through marker assisted-selection to lead to the more rapid development of superior cultivars using informative markers linked to desired traits.Identification of linked markers is the base to improve peanut resistance for the important diseases through marker-assisted breeding. In order to identify quantitative trait loci (QTL) for resistance to diseases,and also identify markers linked to disease resistance traits,a mapping population derived from the cross Tifrunner x GT-C20 was developed and two genetic linkage maps were constructed based on F2 population and F9 recombinant inbred lines (RILs). Then we identify QTL using these genetic linkage maps and phenotyping data generated at several generations, and the main results are as follows:1. A total of 111 SSR markers were selected to assess the genetic diversity and population structure of 79 peanut cultivars and breeding lines from different breeding programs in China,India and the United States (US). A total of 472 alleles were detected with an average of 4.25 alleles per locus. The mean values of gene diversity and polymorphic information content (PIC)were 0.480 and 0.429, respectively. Furthermore, country wise analysis revealed that the genetic diversity of the lines from China is the highest, while within the country the genetic diversity of peanut lines from HAAS in China was the highest. A dendrogram based on DARwin6 showed the 79 peanut lines were divided into two major groups (G1 and G2) and G2 was further divided into five subgrops (G2a, G2b, G2c, G2d and G2e), which was basically consistent to the model-based structure analysis and the clustering using principal component analysis. The structuring was basically related to the geographic origin with a few admixtures, and had a correlation with the peanut market types. The results generated in this study could be used for designing effective breedi’ng programs to broaden the genetic base and to facilitate the process of developing peanut cultivars.2. A mapping population derived from the cross Tifrunner×GT-C20 was developed through single seed descent method. Tifrunner has high resistance to TSWV and moderate resistance to leaf spot, GT-C20 is highly susceptible to TSWV and leaf spots. There were 94 F2 individual plants and as of now, this mapping population consists of 248 recombinant inbred lines (RILs).Identification of F1 hybrids is very important to traditional hybridization breeding in peanut. In this study, we did identification of peanut F1 hybrid using 40 SSR molecular markers. Our practice showed that it is difficult to identify all the F1 by field agronomic investigation. The polymorphism of parents from different combinations is different and the highest polymorphism is 65%. The identification results showed that there are 46 F1 plants among the 92 F1 individual plants were true hybrids, accounting for 53.3%. Based on phenotype and agronomic traits, 62 F1 plants were identified as true hybrids and there is bigger different between marker identification and phenotype identification.3. Two genetic linkage maps were constructed based on different generation of this population in different years. Based on F2 population and more than 5000 SSR markers, a linkage map was constructed with 318 SSR marker loci distributed on 21 linkage groups with genome coverage of 1674.4 cM and a marker density of 5.3 cM per locus. Based on F9 RIL population, a genetic map with 426 SSR markers was constructed. There were 20 linkage groups in all and the total length of the map was 1980.78 cM with an average marker interval length of 4.6 cM. This map has the highest number of SSR markers for a single population.4. The entire set of RILs with 248 individuals was planted and phenotyped for several important traits including resistance to thrips, TSWV, and leaf spots in several environments from 2009 to 2013.5. Base on the F2 genetic maps constructed in this study and another F5 map (Qin et al., 2012),QTL analysis conducted using multi-environment phenotyping data for disease resistance identified a total of 54 QTLs in the F2 population which include two QTLs for thrips (12.14 -19.43% PVE),15 for TSWV (4.40-34.92% PVE) and 37 for LS (6.61-27.35% PVE).Similarly with the F5 map, total 23 QTLs could be identified which include a single QTL for thrips (5.86%PVE),nine for TSWV (5.20-14.14% PVE) and 13 for LS (5.95-21.45% PVE). 15 consistant QTL were identified in this study and consistent QTLs have shown higher phenotypic variance than non-consistent QTLs. As expected, the number of QTLs and their estimates of phenotypic variance were reduced in the F5 population.6. Base on the F9 genetic maps, a total of 49 QTL were identified on 16 LGs for these disease resistance traits with phenotypic variance explained (PVE) ranged from 6.26% to 15.54%. Of these QTL, 14 were for ELS, 22 for LLS, and 11 for TSWV, respectively. Among the 49 QTL, 13 QTL were found to be major QTL, including 7 for ELS, 5 for LLS and 1 for TSWV. Four consistently expressed QTL regions were identified in more than two environments. Interestingly,LGa05 was identified as a "resistance gene rich" LG for both ELS and LLS. This is the first QTL study reporting novel QTLs for thrips, TSWV and LS in peanut, and thus, markers linked to these QTLs may be of great use in genetic improvement in peanut disease resistance. |
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