Genetic Map Construction And QTL Mapping For Important Agronomic Traits In Synthetic Hexaploid Wheat

Most traits in wheat such as growling period, plant height, quality, phosynthesis associated traits, biotic stress and abiotic stress tolerance are generally controlled by multiple genes. Accurate phenotyping and genetic mapping are essential for marker-assisted selection and map-based cloning. And genetic maps are powerful tools for identification of such quantitative trait loci. Genetic variation in Common wheat is poorly, but highly in wild ancestral species of wheat. Therefore, utilization of genetic resources in wild ancestral species of wheat can break the bottleneck of yield breeding in common wheat. Synthetic hexaploid wheats offer breeder ready access to potentially novel genetic variations in wild ancestral species. In present study, we constructed genetic map for recombinant intercross lines (RILs) population derived from the cross of synthetic wheat, and performed QTL mapping for important agronomic traits, to identified novel genetic regions associated investigated traits in synthetic wheat. The major study and result were described as follows:1) Genetic map construction was conducted in a SC (SHW-L1×Chuanmai32) population which contains171lines derived from the cross of synthetic wheat SHW-L1. A total of1,862makers were assigned to21chromosomes and covered a total of3,766.9cM of genetic distance with an average of2.0cM between markers in map constriction. This map is essential to identify novel loci associated agronomic traits in SHW-L1. In addition, we also performed genotyping for SHW-L1, and its parents of AS60and AS2255through the markers located on the map. The genotypic analysis identified129loci were eliminated in SHW-L1, and these loci clustered in32genetic regions. It was suggested that D genomes of SHW-L1suffered more shake than AB genomes in allopolyploidization. These eliminated regions identification will help domestication research and genomic evolution research in wheat.2) QTL mapping for emergence date, tillering date, heading date, flowering date, maturation period, and growling period were performed through the high resolution map of SC population. We also conducted conditional QTL analysis for growling period and other investigated traits to determine the genetic relationships between growling period and its components. A total of30QTLs were identified in present study, which explained3.3-34.7%of the phenotypic variation,10of which explained the phenotypic variation higher than10%, and4of which were environments independent. Conditional analysis suggested that growling period were major effected by heading date, especially the major locus for heading date on2D, which also showed major effect on emergence date and maturation period. In addition, the effect of maturation period on growling period was lower. No significant effect of emergence date and tilling date on growling period were observed, and the effect or flowering date was not sure. This QTL mapping study and genetic relationship analysis between growling period and its components traits should be great practical value and theoretical value for wheat growling period breeding.3) QTL mapping for yield related traits were performed through the high resolution map of SC population. A total of30QTLs were detected in present study, which explained3.87-31.85%of the phenotypic variation,11of which explained the phenotypic variation higher than10%, and21of which were environments independently. For these QTLs,15alleles from synthetic wheat SHW-L1contributed positively to phenotypic variation, especially QTLs on1A,2D (wPt-730529-wPt-8134),4D (wPt-732586-wPt-73040),and6A(wPt-2216-rPt-6189), these environments independent QTLs should be great value for yield breeding in wheat.4) QTL mapping for uppermost internode length, spike length, and plant height were performed in ITMI (W7984×Opata85) and SC populations. We also conducted conditional QTL mapping for plant height and other two investigated traits. A total of40QTLs were identified in ITMI population, which explained2.5-33.3%of the phenotypic variation,15of which explained the phenotypic variation higher than10%, and7of which were detected in all investigated environments. A total of40QTLs were identified in ITMI population, which explained3.4-44.8%of the phenotypic variation,11of which explained the phenotypic variation higher than10%, and6of which were environments independently. Conditional QTL analysis suggests that:Most QTLs identified for UTL and SL were independent to PH, and PH were more independent to SL. The QTL identification and genetic relationship analysis will accelerate selection of suitable locus to improve the commercial wheat morphology to avoid the change of PH.5) QTL mapping for waterlogging tolerance was performed at seedling stage in ITMI population (W7984×Opata85) and SC population. The waterlogging tolerance waterlogging tolerance traits include total dry weight index (TDWI), shoot dry weight index (SDWI), and root dry weight index (RDWI). We also conducted conditional QTL analysis for total dry weight index with other two traits. A total of18QTLs were identified in ITMI population, which explained4.2-18%of the phenotypic variation,13of which explained the phenotypic variation higher than10%. For these QTLs,9alleles from synthetic wheat W7984contributed positively to phenotypic variation. A total of7QTLs were identified in ITMI population, which explained5.9-13.2%of the phenotypic variation,4of which explained the phenotypic variation higher than10%. For these QTLs,2alleles from synthetic wheat W7984contributed positively to phenotypic variation. These results indicated that SDWI showed tighter genetic correlation with TDWI than RDWI in both ITMI population and SC population. Several QTLs for RDWI were coordinated with SDWI to affect the expression of QTL for TDWI, and most of them showed suppression. Breakthrough these suppressed relationship will be notable improvement for waterlogging breeding in wheat.