ABSTRACT
Fusarium head blight (FHB) is a devastating disease that occurs in warm and humid environments. The German wheat Centrum has displayed moderate to high levels of FHB resistance in the field for many years. In this study, an F6:8 recombinant inbred line (RIL) population derived from cross Centrum × Xinong 979 was evaluated for FHB response following point inoculation in five environments. The population and parents were genotyped using the GenoBaits Wheat 16 K Panel. Stable quantitative trait loci (QTL) associated with FHB resistance in Centrum were mapped on chromosome arms 2DS and 5BS. The most effective QTL, located in 2DS, was identified as a new chromosome region represented by a 1.4 Mb interval containing 17 candidate genes. Another novel QTL was mapped in chromosome arm 5BS of a 5BS-7BS translocation chromosome. In addition, two environmentally-sensitive QTL were mapped on chromosome arms 2BL from Centrum and 5AS from Xinong 979. Polymorphisms of flanking allele-specifc quantitative PCR (AQP) markers AQP-6 for QFhb.nwafu-2DS and 16K-13073 for QFhb.nwafu-5BS were validated in a panel of 217 cultivars and breeding lines. These markers could be useful for marker-assisted selection of FHB resistance and also provide a starting point for fine mapping and marker-based cloning of the resistance genes.
ABSTRACT
Stripe rust is a devastating disease of wheat worldwide. Chinese wheat cultivar Lanhangxuan 121 (LHX121), selected from an advanced line L92-47 population that had been subjected to space mutation breeding displayed a consistently higher level of resistance to stipe rust than its parent in multiple field environments. The aim of this research was to establish the number and types of resistance genes in parental lines L92-47 and LHX121 using separate segregating populations. The first population developed from a cross between LHX121 and susceptible cultivar Xinong 822 comprised 278 F2:3 lines. The second validation population comprised 301 F2:3 lines from a cross between L92-47 and susceptible cultivar Xinong 979. Lines of two population were evaluated for stripe rust response at three sites during the 2018-2020 cropping season. Affymetrix 660 K SNP arrays were used to genotype the lines and parents. Inclusive composite interval mapping detected QTL QYrLHX.nwafu-2BS, QYrLHX.nwafu-3BS, and QYrLHX.nwafu-5BS for resistance in all three environments. Based on previous studies and pedigree information, QYrLHX.nwafu-2BS and QYrLHX.nwafu-3BS were likely to be Yr27 and Yr30 that are present in the L92-47 parent. QYrLHX.nwafu-5BS (YrL121) detected only in LHX121 was mapped to a 7.60 cM interval and explained 10.67-22.57% of the phenotypic variation. Compared to stripe rust resistance genes previously mapped to chromosome 5B, YrL121 might be a new adult plant resistance QTL. Furthermore, there were a number of variations signals using 35 K SNP array and differentially expressed genes using RNA-seq between L92-47 and LHX121 in the YrL121 region, indicating that they probably impair the presence and/or function of YrL121. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01461-0.
ABSTRACT
A spatial colocation pattern represents a subset of spatial features with instances that are prevalently located together in a geographic space. Although many algorithms for mining spatial colocation patterns have been proposed, the following problems still remain. these methods miss certain meaningful patterns (e.g., {Ganoderma_lucidumnew, maple_treedead} and {water_hyacinthnew(increase), algaedead(decrease)}) and obtain a wrong conclusion if the instances of two or more features increase/decrease (i.e., new/dead) in the same/approximate proportion, which has no effect on the prevalent patterns; and the efficiency of existing methods is low in mining prevalent spatial colocation patterns, because the number of prevalent spatial colocation patterns is quite large. Therefore, we first propose the concept of a dynamic spatial colocation pattern that can reflect the dynamic relationships among spatial features. Second, we mine a small number of prevalent maximal dynamic spatial colocation patterns that can derive all prevalent dynamic spatial colocation patterns, which can improve the efficiency of obtaining all prevalent dynamic spatial colocation patterns. Third, we propose an algorithm for mining prevalent maximal dynamic spatial colocation patterns and two pruning strategies. Finally, the effectiveness and efficiency of the proposed method and the pruning strategies are verified by extensive experiments over real/synthetic data sets.
