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| 1 | Dissection of two quantitative trait loci for grain weight linked in repulsion on the long arm of chromosome 1 of rice(Oryza sativa L.)显示文摘Grain weight is a key determinant of grain yield in rice. Three sets of rice populations with overlapping segregating regions in isogenic backgrounds were established in the generations of BC2 F5, BC2 F6 and BC2 F7, derived from Zhenshan 97 and Milyang 46, and used for dissection of quantitative trait loci(QTL) for grain weight. Two QTL linked in repulsion phase on the long arm of chromosome 1 were separated. One was located between simple sequence repeat(SSR) markers RM11437 and RM11615, having a smaller additive effect with the enhancing allele from the maintainer line Zhenshan 97 and a partially dominant effect for increasing grain weight. The other was located between SSR markers RM11615 and RM11800, having a larger additive effect with the enhancing allele from the restorer line Milyang 46 and a partially dominant effect for increasing grain weight. When the two QTL segregated simultaneously, a residual additive effect with the enhancing allele from Milyang 46 and an over-dominance effect for increasing grain weight were detected. This suggests that dominant QTL linked in repulsion phase might play an important role in heterosis in rice. Our study also indicates that the use of populations with overlapping segregating regions in isogenic backgrounds is helpful for the dissection of minor linked QTL. | Liang Guo Kai Wang Junyu Chen Derun Huang Yeyang Fan Jieyun Zhuang | 2013 | The Crop Journal2013，1，1： | 6 |
| 2 | Genetic dissection of a thousand-grain weight quantitative trait locus on rice chromosome 1显示文摘Thousand-grain weight (TGWT) is an important factor affecting grain yield as well as grain quality in rice. A quantitative trait locus (QTL) qTGWT1-1 for TGWT was detected previously near DNA marker RG532 on the short arm of chromosome 1 in a recombinant inbred line (RIL) population derived from the indica-indica rice cross Zhengshan97B (ZS97B)/Milyang46 (MY46). In this study, two residual het-erozygous lines (RHLs), Ch1 and Ch2, derived from the ZS97B/MY46 RIL F7 population, were used to develop two F6 populations, RIL-1 and RIL-2. The genome of Ch1 and Ch2 contains a heterozygous region flanked by RM1―RM3746 and RM151―RM243 on the short arm of chromosome 1, respectively, but is homozygous in other regions. Two tightly linked QTLs, Gw1-1 and Gw1-2, with the same additive direction and similar effect on TGWT, were detected in the region of QTL qTGWT1-1 in population RIL-2. No QTL was detected in the population RIL-1. Four individual RHLs from the population RIL-2 carrying heterozygous segments flanked by RM151―RM10404, RM10381―RM243, RM10435―RM259 and RM10398―RM5359, respectively, were chosen to develop four F2 populations. Ten maternal homozy-gotes and 10 paternal homozygotes were selected from each of the four F2 populations derived from the four RHLs. The four sets of near isogenic lines (NILs) were grown for phenotyping of TGWT and delimitation of Gw1-1 and Gw1-2. Results showed that Gw1-1 and Gw1-2 were located in the intervals RM10376―RM10398 and RM10404―RM1344 which cover 392.9 and 308.5 kb regions, respectively. The enhancing alleles were from ZS97B at both loci, and no significant interactions were detected. Genetic dissection of Gw1-1 and Gw1-2 has laid a foundation for their cloning and molecular breeding of grain yield and quality in rice. | YU ShouWu YANG ChangDeng FAN YeYang ZHUANG JieYun LI XiMing | 2008 | Chinese Science Bulletin2008，53，15： | 5 |
| 3 | Fine-Mapping of qTGW1.2a, a Quantitative Trait Locus for 1000-Grain Weight in Rice显示文摘Thousand-grain weight (TGW) is a key component of grain yield in rice. This study was conducted to validate and fine-map qTGW1.2a, a quantitative trait locus for grain weight and grain size previously located in a 933.6-kb region on the long arm of rice chromosome 1. Firstly, three residual heterozygotes (RHs) were selected from a BC2F11 population of the indica rice cross Zhenshan 97 (ZS97)///ZS97//ZS97/Milyang 46. The heterozygous segments in these RHs were arranged successively in physical positions, forming one set of sequential residual heterozygotes (SeqRHs). In each of the populations derived, non-recombinant homozygotes were identified to produce near isogenic lines (NILs) comprising the two homozygous genotypes. The NILs were tested for grain weight, grain length and grain width. QTL analyses for the three traits were performed. Then, the updated QTL location was followed for a new run of SeqRHs identification-NIL development-QTL mapping. Altogether, 11 NIL populations derived from four sets of SeqRHs were developed and used. qTGW1.2a was finally delimitated into a 77.5-kb region containing 13 annotated genes. In the six populations segregating this QTL, which were in four generations and were tested across four years, the allelic direction of qTGW1.2a remained consistent and the genetic effects were stable. For TGW, the additive effects ranged from 0.23 to 0.38 g and the proportions of phenotypic variance explained ranged from 26.15% to 41.65%. These results provide a good foundation for the cloning and functional analysis of qTGW1.2a. | WANG Wenhui WANG Linlin ZHU Yujun FAN Yeyang ZHUANG Jieyun | 2019 | Rice science2019，26，4： | 4 |
| 4 | Minor-effect QTL for heading date detected in crosses between indica rice cultivar Teqing and near isogenic lines of IR24显示文摘Identification of quantitative trait loci(QTL) having small effects on heading date(HD) is important for fine-tuning flowering time in rice(Oryza sativa L.). In this study, minor-effect QTL for HD were identified using five segregating rice populations, including a recombinant inbred line population derived from crosses between indica cultivar Teqing and near isogenic lines of IR24,and four populations derived from residual heterozygotes identified in the original population.HD data from these populations were obtained in multiple years or at two locations with different photoperiods. A total of 11 QTL were detected; they had small additive effects ranging from 0.21 to1.63 days. The QTL were all detected in different populations, locations and/or years, having consistent allelic effects across experiments and a stable magnitude across years at the same location. These QTL, and other minor-effect QTL that have been cloned or fine-mapped, generally do not have strong photoperiod sensitivity, and thus can be used in a wide range of ecogeographical conditions. Seven of the 11 QTL were different from those that have been cloned or fine-mapped, providing new candidates for gene cloning and marker-assisted breeding. Allelic effects of QTL corresponding to those that had been cloned or fine-mapped, were much smaller in this study than previously reported. The results supported the assumption that qualitative and quantitative genes may be different alleles at the same loci, suggesting that it may be promising to identify minor-effect QTL from major heading date genes/QTL that have been cloned. | Zhichao Sun Yujun Zhu Junyu Chen Hui Zhang Zhenhua Zhang Xiaojun Niu Yeyang Fan Jieyun Zhuang | 2018 | The Crop Journal2018，6，3： | 2 |
| 5 | Genetic Interaction of Hd1 with Ghd7, DTH8 and Hd2 Largely Determines Eco-Geographical Adaption of Rice Varieties in Southern China显示文摘Although cultivated rice originated from the tropical region,a long process of domestication and human selection has enabled cultivated rice to grow in a wide range of geographical regions.Diversification of photoperiodic flowering provides a foundation for this diverse adaptation.Intensive studies have focused on elucidating how japonica varieties lost photoperiodic sensitivity(PS)to expand their cultivation areas to high latitudes,where rice is grown in a short season when day-length is long(Fujino et al,2013;Naranjo et al,2014;Gomez-Ariza et al,2015;Li et al,2015,2018;Goretti et al,2017;Ye et al,2018).By contrast,limited attention has been paid to the genetic architecture of heading date(HD)variation among varieties in middle and low latitudes.The southern China rice region,located in middle and low latitudes,occupies the most important rice cultivation region in China.This region is predominantly planted with indica varieties,which presents a rich diversity of regional and seasonal adaptations.In the present study,improved varieties and landraces used in this region were analyzed for allelic variations of 12 cloned QTLs controlling HD,as well as for the genotypic effects of these genes on HD and PS.Our objective was to clarify predominant genetic factors influencing ecogeographical adaption of rice varieties in southern China by comparing improved varieties with landraces. | ZHANG Zhenhua ZHU Yujun WANG Shilin FAN Yeyang ZHUANG Jieyun | 2021 | Rice science2021，28，2： | 2 |
| 6 | Fine mapping of qTGW10-20.8, a QTL having important contribution to grain weight variation in rice显示文摘Grain weight is one of themost important determinants of grain yield in rice.In this study,QTL analysis for grain weight,grain length,and grainwidthwas performed using populations derived from crosses between major parental lines of three-line indica hybrid rice.A total of 27 QTL for grain weight were detected using three recombinant inbred line populations derived from the crosses Teqing/IRBB lines,Zhenshan 97/Milyang 46,and Xieqingzao/Milyang 46.Of these,10 were found in only a single population and the other 17 in two or all three populations.Nine of the 17 common QTL were located in regions where no QTL associated with grain weight have been cloned and onewas selected for fine-mapping.Eight populations segregating in an isogenic background were derived from one F7 residual heterozygote of Teqing/IRBB52.The target QTL,qTGW10-20.8 controlling grain weight,grain length,and grain width,was localized to a 70.7-kb region flanked by InDel markers Te20811 and Te20882 on the long arm of chromosome 10.The QTL region contains seven annotated genes,ofwhich six encode proteins with known functional domains and one encodes a hypothetical protein.One of the genes,Os10g0536100 encoding the MIKC-type MADS-box protein OsMADS56,is the most likely candidate for qTGW10-20.8.These results provide a basis for cloning qTGW10-20.8,which has an important contribution to grain weight variation in rice. | Yujun Zhu Zhenhua Zhang Junyu Chen Yeyang Fan Tongmin Mou Shaoqing Tang Jieyun Zhuang | 2019 | The Crop Journal2019，7，5： | 2 |
| 7 | Genetic diversity and association mapping for salinity tolerance in Bangladeshi rice landraces显示文摘Breeding for salinity tolerance using Bangladeshi rice landraces and understand genetic diversity has been limited by the complex and polygenic nature of salt tolerance in rice genotypes. A genetic diversity and association mapping analysis was conducted using 96 germplasm accessions with variable response to salt stress at the seedling stage. These included86 landraces and 10 indica varieties and lines including Nona Bokra, from southern Bangladesh. A total of 220 alleles were detected at 58 Simple Sequence Repeat(SSR) marker loci randomly distributed on all 12 rice chromosomes and 8 Sequence Tagged Site(STS) markers developed for genes SKC1, DST, and SalT. The average gene diversity was 0.5075 and polymorphism information content value was 0.4426, respectively. Cluster analysis revealed that 68 and 21 accessions were clustered into 2 distinct groups, possibly corresponding to indica and japonica groups, respectively and the remaining 7 landraces were classified as an admixed group. In addition to Wn11463, the STS marker for SKC1, RM22418 on Chr. 8 was significantly associated with salinity tolerance, at the location of a QTL detected in previous studies. Our findings of favorable alleles associated with salinity tolerance in Bangladeshi rice landraces, as well as the development of STS markers for salt tolerance genes, will be helpful in future efforts to breed salinity tolerance in rice. | Reza M.Emon Mirza M.Islam Jyotirmoy Halder Yeyang Fan | 2015 | The Crop Journal2015，3，5： | 1 |
| 8 | Progress in research and development on hybrid rice: A super-domesticate in China显示文摘 | Cheng Shihua Zhuang Jieyun Fan Yeyang | 2007 | Annals of Botany2007，100，5： | 1 |
| 9 | Fine mapping of qHUS6.1,a quantitative trait locus for silicon content in rice(Oryza sativa L.)显示文摘Silicon is essential for optimal growth of rice(Oryza sativa L.).This study was conducted to fine map qHUS6.1,a quantitative trait locus(QTL) for rice hull silicon content previously located in the interval RM510-RM19417 on the short arm of chromosome 6,and to analyze the effect of this QTL on the silicon content in different organs of rice.Selfed progenies of a residual heterozygous line of rice were detected using 13 microsatellite markers in the vicinity of qHUS6.1.Three plants with overlapping heterozygous segments were selected.Three sets of near isogenic lines(NILs) were developed from the selfed progenies of the 3 plants.They were grown in a paddy field and the silicon contents of the hull,flag leaf,and stem were measured at maturity.Based on analyses of the phenotypic distribution and variance among different genotypic groups in the same NIL set,a significant genotypic effect was shown in the NIL set that was heterogenous in the interval RM19410-RM5815,whereas a significant effect was not found in the remaining 2 NIL sets that were heterogenous in either of the intervals RM4923-RM19410 or RM19417-RM204.On comparison among the physical positions of the 3 heterogenous segments,qHUS6.1 was delimited to a 64.2-kb region flanked by RM19410 and RM19417 that contains nine annotated genes according to the genome sequence of Nipponbare.This QTL showed strong effects on all of the three traits tested,and the enhancing alleles were always derived from the paternal line Milyang 46.The present study will facilitate the cloning of qHUS6.1 and the exploration of new genetic resources for QTL fine mapping. | GONG JunYi WU JiRong WANG Kai FAN YeYang ZHUANG JieYun | 2010 | Chinese Science Bulletin2010，55，29： | 1 |
| 10 | The Short-Term Prediction ofWind Power Based on the Convolutional Graph Attention Deep Neural Network显示文摘The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale.Therefore,wind power forecasting plays a key role in improving the safety and economic benefits of the power grid.This paper proposes a wind power predicting method based on a convolutional graph attention deep neural network with multi-wind farm data.Based on the graph attention network and attention mechanism,the method extracts spatial-temporal characteristics from the data of multiple wind farms.Then,combined with a deep neural network,a convolutional graph attention deep neural network model is constructed.Finally,the model is trained with the quantile regression loss function to achieve the wind power deterministic and probabilistic prediction based on multi-wind farm spatial-temporal data.A wind power dataset in the U.S.is taken as an example to demonstrate the efficacy of the proposed model.Compared with the selected baseline methods,the proposed model achieves the best prediction performance.The point prediction errors(i.e.,root mean square error(RMSE)and normalized mean absolute percentage error(NMAPE))are 0.304 MW and 1.177%,respectively.And the comprehensive performance of probabilistic prediction(i.e.,con-tinuously ranked probability score(CRPS))is 0.580.Thus,the significance of multi-wind farm data and spatial-temporal feature extraction module is self-evident. | Fan Xiao Xiong Ping Yeyang Li Yusen Xu Yiqun Kang Dan Liu Nianming Zhang | 2024 | Energy Engineering2024，121，2： | 0 |
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