2篇
您的检索式：作者名="Yangjun Wen"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | A Genomic Variation Map Provides Insights into the Genetic Basis of Spring Chinese Cabbage (Brassica rapa ssp.pekinensis)Selection显示文摘Chinese cabbage is the most consumed leafy crop in East Asian countries.However,premature bolting induced by continuous low temperatures severely decreases the yield and quality of the Chinese cabbage, and therefore restricts its planting season and geographic distribution.In the past 40years,spring Chinese cabbage with strong winterness has been selected to meet the market demand.Here,we report a genome variation map of Chinese cabbage generated from the resequencing data of 194 geographically diverse accessions of three ecotypes.In-depth analyses of the selection sweeps and genome-wide patterns revealed that spring Chinese cabbage was selected from a specific population of autumn Chinese cabbage around the area of Shandong peninsula in northern China.We identified 23 genomic loci that underwent intensive selection,and further demonstrated by gene expression and haplotype analyses that the incorporation of elite alleles of VERNALISATION INSENTIVE 3.1(BrVIN3.1)and FLOWER LOCUS C 1(BrFLC1)is a determinant genetic source of variation during selection.Moreover,we showed that the quantitative response of BrVIN3.1 to cold due to the sequence variations in the cis elements of the BrVlN3.1 promoter significantly contributes to bolting-time variation in Chinese cabbage.Collectively, our study provides valuable insights into the genetic basis of spring Chinese cabbage selection and will facilitate the breeding of bolting-resistant Varieties by molecular-marker-assisted selection,transgenic or gene editingapproaches. | Tongbing Su Weihong Wang Peirong Li Bin Zhang Pan Li Xiaoyun Xin Honghe Sun Yangjun Yu Deshuang Zhang Xiuyun Zhao Changlong Wen Gang Zhou Yuntong Wang Hongkun Zheng Shuancang Yu Fenglan Zhang | 2018 | Molecular Plant2018，11，11： | 6 |
| 2 | The improved FASTmrEMMA and GCIM algorithms for genome-wide association and linkage studies in large mapping populations显示文摘Owing to high power and accuracy and low false positive rate in our multi-locus approaches for genome-wide association studies and linkage analyses,these approaches have attracted considerable attention in plant and animal genetics.In large mapping population,however,fast multi-locus random-SNP-effect efficient mixed model association(FASTmrEMMA)and genome-wide composite interval mapping(GCIM)run a relatively long time.To address this issue,we proposed the improved FASTmrEMMA and GCIM algorithms in this study.In the new algorithms,some matrix identities,such as the Woodbury matrix identity,were used.In scanning each marker on the entire genome,in other words,the improved algorithms effectively replace the expensive eigenvector solutions in(restricted)maximum likelihood estimations in original algorithms with two(one)updated inner products and one updated vector-matrix-vector multiplication.Simulated and real data analyses showed that their computational efficiencies are increased sharply in large mapping population,although there are no mapping result differences between original and improved algorithms.In addition,the related software packages(mrMLM.GUI and QTL.gCIMapping.GUI)can be downloaded from the R and BioCode websites. | Yangjun Wen Yawen Zhang Jin Zhang Jianying Feng Yuanming Zhang | 2020 | The Crop Journal2020，8，5： | 4 |
您所在的机构暂未开通此部分的功能，请联系管理员