Genetic variation and population structure of Rosa roxburghii by EST-based and genomic SSR markers

2020 ◽  
Vol 52 (4) ◽  
Author(s):  
Min Lu ◽  
Huaishan Zhang ◽  
Huaming An ◽  
Wen Zhou
Mycobiology ◽  
2020 ◽  
Vol 48 (2) ◽  
pp. 115-121
Author(s):  
Hwa-Yong Lee ◽  
Suyun Moon ◽  
Hyeon-Su Ro ◽  
Jong-Wook Chung ◽  
Hojin Ryu

Hereditas ◽  
2016 ◽  
Vol 153 (1) ◽  
Author(s):  
Yu Zhang ◽  
Haidong Yan ◽  
Xiaomei Jiang ◽  
Xiaoli Wang ◽  
Linkai Huang ◽  
...  

2021 ◽  
Author(s):  
Yu Zhang ◽  
Yewen Wang ◽  
Peijiang Li ◽  
Yuexing Wang ◽  
Shimao Zheng ◽  
...  

Abstract Background: The Qinba region is the transition region between Indica and Japonica varieties in China. It has a long history of Indica rice planting of more than 7000 years and is also a planting area for fine-quality Indica rice. The aims of this study are to explore different genetic markers applied to the analysis population structure, genetic diversity, selection and optimization of molecular markers of Indica rice, thus providing more information for the protection and utilization on germplasm resources of Indica rice. Methods: 15 phenotypic traits, a core set of 48 SSR markers as well as SNPs data obtained by genotyping-by-sequencing (GBS, NlaIII and MseI digestion, referred to as SNPs-NlaIII and SNPs-MseI, respectively) for this panel of 93 samples using the Illumina HiSeq2000 sequencing platform, were employed to explore the genetic diversity and population structure of 93 samples.Results: The average of coefficient of variation (CV) and diversity index (He) were 29.72% and 1.83 ranging from 3.07% to 137.43%, and from 1.45 to 2.03, respectively. The correlation coefficient between 15 phenotypic traits ranged from 0.984 to -0.604. The first four PCs accounted for 70.693% phenotypic variation based on phenotypic analysis. A total of 379 alleles were obtained using SSR markers, encompassing an average of 8.0 alleles per primer. Polymorphic bands (PPB) and polymorphism information content (PIC) was 88.65% and 0.77, respectively. The Mantel test showed that the correlation between the genetic distance matrix based on SNPs-NlaIII and SNPs-MseI was the largest (R2=0.88), and that based on 15 phenotypic traits and SSR was the smallest (R2=0.09). The 93 samples could be clustered into two subgroups by 3 types of genetic markers. Molecular variance analysis revealed that the genetic variation was 2% among populations and 98% within populations (the Nm was 0.16), Tajima’s D value was 1.66, the FST between the two populations was 0.61 based on 72,824 SNPs. Conclusions: The population genetic variation explained by SNPs was larger than that explained by SSRs. The gene flow of 93 samples used in this study was larger than that of naturally self-pollinated crops, which may be caused by long-term breeding selection of Indica rice in the Qinba region. The genetic structure of the 93 samples was simple and lacked rare alleles.


Gene ◽  
2015 ◽  
Vol 566 (2) ◽  
pp. 175-183 ◽  
Author(s):  
Honglin Chen ◽  
Ling Qiao ◽  
Lixia Wang ◽  
Suhua Wang ◽  
Matthew Wohlgemuth Blair ◽  
...  

2022 ◽  
Author(s):  
Yu Zhang ◽  
Qiaoqiao He ◽  
Xixi Zhou ◽  
Yewen Wang ◽  
Peijiang Li ◽  
...  

Abstract Background: The Qinba region is the transition region between Indica and Japonica varieties in China. It has a long history of Indica rice planting of more than 7000 years and is also a planting area for fine-quality Indica rice. The aims of this study are to explore different genetic markers applied to the analysis population structure, genetic diversity, selection and optimization of molecular markers of Indica rice, thus providing more information for the protection and utilization on germplasm resources of Indica rice. Methods: 15 phenotypic traits, a core set of 48 SSR markers as well as SNPs data obtained by genotyping-by-sequencing (GBS, NlaIII and MseI digestion, referred to as SNPs-NlaIII and SNPs-MseI, respectively) for this panel of 93 samples using the Illumina HiSeq2000 sequencing platform, were employed to explore the genetic diversity and population structure of 93 samples.Results: The average of coefficient of variation (CV) and diversity index (He) were 29.72% and 1.83 ranging from 3.07% to 137.43%, and from 1.45 to 2.03, respectively. The correlation coefficient between 15 phenotypic traits ranged from 0.984 to -0.604. The first four PCs accounted for 70.693% phenotypic variation based on phenotypic analysis. A total of 379 alleles were obtained using SSR markers, encompassing an average of 8.0 alleles per primer. Polymorphic bands (PPB) and polymorphism information content (PIC) was 88.65% and 0.77, respectively. The Mantel test showed that the correlation between the genetic distance matrix based on SNPs-NlaIII and SNPs-MseI was the largest (R2=0.88), and that based on 15 phenotypic traits and SSR was the smallest (R2=0.09). The 93 samples could be clustered into two subgroups by 3 types of genetic markers. Molecular variance analysis revealed that the genetic variation was 2% among populations and 98% within populations (the Nm was 0.16), Tajima’s D value was 1.66, the FST between the two populations was 0.61 based on 72,824 SNPs. Conclusions: The population genetic variation explained by SNPs was larger than that explained by SSRs. The gene flow of 93 samples used in this study was larger than that of naturally self-pollinated crops, which may be caused by long-term breeding selection of Indica rice in the Qinba region. The genetic structure of the 93 samples was simple and lacked rare alleles.


2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Lei Xue ◽  
Qingwen Liu ◽  
Mengfan Qin ◽  
Mingyue Zhang ◽  
Xiao Wu ◽  
...  

2020 ◽  
Vol 52 (6) ◽  
Author(s):  
Anpei Zhou ◽  
Dan Zong ◽  
Peihua Gan ◽  
Yao Zhang ◽  
Dan Li ◽  
...  

2011 ◽  
Vol 37 (1) ◽  
pp. 74-78 ◽  
Author(s):  
Ming-Fu WEN ◽  
Xin CHEN ◽  
Hai-Yan WANG ◽  
Cheng LU ◽  
Wen-Quan WANG
Keyword(s):  

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