Analysis of genetic diversity and population structure in Saharan maize (Zea mays L.) populations using phenotypic traits and SSR markers

2018 ◽  
Vol 66 (1) ◽  
pp. 243-257 ◽  
Author(s):  
Nawel Belalia ◽  
Antonio Lupini ◽  
Abderrahmane Djemel ◽  
Abdelkader Morsli ◽  
Antonio Mauceri ◽  
...  
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.


2008 ◽  
Vol 87 (3) ◽  
pp. 287-291 ◽  
Author(s):  
Yao Qi-Lun ◽  
Fang Ping ◽  
Kang Ke-Cheng ◽  
Pan Guang-Tang

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.


Author(s):  
Shalini Singh ◽  
B. Singh ◽  
V.R. Sharma ◽  
M. Kumar ◽  
U. Sirohi

Background: The study was undertaken to assess the genetic diversity and genetic structure among fifty-five pea accessions using morphological traits and SSR markers. Methods: A total of 55 pea accessions were analyzed using eleven phenotypic traits and twenty SSR markers. The data obtained by morphological and molecular profiling was used for the analysis of genetic diversity and for the estimation of genetic diversity estimates, correlation, principal components analysis and population structure. Result: This study reveals that majority of genetic variation was due to variation within population and were clustered into two distinct groups, which reveals a high admixture within individuals. Accessions viz., VRP-82, VRP-320, VRP-194, VRP-375, EC-97280 and EC-8724, showed great diversity as compared to the other accessions based on both morphological and molecular markers. These accessions may assist in developing and planning breeding strategies aimed to produce new varieties in the future.


2016 ◽  
Vol 11 (24) ◽  
pp. 2118-2128 ◽  
Author(s):  
Pandit Madhav ◽  
Chakraborty Manigopa ◽  
A Haider Z ◽  
Pande Anita ◽  
Prasad Sah Rameshwar ◽  
...  

2008 ◽  
Vol 17 (2) ◽  
pp. 133-140 ◽  
Author(s):  
Bhupender Kumar ◽  
Sujay Rakshit ◽  
R. D. Singh ◽  
R. N. Gadag ◽  
Ravindra Nath ◽  
...  

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