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"STRUCTURE"

Research Articles
Genetic Variation of Common Millet (Panicum miliaceum L.) Collected from East Asia Based on Simple Sequence Repeats (SSRs)
Sang-Yun Han, Kyu Jin Sa, Ju Kyong Lee
Plant Breed. Biotech. 2020;8(2):186-195.   Published online June 1, 2020
DOI: https://doi.org/10.9787/PBB.2020.8.2.186

This study was conducted to evaluate the genetic variation for 75 accessions of common millet collected from Korea, Japan, and China. Genetic diversity analysis was performed on 75 accessions from Korea, Japan, and China using 9 SSR primers. A total of 30 alleles was identified with an average of 3.33 alleles per locus. The GD values measured in these groups ranged from 0.127 to 0.377 with an average of 0.266. The PIC values ranged from 0.124-0.347 with an average of 0.245. The Chinese common millet accessions showed higher genetic diversity than the Korean and Japanese accessions. From the analysis of population structure using the software program STRUCTURE 2.2, the 75 common millet accessions divided into two groups because the highest value of ΔK values was revealed for K = 2. Group I included 40 Korean accessions, and Group II included 14 Korean accessions, 12 Japanese accessions, and 9 Chinese accessions. The UPGMA phylogenetic tree revealed that the 75 common millet accessions were clustered into three major groups. The clustering patterns did not permit any clear distinction of the accessions of common millet collected in East Asia. The results of genetic diversity, genetic relationships, and population structure in the 75 common millet accessions from Korea, Japan, and China identified in this study will provide useful information for the development of common millet breeding lines and breeding programs and also genetic resource conservation strategies in Korea.

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  • Development of iron and zinc transporter based genic SSR markers in foxtail millet and their cross- genera transferability in little millet
    Kumari Anjani, Kaushal Kumar, V. K. Sharma
    Cereal Research Communications.2026; 54(2): 875.     CrossRef
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Genetic Diversity and Association Analyses of Chinese Maize Inbred Lines Using SSR Markers
Yin Vathana, Kyu Jin Sa, Su Eun Lim, Ju Kyong Lee
Plant Breed. Biotech. 2019;7(3):186-199.   Published online September 1, 2019
DOI: https://doi.org/10.9787/PBB.2019.7.3.186

We selected 68 Chinese maize inbred lines to understand the genetic diversity, population structure, and marker-trait associations for eight agronomic traits and 50 simple sequence repeats (SSRs) markers. In this study, effective traits, such as days of anthesis (DA), days of silking (DS), ear height (EH), plant to ear height ratio (ER), plant height (PH), and leaf width (LW) were divided into PC1 and PC2 by PCA analysis for maize inbred lines. Genetic diversity analysis revealed a total of 506 alleles at 50 SSR loci. The mean number of alleles per locus was 10.12. The averages of genetic diversity (GD) and polymorphic information content (PIC) values were 0.771 and 0.743, respectively. Based on a membership probability threshold of 0.80, the population structure revealed that the total inbred lines were divided into three major groups with one admixed group. A marker-trait association using Q + K MLM showed that nine SSR markers (bnlg1017, umc2041, umc2400, bnlg105, umc1229, umc1250, umc1066, umc2092, and umc1426) were related with seven agronomic traits. Among these SSR markers, eight SSR markers were associated with only one agronomic trait (DA, DS, ER, LL, LW, PH, and ST), whereas one SSR marker (umc1229) was associated with two agronomic traits (DA and ST). These results will help in optimizing the choice of inbred lines for cross combinations, as well as in selecting markers for further maize breeding programs.

Citations

Citations to this article as recorded by  
  • Assessment of combining ability for grain yield and its attributing traits in maize (Zea mays L.)
    Jiban Shrestha, Surya Kant Ghimire, Krishna Hari Dhakal, Mahendra Prasad Tripathi
    Discover Agriculture.2026;[Epub]     CrossRef
  • Mapping of quantitative trait loci associated with fodder quality traits in forage maize (Zea mays L.)
    Palaniyappan Subramani, Ganesan Kalipatty Nalliappan, Manivannan Narayana, Senthil Natesan
    Euphytica.2025;[Epub]     CrossRef
  • Phylogenetic analysis of Perilla crop (Perilla frutescens L.) based on morphological characteristics and volatile substances
    Jungeun Cho, Hyeon Park, Tae Hyeon Heo, Kyu Jin Sa, Ju Kyong Lee
    Genetic Resources and Crop Evolution.2025; 72(3): 2959.     CrossRef
  • Molecular diversity, population structure analysis, and assessment of parent hybrid relationships in fodder maize
    Palaniyappan Subramani, Ganesan Kalipatty Nalliappan, Manivannan Narayana, Ravichandran Veerasamy, Senthil Natesan
    Crop Breeding and Applied Biotechnology.2024;[Epub]     CrossRef
  • Selection of superior and stable fodder maize hybrids using MGIDI and MTSI indices
    Palaniyappan Subramani, Ganesan Kalipatty Nalliappan, Manivannan Narayana, Ravichandran Veerasamy, Senthil Natesan
    Crop Breeding and Applied Biotechnology.2024;[Epub]     CrossRef
  • Association Mapping for Evaluation of Population Structure, Genetic Diversity, and Physiochemical Traits in Drought-Stressed Maize Germplasm Using SSR Markers
    Muhammad Zahaib Ilyas, Hyeon Park, So Jung Jang, Jungeun Cho, Kyu Jin Sa, Ju Kyong Lee
    Plants.2023; 12(24): 4092.     CrossRef
  • Uncovering microsatellite markers associated with agronomic traits of South Sudan landrace maize
    Emmanuel Andrea Mathiang, Hyeon Park, So Jung Jang, Jungeun Cho, Tae Hyeon Heo, Ju Kyong Lee
    Genes & Genomics.2023; 45(12): 1587.     CrossRef
  • Morphological Variation in Normal Maize Landrace Accessions Collected from South Sudan
    Emmanuel Andrea Mathiang, Kyu Jin Sa, Hyeon Park, So Jung Jang, Ju Kyong Lee
    Plant Breeding and Biotechnology.2023; 11(1): 15.     CrossRef
  • Genetic diversity and population structure analysis in early generations maize inbreds derived from local germplasm of Eastern Himalayan regions using microsatellite markers
    E. Lamalakshmi Devi, Umakanta Ngangkham, Sunil Kumar Chongtham, Bhuvaneswari S, Ingudam Bhupenchandra, Konsam Sarika, Harendra Verma, Akoijam Ratankumar Singh, Amit Kumar, Tensubam Basanta Singh, Amit Kumar, T. L. Bhutia, S. K. Dutta, Shaon Kumar Das, Ram
    Plant Genetic Resources: Characterization and Utilization.2023; 21(5): 418.     CrossRef
  • Identification of SSR Markers Associated with Yield-Related Traits and Heterosis Effect in Winter Oilseed Rape (Brassica Napus L.)
    Joanna Wolko, Agnieszka Łopatyńska, Łukasz Wolko, Jan Bocianowski, Katarzyna Mikołajczyk, Alina Liersch
    Agronomy.2022; 12(7): 1544.     CrossRef
  • Genetic Diversity and Population Structure of Normal Maize Germplasm Collected in South Sudan Revealed by SSR Markers
    Emmanuel Andrea Mathiang, Kyu Jin Sa, Hyeon Park, Yeon Joon Kim, Ju Kyong Lee
    Plants.2022; 11(20): 2787.     CrossRef
  • Using of Molecular Markers in Prediction of Wheat (Triticum aestivum L.) Hybrid Grain Yield Based on Artificial Intelligence Methods and Multivariate Statistics
    E. E. Shamsabadi, H. Sabouri, H. Soughi, S. J. Sajadi
    Russian Journal of Genetics.2022; 58(5): 603.     CrossRef
  • Genetic characterization and association mapping in near-isogenic lines of waxy maize using seed characteristics and SSR markers
    Hae Ri Kim, Kyu Jin Sa, Min Nam-Gung, Ki Jin Park, Si-Hwan Ryu, Chang Yeun Mo, Ju Kyong Lee
    Genes & Genomics.2021; 43(1): 79.     CrossRef
  • Genetic variation and association mapping in the F2 population of the Perilla crop (Perilla frutescens L.) using new developed Perilla SSR markers
    Ju Yeon Kim, Kyu Jin Sa, Ye Ju Ha, Ju Kyong Lee
    Euphytica.2021;[Epub]     CrossRef
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Genetic Diversity and Association Analyses of Canadian Maize Inbred Lines with Agronomic Traits and Simple Sequence Repeat Markers
Kyu Jin Sa, Tak Ki Hong, Ju Kyong Lee
Plant Breed. Biotech. 2018;6(2):159-169.   Published online June 1, 2018
DOI: https://doi.org/10.9787/PBB.2018.6.2.159

We evaluated genetic diversity and population structure in 32 Canadian maize inbred lines and performed association analysis for five agronomical traits and 50 simple sequence repeat (SSR) markers. Genetic diversity analysis revealed a total of 381 alleles at the 50 SSR loci. The average number of alleles per locus was 7.6. The average genetic diversity and polymorphic information content values were 0.709 and 0.676, respectively. The average major allele frequency was 0.414. Population structure analysis indicated that these maize inbred lines were comprised of four major groups and one admixed group based on a membership probability threshold of 0.80. A general linear model showed 20 marker-trait associations involving 12 SSR markers associated with the four agronomic traits except for leaf length. For these marker-trait associations, phi056, mmc0022, bnlg1621, bnlg1695, phi116, and bnlg1028 were associated with only one trait. The other nc005, bnlg1012, phi065, and umc1982 were associated with two traits. Two SSR markers, mmc0111 and umc1038, were associated with three traits. These results will help in optimizing the choice of parents for crossing combinations, as well as in selecting markers for marker-assisted selection for maize improvement.

Citations

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  • Harnessing teosinte for quality traits enhancement and genetic diversity in maize
    Pardeep Kumar, Mukesh Choudhary, Seema Sheoran, Bhupender Kumar, Sushil Kumar, Ankush Sharma, Bharat Bhushan, Bahadur Singh Jat, Dharam Paul, Sumit Kumar Aggarwal, Shyam Bir Singh
    Cereal Research Communications.2026; 54(1): 645.     CrossRef
  • Genetic diversity of Turkish colored maize landraces assessed by simple sequence repeat (SSR) markers
    Ezgi Alaca Yıldırım, Fatih Kahrıman, Ferhat Matur
    Genetic Resources and Crop Evolution.2025; 72(8): 9623.     CrossRef
  • DNA Profiling of Indonesian Maize Hybrids and their Parental Lines Using SSR Markers
    Slamet Bambang Priyanto, Lesty Ayu Bidhari, Roy Efendi, Bunyamin Zainuddin, Nining Nurini Andayani, Muhammad Azrai
    Agriculture (Pol'nohospodárstvo).2025; 71(2): 53.     CrossRef
  • Genetic Diversity and Population Structure of Maize (Zea mays L.) Inbred Lines in Association with Phenotypic and Grain Qualitative Traits Using SSR Genotyping
    Rumit Patel, Juned Memon, Sushil Kumar, Dipak A. Patel, Amar A. Sakure, Manish B. Patel, Arna Das, Chikkappa G. Karjagi, Swati Patel, Ujjaval Patel, Rajib Roychowdhury
    Plants.2024; 13(6): 823.     CrossRef
  • Association Mapping for Evaluation of Population Structure, Genetic Diversity, and Physiochemical Traits in Drought-Stressed Maize Germplasm Using SSR Markers
    Muhammad Zahaib Ilyas, Hyeon Park, So Jung Jang, Jungeun Cho, Kyu Jin Sa, Ju Kyong Lee
    Plants.2023; 12(24): 4092.     CrossRef
  • Application Marker-Assisted Selection (MAS) and Multiplex PCR Reactions in Resistance Breeding of Maize (Zea mays L.)
    Aleksandra Sobiech, Agnieszka Tomkowiak, Jan Bocianowski, Bartosz Nowak, Dorota Weigt, Danuta Kurasiak-Popowska, Michał Kwiatek, Sylwia Mikołajczyk, Janetta Niemann, Katarzyna Szewczyk
    Agriculture.2022; 12(9): 1412.     CrossRef
  • Fuzzy model for clustering open pollinated maize variety released in Indonesia
    Muhammad Aqil, N.N. Andayani, T Fahdiana, Suwardi
    IOP Conference Series: Earth and Environmental Science.2020; 484: 012046.     CrossRef
  • Characterization of Mimban maize landrace from North-Eastern Himalayan region using microsatellite markers
    Nenavath Krishna Kumar Rathod, Jyoti Kumari, Firoz Hossain, Rashmi Chhabra, Somnath Roy, Ganjalagatta Dasaiah Harish, Rakesh Bhardwaj, Raveendra N. Gadag, Anup Kumar Misra
    Journal of Plant Biochemistry and Biotechnology.2020; 29(2): 323.     CrossRef
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Genetic Diversity and Population Structure of Mongolian Wheat Based on SSR Markers: Implications for Conservation and Management
Narantsetseg Ya, Sebastin Raveendar, N Bayarsukh, Myagmarsuren Ya, Jung-Ro Lee, Kyung-Jun Lee, Myoung-Jae Shin, Gyu-Taek Cho, Kyung-Ho Ma, Gi-An Lee
Plant Breed. Biotech. 2017;5(3):213-220.   Published online September 1, 2017
DOI: https://doi.org/10.9787/PBB.2017.5.3.213

Production of spring wheat, the major crop in Mongolia, accounts for 98% of the cultivated area. Understanding genetic variability in existing gene bank accessions is critical for collection, conservation and use of wheat germplasms. To determine genetic diversity and population structure among a representative collection of Mongolian local wheat cultivars and lines, 200 wheat accessions were analyzed with 15 SSR markers distributed throughout the wheat genome. A total of 85 alleles were detected, with three to five alleles per locus and a mean genetic richness of 5.66. Average genetic diversity index was 0.69, with values ranging from 0.37–0.80. The 200 Mongolian wheat accessions were mainly divided into two subgroups based on structure and phylogenetic analyses, and some phenotypes were divergent by the subgroups. Results from this study will provide valuable information for conservation and sustainable use of Mongolian wheat genetic resources.

Citations

Citations to this article as recorded by  
  • The Genetic Diversity of Tunisian Sea Barley (Hordeum marinum ssp. marinum): Insights from Cross-species SSRs
    Warda Saoudi, Wael Taamalli, Mounawer Badri, António Martin, Chedly Abdelly
    Plant Molecular Biology Reporter.2026;[Epub]     CrossRef
  • Harnessing genetic potentials for drought tolerance in wheat (Triticum aestivum L.) using tolerance indices and molecular markers
    Mst. Anamika Amzad, Md. Arifuzzaman, Md. Ashraful Alam
    Gene Reports.2025; 40: 102230.     CrossRef
  • Morphological characterization and molecular diversity assessment of rust resistant genetic stocks of wheat
    Sneha Adhikari, S. C. Bhardwaj, O. P. Gangwar, Pramod Prasad, Charu Lata, Subodh Kumar, Gulab Chand
    Tropical Plant Pathology.2024; 49(4): 525.     CrossRef
  • Structure and genetic diversity of macauba [Acrocomia aculeata (Jacq.) Lodd. ex Mart.] approached by SNP markers to assist breeding strategies
    Bruno Galvêas Laviola, Adriano dos Santos, Erina Vitório Rodrigues, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Tatiana Barbosa Rosado, Cíntia Gonçalves Guimarães, Léo Duc Haa Carson Schwartzhaupt da Conceição
    Genetic Resources and Crop Evolution.2022; 69(3): 1179.     CrossRef
  • Genetic diversity, population structure and relationship of Ethiopian barley (Hordeum vulgare L.) landraces as revealed by SSR markers
    Allo A. Dido, M. S. R. Krishna, Ermias Assefa, Dawit T. Degefu, B. J. K. Singh, Kassahun Tesfaye
    Journal of Genetics.2022;[Epub]     CrossRef
  • Genetic diversity and population structure in Jatropha (Jatropha curcas L.) based on molecular markers
    Adriana de Souza Carneiro, Adriano dos Santos, Bruno Galvêas Laviola, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Erina Vitório Rodrigues
    Genetic Resources and Crop Evolution.2022; 69(1): 245.     CrossRef
  • Association analysis for agronomic traits in wheat under terminal heat stress
    Adeel Khan, Munir Ahmad, Mukhtar Ahmed, Kulvinder Singh Gill, Zahid Akram
    Saudi Journal of Biological Sciences.2021; 28(12): 7404.     CrossRef
  • Genetic Diversity and Genome-Wide Association Study of Seed Aspect Ratio Using a High-Density SNP Array in Peanut (Arachis hypogaea L.)
    Kunyan Zou, Ki-Seung Kim, Kipoong Kim, Dongwoo Kang, Yu-Hyeon Park, Hokeun Sun, Bo-Keun Ha, Jungmin Ha, Tae-Hwan Jun
    Genes.2020; 12(1): 2.     CrossRef
  • Population structure of Nepali spring wheat (Triticum aestivum L.) germplasm
    Kamal Khadka, Davoud Torkamaneh, Mina Kaviani, Francois Belzile, Manish N. Raizada, Alireza Navabi
    BMC Plant Biology.2020;[Epub]     CrossRef
  • Development of genomic simple sequence repeat markers for Glycyrrhiza lepidota and cross-amplification of other Glycyrrhiza species
    Jun Hyoung Bang, Chi Eun Hong, Sebastin Raveendar, Kyong Hwan Bang, Kyung Ho Ma, Soon Wook Kwon, Hojin Ryu, Ick Hyun Jo, Jong-Wook Chung
    PeerJ.2019; 7: e7479.     CrossRef
  • Genome-Wide Genetic Diversity and Population Structure of Tunisian Durum Wheat Landraces Based on DArTseq Technology
    Cyrine Robbana, Zakaria Kehel, M’barek Ben Naceur, Carolina Sansaloni, Filippo Bassi, Ahmed Amri
    International Journal of Molecular Sciences.2019; 20(6): 1352.     CrossRef
  • Melatonin Mitigates Salt Stress in Wheat Seedlings by Modulating Polyamine Metabolism
    Qingbo Ke, Jun Ye, Bomei Wang, Jianhong Ren, Lina Yin, Xiping Deng, Shiwen Wang
    Frontiers in Plant Science.2018;[Epub]     CrossRef
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Genetic Diversity and Population Structure of Rubus Accessions Using Simple Sequence Repeat Markers
Kyung Jun Lee, Gi-An Lee, Hee-Kyoung Kang, Jung-Ro Lee, Sebastin Raveendar, Myoung-Jae Shin, Yang-Hee Cho, Kyung-Ho Ma
Plant Breed. Biotech. 2016;4(3):345-351.   Published online August 31, 2016
DOI: https://doi.org/10.9787/PBB.2016.4.3.345

Sixty-nine Rubus accessions were analyzed to determine the genetic relationships using simple sequence repeat (SSR) markers. Twenty-three SSR markers generated a total of 351 alleles from all accessions, with an average of 15.3 alleles per locus. The average value of polymorphism information content was 0.76, ranging from 0.52 to 0.91. As a result of population structure analysis, 69 Rubus accessions of six Rubus species were subdivided into six subpopulations. Four subpopulations included distinct Rubus species accessions; pop2 (Rubus crataegifolius var. subcuneatus, 2 accessions), pop3 (R. crataegifolius Bunge., 18 accessions), pop4 (R. fruticosus L., 3 accessions) and pop6 (R. coreanus Miq., 36 accessions) while The pop1 and pop5 mainly included R. idaeus L. and R. parvifolius L., respectively. In cluster analysis, 69 Rubus accessions were divided into three groups. Group I contained 35 Rubus accessions, which consisted of six Rubus species. Groups II and III had 30 and 4 Rubus accessions, respectively. They consisted of only R. coreanus. The uncertain diversity of species and artificial groups of the Rubus genus has created confusion with respect to the correct classification of the species at both commercial and scientific levels. The results of the present study will provide basic information for phylogeny, taxonomy and breeding programs of Rubus species.

Citations

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  • Genetic diversity and population structure of some blackberry genotypes collected from different parts of Türkiye using inter simple sequence repeat (ISSR) markers
    Fatma Alan, Aydın Uzun, Hasan Pınar
    Genetic Resources and Crop Evolution.2025; 72(7): 9001.     CrossRef
  • Cross-transferability of Rubus ellipticus EST–SSR markers for genetic diversity analysis of peach (Prunus persica)
    Samriti Sharma, Rajinder Kaur, Krishan Kumar, Heerendra Sagar
    Genetic Resources and Crop Evolution.2024; 71(4): 1615.     CrossRef
  • Genetic diversity and population relationships in wild Korean black raspberry (Rubus coreanus Miq.) based on microsatellite markers: establishing a fruit tree breeding strategy
    Sung-Kyung Han, Hanna Shin, Jei-Wan Lee, Kyung-Nak Hong, Ji-Young Ahn
    Horticulture, Environment, and Biotechnology.2024; 65(2): 293.     CrossRef
  • Genetic differentiation between Czech and Norwegian raspberry populations: new options for breeding
    Jiří Sedlák, Leona Leišová-Svobodová, Inger Martinussen, Vojtěch Holubec
    Euphytica.2022;[Epub]     CrossRef
  • Genetic variability in Rubus ellipticus collections assessed by morphological traits and EST-SSR markers
    Samriti Sharma, Rajinder Kaur, Krishan Kumar, Dinesh Kumar, Amol Kumar U. Solanke
    Journal of Plant Biochemistry and Biotechnology.2021; 30(1): 37.     CrossRef
  • Evaluación de marcadores microsatélites (SSRs) heterólogos en Rubus niveus para estudios de diversidad genética en las Islas Galápagos
    Pablo Alarcón Bolaños, María de Lourdes Torres, Gabriela Pozo, María Paula Erazo, Mayra Ortega, Estefanía Rojas, Noelia Barriga, Antonio Leon Reyes
    ACI Avances en Ciencias e Ingenierías.2021; 13(2): 20.     CrossRef
  • Recent Large-Scale Genotyping and Phenotyping of Plant Genetic Resources of Vegetatively Propagated Crops
    Hilde Nybom, Gunārs Lācis
    Plants.2021; 10(2): 415.     CrossRef
  • MODERN WAYS OF RASPBERRY BREEDING
    L. V. FROLOVA, T. A. HASHENKO, O. A. HASHENKO
    Fruit-Growing.2021; 33: 211.     CrossRef
  • Genetic diversity of the Andean blackberry (Rubus glaucusBenth.) in Ecuador assessed by AFLP markers
    Patricia Garrido, Eduardo Morillo, Wilson Vásquez-Castillo
    Plant Genetic Resources: Characterization and Utilization.2020; 18(4): 243.     CrossRef
  • Molecular markers in the genetic diversity studies of representatives of the genus Rubus L. and prospects of their application in breeding
    A. M. Kamnev, O. Yu. Antonova, S. E. Dunaeva, T. A. Gavrilenko, I. G. Chukhina
    Vavilov Journal of Genetics and Breeding.2020; 24(1): 20.     CrossRef
  • Genetic and genomic resources for Rubus breeding: a roadmap for the future
    Toshi M. Foster, Nahla V. Bassil, Michael Dossett, Margaret Leigh Worthington, Julie Graham
    Horticulture Research.2019;[Epub]     CrossRef
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Genetic Diversity and Population Structure of Asian Tomato Accessions Based on Simple-Sequence Repeats
Sebastin Raveendar, Jong-Wook Chung, Gi-An Lee, Jung-Ro Lee, Kyung-Jun Lee, Myoung-Jae Shin, Yang-Hee Cho, Kyung-Ho Ma
Plant Breed. Biotech. 2016;4(3):306-314.   Published online August 31, 2016
DOI: https://doi.org/10.9787/PBB.2016.4.3.306

Tomato (Solanum lycopersicum L.) is one of the most economically important plants in the family Solanaceae. Understanding its genetic diversity of accessions is vital for additional collection of tomato germplasms. The
objective
of this study was to determine the genetic diversity and population structure of 355 tomato accessions from Asia using 18 simple-sequence repeats (SSRs). A total of 176 alleles were detected at an average of ten alleles per SSR locus. The average major allele frequency and polymorphic information content were 0.69 and 0.39, respectively. Model-based structure analysis revealed two subpopulations (88%), including admixtures (11%) in the 355 Asian tomato accessions, consistent with clustering results based on genetic distance. The overall FST value was 0.135, indicating a moderate differentiation between the inferred subpopulations. Analysis of molecular variance showed that the genetic variance among geographical groups was less than 6%, in contrast to 86% of genetic variance among individuals. The results from this study will provide important information for future germplasm conservation and improvement programs for tomato.

Citations

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  • Next generation sequencing technologies to explore the diversity of germplasm resources: Achievements and trends in tomato
    Pasquale Tripodi
    Computational and Structural Biotechnology Journal.2022; 20: 6250.     CrossRef
  • Genetic diversity, population structure and validation of SSR markers linked to Sw-5 and I-2 genes in tomato germplasm
    Saidaiah Pidigam, Vishnukiran Thuraga, Suchandranath Babu Munnam, Geetha Amarapalli, Gopal Kuraba, Someswara Rao Pandravada, Srinivas Nimmarajula, Hari Kishan Sudini
    Physiology and Molecular Biology of Plants.2021; 27(8): 1695.     CrossRef
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