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Research Article Open access CC BY 3.0

Inter Simple Sequence Repeats Polymorphism in Sudanese Sorghum bicolor (L.) Moench Accessions

Haitham K. A. El-Amin, Nada B. Hamza

Biotechnology Journal International · pp. 846–857 · Published 18 Jul 2014

10.9734/BBJ/2014/4275

Abstract

Aims: Investigate the degree of polymorphism using 41 ISSR (Inter-simple sequence repeat) markers in 50 sorghum accessions from 11 different regions in Sudan and Republic of South Sudan. Study Design: UPGMA cluster analysis using STATISTCA- SPSS software Ver. 9. Place and Duration of Study: Department of Molecular Biology, Commission for Biotechnology and Genetic Engineering, National Center for Research, Khartoum, Sudan (2010-2012). Methodology: 50 sorghum accessions with important agronomic traits, representing 11 regions in Sudan and Republic of South Sudan were assayed for polymorphism using Inter-simple sequence repeats (ISSRs). Seven primers out of 41 tested (807, 808, 810, 814, 848, 872 and 879) showed high polymorphism among the Sorghum accessions. Results: The results indicated 75 polymorphic bands out of 78 bands with percentage of polymorphic bands of 97%. UPGMA analysis showed ISSR distance matrix ranged between (0.04-0.47) which reflected high genetic diversity. The ISSR UPGMA dendrogram showed high molecular variance within regions. Based on the results of this study ISSR technique showed differences among closely related accessions of sorghum. Also it proved to be useful technique to study genetic variation among the Sudanese Sorghum accessions. Conclusion: Sorghum accessions from Sudan exhibits high genetic variation within and among regions. ISSR marker technique used in this study proved that it is efficient and could be very useful for breeders and researchers community in various fields of sorghum improvement in Sudan.

Sorghum ISSR markers genetic variation cluster UPGMA Sudan

Cited by 1

MicroRNA expression profiles in response to drought stress in Sorghum bicolor

Nada Babiker Hamza, Neha Sharma, Anita Tripathi · Gene Expression Patterns · 2016

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