Microsatellite-based diversity analysis and genetic relationships of three Indian sheep breeds

2006 ◽  
Vol 123 (4) ◽  
pp. 258-264 ◽  
Author(s):  
M. Mukesh ◽  
M. Sodhi ◽  
S. Bhatia
2011 ◽  
Vol 96 (2-3) ◽  
pp. 111-119 ◽  
Author(s):  
Emiliano Lasagna ◽  
Matteo Bianchi ◽  
Simone Ceccobelli ◽  
Vincenzo Landi ◽  
Amparo Martínez Martínez ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ranganathan Kamalakkannan ◽  
Satish Kumar ◽  
Karippadakam Bhavana ◽  
Vandana R. Prabhu ◽  
Carolina Barros Machado ◽  
...  

AbstractIndia ranks the second in the world in terms of its sheep population with approximately 74.26 million represented by 44 well-described breeds in addition to several non-descript populations. Genetic diversity and phylogeography of Indian sheep breeds remain poorly understood, particularly for south Indian breeds. To have a comprehensive view of the domestication history of Indian sheep, we sequenced the mitochondrial DNA (mtDNA) control region (D-loop) and cytochrome b gene (CYTB) of 16 Indian domestic sheep breeds, most of them (13) from the south India. We analysed these sequences along with published data of domestic and wild sheep from different countries, including India. The haplotype diversity was relatively high in Indian sheep, which were classified into the three known mtDNA lineages, namely A, B and C. Lineage A was predominant among Indian sheep whereas lineages B and C were observed at low frequencies but C was restricted to the breeds of north and east India. The median joining network showed five major expanding haplogroups of lineage A (A1–A5). Out of which, A2, A4 and A5 were more frequent in Indian sheep in contrast to breeds from other parts of the world. Among the 27 Indian sheep breeds analysed, Mandya and Sonadi breeds were significantly different from other Indian breeds in the MDS analyses. This was explained by a very high contribution of lineage B into these two breeds. The Approximate Bayesian Computation (ABC) provided evidence for the domestication of lineage A sheep in the Indian subcontinent. Contrary to the current knowledge, we also found strong support for the introduction of lineage B into Indian subcontinent through sea route rather than from the Mongolian Plateau. The neighbour-joining tree of domestic and wild sheep revealed the close genetic relationship of Indian domestic sheep with Pakistani wild sheep O. vignei blanfordi. Based on our analyses and archaeological evidences, we suggest the Indian subcontinent as one of the domestication centres of the lineage A sheep, while lineage B sheep might have arrived into India from elsewhere via Arabian sea route. To the best of our knowledge, this is the first comprehensive study on Indian sheep where we have analysed more than 740 animals belonging to 27 sheep breeds raised in various regions of India. Our study provides insight into the understanding of the origin and migratory history of Indian sheep.


2021 ◽  
pp. 36-48
Author(s):  
Farhana Afrin Vabna ◽  
Mohammad Zahidul Islam ◽  
Md. Ferdous Rezwan Khan Prince ◽  
Md. Ekramul Hoque

Aims: The aim of the study was to determine the genetic diversity of twenty four Boro rice landraces using rice genome specific twelve well known SSR markers. Study Design: Genomic DNA extraction, PCR amplification, Polyacrylamide gel electrophoresis (PAGE) and data analysis-these steps were followed to perform the research work. Data was analysed with the help of following software; POWERMAKER version 3.25, AlphaEaseFC (Alpha Innotech Corporation) version 4.0. UPGMA dendrogram was constructed using MEGA 5.1 software. Place and Duration of Study: The study was conducted at the Genetic Resources and Seed Division (GRSD), Bangladesh Rice Research Institute (BRRI), Joydebpur, Gazipur, Bangladesh during the period of November 2017 to March 2018. Methodology: Simple Sequence Repeat (SSR) markers were used to assay 24 landraces of Boro rice collected from the Gene Bank of Bangladesh Rice Research Institute (BRRI). Results: A total fifty four (54) alleles were detected, out of which forty five (45) polymorphic alleles were identified. The Polymorphic Information Content (PIC) of SSR markers ranged from 0.08 (RM447) to 0.84 (RM206) with an average value of PIC = 0.49. Gene diversity ranges from 0.08 (RM447) to 0.86 (RM206) with an average value of 0.52. The RM206 marker can be considered as the best marker among the studied markers for 24 rice landraces. Dendrogram based on Nei’s genetic distance using Unweighted Pair Group Method of Arithmetic Mean (UPGMA) indicated the segregation of 24 genotypes into three main clusters. Conclusion: The result revealed that SSR markers are very effective tools in the study of genetic diversity and genetic relationships and this result can be conveniently used for further molecular diversity analysis of rice genotypes to identify diverse parent for the development of high yielding variety in rice.


2015 ◽  
Vol 39 ◽  
pp. 576-582 ◽  
Author(s):  
Onur YILMAZ ◽  
Tamer SEZENLER ◽  
Semih SEVİM ◽  
İbrahim CEMAL ◽  
Orhan KARACA ◽  
...  

2011 ◽  
Vol 11 (spe) ◽  
pp. 66-72 ◽  
Author(s):  
Maria Celeste Gonçalves-Vidigal ◽  
Luciana Benchimol Rubiano

Molecular markers are powerful tools for analyzing genome diversity within a species, and to evaluate genetic relationships between individuals and populations. Among them, microsatellites (SSRs) are one of the most important polymorphic markers that can be used effectively to distinguish germplasm accessions. These markers present high informative content due to their codominant inheritance, multiallelism, mendelian pattern and good genome coverage. The enrichment methodology for microsatellite development has a superior efficiency in plants, especially when performed using biotin-labeled microsatellite oligoprobes and streptavidin-coated magnetic beads. The development of EST-SSR markers has become a fast and relatively inexpensive way but it is limited to species for which this type of database exists. Given the high polymorphism level of microsatellites when compared to other markers, SSRs have been used to study population structure, for genetic diversity analysis, genetic mapping and marker assisted selection.


2016 ◽  
Vol 22 (2) ◽  
pp. 170
Author(s):  
Rajiv Kumar ◽  
Shringarika Gupta ◽  
A.S. Meena ◽  
S.M.K. Naqvi ◽  
S. Kumar

2015 ◽  
Vol 21 (1) ◽  
pp. 13
Author(s):  
M.G. Sahare ◽  
A.D. Sawaimul ◽  
S.Z. Ali ◽  
A.R. Sirothia ◽  
S. Kumar

2008 ◽  
Vol 80 (1-3) ◽  
pp. 39-44 ◽  
Author(s):  
E. Legaz ◽  
I. Álvarez ◽  
L.J. Royo ◽  
I. Fernández ◽  
J.P. Gutiérrez ◽  
...  

2015 ◽  
Vol 46 (2) ◽  
pp. 220-223 ◽  
Author(s):  
R. Mukiibi ◽  
C. M. Rochus ◽  
G. Andersson ◽  
A. M. Johansson

Sign in / Sign up

Export Citation Format

Share Document