Genetic Diversity of Indonesian Cattle Breeds Based on Microsatellite Markers

Agung, Paskah Partogi; Saputra, Ferdy; Zein, Sharif Hussein Sharif; Wulandari, Ari Sulistyo; Putra, Widya Pintaka Bayu; Said, Syahruddin; Jakaria, Jakaria

doi:10.5713/ajas.18.0283

Cited by 37 publications

(42 citation statements)

References 27 publications

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“…In general, the levels of heterozygosity (H e and H o ) that were observed in this study are similar to previous reports (Agung et al, 2019;Egito et al, 2007;Freeman et al, 2004;Suh et al, 2014;Zhao et al, 2017). Given that these observed levels of heterozygosity were greater than 0.5, this indicates that the 10 microsatellite markers are suitable for genetic diversity studies in the three dairy breeds (Sheriff & Alemayehu, 2017).…”

Section: Discussionsupporting

confidence: 91%

“…Additionally, microsatellite markers are used for identification of parental lineages (Erhardt & Weimann, 2007), mapping genes for economically important traits (Goddard & Hayes, 2007;Naicy, 2008), and aiding in marker-assisted selection (MAS) (Oltenacu & Broom, 2010;Schefers & Weigel, 2012). Microsatellites have been commended for use in genetic diversity studies because of their accuracy, versatility, abundancy and being spread over the entire genome (Agung et al, 2019;Erhardt & Weimann, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Genetic diversity and differentiation among Korean-Holstein, Hanwoo, and Uganda-Holstein breeds

Bigirwa¹,

Kim²,

Acai³

et al. 2020

SA J. An. Sci.

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The aim of this research was to assess genetic diversity of Korean-Holstein, Korean Hanwoo, and Ugandan-Holstein dairy cattle. DNA was extracted from either blood or hair of Korean-Holstein (n=74), Korean-Hanwoo (n=75) and Ugandan-Holstein (N=77) using AccuPrep® PCR purification kit. The DNA samples were amplified by multiplex polymerase chain reaction, using GeneTrackTM Hanwoo genotyping kit and assayed using ABI genetic analyser 3130XL. Number of alleles, expected heterozygosity (He), observed heterozygosity (Ho), and the polymorphism information content (PIC) were estimated from 10 microsatellite loci in the three breeds. In addition, F-statistics for each of the 10 microsatellites in the three cattle breeds were estimated using fstat version 2.9.3.2 computer program. GENETIX (v.4.02) was used to perform factorial correspondence analysis (FCA) from the allele frequencies and multi-locus clustering was done using STRUCTURE analyses. A total of 124 alleles were detected. The number of alleles per locus varied from eight (TGLA126) to 22 (TGLA122), with an overall mean of 12.2. Expected heterozygosity ranged from 0.617 (SPS115) to 0.854 (TGLA53) and averaged 0.761. Observed heterozygosity ranged from 0.6 (SPS115) to 0.859 (TGLA53); and averaged 0.761. The mean PIC was 0.723; and means of the F-statistics FIT, FST and FIS were 0.077, 0.076 and 0.001 respectively. Although FCA revealed clear differentiation of Uganda-Holstein, Korean-Holstein, and Hanwoo, clustering assignments showed genetic admixture between Ugandan dairy cattle (Uganda-Holstein) and Hanwoo. In conclusion, the allelic variation present at the 10 loci was sufficient to categorize these cattle into distinct breed groups. Keywords: dairy cattle, genetic differentiation, F-statistics, heterozygosity, microsatellite

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Genetic diversity and differentiation among Korean-Holstein, Hanwoo, and Uganda-Holstein breeds

Bigirwa¹,

Kim²,

Acai³

et al. 2020

SA J. An. Sci.

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“…Previously wide studies on buffalo domestication have been conducted and it was concluded that the origin of buffalo domestication occurred in China (Yue et al, 2013). Microsatellites have been widely used for the study of genetic diversity on various types of livestock in Indonesia such as in cattle (Abdullah et al, 2012;Agung et al, 2016;Agung et al, 2019), goats (Zein et al, 2012), and sheep (Jakaria et al, 2012). Microsatellite studies in local swamp buffaloes have been reported in Indonesia, but are still limited.…”

Section: Introductionmentioning

confidence: 99%

Genetic Diversity of Indonesian Swamp Buffalo Based on Microsatellite Markers

Saputra¹,

Jakaria

Anggraeni³

et al. 2020

Trop. Anim. Sci. J.

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Indonesia has high genetic resources of local swamp buffalo with good adaptation across regions. However, these animals decline in both population and genetic quality. This research was conducted to study the genetic diversity of Indonesian swamp buffalo. A total of 199 DNA samples (swamp buffalo) from seven provincial populations were used in this study. Genetics identification used three microsatellite markers (CSSM66, ILSTS61, and ILSTS17). Microsatellites were visualized by Polyacrylamide Gel Electrophoresis (PAGE) 10% with silver staining method. Microsatellite data were analyzed using GenAlEx 6.41, Cervus 3.0, and POPTREE2 software. The results showed that a total of 9 alleles were found from the three loci. ILSTS61 had a high PIC (Polymorphism Information Content) compared to the other loci. The high observed heterozygosity of ILSTS61 was found in swamp buffalo from Riau Province, while the Ho value of ILSTS17 ranged from 0.000 to 0.170. This study identified two clusters for Indonesian swamp buffalo, i.e., cluster I (Aceh, North Sumatra, and Riau) and cluster II (Banten, Central Java, West Nusa Tenggara, and South Sulawesi). The two major divergent directions are considered in Indonesia swamp buffaloes across the observed provinces.

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“…Multivariate methods can summarize the genetic variability without making assumptions about an evolution model, the absence of linkage disequilibrium, and it does not rely on Hardy-Weinberg equilibrium [7]. Principal Component Analysis (PCA) is a multivariate method used in analyzing microsatellite data on Indonesian animal genetic resources and is able to distinguish between species of cattle [8]. The purpose of this study was to examine several multivariate methods in classifying genetic diversity, especially in Indonesian chickens and provides recommendations of multivariate methods that can be used to see genetic differences based on allele frequency data.…”

Section: Introductionmentioning

confidence: 99%

Multivariate analysis of five chicken breed in Indonesia based on microsatellite allele frequency

Saputra¹,

Sartika²,

Anggraeni³

et al. 2021

Livest. Anim. Res.

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Objective: This study tries to examine several multivariate methods in classifying genetic diversity using microsatellite allele frequency data.Methods: This study used microsatellite allele frequency data from White Leghorn (n = 48), Kampung (n = 48), Pelung (n = 24), Sentul (n = 24), and Black Kedu (n = 25) from Indonesian Research Institute for Animal Production. Allele frequency data were analyzed by the Neighbor-Joining (NJ) method using the POPTREE2 program. The data was also analyzed by the Principal Component Analysis (PCA), Correspondence Analysis (CA), and Hierarchical Clustering on Principal Components (HCPC) methods using the factoextra and FactoMineR packages in the R 4.0.0 program.Results: Correspondence Analysis (CA) found Sentul is more closer to Black Kedu. However, based on NJ, PCA, and HCPC showed Sentul is closer to Kampung. Based on the value of Dimension 1, Correspondence Analysis (80.7%) can explain greater variation than PCA (58.9%). However, CA method generated different results compared to NJ, PCA, and HCPC. NJ, PCA, and HCPC found four chicken clusters, namely cluster 1 (White Leghorn), cluster 2 (Pelung), cluster 3 (Black Kedu), and cluster 4 (Kampung and Sentul).Conclusions: In conclusion, HCPC is a better multivariate method for analyzing allele frequency data than PCA and CA. HCPC can be used to analyze allele frequency data better than PCA, because HCPC is a combination of methods from hierarchical clustering and principal components.

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Genetic Diversity of Indonesian Cattle Breeds Based on Microsatellite Markers

Cited by 37 publications

References 27 publications

Genetic diversity and differentiation among Korean-Holstein, Hanwoo, and Uganda-Holstein breeds

Genetic diversity and differentiation among Korean-Holstein, Hanwoo, and Uganda-Holstein breeds

Genetic Diversity of Indonesian Swamp Buffalo Based on Microsatellite Markers

Multivariate analysis of five chicken breed in Indonesia based on microsatellite allele frequency

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