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All Journal Livestock and Animal Research
Komarudin Komarudin
Indonesian Research Institute for Animal Production
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Veterinary

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Multivariate analysis of five chicken breed in Indonesia based on microsatellite allele frequency Ferdy Saputra; Tike Sartika; Anneke Anggraeni; Andi Baso Lompengeng Ishak; Komarudin Komarudin; Nurul Pratiwi
Livestock and Animal Research Vol 19, No 1 (2021): Livestock and Animal Research
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (399.538 KB) | DOI: 10.20961/lar.v19i1.43459

Abstract

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.
Co-Authors Andi Baso Lompengeng Ishak Anneke Anggraeni Ferdy Saputra Nurul Pratiwi Tike Sartika