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The cost of SNP genotyping to screen different breeds and to estimate the exact proportion of ancestry level is quite high, which can be compensated through deriving a small panel of ancestry informative markers (AIMs). Hence, we carried out the present study to provide an insight into ancestry level inferred from a panel of informative markers in the crossbred Vrindavani population developed at ICAR-IVRI, India. We have performed a new method i.e., discriminant analysis of principal components (DAPC) for the first time on the dataset of Vrindavani cattle. To confirm our method, we had performed DAPC on two other well-known crossbred cattle, i.e., Frieswal and Beefmaster. Three sets of panels (500, 1000 and 2000 markers) were tested for clustering of individuals. Among all the panels, we found the panel (1000 markers) with DAPC based contribution method was of the smallest size and comparatively of the highest accuracy.
Pubmed ID: 31678154
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Software package for using multi locus genotype data to investigate population structure. Used for inferring presence of distinct populations, assigning individuals to populations, studying hybrid zones, identifying migrants and admixed individuals, and estimating population allele frequencies in situations where many individuals are migrants or admixed. Can be applied to most of commonly used genetic markers, including SNPS, microsatellites, RFLPs and Amplified Fragment Length Polymorphisms.
View all literature mentionsSoftware package for using multi locus genotype data to investigate population structure. Used for inferring presence of distinct populations, assigning individuals to populations, studying hybrid zones, identifying migrants and admixed individuals, and estimating population allele frequencies in situations where many individuals are migrants or admixed. Can be applied to most of commonly used genetic markers, including SNPS, microsatellites, RFLPs and Amplified Fragment Length Polymorphisms.
View all literature mentions