Comparison of principal component analysis (PCA) and discriminant analysis of principal component (DAPC) methods for analysis of population structure in Akhal-Take, Arabian and Caspian horse breeds using genomic data

Introduction Development of high-power and cost-effective genotyping methods in recent years has provided the possibility of evaluation the genetic structure and the relationship among species populations utilizing genomic data. Genome wide inference of population structure using genetic markers could provide invaluable information associated with evolutionary relationships and clustering of subpopulations for performing animal breeding programs. In large scale studies, one of the interesting subjects is to study the existence of genetic differences among subdivided groups ascertained from different geographic locations. The objective of this study was to compare the principal component analysis (PCA) and discriminant analysis of principal component (DAPC) approaches for determining the population structure and study how an individual allocated to the true population of origin, in three Horse breeds located in Middle East consisting Akhal Take, Arabian and Caspian using genomic data.Materials and Methods In this study, the genomic data obtained from 61 animals consisting Akhal Take (19), Arabian (24) and Caspian (18) were used to investigate the population structure of some Asian horse breeds. The data were obtained from the Equine Genetic Diversity Consortium (EGDC) project. Hair or tissue samples were collected from animals. DNA extraction was performed using an optimized Pure gene (Qiagen) assay and approximately 1 μg of DNA was used for genotyping of the samples. Genotyping was performed using Illumina SNP 50K BeadChip arrays that allow to genotype 52603 SNP marker loci, according to the Illumina standard guidelines. In this study, different quality control steps were applied on preliminary data to ensure the quality of genotyping data. Quality control carried out using PLINK v.1.07 program. The samples with more than 5% missing data were excluded from analysis. Then for each SNP, MAF and call percentage were calculated and the SNPs with a call rate