Phenotypic diversity in poultry has been mainly driven by artificial selection and genetic drift. These led to the adaptation to the environment and the development of specific phenotypic traits of chickens in response to their economic use. This study evaluated genetic diversity within and between Russian breeds and populations using Illumina Chicken 60K SNP iSelect BeadChip by analysing genetic differences between populations with Hudson's fixation index (F_ST statistic) and heterozygosity. We estimated the effect of rare alleles and linkage disequilibrium (LD) on these measurements. To assess the effect of LD on the genetic diversity population, we carried out the LD-based pruning (LD<0.5 and LD<0.1) for seven chicken populations combined (I) or separately (II). LD pruning was specific for different dataset groups. Because of the noticeably large sample size in the Russian White RG population, pruning was substantial for Dataset I, and F_ST values were only positive when LD<0.1 pruning was applied. For Dataset II, the LD pruning results were confirmed by examining heterozygosity and alleles' frequency distribution. LD between single nucleotide polymorphisms was consistent across the seven chicken populations, except the Russian White RG population with the smallest r² values and the largest effective population size. Our findings suggest to study variability in each population LD pruning has to be carried separately not after merging to avoid bias in estimates.

Background: Due to the advent of SNP array technology, a genome-wide analysis of genetic differences between populations and breeds has become possible at a previously unattainable level. The Wright’s fixation index (F_st) and the principal component analysis (PCA) are widely used methods in animal genetics studies. In paper we compared the power of these methods, their complementing each other and which of them is the most powerful. Results: Comparative analysis of the power Principal Components Analysis (PCA) and F_st were carried out to reveal genetic differences between herds of Holsteinized cows. Totally, 803 BovineSNP50 genotypes of cows from 13 herds were used in current study. Obtained F_st values were in the range of 0.002–0.012 (mean 0.0049) while for rare SNPs with MAF 0.0001–0.005 they were even smaller in the range of 0.001–0.01 (mean 0.0027). Genetic relatedness of the cows in the herds was the cause of such small F_st values. The contribution of rare alleles with MAF 0.0001–0.01 to the F_st values was much less than common alleles and this effect depends on linkage disequilibrium (LD). Despite of substantial change in the MAF spectrum and the number of SNPs we observed small effect size of LD - based pruning on F_st data. PCA analysis confirmed the mutual admixture and small genetic difference between herds. Moreover, PCA analysis of the herds based on the visualization the results of a single eigenvector cannot be used to significantly differentiate herds. Only summed eigenvectors should be used to realize full power of PCA to differentiate small between herds genetic difference. Finally, we presented evidences that the significance of F_st data far exceeds the significance of PCA data when these methods are used to reveal genetic differences between herds. Conclusions: LD - based pruning had a small effect on findings of F_st and PCA analyzes. Therefore, for weakly structured populations the LD - based pruning is not effective. In addition, our results show that the significance of genetic differences between herds obtained by F_st analysis exceeds the values of PCA. Proposed, to differentiate herds or low structured populations we recommend primarily using the F_st approach and only then PCA.

In this study, 371 Holstein cows from six herds and 26 Holstein bulls, which were used in these herds, were genotyped by the Illumina BovineSNP50 array. For runs of homozygosity (ROH) identification, consecutive and sliding runs were performed by the detectRUNS and Plink software. The missing calls did not significantly affect the ROH data. The mean number of ROH identified by consecutive runs was 95.4 ± 2.7, and that by sliding runs was 86.0 ± 2.6 in cows, while this number for Holstein bulls was lower 58.9 ± 1.9. The length of the ROH segments varied from 1 Mb to over 16 Mb, with the largest number of ROH having a length of 1–2 Mb. Of the 29 chromosomes, BTA 14, BTA 16, and BTA 7 were the most covered by ROH. The mean coefficient of inbreeding across the herds was 0.111 ± 0.003 and 0.104 ± 0.004 based on consecutive and sliding runs, respectively, and 0.078 ± 0.005 for bulls based on consecutive runs. These values do not exceed those for Holstein cattle in North America. The results of this study confirmed the more accurate identification of ROH by consecutive runs, and also that the number of allowed heterozygous SNPs may have a significant effect on ROH data.

Holstein bulls and semen have been imported to Russia from Western countries since the 1970s. The objective of our study was to examine the effect of this introgression on genetic diversity between various commercial Holstein herds in the Leningrad region. A total of 803 Holstein cows from 13 herds were genotyped using the Illumina BovineSNP50 v.2 array. The pairwise Hudson’s estimator of Fst values between 13 herds varied from 0.002 to 0.015, which is less than values usually obtained between dairy cattle breeds (> 0.1). The mean of these pairwise Fst values revealed differences between herds depending, mainly, on the proportion of common sires shared between the herds. In addition, we investigated the cause of negative Fst values. Based on our results, these negative values could be interpreted as an excess of within-herd genetic diversity over the between-herds genetic diversity. Our results show that introgressions of Holstein genes into Russian Black and White cattle of the Leningrad region have created genetic separation between herds similar with those for Jersey cows in USA, Australia and New Zealand.

Genomes of animals harbor not only homozygous (ROH) but also HRR segments of chromosomes. In the current study, the HRRs in Holstein cattle were analyzed using a 50K array. On average, 366 Holstein cows from six herds had 439 ± 7 and 111 ± 3 HRRs per cow when HRRs with a minimum length of 50 kb and 250 kb were detected, respectively. The average HRRs values did not differ between herds. Removal of SNP with an MAF less than 0.01 leads to a noticeable increase in the number of HRRs. The distribution of HRRs in the chromosomes of cows is not uniform and depends on the scanned lengths of the HRR segments. No clear correlations were found between interchromosomal HRR specific density and the occurrence of HRRIs. When HRR segments with a length of 50 kb or more have been detected, the significant HRRIs were located on BTA1, BTA10, BTA20, and BTA21(first set); for HRR segments with a length of 250 kb or more, significant islands were found on BTA9, BTA11, and BTA29 (second set). According to the Tajima D test, the islands on the BTA of the first set have maximum values of 3.47–2.82, while the islands on the BTA of the second set have values of less than 2.49. The genes in the HRRIs are involved in immune response, oogenesis, and reproduction but are not related to milk production traits. These results show that the recognition of HRRIs strongly depends on the length of the scanned HRR segments. No single “mechanism” was identified as the main cause of HRRIs.