2.2. Pedigree Analysis
An active population of 324 dams and sires (animals used in breeding) in 2020 was considered the reference population for pedigree analysis of the PBP breed. Basic characteristics of the population are summarized in
Table 1. The information about sire, dam, date of birth, sex, breeding line, and herd for 1971 individuals were considered for pedigree analysis. The pedigree of each individual in the reference population was tracked back to its earliest ancestor. The analyzed pedigree contains animals born between 1982 and 2020. Average generation interval (GI) was calculated as the average age of parents when their selected offspring was born (in years). The GI was computed for the four selection pathways (SS: sires to sons, SD: sires to daughters, DS: dams to sons, and DD: dams to daughters).
Inbreeding coefficient (F) of an individual was calculated using Wright’s coefficients and Meuwissen and Luo’s algorithm [
10,
11]. The differences of the inbreeding effect which were achieved during recent generations compared with inbreeding achieved during the more distant past were evaluated. Therefore, the total inbreeding coefficient of a given animal (F
AnT) has been split into the “new” inbreeding coefficient (F
AnN) and the “old” inbreeding coefficient (F
AnO). The F
AnT was calculated as inbreeding coefficient of ancestors up to five generations back. In addition to the calculation of the inbreeding coefficient of an individual, the influence of inbreeding was also evaluated by the sires (F
SrN, F
SrO, and F
SrT as new, old, and total inbreeding, respectively), and dams (F
DmN, F
DmO, and F
DmT as new, old, and total inbreeding, respectively). Expected inbreeding coefficient was computed as the co-ancestry of the breeding animals assuming random mating [
12]. The co-ancestry coefficient was obtained by applying Colleau’s algorithm [
13].
Pedigree completeness index (PCI) was calculated to express the quality of the pedigree using the algorithm devised by MacCluer et al., which summarized the proportion of known ancestors in each ascending generation [
14]. It quantifies the change in observed inbreeding in the pedigree [
15]. The following equations were used:
And
where
k is the dam or sire line of an animal,
ai is the proportion of known ancestors in generation
i, and
d is number of generations considered in the calculation of the pedigree completeness. For description of methods used to calculate maximum generations traced back, average complete equivalent generations, number of founders and number of ancestors explaining appropriate proportion of gene pool [
16].
Different methods were used to compute the effective population size (N
e). The N
e was calculated based on standard equation:
where ΔF is rate of inbreeding coefficient. Here, ΔFp is the ratio of the average inbreeding in an offspring and their direct parents and ΔFg was the ratio of the average inbreeding in an offspring to the average inbreeding in average parent’s generation. Both methods were described previously [
12]. An algorithm devised by Pérez-Ensico was used for the calculation of N
e-Ln and used the slope from logarithmic regression performed on year of birth of the PBP pigs in the pedigree analysis [
17]. The approach defined by Gutierrez et al. [
18], which uses the sum of the inbreeding coefficients of all individuals and their known ancestors, was used to compute the N
e-Ecg. N
e-Coan was computed using the additive genetic relationship against the inbreeding coefficient as described by Falconer and Mackay [
12].
The basic pedigree data manipulations were performed using the R-project package [
19]. The POPREP package v. 2.0, the CFC software v. 1.0, the PEDIG package, and the Endog software v. 4.5 were used for the pedigree analysis [
20,
21,
22,
23].
2.3. SNP Analysis
The genome-wide data were analyzed for 181 individuals (65 sows and 116 boars) born between 2005 and 2020. Bristles, frozen blood samples, and used insemination doses were the sources of DNA. All DNA analyses were performed at the laboratory of genetics of the Czech-Moravian Breeders’ Corporation. DNA was isolated using the magnetic bead method and the silicate column method for bristles and insemination doses, respectively. DNA from the blood samples was isolated using the GeneAll Exgene DNA micro isolation kit (GeneAll Biotechnology, Seoul, Korea). Selected DNA isolates were applied to PorcineSNP60 v2 BeadChips (Illumina, San Diego, CA, USA) using the Illumina HD Infinium technology protocol. A total of 61 565 SNPs were identified in the selected DNA isolates. The average call rate was 0.99, 0.97, and 0.94 for the bristles, blood samples, and insemination doses, respectively. The animals with call rate > 0.90 were used in the subsequent analyses.
Only the SNPs mapped on autosomes were considered in this study. The PLINK v. 1.9 software was used for quality control and subsequent analyses [
24]. The SNPs with call rate > 0.90,
p-value of Hardy–Weinberg equilibrium > 0.0001, and minor allele frequency (MAF) > 0.05 were included in the SNP analyses. After the quality control analysis of all SNPs, the data from 174 animals containing 39 779 SNPs were used for further analysis. Runs of homozygosity (ROH) segments were expressed by a specific minimum number of continuous homozygous SNP markers with a maximum spacing of 1 Mb and a minimum density of one SNP marker per 100 kb. The minimum number of SNPs in ROH (
n = 39) was determined based on the approach proposed in the study by Lencz et al. to avoid the detection of false-positive ROH segments (α = 0.05) [
25]. As shown in previous studies, the length of ROH segments is related to the proportion of genomic information coming from different ancestral generations [
26,
27,
28,
29]. Therefore, based on the terminology proposed previously, the ROH segments were divided into five classes on the basis of their length (>1 Mb, >2 Mb, >4 Mb, >8 Mb, and >16 Mb) [
27,
29]. These trace back approximately 50, 25, 12, 6, and 3 ancestral generations.
The inbreeding coefficients, calculated based on the SNP data, were calculated by four different methods. The genomic inbreeding coefficient was calculated for each ROH (F
ROH) class by dividing the sum of the ROH segments length (kb) in a particular class by the total length of the autosomal genome (kb) covered by the SNPs and expressed in percentage. The inbreeding coefficient, F
hat1 is the variance-standardized relationship minus one. The inbreeding coefficient, F
hat2 is approximately equal to the estimates (observed and expected autosomal homozygous genotype counts for each sample and reported method of moments F coefficient estimates). The inbreeding coefficient F
hat3 is based on the correlation between uniting gametes. All the inbreeding coefficients were calculated using PLINK 1.9 software [
24]. The correlations between evaluated inbreeding coefficients were calculated. The effective population size based on the genomic data was calculated by GONE software [
30]. The method is based on the linkage disequilibrium (LD) between SNP markers and implements a genetic algorithm published previously [
31]. The parameters were set to 2000 generations for which the linkage data is obtained in bins. Only pairs of SNPs with recombination fraction, c > 1/4000 were considered. Numbers of bins were set to 400, making a five-year generations gap, obtained by dividing the number of generations with the number of bins. However, the first 10 generations were analyzed using 2-generation gaps and the rest using 5-generation gaps. The N
e was calculated separately for K1, K2, K3, K4, and K8, which represent 1, 2, 4, and 8 centiMorgans per mega base, respectively. The overall degree of population stratification was assessed by calculating Nei’s genetic distances (Da) between animals and the pair-wise Wright’s index at the group level using the StAMPP R package [
32].