Genetic parameters for fertility traits in Czech Holstein population were estimated. The database obtained from the Czech-Moravian Breeders Corporation with 6 414 486 insemination records between years 2005–2015 was used. Date of calving of the selected animals was taken from the database of milk records from 2005–2015. Fertility traits were age at first service (AFS), age at first calving (AFC), days open (DO), calving interval (CI) and first service to conception interval in cows (FSC-C) and heifers (FSC-H). The heritability of each trait was estimated using single-trait animal models. The model included fixed effects of herd-year-season of birth, herd-year-month of calving, lactation order, parity, last calving ease, linear and quadratic regressions on age at first insemination in heifers or on age at first calving in cows. Random effects were animal, permanent environmental effect and random residual error. After edits, the final data set included up to 599 901 observations from up to 448 037 animals dependent on traits. The range of heritability estimates was from 0.010 to 0.058. The lowest heritability was for first service to conception interval in heifers, and the highest heritability was for age at first service. Variances of random permanent effects were higher than variance of additive genetic effect in all traits manifested in mature cows. Repeatability ranged from 0.060 to 0.090. Genetic correlations between traits were estimated using a bivariate animal model. High positive genetic correlations were found between AFS–AFC, DO–CI, FSC-C–DO and FSC-C–CI. A moderate genetic correlation was found between AFS–FSC-H and between AFC. A negative correlation was found between AFS–FSC-C. Correlations between other traits were close to zero. The results suggest that the level of these reproductive traits can be improved by selection of animals with high genetic merit.

Loss off genetic diversity negatively affects most of the modern dog breeds. However, no breed created strictly for laboratory purposes has been analyzed so far. In this paper, we sought to explore by pedigree analysis exactly such a breed—the Czech Spotted Dog (CSD). The pedigree contained a total of 2010 individuals registered since the second half of the 20th century. Parameters such as the mean average relatedness, coefficient of inbreeding, effective population size, effective number of founders, ancestors and founder genomes and loss of genetic diversity—which was calculated based on the reference population and pedigree completeness—were used to assess genetic variability. Compared to the founding population, the reference population lost 38.2% of its genetic diversity, of which 26% is due to random genetic drift and 12.2% is due to the uneven contribution of the founders. The reference population is highly inbred and related. The average inbreeding coefficient is 36.45%, and the mean average relatedness is 74.83%. The effective population size calculated based on the increase of inbreeding coefficient is 10.28. Thus, the Czech Spotted Dog suffered significant losses of genetic diversity that threaten its future existence.

No abstract

The effect of the reference population size and the number of missing single nucleotide polymorphisms (SNPs) on imputation accuracy was determined. The population imputation method using the FImpute software was applied. The dataset used for the purpose of this study was taken from the database of the Holstein Cattle Breeders Association of the Czech Republic. It contains 1000 animals genotyped with the Illumina BovineSNP50 v.2 BeadChip. Two datasets were created, the first containing the original genotypes, including the missing SNPs, the second containing the same genotypes modified to avoid missing data. In these datasets, animals were randomly selected for a reference population (10, 25, 50 and 75%) and there were randomly selected SNPs for deletion (15, 30, 55, 70, and 95%) in animals that were not used as the reference population. Subsequently, the data accuracy was determined by two parameters: correlation between original and imputed SNPs and percentage of correctly imputed SNPs. Since animals and SNPs were randomly selected, the process including data imputation was repeated 100 times. Accuracy was determined as the average accuracy over all repetitions. It was found that the imputation accuracy is influenced by both parameters. If the size of the reference population is sufficient, the imputation accuracy is higher despite the large number of missing SNPs.

An objective of our study was to evaluate gestation length and its genetic variability in the Czech Holstein population. Data set consisted of 770 865 records of gestation length in 375 574 Holstein cows and covered the period from 2012 to 2018. Mean gestation length was 277 ± 4.9 days, and it was 1.4 days longer in male calves compared to females, and 1.1 days longer in cows compared to heifers. Animal repeatability model with maternal effect was employed for variance component estimation. The direct genetic effect explained the highest proportion of variability, and it corresponded with moderate direct heritability (0.48), while maternal heritability was much lower (0.06). We estimated conventional and genomic breeding values with the genomic matrix based on 39 145 single nucleotide polymorphisms in 13 844 animals. Genomic breeding values were weakly (< 0.25) but significantly correlated with breeding values for type, production and fitness traits. Pearson correlations between breeding values indicated a negative association of direct gestation length with milk production, longevity and fertility of bulls, and a positive association of maternal gestation length with most of the type traits related to the body composition. Genetic trends for male and female parts of the population showed a tendency to the shortening of gestation, which should be of concern, as short gestation could be reflected in a negative indirect response in other correlated traits, such as the incidence of stillbirth, the health status of cows after calving, culling, or conception rate.