Background: PLINK is probably the most used program for analyzing SNP genotypes and runs of homozygosity (ROH), both in human and in animal populations. The last decade, ROH analyses have become the state-of-the-art method for inbreeding assessment. In PLINK, the --homozyg function is used to perform ROH analyses and relies on several input settings. These settings can have a large impact on the outcome and default values are not always appropriate for medium density SNP array data. Guidelines for a robust and uniform ROH analysis in PLINK using medium density data are lacking, albeit these guidelines are vital for comparing different ROH studies. In this study, 8 populations of different livestock and pet species are used to demonstrate the importance of PLINK input settings. Moreover, the effects of pruning SNPs for low minor allele frequencies and linkage disequilibrium on ROH detection are shown. Results: We introduce the genome coverage parameter to appropriately estimate F_ROH and to check the validity of ROH analyses. The effect of pruning for linkage disequilibrium and low minor allele frequencies on ROH analyses is highly population dependent and such pruning may result in missed ROH. PLINK’s minimal density requirement is crucial for medium density genotypes and if set too low, genome coverage of the ROH analysis is limited. Finally, we provide recommendations for the maximal gap, scanning window length and threshold settings. Conclusions: In this study, we present guidelines for an adequate and robust ROH analysis in PLINK on medium density SNP data. Furthermore, we advise to report parameter settings in publications, and to validate them prior to analysis. Moreover, we encourage authors to report genome coverage to reflect the ROH analysis’ validity. Implementing these guidelines will substantially improve the overall quality and uniformity of ROH analyses.

Background: Runs of homozygosity (ROH) have become the state-of-the-art method for analysis of inbreeding in animal populations. Moreover, ROH are suited to detect signatures of selection via ROH islands and are used in other applications, such as genomic prediction and genome-wide association studies (GWAS). Currently, a vast amount of single nucleotide polymorphism (SNP) data is available online, but most of these data have never been used for ROH analysis. Therefore, we performed a ROH analysis on large medium-density SNP datasets in eight animal species (cat, cattle, dog, goat, horse, pig, sheep and water buffalo; 442 different populations) and make these results publicly available. Results: The results include an overview of ROH islands per population and a comparison of the incidence of these ROH islands among populations from the same species, which can assist researchers when studying other (livestock) populations or when looking for similar signatures of selection. We were able to confirm many known ROH islands, for example signatures of selection for the myostatin ( MSTN ) gene in sheep and horses. However, our results also included multiple other ROH islands, which are common to many populations and not identified to date (e.g. on chromosomes D4 and E2 in cats and on chromosome 6 in sheep). Conclusions: We are confident that our repository of ROH islands is a valuable reference for future studies. The discovered ROH island regions represent a unique starting point for new studies or can be used as a reference for future studies. Furthermore, we encourage authors to add their population-specific ROH findings to our repository.

Summary: The Piétrain pig originates from the Belgian village Piétrain some time between 1920 and 1950. Owing to its superior conformation, the Piétrain has spread worldwide since the 1960s. As initial population sizes were limited and close inbreeding was commonplace, the breed’s genetic diversity has been questioned. Therefore, this study examines Piétrain breed substructure, diversity and selection signatures using SNP data in comparison with Duroc, Landrace and Large White populations. Principal component analysis indicated three subpopulations, andF_STanalysis showed that US Piétrains differ most from European Piétrains. Average inbreeding based on runs of homozygosity (ROH) segments larger than 4 Mb ranged between 16.7 and 20.9%. The highest chromosomal inbreeding levels were found on SSC8 (42.7%). ROH islands were found on SSC8, SSC15 and SSC18 in all Piétrain populations, but numerous population‐specific ROH islands were also detected. Moreover, a large ROH island on SSC8 (34–126 Mb) appears nearly fixed in all Piétrain populations, with a unique genotype. Chromosomal ROH patterns were similar between Piétrain populations. This study shows that Piétrain populations are genetically diverging, with at least three genetically distinct populations worldwide. Increasing genetic diversity in local Piétrain populations by introgression from other Piétrain populations seems to be only limited. Moreover, a unique 90 Mb region on SSC8 appeared largely fixed in the Piétrain breed, indicating that fixation was already present before the 1960s. We believe that strong selection and inbreeding during breed formation fixed these genomic regions in Piétrains. Finally, we hypothesize that independent coat color selection may have led to large ROH pattern similarities on SSC8 between unrelated pig breeds.

Background: Increasing resilience is a priority in modern pig breeding. Recent research shows that general resilience can be quantified via variability in longitudinal data. The collection of such longitudinal data on weight, feed intake and feeding behaviour in pigs has been facilitated by the development of technologies such as automated feeding stations. The goal of this study was to investigate resilience traits, which were estimated as deviations from longitudinal weight, feed intake and feeding behaviour data during the finishing phase. A dataset with 324,207 records between the age of 95 and 155 days on 5,939 Piétrain pigs with known pedigree and genomic information was used. We provided guidelines for a rigid quality control of longitudinal body weight data, as we found that outliers can significantly affect results. Gompertz growth curve analysis, linear modelling and trajectory analyses were used for quantifying resilience traits. Results: To our knowledge, this is the first study comparing resilience traits from longitudinal body weight, feed intake and feeding behaviour data in pigs. We demonstrated that the resilience traits are lowly to moderately heritable for deviations in body weight (h² = 2.9%–20.2%), in feed intake (9.4%–23.3%) and in feeding behaviour (16.2%–28.3%). Additionally, these traits have good predictive abilities in cross-validation analyses. Deviations in individual body weight and feed intake trajectories are highly correlated (r_g = 0.78) with low to moderate favourable genetic correlations with feed conversion ratio (r_g = 0.39–0.49). Lastly, we showed that some resilience traits, such as the natural logarithm of variances of observed versus predicted body weights (lnvar_weight), are more robust to lower observation frequencies and are repeatable over three different time periods of the finishing phase. Conclusions: Our results will help future studies investigating resilience traits and resilience-related traits. Moreover, our study provides first results on standardization of quality control and efficient data sampling from automated feeding station data. Our findings will be valuable for breeding organizations as they offer evidence that pigs’ general resilience can be selected on with good accuracy. Moreover, this methodology might be extended to other species to quantify resilience based on longitudinal data.

Summary: The present study focuses on the Belgian Milk Sheep in Flanders (Belgium) and compares its genetic diversity and relationship with the Flemish Sheep, the Friesian Milk Sheep, the French Lacaune dairy sheep and other Northern European breeds. For this study, 94 Belgian Milk Sheep, 23 Flemish Sheep and 22 Friesian Milk Sheep were genotyped with the OvineSNP50 array. In addition, 29 unregistered animals phenotypically similar to Belgian Milk Sheep were genotyped using the 15K ISGC chip. Both Belgian and Friesian Milk Sheep as well as the East Friesian Sheep were found to be less diverse than the other seven breeds included in this study. Genomic inbreeding coefficients based on runs of homozygosity (ROH) were estimated at 14.5, 12.4 and 10.2% for Belgian Milk Sheep, Flemish Sheep and Friesian Milk Sheep respectively. Out of 29 unregistered Belgian Milk Sheep, 28 mapped in the registered Belgian Milk Sheep population. Ancestry analysis, PCA and F_ST calculations showed that Belgian Milk Sheep are more related to Friesian Milk Sheep than to Flemish Sheep, which was contrary to the breeders' expectations. Consequently, breeders may prefer to crossbreed Belgian Milk Sheep with Friesian sheep populations (Friesian Milk Sheep or East Friesian Sheep) in order to increase diversity. This research underlines the usefulness of SNP chip genotyping and ROH analyses for monitoring genetic diversity and studying genetic links in small livestock populations, profiting from internationally available genotypes. As assessment of genetic diversity is vital for long‐term breed survival, these results will aid flockbooks to preserve genetic diversity.

Background: Previous research showed that deviations in longitudinal data are heritable and can be used as a proxy for pigs’ general resilience. However, only a few studies investigated the relationship between these resilience traits and other traits related to resilience and welfare. Therefore, this study investigated the relationship between resilience traits derived from deviations in longitudinal data and traits related to animal resilience, health and welfare, such as tail and ear biting wounds, lameness and mortality. Results: In our experiment, 1919 finishing pigs with known pedigree (133 Piétrain sires and 266 crossbred dams) were weighed every 2 weeks and scored for physical abnormalities, such as lameness and ear and tail biting wounds (17,066 records). Resilience was assessed via deviations in body weight, deviations in weighing order and deviations in observed activity during weighing. The association between these resilience traits and physical abnormality traits was investigated and genetic parameters were estimated. Deviations in body weight had moderate heritability estimates (h ² = 25.2 to 36.3%), whereas deviations in weighing order (h ² = 4.2%) and deviations in activity during weighing (h ² = 12.0%) had low heritability estimates. Moreover, deviations in body weight were positively associated and genetically correlated with tail biting wounds (r _g = 0.22 to 0.30), lameness (r _g = 0.15 to 0.31) and mortality (r _g = 0.19 to 0.33). These results indicate that events of tail biting, lameness and mortality are associated with deviations in pigs’ body weight evolution. This relationship was not found for deviations in weighing order and activity during weighing. Furthermore, individual body weight deviations were positively correlated with uniformity at the pen level, providing evidence that breeding for these resilience traits might increase both pigs’ resilience and within-family uniformity. Conclusions: In summary, our findings show that breeding for resilience traits based on deviations in longitudinal weight data can decrease pigs’ tail biting wounds, lameness and mortality while improving uniformity at the pen level. These findings are valuable for pig breeders, as they offer evidence that these resilience traits are an indication of animals’ general health, welfare and resilience. Moreover, these results will stimulate the quantification of resilience via longitudinal body weights in other species.