The forgotten variable? Does the euthanasia method and sample storage condition influence an organisms transcriptome - a gene expression analysis on multiple tissues in pigs.

BACKGROUND: Transcriptomic studies often require collection of fresh tissues post euthanasia. The chosen euthanasia method might have the potential to induce variations in gene expressions that are unlinked with the experimental design. The present study compared the suitability of 'nitrogen gas in foam' (ANOXIA) in comparison to a non-barbiturate anaesthetic, T-61® (T61), for euthanizing piglets used in transcriptome research. Further, the effect of common tissue storage conditions, RNAlater™ (RL) and snap freezing in liquid nitrogen (LN2), on gene expression profiles were also analysed. RESULTS: On comparison of the 3'mRNA-Seq data generated from pituitary, hypothalamus, liver and lung tissues, no significant differential expression in the protein coding genes were detected between the euthanasia methods. This implies that the nitrogen anoxia method could be a suitable alternative for euthanasia of piglets used in transcriptomic research. However, small nuclear RNAs (snRNAs) that constitute the eukaryotic spliceosomal machinery were found to be significantly higher (log2fold change ≥ 2.0, and adjusted p value ≤ 0.1) in pituitary samples collected using ANOXIA. Non-protein coding genes like snRNAs that play an important role in pre-mRNA splicing can subsequently modify gene expression. Storage in RL was found to be superior in preserving RNA compared to LN2 storage, as evidenced by the significantly higher RIN values in representative samples. However, storage in RL as opposed to LN2, also influenced differential gene expression in multiple tissues, perhaps as a result of its inability to inhibit biological activity during storage. Hence such external sources of variations should be carefully considered before arriving at research conclusions. CONCLUSIONS: Source of biological variations like euthanasia method and storage condition can confound research findings. Even if we are unable to prevent the effect of these external factors, it will be useful to identify the impact of these variables on the parameter under observation and thereby prevent misinterpretation of our results.

Myostatin mutation causing double muscling could affect increased psoroptic mange sensitivity in dual purpose Belgian Blue cattle.

Belgian Blue cattle are known for their high degree of muscling and good carcass qualities. This high degree of muscling is mainly caused by a mutation in the myostatin gene (MSTN). Although the MSTN mutation is considered as fixed in the Belgian Blue breed, segregation is occurring in a sub-population bred for dual purpose. In the latter population, we observed an association between the mutation in MSTN and susceptibility to psoroptic mange, a skin disease caused by Psoroptes ovis mites that heavily plagues Belgian Blue cattle. In total, 291 animals were sampled and screened for their susceptibility for mange lesions and their MSTN genotype. Via linear mixed modelling, we observed that homozygous mutant animals had a significant increase in the size of mange lesions (+2.51% lesion extent) compared to homozygous wild type. These findings were confirmed with zero-inflated modelling, an animal model and odds analysis. Risk ratios for developing severe mange lesions were 5.9 times as high for homozygous mutant animals. All analyses confirmed an association between the MSTN genotype and psoroptic mange lesion size.

How to study runs of homozygosity using PLINK? A guide for analyzing medium density SNP data in livestock and pet species.

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 FROH 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.

Unraveling the genetic diversity of Belgian Milk Sheep using medium-density SNP genotypes.

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 FST 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.

SNP genotypes reveal breed substructure, selection signatures and highly inbred regions in Piétrain pigs.

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, and FST analysis 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.