The genetic differentiation of the current horse population was evolutionarily created by natural or artificial selection which shaped the genomes of individual breeds in several unique ways. The availability of high throughput genotyping methods created the opportunity to study this genetic variation on a genome-wide level allowing detection of genome regions divergently selected between separate breeds as well as among different horse types sharing similar phenotypic features. In this study, we used the population differentiation index (F_ST) that is generally used for measuring locus-specific allele frequencies variation between populations, to detect selection signatures among six horse breeds maintained in Poland. These breeds can be classified into three major categories, including light, draft and primitive horses, selected mainly in terms of type (utility), exterior, performance, size, coat color and appearance. The analysis of the most pronounced selection signals found in this study allowed us to detect several genomic regions and genes connected with processes potentially important for breed phenotypic differentiation and associated with energy homeostasis during physical effort, heart functioning, fertility, disease resistance and motor coordination. Our results also confirmed previously described association of loci on ECA3 (spanning LCORL and NCAPG genes) and ECA11 (spanning LASP1 gene) with the regulation of body size in our draft and primitive (small size) horses. The efficiency of the applied F_ST-based approach was also confirmed by the identification of a robust selection signal in the blue dun colored Polish Konik horses at the locus of TBX3 gene, which was previously shown to be responsible for dun coat color dilution in other horse breeds. F_ST-based method showed to be efficient in detection of diversifying selection signatures in the analyzed horse breeds. Especially pronounced signals were observed at the loci responsible for fixed breed-specific features. Several candidate genes under selection were proposed in this study for traits selected in separate breeds and horse types, however, further functional and comparative studies are needed to confirm and explain their effect on the observed genetic diversity of the horse breeds.

Identification of selection signatures can provide a direct insight into the mechanism of artificial selection and allow further disclosure of the candidate genes related to the animals’ phenotypic variation. Domestication and subsequent long-time selection have resulted in extensive phenotypic changes in domestic pigs, involving a number of traits, like behavior, body composition, disease resistance, reproduction and coat color. In this study, based on genotypes obtained from PorcineSNP60 Illumina assay we attempt to detect both diversifying and within-breed selection signatures in 530 pigs belonging to four breeds: Polish Landrace, Puławska, Złotnicka White and Złotnicka Spotted, of which the last three are a subject of conservative breeding and substantially represent the native populations. A two largely complementary statistical methods were used for signatures detection, including: pairwise FST and relative extended haplotype homozygosity (REHH) test. Breed-specific diversifying selection signals included several genes involved in processes connected with fertility, growth and metabolism which are potentially responsible for different phenotypes of the studied breeds. The diversifying selection signals also comprised PPARD gene that was previously found to have a large effect on the shape of the external ear in pigs or two genes encoding neuropeptide Y receptors (Y2 and Y5) involved in fat deposition and stress response which are important features differentiating the studied breeds. REHH statistics allowed detecting several within-breed selection signatures overlapping with genes connected with a range of functions including, among others: metabolic pathways, immune system response or implantation and development of the embryo. The study provides many potential candidate genes with implication for traits selected in the individual breeds and gives strong basis for further studies aiming at identification of sources of variation among the studied pig breeds.

MicroRNAs are vital gene expression regulators, extensively studied worldwide. The large-scale characterization of miRNAomes is possible using next-generation sequencing (NGS). This technology offers great opportunities, but these cannot be fully exploited without proper and comprehensive bioinformatics analysis. This may be achieved by the use of reliable dedicated software; however, different programs may generate divergent results, leading to additional discrepancies. Thus, the aim of this study was to compare three bioinformatic algorithms dedicated to NGS-based microRNA profiling and validate them using an alternative method, namely RT-qPCR. The comparison analysis revealed differences in the number and sets of identified miRNAs. The qPCR confirmed the expression of the investigated microRNAs. The correlation analysis of NGS and qPCR measurements showed strong and significant coefficients for a subset of the tested miRNAs, including those detected by all three algorithms. Single miRNA variants (isomiRs) showed different levels of correlation with the qPCR data. The obtained results revealed the good performance of all tested programs, despite the observed differences. Moreover, they implied that some specific miRNAs may be differentially estimated using NGS technology and the qPCR method, regardless of the used bioinformatics software. These discrepancies may stem from many factors, including the composition of the isomiR profile, their abundance, length, and investigated species. In conclusion, in this study, we shed light on the bioinformatics aspects of miRNAome profiling, elucidating its complexity and pinpointing potential features influencing validation. Thus, qPCR validation results should be open to interpretation when not fully concordant with NGS results until further, additional analyses are conducted.