Selective breeding has led to gradual changes at the genome level of horses. Deciphering selective pressure patterns is progressive to understand how breeding strategies have shaped the sport horse genome; although, little is known about the genomic regions under selective pressures in sport horse breeds. The major goal of this study was to shed light on genomic regions and biological pathways under selective pressures in sport horses. In this study, whole-genome sequences of 16 modern sport and 35 non-sport horses were used to investigate the genomic selective signals of sport performance, by employing fixation index, nucleotide diversity, and Tajima’s D approaches. A total number of 49 shared genes were identified using these approaches. The functional enrichment analysis for candidate genes revealed novel significant biological processes related to musculoskeletal system development, such as limb development and morphogenesis, having been targeted by selection in sport breeds.

Improvement of prediction accuracy of estimated breeding values (EBVs) can lead to increased profitability for swine breeding companies. This study was performed to compare the accuracy of different popular genomic prediction methods and traditional best linear unbiased prediction (BLUP) for future performance of back-fat thickness (BFT), average daily gain (ADG), and loin muscle depth (LMD) in Canadian Duroc, Landrace, and Yorkshire swine breeds. In this study, 17,019 pigs were genotyped using Illumina 60K and Affymetrix 50K panels. After quality control and imputation steps, a total of 41,304, 48,580, and 49,102 single-nucleotide polymorphisms remained for Duroc (n = 6,649), Landrace (n = 5,362), and Yorkshire (n = 5,008) breeds, respectively. The breeding values of animals in the validation groups (n = 392–774) were predicted before performance test using BLUP, BayesC, BayesCπ, genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods. The prediction accuracies were obtained using the correlation between the predicted breeding values and their deregressed EBVs (dEBVs) after performance test. The genomic prediction methods showed higher prediction accuracies than traditional BLUP for all scenarios. Although the accuracies of genomic prediction methods were not significantly (P > 0.05) different, ssGBLUP was the most accurate method for Duroc-ADG, Duroc-LMD, Landrace-BFT, Landrace-ADG, and Yorkshire-BFT scenarios, and BayesCπ was the most accurate method for Duroc-BFT, Landrace-LMD, and Yorkshire-ADG scenarios. Furthermore, BayesCπ method was the least biased method for Duroc-LMD, Landrace-BFT, Landrace-ADG, Yorkshire-BFT, and Yorkshire-ADG scenarios. Our findings can be beneficial for accelerating the genetic progress of BFT, ADG, and LMD in Canadian swine populations by selecting more accurate and unbiased genomic prediction methods.

Selection, both natural and artificial, leaves patterns on the genome during domestication of animals and leads to changes in allele frequencies among populations. Detecting genomic regions influenced by selection in livestock may assist in understanding the processes involved in genome evolution and discovering genomic regions related to traits of economic and ecological interests. In the current study, genetic diversity analyses were conducted on 34,206 quality‐filtered SNP positions from 450 individuals in 15 sheep breeds, including six indigenous breeds from the Middle East, namely Iranian Balouchi, Afshari, Moghani, Qezel, Karakas and Norduz, and nine breeds from Europe, namely East Friesian Sheep, Ile de France, Mourerous, Romane, Swiss Mirror, Spaelsau, Suffolk, Comisana and Engadine Red Sheep. The SNP genotype data generated by the Illumina OvineSNP50 Genotyping BeadChip array were used in this analysis. We applied two complementary statistical analyses, FST (fixation index) and xp‐EHH (cross‐population extended haplotype homozygosity), to detect selection signatures in Middle Eastern and European sheep populations. FST and xp‐EHH detected 629 and 256 genes indicating signatures of selection, respectively. Genomic regions identified using FST and xp‐EHH contained the CIDEA, HHATL, MGST1, FADS1, RTL1 and DGKG genes, which were reported earlier to influence a number of economic traits. Both FST and xp‐EHH approaches identified 60 shared genes as the signatures of selection, including four candidate genes (NT5E, ADA2, C8A and C8B) that were enriched for two significant Gene Ontology (GO) terms associated with the adenosine metabolic procedure. Knowledge about the candidate genomic regions under selective pressure in sheep breeds may facilitate identification of the underlying genes and enhance our understanding on these genes role in local adaptation.

Natural selection and domestication have shaped modern horse populations, resulting in a vast range of phenotypically diverse breeds. Horse breeds are classified into three types (pony, light, and draft) generally based on their body type. Understanding the genetic basis of horse type variation and selective pressures related to the evolutionary trend can be particularly important for current selection strategies. Whole-genome sequences were generated for 14 pony and 32 light horses to investigate the genetic signatures of selection of the horse type in pony and light horses. In the overlapping extremes of the fixation index and nucleotide diversity results, we found novel genomic signatures of selective sweeps near key genes previously implicated in body measurements including C4ORF33, CRB1, CPN1, FAM13A, and FGF12 that may influence variation in pony and light horse types. This study contributes to a better understanding of the genetic background of differences between pony and light horse types.

The genetic structure and characteristics of Iranian native breeds are yet to be comprehensibly investigated and studied. Therefore, we employed genomic information of 364 Iranian native horses representing the Asil (n = 109), Caspian (n = 40), Dareshuri (n = 44), Kurdish (n = 95), and Turkoman (n = 76) breeds to reveal the genetic structure and characteristics. For these and 19 other horse breeds, principal component analysis, Bayesian model-based, Neighbor-Net, and bootstrap-based TreeMix approaches were applied to investigate and compare their genetic structure. Additionally, three haplotype-based methods including haplotype homozygosity pooled, integrated haplotype score, and number of segregating sites by length were applied to trace genomic footprints of selection of Asil, Caspian, Dareshuri, Kurdish, and Turkoman groups. Then, the Mahalanobis distance based on the negative-log 10 rankbased P-values was estimated based on the haplotype homozygosity pooled, integrated haplotype score, and number of segregating sites by length values. Asil, Caspian, Dareshuri, Kurdish, and Turkoman can be categorized into five different genetic clusters. Based on the top 1% of Mahalanobis distance based on the negative-log 10 rank-based P-values of SNPs, we identified 24 SNPs formerly reported to be associated with different traits and >100 genes undergoing selection pressures in Asil, Caspian, Dareshuri, Kurdish, and Turkoman. The detected QTL undergoing selection pressures were associated with withers height, equine metabolic syndrome, overall body size, insect bite hypersensitivity, guttural pouch tympany, white markings, Rhodococcus equi infection, jumping test score, alternate gaits, and body weight traits. Our findings will aid to have a better perspective of the genetic characteristics and population structure of Asil, Caspian, Dareshuri, Kurdish, and Turkoman horses as Iranian native horse breeds.