The horse is central to many Indigenous cultures across the American Southwest and the Great Plains. However, when and how horses were first integrated into Indigenous lifeways remain contentious, with extant models derived largely from colonial records. We conducted an interdisciplinary study of an assemblage of historic archaeological horse remains, integrating genomic, isotopic, radiocarbon, and paleopathological evidence. Archaeological and modern North American horses show strong Iberian genetic affinities, with later influx from British sources, but no Viking proximity. Horses rapidly spread from the south into the northern Rockies and central plains by the first half of the 17th century CE, likely through Indigenous exchange networks. They were deeply integrated into Indigenous societies before the arrival of 18th-century European observers, as reflected in herd management, ceremonial practices, and culture.

Inbreeding coefficients can be estimated either from pedigree data or from genomic data, and with genomic data, they are either global or local (when the linkage map is used). Recently, we developed a new hidden Markov model (HMM) that estimates probabilities of homozygosity-by-descent (HBD) at each marker position and automatically partitions autozygosity in multiple age-related classes (based on the length of HBD segments). Our objectives were to: (1) characterize inbreeding with our model in an intensively selected population such as the Belgian Blue Beef (BBB) cattle breed; (2) compare the properties of the model at different marker densities; and (3) compare our model with other methods. When using 600 K single nucleotide polymorphisms (SNPs), the inbreeding coefficient (probability of sampling an HBD locus in an individual) was on average 0.303 (ranging from 0.258 to 0.375). HBD-classes associated to historical ancestors (with small segments ≤ 200 kb) accounted for 21.6% of the genome length (71.4% of the total length of the genome in HBD segments), whereas classes associated to more recent ancestors accounted for only 22.6% of the total length of the genome in HBD segments. However, these recent classes presented more individual variation than more ancient classes. Although inbreeding coefficients obtained with low SNP densities (7 and 32 K) were much lower (0.060 and 0.093), they were highly correlated with those obtained at higher density (r = 0.934 and 0.975, respectively), indicating that they captured most of the individual variation. At higher SNP density, smaller HBD segments are identified and, thus, more past generations can be explored. We observed very high correlations between our estimates and those based on homozygosity (r = 0.95) or on runs-of-homozygosity (r = 0.95). As expected, pedigree-based estimates were mainly correlated with recent HBD-classes (r = 0.56). Although we observed high levels of autozygosity associated with small HBD segments in BBB cattle, recent inbreeding accounted for most of the individual variation. Recent autozygosity can be captured efficiently with low-density SNP arrays and relatively simple models (e.g., two HBD classes). The HMM framework provides local HBD probabilities that are still useful at lower SNP densities.

Infrared thermography (IRT) is a popular technology used for the detection of thermal changes given its non-invasive nature and lack of direct contact with the individual. Accordingly, the maximal eye temperature (MaxET) measured with IRT has been extensively applied in equine research. However, there is little information available about the potential limitations of the MaxET in field studies. Thus, the aims of this study were to 1) quantify the individual variation of MaxET in field conditions and the effects of individual, breed, body size (height at withers), eye side, sex and age, 2) determine the effects of environment and operator, and 3) explore the relationship between MaxET and rectal temperature (RT) at rest. To accomplish these aims, 791 MaxET measures from 32 horses were collected in Sweden in five different months and five farms over a period of 12 months. There was an effect of individual on IRT (P < .05) and individual MaxET varied from 29.4 to 37.6 °C. IRT was also affected (P < .05) by breed and sex (maximal difference 1.1 °C and 0.3 °C, respectively) but not by eye side, age and height at withers. There were significant effects of month and farm (maximal differences; 2.4 and 2.3 °C, respectively), between outdoor and indoor measurements (0.8 °C) and also between operators (0.2 °C). There were no correlations between MaxET and RT. These results demonstrate that in horses observed at rest in their home environment, MaxET is affected by endogenous (sex and breed) and environmental factors (farm, location and month of the year) and shows no relationship to RT. We strongly suggest that IRT technology should be used with great caution in field studies and only under conditions where these factors can be accurately accounted for.

Background: Copy Number Variation (CNV) is a common form of genetic variation underlying animal evolution and phenotypic diversity across a wide range of species. In the mammalian genome, high frequency of CNV differentiation between breeds may be candidates for population-specific selection. However, CNV differentiation, selection and its population genetics have been poorly explored in horses. Results: We investigated the patterns, population variation and gene annotation of CNV using the Axiom® Equine Genotyping Array (670,796 SNPs) from a large cohort of individuals (N = 1755) belonging to eight European horse breeds, varying from draught horses to several warmblood populations. After quality control, 152,640 SNP CNVs (individual markers), 18,800 segment CNVs (consecutive SNP CNVs of same gain/loss state or both) and 939 CNV regions (CNVRs; overlapping segment CNVs by at least 1 bp) compared to the average signal of the reference (Belgian draught horse) were identified. Our analyses showed that Equus caballus chromosome 12 (ECA12) was the most enriched in segment CNV gains and losses (~ 3% average proportion of the genome covered), but the highest number of segment CNVs were detected on ECA1 and ECA20 (regardless of size). The Friesian horses showed private SNP CNV gains (> 20% of the samples) on ECA1 and Exmoor ponies displayed private SNP CNV losses on ECA25 (> 20% of the samples). The Warmblood cluster showed private SNP CNV gains located in ECA9 and Draught cluster showed private SNP CNV losses located in ECA7. The length of the CNVRs ranged from 1 kb to 21.3 Mb. A total of 10,612 genes were annotated within the CNVRs. The PANTHER annotation of these genes showed significantly under- and overrepresented gene ontology biological terms related to cellular processes and immunity (Bonferroni P-value < 0.05). We identified 80 CNVRs overlapping with known QTL for fertility, coat colour, conformation and temperament. We also report 67 novel CNVRs. Conclusions: This work revealed that CNV patterns, in the genome of some European horse breeds, occurred in specific genomic regions. The results provide support to the hypothesis that high frequency private CNVs residing in genes may potentially be responsible for the diverse phenotypes seen between horse breeds.

Although harness racing is of high economic importance to the global equine industry, significant genomic resources have yet to be applied to mapping harness racing success. To identify genomic regions associated with harness racing success, the current study performs genome-wide association analyses with three racing performance traits in the Norwegian-Swedish Coldblooded Trotter using the 670 K Axiom Equine Genotyping Array. Following quality control, 613 horses and 359,635 SNPs were retained for further analysis. After strict Bonferroni correction, nine genome-wide significant SNPs were identified for career earnings. No genome-wide significant SNPs were identified for number of gallops or best km time. However, four suggestive genome-wide significant SNPs were identified for number of gallops, while 19 were identified for best km time. Multiple genes related to intelligence, energy metabolism, and immune function were identified as potential candidate genes for harness racing success. Apart from the physiological requirements needed for a harness racing horse to be successful, the results of the current study also advocate learning ability and memory as important elements for harness racing success. Further exploration into the mental capacity required for a horse to achieve racing success is likely warranted.