Browsing by Subject "horse"

Now showing 1 - 5 of 5
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    Advances in Horse Health and Management: Estimating Bodyweight and Grazing Legumes
    (2016-07) Catalano, Devan
    The role of the horse (Equus caballus L.) has evolved since it first appeared four million years ago (Hunt, 1995). According to a survey conducted by the United States Department of Agriculture (USDA), 45.7% of farms use horses for pleasure, 24.8% use horses for farm or ranch work, and 15.9% use horses for breeding (USDA, 2007). Within the sector of horses used as pleasure, workloads can vary drastically from minimal work (maintenance) to intense work (horses participating at the highest levels of competition). Within this range of workload, there are also horses described as hard keepers or easy keepers. The range of energy output of horses varies drastically; therefore, there is not a single ratio or feedstuff that applies to all horses. These different categories of horses have led to two different management problems; how to keep bodyweight (BW) off easy keepers and maintenance horses, and how to keep BW on hard keepers and performance horses. The objectives of the following studies were: 1. to determine the forage nutritive value, yield, and preference of legumes when grazed by adult horses and 2. to assess the accuracy of previously derived BW estimation equations, and if warranted, develop new BW estimation equations for adult draft and warmblood horse breeds using morphometric measurements. To determine objective 1, research was conducted in 2014 and 2015 in St. Paul, MN. Legumes were established as monocultures and in binary mixtures with cool-season grasses in a randomized complete block design with four replicates. Stands were established on May 16, 2014 and April 27, 2015. Adult horses grazed eight alfalfa (Medicago sativa L.) varieties, one red clover (Trifolium pratense L.), and one white clover (Trifolium repens L) when legumes reached the pre-bud stage. Legumes were measured for yield and samples to determine forage nutritive values were harvested prior to grazing. Plots were visually assessed for the percentage of forage removal on a scale of 0 to 100 to determine horse preference. White clover had the greatest amount of equine digestible energy (DE; 2.58 to 2.75 Mcal/kg) in monocultures and mixtures in 2014 and in monoculture in 2015. Digestible energy of all legumes exceeded equine DE requirements for adult horses at maintenance. In both years, alfalfa varieties yielded more compared to white clover (P < 0.0001). The top alfalfa variety yielded 17.4 and 12.9 Mt/ha in 2014 and 2015, respectively. In both years, horses had similar preference for all legumes and removed between 72 to 99% of available forage. This research helps to confirm that legumes are a nutrient dense, high yielding and preferred forage when grazed by adult horses. To determine objective 2, morphometric measurements were collected on adult (≥3 yr), non-pregnant draft (n = 138) and warmblood (n = 89) horse breeds at two separate shows in Minnesota in 2014. Trained personnel assessed body condition score (BCS) on a scale of 1 to 9, measured wither height at the third thoracic vertebra, body length from the point of the shoulder to the point of the buttock (BL wrap), body length from the point of the shoulder to a line perpendicular to the point of the buttock (BL straight), neck circumference at the midway point between the poll and the withers, and girth circumference at the third thoracic vertebra. Each horse was weighed using a portable livestock scale. Individuals were grouped into breed types using multivariate ANOVA analysis of morphometric measurements. Bodyweight estimations equations were developed using linear regression modeling. For estimated BW, the model was fit using all individuals and all morphometric measurements, except BL wrap. For ideal BW, the model was fit using individuals with a BCS of 5 and morphometric measurements not affected by adiposity; BL straight and height. Mean (± SD) BCS was 6.3 (± 0.9) and 5.2 (± 0.6) for draft and warmblood horses, respectively. BW (kg) was estimated by taking [girth (cm)1.528 x BL straight (cm)0.524 x height (cm)0.246 x neck (cm)0.261] / 1,181 (draft) or 1,209 (warmblood)] (R2 = 0.96; rMSE = 28 kg). This is an improvement over the previous BW estimation equation for light-breed horses, which utilized BL wrap and girth circumference to estimate BW (R2 = 0.94; rMSE = 34 kg). Ideal BW (kg) was estimated by [(4.92 x BL straight (cm)) + (4.64 x height (cm)) – 951 (draft) or 1,016 (WB)] (R2= 0.90 and rMSE = 33 kg). Morphometric measurements were successfully used to develop new and improved BW-related equations for draft and warmblood horses. The equations will assist draft and warmblood horse owners and professionals with managing horse BW, nutrition and health.
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    Economic Contribution of Minnesota's Horse Racing Industry
    (University of Minnesota, 2017-02) Tuck, Brigid
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    Forage quality and blood metabolites of horses grazing alfalfa, cool-season perennial grass, and teff
    (2018-07) DeBoer, Michelle
    The impact of forage species on blood metabolites concentrations of grazing horses (Equus caballus L.) is unknown. However, these differences can be crucial as plasma amino acid (AA) concentrations as well as the glucose and insulin response of grazing horses can be indicators of nutritional status or metabolic health. As a result, the objectives of these studies were to determine the impact of different forage species on plasma AA concentrations, protein synthesis, as well as the glucose and insulin response across seasons. Research was conducted in May (spring), July (summer), September (fall), and late October (late-fall) in St. Paul, MN in 2016. However, the data collected was divided into three different studies (1) July samples taken during the first 4 hours were used to evaluate the forage and plasma AA concentrations (2) samples collected in July and September were included in the glucose and insulin response analysis of the regular grazing season and (3) May and October samples were used to analyze the glucose and insulin response during the extended grazing season. Forage treatments included alfalfa (Medicago sativa L.), a mixed perennial cool-season grass (CSG) and teff (Eragrostis tef [Zucc.] Trotter), however, not all forage species were grazed every season. Alfalfa and CSG were grazed in May while CSG and teff were grazed during the October, with all three species grazed in July and September. During these times, forages were grazed by six horses (24 ± 2 yr) randomly assigned to a forage in either a Latin-square or cross-over design. Jugular catheters were inserted 1 h prior to the start of grazing and horses had access to pasture starting at 08:00 h for either 4 or 8 h depending on the season. Jugular venous blood samples were collected from each horse prior to being turned out (0 h) and then at 2 hour intervals following turnout. Plasma and serum samples were collected and analyzed for AA, glucose, and insulin. Corresponding forage samples were taken by hand harvest. Equine muscle satellite cell cultures were treated with sera from grazing horses to assess de novo protein synthesis. Seasons were analyzed separately and data were analyzed using the MIXED procedure in SAS with P ≤ 0.05. When evaluating forage, AA were generally lowest in teff and highest in CSG (P ≤ 0.05). Significant differences in threonine concentration in the plasma were observed; there was no effect on de novo protein synthesis of cultured equine myotubes treated with plasma obtained from the grazing horses (P ≥ 0.20). As a result, although there were significant differences in forage AA content only plasma threonine concentration was different at 4 h with no effect on protein synthesis of cultured equine satellite cells. When evaluating the glucose and insulin response during the regular grazing season, teff generally had lower (P ≤ 0.05) equine digestible energy (DE), crude protein (CP) and nonstructural carbohydrates (NSC) compared to the other forages. Differences in peak insulin were observed between horses grazing CSG and teff during the fall grazing (P ≤ 0.05). Additionally, when evaluating the extended grazing season, teff had lower NSC than CSG in the late-fall (P ≤ 0.05) with subsequently lower average glucose, average insulin, and peak insulin in horses grazing teff compared to CSG (P ≤ 0.05).These results suggest grazing teff could lower the glucose and insulin response of some horses, specifically in the fall and late-fall, and may provide an alternative forage for horses with metabolic concerns,
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    Genetic Factors Underlying Disease and Performance Traits in Standardbreds
    (2014-05) McCoy, Annette
    Many diseases and performance characteristics of the horse are considered to be "complex" traits because they are influenced by both genetic and environmental factors. Furthermore, many are polygenic in nature, reflecting the combined effects of multiple genes. Traditional methodological approaches, such as family linkage analysis and candidate gene sequencing are not ideal for identifying the multiple interacting alleles underlying complex/polygenic traits. An alternative investigational approach is needed that can account for environmental risk factors, issues related to population structure in large study cohorts, and epistatic interactions. In the work presented here, whole-genome approaches, including genome-wide association (GWA) analysis, whole-genome sequencing (WGS), and high-throughput genotyping, were used to investigate the genetic factors underlying three complex traits in Standardbred horses, a breed primarily used for harness racing. These were 1) osteochondrosis (OC; a disease of young horses in which the cartilage at the end of long bones does not form normally); 2) pacing (an alternative pattern of locomotion); and 3) performance (using speed as the phenotype). GWA analysis identified chromosomal regions of association for all three traits of interest, although the significance of the findings for speed was marginal, reflecting the challenge of appropriately phenotyping a complex trait such as performance. WGS performed in eighteen horses identified thousands of variants within chromosomal regions of association identified for OC and pacing, of which a small fraction were predicted to have functional effect. These variants were prioritized and a subset was selected for high-throughput genotyping in the study cohorts (180 horse phenotyped for OC, 500 phenotyped for gait). A few of the markers selected for OC were moderately associated with disease status, while the majority of the markers selected for gait were highly associated with this trait. A crucial next step for interpreting these data will be trying to understand the potential interactions between markers, using a combination of pathway analysis and random forest analysis. Knowledge of gene variants that affect complex traits in the horse - and how they interact with each other - may help reduce the incidence of disease and assist selection for desirable characteristics.
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    Identification of genetic loci underlying equine metabolic syndrome and laminitis risk
    (2019-10) Norton, Elaine
    Laminitis is a painful, debilitating disease of the hoof, often resulting in these horses being humanely euthanized due to uncontrolled pain. The most commonly cited cause of this life-threatening disease is a clustering of clinical signs resulting from a metabolically efficient phenotype, termed equine metabolic syndrome (EMS). While EMS is a commonly diagnosed syndrome, knowledge of the underlining pathophysiology is lacking and recommendations for diagnostic criteria are vague and inconsistent. EMS is thought to be complex disease, and identification of its underlying genetic risk factors and key gene-by-environment interactions will improve our understanding of EMS pathophysiology and allow for early detection of high-risk individuals and intervention prior to the onset of laminitis. We hypothesized that major genetic risk factors leading to EMS and laminitis susceptibility are shared across breeds of horses, and that differences in the severity and secondary features of the EMS phenotype between breeds, or between individuals within a breed, are the result of modifying genetic risk alleles with variable frequencies between breeds. To test these hypotheses, my PhD thesis has consisted of using phenotype and genotype data on 286 Morgan horses and 264 Welsh ponies, two high risk breeds for EMS. Phenotype data collected on all horses included: signalment, medical history, laminitis status, environmental management (feed, supplements, turnout and exercise regimen), and morphometric measurements (body condition score (BCS), wither height, and neck and girth circumference). After an eight hour fast, an oral sugar test (OST) was performed using 0.15mg/kg Karo lite corn syrup. Biochemical measurements included baseline insulin, glucose, non-esterified fatty acids (NEFA), triglycerides (TG), adiponectin, leptin and ACTH; and measurements 75 minutes after the OST included insulin (INS-OST) and glucose (GLU-OST). For inclusion in the study, each farm had to have at least one control and one horse with clinical signs consistent with EMS under the same management. Single nucleotide polymorphism (SNP) genotyping was performed on all horses. Haplotype phasing and genotype imputation up to two million SNPs was performed on horses genotyped on lower density arrays using Beagle software. Quality control on the imputed data was performed using the Plink software package. After genotype pruning, 1,428,337 and 1,158,831 SNPs remained for subsequent analysis in the Welsh ponies and Morgan horses, respectively. In chapter 2, SNP genotype data from the Welsh ponies and Morgan horses were used to estimate the heritability of the nine EMS biochemical measurements. Heritability (h2SNP) was estimated using a restricted maximum likelihood statistic with the inclusion of genetic relationship matrix, which was corrected for linkage disequilibrium (regions of the genome which are not independent as they are inherited together). The confounders of age, sex and season were included in the model based on the Akaike information criteria. In the Welsh ponies, seven of the nine biochemical traits had h2SNP estimates with p-values that exceeded the Holm-Bonferroni corrected cut-off as follows: triglycerides (0.31), glucose (0.41), NEFA (0.43), INS-OST (0.44), adiponectin (0.49), leptin (0.55), and insulin (0.81). Six of the nine EMS traits in the Morgans had h2SNP estimates with p-values that exceeded the Holm-Bonferroni cutoff as follows: INS-OST (0.36), leptin (0.49), GLU-OST (0.57), insulin (0.59), NEFA (0.68), and adiponectin (0.91). Insufficient population size and high trait variability may have limited power to obtain statistically significant h2SNP estimates for ACTH (both breeds), glucose and triglycerides in Morgans and GLU-OST in Welsh ponies. These data provide the first concrete evidence of a genetic contribution to key phenotypes associated with EMS and demonstrate that continued research for identification of the genetic risk factors for EMS phenotypes within and across breeds is warranted. Although heritability estimates provide valuable insight on the genetic contribution to a trait, they do not provide information on the number of contributing genes, specific genes involved, or where these genes are located within the genome. Genome wide association analyses (GWA) use SNP genotype data to identify those key regions of the genome that are associated with a trait. The objectives of chapter 3 were to (i) perform within breed GWA to identify significant contributing loci in Welsh ponies and Morgans, and (ii) use a meta-analysis approach to identify shared and unique loci between both breeds. For each trait, within breed GWA were performed from the imputed SNP genotype data using custom code for an improved mixed linear regression analysis. Prior to analysis, traits were adjusted to account for known covariates, with sex and age included as fixed effects and farm as a random effect. GWA meta-analysis was performed with a random effects model using the Morgans and Welsh pony GWA summary data from the 688,471 SNPs that were shared between breeds. To define the boundaries of the region, a pairwise comparison of linkage disequilibrium (LD) was calculated for all SNPs within the region. A custom code was used to identify regions where LD for all SNPs dropped below the LD threshold of 0.3 and spanned at least 100kb both 5' and 3' to the widest peak of LD within the window, which was used to define the boundaries of the ROI. An LD-region was identified as shared if it was within the boundaries of another LD-region and prioritized as described above for the fixed regions. Regions were prioritized based on whether they were identified as shared between breeds on meta-analysis (high priority), shared across traits (medium priority), or found in a single breed but not shared across traits (low priority). Prioritization resulted in 56 high, 26 medium, and 7 low priority genomic regions for a total of 1853 candidate genes in the Welsh ponies, and 39 high, 8 medium and 9 low priority regions for a total of 1167 candidate genes in the Morgan horses. Meta-analysis identified 65 of these regions that were shared across breeds. These data demonstrate that EMS is a polygenic trait with both across breed and breed specific genetic variants. In chapter 4, we utilized imputed whole-genome sequencing (WGS) and linear regression analysis in order to fine-map selected high priority LD-ROI in both the Morgan horses and Welsh ponies. LD-ROI were fine-mapped if they contained at least 5 SNPs with one SNP exceeding the threshold for genome-wide significance. Five fine-mapped regions from each breed were further interrogated for predicted impact using variant annotation. Protein-coding genes containing non-coding or coding variants within the fine-mapping region were then further prioritized based on known function and biological evidence in other species utilizing the PubMed search engine. A total of 19 positional candidate genes were identified as having biological evidence for a role in EMS. These data provide support for the process of fine-mapping GWA ROI by increasing marker density and using biological evidence across species to further prioritize candidate genes. In chapter 5, a missense mutation in the first exon of HMGA2 was identified as a putative functional allele for height and EMS phenotypes in Welsh ponies. It is well recognized that ponies (short horses) are at high risk for developing EMS; and in humans shorter individuals have an increased risk of developing cardiovascular disease, type II diabetes and metabolic syndrome. We hypothesized that genetic loci affecting height in ponies have pleiotropic effects on metabolic pathways and increase the susceptibility to EMS. Pearson’s correlation coefficient identified an inverse relationship between height and baseline insulin (-.26) in the Welsh ponies. Genomic signature of selection analysis was performed using a di statistic and identified a ~1.3 megabase region on chromosome 6, that was also identified on GWA. Haplotype analysis using HapQTL confirmed that there was a shared ancestral haplotype between height and insulin. This region contributed ~40% of the heritability for height and ~20% of the heritability for insulin. HMGA2 was identified as a candidate gene, and sequencing identified a single a c.83G>A variant (p.G28E) in HMGA2, previously described in other small stature horse breeds. In our cohort of ponies, the A allele had a frequency of .76, was strongly correlated with height (-.75) and was low to moderately correlated with metabolic traits including: insulin (.32), insulin after an oral sugar test (.25), non-esterified fatty acids (.19) and triglyceride (.22) concentrations. For this allele, model analysis suggested an additive mode of inheritance with height and a recessive mode of inheritance with the metabolic traits. This was the first gene identified as having a pleotropic effect for EMS. In conclusion, the results of my thesis are major steps forward in understanding the genetic contributions of EMS in two high risk breeds. Future directions include the continued identification of the specific genes and alleles contributing to EMS and could include prioritization of the positional candidate genes identified in aim 2 via (1) identification of biological candidate genes based on known gene function and evidence from other species; (2) use of whole genome sequencing and linear regression analysis to fine map regions; (3) use of intermediate phenotypes such as metabolomics or transcriptomics to identify shared regions; or (4) network analysis for identification of genes within similar, relevant pathways.