Browsing by Subject "equine metabolic syndrome"

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    Characterization of equine metabolic syndrome and mapping of candidate genetic loci
    (2016-01) Schultz, Nichol
    Equine metabolic syndrome (EMS) is a clustering of clinical signs associated with increased risk of laminitis, a potentially life-threatening condition of the foot. Similar to human metabolic syndrome (MetS), generalized and/or regional adiposity, hyperinsulinemia, insulin resistance and dyslipidemia, are reported components of EMS. However, there is ongoing debate regarding the definition of EMS, its etiology and pathogenesis, and the mechanisms linking EMS to its secondary consequences. Conflicting reports regarding EMS reflect the limitations of prior EMS studies, and that EMS is likely a complex, multifactorial condition similar to MetS. The primary objectives of this thesis were to characterize metabolic variation and EMS across horse and pony breeds and to identify candidate genes for EMS risk. Chapter 2 details the largest-ever epidemiological investigation of EMS in which 11 metabolic traits were measured in >600 horses and ponies from 166 farms. The use of multivariate, multilevel regression modeling allowed, for the first time, quantification of the relative importance of environmental (farm, dietary composition, exercise, etc.) and individual (age, breed, sex etc.) factors on these metabolic traits, while accounting for the often strong correlation between the trait measures. Age, sex, breed, obesity, prior laminitis status, and time of year were all strongly associated with one or more metabolic traits. Despite strong associations, these factors only explained 9.6% to 36.3% of the variation across these 11 traits, thus the majority of the variability in these measures remained unexplained. Unexplained variation at the farm level after accounting for diet, exercise, and sampling time of year, suggests that additional unmeasured environmental factors explain the similarity in metabolic measures between horses sampled from the same farm. Similarly, unexplained variation at the individual level suggests that unmeasured individual characteristics, for example genetics, are responsible for a large proportion of individual trait variation. Differences in the incretin response may also contribute to individual trait variation. The incretin response, defined as the difference in insulinemic responses between an oral and intravenous glucose challenge, is controlled by intestinal secretion of peptides, such as GLP-1, that stimulate pancreatic insulin secretion. While the incretin response has been hypothesized to play a role in the EMS pathogenesis, this hypothesis has not been adequately tested. In Chapter 3, the glycemic, insulinemic, and total and active GLP-1 responses to an oral sugar challenge, and the activity of DPP4, the major protease that breaks down GLP-1, were characterized. The use of a longitudinal analysis, rather than the traditional area under the curve analysis, allowed for more power to detect differences in these responses, including variation due to breed, obesity, and prior laminitis status. Unexplained individual level variation and breed differences in metabolic phenotypes support the hypothesis that there is an underlying genetic susceptibility to EMS. The final objective of this thesis was to identify candidate genes associated with EMS. MetS is a highly polygenic syndrome where numerous candidate genes have been identified. Whereas MetS associated variants are typically of small effect size; it was hypothesized that in EMS a small number of moderate to large effect loci contribute to variation in metabolic traits due to the fact that horse populations do not randomly mate and experience substantial selection pressure. 286 Morgan horses were genotyped on the Illumina SNP50 chip and imputed up to >800,000 SNPs to perform a genome wide association study (GWAS) to identify candidate genes for EMS. Additive genetic variance estimated from a genomic relationship matrix calculated from genotyped SNPs (“chip heritability”) indicated that the 11 measured metabolic traits were moderately heritable. Yet initial genome-wide scans using standard linear mixed models failed to detect significant associations. In Chapter 4, an improved linear mixed model for mapping polygenic traits in a population with familial relationships similar to that in many equine GWAS was developed and validated. The model incorporates a Bayesian variable selection method to rank SNPs and a stepwise feature selection process to determine the optimal SNPs to model the random polygenic effect, while including a random effect for each sampled herd or “familial cluster”. The method was validated using the QTL-MAS 2010 dataset, and Morgan horse and Welsh pony height datasets, and demonstrated increased power while controlling the false positive rate. Using this improved linear mixed model, 76 suggestive and 17 genome-wide significant candidate loci were identified for the 11 metabolic traits in the 286 Morgan horse cohort. Candidate genes had a substantial overlap with MetS candidate genes such as VEGFA, NRXN3, GRIK2, and TRIB2. Other interesting candidate genes included ISL, which encodes insulin enhancer protein that is thought to play an important role in regulating insulin gene expression; and AHR which encodes the aryl hydrocarbon receptor, a ligand activated transcription factor known to bind endocrine disrupting chemicals such as polycyclic aromatic hydrocarbons and dioxins. AHR is an interesting candidate gene given the potential role of endocrine disrupting chemical in the pathophysiology of MetS, and unexplained sources of farm level variation in Chapter 2. A unifying theme of Chapters 2-5 was the similarities between EMS and MetS, and the complex phenotypic and genetic architecture in both species. The use of advanced statistical modeling approaches allowed for a more complete understanding of the metabolic phenotypic variation in Chapters 2 and 3, and for the identification of many associated genetic loci in Chapter 5. The shared candidate genes for metabolic syndrome in humans and horses suggests similar underlying pathophysiological mechanisms and provides opportunity for exploring similar preventative and therapeutic management strategies.
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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.