Browsing by Author "Durward-Akhurst, Sian"
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Item Immune-Mediated Myositis in Horses: From phenotype to genotype(2016-05) Durward-Akhurst, SianBackground: Equine immune-mediated myositis (IMM) is a painful and debilitating condition of predominantly Quarter Horse (QH) and related breeds. The epaxial and gluteal muscles are most severely affected and muscle atrophy can be dramatic, with 50% of the affected muscle mass being lost in <72 hours. Diagnosis is based on a muscle biopsy of affected muscles and the identification of lymphocytes invading myofibers and in some cases surrounding blood vessels. The pathophysiology is presumed to be immune-mediated, but further evidence is needed to confirm this. Abnormal expression of major histocompatibility Complex (MHC) has been identified on muscle fibers from most human IMMs and provides the most consistent indication of an immune-mediated mechanism. The restriction of IMM to primarily QH and related breeds, particularly in certain bloodlines suggests that there is a genetic susceptibility underlying IMM. Hypothesis: Quarter Horses are genetically susceptible to an immune mediated myositis that is characterized by abnormal expression of MHC class I and/or class II on the sarcolemma of myofibers. Specific Aim 1: To determine if abnormal MHC class I and II expression is present on the sarcolemma of myofibers of horses with active IMM in the presence or absence of myofiber lymphocytic infiltrates. Specific Aim 2: To characterize the subtypes of lymphocytes in the myofibers of horses with active IMM and correlate this with MHC expression. Methods: Immunohistochemical staining for MHC I, II, CD4+, CD8+, CD20+ lymphocytes was performed on archived muscle samples of IMM (21 horses) and controls (3 healthy and 6 disease controls). Scores were given for MHC I and II and for lymphocytic subtypes. Results: A degree of sarcolemmal MHC I and II expression was present in 81% and 71% of IMM horses, respectively. CD4+, CD8+, and CD20+ cells were present in 20/21 IMM horses with a CD4+ predominance in 48% of cases. MHC I score was positively correlated with MHC II (r = 0.89, p = <0.001) and CD8+ (r= 0.64, p = 0.002) and CD20+ (r = 0.66, p = 0.001) lymphocyte and macrophage scores (r = 0.70, p = <0.001). MHC II scores were positively correlated to CD8+ (r = 0.59, p = 0.005), CD20+ (r = 0.61, p = 0.004) lymphocyte and macrophage (r = 0.70, p = <0.001) scores. Specific Aim 3: To determine if equine IMM is significantly associated with a region of the equine genome using a genome-wide association (GWA) study. Methods: DNA was extracted from blood and muscle of 36 IMM horses and 54 healthy controls of QH-related breeds that were housed in similar environments. Sequencing was performed on equine 50K and 70K single nucleotide polymorphism (SNP) arrays. A GWA was performed across 40,811 SNPs that passed quality control. To account for elevated genomic inflation, statistical analysis was performed using GEMMA and GRAMMAR-GC software. Results: A significant association was identified between IMM and a 2 MB region on equine chromosome 11. Five SNPs in 3 haplotype blocks reached genome-wide significance using the 2 different statistical methods to account for population stratification. The significant region contains 6 myosin heavy chain genes expressed in skeletal muscle, including MYH2, which has been associated with a human IMM. Conclusions: Equine IMM is characterized by MHC I and II expression on the sarcolemma of myofibers during an acute CD4+ and CD8+ lymphocytic inflammatory episode. There is an approximately 2MB region on equine chromosome 11 that is associated with the development of the disease. Sequencing of the MYH genes in IMM cases and unaffected controls is warranted to identify variants that cause IMM in Quarter Horses.Item Tools For Precision Medicine In The Horse(2020-03) Durward-Akhurst, SianOf the 239 recognized genetic traits and disorders in the horse, the causative variants have been identified for less than 50 genetic disease phenotypes, leaving a vast deficit in our understanding of genetic disease in this species. Large-scale studies of genetic variation from genome sequencing have been used with great success in humans, dogs, cats, and other species, to improve understanding of genetic variation in the general population (i.e.; not phenotyped for a single disease) and to facilitate identification of causative variants. These steps are two of the first and most important steps towards Precision Medicine, or genome-driven medical decision making. Precision Medicine has been demonstrated to facilitate disease diagnosis, allow for target treatment options addressing the specific disease-causing variant, and provide more accurate prognostic information based on the response of patients with the same disease-causing variants to treatment. At this time, Precision Medicine is in its infancy in the equine field and the largest catalog of genetic variation is from < 100 horses. In this thesis, we develop the first large-scale studies of single nucleotide polymorphism (SNP) and structural (copy number variants [CNVs], insertions, deletions, inversions, and intra- and interchromosomal translocations) variation in the horse. We also calculate the number of variants computationally predicted to have a detrimental effect on phenotype (i.e.; the genetic burden) and the frequencies of 154 previously identified phenotype-causing and -associated variants. Lastly, we demonstrate the utility of these resources for prioritizing putative phenotype-causing variants for 11 equine phenotypes that are analogous to Mendelian phenotypes in humans (alopecia areata, atrial fibrillation, congenital bilateral absence of the vas deferens, eosinophilic myositis, hemochromatosis, hyperkalemic periodic paralysis, skeletal muscle hypertrophy (hypertrophy), idiopathic renal hematuria, malignant hyperthermia, microphthalmia, and myotonia). We identified SNPs and small and large structural variants from whole genome sequence of 534 horses from 46 breeds and discovered 28,273,058 SNPs, 1,609,215 small insertions and deletions, 500,780 copy number variants (CNVs), 16,982,525 structural variants (insertions, deletions, inversions, and intrachromosomal translocations), and 11,149,562 interchromosomal translocations. The genetic burden in this population was 0.02% (5,852 variants), with 5,020 of those variants predicted to lead to complete loss of function (LOF) of the gene carrying the variant. Each individual horse carried 865 genetic burden variants (705 LOF variants). Of the 154 phenotype-causing and -associated variants, 94 were identified in this catalog of genetic variation. Finally, we developed 3 pipelines for prioritizing putative-phenotype-causing variants. A stringent candidate gene approach excluded ~100% of variants for our 11 equine phenotypes, and this pipeline was determined to be too stringent. The other 2 pipelines prioritized variants using the expected allele frequency of the causative variant based on the disease prevalence and Hardy-Weinberg equilibrium, with a measure of gene constraint (A) and without (B). The pipelines led to a decrease in the number of possible variants by 99.99% (A) and 99.96% (B), leaving on average 211 (A) and 554 (B) putative phenotype-causing variants for follow-up. Overall, we have produced the largest catalog of genetic variation in the horse to date. This resource can be used to prioritize phenotype-causing variants for all future genetic investigations in the horse. The genetic burden is higher in horses than in humans, which is expected given the reduced genetic diversity in the horse compared to humans and the reduced quality of the horse reference genome and annotation files compared with humans. This catalog of variation was successfully used to provide additional evidence that all the previously published disease-causing variants are likely the true variants, based on their low frequency in this population. We also demonstrated the utility of this catalog for prioritizing phenotype-causing variants for suspected equine genetic phenotypes that are analogous to Mendelian phenotypes in humans. These putative variants will be followed up using additional computational tools and through genotyping in additional cases to identify the true phenotype-causing variants.