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Of 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.