Hypertrophic cardiomyopathy (HCM) is the most common monogenic heart disease and the most frequent cause of sudden cardiac death (SCD) in the young. It is characterized by unexplained left ventricular hypertrophy (LVH), diffuse and patchy fibrosis, and myofibrillar disarray. While the majority of patients remain asymptomatic, prognosis is poor in a subset who present with SCD or progress to heart failure (HF). Current methods to predict risk of these adverse events and to target therapy are limited. Current medical therapy does not protect against SCD, nor does it prevent development of HF. Therefore, the identification of novel risk markers would help develop therapeutic targets aimed at altering the phenotypic expression to impact the natural history, especially SCD and HF. Cardiovascular magnetic resonance (CMR) is emerging as a powerful tool for diagnosis and risk stratification in HCM including assessment of LV mass and pattern of hypertrophy. Late gadolinium enhancement by CMR is a marker of focal myocardial fibrosis which is thought to underlie the arrhythmogenic substrate as well as promote development of HF. The investigators hypothesize that HCM patients with a higher primary outcome event rate can be identified by novel CMR findings. The majority of cases of HCM are autosomal dominant and about 60% are caused by mutations in genes encoding cardiac sarcomeric proteins. However, the relationship between genetic mutation, disease phenotype, and clinical outcomes remains poorly understood. The investigators hypothesize that HCM patients with sarcomeric HCM mutations will have a higher primary outcome event rate and more marked myocardial pathology on CMR than those without. Furthermore, there may be a link between sarcomeric mutations and fibrosis, as mutation carriers with overt HCM as well as those without hypertrophy have elevated markers of collagen turnover. The investigators therefore hypothesize that serum biomarkers of collagen metabolism in HCM will predict outcomes. Thus, the Specific Aim is to develop a predictive model of cardiovascular outcomes in HCM by: 1) using exploratory data mining methods to identify demographic, clinical, and novel CMR, genetic and biomarker variables associated with the outcomes and 2) develop a score from the predictive model that can be used to assess risk given a patient's combination of risk factors, thus establishing the evidence base to enable clinical trial design to reduce morbidity and mortality in HCM in a cost-effective manner.