As indicated in red, the phenotype of hypertrophic cardiomyopathy (HCM) can arise from: 1) excessive energy use (e.g., by aberrant sarcomeres); 2) inadequate energy production (e.g., from poorly functioning mitochondria), inadequate metabolic substrates, or a failure to transfer energy across cellular compartments owing to cytoarchitectural defects as exemplified by muscle LIM protein (MLP) mutations; or 3) aberrant signaling of energy deficiency (e.g., with AMP-activated protein kinase [AMPK] mutations). The final common path for these diverse defects is energy deficiency and ensuing hypertrophy. ADP = adenosine diphosphate; AMP = adenosine monophosphate; ATP = adenosine triphospate; Cr = creatine; FAM = fatty acid metabolism.

Diverse cardiac stresses, either through direct toxicity to myocytes or through processes such as inflammation and/or aberrant calcium dynamics, result in myocyte apoptosis. The heart initially instigates compensatory responses according to the nature and severity of the inciting influence. If energy deficiency is a prominent feature of the inciting stress, then hypertrophy may initially ensue. If compensatory adaptation through hypertrophy and perhaps stem cell recruitment is inadequate, unchallenged apoptosis will result in myocyte depletion and myocardial decompensation. The resulting low cardiac output state promotes an initially adaptive but a chronically maladaptive neurohormonal state that perpetuates myocyte apoptosis. This common maladaptive chronic heart failure state is, at least in part, independent of the proximate inciting influence and is characterized by neurohormonal activation, metabolic impairment, and maladaptive cardiac remodeling that results in dilated-hypokinetic ventricles. ACE-I = angiotensin-converting enzyme inhibition; DCM = dilated cardiomyopathy; HCM = hypertrophic cardiomyopahy.

The ultimate determinant of biologic phenotype is the pattern of cellular protein expression (i.e., the proteome). The proteome is in turn determined largely by transcription within the cell (i.e., the transcriptome). Microarray technology permits a comprehensive and quantitative description of the transcriptome. Traditional approaches to understanding disease pathogenesis have attempted to correlate genetic variants with gross phenotype. Instead, genetic correlations can be made with messenger RNA expression patterns as measured by arrays. These intermediate phenotypes are termed "expression quantitative traits" (eQTs). The combination of the systematic power of arrays to measure transcription and the power of genetic analysis to identify causality has proved to be powerful. The genetic determinants of gene expression, termed "expression quantitative trait loci" (eQTL), represent cis and trans regulatory elements in which variations cause alterations in cells' gene expression patterns. Identification of variants in these regulatory elements, termed "master-switches," not only identifies key molecular protagonists in health and disease per se, but also implicates downstream pathways with therapeutic implications.