The main phenocopies of HCM are Wolff-Parkinson-White syndrome caused by mutations in PRKAG2 [the gamma2 subunit of AMP kinase (Murphy et al. 2005)], Fabry disease caused by mutations in GLA [alpha-galactosidase (Monserrat et al. 2007a)] and Danon disease caused by mutations in LAMP2 [lysosome-associated membrane protein 2 (Yang et al. 2005)]. Of note, Fabry disease and Danon disease are X-linked. Likewise, mutations in FHL1 (coding for four-and-a-half-LIM-domains 1) cause X-linked myopathy with HCM, but isolated HCM cases have been reported (Friedrich et al. 2012; Hartmannova et al. 2013).

Moreover, gene mutations affecting the mitochondria can cause HCM-like phenotypes (Govindaraj et al. 2014). If they are caused by mutations in the mitochondrial genome, maternal inheritance patterns are observed (Taylor et al. 2003).

Following closely behind the genetics of HCM, in vitro and animal model experiments have yielded significant insights into disease mechanisms. On a cellular level, HCM is associated with abnormal sarcomeric calcium handing, energy homeostasis, biomechanical stress signalling, fibrosis and microvascular changes, which are all proposed to underlie or contribute to the pathogenesis of disease.

The vast majority of HCM mutations affect proteins of the cardiac contractile apparatus. HCM mutations typically increase calcium sensitivity and actin-dependent ATPase activity (Robinson et al. 2002), leading to increased myofilament contractility (Robinson et al. 2007; Sequeira et al. 2013; Witt et al. 2001). Increased cross-bridge cycling and ATPase activity (Belus et al. 2008) has a higher energy requirement to produce a given force—‘tension cost’ (Frey et al. 2006). This points at inefficient energy usage and relative energy depletion, which itself is proposed to drive disease pathogenesis (Ashrafian et al. 2003).

The energy depletion hypothesis is supported by the finding that HCM patients have a lower ratio of cardiac phosphocreatine (PCr) to adenosine triphosphate (ATP), independent of the underlying HCM mutation and occurring prior to the development of hypertrophy (Crilley et al. 2003). Cellular energy depletion has also been observed in mouse models and in vitro experiments (Ferrantini et al. 2009; Song et al. 2013; Witjas-Paalberends et al. 2014). Additionally, an HCM-like phenotype develops in a range of metabolic and mitochondrial disorders which compromise cellular energetics. Treatment of HCM patients with perhexiline, which optimises cardiac substrate metabolism with a shift towards carbohydrate use, improves the PCr/ATP ratio and improves functional capacity (Abozguia et al. 2010).

Downstream of the sarcomere, HCM mutations are associated with altered calcium signalling and calcium handling proteins such as SERCA2a and phospholamban (PLN). Changes in calcium kinetics have been proposed to underlie arrhythmogenesis, which can occur in the absence of significant hypertrophy (Knollmann et al. 2003). Cardiac troponin T HCM mouse models show alterations in SERCA2a:PLN ratio, PLN phosphorylation pattern and sarcoplasmic reticulum calcium uptake (Guinto et al. 2009). In support of a disease-causing role for aberrant calcium signalling, neonatal adenoviral delivery of SERCA2a to a tropomyosin mutant HCM mouse model was found to delay the development of hypertrophy (Pena et al. 2010).

Patients with HCM develop significant cardiac fibrosis, although the molecular triggers remain incompletely defined (Ho et al. 2010). Fibrosis occurs early, often prior to overt hypertrophy, and is a cause of arrhythmia and diastolic dysfunction and an important predictor of adverse survival (O’Hanlon et al. 2010). In addition, intimal hyperplasia and medial hypertrophy of the intramyocardial microvasculature lead to reduced flow and ischaemia, further promoting fibrosis and leading to impaired contractile function (Olivotto et al. 2011).

The pathomechanisms of HCM caused by mutations in non-contractile proteins remain enigmatic and will be the subject of future research. A role for downstream biomechanical stress signalling is suggested by identification of mutations in signalling molecules such as CSRP3 and ANKRD1. Dissecting the cellular pathomechanisms in HCM holds great promise for developing clinical treatments, for patients with not only cardiomyopathy but also other diseases characterised by inappropriate hypertrophy or heart failure.

Next-generation sequencing (NGS) technology has dramatically enhanced understanding of the genetic architecture of disease. For genetic cardiomyopathies, NGS holds great promise for elucidating the more complex genetic background to these diseases, for example, in HCM patients where a mutation is not identified by traditional candidate gene screening. However, the increased sensitivity of genetic sequencing also raises the substantial challenge of how to interpret variants of unknown clinical significance (VUS) and whether they are causal, contributory or bystanders to a disease phenotype.

Rare coding variation in the genome has historically been significantly underestimated (Tennessen et al. 2012). Overall, in a study of over 5,000 exomes, over half the sequence variants identified were unique to one patient (Pan et al. 2012), although this was substantially lower for known cardiomyopathy genes. Previously, pathogenicity has been ascribed to ‘mutations’ identified in candidate gene sequencing studies on the basis of absence from a small sample of controls. It is now clear that many of these variants are present in the normal population at low frequency, casting significant doubt on their pathogenicity in disease (Andreasen et al. 2013; Christensen et al. 2010).

NGS thus poses a significant challenge to researchers—how to pick out genuine disease-causing mutations from rare, benign variants existing at low frequency in the genome. Furthermore, genetic variants may not be acting in isolation, but as compound mutations, which are disease-causing when in the presence of another mutation, or as modifiers of the phenotype. Early use of targeted high-throughput sequencing in patients with HCM hints at the level of genetic complexity to be unravelled: 11 % had multiple candidate variants within the eight sarcomeric genes alone. Over 40 % of this HCM clinical cohort also had VUS in ion channel and AC candidate genes (Lopes et al. 2013b).

NGS has also been employed to overcome the technical challenges inherent in sequencing the enormous TTN gene, containing 363 exons (Herman et al. 2012). Using massively parallel sequencing, truncating variants were identified in 22 % patients with idiopathic DCM, significantly higher than in HCM or controls. Confirming pathogenicity in a given patient remains difficult, however, given that TTN truncating variants were also found to exist in the control population at a frequency of 3 %. Presumably pathogenicity is influenced by as yet unknown modifying genetic or epigenetic loci, alongside environmental factors such as hypertension.

The strongest evidence for pathogenicity for a given mutation comes from linkage analysis or co-segregation with a phenotype—as established for the sarcomeric HCM genes—but frequently this is not possible, for example, in small families, for sporadic disease or where penetrance is low. In general, disease-causing variants would be expected to have a low frequency in libraries of control populations (e.g. 1,000 Genomes Project, the Exome Variant Server http://​evs.​gs.​washington.​edu/​EVS/​ and Exome Aggregation Consortium Browser http://​exac.​broadinstitute.​org/​) and to be enriched in disease cases (e.g. ClinVar http://​www.​ncbi.​nlm.​nih.​gov/​clinvar/​). Pathogenic mutations are more likely to be those which disrupt the amino acid reading frame. In silico or isolated in vitro effects on protein function or structure are supportive but are a ‘low bar’ to meet (Watkins 2013) and have low sensitivity and specificity. Recapitulation of the phenotype in animal models, with subsequent rescue by silencing the variant, is strong evidence but costly and time-consuming. Hence, new cost-effective and efficient test systems, e.g. cardiac-specific cellular assays, are required to evaluate the pathogenicity of variants.

Overall, the resources needed for NGS interpretation are substantial and for the moment remain prohibitive outside a research environment. In addition to VUS assessment and interpretation, there are challenges inherent in bioinformatic processing, management of incidental findings and updating results over time as VUS are identified as benign or pathogenic (Dewey et al. 2014). As large databanks of genomes are acquired and automation and filtering of sequence results are improved, NGS will become an extremely powerful tool in probing beyond the ‘one mutation, one gene’ description of genetic disease of the heart.