The Complex and Diverse Genetic Architecture of Dilated Cardiomyopathy

Abstract

Our insight into the diverse and complex nature of dilated cardiomyopathy (DCM) genetic architecture continues to evolve rapidly. The foundations of DCM genetics rest on marked locus and allelic heterogeneity. While DCM exhibits a Mendelian, monogenic architecture in some families, preliminary data from our studies and others suggests that at least 20% to 30% of DCM may have an oligogenic basis, meaning that multiple rare variants from different, unlinked loci, determine the DCM phenotype. It is also likely that low-frequency and common genetic variation contribute to DCM complexity, but neither has been examined within a rare variant context. Other types of genetic variation are also likely relevant for DCM, along with gene-by-environment interaction, now established for alcohol- and chemotherapy-related DCM. Collectively, this suggests that the genetic architecture of DCM is broader in scope and more complex than previously understood. All of this elevates the impact of DCM genetics research, as greater insight into the causes of DCM can lead to interventions to mitigate or even prevent it and thus avoid the morbid and mortal scourge of human heart failure.

Keywords: cardiomyopathies; genetics; genomics; heart failure; humans.

Figure 1.. The asymptomatic and symptomatic phases of DCM.

The causal pathway of DCM is illustrated, as adapted,^, in adult-onset DCM. Phase 1 includes two periods, both asymptomatic. In the first period (1A), individuals who harbor rare DCM variants have risk of developing DCM over time. Genetic information identifies the individuals who would benefit from periodic clinical screening to detect early clinical disease. In Phase 1B, DCM can be clinically detected by appropriate imaging studies but remains asymptomatic, evading detection for years unless surveillance clinical screening is undertaken to detect it. In Phase 2, the individual with DCM develops symptoms, most commonly with HF, which triggers a medical evaluation with a diagnosis of HF and the eventual discovery of the underlying DCM. Factors that may diminish or block these causal pathways to DCM or HF are shown in green (A1, B1, C1)); environmental factors could include good nutrition, a low salt diet, low blood pressure, or drug therapy (e.g., angiotensin-converting enzyme inhibitors or β-blockers). Factors that may accelerate the causal pathway to DCM or HF are shown in red (A2, B2); examples include hypertension, alcohol use, or exposure to chemotherapy or other myocardial toxins. Genomic modifiers, both injurious or protective, include rare, low frequency or common variants that could be operative at all points. Detection of a “pre-DCM phenotype” that portends the transition from Phase 1A to Phase 1B could permit earlier introduction of conventional medical therapy in an effort to block the development of DCM. Following the transition to Phase 1B, all efforts need to be made to prolong this phase to avert late phase symptomatic disease in Phase 2.

Figure 2:

A. Schematic representation of the cardiac sarcomere. Depicted are actin and troponin-complex containing thin filaments and myosin thick filaments interacting with Z-disk and M-band anchored Titin. A-band, I-band, and H-band segments are indicated by horizontal bars. B. TTN exon and protein structure. The TTN canonical transcript (NM_001267550.1) is encoded by 363 exons alternatively spliced into several variably sized isoforms. N2BA (NM_001256850.1), N2B (NM_003319.4), Novex-1 (NM_133432.3), Novex-2 (NM_133437.3) and Novex-3 (NM_133379.3) isoforms are cardiac expressed, while N2BA (NM_133378.4) is expressed in skeletal muscle. Exon structure of each isoform is depicted. Exons are not drawn to scale. Grey color indicates exons spliced from the processed transcript. Exons encoding Ig-like (red, triangle), Z-repeat (pink, square), PEVK (blue, hexagon), fibronectin type III (yellow, star), and protein kinase (cyan, circle) domains are indicated by the indicated colors and symbols. For visual simplicity, symbols are used to mark only the first exon encoding each domain, the exon immediately following encoding of a different domain, or exons encoding multiple domains. Encoded proteins are depicted below each exon structure. Proteins are scaled to show relative proportions of each transcript occupied by Z-disk (grey), near Z-disk (yellow), I-band (blue), A-band (red), and M-band (grey-green) domains. Depicted colors match those in A. Boundaries and amino acid ranges are indicated for each isoform.

Figure 3.. DCM genetic architecture spans ten gene ontologies.

19 genes deemed highly clinically relevant (definitive and strong noted in bold; moderate in regular text) for DCM are in the middle ring. The outermost ring lists other high evidence phenotypic associations and the innermost risk provides an ontology classification for each gene, respectively. Of 19 DCM genes, 14 have also been classified as high levels of evidence in HCM, ARVC, LQTS, and/or Brugada Syndrome, except for NEXN, noted with an asterisk, which has limited evidence in HCM. Ontology abbreviations include: SR, sarcoplasmic reticulum; Co-Chap HSP, co-chaperone, heat shock protein. Figure from.

Figure 4.. A threshold model for DCM.

Lines denote the mean quantitative trait measurement (vertical axis) as a function of age (horizontal axis) in individuals with a particular genetic background. The distribution of the actual measurements in these individuals at a particular age arising from non-genetic variation is represented by the shaded bell curve centered on the mean for that age. The dashed horizontal line indicates the threshold below which an individual is considered to have DCM; the amount of total area in each bell curve shaded red indicates risk of DCM. Panel (a) illustrates how the number, or burden, of deleterious rare variants might impact the age trajectory with other factors held fixed. In the absence of such variants (light gray), neither the mean nor risk changes appreciably with age. While all groups have similar means at birth, individuals with higher burdens of deleterious rare variants (denoted by darker lines) have more rapid declines in the mean quantitative trait with age, which result in greater risk and more severe phenotypes, on average, at a given age as well as earlier onset. Panel (b) illustrates how polygenic effects arising from common variants might modify the average trajectory for the rare variant burden in the dashed middle curve from panel (a). In this example, the polygenic effect shifts the entire age trajectory up or down, either nearly eliminating the increased risk due to the rare variant burden (light gray) or exacerbating it (black). More complex descriptions of the age trajectory as a function of rare variants, common variants, and non-genetic factors are possible under this model; this example serves primarily to demonstrate that even a simple multifactorial model involving multiple genetic components can explain empirically observed age-dependent penetrance and variable expressivity.