Leveraging the shared and opposing genetic mechanisms in the heritable cardiomyopathies for gene discovery
Bibliographic record
Abstract
Abstract Background The heritable cardiomyopathies represent groups of heart muscle diseases with partially overlapping characteristics and genetic mechanisms. The most common subtypes, dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM), show largely opposing phenotypic characteristics, yet both lead to arrhythmia, heart failure, and sudden death. Recent genome-wide association studies (GWAS) have identified dozens of common genetic variants linked to DCM and HCM, of which several loci are shared across both disorders. Purpose We aimed to interrogate the shared and opposing genetic mechanisms of DCM and HCM on a global and local level. We subsequently aimed to leverage these mechanisms to improve locus and gene discovery. Methods and results We leveraged summary-level data from the latest GWAS meta-analyses for HCM[1] (N=5,900 cases) and DCM[2] (N=9,365 cases).(Figure 1a) Across both GWAS, 51 distinct loci were identified, of which 18 overlapped. Bivariate LD score regression revealed a strong negative genetic correlation on a global level (rg=-0.56, p= 3.8-E27). To investigate regional genetic overlap, we analyzed 2,495 genome partitions using Local Analysis of Variant Association (LAVA)[3]. LAVA identified 14 regions with significant genetic correlation, all with opposing genetic effects, of which 3 were not overlapping previous loci (Figure 1b). Given the pervasive inverse genetic relationship, we then performed a case-case GWAS (CC-GWAS)[4], in which DCM and HCM were modeled as opposite entities on a singular disease spectrum. CC-GWAS identified 67 significant loci (26 novel GWAS loci). When integrated into a multi-trait GWAS (MTAG) with MRI-derived left ventricular traits[1] (N=36,083), we subsequently identified 95 significant loci (17 novel loci) (Figure 1c). Through contemporary locus-to-gene mapping, we found that our novel loci were enriched for genes previously implicated in cardiac function (eg, CACNA2D2, MYPN, LDB3, ADM, NOS1AP) and relevant biology (eg, muscle cell contraction, cytoskeletal organization, regulation of potassium channel activity). In contrast, a shared-effects meta-analysis, in which DCM and HCM were treated as similar diseases, identified only one significant locus. The lead variant in this locus was a missense variant in CASQ2, which encodes a Ca-binding protein in the endoplasmic reticulum (Figure 1c). Conclusions DCM and HCM represent opposite entities on a genetic spectrum. By leveraging this spectrum, we highlight several novel players underlying cardiomyopathy pathogenesis. The opposing genetics further point to pervasive opposite molecular mechanisms underlying DCM and HCM, although specific mechanisms related to calcium-handling might be concordant. Our findings inform the genetic architecture of the cardiomyopathy spectrum, with potential implications for therapeutics development.Figure 1.Overview of the study
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".