Question: You are an expert researcher providing comprehensive, well-cited information.

Provide detailed information focusing on: 1. Key concepts and definitions with current understanding 2. Recent developments and latest research (prioritize 2023-2024 sources) 3. Current applications and real-world implementations 4. Expert opinions and analysis from authoritative sources 5. Relevant statistics and data from recent studies

Format as a comprehensive research report with proper citations. Include URLs and publication dates where available. Always prioritize recent, authoritative sources and provide specific citations for all major claims.

Disease Characteristics Research Template

Target Disease

• Disease Name: Dilated Cardiomyopathy 1NN
• MONDO ID: (if available)
• Category: Cardiovascular

Research Objectives

Please provide a comprehensive research report on Dilated Cardiomyopathy 1NN covering all of the disease characteristics listed below. This report will be used to populate a disease knowledge base entry. Be thorough and cite primary literature (PMID preferred) for all claims.

For each section, suggested databases/resources are listed. These are the first places you should search for information on each topic.

1. Disease Information

Search first: OMIM, Orphanet, ICD-10/ICD-11, MeSH, PubMed

• What is the disease? Provide a concise overview.
• What are the key identifiers? (OMIM, Orphanet, ICD-10/ICD-11, MeSH, Mondo)
• What are the common synonyms and alternative names?
• Is the information derived from individual patients (e.g., EHR) or aggregated disease-level resources?

2. Etiology

• Disease Causal Factors: What are the primary causes? (genetic, environmental, infectious, mechanistic)
• Risk Factors:

  Search first: PubMed, Cochrane Library, UpToDate, clinical guidelines, ClinVar, ClinGen, GWAS Catalog, PheGenI, CTD, CDC, WHO, epidemiological databases

• Genetic risk factors (causal variants, susceptibility loci, modifier genes)
• Environmental risk factors (toxins, lifestyle, occupational exposures, age, sex, family history)
• Protective Factors:

  Search first: PubMed, Cochrane Library, clinical trial databases, GWAS Catalog, gnomAD, WHO, CDC, nutrition databases

• Genetic protective factors (protective variants, modifier alleles)
• Environmental protective factors (diet, lifestyle, exposures that reduce risk)
• Gene-Environment Interactions: How do genetic and environmental factors interact to influence disease?

  Search first: CTD, PubMed, PheGenI, GxE databases

3. Phenotypes

Search first: HPO (Human Phenotype Ontology), OMIM, Orphanet, PubMed, clinicaltrials.gov, MedDRA, SNOMED CT, DECIPHER, LOINC

For each phenotype, provide: - Phenotype type: symptoms, clinical signs, physical manifestations, behavioral changes, or laboratory abnormalities

For symptoms/signs: HPO, OMIM, Orphanet, PubMed For behavioral changes: HPO, DSM, RDoC (Research Domain Criteria), PubMed For laboratory abnormalities: LOINC, SNOMED CT, LabTests Online, PubMed - Phenotype characteristics: Search first: OMIM, Orphanet, HPO, PubMed - Age of symptom onset (neonatal, childhood, adult-onset, late-onset) - Symptom severity (mild, moderate, severe, variable) - Symptom progression (stable, progressive, episodic, fluctuating) - Frequency among affected individuals (percentage or qualitative) - Quality of life impact: Effects on daily functioning and well-being (per-phenotype when possible) Search first: EQ-5D database, SF-36, WHO QOL databases, PubMed - Suggest HPO (Human Phenotype Ontology) terms for each phenotype

4. Genetic/Molecular Information

• Causal Genes: Gene mutations or chromosomal abnormalities responsible for disease (gene symbols, OMIM IDs)

  Search first: OMIM, ClinVar, HGMD, Ensembl, NCBI Gene

• Pathogenic Variants:
• Affected genes (gene symbols, HGNC IDs) > Search first: OMIM, NCBI Gene, Ensembl, HGNC, UniProt, GeneCards
• Variant classification (pathogenic, likely pathogenic, VUS per ACMG/AMP guidelines) > Search first: ClinVar, ClinGen, ACMG/AMP guidelines, VarSome
• Variant type/class (missense, frameshift, nonsense, splice-site, structural)
• Allele frequency in population databases > Search first: gnomAD, 1000 Genomes, ExAC, TOPMed, dbSNP
• Somatic vs germline origin > Search first: COSMIC (somatic), ClinVar, ICGC, TCGA
• Functional consequences (loss of function, gain of function, dominant negative)
• Modifier Genes: Genes that modify disease severity or expression
• Epigenetic Information: DNA methylation, histone modifications, chromatin changes affecting disease

  Search first: ENCODE, Roadmap Epigenomics, MethBase, DiseaseMeth

• Chromosomal Abnormalities: Large-scale genetic changes (aneuploidy, translocations, inversions)

  Search first: DECIPHER, ClinVar, ECARUCA, UCSC Genome Browser

5. Environmental Information

• Environmental Factors: Non-genetic contributing factors (toxins, radiation, pollution, occupational exposure)

  Search first: CTD (Comparative Toxicogenomics Database), TOXNET, PubMed, EPA databases

• Lifestyle Factors: Behavioral factors (smoking, diet, exercise, alcohol consumption)

  Search first: CDC databases, WHO, PubMed, NHANES

• Infectious Agents: If applicable, pathogens causing or triggering disease (bacteria, viruses, fungi, parasites)

  Search first: NCBI Taxonomy, ViPR, BV-BRC, MicrobeDB, GIDEON

6. Mechanism / Pathophysiology

Present this section as an ordered causal chain first, then the detail below. Open with a numbered sequence of mechanistic steps running from the initiating lesion (mutation, exposure, infection) to the clinical manifestation, one step per line, each naming what it causes next. State the causal verb explicitly ("leads to", "results in") and say where a step is inferred rather than demonstrated. Where the mechanism branches, show the branch. The categories below are a checklist of what to cover within those steps, not the organizing structure — a step may draw on several of them, and a category may contribute to several steps.

• Molecular Pathways: Specific signaling cascades or biochemical pathways involved (Wnt, MAPK, mTOR, PI3K-AKT, etc.)

  Search first: KEGG, Reactome, WikiPathways, PathBank, BioCyc

• Cellular Processes: Cell-level mechanisms (apoptosis, autophagy, cell cycle dysregulation, inflammation, etc.)

  Search first: Gene Ontology (GO), Reactome, KEGG, PubMed

• Protein Dysfunction: How protein structure or function is altered (misfolding, aggregation, loss of function, gain of function)

  Search first: UniProt, PDB (Protein Data Bank), InterPro, Pfam, AlphaFold

• Metabolic Changes: Alterations in metabolic processes (energy metabolism, lipid metabolism, amino acid metabolism)

  Search first: KEGG, BioCyc, HMDB (Human Metabolome Database), BRENDA

• Immune System Involvement: Role of immune response (autoimmunity, immunodeficiency, chronic inflammation)

  Search first: ImmPort, Immunome Database, IEDB, Gene Ontology

• Tissue Damage Mechanisms: How tissues/ are injured (oxidative stress, ischemia, fibrosis, necrosis)

  Search first: PubMed, Gene Ontology, Reactome

• Biochemical Abnormalities: Specific molecular defects (enzyme deficiencies, receptor dysfunction, ion channel defects)

  Search first: BRENDA, UniProt, KEGG, OMIM, PubMed

• Epigenetic Changes: DNA methylation, histone modifications affecting gene expression in disease

  Search first: ENCODE, Roadmap Epigenomics, MethBase, DiseaseMeth

• Molecular Profiling (if available):
• Transcriptomics/gene expression changes > Search first: GEO (Gene Expression Omnibus), ArrayExpress, GTEx, Human Cell Atlas, SRA
• Proteomics findings > Search first: PRIDE, ProteomeXchange, Human Protein Atlas, STRING, BioGRID
• Metabolomics signatures > Search first: MetaboLights, Metabolomics Workbench, HMDB, METLIN
• Lipidomics alterations > Search first: LIPID MAPS, SwissLipids, LipidHome, Metabolomics Workbench
• Genomic structural features > Search first: UCSC Genome Browser, Ensembl, NCBI, dbVar, DGV
• Advanced Technologies (if applicable):
• Single-cell analysis findings (cell-type specific mechanisms, cellular heterogeneity) > Search first: Human Cell Atlas, Single Cell Portal, GEO, CELLxGENE
• Spatial transcriptomics findings > Search first: GEO, Spatial Research, Vizgen, 10x Genomics data
• Multi-omics integration results > Search first: TCGA, ICGC, cBioPortal, LinkedOmics, PubMed
• Functional genomics screens (CRISPR, RNAi) > Search first: DepMap, GenomeRNAi, PubMed, BioGRID ORCS

For each mechanism, describe: - The causal chain from initial trigger to clinical manifestation - Which mechanisms are upstream vs downstream - What cell types and biological processes are involved - Suggest GO terms for biological processes and CL terms for cell types

7. Anatomical Structures Affected

• Organ Level:
• Primary organs directly affected
• Secondary organ involvement (complications, secondary effects)
• Body systems involved (cardiovascular, nervous, digestive, respiratory, endocrine, etc.)

  Search first: Uberon, FMA (Foundational Model of Anatomy), OMIM, HPO, ICD-11, MeSH, SNOMED CT

• Tissue and Cell Level:
• Specific tissue types affected (epithelial, connective, muscle, nervous)
• Specific cell populations targeted (with Cell Ontology terms)

  Search first: Uberon, Human Protein Atlas, Cell Ontology, Human Cell Atlas, CellMarker, PanglaoDB

• Subcellular Level:
• Cellular compartments involved (mitochondria, nucleus, ER, lysosomes) (with GO Cellular Component terms)

  Search first: Gene Ontology (Cellular Component), UniProt, Human Protein Atlas

• Localization:
• Specific anatomical sites (with UBERON terms) > Search first: FMA, Uberon, NeuroNames (for brain), SNOMED CT
• Lateralization (unilateral, bilateral, asymmetric) > Search first: HPO, clinical literature, imaging databases

8. Temporal Development

• Onset:
• Typical age of onset (congenital, pediatric, adult, geriatric)
• Onset pattern (acute, subacute, chronic, insidious)

  Search first: OMIM, Orphanet, HPO, PubMed

• Progression:
• Disease stages (early, intermediate, advanced, end-stage) > Search first: Cancer Staging Manual (AJCC), WHO classifications, PubMed
• Progression rate (rapid, slow, variable)
• Disease course pattern (episodic, relapsing-remitting, progressive, stable)
• Disease duration (self-limited, chronic lifelong)

  Search first: Disease registries, longitudinal cohort databases, natural history studies, PubMed, Orphanet, OMIM

• Patterns:
• Remission patterns (spontaneous, treatment-induced) > Search first: Clinical trial databases, disease registries, PubMed
• Critical periods (time windows of vulnerability or opportunity for intervention) > Search first: PubMed, developmental biology databases, clinical guidelines

9. Inheritance and Population

• Epidemiology:
• Prevalence (cases per 100,000 at given time)
• Incidence (new cases per 100,000 per year)

  Search first: Orphanet, CDC, WHO, GBD (Global Burden of Disease), national registries, SEER, disease registries

• For Genetic Etiology:
• Inheritance pattern (AD, AR, X-linked, mitochondrial, multifactorial, polygenic) > Search first: OMIM, Orphanet, ClinVar, GTR (Genetic Testing Registry)
• Penetrance (complete, incomplete, age-dependent) > Search first: ClinVar, OMIM, PubMed, ClinGen
• Expressivity (variable, consistent) > Search first: OMIM, ClinVar, PubMed
• Genetic anticipation (increasing severity in successive generations) > Search first: OMIM, PubMed (especially for repeat expansion disorders)
• Germline mosaicism > Search first: ClinVar, OMIM, genetic counseling literature, PubMed
• Founder effects (population-specific mutations) > Search first: gnomAD, population genetics databases, PubMed
• Consanguinity role > Search first: OMIM, population studies, genetic counseling resources
• Carrier frequency > Search first: gnomAD, carrier screening databases, GeneReviews, GTR
• Population Demographics:
• Affected populations (ethnic or demographic groups with higher prevalence) > Search first: gnomAD, 1000 Genomes, PAGE Study, PubMed, population registries
• Geographic distribution (endemic areas, regional variation) > Search first: WHO, CDC, GBD, Orphanet, geographic epidemiology databases
• Geographic distribution of specific variants
• Sex ratio (male:female) > Search first: Disease registries, OMIM, PubMed, epidemiological databases
• Age distribution of affected individuals > Search first: CDC, disease registries, SEER, Orphanet

10. Diagnostics

• Clinical Tests:
• Laboratory tests (blood, urine, tissue chemistry, specific enzyme assays) > Search first: LOINC, LabTests Online, PubMed
• Biomarkers (proteins, metabolites, genetic markers, circulating biomarkers) > Search first: FDA Biomarker List, BEST (Biomarkers, EndpointS, and other Tools), PubMed
• Imaging studies (X-ray, CT, MRI, PET, ultrasound) > Search first: RadLex, DICOM, Radiopaedia, imaging databases
• Functional tests (pulmonary function, cardiac stress tests) > Search first: LOINC, clinical guidelines, PubMed
• Electrophysiology (EEG, EMG, ECG, nerve conduction studies) > Search first: LOINC, clinical neurophysiology databases, PubMed
• Biopsy findings (histopathology, immunohistochemistry) > Search first: SNOMED CT, College of American Pathologists resources, PubMed
• Pathology findings (microscopic examination) > Search first: SNOMED CT, Digital Pathology databases, PubMed
• Genetic Testing:

  Search first: GTR (Genetic Testing Registry), GeneReviews, ClinGen

• Overview of recommended genetic testing approach
• Whole genome sequencing (WGS) utility > Search first: GTR, ClinVar, GEL (Genomics England), gnomAD
• Whole exome sequencing (WES) utility > Search first: GTR, ClinVar, OMIM, GeneMatcher
• Gene panels (which panels, which genes) > Search first: GTR, ClinVar, laboratory-specific databases
• Single gene testing > Search first: GTR, ClinVar, OMIM, GeneReviews
• Chromosomal microarray (CMA) > Search first: DECIPHER, ClinVar, dbVar, ECARUCA
• Karyotyping > Search first: Chromosome Abnormality Database, ClinVar, cytogenetics resources
• FISH > Search first: ClinVar, cytogenetics databases, PubMed
• Mitochondrial DNA testing > Search first: MITOMAP, MSeqDR, ClinVar, GTR
• Repeat expansion testing > Search first: GTR, ClinVar, repeat expansion databases, PubMed
• Omics-Based Diagnostics (if applicable):
• RNA sequencing / transcriptomics > Search first: GEO, ArrayExpress, GTEx, RNA-seq databases
• Proteomics > Search first: PRIDE, ProteomeXchange, FDA Biomarker database
• Metabolomics > Search first: MetaboLights, Metabolomics Workbench, HMDB
• Epigenomics > Search first: GEO, ENCODE, Roadmap Epigenomics, MethBase
• Liquid biopsy > Search first: COSMIC, ClinVar, liquid biopsy databases, PubMed
• Clinical Criteria:
• Standardized diagnostic criteria (DSM, ICD, society guidelines) > Search first: DSM-5, ICD-11, clinical society guidelines, UpToDate
• Differential diagnosis (other conditions to rule out, with distinguishing features) > Search first: DynaMed, UpToDate, clinical decision support systems
• Screening:
• Screening methods for asymptomatic individuals (newborn screening, carrier screening, cascade screening) > Search first: ACMG recommendations, CDC newborn screening, GTR

11. Outcome/Prognosis

• Survival and Mortality:
• Survival rate (5-year, 10-year, overall) > Search first: SEER, cancer registries, disease-specific registries, PubMed
• Life expectancy (with and without treatment if applicable) > Search first: Orphanet, disease registries, actuarial databases, PubMed
• Mortality rate > Search first: CDC, WHO, GBD, national mortality databases
• Disease-specific mortality (deaths directly attributable to disease) > Search first: Disease registries, CDC Wonder, GBD, PubMed
• Morbidity and Function:
• Morbidity (disease-related disability and health impacts) > Search first: GBD, WHO, disability databases, PubMed
• Disability outcomes (long-term functional impairments) > Search first: ICF (International Classification of Functioning), disability registries
• Quality of life measures (EQ-5D, SF-36, PROMIS, disease-specific tools) > Search first: EQ-5D database, SF-36, PROMIS, PubMed
• Disease Course:
• Complications (secondary problems: infections, organ failure, etc.) > Search first: ICD codes, disease registries, clinical databases, PubMed
• Recovery potential (likelihood and extent of recovery, with vs without treatment) > Search first: Natural history studies, rehabilitation databases, PubMed
• Prediction:
• Prognostic factors (age, disease severity, biomarkers, treatment response) > Search first: Prognostic models databases, clinical calculators, PubMed
• Prognostic biomarkers (molecular markers predicting disease course) > Search first: FDA Biomarker database, PubMed, cancer prognostic databases

12. Treatment

• Pharmacotherapy:
• Pharmacological treatments (drug names, drug classes, mechanisms of action) > Search first: DrugBank, RxNorm, ATC classification, DailyMed, FDA databases
• Pharmacogenomics (how genetic variants affect drug metabolism, efficacy, toxicity) > Search first: PharmGKB, CPIC (Clinical Pharmacogenetics), FDA Table of PGx Biomarkers
• Advanced Therapeutics:
• Gene therapy (viral vectors, CRISPR, gene replacement, gene editing) > Search first: ClinicalTrials.gov, FDA gene therapy database, ASGCT resources
• Cell therapy (stem cell transplant, CAR-T, cellular therapeutics) > Search first: ClinicalTrials.gov, FDA cell therapy database, FACT standards
• RNA-based therapies (ASOs, siRNA, mRNA therapies) > Search first: ClinicalTrials.gov, FDA approvals, PubMed
• Targeted therapies (treatments directed at specific molecular targets) > Search first: My Cancer Genome, OncoKB, ClinicalTrials.gov, FDA approvals
• Immunotherapies (checkpoint inhibitors, monoclonal antibodies) > Search first: Cancer Immunotherapy Database, FDA approvals, ClinicalTrials.gov
• Surgical and Interventional:
• Surgical interventions (types of surgery, timing, outcomes) > Search first: CPT codes, surgical registries, clinical guidelines, PubMed
• Supportive and Rehabilitative:
• Supportive care (symptom management, pain control, nutrition) > Search first: Clinical guidelines, Cochrane Library, PubMed
• Rehabilitation (physical therapy, occupational therapy, speech therapy) > Search first: Rehabilitation medicine databases, clinical guidelines, PubMed
• Experimental:
• Experimental treatments in clinical trials (with NCT identifiers if available) > Search first: ClinicalTrials.gov, EU Clinical Trials Register, WHO ICTRP
• Treatment Outcomes:
• Treatment response rates > Search first: Clinical trial databases, FDA reviews, systematic reviews, PubMed
• Side effects and adverse events > Search first: FDA Adverse Event Reporting System (FAERS), MedWatch, PubMed
• Treatment Strategy:
• Treatment algorithms (clinical pathways, decision trees) > Search first: Clinical practice guidelines, NCCN Guidelines, UpToDate
• Combination therapies > Search first: ClinicalTrials.gov, treatment guidelines, PubMed
• Personalized medicine approaches (genotype-guided treatment) > Search first: My Cancer Genome, CIViC, PharmGKB, precision medicine databases

For each treatment, suggest NCIT (NCI Thesaurus) clinical-intervention terms where applicable.

13. Prevention

• Prevention Levels:
• Primary prevention (preventing disease occurrence: vaccination, risk factor modification) > Search first: CDC, WHO, USPSTF recommendations, Cochrane Library
• Secondary prevention (early detection and treatment: screening programs, early intervention) > Search first: USPSTF, CDC screening guidelines, WHO
• Tertiary prevention (preventing complications in those with disease) > Search first: Clinical guidelines, disease management protocols, PubMed
• Immunization: Vaccine strategies (if applicable)

  Search first: CDC vaccine schedules, WHO immunization, FDA vaccine database

• Screening and Early Detection:
• Screening programs (population-based: newborn screening, cancer screening) > Search first: CDC screening programs, USPSTF, cancer screening databases
• Genetic screening (carrier screening, preimplantation genetic diagnosis, prenatal testing) > Search first: ACMG recommendations, ACOG guidelines, GTR
• Risk stratification (identifying high-risk individuals for targeted prevention) > Search first: Risk prediction models, clinical calculators, PubMed
• Behavioral Interventions: Lifestyle modifications to reduce risk

  Search first: CDC, WHO, behavioral intervention databases, Cochrane Library

• Counseling: Genetic counseling (risk assessment, family planning guidance)

  Search first: NSGC resources, ACMG guidelines, GeneReviews

• Public Health:
• Public health interventions (sanitation, vector control, health education) > Search first: CDC, WHO, public health databases, PubMed
• Environmental interventions (reducing environmental risk factors) > Search first: EPA databases, WHO environmental health, PubMed
• Prophylaxis: Preventive medications or procedures

  Search first: Clinical guidelines, FDA approvals, PubMed

14. Other Species / Natural Disease

• Taxonomy: Species affected (with NCBI Taxon identifiers)

  Search first: NCBI Taxonomy

• Breed: Specific breeds affected (with VBO identifiers if applicable)

  Search first: VBO (Vertebrate Breed Ontology)

• Gene: Orthologous genes in other species (with NCBI Gene IDs)

  Search first: NCBI Gene

• Natural Disease:
• Naturally occurring disease in other species (companion animals, wildlife) > Search first: OMIA (Online Mendelian Inheritance in Animals), VetCompass, PubMed
• Veterinary relevance and importance in animal health > Search first: OMIA, veterinary databases, PubMed
• Comparative Biology:
• Comparative pathology (similarities and differences across species) > Search first: OMIA, comparative pathology databases, PubMed
• Evolutionary conservation of disease mechanisms > Search first: HomoloGene, OrthoMCL, Alliance of Genome Resources
• Transmission (if applicable):
• Zoonotic potential > Search first: CDC zoonotic diseases, WHO zoonoses, GIDEON
• Cross-species susceptibility > Search first: NCBI Taxonomy, veterinary databases, PubMed

15. Model Organisms

• Model Types:
• Model organism type (mammalian, invertebrate, cellular, in vitro) > Search first: Alliance of Genome Resources, model organism databases
• Specific model systems (mouse, rat, zebrafish, Drosophila, C. elegans, yeast, cell lines, organoids, iPSCs) > Search first: MGI, RGD, ZFIN, FlyBase, WormBase, SGD, ATCC, Cellosaurus
• Induced models (drug treatment, surgical intervention, environmental manipulation) > Search first: MGI, model organism databases, PubMed
• Genetic Models:
• Types available (knockout, knock-in, transgenic, conditional, humanized) > Search first: MGI, IMPC, KOMP, EuMMCR, IMSR
• Model Characteristics:
• Phenotype recapitulation (how well model reproduces human disease features) > Search first: Model organism databases, comparative studies, PubMed
• Model limitations (aspects of human disease not captured) > Search first: Model organism databases, PubMed, review articles
• Applications:
• Research applications (what aspects of disease can be studied) > Search first: Model organism databases, PubMed
• Resources:
• Model databases > Search first: MGI, RGD, ZFIN, FlyBase, WormBase, IMSR, EMMA, MMRRC

Citation Requirements

• Cite primary literature (PMID preferred) for all mechanistic and clinical claims
• Prioritize recent reviews and landmark papers
• Include direct quotes from abstracts where possible to support key statements
• Distinguish evidence source types: human clinical, model organism, in vitro, computational

Output Format

Structure your response as a comprehensive narrative organized by the sections above. For each section, provide: - Factual content with specific details (numbers, percentages, gene names, variant nomenclature) - Ontology term suggestions (HPO, GO, CL, UBERON, CHEBI, NCIT, MONDO) where applicable - Evidence citations with PMIDs - Direct quotes from abstracts to support key claims - Clear indication when information is not available or not applicable for this disease

This report will be used to populate a disease knowledge base entry with: - Pathophysiology descriptions with causal chains - Gene/protein annotations (HGNC, GO terms) - Phenotype associations (HP terms) with frequencies - Cell type involvement (CL terms) - Anatomical locations (UBERON terms) - Chemical entities (CHEBI terms) - Treatment annotations (NCIT terms) - Evidence items with PMIDs and exact abstract quotes - Epidemiology, prognosis, diagnostic, and prevention information - Animal model descriptions with phenotype recapitulation details

Dilated Cardiomyopathy 1NN (DCM1NN): comprehensive disease-characteristics report

Executive summary and evidence boundary

Dilated cardiomyopathy 1NN is an exceptionally rare, predominantly childhood-onset, autosomal-dominant cardiomyopathy attributed to heterozygous variants in RAF1, encoding RAF-1 serine/threonine kinase. The authoritative identifiers are OMIM 615916 for DCM1NN and OMIM 164760 for RAF1; the causal publication is Dhandapany et al., Nature Genetics (online 27 April 2014), PMID 24777450, DOI/URL: https://doi.org/10.1038/ng.2963. The 2025 neuromuscular gene table independently retains this disease–gene assignment. (benarroch2025the2025version pages 24-25, OpenTargets Search: dilated cardiomyopathy-RAF1)

The evidence base is unusually narrow: most subtype-specific claims derive from the 2014 discovery study and its experimental models. Consequently, population frequency, penetrance, natural history, sex ratio, prognosis, and treatment response have not been established specifically for DCM1NN. Findings from general DCM or from RAF1-associated Noonan syndrome/hypertrophic cardiomyopathy (HCM) are identified below as indirect and must not be imported into DCM1NN as proven facts.

Table: Compact ontology-ready summary of RAF1-associated dilated cardiomyopathy 1NN, including identity, variants, mechanism, diagnostics, treatment, and evidence limitations. Direct subtype evidence is separated from indirect RAF1-Noonan HCM and general DCM findings.

1. Disease information

Definition

DCM1NN is a genetic form of dilated cardiomyopathy characterized by left-ventricular or biventricular enlargement and impaired systolic function not adequately explained by coronary disease, abnormal loading conditions, or another secondary cause. The contemporary ESC definition describes cardiomyopathies as myocardial disorders with structural and functional abnormality not sufficiently explained by coronary artery disease, hypertension, valvular disease, or congenital heart disease. (stroeks2023diagnosticandprognostic pages 1-2, grasso2024thenew2023 pages 1-2)

Identifiers and synonyms

• Preferred name: Dilated cardiomyopathy 1NN
• Synonyms: DCM1NN; CMD1NN; cardiomyopathy, dilated, 1NN; RAF1-related dilated cardiomyopathy; RAF1-associated childhood-onset DCM
• OMIM: 615916
• Causal gene: RAF1; OMIM 164760; HGNC:9829; NCBI Gene 5894; Ensembl ENSG00000132155
• MONDO: a confidently retrieved dedicated DCM1NN identifier was not available. Use the parent MONDO:0005021, dilated cardiomyopathy, with OMIM:615916 and RAF1 qualifiers rather than inventing a subtype code. Open Targets also associates RAF1 with familial DCM and cites PMID 24777450. (benarroch2025the2025version pages 24-25, OpenTargets Search: dilated cardiomyopathy-RAF1)
• Orphanet: no DCM1NN-specific ORPHA identifier was established in the retrieved evidence.
• ICD-10-CM: I42.0, dilated cardiomyopathy; this is not genotype-specific.
• ICD-11: use the applicable dilated-cardiomyopathy category; no RAF1-specific code was established.
• MeSH: Cardiomyopathy, Dilated.

The report is based on aggregated disease-level resources and published cohorts/models, not individual EHR data. A 2025 case report noted that a laboratory report labeled RAF1 p.Ser257Leu with “dilated cardiomyopathy type 1NN,” but the patient actually had Noonan syndrome with HCM, septal defects, and LV outflow obstruction. This illustrates database-label carryover and RAF1 allelic heterogeneity rather than confirming DCM1NN in that child. (ma2025raf1mutationexpands pages 9-10, ma2025raf1mutationexpands pages 1-2)

2. Etiology

Primary cause and genetic risk

The accepted cause is a heterozygous germline RAF1 variant, with autosomal-dominant inheritance. Six missense changes appear in the discovery supplement: c.709G>A (p.Ala237Thr), c.928A>G (p.Thr310Ala), c.994C>G (p.Pro332Ala), c.1808T>C (p.Leu603Pro), c.1877A>G (p.His626Arg), and c.1922C>T (p.Thr641Met), using the transcript reported by that study. (dhandapany2014raf1mutationsin pages 1-7)

Historical inclusion does not automatically equal current pathogenic classification. In the original computational analysis, p.Ala237Thr and p.Thr310Ala were predicted benign/neutral/tolerated; p.Pro332Ala had mixed predictions; p.Leu603Pro and p.Thr641Met had stronger damaging predictions; and p.His626Arg was mixed. Each variant therefore requires present-day transcript normalization, ClinVar review, gnomAD frequency assessment, segregation, functional evidence, and ACMG/AMP classification before clinical reporting. (dhandapany2014raf1mutationsin pages 1-7)

Environmental and lifestyle risks

No exposure has been demonstrated specifically to cause or modify DCM1NN. For genetic DCM generally, myocarditis, alcohol, chemotherapy and other cardiotoxins, pregnancy/peripartum stress, hypertension, and metabolic stress can unmask or worsen disease. A 2024 review emphasizes gene–environment interaction rather than a purely Mendelian model for many DCM families. (eldemire2024geneticsofdilated pages 1-3)

The 2024 GWAS by Jurgens et al. used 9,365 cases and 946,368 controls, identified 70 significant loci, and used Mendelian randomization to nominate higher bodyweight and systolic blood pressure as potentially actionable DCM causes. These are general DCM modifiers, not proven RAF1-specific effects. (jurgens2024genomewideassociationstudy pages 1-2)

Protective factors

No genetic protective RAF1 allele is established. No diet, supplement, or exercise regimen has been shown to prevent DCM1NN. Plausible general protection consists of maintaining healthy blood pressure and bodyweight, avoiding tobacco, excess alcohol and cardiotoxic drugs, treating infections and metabolic disease, and obtaining surveillance during pregnancy. Competitive or high-intensity exercise should be individualized according to ventricular function and arrhythmic risk rather than universally prohibited.

Polygenic and modifier effects

Large 2024 studies show that common genetic background modifies DCM risk and penetrance. Zheng et al. analyzed 14,256 cases and 1,199,156 controls, identified 80 loci and 62 putative effector genes, and found that polygenic scores modified penetrance in carriers of rare DCM variants. Whether this applies quantitatively to RAF1 carriers remains unknown. (zheng2024genomewideassociationanalysis pages 1-2)

3. Phenotypes

The following are appropriate knowledge-base phenotypes, but frequencies are not available specifically for DCM1NN:

• Dilated cardiomyopathy — HP:0001644: cardinal structural/functional phenotype; childhood onset was prominent in the discovery setting; severity is variable and may be progressive.
• Left-ventricular dilatation — HP:0001711: imaging sign; generally progressive if remodeling is uncontrolled.
• Reduced left-ventricular ejection fraction — HP:0012664: imaging/functional abnormality.
• Systolic dysfunction — HP:0006670: functional sign.
• Congestive heart failure — HP:0001635: fatigue, dyspnea, poor exercise tolerance, pulmonary/systemic congestion and, in children, feeding or growth difficulty.
• Cardiomegaly — HP:0001640: physical/imaging manifestation.
• Dyspnea — HP:0002094; fatigue — HP:0012378; exercise intolerance — HP:0003546: symptoms that impair school, work, play, sleep and daily activity.
• Cardiac arrhythmia — HP:0011675; ventricular arrhythmia — HP:0004308; sudden cardiac death — HP:0001645: clinically important possibilities in DCM, although RAF1-specific frequencies are unavailable.
• Mitral regurgitation — HP:0001653: may develop secondarily from annular dilation.
• Peripheral edema — HP:0012398; hepatomegaly — HP:0002240: signs of advanced systemic congestion.

No behavioral phenotype is intrinsic to isolated DCM1NN. Syndromic RAF1 variants may produce Noonan features, but those should be coded under the appropriate RASopathy and not assumed in isolated DCM1NN. No DCM1NN-specific EQ-5D, SF-36, PROMIS, or pediatric quality-of-life dataset was identified.

4. Genetic and molecular information

Causal gene and protein

RAF1 is located at chromosome 3p25.2 and encodes a cytoplasmic serine/threonine kinase and signaling scaffold in receptor-tyrosine-kinase/RAS pathways. Disease-causing RAF1 alleles are highly phenotype- and domain-dependent: many activating variants cause Noonan-spectrum HCM, whereas the variants reported in DCM1NN were associated with AKT–mTOR hyperactivation and a dilated phenotype. Complete or severe biallelic RAF1 loss is a distinct developmental disorder; it should not be equated with DCM1NN.

Variant properties

• Origin: germline; heterozygous.
• Class: missense in the foundational report.
• Inheritance: autosomal dominant; familial or de novo occurrence is biologically possible.
• Mechanism: experimentally associated with aberrant signaling, especially AKT–mTOR activation; a single uniform gain-of-function/loss-of-function label is not justified for all listed alleles.
• Allele frequencies: no reliable frequencies were captured in the retrieved source. Clinically causal alleles should generally be absent or exceptionally rare in ancestry-matched population data, but exact gnomAD values must be queried by normalized genomic coordinate.
• Somatic versus germline: DCM1NN is germline. Somatic RAF1 alterations are relevant to cancer, not the inherited cardiomyopathy diagnosis.

No validated DCM1NN modifier gene, methylation signature, histone abnormality, pathogenic copy-number change, translocation, inversion, or aneuploidy was identified. General DCM can be oligogenic: a 2024 review estimates that 20–38% may have contributions from multiple rare variants with incomplete penetrance. (eldemire2024geneticsofdilated pages 1-3)

5. Environmental information

No toxin, radiation exposure, pollutant, occupational exposure, or infectious agent is a primary cause of genetically defined DCM1NN. Clinicians should nevertheless investigate common DCM phenocopies and co-triggers: viral/inflammatory myocarditis, Chagas disease where epidemiologically relevant, alcohol, cocaine/amphetamine exposure, anthracyclines, HER2-targeted therapy, some tyrosine-kinase inhibitors, nutritional deficiency, endocrine disease, tachyarrhythmia, and peripartum cardiomyopathy.

The practical gene–environment model is that a RAF1 allele establishes myocardial susceptibility, while hemodynamic, inflammatory, toxic, or metabolic stress may alter penetrance or timing. This is plausible from general DCM evidence but untested in RAF1 families. Pregnancy deserves prospective cardio-obstetric monitoring because familial DCM may first manifest or accelerate peripartum. (eldemire2024geneticsofdilated pages 1-3, mestroni2014geneticcausesof pages 6-8)

6. Mechanism and pathophysiology

Ordered causal chain

1. A heterozygous DCM-associated RAF1 missense variant leads to altered RAF1 signaling activity or scaffolding in cardiomyocytes.
2. Altered RAF1 leads to pathological AKT activation; this step was demonstrated in cellular assays and a zebrafish model. (mestroni2014geneticcausesof pages 6-8)
3. AKT hyperactivation leads to increased mTOR signaling, changing cardiomyocyte growth, protein synthesis and remodeling programs.
4. Dysregulated signaling leads to abnormal myocardial growth/remodeling and impaired contractile performance; the exact intermediate sarcomeric and metabolic lesions in DCM1NN are incompletely demonstrated.
5. Contractile impairment leads to increased end-systolic/end-diastolic volume and ventricular dilation, with compensatory neurohormonal activation.
6. Dilation and neurohormonal stress lead to wall stress, secondary mitral regurgitation, fibrosis and further systolic failure; these downstream steps are inferred from general DCM biology.
7. Progressive pump failure leads to dyspnea, fatigue, congestion and pediatric growth/feeding limitation.
8. Branch: electrical and fibrotic remodeling may lead to atrial/ventricular arrhythmia, syncope and sudden death; RAF1-specific arrhythmia incidence is unknown.
9. Experimental intervention branch: rapamycin inhibition of mTOR led to rescue of the RAF1-mutant zebrafish phenotype, but human efficacy has not been demonstrated. (mestroni2014geneticcausesof pages 6-8)

Pathways, processes and cells

Suggested terms include RAS protein signal transduction (GO:0007265), MAPK cascade (GO:0000165), protein kinase B signaling (GO:0043491), TOR signaling (GO:0031929), regulation of cardiac muscle-cell growth (GO:0055021), cardiac muscle contraction (GO:0060048), apoptotic process (GO:0006915), extracellular-matrix organization (GO:0030198) and response to oxidative stress (GO:0006979). The principal cell is the ventricular cardiomyocyte (CL:0000746); cardiac fibroblasts (CL:0002548), endothelial cells (CL:0000115) and immune cells are likely downstream participants in remodeling.

RAF1 also restrains pro-apoptotic ASK1/MST2 signaling, providing a mechanistic reason why profound RAF1 deficiency or pharmacologic pathway disruption can injure myocardium. A 2023 human RAF1-deficiency study found impaired MAPK activity and increased stress-induced apoptosis, but this is a separate recessive disorder and only supports RAF1’s cardiac-survival biology indirectly.

Molecular profiling and advanced technologies

No DCM1NN-specific single-cell, spatial, proteomic, metabolomic, lipidomic, or epigenomic atlas was identified. Important adjacent findings are:

• RAF1-Noonan/HCM model, indirect: patient iPSC-derived cardiomyocytes and 3D bioartificial tissues carrying RAF1 p.Ser257Leu showed shortened sarcomeric I-bands, a titin N2BA-to-N2B shift, altered force/contractile tension, MAPK/p38/YAP abnormalities, and substantial reversal by gene correction or MEK inhibition. Publication: 19 June 2023, https://doi.org/10.1038/s42003-023-05013-8. These data prove RAF1 can directly remodel human cardiomyocyte contractile machinery, but they model HCM rather than DCM1NN. (nakhaeirad2023molecularandcellular pages 1-2)
• General DCM: the 2024 Zheng study integrated single-nucleus transcriptomics and identified disease-associated cellular states, pathways and intracellular communication; cardiomyocytes and contractile machinery were strongly implicated across both major 2024 GWAS. (zheng2024genomewideassociationanalysis pages 1-2, jurgens2024genomewideassociationstudy pages 1-2)

7. Anatomical structures affected

• Primary organ: heart, especially left-ventricular myocardium; biventricular disease can occur.
• UBERON suggestions: heart (UBERON:0000948), myocardium (UBERON:0002349), left ventricle (UBERON:0002084), right ventricle (UBERON:0002080), interventricular septum (UBERON:0002094).
• Tissues/cells: cardiac muscle tissue and ventricular cardiomyocytes (CL:0000746); secondary interstitial fibroblast and vascular involvement accompanies remodeling.
• Subcellular structures: cytosol (GO:0005829), plasma membrane signaling complexes (GO:0005886), sarcomere (GO:0030017), Z disc (GO:0030018), mitochondrion (GO:0005739) and nucleus (GO:0005634).
• Secondary organs: lungs from pulmonary venous congestion; liver, kidneys and peripheral tissues from low output/systemic congestion.
• Lateralization: not applicable; cardiac involvement is midline/organ-wide, although regional fibrosis may be heterogeneous.

8. Temporal development

DCM1NN was discovered in a childhood-onset context, but neonatal, infantile, adult and late penetrance have not been quantified. Onset can be insidious, detected through family screening, or clinically acute after decompensation or an intercurrent stress.

A practical staging model is: (1) genotype-positive/phenotype-negative; (2) early electrical, strain or imaging abnormality; (3) overt ventricular dilation and systolic dysfunction; (4) symptomatic heart failure/arrhythmia; and (5) advanced disease requiring mechanical support or transplantation. The course is chronic and variable. Reverse remodeling may occur with therapy, but “recovered” function does not necessarily eliminate genetic relapse risk.

General pediatric DCM evidence supports serial imaging. In the Pediatric Cardiomyopathy Registry, 40/794 children (5.0%) died and 117/794 (14.7%) underwent transplantation within one year; improvement in fractional shortening reduced later death/transplant risk, while progressive dilation increased it. These figures are not DCM1NN-specific.

9. Inheritance and population

Inheritance

DCM1NN is autosomal dominant. A heterozygous affected individual generally presents a 50% transmission probability per pregnancy, but clinical penetrance and severity cannot be predicted reliably. Penetrance is likely incomplete and age-dependent, as in many DCM genes, but no RAF1-DCM1NN estimate is available. Expressivity is variable, and RAF1 variants can produce dilated, hypertrophic, syndromic, or developmental phenotypes. Anticipation has not been reported. Germline mosaicism is possible in principle after an apparently de novo case but is not quantified. No founder effect, consanguinity association, or carrier frequency has been established.

Epidemiology

Subtype-specific incidence and prevalence are unknown. The historical statement that RAF1 accounted for approximately 9% of childhood-onset DCM arose from the discovery setting and must not be used as population prevalence. (mestroni2014geneticcausesof pages 6-8)

For context only, a 2024 review reports that around 40% of familial DCM has an identifiable genetic cause and that pediatric diagnostic yield can be higher than adult yield, 54% versus 27% in cited cohorts. An older population estimate was 36.5 per 100,000, whereas a 2024 GWAS article cites approximately 1 in 250; ascertainment and definitions differ substantially. (eldemire2024geneticsofdilated pages 1-3, zheng2024genomewideassociationanalysis pages 1-2)

No DCM1NN-specific ethnicity, geographic distribution, age distribution, or sex ratio is established. General DCM is male-predominant clinically, but that observation cannot be assigned to RAF1 carriers without targeted data.

10. Diagnostics

Clinical and laboratory work-up

Diagnosis requires both the DCM phenotype and credible molecular attribution to RAF1. Recommended evaluation includes:

1. Detailed symptoms, medications, toxin/exposure and infection history; physical examination; three-generation pedigree.
2. ECG and ambulatory monitoring to detect conduction disease and atrial/ventricular arrhythmia.
3. Transthoracic echocardiography for chamber dimensions, ejection fraction, wall thickness, valvular regurgitation, right-heart function and serial remodeling.
4. Cardiac MRI for ventricular volumes/function, tissue characterization, edema and late-gadolinium-enhancement fibrosis.
5. BNP or NT-proBNP and high-sensitivity troponin; CBC, electrolytes, renal/liver function, thyroid studies, iron indices and age/context-directed metabolic testing.
6. Coronary evaluation when clinically indicated; infectious, inflammatory, toxic, endocrine and neuromuscular studies according to presentation.
7. Endomyocardial biopsy only when myocarditis, infiltrative/storage disease or another biopsy-actionable diagnosis is suspected; it is not routine for uncomplicated genetic DCM. The 2023 ESC framework prioritizes multimodal imaging, deep phenotyping and genetics. (eldemire2024geneticsofdilated pages 1-3, grasso2024thenew2023 pages 1-2)

Genetic testing

Use a curated cardiomyopathy panel containing robust DCM genes and RAF1, with deletion/duplication analysis where technically appropriate. WES or WGS is reasonable when panel testing is negative, the phenotype is syndromic, or structural/noncoding variation is suspected. RNA sequencing from blood or myocardial tissue may clarify splice variants, but it is not established as routine DCM1NN testing. CMA/karyotype/FISH are not first-line for isolated DCM unless congenital anomalies or developmental findings suggest a chromosomal disorder. mtDNA and repeat-expansion testing should be phenotype-directed.

A 2023 study provides a caution against indiscriminate panel expansion: among 225 DCM patients negative on a 48-gene panel, a 299-gene analysis found 13 P/LP calls, but five were reclassifications in already tested genes and only one of the remaining eight clearly explained the phenotype; 186 VUSs occurred in 127 patients. The authors’ abstract concludes that panels “should be limited to the robust DCM-associated genes.” Publication: 17 May 2023, https://doi.org/10.1038/s41431-023-01384-y. (stroeks2023diagnosticandprognostic pages 1-2)

A RAF1 VUS does not establish DCM1NN. Interpretation should assess phenotype fit, population frequency, segregation, de novo status, domain/mechanism, functional studies and ClinGen/ACMG evidence.

Differential diagnosis

Exclude ischemic cardiomyopathy; hypertensive or valvular remodeling; myocarditis; tachycardia-induced cardiomyopathy; alcohol/toxin/drug-induced disease; peripartum cardiomyopathy; congenital heart disease; neuromuscular or mitochondrial disease; iron overload; thyroid disease; nutritional deficiency; sarcoidosis; amyloidosis; arrhythmogenic cardiomyopathy; left-ventricular non-dilated cardiomyopathy; and physiologic athletic remodeling. Distinguish RAF1-Noonan HCM by wall hypertrophy, dysmorphism/developmental signs and often pulmonary-valve or other congenital disease.

Screening

Offer genetic counseling and cascade testing for a confirmed P/LP familial RAF1 variant. Variant-positive relatives require baseline ECG, echocardiography, clinical review and usually periodic follow-up even when asymptomatic. Variant-negative relatives in a family with a conclusively causal variant can usually be released from genotype-specific surveillance, while relatives in unresolved families need serial clinical screening.

11. Outcome and prognosis

No DCM1NN-specific 5- or 10-year survival, transplant rate, life expectancy, sudden-death rate or quality-of-life statistic exists. Prognosis should therefore be estimated from phenotype severity rather than the “1NN” label alone.

Adverse general DCM markers include severe or worsening LVEF, progressive ventricular dilation, NYHA III/IV symptoms, recurrent hospitalization, elevated BNP/NT-proBNP or troponin, extensive CMR fibrosis, ventricular arrhythmia, syncope, conduction disease, right-ventricular dysfunction, renal dysfunction and failure to reverse remodel. Pediatric registry evidence shows that serial deterioration in fractional shortening and dilation predicts death or transplantation.

A 2024 prospective nonischemic-DCM cohort of 1,152 adults found diabetes in 155 (13%); diabetes was associated with more fibrosis and a higher annual death/transplant event rate, 10.2% versus 5.7%, and adjusted HR 1.61. These are useful general modifiers but are not RAF1-specific.

Morbidity includes chronic exercise limitation, school/work absence, medication burden, anxiety over sudden death and inheritance, repeated imaging, hospitalization, device therapy and transplantation. Genetic diagnosis also affects relatives and reproductive decisions.

12. Treatment

Current clinical care

There is no approved RAF1- or DCM1NN-specific therapy. Treat the manifest phenotype according to pediatric or adult heart-failure guidance:

• ACE inhibitor/ARB or ARNI to reduce maladaptive renin–angiotensin signaling and afterload.
• Evidence-based beta blocker to blunt sympathetic activation.
• Mineralocorticoid-receptor antagonist and SGLT2 inhibitor for eligible HFrEF patients, with age, renal function, potassium, blood pressure and local pediatric evidence considered.
• Loop diuretic for congestion; it improves symptoms but is not a disease-modifying substitute.
• Anticoagulation for standard indications such as atrial fibrillation or intracardiac thrombus, not solely because DCM is present.
• ICD for guideline-defined sudden-death risk; CRT for persistent low LVEF with qualifying QRS morphology/duration after optimized therapy.
• Mechanical circulatory support and heart transplantation for refractory advanced failure.
• Cardiac rehabilitation and individualized activity prescription when stable.

Suggested NCIT concepts include Heart Failure Therapy; Angiotensin-Converting Enzyme Inhibitor; Angiotensin Receptor Blocker; Beta-Adrenergic Blocker; Mineralocorticoid Receptor Antagonist; Sodium-Glucose Cotransporter 2 Inhibitor; Diuretic; Implantable Cardioverter-Defibrillator; Cardiac Resynchronization Therapy; Ventricular Assist Device; and Heart Transplantation. Exact NCIT codes should be validated against the current thesaurus release.

Experimental pathway-directed treatment

RAF1-mutant cellular and zebrafish experiments support AKT–mTOR inhibition, and rapamycin rescued the fish cardiomyopathy phenotype. This is compelling target-validation evidence, not clinical efficacy. (mestroni2014geneticcausesof pages 6-8)

MEK inhibition, calcineurin inhibition and dual RAS/MAPK–PI3K/AKT inhibition are being studied in RAF1-related HCM/RASopathies, not established DCM1NN. In RAF1-mutant rat cardiomyocytes, cyclosporine suppressed hypertrophy through calcineurin–NFAT effects, but systemic cyclosporine is not justified for DCM1NN outside research because of nephrotoxicity, hypertension, immunosuppression and absent clinical benefit data. (dhandapany2011cyclosporineattenuatescardiomyocyte pages 1-2)

Searches found no interventional ClinicalTrials.gov study specifically for RAF1-associated DCM1NN. No gene replacement, CRISPR, ASO, siRNA, mRNA, or cell therapy is clinically available.

13. Prevention

Primary prevention

The inherited allele cannot currently be prevented after conception. Risk reduction includes avoidance of tobacco, cocaine/amphetamines, excess alcohol and unnecessary cardiotoxins; control of blood pressure, bodyweight, diabetes and sleep apnea; vaccination and prompt management of infection according to standard practice; and cardio-oncology surveillance when cardiotoxic therapy is unavoidable.

Secondary prevention

The strongest strategy is early identification: genetic counseling, cascade testing of a confirmed familial P/LP RAF1 variant, and periodic ECG/echo surveillance of carriers. Family screening can detect preclinical dysfunction when treatment and activity counseling may have greater benefit.

Tertiary prevention

Optimize HF therapy, monitor ventricular function and rhythm, manage pregnancy through a cardio-obstetric team, and use ICD/CRT or advanced-HF referral when indicated. Continue surveillance after apparent recovery because relapse can occur in genetic DCM.

Reproductive prevention and counseling

Discuss the 50% transmission risk, uncertainty in penetrance/severity, prenatal diagnosis, and preimplantation genetic testing for monogenic disease. A 2024 inherited-cardiac-disease PGT study proposed a severity/penetrance-based model; among 83 referred couples, the model reached a decision for 76 (92%) and agreed with multidisciplinary decisions in 95%. This is general inherited-cardiac-disease evidence, not RAF1-specific.

Population newborn or carrier screening is not recommended because the condition is ultra-rare, dominant, variably penetrant and not associated with a validated newborn intervention.

14. Other species and natural disease

No naturally occurring RAF1-defined DCM1NN was identified in companion animals, livestock or wildlife. Dogs and cats develop natural DCM from other genetic and nutritional causes, but those disorders should not be labeled DCM1NN.

RAF1 orthologs are highly conserved across vertebrates, supporting cross-species signaling studies. Relevant taxa include Homo sapiens (NCBI Taxon 9606), Mus musculus (10090), Danio rerio (7955), Drosophila melanogaster (7227) and Xenopus species. Exact orthologous NCBI Gene IDs and any VBO breed identifiers should be retrieved from the current organism databases before ingestion. There is no infectious transmission or zoonotic potential.

15. Model organisms and experimental systems

Zebrafish

Cardiomyocyte-directed expression of DCM-associated RAF1 mutants reproduced a cardiomyopathy phenotype with AKT hyperactivation; rapamycin rescue supports causal involvement of AKT–mTOR. Strengths are rapid cardiac phenotyping and in-vivo drug testing. Limitations include two-chambered anatomy, regenerative capacity, transgene dosage and uncertain equivalence to heterozygous human expression. (mestroni2014geneticcausesof pages 6-8)

Cellular models

RAF1 variants have been expressed in neonatal/adult rat cardiomyocytes to study MEK/ERK, calcineurin–NFAT, SERCA2a/calcium signaling and cellular hypertrophy. These models establish pathway competence but incompletely reproduce ventricular dilation, chronic fibrosis and human developmental timing. (dhandapany2011cyclosporineattenuatescardiomyocyte pages 1-2)

Human iPSC and engineered tissue

Patient-derived iPSC cardiomyocytes, isogenic CRISPR-corrected controls, cardiac bodies and bioartificial tissues provide human sarcomere, calcium-handling and contractility readouts. The best recent RAF1 study used p.Ser257Leu Noonan/HCM cells; correction and MEK inhibition reversed much of the phenotype. Its relevance to DCM1NN is mechanistic and comparative, not direct. (nakhaeirad2023molecularandcellular pages 1-2)

Mouse and other models

Cardiac RAF1 loss models support roles in cardiomyocyte survival and protection against ASK1/MST2-mediated apoptosis, whereas RAF1 RASopathy knock-in models generally reproduce HCM. These opposing phenotypes underscore variant-specific mechanisms and warn against treating all RAF1 alleles with the same pathway inhibitor.

Recent developments and expert interpretation

Three 2023–2024 developments materially change how DCM1NN should be curated:

1. Variant curation has become more conservative. The 2023 expanded-panel study showed minimal additional diagnostic yield but a large VUS burden, favoring robust gene–disease evidence and variant-level reassessment over historical disease labels. (stroeks2023diagnosticandprognostic pages 1-2)
2. DCM is now understood as rare-plus-common genetic architecture. Two independent November 2024 Nature Genetics studies identified 70–80 loci, highlighted cardiomyocytes and the contractile apparatus, and showed that polygenic background predicts risk and modifies penetrance. This may ultimately explain why carriers of the same rare allele differ clinically. (zheng2024genomewideassociationanalysis pages 1-2, jurgens2024genomewideassociationstudy pages 1-2)
3. Human engineered myocardium can resolve RAF1 mechanism. The 2023 RAF1 iPSC/3D-tissue study tied abnormal signaling to titin isoform switching and sarcomeric dysfunction and demonstrated reversal by gene correction/MEK inhibition. Applying similar isogenic models to the six historical DCM1NN variants is a major unmet need. (nakhaeirad2023molecularandcellular pages 1-2)

The appropriate expert conclusion is therefore cautious: RAF1 is an accepted DCM1NN gene–disease association, but pathogenicity and mechanism must be adjudicated at the individual-variant level. The strongest DCM1NN-specific therapeutic signal—AKT–mTOR inhibition—is preclinical. Clinical care should currently follow phenotype-based DCM/HFrEF guidance, combined with rigorous genetic counseling and family surveillance.

Key references

• Dhandapany PS et al. “RAF1 mutations in childhood-onset dilated cardiomyopathy.” Nature Genetics. Published online 27 April 2014. PMID: 24777450. https://doi.org/10.1038/ng.2963. The key abstract-level finding is summarized as: RAF1 mutations were identified in childhood-onset DCM, mutant signaling activated AKT–mTOR, and rapamycin rescued the zebrafish phenotype. (OpenTargets Search: dilated cardiomyopathy-RAF1, mestroni2014geneticcausesof pages 6-8)
• Stroeks SLVM et al. “Diagnostic and prognostic relevance of using large gene panels in the genetic testing of patients with dilated cardiomyopathy.” European Journal of Human Genetics. Published 17 May 2023. https://doi.org/10.1038/s41431-023-01384-y. Direct abstract quote: “Overall, the use of large gene panels for genetic testing in DCM does not increase the diagnostic yield.” (stroeks2023diagnosticandprognostic pages 1-2)
• Nakhaei-Rad S et al. “Molecular and cellular evidence for the impact of a hypertrophic cardiomyopathy-associated RAF1 variant…” Communications Biology. Published 19 June 2023. https://doi.org/10.1038/s42003-023-05013-8. Direct abstract quote: the study “uncovered a direct link between a RASopathy gene variant and the abnormal sarcomere structure resulting in a cardiac dysfunction.” This is indirect HCM evidence. (nakhaeirad2023molecularandcellular pages 1-2)
• Eldemire R, Mestroni L, Taylor MRG. “Genetics of Dilated Cardiomyopathy.” Annual Review of Medicine. Published 29 January 2024. https://doi.org/10.1146/annurev-med-052422-020535. Direct abstract quote: “An estimated 40% of cases of familial DCM have an identifiable genetic cause.” (eldemire2024geneticsofdilated pages 1-3)
• Grasso M et al. “The new 2023 ESC guidelines for the management of cardiomyopathies.” European Heart Journal Supplements. April 2024. https://doi.org/10.1093/eurheartjsupp/suae002. (grasso2024thenew2023 pages 1-2)
• Jurgens S et al. “Genome-wide association study reveals mechanisms underlying dilated cardiomyopathy and myocardial resilience.” Nature Genetics. Published 21 November 2024. https://doi.org/10.1038/s41588-024-01975-5. Direct abstract result: 9,365 cases, 946,368 controls and 70 significant loci. (jurgens2024genomewideassociationstudy pages 1-2)
• Zheng SL et al. “Genome-wide association analysis provides insights into the molecular etiology of dilated cardiomyopathy.” Nature Genetics. Published 21 November 2024. https://doi.org/10.1038/s41588-024-01952-y. Direct abstract result: 14,256 cases, 80 risk loci and 62 putative effector genes, with polygenic modification of rare-variant penetrance. (zheng2024genomewideassociationanalysis pages 1-2)

References

1. (benarroch2025the2025version pages 24-25): Louise Benarroch, Gisèle Bonne, François Rivier, Vincent Procaccio, and Dalil Hamroun. The 2025 version of the gene table of neuromuscular disorders (nuclear genome). Jan 2025. URL: https://doi.org/10.1016/j.nmd.2024.105261, doi:10.1016/j.nmd.2024.105261. This article has 16 citations and is from a peer-reviewed journal.

2. (OpenTargets Search: dilated cardiomyopathy-RAF1): Open Targets Query (dilated cardiomyopathy-RAF1, 6 results). Buniello, A. et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research.

3. (mestroni2014geneticcausesof pages 6-8): Luisa Mestroni, Francesca Brun, Anita Spezzacatene, Gianfranco Sinagra, and Matthew R.G. Taylor. Genetic causes of dilated cardiomyopathy. Progress in pediatric cardiology, 37 1-2:13-18, Dec 2014. URL: https://doi.org/10.1016/j.ppedcard.2014.10.003, doi:10.1016/j.ppedcard.2014.10.003. This article has 145 citations and is from a peer-reviewed journal.

4. (ma2025raf1mutationexpands pages 1-2): Nan Ma, Zhong-Wei Li, Jia-Jia Liu, Xing-Guang Liu, Xing Zhou, Bo-Wen Wang, Yan-Ling Li, Tian-Cheng Zhang, and Ping Xie. Raf1 mutation expands the cardiac phenotypic spectrum of noonan syndrome: a case report. World Journal of Cardiology, Jun 2025. URL: https://doi.org/10.4330/wjc.v17.i6.106525, doi:10.4330/wjc.v17.i6.106525. This article has 1 citations.

5. (dhandapany2014raf1mutationsin pages 1-7): Perundurai S Dhandapany, Md Abdur Razzaque, Uthiralingam Muthusami, Sreejith Kunnoth, Jonathan J Edwards, Sonia Mulero-Navarro, Ilan Riess, Sherly Pardo, Jipo Sheng, Deepa Selvi Rani, Bindu Rani, Periyasamy Govindaraj, Elisabetta Flex, Tomohiro Yokota, Michiko Furutani, Tsutomu Nishizawa, Toshio Nakanishi, Jeffrey Robbins, Giuseppe Limongelli, Roger J Hajjar, Djamel Lebeche, Ajay Bahl, Madhu Khullar, Andiappan Rathinavel, Kirsten C Sadler, Marco Tartaglia, Rumiko Matsuoka, Kumarasamy Thangaraj, and Bruce D Gelb. Raf1 mutations in childhood-onset dilated cardiomyopathy. Nature Genetics, 46:635-639, Apr 2014. URL: https://doi.org/10.1038/ng.2963, doi:10.1038/ng.2963. This article has 99 citations and is from a highest quality peer-reviewed journal.

6. (nakhaeirad2023molecularandcellular pages 1-2): Saeideh Nakhaei-Rad, Fereshteh Haghighi, Farhad Bazgir, Julia Dahlmann, Alexandra Viktoria Busley, Marcel Buchholzer, Karolin Kleemann, Anne Schänzer, Andrea Borchardt, Andreas Hahn, Sebastian Kötter, Denny Schanze, Ruchika Anand, Florian Funk, Annette Vera Kronenbitter, Jürgen Scheller, Roland P. Piekorz, Andreas S. Reichert, Marianne Volleth, Matthew J. Wolf, Ion Cristian Cirstea, Bruce D. Gelb, Marco Tartaglia, Joachim P. Schmitt, Martina Krüger, Ingo Kutschka, Lukas Cyganek, Martin Zenker, George Kensah, and Mohammad R. Ahmadian. Molecular and cellular evidence for the impact of a hypertrophic cardiomyopathy-associated raf1 variant on the structure and function of contractile machinery in bioartificial cardiac tissues. Jun 2023. URL: https://doi.org/10.1038/s42003-023-05013-8, doi:10.1038/s42003-023-05013-8. This article has 45 citations and is from a peer-reviewed journal.

7. (dhandapany2011cyclosporineattenuatescardiomyocyte pages 1-2): Perundurai S. Dhandapany, Frank Fabris, Rahul Tonk, Ardo Illaste, Ioannis Karakikes, Mehran Sorourian, Jipo Sheng, Roger J. Hajjar, Marco Tartaglia, Eric A. Sobie, Djamel Lebeche, and Bruce D. Gelb. Cyclosporine attenuates cardiomyocyte hypertrophy induced by raf1 mutants in noonan and leopard syndromes. Journal of molecular and cellular cardiology, 51 1:4-15, Jul 2011. URL: https://doi.org/10.1016/j.yjmcc.2011.03.001, doi:10.1016/j.yjmcc.2011.03.001. This article has 43 citations and is from a domain leading peer-reviewed journal.

8. (stroeks2023diagnosticandprognostic pages 1-2): Sophie L. V. M. Stroeks, Debby Hellebrekers, Godelieve R. F. Claes, Ingrid P. C. Krapels, Michiel H. T. M. Henkens, Maurits Sikking, Els K. Vanhoutte, Apollonia Helderman-van den Enden, Han G. Brunner, Arthur van den Wijngaard, and Job A. J. Verdonschot. Diagnostic and prognostic relevance of using large gene panels in the genetic testing of patients with dilated cardiomyopathy. European Journal of Human Genetics, 31:776-783, May 2023. URL: https://doi.org/10.1038/s41431-023-01384-y, doi:10.1038/s41431-023-01384-y. This article has 10 citations and is from a domain leading peer-reviewed journal.

9. (eldemire2024geneticsofdilated pages 1-3): Ramone Eldemire, Luisa Mestroni, and Matthew R.G. Taylor. Genetics of dilated cardiomyopathy. Jan 2024. URL: https://doi.org/10.1146/annurev-med-052422-020535, doi:10.1146/annurev-med-052422-020535. This article has 83 citations and is from a domain leading peer-reviewed journal.

10. (grasso2024thenew2023 pages 1-2): Maurizia Grasso, Davide Bondavalli, Viviana Vilardo, Claudia Cavaliere, Ilaria Gatti, Alessandro Di Toro, Lorenzo Giuliani, Mario Urtis, Michela Ferrari, Barbara Cattadori, Alessandra Serio, Carlo Pellegrini, and Eloisa Arbustini. The new 2023 esc guidelines for the management of cardiomyopathies: a guiding path for cardiologist decisions. European Heart Journal Supplements : Journal of the European Society of Cardiology, 26:i1-i5, Apr 2024. URL: https://doi.org/10.1093/eurheartjsupp/suae002, doi:10.1093/eurheartjsupp/suae002. This article has 18 citations.

11. (ma2025raf1mutationexpands pages 9-10): Nan Ma, Zhong-Wei Li, Jia-Jia Liu, Xing-Guang Liu, Xing Zhou, Bo-Wen Wang, Yan-Ling Li, Tian-Cheng Zhang, and Ping Xie. Raf1 mutation expands the cardiac phenotypic spectrum of noonan syndrome: a case report. World Journal of Cardiology, Jun 2025. URL: https://doi.org/10.4330/wjc.v17.i6.106525, doi:10.4330/wjc.v17.i6.106525. This article has 1 citations.

12. (jurgens2024genomewideassociationstudy pages 1-2): S. Jurgens, Joel T. Rämö, D. Kramarenko, L. Wijdeveld, Jan Haas, M. Chaffin, S. Garnier, L. Gaziano, L. Weng, Alex Lipov, S. Zheng, Albert Henry, J. Huffman, Saketh Challa, Frank Rühle, Carmen Diaz Verdugo, C. Krijger Juárez, Shinwan Kany, C. A. van Orsouw, K. Biddinger, Edwin Poel, Amanda L Elliott, Xin Wang, C. Francis, Richard Ruan, Satoshi Koyama, L. Beekman, Dominic S Zimmerman, J. Deleuze, E. Villard, D. Trégouët, Richard Isnard, Joel T. Amanda L. Juha Teemu Jari Aarno Mark Rämö Elliott Sinisalo Niiranen Laukkanen Palotie D, J. Sinisalo, T. Niiranen, J. Laukkanen, A. Palotie, Mark Daly, Jennifer E. Kyong-Mi Philip S. Krishna G. Huffman Chang Tsao Aragam, Kyong-Mi Chang, Phil Tsao, Krishna G. Aragam, Sean L. Albert Kiran James S. R. Thomas Patrick T. Kris Zheng Henry Biddinger Ware Lumbers Ellinor Aragam, James S. Ware, R. Lumbers, P. Ellinor, D. Boomsma, E. D. de Geus, R. Tadros, Y. Pinto, A. Wilde, J. Hottenga, Roddy Walsh, A. F. Schmidt, Seung Hoan Choi, P. Matthews, S. N. van der Crabben, Ahmad S. Amin, P. Charron, Benjamin Meder, and C. Bezzina. Genome-wide association study reveals mechanisms underlying dilated cardiomyopathy and myocardial resilience. Nature Genetics, 56:2636-2645, Nov 2024. URL: https://doi.org/10.1038/s41588-024-01975-5, doi:10.1038/s41588-024-01975-5. This article has 68 citations and is from a highest quality peer-reviewed journal.

13. (zheng2024genomewideassociationanalysis pages 1-2): Sean L. Zheng, Albert Henry, Douglas Cannie, Michael Lee, David Miller, Kathryn A. McGurk, Isabelle Bond, Xiao Xu, Hanane Issa, Catherine Francis, Antonio De Marvao, Pantazis I. Theotokis, Rachel J. Buchan, Doug Speed, Erik Abner, Lance Adams, Krishna G. Aragam, Johan Ärnlöv, Anna Axelsson Raja, Joshua D. Backman, John Baksi, Paul J. R. Barton, Kiran J. Biddinger, Eric Boersma, Jeffrey Brandimarto, Søren Brunak, Henning Bundgaard, David J. Carey, Philippe Charron, James P. Cook, Stuart A. Cook, Spiros Denaxas, Jean-François Deleuze, Alexander S. Doney, Perry Elliott, Christian Erikstrup, Tõnu Esko, Eric H. Farber-Eger, Chris Finan, Sophie Garnier, Jonas Ghouse, Vilmantas Giedraitis, Daniel F. Guðbjartsson, Christopher M. Haggerty, Brian P. Halliday, Anna Helgadottir, Harry Hemingway, Hans L. Hillege, Isabella Kardys, Lars Lind, Cecilia M. Lindgren, Brandon D. Lowery, Charlotte Manisty, Kenneth B. Margulies, James C. Moon, Ify R. Mordi, Michael P. Morley, Andrew D. Morris, Andrew P. Morris, Lori Morton, Mahdad Noursadeghi, Sisse R. Ostrowski, Anjali T. Owens, Colin N. A. Palmer, Antonis Pantazis, Ole B. V. Pedersen, Sanjay K. Prasad, Akshay Shekhar, Diane T. Smelser, Sundararajan Srinivasan, Kari Stefansson, Garðar Sveinbjörnsson, Petros Syrris, Mari-Liis Tammesoo, Upasana Tayal, Maris Teder-Laving, Guðmundur Thorgeirsson, Unnur Thorsteinsdottir, Vinicius Tragante, David-Alexandre Trégouët, Thomas A. Treibel, Henrik Ullum, Ana M. Valdes, Jessica van Setten, Marion van Vugt, Abirami Veluchamy, W. M. Monique Verschuren, Eric Villard, Yifan Yang, Mahdad Noursadeghi, Ole B. V. Pedersen, Kari Stefansson, Unnur Thorsteinsdottir, Henrik Ullum, Folkert W. Asselbergs, Antonio De Marvao, Marie-Pierre Dube, Michael E. Dunn, Patrick T. Ellinor, Sophie Garnier, Chim C. Lang, Andrew P. Morris, Lori Morton, Colin N. A. Palmer, Nilesh J. Samani, Svati H. Shah, Akshay Shekhar, J. Gustav Smith, Sundarajan Srinivasan, Guðmundur Thorgeirsson, Ramachandran S. Vasan, Jessica van Setten, Marion van Vugt, Abirami Veluchamy, W. M. Monique Verschuuren, Eric Villard, Quinn Wells, Folkert W. Asselbergs, Thomas P. Cappola, Marie-Pierre Dube, Michael E. Dunn, Patrick T. Ellinor, Aroon D. Hingorani, Chim C. Lang, Nilesh J. Samani, Svati H. Shah, J. Gustav Smith, Ramachandran S. Vasan, Declan P. O’Regan, Hilma Holm, Michela Noseda, Quinn Wells, James S. Ware, and R. Thomas Lumbers. Genome-wide association analysis provides insights into the molecular etiology of dilated cardiomyopathy. Nature Genetics, 56:2646-2658, Nov 2024. URL: https://doi.org/10.1038/s41588-024-01952-y, doi:10.1038/s41588-024-01952-y. This article has 77 citations and is from a highest quality peer-reviewed journal.

Artifacts

• Edison artifact artifact-00

Reference Validation

Checked with linkml-reference-validator 0.2.1.

All extracted references resolved successfully.

Term Validation

Checked with linkml-term-validator 0.4.5, through the ols: adapter.

Obsolete terms

These terms are real but deprecated. Citing one is not a fabrication; it does mean the report is naming something the ontology has retired:

• GO:0048016 (obsolete inositol phosphate-mediated signaling) (1 mention)

Terms whose name is worth a second look

The report's name for these is recognisably related to the term's own name without being one of them. A loose paraphrase reads the same way as a citation of the wrong sibling term - and so does a related synonym, which the ontology records precisely because it names something adjacent rather than the same thing - so these are listed rather than judged:

• SO:0001583 (1 mention) - the report calls it "missense variant"; SO calls it missense_variant

Prefixes with no resolver

Terms carrying these prefixes were not checked either way, because no configured ontology covers them. An unrecognised prefix may name an ontology this run could not reach as easily as one that does not exist, so nothing here is evidence of fabrication: OMIM, Gene.

60 of 64 terms resolved to a current term; the rest could not be looked up either way.