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Northwestern Medicine researchers have combined three distinct genetic testing strategies into a single, more powerful risk score that improves prediction of arrhythmias — irregular heart rhythms that can progress to atrial fibrillation or sudden cardiac death. The integrated approach uses whole-genome sequencing to merge rare variant detection, polygenic risk information, and broad genomic context, opening a path toward earlier diagnosis and more personalized cardiac care.
A practical roadmap for genetic risk in cardiology
For years, genetic testing in cardiology has been split across three main approaches: monogenic panels that flag rare, high-impact mutations; polygenic risk scores that aggregate many common variants to estimate relative risk; and whole-genome sequencing that reads the entire genetic code. Each method has value, but working in isolation leaves gaps.
Northwestern’s team created a composite genetic score that blends all three. The result: a 360-degree genomic profile that captures rare, strong-effect changes; the cumulative influence of many small-effect variants; and non-coding genomic signals that traditional panels miss. The study — published November 11 in Cell Reports Medicine and led by Dr. Elizabeth McNally — included 1,119 participants and shows how this integrative framework can sharpen predictions of who will develop dangerous heart rhythm disorders.
How the integrated score was constructed
The study enrolled 523 people with confirmed arrhythmias (some also had heart failure) and compared their genomes with 596 control participants from the NUgene biobank who were aged 40+ with no known cardiac disease. Investigators painstakingly reviewed clinical records and device data to ensure case accuracy, then performed whole-genome sequencing on all participants.
Researchers combined data from three complementary sources:
- Monogenic testing: detection of rare, high-penetrance variants in genes already linked to cardiac disease.
- Polygenic risk scores: a weighted sum of many common variants that together influence arrhythmia risk.
- Genome-wide context: non-coding regions and broader genomic patterns captured by whole-genome sequencing.
“It’s a very cool approach in which we are combining rare gene variants with common gene variants and then adding in non-coding genome information,” Dr. Elizabeth McNally said. She noted that, to their knowledge, no prior study had stitched all three layers together in this way. By integrating these data, clinicians can achieve a much higher odds ratio for identifying patients at greatest risk.
Why this matters for patients and clinicians
Traditional cardiology relies on symptoms, family history, electrocardiograms (EKGs), echocardiograms, and imaging. Genetic information can augment those tools by revealing risk long before symptoms appear. In practice, McNally says the integrated score helps stratify patients — identifying those who might benefit from closer monitoring, lifestyle interventions, or even proactive devices like implantable defibrillators when risk is very high.
That said, genetic testing remains underused. Estimates cited by the research team suggest only 1–5% of people who should receive genetic testing do so. Even in cancer care — a field with established genetic testing pathways — only 10–20% of eligible patients undergo testing. The key obstacles are workforce training and practical tools: many physicians lack the education, time, or integrated systems needed to apply genomic data in routine care.
Scientific context and potential beyond arrhythmia
The integrated model isn’t just relevant to cardiac rhythm disorders. The authors suggest this unified approach could be a template for other complex, genetically influenced diseases — from certain cancers to neurodegenerative conditions like Parkinson’s disease and even some neurodevelopmental disorders. Whole-genome sequencing provides a comprehensive resource that, when paired with smart analytic frameworks, can surface both obvious and subtle genetic contributors to disease.
A graphical abstract from the paper
Clinical hurdles and next steps
Translating this work to clinical practice will require several steps: validation in larger and more diverse populations, development of clinician-friendly reporting tools, and education programs for non-specialist physicians. Health systems will need workflows that connect genomic labs, electronic health records, and decision support so genetic information can inform real-time care decisions.
Regulatory and ethical considerations also matter. Broad genome sequencing raises questions about incidental findings, privacy, and how to counsel patients about risk that may not be modifiable today. Still, the promise is clear: earlier risk detection, more precise prevention strategies, and treatments tailored to a person’s full genetic profile.
Expert Insight
“Integrating rare and common variant data with whole-genome context is the logical next step for genomic medicine,” says Dr. Maya Singh, a clinical geneticist and cardiology researcher unaffiliated with the study. “This approach increases sensitivity without sacrificing specificity, and it helps clinicians prioritize interventions. The challenge will be delivering these insights in a way that’s actionable at the bedside.”
Dr. Singh adds that scalable clinician education and thoughtful implementation science will be essential: “Technology is moving faster than practice. To realize benefits for patients, we need tools that simplify interpretation and clear guidelines for how to act on genomic risk.”
As polygenic risk scores become more common and whole-genome sequencing costs continue to fall, integrated genetic profiles like the one from Northwestern may shift how cardiologists identify and prevent life-threatening arrhythmias. For patients, that could mean knowing risk years earlier — and receiving care tailored to the full complexity of their genome.
Source: scitechdaily
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