How Do You Prioritize Variants Without Patient Cohorts?

🧬 Decoded Series – Unpacking 3billion’s Diagnostic Technologies

Welcome to another edition of Decoded, our series breaking down the patented technologies behind 3billion’s diagnostics. In this fourth entry, we spotlight an innovative system designed to identify disease-causing variants—without requiring large-scale patient genome datasets.

Here’s the key question this technology answers:
Can we find causal variants using only symptom and variant data?
Yes—we can. Let’s break down how.

Why Was This Technology Needed?

In rare disease diagnostics, traditional methods rely on comparing the genomes of affected individuals (cases) and unaffected individuals (controls) to find statistically meaningful variants.

But in rare diseases, patient data is limited—many diseases affect fewer than 1 in 100,000 people. That makes case-control comparisons impractical. So how can we still identify meaningful variants?

The 3billion Breakthrough

Instead of relying on patient cohort comparisons, 3billion developed a system that starts with symptom-variant associations and works upward.

The logic goes like this:
👉 “How strongly is this variant associated with this symptom?”
Then—
👉 “Given this set of symptoms that define a disease, how likely is this variant to be the cause?”

It flips the diagnostic process on its head—working from observed clinical features to genome data, not the other way around.

How It Works

The system works in three key steps:

1. Symptom-to-Variant Correlation
   For each symptom, the system calculates how strongly a specific variant is associated with that symptom.
   
2. Variant-to-Disease Probability Calculation
   Diseases are defined as a set of symptoms. The system combines the variant’s association scores across all symptoms of a disease to compute how likely that variant causes the disease.
   
3. Bayesian Odds Model
   The final probability is refined through a Bayesian odds framework to improve diagnostic accuracy.
   

This approach allows meaningful variant interpretation—without relying on patient group data.

Why It Matters

• Designed for rare diseases
  Works even when patient data is too limited for conventional analysis.
  
• Improves interpretation of uncertain variants (VUS)
  Adds clinical weight by incorporating symptom associations.
  
• Bridges phenotype-genotype gaps
  Helps prioritize variants in cases where symptoms provide stronger clues than genetic findings alone.
  

Real-World Testing

We tested this model using pathogenic and benign variants publicly available in the ClinVar database. The result? The system clearly distinguished between disease-causing and non-disease-causing variants—using only symptom and variant information.

This logic now powers parts of our variant interpretation engine within GEBRA, our internal genomic analysis platform.

Frequently Asked Questions

Q. How is this different from standard gene-disease databases?
A. This approach doesn’t depend on large patient datasets. It calculates diagnostic relevance by connecting symptoms to variants, then variants to diseases—enhancing traditional gene-disease logic.

Q. Does it help interpret VUS?
A. Yes. It adds a probabilistic score that complements other classification criteria.

Q. Is this used in every 3billion test?
A. Yes. This system is integrated into the interpretation logic of GEBRA and supports every diagnostic analysis we deliver.

Learn More

📄 Patent Number: 1020200100095 – View Full Patent (DOI)
📘 How GEBRA integrates phenotypic signals into variant analysis