The Hidden Problem Behind Slow Turnaround Times
Even as sequencing becomes faster and more affordable, the downstream interpretation steps have not kept pace. Laboratories still confront large volumes of candidate variants, most of which are benign, and the task of reducing these lists to plausible disease-relevant candidates remains a manual, laborious process.
Defining the Problem
At the core of today’s interpretation workflow is a structural imbalance between data generation and evidence availability. Each exome or genome introduces tens of thousands of variants, yet only a small fraction are functionally significant. Most are benign, but distinguishing them from pathogenic signals requires time, context, and expertise.
Three forces drive the persistent manual burden:
- Variant burden: Human-only datasets produce an overwhelming number of rare or novel variants that cannot be confidently triaged using conventional evidence categories.
- Persistent VUS: Large catalogs of variants remain unclassified due to lack of functional evidence, limited case data, and absence of population-level clarity.
- Evidence gaps: Population frequency, clinical assertions, and computational predictions provide partial views, but cannot fully describe which variants genes can naturally tolerate.
From Cornerstone Genomic’s perspective, these challenges originate in a narrow evidence ecosystem. Current workflows rely almost entirely on human variation and limited functional assays, which cannot reveal how a gene behaves over evolutionary time. Without broader comparative context, most variants enter the pipeline as uncertain, and manual review becomes the default resolution method.
Evidence for Systemic Bottlenecks
Peer-reviewed studies highlight that VUS remain prevalent across disease genes even with expanding population datasets (Lek et al. 2016). Functional assays, while informative, scale poorly and often yield discordant or context-specific results (Brnich et al. 2019). Many in-silico predictors also produce inconsistent outputs, limiting their standalone clinical utility (Niroula and Vihinen 2019).
Additionally, population databases remain demographically skewed. Underrepresentation complicates allele frequency interpretation and inflates uncertainty for many groups (Martin et al. 2019). These structural limitations reinforce the need for extended evidence sources that do not depend on human population sampling alone.
Why Existing Approaches Fail
Current approaches struggle because they share three intrinsic constraints.
1. Structural limits of population frequency evidence
Allele frequency can help rule out pathogenicity for common variants, but it cannot distinguish rare benign variants from pathogenic ones. Many benign variants remain rare due to demography rather than functional constraint, and population skews further confound interpretation.
2. Limits and discordance of computational prediction
Predictive models are trained on incomplete datasets and often disagree. They lack direct information about which amino acids or nucleotides have been tolerated over evolutionary timescales.
3. Lack of scalable functional evidence
Experimental studies remain essential but cannot feasibly evaluate most human variation. They are resource-intensive and context-dependent, and they rarely provide comprehensive coverage across all possible variants.
4. Bias and incompleteness in clinical datasets
ClinVar and similar registries contain expert-curated assertions, but large proportions remain conflicting or uncertain due to limitations in case data and phenotypic diversity.
5. Manual curation bottlenecks
Because available evidence cannot resolve most variants, expert review remains the final step. Reviewers integrate disparate evidence categories variant by variant, often spending hours per case.
The result is an interpretation system that scales poorly and remains dependent on manual, time-intensive adjudication.
Introducing Evolutionary Evidence
Evolutionary constraint provides an orthogonal axis of evidence that does not depend on human population size, sampling bias, or functional assay throughput. Across millions of years, natural selection has tested which sites in a gene can tolerate change and which cannot.
Cross-species comparative genomics captures:
- Constraint: Sites that remain unchanged across primates or broader taxa indicate intolerance to variation.
- Natural variation: Positions that vary across species reveal which changes are benign and functionally tolerated.
- Neutral vs. adaptive patterns: Distinguishing conserved from flexible regions clarifies mechanism, not just classification.
This evolutionary information directly addresses the unresolved categories in variant interpretation:
- It reduces uncertainty by showing which variants co-occur with normal biological function.
- It identifies which positions are highly constrained and therefore more likely to be pathogenic when mutated.
- It operates independently of human-only datasets, enabling clearer interpretation in ancestrally diverse or underrepresented populations.
Cornerstone’s internal analyses confirm these principles. Across 55 primate genera and 80 million years of divergence, benign variants persist while pathogenic variants do not co-occur with natural variation. This pattern provides a mechanistic, evolution-rooted framework for resolving uncertainty.
Cornerstone’s Solution: The CodeXome Evolutionary Filtering Platform
CodeXome integrates evolutionary evidence at scale by combining:
- Proprietary primate exome datasets spanning 55 genera and ~2 billion sequence datapoints.
- Alignments mapped to GRCh38 for 19,244 genes.
- Integrated clinical and population datasets, including ClinVar, gnomAD, UniProt, and NCBI summaries.
- An evolutionary filtering engine that separates natural variation from human-unique variation in minutes.
- AI and machine-learning variant scoring trained on evolutionary patterns across millions of years.
How evolutionary filtering works in practice
When users upload a VCF, CodeXome:
- Cross-references each variant against primate-aligned evolutionary data.
- Automatically filters out all naturally occurring, benign variants in one step.
- Retains only human-unique variants for further assessment.
- Applies evolutionary scoring to prioritize remaining variants on the basis of functional constraint and deep-time patterns.
- Provides integrated structural and clinical context for interpretation.
Validation studies show that pathogenic variants classified by expert panels are not shared with primate variation, while benign variants consistently co-occur with evolutionary diversity. On average, 20 percent of ClinVar VUS across more than 14,000 genes can be confidently reclassified as likely benign.
This transforms the workload. Instead of manually sifting through thousands of variants, users begin with a drastically reduced and biologically enriched candidate list.
What This Means for the Field
The integration of evolutionary filtering into variant interpretation has several immediate implications.
Clinical laboratories
- Significant reduction in variant review volume.
- More consistent triage of VUS and rare missense variants.
- Improved scalability without proportional increases in personnel.
- Faster turnaround times and clearer reporting decisions.
Medical geneticists
- Stronger evidence for separating benign from pathogenic signals.
- More mechanistic insight into which gene regions are functionally constrained.
- Enhanced interpretation for individuals from ancestrally diverse populations.
Researchers and consortia
- Ability to identify evolutionarily significant positions for follow-up functional studies.
- Clearer prioritization of candidate variants in gene discovery projects.
- Integration-ready data for pipelines, enabling automated downstream workflows.
By reducing noise, evolutionary filtering allows focus on variants that plausibly alter gene function, accelerating decision-making and improving the reproducibility of interpretation.
Conclusion
Manual variant filtering persists because current evidence sources cannot definitively resolve most rare variants. Human-only data provide too narrow a view, and functional evidence cannot scale. Evolutionary genomics fills this gap by revealing which variants have been naturally tolerated over millions of years, enabling confident separation of benign and pathogenic signals.
CodeXome operationalizes this evidence into a practical, scalable platform that reduces months of manual filtering to minutes, providing an essential new evidence axis for the field.
If your team is exploring ways to improve variant interpretation efficiency, reduce VUS burden, or integrate evolutionary filtering into existing workflows, we welcome collaboration and invite you to evaluate CodeXome in your environment. Book a call to discuss today.
References
- Brnich SE et al. 2019. ACMG laboratory quality assurance standards for functional assays. Genetics in Medicine.
- Lek M et al. 2016. Analysis of protein-coding genetic variation in 60,706 humans. Nature.
- Martin AR et al. 2019. Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics.
- Niroula A and Vihinen M. 2019. How good are pathogenicity predictors? PLoS Computational Biology.
