Our Approach


We perform polygenic interpretation services built on top of your existing assays.
  • Polygenic Risk Scores are increasingly being integrated into clinical practice across healthcare systems in the United States and Europe. Learn about how PRS is used as a cutting-edge tool for precision genomic risk prediction, empowering healthcare providers to provide better patient outcomes through data-driven disease risk management.
  • Our offering involves polygenic interpretation services built on top of existing microarrays, panels, or sequencing methods. Use our computational solution to 10x the predictive power and informativeness of your methods.
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  • Accuracy: Polygenic risk improves sensitivity of genomic risk prediction significantly compared to traditional monogenic panels.
  • Multi-ancestry: Unlike single-SNP based approaches relevant to a subset of ancestries, our multi-ancestry polygenic risk score is tested for individuals of diverse ancestral background, including European, African, East Asian, South Asian, Indigenous, Latin, Arab, and Mixed.
  • Granularity: Our risk reports include 5-year, 10-year, and overall lifetime risk.


We expedite biomarker-driven drug development from discovery to commercialization.
  • Dataset construction: We help construct large-scale retrospective longitudinal cohorts with our existing data or in tandem with client data. We ensure datasets are representative of target populations and can augment with synthetic data as needed using generative techniques. We help design prospective datasets to ensure sufficient statistical power for subsequent analyses.
    dataset creation - cohort design - synthetic data generation
  • Research and bioninformatics support: We help create a body of evidence supporting the performance of a particular diagnostic or drug with statistical rigor. We conduct retrospective analyses on large-scale longitudinal datasets using statistical and machine learning techniques. We analyze prospective data to quantify the performance and side effects of an intervention.
    evidence generation - statistical analyses - bioinformatics
  • Biomarker creation: Using whole-genome, microarray, or panel data, we can extract and analyze a variety of genomic biomarkers including single nucleotide polymorphisms, insertions and deletions, copy number variants, or even polygenic predictions (e.g., polygenic risk scores or other whole-genome measures constructed using machine learning techniques). We can combine genomic biomarkers with other genetically-derived markers (e.g., ancestry, sex) or other clinical indications (e.g., cholesterol levels, disease history) to create holistic biomarkers for use in drug development and clinical trials.
    genomic biomarkers - polygenic risk scores - whole genome analysis - machine-learning-based biomarkers
  • CDx co-development: Genetic biomarkers are often used as companion diagnostics. We integrate with pharmaceutical companies in their drug development journey to co-create companion diagnostics for a particular therapeutic, helping improve yield and performance for target patients.
    companion diagnostics - clinical trials - co-development