For years, we've watched research and clinical teams get stuck in the same costly trap. They design an elegant study for a specific genetic profile, only to spend years and a fortune screening thousands of patients just to find a handful of eligible participants.
For these critical studies, we're doing it backwards by starting with a phenotype, hoping that symptoms will point us to the underlying biology. Our recruitment strategy needs to keep up with the science and start with the true signal: the genome itself.
Key Takeaways
- 500,000+ whole Exome+® sequenced individuals available for genotype-first participant identification
- Pre-consented network every participant across 16+ U.S. health systems has agreed to recontact Rare variant programs (sub-1% allele frequency) reduce screen-fail timelines from months to weeks
- Covers cardiometabolic, GLP-1 response, autoimmune, MASH, and other therapeutic areas with an average of 13+ years of longitudinal EHR history per participant
- Applications span the drug development lifecycle: target validation, trial enrichment, rare disease cohort assembly, and outcomes research
The Problem with Phenotype-First Recruitment
Traditional patient recall for genetically-driven indications follows a familiar pattern: cast a wide net of thousands of candidates, run broad screening, and absorb genotyping late in the process. For common variants or well-characterized diseases, this works well enough. However, for programs targeting rare genetic variants with sub-1% loss-of-function mutations, rare polygenic profiles and genetically-defined disease subtypes; phenotypic screening at scale is an expensive and inefficient way to find a meaningful signal in a lot of signal noise.
A variant present in fewer than 1% of the population means teams may need to screen hundreds of individuals just to find one eligible participant. Multiply that by the sample sizes required for meaningful target validation or Phase II enrollment, and you're looking at broad outreach campaigns, long timelines, and screen-fail rates that drain budgets before the study even begins.
The underlying issue: you're chasing a genomic signal through a phenotypic shadow — waiting for symptoms, labs, or clinical codes to point you toward the biology that was always in the genome from the beginning.
Recall-by-Genotype: Start with the Genetic Signal
Recall-by-Genotype (RbG) is built on a different premise: identify participants by their genetic profile first, then recall and characterize them. Rather than screening a broad population to find the target genotype, RbG queries a pre-sequenced, clinically-linked dataset to identify carriers before a single screening visit occurs.
Helix's RbG solution is built on GenoSphere™, a clinico-genomic research database of over 500,000 whole Exome+ sequenced individuals with linked longitudinal EHR averaging 13+ years of clinical history per participant across cardiometabolic, GLP-1, autoimmune, MASH, and other therapeutic areas. Every participant in the network has consented to recontact, meaning they're not just records in a database. They are real individuals who can be engaged for follow-up studies, surveys, and clinical trial participation.
The model is simple but powerful: instead of genotyping your way to a cohort, you query your way to one.
How It Works
The RbG process is designed to move from scientific question to consented cohort efficiently, without sacrificing rigor:
- 1. Define your genetic and phenotypic criteria. Specify the target gene, variant, inheritance model, or polygenic threshold that aligns with your study objectives such as a rare loss-of-function variant for target validation, a specific HLA haplotype for an autoimmune program, or a threshold for cardiometabolic research.
- 2. Assess feasibility. Helix queries the linked GenoSphere data to estimate cohort size and stratification options including ancestry, age distribution, and co-occurring phenotype signals so researchers can inform protocol design with real data before committing to a study timeline.
- 3. Recall and consent. Helix works with partners to initiate participant engagement and consent flows appropriate for the required follow-up activities. Because network participants have pre-consented to recontact, this step is built into the infrastructure rather than being a downstream blocker.
- 4. Collect data and generate evidence. Enable prospective phenotyping, surveys, or clinical assessments to analyze outcomes and study progression.
What This Looks Like in Practice
Consider a common scenario in early drug development: a biopharma team has identified a loss-of-function variant in a cardiometabolic gene, but it's present in fewer than 1% of the population. The team needs phenotypic data from variant carriers and matched controls to validate the variant's functional impact before committing resources to a full development program.
Under a traditional recruitment model, this means broad population screening, late-stage genotyping, high screen-fail, and a timeline measured in months before a meaningful cohort exists. The cost of that uncertainty compounds at every stage.
With RbG, the starting point is a query to identify carriers of the target variant, assess their longitudinal clinical profiles, and stratify against matched controls — all before a single participant is contacted. The result is a dramatically smaller screen-fail problem, because participant selection was precise from the start, and a timeline compressed from months to weeks. One engagement, one protocol, a consented cohort with longitudinal potential for future studies.
Where RbG Creates the Most Value
Recall-by-Genotype isn't a single-use tool. The same multi-site network protocol that supports rare variant validation can serve multiple programs across the research lifecycle:
- Biomarker and target validation: Characterize the phenotypic impact of novel genetic signals with a genetically-defined, longitudinally-tracked cohort before Phase I.
- Clinical trial recruitment: Pre-identify and pre-consent patients who meet genetic eligibility criteria, reducing screen-fail and shortening enrollment timelines.
- Rare disease cohort assembly: Build rare genotype cohorts that simply can't be assembled through traditional clinical recruitment at any reasonable scale or cost.
- Outcomes and comparative effectiveness research: Link genetic profiles to real-world treatment patterns and outcomes across years of longitudinal EHR and claims data.
For teams working in areas where genetic stratification matters (which increasingly means almost every therapeutic area), RbG changes the unit economics of early research. Less time screening the wrong patients means more time and budget focused on the right ones.
Our Path Forward
We believe the future of medicine lies in our ability to translate genomic discoveries into clinical realities, but we will remain hampered if we are still using outdated approaches for patient identification. The entire field, from targeted therapies to polygenic medicine, depends on finding the right patients efficiently.
We built Helix's Recall-by-Genotype, backed by the depth and scale of GenoSphere cohorts, because we knew there had to be a better way. The genome is the most precise entry point we have and the foundation for the next wave of precision medicine. We are now equipped to build upon it.
Learn More About Recall-by-Genotype
The Helix GenoSphere platform gives life sciences teams access to one of the largest pre-sequenced, recontact-consented clinico-genomic cohorts available that are built for genotype-first participant identification.
Connect with our team to discuss your program and explore what's feasible with GenoSphere: www.helix.com/life-sciences/recall-by-genotype