In a groundbreaking study that challenges the conventional diagnostic paradigm for rare genetic disorders (RGDs), researchers at Geisinger have pioneered a “genomic-first” screening approach to identify these conditions more efficiently and at an earlier stage than traditional symptom-driven genetic testing. This innovative strategy leverages large-scale genome sequencing data and automated analytic techniques to uncover pathogenic variants linked to an extensive catalog of RGDs, offering a transformative potential in precision medicine and population health management.
Historically, the identification of RGDs relied heavily on a “phenotype-first” approach, wherein patients exhibiting clinical symptoms prompted genetic testing to reach a diagnosis. Such reliance on phenotypic presentation, however, inherently biases detection toward individuals with pronounced or classical symptoms, inadvertently overlooking those with mild manifestations or asymptomatic carriers. Consequently, disease prevalence estimates based only on clinical referrals are likely underrepresentative, masking the true genetic burden within populations.
To address these limitations, the Geisinger team curated a comprehensive list of 2,701 rare genetic disorders that are not typically included in standard population-wide genetic screening protocols. Utilizing the vast genomic dataset from over 218,000 participants enrolled in Geisinger’s MyCode Community Health Initiative, the researchers deployed robust computational pipelines capable of detecting disease-causing variants across this expansive gene set. This large-scale “genomic-first” methodology circumvents the need for initial clinical symptomatology, enabling the capture of a broader spectrum of affected individuals.
A key innovation in the study was the development of an automated diagnostic concordance metric referred to as “diagnostic fit” (DxFit), which systematically compares genetic findings with existing clinical diagnoses recorded within participants’ electronic health records (EHRs). This analytical tool quantifies the extent to which genomic data align with documented phenotypic information, thus providing a nuanced understanding of diagnostic gaps and potential underdiagnosis.
Remarkably, the findings revealed that approximately 2.5% of the cohort harbored high-confidence pathogenic variants associated with RGDs. However, a significant majority of these individuals had no corresponding diagnosis annotated in their medical records, underscoring a substantial discrepancy between molecular evidence and clinical recognition. This disconnect indicates that many carriers of pathogenic variants remain unidentified and untreated under current healthcare frameworks, which predominantly depend on phenotypic criteria for genetic testing referrals.
The implications of these results are profound. The genomic-first approach not only enhances the detection rate of RGDs by capturing individuals with subclinical or asymptomatic genotypes but also offers valuable insights into the penetrance and expressivity of these disorders. By expanding diagnostic reach beyond symptom-based triggers, healthcare providers can initiate earlier interventions and personalized management plans that may mitigate disease progression and improve patient outcomes.
Furthermore, the study calls into question previous assumptions about the penetrance of certain pathogenic variants. The observation that many individuals with confirmed disease-causing mutations lack corresponding clinical symptoms suggests that the phenotypic impact of some RGDs may be more variable or attenuated than traditionally appreciated. This revelation has significant ramifications for genetic counseling, risk assessment, and the design of future screening programs.
Published in the latest issue of the American Journal of Human Genetics, this research exemplifies the integration of genomics with clinical informatics to reshape the landscape of rare disease detection. The adoption of scalable genomic-first methodologies can facilitate comprehensive health surveillance on a population level, enabling a shift from reactive to proactive healthcare paradigms.
Kyle Retterer, MS, chief data science officer at Geisinger and senior author of the study, emphasizes that the genomic-first strategy is poised to revolutionize rare disorder diagnosis and management. By capitalizing on vast genomic data coupled with sophisticated analytic frameworks, this approach holds the promise of refining disease characterization and delivering tailored therapeutic measures to a broader patient population.
The scalability of this model is particularly noteworthy given the increasing availability of genomic sequencing technologies and population biorepositories. The integration of automated variant interpretation and diagnostic fit algorithms can be adapted across diverse healthcare settings, thus democratizing access to precision diagnostics and mitigating disparities in rare disease detection.
In summary, this study provides compelling evidence that “genomic-first” population screening can uncover numerous undiagnosed cases of rare genetic disorders, many of which evade detection under conventional clinical practice. By enabling earlier and more accurate diagnoses, this approach lays the groundwork for transformative improvements in personalized medicine, patient care, and public health genetics.
Subject of Research:
Article Title: A scalable approach for genomic-first rare disorder detection in a healthcare-based population
News Publication Date: 6-Oct-2025
Web References: https://www.cell.com/ajhg/abstract/S0002-9297(25)00366-0, http://dx.doi.org/10.1016/j.ajhg.2025.09.010
References: American Journal of Human Genetics
Keywords: Genetics, Genome sequencing, Genomes, Genomic analysis
Tags: asymptomatic genetic carriersautomated analytic techniques in genomicscomprehensive genetic disorder catalogearly detection of rare genetic disordersgenomic-first screening approachlarge-scale genome sequencing dataMyCode Community Health Initiative researchpathogenic variants identificationphenotype-first limitationspopulation health management strategiesPrecision Medicine Advancementstraditional genetic testing methods
