Genetic testing is now embedded across a wide range of clinical applications, from diagnosing neurocognitive disorders in children to guiding cancer treatment in adults. But as genomic technologies advance and testing volumes grow, a critical challenge intensifies: interpreting genetic results accurately. This challenge affects healthcare providers and patients alike, and the consequences of getting it wrong are significant.
As genetic testing expands into more clinical settings, healthcare professionals from diverse backgrounds are now ordering and interpreting these tests, including those who may not have specialized training in genetics. At the same time, the supply of trained medical geneticists and genetic counselors has not kept pace with demand. This gap means that interpretation increasingly falls to clinicians who may lack the depth of training required to navigate complex results.
Misinterpreting genetic test results can have profound implications, including the risks of false positives, false negatives, and the identification of variants of unknown significance (VUS). This article examines the challenges of genetic data interpretation, including the reasons testing is on the rise, how results are analyzed, and the consequences of misinterpretation.
Genetic testing has become a standard tool across clinical disciplines, generating data that informs diagnosis, treatment selection, and risk stratification. Its applications span diagnostic confirmation, treatment stratification, pharmacogenomics, and hereditary risk assessment across multiple therapeutic areas. Yet, the task of deciphering the complex information encoded in over 3 billion nucleotide pairs of the human genome is far from straightforward.
A variety of genetic tests are used in both research and clinical settings to investigate DNA, each generating varying volumes of data. This diversity of testing approaches reflects the complexity and breadth of information that genetics can provide about individual health and well-being. Some of the most common types of genetic testing include:
Genetic test results are typically reported as positive, negative, or uncertain, but those categories only become meaningful when interpreted alongside medical history, family history, and the type of test performed. MedlinePlus notes that the same reported result can carry different implications depending on the clinical context.
A positive result identifies a genetic change associated with a condition or an increased risk of developing one. In predictive or presymptomatic settings, a positive result can indicate elevated risk rather than confirming that disease will occur, and the implications depend on factors such as penetrance, age of onset, and available interventions.
A negative result can be either informative or uninformative. A true negative may rule out a known familial variant, while an uninformative negative may simply mean that the test did not detect a causative change, that the relevant gene was not included, or that the variant is not yet well-characterized in reference databases.
An uncertain result is commonly reported as a variant of uncertain significance (VUS), meaning the available evidence is insufficient to classify the variant as benign or pathogenic. Larger panels and broader sequencing approaches can increase the likelihood of VUS findings, and classification may change as new evidence emerges. FORCE provides additional detail on how VUS results are interpreted and reclassified over time.
In practice, interpretation decisions often require multidisciplinary input, including clinical genetics expertise, laboratory classification frameworks, and careful alignment with the clinical question the test was ordered to answer.
Direct-to-consumer (DTC) genetic testing has become widely accessible, allowing individuals to explore their DNA without a physician's referral. These tests are used for ancestry, health risk assessment, and carrier screening. However, their accessibility does not guarantee accuracy, and the interpretation challenges they introduce are significant.
Most DTC tests use SNP-chip genotyping to explore specific genetic variations, such as single nucleotide polymorphisms (SNPs), across one's genome. While this method is effective at identifying common genetic variants, SNP-chips commonly yield false positives for rare variants.
In the UK Biobank analysis of BRCA1 and BRCA2, 96% of disease-causing very rare variants identified by SNP-chip genotyping were false positives. 96% of disease-causing very rare variants identified by SNP-chip genotyping were false positives. Genome sequencing, another method gaining traction, sequences the entire genetic code but also has interpretation challenges.
DTC genetic tests can provide a variety of health-related information, such as polygenic risk scores, specific genotypes related to disease risk, carrier status for certain conditions, and even unanalyzed "raw" genetic data. However, the predictive value of DTC genetic tests diminishes when there is no family history of the disease. Identifying a "disease-causing variant" in isolation does not guarantee disease development. Without the clinical context that a healthcare provider would apply, including medical history, family history, and test methodology, DTC results can mislead rather than inform.
Bioinformatics uses computer technology to transform raw genetic data into clinically useful information. Bioinformaticians then use specialised pipelines for filtering and analyzing these files, tailoring their approach to the clinical question and the type of sample sequenced.
Bioinformaticians process the data, employing various computational tools and methods to identify and annotate variants within the genome. The result is a comprehensive list of annotated variants, providing a foundation for clinical interpretation. This interpretation is a collaborative effort, with clinical scientists using bioinformatics-generated data to produce a tailored clinical report. This report guides healthcare management decisions for patients and their families.
Bioinformatics has a structural limitation: the accuracy of its outputs depends on existing reference databases. This means bioinformatics can primarily shed light on genetic variants that are already understood. As knowledge of genetics and disease-associated variants expands, the depth and utility of bioinformatics can improve, but at present, these gaps remain a meaningful constraint.
Cloud-based genomic analysis platforms such as Terra and Seven Bridges address the scale of data and computational demands involved in next-generation sequencing (NGS) analysis. By centralizing data storage and standardizing analytical workflows, these platforms reduce barriers to reproducible research and support more consistent translation of genetic findings into clinical guidance.
A documented legal case illustrates the real-world consequences of genetic test misinterpretation. A child with epilepsy died after a genetics laboratory incorrectly classified a variant in the SCN1A gene as being of uncertain significance. This variant, erroneously categorized as of uncertain significance, was actually known in the scientific literature to cause Dravet syndrome, a severe form of epilepsy. This case is not exceptional. Comparable misclassifications have been documented across the field, reinforcing the structural importance of standardised variant interpretation protocols.
In response, diagnostic laboratories are actively working to establish consensus standards and best practice guidelines for the classification and management of VUS in genetic testing. Leading organizations, including the UK Association of Clinical Genetic Science and the American College of Medical Genetics, have developed comprehensive algorithms to assist laboratories in the accurate interpretation of genetic variants. While following these guidelines is not a legal requirement, compliance significantly enhances the quality and reliability of genetic testing outcomes.
Genetic testing is increasingly used to define eligibility, stratify cohorts, and guide treatment decisions across precision medicine programs. The limiting factor is often not sequencing capability, but interpretation: as testing volume grows and result complexity increases, the risk of misclassification and inconsistent clinical meaning also increases.
Reliable interpretation depends on interpretive infrastructure, including validated bioinformatics pipelines, high-quality reference databases, and consistent application of variant classification standards. It also depends on cross-disciplinary collaboration, because the clinical significance of a variant is inseparable from the patient context, the test methodology, and the clinical question the test is intended to answer.
For sponsors running genetically stratified trials, interpretation accuracy affects who is screened in or out, how outcomes are attributed to biology, and how much downstream risk is carried into regulatory review. Standardized interpretation and multidisciplinary review are prerequisites for reducing screen failures, improving cohort definition, and maintaining confidence that genetic eligibility criteria are being applied consistently across sites and regions.