Genomic sequencing reveals the full DNA sequence of an individual, uncovering millions of genetic variants—small differences in the nucleotide sequence that make each person unique. Among these, some variants occur in genes or regulatory regions and may influence health or disease. But identifying a variant in the genome is only the beginning.

The real challenge lies in linking specific variants to diseases—a process that requires assessing the clinical validity of the gene-disease relationship. Clinical validity refers to the strength of the evidence connecting a genetic variant to a disease, including how consistently it appears in affected individuals and whether it alters biological function in a meaningful way.

This is where variant annotation comes in. Variant annotation is the process of interpreting a variant’s potential impact by combining genomic, clinical, and population data. Key steps include:

• Mapping the variant to a gene or region
• Predicting the functional impact (e.g. protein disruption)
• Comparing with known disease-associated variants in public databases

Critical aspects include data quality, completeness of reference databases, and integration of multiomic context.

To address the complexity and scale of this task, NextGen is developing advanced machine learning algorithms for variant prioritisation. These tools rank gene sequences based on their likelihood of being implicated in disease, making the analysis faster and more resource-efficient—without compromising reliability.

NextGen’s algorithms help researchers and clinicians focus on the most relevant variants linked to specific diseases, improving the speed and accuracy of genetic interpretation.

Impact:

• Significantly reduced manual processing time
• Shorter diagnostic pathways, from patient presentation to treatment planning
• Improved outcomes through earlier and more precise interventions