Understanding the Importance of Actionable vs Non-Actionable Interpretation

Advances in genomic research and data analysis have opened new opportunities for understanding an individual’s genetic makeup and its potential health implications. Genomic data, which comprises the complete set of an individual’s genes and their variations, can provide valuable insights into an individual’s genetic predisposition to certain health conditions. However, not all genomic data is created equal, and the distinction between actionable and non-actionable genomic data interpretation plays a crucial role in harnessing the full potential of genomic information for clinical decision-making.

Actionable genomic data refers to genetic information that has clear clinical relevance and can guide specific interventions or treatments to improve patient outcomes. For example, identifying a specific genetic mutation associated with a certain disease, such as the BRCA1 gene mutation in breast cancer, can prompt preventive measures like increased surveillance, risk-reducing surgeries, or targeted therapies. Actionable genomic data can inform clinical decision-making, drive personalised treatment plans, and facilitate proactive disease management.

Alternatively, non-actionable genomic data refers to genetic information that currently lacks clear clinical significance or does not have established interventions or treatments available. This may include genetic variants with unknown or uncertain clinical implications, benign variants, or variants with limited evidence supporting their association with specific diseases. Non-actionable genomic data, while still important for research and scientific understanding, may not have immediate clinical implications and may not guide direct interventions or treatments.

The distinction between actionable and non-actionable genomic data interpretation is essential in a clinical setting for several reasons. Firstly, actionable genomic data can lead to precise and targeted interventions, allowing for personalised treatment plans tailored to an individual’s genetic profile. This can optimise patient care, improve treatment outcomes, and potentially prevent or delay the onset of certain diseases.

Secondly, focusing on actionable genomic data helps healthcare providers avoid unnecessary interventions or treatments based on non-actionable or uncertain genetic information. This prevents unwarranted medical interventions, unnecessary costs, and potential patient harm.

Thirdly, actionable genomic data can facilitate research and clinical trials to develop evidence-based interventions and treatments for specific genetic variants or mutations. This can accelerate the translation of genomic research into clinical practice, leading to more effective and targeted therapies in the future.

In conclusion, the distinction between actionable and non-actionable genomic data interpretation is crucial in harnessing the full potential of genomic information for clinical decision-making. While both types of genomic data have their importance, actionable genomic data provides actionable insights that can guide personalised treatment plans, optimise patient care, and improve patient outcomes. As genomic research continues to advance, the identification and integration of actionable genomic data into clinical practice will play a pivotal role in delivering personalised and effective healthcare interventions based on an individual’s genetic makeup.

The AIRGenomix team which comprises molecular biologists, doctors, dietitians, and fitness experts has been working for several years to develop an algorithm that works towards the delivery of actionable information to the doctors and dietitians so that the right advice can be provided and appropriate use of the genomic data can be achieved.