Predictive Genomics: Harnessing Genetic Data

Predictive Genomics: Harnessing Genetic Data

by Leo Petersen-Khmelnitski

In 2021, the Nature Journal stated that predictive genomics is the culmination of the Human Genome Project. Predictive genomics allows using genetic data to help predict common disease risks and drug responses.


Predictive genomics has arisen at the intersection of predictive medicine (focuses on predicting the probability of disease and on preventive actions), personal genomics (engaged in analysis and interpretation of the genome of an individual), and translational bioinformatics, the goal of this new area in health informatics is to formulate knowledge and medical tools based on the increasing amount of biomedical and genomic data.

Genes provide operating templates for all biological processes. Sequence variations in genetic material can sometimes confer genetic disadvantages, such as a higher risk of cancer. Genetic variants, such as those in coronary artery disease or type 2 diabetes, play a greater role in most common diseases than mutations in single genes.

In predictive genomics, genetic information can be harnessed and interpreted in a clinically actionable manner, to prevent disease and to maintain healthy lifestyles.


Predictive genomics helps identify disease risks with polygenic risk scores (tells you how a person’s risk compares to others with a different genetic constitution) and manage adverse drug responses (a broad term referring to unwanted, uncomfortable, or dangerous effects that a drug may have) with pharmacogenomics (a person’s response to a certain medication based on her/his genetic profile). The understanding of risks and responses will significantly change personalised and population health management in the future.

Complex diseases may be caused by genetic variants. Developing preventative, prognostic, and diagnostic tools for these diseases depends on discovering and mapping causal mutations. In result, an individual genomic profile can be created to be used for prediction of disease prior to onset, and to identify causal variants.

Research and clinical applications can be bridged by identifying causal variants, genes, and pathways. When therapeutic targets are discovered within implicated biological pathways, they have implications for both treatment and prevention. Further, identifying disease relevant biomarkers improves the monitoring of disease progression and response to treatment downstream, facilitating personalised medicine and outcomes by incorporating the results into clinical decision support systems (CDSS). Replication of associated variants can be of significant translational value even if it is marginally effective.

Using the complete knowledge of an individual to develop personalised approaches to disease management is expected to result in the transfer from scientific research to clinical practice. Predictive genomics is expected to be used for personalising healthcare in several ways, including monitoring an individual’s progress during treatment or using their genomic profile to assist with selecting dosages.


There are a few things missing on the way to the vision outlined above: ethics in adoption, sharing genetic data, awareness of genetic technologies, reimbursement models and market pathways for predictive genomics:

Future ethical issues

Future adoption of predictive genomics rises a range of ethical issues yet to be addressed. An example: insurance companies may be inclined to alter insurance policies, to have people with ‘high’ predictive risk scores pay more on their insurance.

Further dystopian visions of dividing people by ‘good genes’ and ‘bad genes’ are not in the research domain, as there is no such thing as a good gene or a bad gene. Genes are ‘good’ or ‘bad’ depending on the other genes around them and the environment you live in. However, such concerns may be with certainty expected to arise in public debates, once predictive genomics expands adoption.

Safe and efficient sharing of health data

Health risk data could cause harm if not stored in a safe, private environment. However, predictive genomics cannot be achieved without robust and comprehensive sharing of health data. For health data sharing, governments (as healthcare providers) and industry must establish a new social contract. Predictive genomics can be integrated into the European Health Data Space in a safe and effective manner.

Genetics education for healthcare professionals

Even though predictive genomics is not new (the technology that underpins it, microarrays, is already well established), it has not entered doctors’ curricula and treatment guidelines. Practitioners need to be informed about predictive genomics and prepared for the implementation of PG solutions in near future.

Patients can choose from a variety of genetic tests, and genetic counsellors provide excellent advice on how to interpret results. Nevertheless, genetic counselling across Europe is not harmonised, and they often have little experience with predictive genomics.

Predictive genomics’ potential should be made aware to millions of patients across the continent, and they should be empowered to co-design PG solutions that fit their needs and expectations. Providing responsible and in line with prevailing scientific opinions, industry has a role to play in raising awareness in all three stakeholder categories.

Reimbursement models

Currently, there is no clear pathway for reimbursement of predictive genomics solutions. There are some digital health interventions that are slowly entering the healthcare system, but they are not being adopted as quickly as they should. Genomics tests, particularly those requiring prospective clinical trials, are often underfunded or underreimbursed. Since laboratories are rewarded for offering tests that are better reimbursed, they are hesitant to offer tests that might be unprofitable because of such lack of funding and reimbursement. As a result, some treatment pathways that lean on personalised genetic data, may not be adequately explored.