Personalized medicine must go beyond genetics to be effective

Personalized medicine, which looks at genetic risk scores to understand a person's health, has growing support among doctors and scientists.

Two experts discuss the issue in a Sept. 9 commentary in Nature, a top scientific journal, arguing that "clinical medicine must learn to develop more holistic measures of individual risk, both genetic and non-genetic." They enthusiastically endorse personalized medicine as the future in their field, but say that risk assessment must include environmental and other factors. 

The authors are Mark McCarthy, executive director of human genetics at Genetech in California, and Ewan Birney, deputy director general of the European Molecular Biology Laboratory in the United Kingdom.

McCarthy and Birney summarize what's left out of the genetic risk approach, noting that the current data "work best for the predominantly white, wealthy populations in which most genetic studies have been performed."

Non-genetic factors also influence disease risk and progression, they say, as well as "real time measurements of clinical state that are especially important in diseases linked to aging ... There is more to disease risk than genetics."

The authors suggest three recommendations to make medicine "truly personalized." 

First, they say, researchers and physicians need to use "more disparate types of data" from more diverse populations. Second, both genetic and non-genetic risks should be included. In other words, risk also involves socio-economic status, diet, exercise, access to health care, good housing, sanitation and gut microbiome diversity, among other factors.

Third, they state, "the field needs to move away from its tendency to collapse all these rich, individual-level data into rigid clinical categories." Researchers need to use gradations of risk and look at the various different processes involved with common diseases.

Limits of risk data

The present polygenic risk scores use data from the genome-wide study data in calculating an individual's genetic possibility for developing a disease. But these data now come predominantly from "individuals of recent European descent," they note. As an example they write that measures such as BMI (body mass index) lose more than 60% of their predictive power when applied to individuals of recent African descent.

Another problem the authors point out is that risk scores currently incorporate only the most common genetic variants into a risk analysis, leaving out rare variants that may have a greater impact on particular diseases. An example of this are the rare gene variants BRCA1 and BRCA2 that have "high penetrance" for risk estimates in breast cancer and ovarian cancer.

More accurate risk measures that incorporate rare genetic variants will give individuals a more informed basis on which to make a decision about prophylactic surgery, for example.

A holistic approach that preserves complexity

The authors review the pitfalls of looking only at genetics when it comes to assessing risk.

Environmental effects can interact with genetic factors and alter the patient's risk for a disease. They emphasize the need for "real-time clinical data," which can help "counter the fatalism that can seep into the interpretation of genetic risk." In other words, a patient can mitigate the progression of a disease by interventions.

They also suggest some remedies. For instance clinicians should track the different processes involved with a disease separately, not lump everything together. "Tracking multiple measurements reveals the ebb and flow of each individual's status with respect to health and disease," which is useful when deciding on treatment, such as surgery, at a given point, they note.

Finally, McCarthy and Birney urge a more holistic approach, including standards of data collection. 

"Researchers, funders, and industry need to embrace greater diversity in the design and implementation of studies, focusing not only on gender and ethnicity, but also on social, cultural, and economic factors that influence disease risk and access to health care," they write. "Efforts to base personalized medicine on risk-factor prediction alone will fall short."

Mark McCarthy & Ewan Birney, Personalized profiles for disease risk must capture all facets of health,

Nature, 06 September 2021, doi: https://doi.org/10.1038/d41586-021-02401-0