Antidepressant Response: What’s DNA Got to Do With It?

Anxiety and Depression Association of America (ADAA).

Research using data from the All of Us Research Program seeks to solve the mystery of what makes medications work for some and not others.




More than 13% of adults in the United States take antidepressant medication, according to 2018 statistics from the Centers for Disease Control and Prevention.1 Those numbers have been steadily climbing over the past decade, yet the effectiveness of antidepressants to treat the growing number of people affected by mental health issues still raises questions. Finding the right medication and the right dose is often still a guessing game, leaving patients and providers eager to find answers on how best to treat challenging symptoms.

Julia Sealock, PhD, a postdoctoral research fellow at the Broad Institute, is seeking these answers. Using data from the All of Us Research Program, a historic effort by the National Institutes of Health (NIH) to enroll 1 million or more individuals from diverse backgrounds in building the largest health database of its kind, Dr Sealock has embarked on research to predict antidepressant response.2 Thanks to electronic health record (EHR) data from hundreds of thousands of participants enrolled in All of Us, Dr Sealock developed an algorithm that aims to determine antidepressant response based on whether an individual stayed on, added to, or switched their antidepressant after a 6-week period.

Here, Dr Sealock talks about her work, its inspiration, and what she hopes comes next.

Orlovsky: What was the motivation for your research on mental health?

Sealock: I did my doctoral degree at Vanderbilt University. At the time, I knew I was interested in genetic research and I wanted to focus on brain-related conditions. I thought I wanted to work on Alzheimer disease, but once I started working in the lab, I saw the need for more psychiatric genetic research. I realized that if we can focus more on psychiatric disorders as they relate to genetics, maybe we can develop better treatments in the future.

Orlovsky: What inspired you to focus on antidepressant response?

Sealock: We know a lot about brain function. But we do not know the biology of depression or anxiety or the mechanism of action for antidepressants. There is a disconnect between the biology of the brain and the way antidepressants work. For other disorders, we know the biology that is causing them to occur. However, that isnot true for psychiatric disorders which is why an individual’s drug response is my big interest. My initial research got me thinking about how genetics can influence how we respond to drugs. This work really brings together genetics and the mechanism of action for pharmaceuticals, and that is really interesting to me.

Orlovsky: What gap did you see in current pharmaceutical treatment for depression?

Sealock: With antidepressant response, there is not a lot that we know. When you go to a doctor to get an antidepressant, they usually start off with a general SSRI and then you have to wait to see the effects. That is why I started thinking long-term—wondering if we can find ways to determine who is going to respond to what treatment. Rather than making them wait 6 weeks to see if the medication works, can we have some tools to help select an effective medication from the start?

Orlovsky: How is your research working to achieve this goal?

Sealock: The project I am working on now focuses on creating an algorithm to determine antidepressant response from the start. We are using EHR data to look at whether people switch off an antidepressant and go to another one, or if they use an antipsychotic medication or other medication. Additionally, we are analyzing their genetic history, as well as looking longitudinally at EHR data and some of the survey data from All of Us. By comparing all this data, we can see if our response outcomes associate with other genetic data.

Orlovsky: What is your ultimate goal with this project?

Sealock: The best outcome would be to find genetic variants that really influence the way a person metabolizes a drug. That would be the best-case scenario—and it is going to be a very long-term goal.

Orlovsky: How does the diversity of the All of Us database help you in working to achieve this goal?

Sealock: All of Us is a very large dataset with lots of information to work with—especially when it comes to diverse demographic communities. Thinking about genetic studies, the vast majority of them have been done with individuals of European ancestral descent. That really leaves us at a disadvantage. Diversity in research is really important to make sure all of us are represented.

Ms Orlovsky is a medical copywriter at Scripps Research. She has been writing about health and health care for more than 20 years. Dr Sealock is a postdoctoral research fellow at the Broad Institute.


1. Brody DJ, Gu Q. Antidepressant use among adults: United States, 2015-2018. National Center for Health Statistics. 2020. Accessed June 29, 2023.

2. Validating an algorithm to predict antidepressant response. All of Us Research Hub. October 7, 2022. Accessed June 29, 2023.

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