Summary: Researchers used AI to discover hundreds of genes linked to tobacco use disorder, alongside potential new treatments. Analyzing extensive health data, the team identified genetic variations that contribute to tobacco addiction, which affects 1.3 billion people globally.
Their findings highlight the potential of electronic health records as a valuable resource for genetic research. This study not only advances our understanding of tobacco use disorder but also points to numerous drug candidates that could potentially address this pervasive public health challenge.
Key Facts:
- The study revealed 461 new candidate genes associated with tobacco use disorder, suggesting a strong genetic component to the condition.
- By utilizing AI and genome-wide association studies, researchers significantly accelerated the identification process, making it more efficient and cost-effective.
- The research was enabled by the PsycheMERGE Network and used data from 898,680 individuals, underscoring the scale and potential impact of this genomic research.
Source: UCSD
By analyzing electronic health records, researchers at University of California San Diego School of Medicine have identified hundreds of new genes associated with tobacco use disorder.
They also identified hundreds of potential drug candidates that could help treat the disease.
The study was published on April 17, 2024, in Nature Human Behavior.
“Tobacco use disorder has an enormous impact on public health,” said Sandra Sanchez-Roige, Ph.D., an associate professor in the Department of Psychiatry at UC San Diego School of Medicine.
“However, it’s challenging to develop new therapeutics for tobacco use disorder because so much of its underlying genetics is poorly understood.”
According to the World Health Organization, there are about 1.3 billion tobacco users worldwide, and 80% of these people live in low and middle-income countries.
The public health effects of tobacco use extend far beyond those who use it themselves; tobacco kills more than 8 million people each year and an estimated 1.3 million of these deaths are nonsmokers who were exposed to secondhand smoke.
The official criteria for tobacco use encompass a wide variety of behaviors associated with tobacco use, such as using more tobacco than intended or continuing to use it despite negative consequences.
There are known genes associated with nicotine consumption on its own, but these don’t tell researchers how nicotine use progresses to tobacco use disorder.
“A fraction of people are able to smoke occasionally without developing an addiction,” said Sanchez-Roige. “We want to understand, from a genetic perspective, why occasional tobacco use becomes chronic misuse in some people.”
The researchers leveraged large volumes of electronic health data from several health systems in the United States, which was enabled by the PsycheMERGE Network, an international consortium of researchers that aims to synthetize medical records and genomics data to better understand and treat neuropsychiatric illnesses. Sanchez-Roige leads the substance use disorder workgroup within PsycheMERGE.
For the current study, her team used an approach called genome-wide association, which allows researchers to scan the entire genome and look for variations in our genes associated with certain traits, behaviors, or diseases.
This is one approach scientists have used to find genes associated with smoking, but this is the first time this approach has been able to reveal genes associated with tobacco use disorder.
In their study of 898,680 individuals, they found 461 candidate risk genes for tobacco use disorder, mostly expressed in the brain. These genes are associated with a myriad of other psychiatric and medical conditions, such as HIV infection, heart disease, and chronic pain.
Further, the researchers were able to validate known findings about genes associated with smoking behaviors, which helped validate their approach.
In addition to giving us a more comprehensive view of tobacco use disorder, the researchers were able to use their results to identify hundreds of potential drug candidates that could help doctors treat the disease. However, it will take more research to evaluate these drugs in the lab and the clinic.
In the meantime, the study also supports a growing idea in the field of genetics research: Electronic health records are an underutilized treasure trove of information.
“There’s a world of information hidden in medical records, and we accumulate more of them every day as part of routine clinical care,” said Sanchez-Roige.
“They’re also a relatively untapped resource due to how difficult it is to organize and analyze electronic health record data. This study is part of a growing movement to use this constantly expanding source of information to solve complex medical problems.”
Funding: This study was funded, in part, by the California Tobacco-Related Disease Research Program (grants T29KT0526 and T32IR5226) and the National Institute on Drug Abuse (grant DP1DA054394).
About this genetics, AI, and tobacco addiction research news
Author: Miles Martin
Source: UCSD
Contact: Miles Martin – UCSD
Image: The image is credited to Neuroscience News
Original Research: Closed access.
“Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes” by Sandra Sanchez-Roige et al. Nature Human Behavior
Abstract
Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes
Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD.
Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680).
We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain.
TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain.
This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.
