Studies have found that there is a significant genetic component to alcohol and tobacco use, and that many of the genes associated with these behaviors are shared among individuals of diverse ancestries. However, it’s worth noting that genetics is not the only factor that contributes to alcohol and tobacco use. Environmental and social factors also play a role.
After analyzing data from over 3.4 million people, a large genetic study discovered more than 2,300 genes that predict alcohol and tobacco use. According to the researchers, the majority of these genes are shared by people of European, African, American, and Asian ancestry.
After analyzing data from more than 3.4 million people, Penn State researchers co-led a large genetic study that identified more than 2,300 genes predicting alcohol and tobacco use. They discovered that the majority of these genes were shared by people of European, African, American, and Asian ancestry.
Tobacco and alcohol use are linked to chronic conditions such as cancer and heart disease and are responsible for approximately 15% and 5% of deaths worldwide, respectively. Although the environment and culture can influence a person’s use and the likelihood of becoming addicted to these substances, genetics, according to Penn State College of Medicine researchers, is also a factor. In a previous study, they assisted in the identification of approximately 400 genes associated with certain alcohol and tobacco use behaviors in people.
We’ve now identified more than 1,900 additional genes that are associated with alcohol and tobacco use behaviors. A fifth of the samples used in our analysis were from non-European ancestries, which increases the relevance of these findings to a diverse population.
Dajiang Liu
“We’ve now identified more than 1,900 additional genes that are associated with alcohol and tobacco use behaviors,” said Dajiang Liu, professor and vice chair for research in the Department of Public Health Sciences. “A fifth of the samples used in our analysis were from non-European ancestries, which increases the relevance of these findings to a diverse population.”
Liu and colleagues evaluated genetic datasets from more than 3.4 million people, at least 20% of whom were of non-European ancestry, in collaboration with colleagues from the University of Minnesota and more than 100 other institutions. According to Liu, their study is the most ancestrally diverse genetic study on smoking and drinking behaviors to date. He stated that his previous 2019 study only included data from European ancestry populations.
Liu and colleagues examined smoking and alcohol traits ranging from the initiation of drinking or smoking to the onset of regular use and the amount consumed using genetic datasets from people of African, East Asian, and American ancestry. Using machine learning techniques, the researchers identified genes that were associated with these behaviors.
Comparing the data between samples from different ancestries, Liu and colleagues found that there was a striking similarity in the genes related to alcohol and tobacco use behaviors between the different ancestries, with 80% of the variants showing consistent effects across the studied populations. While some genetic variants had different effects across ancestries or ancestry-specific effects, the genes associated with alcohol and tobacco use were largely consistent between samples from various ancestries.
The researchers used machine learning to create a genetic risk score that could identify people who are at risk of engaging in certain alcohol and tobacco use behaviors. Despite the similarity of genetic effects, the model developed using data from people of European ancestry could only predict alcohol and tobacco use behaviors in people of European ancestry. Because the model was less accurate in predicting risk among people of other ancestries, Liu stated that more sophisticated prediction methods based on larger sample sizes from non-European ancestries are needed to improve risk prediction across diverse human populations.
“It is promising to see that the same genes are associated with addictive behaviors across ancestries,” said Liu, a Penn State Cancer Institute and Penn State Huck Institutes of the Life Sciences researcher. “Having more robust and diverse data will help us develop predictive risk factor tools that can be applied to all populations.”
According to Liu, these genetic risk scores could be refined and made part of routine care for people who have already been identified as being at high risk for alcohol and tobacco use through basic screening. He noted that this research is an example of how big data and sophisticated machine learning methods can help predict health risks so targeted interventions can be developed as interim director of the College of Medicine’s second strategic plan goal, which seeks to develop and apply biomedical artificial intelligence, machine learning, and informatics to make rapid advances in biomedical research.
“This project leveraged large amounts of data to identify common genetic risk factors across diverse populations,” said Kevin Black, MD, interim dean of the College of Medicine. “Using these findings to develop screening tools for diseases of despair is the kind of innovation that will help our College lead the way in using health informatics to contribute to health preservation and disease treatment in our communities.”
Future research, according to Liu, will focus on delving deeper into their findings. Because the majority of the genes identified by the team have unknown functions, the researchers will attempt to understand their functions as well as how changes in those genes, their function, and their interaction with the environment affect the risk for addictive behaviors. He also stated that increasing the diversity of genetic samples in the datasets will aid the team in developing predictive risk models for people of various ancestries.
