Several ancestors share several genes related to the consumption of alcohol and tobacco | central county gazette

HERSHEY — Penn State researchers co-led a large genetic study that identified more than 2,300 genes that predict alcohol and tobacco use after analyzing data from more than 3.4 million people. They said most of these genes were similar among people of European, African, American, and Asian ancestry.

Alcohol and tobacco use are associated with approximately 15% and 5% of deaths worldwide, respectively, and are linked to chronic diseases such as cancer and heart disease. Although environment and culture can affect a person’s use and likelihood of becoming addicted to these substances, genetics is also a contributing factor, according to researchers at the Penn State College of Medicine. In a previous research study, they helped identify about 400 genes that are associated with certain alcohol and tobacco use behaviors.

“We have already identified more than 1,900 additional genes that are associated with alcohol and tobacco use behaviors,” said Dajiang Liu, Ph.D., professor and vice president for research in the Department of Public Health Sciences. “One-fifth of the samples used in our analysis were of non-European descent, increasing the relevance of these findings to a diverse population.”

In collaboration with colleagues at the University of Minnesota and more than 100 other institutions, Liu and his team evaluated genetic data sets of more than 3.4 million people, of whom at least 20% were of non-European descent. According to Liu, her study is the largest genetic study on smoking and drinking behaviors to date and is the most ancestrally diverse. She said that the previous study of hers in 2019 only included data from populations of European descent.

Liu and his colleagues included genetic data sets from people of African, East Asian, and American descent and assessed a variety of smoking and alcohol traits ranging from drinking or smoking onset to regular use onset and amount consumed. Using machine learning techniques, the researchers identified genes that were associated with these behaviors.

By comparing data between samples from different ancestors, Liu and his colleagues found that there was striking similarity in genes related to drinking and smoking behaviors among the different ancestors, with 80% of the variants showing consistent effects on the different ancestors. populations studied. While some genetic variants had different effects between ancestors or ancestor-specific effects, genes associated with alcohol and tobacco use were largely consistent across samples from various ancestors.

The researchers used machine learning to develop a genetic risk score that could identify people at risk for certain alcohol and tobacco use behaviors. Despite the similarity of genetic effects, the model developed using data from people of European descent could only accurately predict alcohol and tobacco use behaviors for people of European descent. Because the model was not as accurate in predicting risk among people of other ancestry, Liu said more sophisticated prediction methods needed to be developed by increasing the sample sizes of non-European ancestors, which could improve risk prediction in various human populations. The results were published in Nature on December 7.

“It’s promising to see that the same genes are associated with addictive behaviors across ancestries,” said Liu, a researcher at the Penn State Cancer Institute. “Having stronger and more diverse data will help us develop risk factor predictive tools that can be applied to all populations.”

Liu said that within two to three years, these genetic risk scores could be refined and become part of routine care for people already identified through basic screening as being at increased risk for alcohol and tobacco use. As acting director of the second strategic goals plan of the School of Medicine, which seeks to develop and apply biomedical artificial intelligence, machine learning, and computing to achieve rapid advances in biomedical research, he noted that this research is an example of how big data and Sophisticated machine learning methods can help predict health risks so that targeted interventions can be developed.

“This project took advantage of vast amounts of data to identify common genetic risk factors in diverse populations,” said Kevin Black, MD, interim dean of the School of Medicine. “Using these findings to develop disease detection tools of despair is the kind of innovation that will help our university lead the use of health informatics to contribute to the preservation of health and the treatment of disease in our communities.” .

According to Liu, future research will focus on digging deeper into their findings. Most of the genes the team identified have unknown functions, so the researchers will try to understand their functions and how changes in those genes, their function, and interaction with the environment affect the risk of addictive behaviors. She also said that increasing the diversity of genetic samples in the data sets will help the team develop predictive risk models for people of diverse ancestries.

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