Scientists harness genomics and statistical learning to predict nicotine biomarkers in multiple ethnicities

Oregon Research Institute (ORI) scientist Andrew Bergen PhD has demonstrated the ability to predict two nicotine biomarkers of cigarette smokers of multiple ethnicities using statistical and machine learning approaches. These models enable estimation of nicotine biomarker levels in research cohorts with DNA for genotyping or existing genomic data. Investigators analyzed genetic findings, nicotine biomarkers, demographics and smoking behaviors to validate these models. Bergen is the lead ORI scientist on the Smokescreen Translational TL) Analysis Platform grant. Andrew Bergen and James Baurley, PhD, of BioRealm, and colleagues from five other institutions collaborated on this work.

This is the first genome-wide modeling of a urinary biomarker for nicotine metabolism (known as NMR) using statistical and machine learning techniques. This is also the first genome-wide modeling of total nicotine consumption (known as TNE) using any technique.

“We demonstrate novel predictive models for urinary nicotine biomarkers in multiple ethnic groups,” noted Bergen. “With further translational research, predicted nicotine metabolism and consumption biomarkers may allow clinicians to better help patients cease smoking and assess risks of tobacco-related disease.”

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The article is published in Nicotine & Tobacco Research: https://doi.org/10.1093/ntr/ntab124

Research funding by the National Institute on Alcohol Abuse and Alcoholism (AA027675 to James Baurley and Andrew Bergen) and by the National Cancer Institute (CA138338 to Daniel Stram, Sharon Murphy, and Lani Park, CA164973 to Löic Le Marchand, and CA232516 to Hilary Tindle).

The authors acknowledge the contribution of data from Genetic Architecture of Smoking and Smoking Cessation accessed through dbGAP. Funding support for genotyping, which was performed at the Center for Inherited Disease Research (CIDR), was provided by 1 X01 HG005274-01. CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C. Assistance with genotype cleaning, as well as with general study coordination, was provided by the Gene Environment Association Studies (GENEVA) Coordinating Center (U01 HG004446). Funding support for collection of datasets and samples was provided by the Collaborative Genetic Study of Nicotine Dependence (COGEND; P01 CA089392) and the University of Wisconsin Transdisciplinary Tobacco Use Research Center (P50 DA019706, P50 CA084724).