Project 460905

The human liver houses many enzymes, proteins which can convert one compound to another. CYP2A6 is the enzyme responsible for metabolically inactivating nicotine; nicotine is the component of cigarettes which causes them to be addictive when smoked. CYP2A6 function varies widely between people; faster CYP2A6 activity is associated with worse cigarette smoking behaviours, such as more cigarettes smoked per day and reduced ability to quit smoking. People with slow CYP2A6 activity tend to smoke less, are more likely to quit, and are at lower risk of lung cancer. Genetics is an important factor in CYP2A6 function, and clinical tools have been developed to predict CYP2A6 function (and therefore the rate of nicotine metabolism) based on the presence or absence of important genetic mutations (or "variants") in the CYP2A6 gene. Most of the variants which need to be assessed are simple to test for, but some of the most important variants (including deletions and duplications of the entire CYP2A6 gene) require more sophisticated and time-consuming methods for testing due to their complexity. My project focuses on developing and using a database of individuals with combined data on simple and complex CYP2A6 gene variants. The goal is to identify complex variants in an individual by examining their combinations of simple variants. Because complex variants are related to the pattern of simple variants (due to complex and simple variants being passed down together from parents to offspring), this database can then be used to predict the presence of complex variants in individuals who only have data on simple variants. This method will allow for rapid, and inexpensive prediction of CYP2A6 function, which can then be used to predict smoking behaviours and for genetic optimization of smoking cessation treatment, an area known as "personalized medicine". The method can also serve as a proof-of-concept for prediction of complex variants in other genes that are similar to CYP2A6.