Tao Wang, of the Department of Epidemiology and Population Health at Albert Einstein College of Medicine, will present:
Abstract: Lung cancer is one of the most common cancers worldwide. While obesity is a strong risk factor for certain types of cancer, such as colon, breast and endometrial cancers, many epidemiological studies have consistently indicated an inverse association between body mass index (BMI) and risk of lung cancer after adjusting for other established risk factors. Mendelian randomization (MR) is an analytical approach that uses genetic variants as the instrumental variable (IV) to infer the causal relationship between an exposure variable and disease. However, because of the lack of efficient statistical tools, MR often requires an extremely large sample size to achieve adequate statistical power. Our goal is to develop a novel statistical method that fully utilizes genomic data to construct the IV for obesity traits in MR analysis. The major advantage of the proposed approach is that it can unravel the genetic variation of obesity traits that is accounted for by multiple genetic variants, and thereby provide substantially improved statistical power. We apply this approach to analyze the existing individual GWAS data as well as detailed epidemiologic data in TRICL (Transdisciplinary Research in Cancer of the Lung). Our results suggest BMI may be a risk factor for lung cancer in both non-smokers and smokers, and our analytic approach may be useful for facilitating investigation of the causal relationship between risk factors and diseases in general.
A social tea will be held at 3:00 p.m. in A434 Mayo. All are Welcome.