University of Pittsburgh

MultiVariate Phenotype Association Test on Chronic Obstructive Pulmonary Disease using Image features

graduate student
Date: 
Friday, April 20, 2018 - 1:00pm - 1:30pm

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death in US and worldwide. Although cigarette smoking is one of the major environmental risk factors, not all smokers develop debilitating COPD. Furthermore, the family-based studies show the importance of the genetic factors in increasing the risk of the disease. Genome Wide Association Studies (GWAS) of COPD have identified several genetic loci associated to the disease. Classically, univariate regression has been done to identify a set of statistically significant genetic variants with respect to a phenotype of interest. Traditionally, GWAS is performed on a one-dimensional phenotype such as respiratory measurements. However, COPD is highly heterogeneous and single measurement cannot characterize the heterogeneity. Computerized Tomography (CT) techniques are increasingly used for diagnosis of the disease. CT captures the anatomical variabilities induced by the disease; hence it can be viewed as a rich high-dimensional phenotype. In this talk, I will review the classical methods to identify risk factors using univariate respiratory measurements. I will discuss why using a high dimension phenotype can be a challenging statistical task and will discuss potential solutions to tackle the challenges.

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