Genetic association tests for cigarattes per day

Abstract

Cigarettes per day (CPD) is one of most commonly used phenotypes in nicotine dependence (ND) study. For example, a genetic association study of ND focuses on identifying single nucleotide polymorphisms (SNPs) that correlate with CPD. However, analysis of CPD data is always challenging, since the estimation of CPD distribution is difficult due to spikes at some specific values, say 10, 20 and so forth. Thus, standard maximum likelihood estimation is not appropriate. In this study, we focus on genetic association tests for identifying SNPs that correlate with CPD. We first reviewed previously proposed approaches applicable to CPD data, in which the CPD data is a dependent variable and the SNP is an ordinal independent variable. We then considered a calibration model in which the SNP is the ordinal dependent variable and the CPD is the independent variable. Unlike a standard modeling approach, this calibration modeling approach becomes sufficiently robust to accommodate distributional assumptions of CPD data. We applied our robust calibration modeling approach to CPD data from the Korean Association Resource project data of 4,183 male samples.