The influence of regression models on genome-wide association studies of alcohol dependence: a comparison of binary and quantitative analyses

Introduction Genome-wide association studies (GWAS) of alcohol dependence syndrome (ADS) offer a platform to detect genetic risk loci. However, the majority of the ADS GWAS undertaken, to date, have utilized a case-control design and have failed to identify consistently replicable loci with the exception of protective variants within the alcohol metabolizing genes, notably ADH1B. The ADS phenotype shows considerable variability which means that the use of quantitative variables as a proxy for the severity of ADS has the potential to facilitate identification of risk loci by increasing statistical power. The current study aims to examine the influences of using binary and adjusted quantitative measures of ADS on GWAS outcomes and on calculated polygenic risk scores (PRS). Methods A GWAS was performed in 1251 healthy controls with no history of excess alcohol use and 739 patients with ADS classified using binary DMS-IV criteria. Two additional GWAS were undertaken using a quantitative score based on DSM-IV criteria, which were applied assuming both normal and non-normal distributions of the phenotypic variables. PRS analyses were performed utilizing the data from the binary and the quantitative trait analyses. Results No associations were identified at genome-wide significance in any of the individual GWAS; results were comparable in all three. The top associated single nucleotide polymorphism was located on the alcohol dehydrogenase gene cluster on chromosome 4, consistent with previous ADS GWAS. The quantitative trait analysis adjusted for the distribution of the criterion score and the associated PRS had the smallest standard errors and thus the greatest precision Conclusion Further exploitation of the use of qualitative trait analysis in GWAS in ADS is warranted.