To contribute to the understanding of the molecular basis of alcohol dependence, we i) attempt to increase the power of detecting genetic signals by developing methods to analyze all functional variants in a gene jointly and ii) use such developed methods to discover and replicate genetic findings from available alcohol related data. In this proposal we focus on expression Quantitative Trait Loci (eQTLs). To achieve the goal of testing eQTLs jointly, we propose to i) construct a tissue specific data base of eQTLs found in dense genotype panels, e.g. 1000 Genomes Project (1KG), ii) develop two statistical methods which use this database information and implement them into fast user friendly software, iii) discover promising genes by applying the developed methods to publicly available alcohol use disorder (AUD) studies and iv) replicate discovered genes in a proprietary AUD study. In the database we include all 1KG genetic variants which are known or predicted to influence gene expression in tissues relevant for AUD, i.e. brain and liver. Both proposed methods have the major advantage of using only summary statistics, i.e. they do not require access to subject level genotypes. The first method directly imputes the univariate summary statistics at unobserved eQTLs based on 1) univariate summary statistics at measured variants nearby and 2) the correlation structure, as estimated from a relevant reference population. The second method uses the univariate statistic at measured and imputed eQTLs, to derive the test for the joint effect on phenotype of all eQTLs in a gene. These methods are subsequently used to discover and replicate findings using summary statistics from data sets which are both publicly available and proprietary. For the discovery phase, we use all the publicly available summary data sets, such as Collaborative Study on the Genetics of Alcoholism among others. We replicate the discovered genes using the internally available Irish Alcohol Study.
Genetic variants, known to affect gene expression, are known to be overrepresented among genetic findings for many diseases. However, the number of consistently replicated genetic variants for alcohol use disorders is relatively small, regardless f their influence on gene expression. We hypothesize that the dearth of findings is due to analyzing variants individually and, to increase the detection power, we i) propose methods/software to jointly analyze such functional variants and ii) apply these methods to relevant alcohol datasets.