Multi-species data integration and gene ranking enrich significant results in an alcoholism genome-wide association study

Zhongming Zhao,Peilin Jia,John Kramer,John I Nurnberger,Michael S Grotewiel,Danielle M Dick,Fazil Aliev,Arpana Agrawal,Andrew G Davies,Brien P Riley,An-Yuan Guo,Howard J Edenberg,Edwin Jcg Van Den Oord,Kenneth S Kendler,Michael F Miles,Jill C Bettinger,Bradley T Webb,Marc A Schuckit

doi:10.1186/1471-2164-13-s8-s16

Abstract

BackgroundA variety of species and experimental designs have been used to study genetic influences on alcohol dependence, ethanol response, and related traits. Integration of these heterogeneous data can be used to produce a ranked target gene list for additional investigation.ResultsIn this study, we performed a unique multi-species evidence-based data integration using three microarray experiments in mice or humans that generated an initial alcohol dependence (AD) related genes list, human linkage and association results, and gene sets implicated in C. elegans and Drosophila. We then used permutation and false discovery rate (FDR) analyses on the genome-wide association studies (GWAS) dataset from the Collaborative Study on the Genetics of Alcoholism (COGA) to evaluate the ranking results and weighting matrices. We found one weighting score matrix could increase FDR based q-values for a list of 47 genes with a score greater than 2. Our follow up functional enrichment tests revealed these genes were primarily involved in brain responses to ethanol and neural adaptations occurring with alcoholism.ConclusionsThese results, along with our experimental validation of specific genes in mice, C. elegans and Drosophila, suggest that a cross-species evidence-based approach is useful to identify candidate genes contributing to alcoholism.

Highlights

A variety of species and experimental designs have been used to study genetic influences on alcohol dependence, ethanol response, and related traits
Five additional data sources were used to construct a score for these genes, including 1) genes showing altered expression in the prefrontal cortex of human alcoholics [15], 2) linkage intervals from published studies of the Collaborative Study on the Genetics of Alcoholism (COGA) and the Irish Study of Alcoholism samples [16,17,18], 3) genes contained on a human addiction/alcoholism array [19], 4) those from a smaller list of ethanol-related genes compiled from Drosophila [20,21], and 5) a short list of ethanolrelated genes compiled from C. elegans [22]
Genes having cross-species hits acquired bonus scores, as cross-species evidence was regarded as an important factor in gene salience

Summary

Introduction

A variety of species and experimental designs have been used to study genetic influences on alcohol dependence, ethanol response, and related traits Integration of these heterogeneous data can be used to produce a ranked target gene list for additional investigation. A wealth of data are available, including linkage studies in human alcoholics, microarray studies of inbred mouse strains’ brains and rat brains exposed to ethanol, and studies of loss or gain of function of genes in organisms association studies in humans. For this and other approaches, evaluation is often needed in order to determine whether the rankings are effective at the end of the data integration process. While there are issues with data reduction or summarization, integration at the level of the gene can be used as a link across a number of commonly used approaches

Methods

Results

Conclusion