Systematic biological prioritization after a genome-wide association study: an application to nicotine dependence.

Abstract

A challenging problem after a genome-wide association study (GWAS) is to balance the statistical evidence of genotype-phenotype correlation with a priori evidence of biological relevance. We introduce a method for systematically prioritizing single nucleotide polymorphisms (SNPs) for further study after a GWAS. The method combines evidence across multiple domains including statistical evidence of genotype-phenotype correlation, known pathways in the pathologic development of disease, SNP/gene functional properties, comparative genomics, prior evidence of genetic linkage, and linkage disequilibrium. We apply this method to a GWAS of nicotine dependence, and use simulated data to test it on several commercial SNP microarrays. A comprehensive database of biological prioritization scores for all known SNPs is available at http://zork.wustl.edu/gin. This can be used to prioritize nicotine dependence association studies through a straightforward mathematical formula-no special software is necessary. Supplementary data are available at Bioinformatics online.