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

Abstract

Motivation: 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.

Results: 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.

Availability: 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 information: Supplementary data are available at Bioinformatics online.

Fig. 1.

The model for a genomic information network. The symbols beneath the names of the nodes represent the prioritization score S_i and the link index L_i.

Fig. 2.

The results of the combined NicSNP GWAS and candidate gene studies. (Top) The original P-values plotted against genomic position. (Bottom) The original P-values plotted against the prioritization score. Signals above the dashed red line correspond to the top 1536 SNPs by weighted P-value with (uncorrected) P ≤ 0.05 (the GIN prioritization method). Signals above the horizontal blue dashed correspond to the top 1536 SNPs by non-weighted P-value (the straight P-value method). The red-shaded knife-shaped and blue-shaded triangular regions show the difference between these two methods.