Genome wide association for addiction: replicated results and comparisons of two analytic approaches

Abstract

Background: Vulnerabilities to dependence on addictive substances are substantially heritable complex disorders whose underlying genetic architecture is likely to be polygenic, with modest contributions from variants in many individual genes. "Nontemplate" genome wide association (GWA) approaches can identity groups of chromosomal regions and genes that, taken together, are much more likely to contain allelic variants that alter vulnerability to substance dependence than expected by chance.

Methodology/principal findings: We report pooled "nontemplate" genome-wide association studies of two independent samples of substance dependent vs control research volunteers (n = 1620), one European-American and the other African-American using 1 million SNP (single nucleotide polymorphism) Affymetrix genotyping arrays. We assess convergence between results from these two samples using two related methods that seek clustering of nominally-positive results and assess significance levels with Monte Carlo and permutation approaches. Both "converge then cluster" and "cluster then converge" analyses document convergence between the results obtained from these two independent datasets in ways that are virtually never found by chance. The genes identified in this fashion are also identified by individually-genotyped dbGAP data that compare allele frequencies in cocaine dependent vs control individuals.

Conclusions/significance: These overlapping results identify small chromosomal regions that are also identified by genome wide data from studies of other relevant samples to extents much greater than chance. These chromosomal regions contain more genes related to "cell adhesion" processes than expected by chance. They also contain a number of genes that encode potential targets for anti-addiction pharmacotherapeutics. "Nontemplate" GWA approaches that seek chromosomal regions in which nominally-positive associations are found in multiple independent samples are likely to complement classical, "template" GWA approaches in which "genome wide" levels of significance are sought for SNP data from single case vs control comparisons.

Figure 1. Validation graph of the relationships between observed (y axis) and expected (x axis) allele frequency data for Affymetrix 6.0 arrays.

“Expected” frequencies come from individual genotyping of individuals. These individuals were assigned to three sets of pools each containing 2, 5 and 15 CEPH individuals (total of 81 individuals). Arctan A/B represent the “observed” measures of allele frequency and are arctangents of the A/B hybridization ratios for this set of pools of individuals. In this figure we have only used SNPs that show at least 10% difference in the expected values across the set of pools (total of 146,000 SNPs). We have obtained similar data from studies validating 500k,100k, 10k and HuSNP arrays –. Note that DNA used for hybridization is less than that recommended for individual genotyping (135 vs 225 ng) in order to avoid saturation of hybridization signals for some array features. Error bars indicate SEM.

Figure 2. Chromosomal distributions of abuser/control t values, clustered positive SNPs, and candidate positive genes (Table 1).

Blue boxes: t values of the abuser control differences from 870,000 SNPs studied here. Values from European-Americans: right side, from African-Americans: left side. Red circles: Positions of the SNPs whose data yield clustered positive values. Yellow triangles: positions of clustered positive results that support genes listed in Table 1. Scale bar (grey): 25 Mb.