Abstract
BackgroundVulnerabilities 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 FindingsWe 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/SignificanceThese 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.
Highlights
Vulnerability to addictions is a complex trait with substantial genetic influences that are documented by data from family, adoption and twin studies [1,2,3,4]
We discuss this work in light of its technical and analytic limitations and in its similarities and differences with ‘‘template’’ genome wide association (GWA) analyses that seek associations that display genome-wide significance, typically in phenotypes that display oligogenic genetic architectures and/or in larger samples that are often recruited in multiple locations
Genes Identified in Other Studies of Addiction Despite the differences in approaches, primary substance of abuse and/or genetic background, there is significant evidence that, compared to chance, the current results identify more of the same genes and chromosomal regions that are identified by a number of independent datasets that compare substance dependence phenotypes to controls
Summary
Vulnerability to addictions is a complex trait with substantial genetic influences that are documented by data from family, adoption and twin studies [1,2,3,4]. We and others have developed ‘‘nontemplate’’ GWA analyses to address highly heritable complex phenotypes for which there is little evidence for many genes of major effect These analyses have focused on identification of nominally significant case vs control allele frequency differences at several nearby SNP markers in multiple independent samples. Several considerations have prompted differing approaches to 1) combining and comparing GWA datasets and 2) declaring that association between sets of nearby SNPs and a complex disorder is ‘‘replicated’’ in the absence of genome wide significance for any result. We discuss this work in light of its technical and analytic limitations and in its similarities and differences with ‘‘template’’ GWA analyses that seek associations that display genome-wide significance, typically in phenotypes that display oligogenic genetic architectures and/or in larger samples that are often recruited in multiple locations. We describe the ways in which these pooled genotype data identify a number of the same genomic regions that are identified by recently available dbGAP datasets that provide individual genotyping for cocaine-dependent and nondependent comparison groups
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