Molecular genetics of addiction and related heritable phenotypes: genome-wide association approaches identify "connectivity constellation" and drug target genes with pleiotropic effects

Abstract

Genome-wide association (GWA) can elucidate molecular genetic bases for human individual differences in complex phenotypes that include vulnerability to addiction. Here, we review (a) evidence that supports polygenic models with (at least) modest heterogeneity for the genetic architectures of addiction and several related phenotypes; (b) technical and ethical aspects of importance for understanding GWA data, including genotyping in individual samples versus DNA pools, analytic approaches, power estimation, and ethical issues in genotyping individuals with illegal behaviors; (c) the samples and the data that shape our current understanding of the molecular genetics of individual differences in vulnerability to substance dependence and related phenotypes; (d) overlaps between GWA data sets for dependence on different substances; and (e) overlaps between GWA data for addictions versus other heritable, brain-based phenotypes that include bipolar disorder, cognitive ability, frontal lobe brain volume, the ability to successfully quit smoking, neuroticism, and Alzheimer's disease. These convergent results identify potential targets for drugs that might modify addictions and play roles in these other phenotypes. They add to evidence that individual differences in the quality and quantity of brain connections make pleiotropic contributions to individual differences in vulnerability to addictions and to related brain disorders and phenotypes. A "connectivity constellation" of brain phenotypes and disorders appears to receive substantial pathogenic contributions from individual differences in a constellation of genes whose variants provide individual differences in the specification of brain connectivities during development and in adulthood. Heritable brain differences that underlie addiction vulnerability thus lie squarely in the midst of the repertoire of heritable brain differences that underlie vulnerability to other common brain disorders and phenotypes.

Figure 1. Pie graph model for the genetic architecture of human vulnerability to dependence on addictive substances

Polygenic additive genetic influences and environmental influences that are largely those that are not shared between members of sibships are depicted. Potential roles for g × g and g × e interactions are not depicted here.

Figure 2. Venn diagram of overlapping genetic contributions to several of the phenotypes discussed here based on genome wide association datasets of about 500000 SNPs

Note that the area of overlap in the figure does not necessarily represent the area of the overlap in the datasets. See text for more details.

Figure 3. Validation of SNP genotyping in DNA pools (From [12])

The relationship (r = 0.95) between individual and pooled genotyping using 500k SNP Affymetrix arrays provides an opportunity to assess the sensitivity of pooled genotyping. Since these validation experiments were the first ones performed with new array sets, these data provide a lower limit. Current results from 1M SNP arrays (6.0) provide relationships ca 0.98 (Drgon et al, in preparation).

Figure 4. Power of genome wide association as assessed using Gene Detective

Simulation of power with 620,000 diallelic markers for samples with n = 400 case and n = 400 controls with nominal 0.05 α levels. Note the striking relationship between power and effect size. Power to detect effects that would produce odds ratios of less than 1.2 –fold is modest, while power to detect effects as high as 1.7 fold is relatively good.

Figure 5. Power of genome wide association as assessed using Gene Detective

Simulation of power with 1,000,000 diallelic markers for samples with n = 2000 case and n = 2000 controls with nominal 0.05 α levels. Note that the striking relationship between power and effect size is retained.

Figure 6. Distributions of data mapped onto cartoons of human chromosomes

Chromosomes 1– 8 (top row), 9 – 16 (second row), 17–21 (third row), 22 (fourth row). Red triangles to the left of the (black) main axis for each chromosome mark locations where genes are identified by clustered positive SNPs from Samples 1 & 2, and support is provided from at least one other sample. Green triangles to the left of the main axis mark locations where the gene is also identified by clustered positive SNPs from both Methanphetamine Samples 4 & 5. Blue triangles to the left of the main axis mark locations where the gene is also identified by clustered positive SNPs from 2 of the 3 Nicotine Abstinance Samples 11–14. Nave squares to the right of the main axis where the gene is also identified by clustered positive SNPs from 2 of the 3 Bipolar Disease Samples 7–9. Purple squares to the right of the main axis mark locations where the gene is also identified by clustered positive SNPs from both Cognitive Function Samples 15 & 16. Grey squares to the right of the main axis mark locations where the gene is also identified by clustered positive SNPs from both Alzheimer’s Disease Samples 17 & 18. Yellow squares to the right of the main axis axis mark locations where the gene is also identified by clustered positive SNPs from the NHLBI Frontal Brain Volume Sample 10. Chromosomal positions are based on National Center for Biotechnology Information MAPVIEWER Build 36.1 coordinates and supplemental data from NETAFFX.