Molecular genetics of addiction and related heritable phenotypes: genome-wide association approaches identify "connectivity constellation" and drug target genes with pleiotropic effects.
- Authors
- Uhl, George R; Drgon, Tomas; Johnson, Catherine; Li, Chuan-Yun; Contoreggi, Carlo; Hess, Judith; Naiman, Daniel; Liu, Qing-Rong
- Year
- 2008
- Journal
- Annals of the New York Academy of Sciences
- PMID
- 18991966
- DOI
- 10.1196/annals.1441.018
- PMCID
- PMC3922196
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.
Pie graph model for the genetic architecture of human vulnerability to dependence on addictive substancesPolygenic additive genetic influences and environmental influences that are largely those that are not shared between members of sibships are depicted. Potential roles for g Γ g and g Γ e interactions are not depicted here.
Venn diagram of overlapping genetic contributions to several of the phenotypes discussed here based on genome wide association datasets of about 500000 SNPsNote 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.
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).
Power of genome wide association as assessed using Gene DetectiveSimulation of power with 620,000 diallelic markers for samples with n = 400 case and n = 400 controls with nominal 0.05 Ξ± levels. Note the striking relationship between power and effect size. Power to detect effects that would produce odds ratios of less than 1.2 βfold is modest, while power to detect effects as high as 1.7 fold is relatively good.
Power of genome wide association as assessed using Gene DetectiveSimulation of power with 1,000,000 diallelic markers for samples with n = 2000 case and n = 2000 controls with nominal 0.05 Ξ± levels. Note that the striking relationship between power and effect size is retained.
Distributions of data mapped onto cartoons of human chromosomesChromosomes 1β 8 (top row), 9 β 16 (second row), 17β21 (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 11β14.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 7β9.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 Alzheimerβs 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.
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|---|---|---|
| 100 | III. Selected Methodological Issues β C. Analyses β 1. Achieving significant genome wide association in single samples vs seeking replication and generalization in multiple samples | There is no clearcut consensus about any single method that will produce only true results from anyβ¦ |
| 101 | III. Selected Methodological Issues β C. Analyses β 1. Achieving significant genome wide association in single samples vs seeking replication and generalization in multiple samples | a reasonable approach. When there is a large effect of a single gene, a number of corrections forβ¦ |
| 102 | III. Selected Methodological Issues β C. Analyses β 1. Achieving significant genome wide association in single samples vs seeking replication and generalization in multiple samples | As the expected effect of each locus falls from the large effects characteristic of oligogenicβ¦ |
| 103 | III. Selected Methodological Issues β C. Analyses β 1. Achieving significant genome wide association in single samples vs seeking replication and generalization in multiple samples | b) Replicate sample approaches: Here we use an alternative analytic approach that focuses onβ¦ |
| 104 | III. Selected Methodological Issues β C. Analyses β 1. Achieving significant genome wide association in single samples vs seeking replication and generalization in multiple samples | It is important to emphasize the ways in which the step wise analyses presented here a) firstβ¦ |
| 105 | III. Selected Methodological Issues β C. Analyses β 1. Achieving significant genome wide association in single samples vs seeking replication and generalization in multiple samples | We thus a) first identify nominally-significant SNPs in each sample, 2) identify the clustering ofβ¦ |
| 106 | III. Selected Methodological Issues β C. Analyses β 1. Achieving significant genome wide association in single samples vs seeking replication and generalization in multiple samples | The criterion used here identifies clustering based on chromosomal position. This approach allowsβ¦ |
| 107 | III. Selected Methodological Issues β C. Analyses β 1. Achieving significant genome wide association in single samples vs seeking replication and generalization in multiple samples | This approach seeks to identify genes with variants that are likely to play roles in addiction andβ¦ |
| 108 | III. Selected Methodological Issues β C. Analyses β 1. Achieving significant genome wide association in single samples vs seeking replication and generalization in multiple samples | us to combine datasets in which different marker sets are used. With each of these limitations, itβ¦ |
| 109 | III. Selected Methodological Issues β C. Analyses β 2. Stepwise approaches to analyses | a) Determination of nominally-significant markers Nominal p values that come from βtβ (forβ¦ |
| 110 | III. Selected Methodological Issues β C. Analyses β 2. Stepwise approaches to analyses | b) Identifying chromosomal clusters of nominally significant markers in single samples: We focus onβ¦ |
| 111 | III. Selected Methodological Issues β C. Analyses β 2. Stepwise approaches to analyses | We focus on clusters of nominally-positive autosomal SNPs that lie within 100 or 25 kb of eachβ¦ |
| 112 | III. Selected Methodological Issues β C. Analyses β 2. Stepwise approaches to analyses | It is important to note that, if stochastic events produce a nominally βsignificantβ associationβ¦ |
| 113 | III. Selected Methodological Issues β C. Analyses β 2. Stepwise approaches to analyses | We test the nonrandomness of clustering of nominally-significant SNPs using Monte Carlo simulations.β¦ |
| 114 | III. Selected Methodological Issues β C. Analyses β 2. Stepwise approaches to analyses | c) Identifying the clustered, nominally positive SNPs within the strongest positive support fromβ¦ |
| 115 | III. Selected Methodological Issues β C. Analyses β 2. Stepwise approaches to analyses | Analyses focus on genes that are identified by clustered positive results from several samples. Thisβ¦ |
| 116 | III. Selected Methodological Issues β C. Analyses β 2. Stepwise approaches to analyses | Clustering of positive results in the same gene in each of several independent samples is much lessβ¦ |
| 117 | III. Selected Methodological Issues β C. Analyses β 2. Stepwise approaches to analyses | d) Identifying the clustered, nominally positive SNPs within the strongest positive support fromβ¦ |
| 118 | III. Selected Methodological Issues β C. Analyses β 2. Stepwise approaches to analyses | Baysian approaches to these analyses suggest that the stronger the evidence for coheritabilities ofβ¦ |
| 119 | III. Selected Methodological Issues β C. Analyses β 2. Stepwise approaches to analyses | Twin data that compares co-occurance frequencies in monozygotic vs dizygotic twin pairs providesβ¦ |
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