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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Ann N Y Acad Sci. Author manuscript; available in PMC 2014 February 12.
Published in final edited form as:
PMCID: PMC3922196

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


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 genome wide association data: genotyping in individual samples vs 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 datasets for dependence on different substances; e) overlaps between GWA data for addictions vs other heritable, brain-based phenotypes that include: i) bipolar disorder, ii) cognitive ability, iii) frontal lobe brain volume, iv) ability to successfully quit smoking, v) neuroticism and vi) 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.

Keywords: pleiotropic, cell adhesion, monte carlo

I. Introduction

Genome wide association is now increasingly the method of choice for identifying allelic variants that contribute to complex genetic disorders, especially those with polygenic genetic bases (eg derived from effects at many gene loci, each with modest effects, as well as from environmental determinants; see also Glossary)[19]. Substance dependence was one of the first complex phenotypes for which replicated association-based genome scanning data was reported [3, 1014]. There is now a torrent of information from genome wide association studies of a number of other complex, brain-based phenotypes that both display substantial heritability and are unlikely (based on linkage study results) to manifest many common gene variants that produce large effects [1, 2, 4, 5]. A number of these other heritable, brain-based phenotypes co-occur with addictions and are thus good candidates to display genetic overlaps with addiction.

No single approach to designing genome wide association studies or to analyzing genome wide association data is now universally accepted. There is now no universal standard for considering genome-wide association results “significant” in ways that allow us to identify polygenic allelic variants in reasonably-sized single experiments. Here, we describe specific sets of working hypotheses about the genetic architecture of addiction (eg vulnerability to develop dependence on an addictive substance). This set of hypotheses is also useful for considering the molecular genetic bases for other common, complex phenotypes that, like addictions, display both substantial evidence for heritability and little evidence for large influences from any single gene (eg single gene, Mendelian influences or oligogenic effects that come from a few genetic loci, each with moderate effects on the phenotype). We then detail experimental design and analytic approaches that arise from working hypotheses about underlying genetic architecture and likely sources of false positive results.

A number of samples provide the bases for these analyses. In analyzing data from these samples, we focus on clusters of genomic markers whose allele frequencies distinguish control individuals from those with substance dependence or addiction-related phenotypes. We describe identification of chromosomal regions that contain clusters of such nominally-positive results in replicate samples for addiction vulnerability. We then describe evidence for generalization that arises from identification of overlapping chromosomal locations of clustered positive results for different phenotypes. These data thus support pleiotropic influences (eg contributions of the same allelic variants to multiple phenotypes) of common allelic variants on several of the brain based phenotypes. The data thus document overlapping heritable influences on several interesting brain phenotypes.

We focus here on clinical phenotypes that co-occur with addiction and a structural brain phenotype, individual differences in frontal cortical volume. Twin studies document sizable heritable components for individual differences in the volumes of brain regions. High heritabilities are especially evident for individual differences in frontal and temporal cerebral cortical regions [15]. Volumes of these brain regions have been reported to be reduced in substance dependent individuals [1619]. Increasing evidence from fMRI and PET studies identifies functional differences in these brain regions in studies of individuals with substance dependence and related phenotypes [20, 21]. We thus focus on this “frontal cortical volume” phenotype.

A number of the genes identified here encode classical “druggable” targets for pharmacological modulation, including enzymes, receptors and transporters. Other genes encode cell adhesion related molecules. We discuss genes in each of these classes below.

A. What is genome wide association and why might it be useful for studies of the molecular bases of heritable influences on vulnerability to addiction and related phenotypes? One way to view genome wide association is in relationship to linkage based genome scanning, since most of the efforts to “positionally clone” gene variants for complex human disorders (eg those that are likely to be caused by multiple genetic and environmental factors) have used linkage-based methods. Linkage asks how addiction phenotypes and genetic markers (typically genotyped approximately every 1/400th – 1/1,000th of the genome) move together through pedigrees of closely-related individuals. Chromosomal regions that contain marker alleles that move through pedigrees together with the trait are said to be “linked” to the trait. Many loci with nominally-significant linkage to addiction phenotypes have been identified [2231] (further references in [32]). Several of the loci identified in independent linkage studies do overlap. However, the large numbers of reported linkage-based studies of addictions yield large numbers of nominally-positive results that cover virtually all chromosomes. These widespread results may not converge more than expected by chance, as we have documented in recent analyses of the reported data for linkage to smoking [32].

Such inconsistent linkage data is consistent with the idea that most of the genetic architecture for human addiction vulnerability is polygenic in most populations. There is now an increasing consensus that genome-wide association (GWA; also termed “whole genome association” or “association genome scanning”) is more likely than linkage approaches to yield positive results in polygenic complex disorders, such as addictions [1, 3, 1012, 3337]. Association asks how addiction phenotypes and genetic markers (genotyped approximately every 1/500,000th to every 1/1,000,000th of the genome in current datasets) are found together in nominally-unrelated individuals (although we are all distantly related to each other, of course). We and others have developed these methods, relying on the increasing densities of single nucleotide polymorphism (SNP) markers that can be assessed using “SNP chip” microarrays of increasing sophistication [1012, 33, 34, 36, 37]. Genome wide association gains power as densities of genomic markers increase. Association identifies much smaller chromosomal regions than linkage-based approaches. Association thus allows us to identify variants in specific genes rather than in large chromosomal regions. Genome wide association fosters pooling strategies that preserve confidentiality and reduce costs, as we discuss below [1012, 33, 3844]. Genome wide association provides ample genomic controls. Proper genomic controls can minimize the chances that disease vs control differences are confounded by occult stratification, such as the stratification that might arise from unintended occult ethnic mismatches between disease and control samples.

B. A likely underlying genetic architecture for addiction (and other common heritable brain-based phenotypes for which linkage data fails to provide evidence for “genes of major effect”) We approach analyses of the molecular genetic bases of addiction and related disorders from perspectives that are based on sets of working hypotheses concerning the underlying genetic architectures of these disorders and phenotypes. In general, the best experimental design and analytic approaches are likely to arise from the best possible working hypotheses concerning: 1) the genetic architectures of the disorders or phenotypes being evaluated 2) the population genetics of the samples being tested 3) the anticipated association signals. It is also desirable to consider and provide controls for alternative hypotheses that might provide alternative explanations for systematic differences between disease and control samples. Alternative hypotheses include: 1) unintended stratification based on eg racial/ethnic differences between “disease” and “control” samples, as noted above, 2) uneven distribution of noise in some assays, so that SNPs with the largest variance might be identified rather than the SNPs whose allelic frequencies truly differ between disease and control individuals, 3) stochastic, chance differences between disease and control samples (at least some of these are highly likely in any single study that uses so many repeated comparisons) and 4) sampling issues so that genetics related to the ways in which the samples are ascertained and obtained (eg features such as differential willingness to consent) is identified rather than true disease/control differences. Many of these concerns become more prominent as hundreds of thousands or millions of repeated comparisons are made using single sample sets. Many of these concerns may be more acute as attempts to rapidly assemble samples with larger and larger “n” drive investigators to include subsamples that may well contribute occult heterogeneities to overall disease and/or control samples (see below).

C. Family, adoption and twin data that each support substantial polygenic heritability for addictions Current models for the genetic architecture for substance dependence in the population are based on information from: 1) family, adoption and twin data that each support substantial heritability for addictions, 2) twin data (in which concordance in genetically-identical monozygotic and genetically-half-identical dizygotic twins are compared) that document that most of this heritable influence is not substance-specific, 3) linkage based (and genome wide association) studies that fail to provide evidence for genes of major effect (eg for any single gene whose variants produce substantial differences in addiction vulnerability) for substance dependence.

Support for the idea that vulnerability to addictions is a complex trait with strong genetic influences that are largely shared by abusers of different legal and illegal addictive substances [4548] comes from classical genetic studies. Family studies document that first degree relatives (eg sibs) of addicts display greater risk for developing substance dependence than more distant relatives [45, 49]. Adoption studies find greater similarities between levels of substance abuse between adoptees vs biological relatives than adoptees vs members of the adoptive families [45]. In twin studies, differences in concordance between genetically identical and fraternal twins also support heritability for vulnerability to addictions [47, 5056]. Twin data allows quantitation of the amount, about half, of addiction vulnerability that is heritable. Twin data also supports the idea that the environmental influences on addiction vulnerability that are not shared among members of twin pairs are much larger than those that are shared by members of twin pairs (eg e2 >> c2 in virtually every such study). Most environmental influences on human addiction vulnerability are thus likely to come from outside of the immediate family environment (Figure 1).

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

D. Twin data document that most of this heritable influence is not substance-specific, but provides “higher order” pharmacogenomics We are fortunate to have data from studies of identical vs fraternal twin pairs that evaluate the degree to which one twin’s dependence on a substance enhances the chance that his or her co-twin will become dependent on a substance of a different class. Results of these analyses document that most of the genetic influences on addiction vulnerability are common to dependence on multiple different substances, though others do appear to be substance-specific [46, 53, 54].

Elsewhere [32] we have suggested levels of analysis for pharmacogenomics and pharmacogenetics: 1) “primary” pharmacogenomics that describes the genetics of individual differences in the adsorption, distribution, metabolism and/or excretion of a drug; 2) “secondary” pharmacogenomics that describes individual differences in drug targets, such as the G-protein coupled receptors, transporters and ligand gated ion channels that are the primary targets of opiates, psychostimulants and barbiturates, respectively and 3) “higher order” pharmacogenomics that provide individual differences in post-receptor drug responses. Such post-receptor drug responses are more likely to be common to actions of abused substances that come from several different chemical classes and act at distinct primary receptor or transporter sites in the brain. Based on the twin data that are currently available, we thus postulate that much, if not most, of the human genetics of addition vulnerability represents “higher order” pharmacogenomics.

E. Failure to document evidence for substance dependence genes of major effect in most populations There are few careful studies of the ways in which most human addiction vulnerabilities move through families (eg segregation analyses). No such study indicates a “major” gene effect on addiction vulnerability in most current populations. There is an exception: the “flushing syndrome” variants at the aldehyde (ALDH) and alcohol (ADH) dehydrogenase loci in Asian individuals do provide genes of major effect in this population. Individuals with these gene variants are at lower risk for becoming dependent on alcohol than individuals with other genotypes [57] in Chinese [58, 59], Korean [60], Japanese [6166] and other populations [67, 68]. Homozygous ALDH2*2 individuals are strongly protected from alcohol dependence [61, 62]. This locus thus provides a good example of “primary” pharmacogenomics, though in a restricted population.

Quantity-frequency data for smoking also provide evidence for a replicable “secondary” pharmacogenomic effect of moderate magnitude. Markers in the chromosome 15 gene cluster that encodes the α3, α5 and β4 nicotinic acetylcholine receptors display different allelic frequencies in heavy vs light smokers in each of several studies [3, 35, 69]. This chromosome 15 locus is likely to provide a good example of “secondary” pharmacogenomics, since it has not been associated as reproducibly with dependence on other substances.

Linkage-based analyses for addiction vulnerabilities would be expected to reproducibly identify many of the genes whose variants exerted major influences on human addiction vulnerability. However, existing linkage data for human dependence on alcohol, nicotine and a number of other substances fails to provide any highly-reproducible results that would support any major gene locus ([2231] and references in [32]). These results add to the conclusion that no locus individually appears to contribute a large fraction of the vulnerability to dependence on any addictive substance. There is a caveat: these data come from subjects with largely European ethnic/racial backgrounds [22, 23, 7084]. Nevertheless, as with many complex human disorders in which initial hopes for a tractable (eg oligogenic) underlying genetic architecture supported use of linkage approaches, the linkage peaks that are identified in each individual study may be more likely to arise on other bases when the underlying architecture is in fact polygenic. Apparent linkage signals identified in single studies might result from polygenic influences from several genes that each happen to lie near each other on human chromosomes or to be found on stochastic bases when there is no true major effect from any single gene variant, for example [85].

F. Current models for the genetic architecture of human dependence on legal and illegal addictive substances in the population thus postulate that each is influenced roughly 50% by polygenic genetic influences, that is by variants in individual genes that each contribute modest amounts to this overall genetic vulnerability. These genetic architectural models posit that many of these genetic vulnerabilities increase risk for addcition to several pharmacologic classes of abused substances, but that some of these genetic influences are specific to drugs of one class [32].

Analyses of twin data for vulnerability to develop dependence on a substance fit with large additive genetic components (a2), large components for nonshared environmental influences (e2) and small components for c2 terms that represents familial or other environmental influences that are shared between members of the twin pair [47, 5056]. What about the possibility that there could be large interactions between these genetic and environmental terms (G × E interactions), invalidating additive models for genetic and environmental contributions? G × E correlations of three types have been described [86, 87]. In one terminology, “passive” G × E correlation occurs when parents transmit both genes and environmental influences that are relevant for a trait [88, 89], “active” G × E correlation occurs where subjects of a certain genotype actively select environments that are correlated with that genotype and “reactive” G × E correlation occurs when an individual’s genotype provides different reactions from the environment. Small values for c2 influences of common environments shared by members of sibpairs appear to provide evidence against “passive” G × E correlations. On these bases, “active” and “reactive” G × E correlations remain of theoretical interest. However, one influential train of thought [88, 90] suggests that G × E correlations are best regarded as parts of the genetic variance because “… the non-random aspects of the environment are… consequence(s) of the genotype(es)…”.

Large interactions between genetic and environmental components would be likely to lead to 1) differences in estimates of heritability from samples obtained in different environments, and 2) differences in molecular genetic findings in individuals from different environments. Data from studies of twins who were sampled from a number of different environments is nevertheless largely convergent. Such convergence supports relatively modest upper limits on (G × E) interactions between genetic and environmental influences on addiction vulnerability. Modest G × E influences are also consistent with molecular genetic results that identify substantial overlaps between molecular genetics of vulnerability to dependence on illegal substances in samples from substantially different environments, such as the United States and Asia (see below).

Gene - gene interactions (G × G) of some magnitude appear likely, a priori, to make at least some contributions to addiction vulnerability. However, if there were large amounts of epistasis, G × G interactions in which specific alleles at one gene locus are required for expression of the effects of allelic variants at a second gene locus, segregation analysis data might provide uneven patterns of familiality. With large amounts of epistasis, second degree relatives (eg cousins) of addicts would be less likely to display specific combinations of G × G alleles than first degree relatives (eg sibs). Substance dependence rates would thus drop more precipitously between first and second degree relatives of addicts than they would if most risk alleles exerted largely independent effects on addiction vulnerability.

There in only a modest amount of family data that allows us to compare concordance in first vs second degree relatives. However, the existing evidence does not support less concordance in second degree relatives than we would anticipate based on the observed concordance in first degree relatives and the assumption that most risk alleles produce largely independent effects [91].

G. The genetic architecture for substance dependence in individuals What about the genetic architecture for substance dependence in individuals? Both “between locus” heterogeneity and “within locus” heterogeneity are likely. If we follow the implications of polygenic genetic models for addiction vulnerability, we can infer that each dependent individual might even display a nearly-distinct set of risk-elevating or risk-reducing allelic variants. As an illustrative example, we might postulate that a) an individual must display at least 50 risk alleles to robustly elevate his/her likelihood of acquiring a substance dependence disorder and b) there are 200 genes that contain common allelic variants that can augment addiction risk. Under such circumstances, it is easy to see that the exact genetic recipe for addiction vulnerability found in one addicted individual might be replicated in only a relatively few other addicted individuals. Such an underlying genetic architecture would be consistent with the failure of linkage-based methods to provide reproducible results in addictions, since linkage relies on identifying consistent patterns in the ways that specific DNA markers and phenotypes move through many families that display high densities of the disorder.

As noted above, the best documented genetic heterogeneity for addictions comes from the chromosome 4 major gene effects found in poorly-alcohol-metabolizing (“flushing”) Asian individuals [6163, 67]. The best documented substance-specific influence comes from the chromosome 15 nicotinic acetylcholinergic receptor gene cluster. There are likely to be other examples of between-locus genetic heterogeneity and of genes whose variants exert substance-specific effects on use and/or dependence that have yet to be elucidated.

We also postulate that within locus heterogeneity is likely, though not yet clearly documented in addiction, to our knowledge. Many common Mendelian disorders and rarer Mendelian phenocopies of common disorders display substantial heterogeneity within their pathogenic loci. A number of variants in the same CFTR gene produce cystic fibrosis disorders [92]. α synuclein missense variants and copy number variants can each provide phenocopies of idiopathic Parkinson’s disease [93]. Evidence for within-locus heterogeneity in complex disorders is just beginning to be accrued; such evidence now includes data from neurexin gene family variants in autism [9497].

H. “Epigenetics” and individual differences in vulnerability to addiction and related phenotypes

“Epigenetics” is now used with both classical and recently-revised definitions. Classical defintions of “epigenetic” emphasize influences of variations that are not encoded in primary DNA sequence but nevertheless inherited “… a change in the state of expression of a gene that does not involve a mutation, but that is nevertheless inherited in the absence of the signal (or event) that initiated the change” [98]. However, more recent definitions of “epigenetic” emphasize gene regulatory mechanisms that do not alter primary DNA sequence while paying less attention to documenting heritability [98].

In the context of this review, heritable epigenetic influences are most relevant. One example of a classical, heritable epigenetic influence is imprinting. Imprinting conveys information from parent to child through mechanisms that include DNA methylation or histone acetylation. These mechanisms retain the primary DNA sequence but can dramatically alter function of specific genes. DNA methylation at CpG sequences in the promoter regions of genes can profoundly alter gene transcription. Since methylation during the course of maternal oocyte (or paternal sperm) development is key to this process, familial patterns of gender-specific transmission can provide evidence for this subset of heritable epigenetic influence. The modest quality of current family datasets for addiction renders them a relatively weak basis for any strong inferences concerning parent-of-origin effects. Nevertheless, there is no segregation data of which we are aware that supports strong parent-of-origin effects on substance dependence. Thus, while there are obvious and large roles for nonheritable “epigenetic” influences in the biology of addiction, there is no current compelling evidence that there are any strong effects of overall heritable “epigenetic” influences, as classically defined. We nevertheless need to be alert for such influences as we unravel effects of variants in specific genes.

I. The nature (and likely evolutionary sources) of the allelic variants likely to contribute to individual differences in vulnerability to addiction and related phenotypes

The analytic strategies described below are based on postulates that common disease/common allele models hold for many of the variants that alter vulnerabilities to addiction and related phenotypes. Rare variants may also explain significant fractions of the genetic risk for common diseases. However, increasing evidence supports roles in addiction vulnerability for allelic variants that are currently common and are thus likely to be “old” in an evolutionary sense. Data indicating that such variants can be identified in diseased individuals from European, African and Asian genetic backgrounds also point, in general, to variants of substantial age. How could genetic selection act on such common functional allelic variants over the large number of generations that are implied by this “substantial age”? It is conceivable that some currently-common allelic variants could exert polygenic influences on addiction vulnerability without exerting positive or negative selective effects during lengthy evolutionary histories. However, it also seems likely that many allelic variants that influence addiction vulnerability can provide balancing selection; favorable in some individuals or organs or circumstances and unfavorable in other individuals or organs or circumstances. Balancing selection might thus maintain relatively high frequencies of multiple functional allelic variants in the population over long periods of time.

The biology of some genes might allow for common, functional allelic variants that could escape selective pressures or exert balancing selection over many generations. However, other genes might not be able to harbor such allelic variations without engendering selective pressures that would reduce the frequency of one of the allelic variants in the population over time. Common allelic variants that are able to influence addiction vulnerability are thus likely to be restricted to a subset of the genes whose products are involved in addictive processes. An important consequence of this logic follows: if a gene fails to display variants that influence vulnerability to addiction, the gene’s products are not at all excluded from involvement in addiction. On the other hand, convincing data that implicates a gene’s common variants in addiction should prompt us to consider mechanisms whereby such variants might provide balancing (eg both positive and negative) selective influences in the differing environments through which the ancestors of current human populations have passed.

How does this discussion of common disease/common allele hypotheses relate to the postulates of genetic heterogeneity noted above? None of the above discussion about common alleles and common variants precludes (or even reduces the likelihood of) contributions of rarer (or even “private”) allelic variants, including those that have arisen more recently in evolutionary time. Recently-arising variations would be much more likely to persist for a number of generations even in the face of even moderately-negative influences on survival or fertility. Indeed, based on experience with other genetic disorders, it may be worthwhile to actively search for effects of rarer “phenocopy” variants in genes that are initially identified based on common (and evolutionarily older) allelic variants [99]. A rarer copy number variant might contribute to addiction vulnerability by altering levels of expression of a gene that also contains more common allelic variants that alter expression via SNPs in other gene elements, for example [9497]. Such considerations support searches within identified loci for molecular genetic heterogeneity relevant to addiction.

In the analyses presented in this review, we focus on addiction-associated allelic variants that lie in genes. Evolutionarily-old common haplotypes (eg groups of nearby variants that travel together through generations) that lie within genes are among the most likely to be tagged by SNP markers that are represented on current microarrays. Haplotypes that involve genes are thus among the most likely variants to exist in currently-reported datasets. It seems reasonable to postulate that many of these allelic variants that lie within genes provide regulatory variants that alter expression or regulation. Other variants are likely to alter mRNA halflives or mRNA splicing. Variants that alter mRNA splicing could occur at the locus of the affected gene (cis) or at genes at different loci that alter generic mRNA splicing processes (trans). Reproducible association of A2BP1 gene variants with addiction vulnerability, for example [12], provide a good candidate for trans effects on mRNA splicing, since this gene’s product regulates splicing and thus are likely to modify the functions of a number of other genes expressed in brain. It also seems likely that a minority of the addiction-associated variants will involve missense effects on expressed proteins.

It also seems likely that many addiction-associated variants will lie outside of genes, at least as we currently understand them. Loci reproducibly associated with diabetes/body mass, for example, lack conventional hallmarks of “genes”, such as expressed sequences [7]. While the analyses in this review focus on the identification of variants within genes, we should also remain alert for roles for “intergenic” variations in chromosomal regions that lie between currently-understood genes.

II. Samples for genome studies of human addiction vulnerabilities and related phenotypes (Figure 2)

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

A. Sample 1) European-American polysubstance abusers and controls. European-Americans volunteered for research at the NIH IRP (NIDA) in Baltimore, Maryland based on word of mouth referrals and newspaper advertisements. Volunteers self-reported their ethnicities, provided drug use histories and provided DSM (diagnostic and statistical manual) diagnoses of substance use disorders [10, 100, 101]. “Abusers” displayed heavy lifetime use of illegal substances [33] and dependence on at least one illegal substance. “Controls” displayed neither abuse nor dependence on any addictive substance and reported no significant lifetime histories of use of any addictive substance. Individuals with intermediate levels of lifetime substance use without dependence were thus not included in analyses of substance dependence, although a number of them were included among samples studied for cognitive abilities (see Samples 15 and 16, below). “Control” individuals thus combined those with no lifetime experience with any addictive substance with those who had modest to moderate exposures to legal addictive substances.

B. Sample 2) African-American polysubstance abusers and controls: African-Americans who volunteered for research at the NIH IRP (NIDA) were also characterized and separated into “abuser”, “control” and samples with intermediate lifetime uses of substance use, as noted above.

Efficacies of recruiting subjects for association studies (and comparison with recruitment for linkage based studies): Little currently-available data documents the features of drug abuse research volunteers who might consent to participate in molecular genetic studies for linkage or association. We thus describe the details of the recruitment of subjects for these studies at the NIH (IRP) facility in Baltimore, MD during a 30 month period (J H, GRU et al, unpublished observations). During this period, 13,969 individuals were screened by telephone and 2633 were interviewed in person for all (genetic and nongenetic) studies at this research facility. These individuals were 68% African American, 29% European American, 1% Hispanic and 72% male, with average age 35.

613 unrelated proband individuals from the group of interviewed subjects were offered participation in this genetic study, based on the availability of screening resources. No individual who was offered participation during this time period refused to participate. The individuals who accepted participation averaged age 34 and were 72% male, 72% African American, 26% European-American and 2% of other ethnicities, based on self-report. Subjects accepting research participation in this study thus appear representative of this overall research volunteer population in this area. They also share some characteristics of the drug abusing population in Baltimore, based on population trends and 1981 data from the Baltimore Epidemiological Catchment Area (ECA) site that identified 57% of the males and 41% of the females who displayed substance abuse and/or dependence as European-American (J. Anthony, personal communication, 1998).

Each volunteering subject was offered three choices concerning family member contacts. 112 (18%) probands provided Type I consents which allowed investigators to contact their family members. Seventy-three percent of these individuals were male, 66% were African-American, 30% European-American and 3% of other ethnicities. 312 (51%) probands gave Type II consents, stating that they would contact their family members. Seventy-two percent were male, 74% were African-American, 23% European-American and 3% of other ethnicities. 189 (31%) refused family member contacts. Seventy-two percent were male, 74% were African-American, 23% European-American and 3% of other ethnicities.

For 33% of the pedigrees for which the proband had provided Type I consents, at least one member could be reached by telephone or mail. At least one member of 12.5% of the pedigrees for whom the proband had provided Type II consent called an investigator and kept an appointment for study participation.

Of the 79 pedigrees from which family members made and kept appointments for study participation over this 30 month time period, 75% had African American probands and 69% had male probands. The sizes of the potentially-accessible sibships in the pedigrees of these individuals, as described by the probands, were: 1 (22%), 2 (23%), 3 (13%), 4 (11%), 5 (12%), 6 (9%), 7 (7%), 8 (1%), and 11 (2%). The numbers of accessible parents from these pedigrees was 0 (10%), 1 (28%) and 2 (62%). Average families for which more than the proband were accessible thus had 1.5 accessible parents and ca. 3.5 accessible sibs.

Over this 30 month time period, DNA and clinical information were collected from 2/3 of the members of the average pedigree from which any member came for an appointment. More complete sampling was obtained from smaller than from larger pedigrees. Of the 79 pedigrees for which DNA and clinical information were obtained, fifty-four had 2 members, 15 had 3 members, 7 had 4 members and 3 had 5 members. By the end of this period, DNA and clinical information was successfully collected from 2.5 members of the average pedigree. 14 of these pedigrees contained same-gender and 15 opposite-gender sibs within 5 years of the age of the proband who were discordant for drug abuse phenotype. 18 sibs were within 5 years of age of the proband and concordant for substance abuse phenotype. Thirteen sibs were more than 5 years different in age from the probands and discordant for phenotype, while 4 were concordant.

Reliability of pedigree structure reports was assessed by comparing family structure information provided by the proband and another first degree relative informant from these 79 families. Parents agreed (completely) with 100% of the proband’s pedigree assignments, while sibs agreed (completely) with 70% of all possible pedigrees. Disagreements were largely due to differential reporting of half vs full siblings.

Reliability of information about drug histories was assessed by comparing drug use survey (DUS) and family history/research diagnostic criteria (FH/RDC) estimates of drug use by the proband with FH/RDC estimations from first-degree relatives. 73% of parental evaluations agreed (completely) with the probands’ evaluations of all pedigree members’ status with respect to all abused substances. 81% of sib evaluations were concordant (completely) with the proband’s evaluations of his/her own drug use. Differences were most prominent in both cases due to family member underestimation and/or under-reporting of offspring/sib drug use.

As estimate of the validity of drug use survey (DUS) quantity/frequency estimates was obtained by comparing DUS ratings for substances with criteria identified as strongly heritable in work of Tsuang and colleagues [102] “never used, used fewer than 5 times, and used more than 5 times”. All individuals who denied using alcohol, cocaine, heroin, cannabis or nicotine when questioned as part of the addiction severity index screening received “0" scores on the DUS for the appropriate substance. All but two of the individuals who scored 2+ or 3+ on the DUS reported use more than 5 times with a separate instrument on a separate occasion. One individual who reported use of a substance five or fewer times during screening obtained a 2+ and one a 3+ score on the DUS for cannabis. Individuals who reported 1–5 lifetime uses during screening with the addiction severity index received 1+ DUS ratings on several occasions. The fraction of individuals who were rated as 1+ on DUS and who reported use of substances between one and 5 times on a different scale was 7%, 12%, 24%, 32% and 39% for alcohol, nicotine, cocaine, cannabis and heroin, respectively.

Although it is encouraging that no individual who was offered participation in this study refused to participate themselves, only one fifth of the probands agreed, during initial evaluations, to provide permission for research staff to directly contact other members of their families. Individuals who agree to contact their own family members do so at rates substantially lower than the contact rates achieved when family contacts are initiated by research staff members. Such differential participation may well provide occult differences between samples collected for association vs linkage studies.

It may also be important to consider that these subjects consented to studies in which genotyping was performed using pooling approaches that provide maximal protection from research risks (see below). Samples collected in studies which propose to conduct unlimited high density individual genotyping in any number of different laboratories might well experience different consent rates from some subgroups of participants, providing an additional confounding factor.

Genotyping: The primary data reviewed here is based on assessments of allele frequencies in multiple DNA pools that each contained equal amounts of DNA from 20 individuals of the same racial and phenotype group. Each DNA pool was assessed on four sets of four arrays, two from “100k” and two from “500k” Affymetrix microarray sets. We also document some of the features of assessments using “1M SNP” Affymetrix 6.0 arrays.

600k methods used for these samples revealed r = 0.95 correlations between pooled and individual genotyping in extensive validation studies [12] (see below). Much of this variation (1–2%) can be attributed to variations in pipetting and DNA quantification required for pool construction (TD and DW, unpublished observations, 2002–2006). The remainder of the variance is reduced for arrays used at the end of a study, in comparison to validating experiments that use data from some of the first arrays of this type that were studied within this laboratory. The r = 0.95 correlation is thus likely to reflect an upper limit of the variance noted in actual disease vs control comparisons. 1M SNP assays can reveal even higher correlations between individual genotyping and pooled genotyping datasets (Liu et al, in preparation).

Data for each SNP provides a score that provides a continuous measure of the percentage of its two alleles in the DNAs from the hetero- and homozygous individuals represented in each pool. For SNPs that displayed nominally-significant differences between addicted and control individuals in 600k studies, there were r ≥ 0.9 correlation between the magnitude of the differences between pooled vs individual genotyping approaches. Variance from array-to-array (assessing the same DNA pool) and variance from pool-to-pool were both modest, ca 3%, supported good validity of this pooling data. Results from 1M SNP assays provide even more modest pool to pool and array to array variances (Liu et al, in preparation).

C. Sample 3) European-American alcohol dependent and control [33] Unrelated individuals sampled from pedigrees collected by the Collaborative Study on the Genetics of Alcoholism provide an interesting sample for several reasons. Dependence on alcohol and other substances has been carefully characterized in these individuals using validated instruments. Unrelated control individuals free from substance abuse or dependence diagnoses, largely individuals who marry into these pedigrees, are available. We thus identified 120 unrelated alcohol dependent individuals and 160 unrelated unaffected controls with self-reported European-American ethnicities.

Genotyping: Allele frequencies were assessed in 14 DNA pools, each containing equal amounts of DNA from 20 individuals of the same phenotype group, using four sets of four arrays, two from “100k” and two from “500k” Affymetrix SNP arrays, using approaches that were extensively validated as noted above.

D. Sample 4) Taiwanese methamphetamine-dependent vs control: Unrelated subjects recruited in Taipei included 140 methamphetamine-dependent individuals diagnosed independently by each of two psychiatrists using DSM IV criteria [103108] and 240 matched Han Chinese controls who denied any history of use of illegal drugs and denied any histories of psychotic symptoms. Subjects were 30% female and averaged 32.5 +/− 10 years of age. Dependent individuals reported methamphetamine use > 20 times/year, or described well-documented methamphetamine psychosis with lower levels of regular use. They denied histories of psychosis either prior to methamphetamine use or in relation to other psychedelic drugs. Most reported use of at least one other addictive substance. Controls denied illegal drug use or psychotic symptoms and were matched for gender and age.

E. Sample 5) JGIDA Japanese methamphetamine-dependent vs control: Japanese subjects were 21% female and averaged 40 years of age. One hundred methamphetamine dependent subjects were in- or outpatients of psychiatric hospitals in these regions that participate in the Japanese Genetics Initiative for Drug Abuse (JGIDA) [12, 104116] and met ICD-10-DCR criteria F15.2 and F15.5 for methamphetamine dependence in independent diagnoses made by each of two trained psychiatrists based on interviews and review of records. Ninety one percent revealed histories of methamphetamine psychosis, 89% used methamphetamine intravenously, 62% also abused organic solvents and most abused at least one other substance. Subjects who displayed clinical diagnoses of schizophrenia, other psychotic disorders, or organic mental syndromes were excluded. Controls were one hundred age-, gender-, and geographically-matched staff recruited at the same institutions who denied use of any illegal substance, abuse or dependence on any legal substance, any psychotic psychiatric illness, or any family history of substance dependence or psychotic psychiatric illness during interviews with trained psychiatrists.

Genotyping: We assessed allele frequencies for methamphetamine-dependent and control subjects in DNA pools, each containing equal amounts of DNA from 20 individuals of the same phenotype group, on four sets of arrays, two arrays from “100k” and two from “500k” Affymetrix sets (Sample 5) or two arrays from “500k” Affymetrix sets (Sample 4).

F. Sample 6) Australian and US dependent vs nondependent smokers of European ancestry [3].

Dependent smokers of European ancestry were diagnosed using Fagerström Test for Nicotine Dependence (FTND score ≥ 4) criteria and compared to smokers did not display FTND dependence (scores = 0) [3]. About 1/4 of the individuals who displayed “dependence” by FTND standards did not display DSM IV nicotine dependence. About ¼ of the individuals used in this study as “controls” did display DSM nicotine dependence.

Genotyping: Allele frequency data was assessed in 16 DNA pools, each containing equal amounts of DNA from 60 individuals using a single set of 49 arrays that assessed 2,427,354 SNPs. Methods used for these samples revealed r = 0.85 overall correlations between pooled and individual genotyping for all SNPs. However, there were much more modest, r = 0.58 correlations between dependent vs nondependent data derived from pooled vs individual genotyping for the SNPs that displayed nominally-significant differences. Individual genotyping followed up nominally-positive results for 39,213 SNPs in 1,050 dependent and 879 nondependent smokers. The convergence analyses presented here use data from the subset of these 39,213 SNPs that lie within genes.

G. Sample 7) WTCCC bipolar disease vs control: Genome wide association for bipolar disorder compared controls to 1868 UK individuals of European descent with bipolar disorders [1]. Bipolar mood disorders were diagnosed using Research Diagnostic Criteria. Uncharacterized control samples include: a) 1480 individuals from a 1958 Birth Cohort sample b) 1458 individuals from a United Kingdom Blood Service sample of consenting blood donors and c) individuals with disease phenotypes whose genetics was deemed unlikely to overlap with the genetics of bipolar disorder [1].

Genotyping for 436,604 autosomal SNPs analyzed was performed using Afymetrix 500k arrays with allele calls made by a CHIAMO algorithm with a ≥ 0.9 a posteriori probability threshold [1]. 436,604 of the 469,557 SNPs assessed could be assigned confident chromosomal localizations. A p value for each SNP was determined based on its χ2 test for significance of allele frequency differences in bipolar vs control (actually control plus other disease) subjects [1].

H. Sample 8) NIMH bipolar vs control: Genome wide association was assessed in controls compared to 461 unrelated bipolar I probands of self-reported European-American ancestry who were selected from families which included at least one affected sibling pair who participated in the NIMH Genetics Initiative ( [2]. Probands were assigned a ‘confident’ diagnosis of DSM-IV bipolar I disorder by each of two trained clinicians. 563 unrelated control individuals of European-American ancestry who failed to display evidence for DSM-IV criteria for major depression, any history of bipolar disorder or any history of psychosis were recruited by a marketing firm.

I. Sample 9) German bipolar vs control: 536,288 autosomal SNP genome-wide association was assessed in controls compared with 772 bipolar I patients diagnosed using DSM-IV criteria who were recruited from consecutive hospital admissions [2]. 876 population-based controls were randomly recruited; individuals with histories of affective disorder or schizophrenia were excluded.

For genotyping, NIMH samples (Sample 8) were divided into seven bipolar and nine control pools of 50–80 subjects/pool. German samples (Sample 9) were divided into 13 bipolar and 10 control pools of 42–60 subjects/ pool. SNP allelic distributions were assessed using duplicate Illumina HumanHap550 assays (Illumina Inc., La Jolla, CA, USA) [2]. Normalized allele frequencies were calculated from raw intensity data averaged across duplicate pools to obtain a relative allele frequency estimate for each SNP in each pool. SNP with allele frequencies that displayed > 2% variance between replicate pools were excluded. t tests compared pool-to-pool variation within phenotypes to phenotype-to-phenotype differences.

J. Sample 10) Unrelated members of NHLBI twin pairs for assessment of frontal brain volume: 242 unrelated individuals were selected randomly from members of twin pairs from a population-based registry of European-American male World War II veteran twin pairs who received volumetric MRI studies as part of the National Heart, Lung, and Blood Institute (NHLBI) Twin Study. Studies were performed when subjects averaged 72.6 years of age and reported 13.6 years of education. Frontal lobar volumes, corrected for intracranial volumes, were obtained [117] in ways that produce inter-rater reliabilities > 0.90.

For genotyping, DNAs were carefully quantitated and combined into 12 pools that each represented about 20 subjects, based on estimates for frontal brain corrected for total cranial volume. We thus constructed 4 DNA pools from individuals with the lowest, 4 pools from individuals with intermediate, and 4 pools from individuals with the highest estimates of total frontal brain volumes and subjected these DNA pools to Affymetrix 500k genotyping as noted above. t tests compared differences between highest and lowest brain volume groups. SNPs that displayed nominally-significant t values and that also displayed rank order of allelic frequencies with either highest tercile > intermediate tercile > lowest tercile or the converse are included in these analyses.

K. Sample 11) Framingham study participants for assessment of frontal brain volume: Subjects were 705 stroke- and dementia-free participants in the Framingham study, age 62 +/− 9 yrs, 50% male, who received volumetric brain MRI studies that were analyzed as noted above for the NHLBI subjects.

Genotyping provided data from 70,987 autosomal SNPs using Affymetrix 100K arrays. Allele frequencies for SNPs that displayed a) minor allele frequencies ≥ 0.10, b) genotype success ≥ 0.80, and c) Hardy-Weinberg equilibrium p ≥ 0.001 were used. A generalized estimating equation provided corrections for familial relatedness and other covariates [8].

L. Sample 12) European American smokers who successfully vs unsuccessfully quit smoking in trials in Philadelphia, Washington DC and Buffalo, New York responded to advertising and physician referrals for help in quitting smoking [118, 119]. Subjects aged 18 – 65 enrolled in a randomized clinical trials for smoking cessation accompanied by standardized behavioral counseling that utilized a blinded, placebo controlled trial of bupropion 300 mg/d or matching placebo × 10 weeks or an open label trial of nicotine nasal spray versus nicotine patch × 8 weeks [118]. 126 individuals with biochemically-confirmed abstinence for at least the 7 days prior to both 8 week and 24 week assessments were contrasted with 140 unsuccessful quitters who were not abstinent at either time point.

M. Sample 13) European American smokers who successfully vs unsuccessfully quit smoking in trials in North Carolina: Participants received either active 21 mg/day or placebo nicotine skin patches for two weeks before the targeted quit date as well as mecamylamine 10 mg/day po prior to the target quit-smoking date [120]. After the quit-date, participants were randomly assigned to mecamylamine 10 mg/day vs matching placebo and 21 mg/24 h vs 42 mg/24 h nicotine skin patch doses. Fifty-five individuals reported continuous abstinence from smoking when assessed 6 weeks after the quit date with biochemical confirmation; 79 were not abstinent.

N. Sample 14) European American smokers who successfully vs unsuccessfully quit smoking in trials in Rhode Island: Participants engaged in a ten week, double-blind placebo controlled trial of placebo or bupropion (150 mg/day for the first 3 days, then 300 mg/day) with a target quit date one week following initiation of drug or placebo [119]. Sixty individuals with biochemically-confirmed abstinence for at least the 7 days prior to both the end of treatment and 24 week assessments were contrasted with 90 unsuccessful quitters who were not abstinent at either time point.

Genotyping for samples 12–14 used Affymetrix 500k arrays and multiple pools of DNAs (n = 16 to 20), as noted above.

O. Sample 15) African American individuals (ages 18 – 65) with different levels of general cognitive ability as assessed by the Shipley Institute of Living scale. Research volunteers with a variety of different levels of lifetime use of addictive substances volunteered for research protocols at the NIH (NIDA) facility in Baltimore, Maryland, as described above. Eighteen pools were constructed with DNA from 20 individuals each (33% female, average age 32.1). Mean cognitive function scores estimated from Shipley Institute of Living scales ranged from “IQ” equivalents of 75.9 to 109.2 for the individuals in these DNA pools.

P. Sample 16) African American individuals (ages 18 – 65) with different levels of general cognitive ability as assessed by the Shipley Institute of Living scale. Eleven pools were constructed with DNA from 16 individuals each (34% female, average age 31.8). Mean estimated “IQ” scores ranged from 79.1 to 112.3 for the individuals represented in these DNA pools.

Genotyping for samples 15–16 used Affymetrix 500k arrays and pools of DNAs (n = 16 to 20), as noted above. The nominal significance of the correlations between pool-to-pool differences in assessments of allele frequency and pool-to-pool differences in Shipley scores was assessed for each SNP.

Q. Sample 17) Alzheimer’s disease vs control: brain donors: Subjects were 1086 brain donors of mean age 82 who were 43% male and at least 65 years of age at death. Brains and clinical data met pathological criteria for Alzheimer’s disease or control status. DNAs were subjected to Affymetrix 500k genotyping. From the files TGEN_WGA_ DATA_ recode_ped.txt [4], genotype calls for 552 control and 859 AD individuals allowed us to calculate χ2 values for the AD vs control differences for each SNP, select SNPs which displayed χ2 values with p < 0.05 as nominally-positive and assess which of these nominally-positive SNPs fell into chromosomal clusters such that at least 3 nominally-positive SNPs that represent both array types lie ≤ 25 kb from each other.

R. Sample 18) Alzheimer’s disease vs control: memory clinic participants: Seven hundred fifty-three AD cases and 736 controls with European ancestry were recruited in Canadian memory clinics. Probable Alzheimer’s disease was diagnosed by clinical criteria, and controls selected who displayed no histories of memory impairment or any impairment on neuropsychological tests [5]. DNAs were subjected to Affymetrix 500k genotyping. From files in, p values for each SNP, derived from Fisher’s exact tests, were extracted and data was analyzed as described above.

S. Sample 19) Individuals with scores on tests of neuroticism 1038 individuals from southwestern England sites with European backgrounds and with high neuroticism (“N”) scores on the revised Eysenck Personality Questionnaire and 1016 individuals with low neuroticism scores (63% female) were studied along with a replication sample of 831 high vs 702 low neuroticism individuals (61% female) [121]. Genotyping of eight pools of DNA from mouth swabs compared: (1) men with high N scores (n = 112), (2) men with low N score (n = 158), (3) men with very high N score (n = 245), (4) men with very low N score (n = 238), (5) women with high N score (n = 320), (6) women with low N score (n = 205), (7) women with very N high score (n = 340) and (8) women with very N low score (n = 436). Very high or low N scores were defined as more than 1.5 s.d. from the mean score adjusted to age and sex (on average 2 s.d.), while high and low N scores were between 1 and 1.5 s.d. from the mean score (on average 1.3 s.d.). Data for relative allele score from the 452 574 SNPs from 100 and 500k Affymetrix arrays with minor allele frequencies above 5% were obtained from five replicate arrays used to assess each sample.

III. Selected Methodological Issues

A. Genotyping using pooled DNAs

Gentyping for genome wide association studies can be performed in either individual samples or in pools of DNAs from individuals with the same racial/ethnic background and the same phenotype. Pooling strategies have several advantages. Pooling fits well with association genetics, can efficiently perform allele typing, preserve confidentiality and reduce costs [10, 122124].

Not all pooling strategies are alike. “Single-pool” strategies seek differences in allele frequencies in comparing data between a single pool of DNA from diseased individuals vs a single pool of DNA from control individuals. Such results generate hypotheses. However, such designs, and related designs with very few pools, provide little ability to differentiate between: 1) the variability between disease vs control samples with 2) the variability within disease or within control samples.

We focus here instead on “multiple pool” strategies. With careful attention to a large number of small details, these approaches can provide accurate allele typing. Multiple pool approaches provide estimates of a) differences between disease vs control samples, b) variability within disease samples and c) variability within control samples. Assessment of the differences between disease and control in the context of assessments of the variability within disease samples and within control samples allows us to use standard statistical approaches to assess the significance of the results.

Here, we provide assessment of several of the steps necessary to validate and characterize features of the power and sensitivity of multipool genome scanning strategies. Used with high densities of genomic markers, carefully-performed multiple pool studies can 1) provide increased study feasibility and 2) preserve virtually absolute genetic confidentiality with 3) only modest effects on the sensitivity and specificity of genome wide association.

1. DNA quality, quantity and contamination: Care in assessing and maintaining the quantity and quality of DNA in every sample is crucial for pooling studies. Rough DNA quantitation procedures that are routinely used in most genotyping laboratories are likely to introduce such substantial errors that many of the apparent disease/control differences will actually arise from occult over- or under-representation of genotypes of individuals with misquantitated DNAs. Uneven DNA quality can provide the same selective over- and under-representation of genotypes of selected individuals in each pool, leading to more false positives and less sensitivity for detection of true positive results. Contamination of pooled DNA samples with even a modest amount of DNA from laboratory personnel or other sources can also provide difficulties for pooling procedures.

2. Numbers of individuals per pool and numbers of pools: To obtain maximal benefits from multiple pool genome wide association: 1) The numbers of individuals in each pool should be sufficient that even sophisticated analyses of pooled data cannot reconstruct individual identities or genotypes. Treatments of this subject suggest that pools need to contain more than 4–5 individuals for maximal confidentiality protection [125]. 2) The numbers of individuals in each pool should allow significant cost/time savings compared to individual genotyping. 3) The numbers of pools should be sufficient to provide good estimates of pool-to-pool variability, which can then be used to compare to the differences between disease and control individuals using standard statistical tests.

Multiple genotyping assessments of each DNA pool can aid the precision of estimates of pool-to-pool variability as well. We have used three to four microarrays to assess DNAs from each pool. These numbers are based on preliminary studies that seek to optimize estimates of “true” relative allele frequencies at acceptable cost. We construct each pool using DNAs from 20 individuals of the same self-reported ethnicity and same disease or “control” phenotype. Thus, we obtain results in quadruplicate at 1/5 – 1/7 the reagent costs of individual genotyping using a single array set per person.

3. Number of different replicate samples: Power calculations assess the likelihood that an experiment can detect a difference of a certain magnitude in a specific SNP. Experiments require reasonable levels of protection against false-positive results, α. They also require reasonable power, β. Genome wide association requires many repeated measures; considerations of α and β thus need to be applied to hundreds of thousands or millions of SNPs.

One approach to the dilemma raised by these large numbers of multiple comparisons has been to propose single studies with larger and larger “n”. However, accretion of very large samples is expensive. Attempts to assemble large samples from smaller subsamples collected at various sites also run greater and greater risks of incorporating increasing numbers of occult heterogenities that could provide confounding influences on the results obtained [126].

The approaches that we outline here rely on initial use of achievable sample sizes that may be more likely to be more homogeneous. Initial samples can nominate sets of SNP markers, genomic regions and genes that can be studied in additional independent replicate samples. A requirement that genes display SNPs whose allelic frequencies distinguish disease from control individuals in multiple samples is one of the few assurances against false-positive results that is likely to pass ultimate statistical muster and also to yield feasible experimental designs. There is a downside to this approach: rates of false negative results are also unavoidably elevated by requirements for replication (see below).

4. Validation studies assess the fits between individual and pooled genotyping in a number of different ways. These include assessments of the concordance between results from sense and antisense probes for the same SNP and concordance between results for the same SNPs obtained using arrays of different types. The same DNAs can be pooled multiple times, and the same pool can be analyzed multiple times, in order to further assess concordance.

We focus here on a core validating test for pooling. This core test comes from analyses of the relationships between a) “observed allele ratios”, background-subtracted, normalized hybridization intensity ratio values obtained from different pools of DNAs and b) “expected allele ratios” the fraction of eg. “A” and “B” alleles obtained from individual genotypes. We and others have compared allelic determinations from individual DNAs vs results from pools with equal of differing amounts of DNA from small numbers or larger numbers of control individuals in use of HuSNP, Affymetrix 10k, 100k, 500k and 1million SNP products, Perlegen arrays and Illumina 300 and 500k arrays [11, 33, 39, 41, 42, 122, 123, 127].

Using 500k Affymetrix arrays, we have evaluated pooling using equal and varying amounts of DNA from CEPH individuals. Overall data for 150,000 SNPs from these comparisons produces correlations between pooled and individually-determined genotypes of r2 = 0.95 (Figure 3). These overall results derive from studies of 5475 and 6230 informative Nsp I and Sty I SNPs in experiments in which equal amounts of DNA from homozygotes were mixed (correlation ca 0.9); 10,032 informative Nsp I and 10,249 informative Sty I SNPs in experiments in which these same DNAs were mixed in 1:1, 1:5, and 1:15 ratios (correlations 0.96 and 0.98, respectively) and 31,201 informative Nsp I and 39,827 Sty I SNPs for studies of one homozygote and one heterozygote (correlations 0.89 and 0.92, respectively). When we compare these results with those reported using long range PCR products, using the reported procedure of eliminating the 9% of SNPs that yielded the more problematic correlations [127], the overall correlations for the remaining SNPs is 0.98. Correlations using 1M Affymetrix SNP arrays are at least as strong, with r2 > 0.98.

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

B. Power assessments

Approaches to assessing power of genome wide association have used a variety of assumptions about the frequencies of disease-causing alleles, the heterogeneity and penetrance of disease-causing alleles, marker frequencies, and the nature and distribution of linkage disequilibrium across the genomic intervals surveyed [128, 129]. Many approaches to this problem use linkage disequilibrium distributions identified in HapMap samples, even though these HapMap individuals represent only very small subsets of several current human populations.

We have been impressed by the variability in the detailed distribution of linkage disequilibrium across different genomic loci in different samples [130, 131]. We have also been impressed by the possibilities that approximations of this variability could be modeled, on average, by simple functions. Under these circumstances, estimates of effects of sample size, locus-specific effect sizes for the underlying functional alleles, genetic heterogeneity, penetrance and marker density can produce reasonable models that can allow assessments of the effects of variation in these parameters on power.

We have focused on diallelic markers and disease/no disease phenotypes. We have developed a model to simulate the effects of varying these parameters that has resulted in a program “Gene Detective”. We can use this approach (see Supplement for details) to simulate the ca 620,000 diallelic markers reported to date for samples with n = 400 case and n = 400 controls with nominal 0.05 α levels (Figure 4). We can observe effects of sample size, heterogeneity/penetrance ratios, marker minor allele frequencies and disease frequencies. Such effects are each relatively modest over a reasonable range of values for genome-wide distributions of linkage disequilibrium. However, there is a 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 4
Power of genome wide association as assessed using Gene Detective

Increments in marker density from 630,000 to 1,000,000 and increases in sample sizes from n = 400 to n = 2,000 samples in case and control improve power (Figure 5). However, the steep relationships between power and effect sizes are also found in these simulations. Such results underscore the distinctions that we have made above concerning analytic approaches to “oligogenic” disorders in which variants at individual gene loci produce relatively large differences in risk vs “polygenic” disorders in which the effects at each locus are likely to be modest.

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

These power calculations apply only to the initial genome scans. As we note above, replicate studies aid in distinguishing false- from true positive associations, but also increase the cumulative number of false-negative results.

Limits on the precision of the power calculations that derive from the approach outlined here include limits of the precision of estimation of the parameters whose estimation is required. Several of these parameters can be estimated based on substantial empirical data. These include the size of the genome or genomic segment under consideration, the sizes of the samples of disease and nondisease control subjects studied and the value for α desired. The frequency P [D] of the disease in the population under study is available from epidemiological studies. It is thus important that the samples for association genome scanning display characteristics similar to those of the populations in which disease probabilities were determined, so that estimates of P [D] are as accurate as possible.

To assess the statistical power of analysis, we have also used more standard power calculations. The program PS v2.1.31 [132] a) α = 0.05, b) sample sizes equal to the numbers of pools from the current dataset, c) mean abuser/control differences of 0.05 and 0.1 and d) standard deviations from the SNPs that provided the largest differences between control and abuser population are used in several of the discussions below. We have also used data from the Genetic Power Calculator ( for some analyses.

C. Analyses

1. Achieving significant genome wide association in single samples vs seeking replication and generalization in multiple samples

a) Single sample approaches: As noted above, genome wide association gains power to detect variants in more and more of the genome as more and more genetic markers, generally SNPs and/or copy number variants, are assayed. Since many hundreds of thousands of SNPs and/or copy number variants are assayed in current datasets, stringent approaches to correct for the large number of multiple comparisons are needed.

There is no clearcut consensus about any single method that will produce only true results from any single sample. One approach to concerns about the large numbers of comparisons that are key components of GWA focuses on achieving “genome wide” significance in single samples. Single samples that demonstrate genome wide significance in this way must contain single SNPs whose association displays a striking nominal p value, often in the neighborhood of ca 10−8 [133, 134]. Such results may be the most likely to be published in prominent journals. However, in most studies with findings of this statistical magnitude, effects of variants at a single locus are sufficiently large that linkage studies also provided significant evidence at the same chromosomal locus [135, 136]. For “oligogenic” contributions to common, complex disorders, seeking association with “genome wide” significance in single samples thus provides a reasonable approach. When there is a large effect of a single gene, a number of corrections for multiple comparisons can be applied without creating many false negatives. Bonferroni corrections for multiple comparisons are advocated by some investigators, though they are generally agreed to provide a conservative correction [137, 138]. False discovery rate corrections can also be applied [139141]. Permutation and Monte Carlo tests provide additional approaches [6, 142, 143].

As the expected effect of each locus falls from the large effects characteristic of oligogenic influences to the small effects that characterize polygenic influences, however, the sample sizes needed to generate p values in these ranges provide a daunting problem. Costs of individually genotyping such large samples become limiting in all but the best-supported enterprises [144]. The risks of introducing occult heterogeneities increase when subsamples are collected at a variety of distinct sites [126]. As more occult heterogeneities are included as disease and control samples need to be assembled from more and more diverse sources to achieve sufficient “n”, more and more of the results obtained may well represent “false positives” based on such occult sample heterogeneity for genetic background or for heritable traits that are not (nominally) being studied.

b) Replicate sample approaches: Here we use an alternative analytic approach that focuses on step-wise assessments. These step wise analyses address the problem of multiple testing by seeking nominally-significant results that can be replicated in several independent samples. We can assess the significance of these replicated, nominally-significant results through use of Monte Carlo methods that correct for multiple comparisons.

It is important to emphasize the ways in which the step wise analyses presented here a) first identify evidence for genes that contain haplotypes that are found at different frequencies in single disease vs control sample comparisons and b) then identify evidence for genes that display haplotypes with such differing frequencies in multiple different samples that results are unlikely to be due to chance.

We thus a) first identify nominally-significant SNPs in each sample, 2) identify the clustering of such SNPs (within small chromosomal regions) in each sample, 3) seek replication, identifying small genomic areas in which clusters from multiple replicate samples from the same phenotype also identify clustered nominally-significant SNPs 4) seek generalization, identifying genes that contain clusters of nominally-positive SNPs from studies of related, genetically determined phenotypes.

The criterion used here identifies clustering based on chromosomal position. This approach allows direct comparison between datasets that assess different sets of SNPs in samples that may well differ in the details of their patterns of linkage disequilibrium. The Monte Carlo simulation methods used here do not make assumptions about the underlying distribution of the data assessed. Monte Carlo methods provide empirical p values based on repeated random samples from the actual datasets analyzed. Such approaches are especially useful when we seek to assess the significance of apparently-reproducible results from convergent data from multiple independent datasets which differ from each other in “n”, number and types of genomic markers, racial/ethnic background of the subjects and other key features. No alternative method of which we are aware provides as tractable a method for assessing the significance of results obtained in multiple samples without assumptions about underlying distributions of the data as do Monte Carlo approaches. We use 10,000 Monte Carlo trials in circumstances in which moderately-high significance is anticipated, and 100,000 trials in circumstances in which extremely-high significance is anticipated.

This approach seeks to identify genes with variants that are likely to play roles in addiction and in related phenotypes. This approach allows for “locus heterogeneity”, and thus does not use the more stringent criterion that the same SNP is required to display nominal significance in each of the samples in which association data is said to support association at a specific gene locus. This approach allows for differences in phase of association, and thus does not use the more stringent criterion that the same allele of the SNP (or haplotype) must be associated with nominal significance in each of the samples in which association data is said to support association at a specific gene locus. The approach allows for different details of the patterns of linkage disequilibrium between marker and functional haplotype from sample to sample. The approach allows us to combine datasets in which different marker sets are used. With each of these limitations, it is clear that subsequent followup analyses are required. Analyses in the same and in additional independent samples are required to untangle any locus heterogeneity, to unequivocally identify which individual SNPs are associated, to identify pathological haplotypes and the phases with which they are associated with phenotypes in samples from different racial and ethnic backgrounds. While we describe examples of such follow-up studies for the NrCAM and NRXN3 genes below, it is important to note the limited numbers of genes for which such confirmatory follow-up data is available. In many circumstances, we believe that this sort of follow-up requires molecular biologic, behavioral and other evidence to buttress the data that comes from association genetics alone.

2. Stepwise approaches to analyses

a) Determination of nominally-significant markers Nominal p values that come from “t” (for pooled data), “χ2” (for individual genotype frequencies) or “ρ” (for correlational approaches) statistics delineate the nominal significance of the differences between disease and control groups for each SNP. For pooled assessments, proper definition of the pool to pool variability is crucial for proper assignment of the appropriate nominal t value. However, the continuous resuslts that come from pooled datasets do provide the additional statistical power characteristic of statistics based on continuous measures.

b) Identifying chromosomal clusters of nominally significant markers in single samples: We focus on the SNPs whose chromosomal positions can be accurately determined. Since gender ratios differ substantially in many of these datasets, we omit data from sex chromosomes for most of these samples.

We focus on clusters of nominally-positive autosomal SNPs that lie within 100 or 25 kb of each other, depending on the density of markers available; the latter figure is closer to the average “haplotype block” length in the samples studied here [11, 12, 33, 34, 36, 37]. To use a valuable technical control that is possible with Affymetrix 500k reagents, we require that SNPs in each cluster come from both Sty I and Nsp I array types where possible [12]. In assessment of the data from each sample set, these criteria thus provide some assurance that haplotypes do occur at differing frequencies in disease vs controls. These criteria provide significant technical controls, based on requirements that multiple nearby SNPs must display positive results and that these positive results must come from two array types.

It is important to note that, if stochastic events produce a nominally “significant” association at a given SNP in a single sample, linkage disequilibrium with nearby SNPs might provide “cluster” of several SNPs with nominal significance in this single sample on stochastic grounds alone. Control for the possibility that these differences in haplotype frequencies are due to stochastic differences between samples thus awaits the next analytic step (c, below).

We test the nonrandomness of clustering of nominally-significant SNPs using Monte Carlo simulations. We can also use these approaches to identify the nonrandomness of clustering within genes. For each simulation trial, a random set of SNPs from the database that contains the results from these studies is subjected to the same analytic procedures that had been used for the actual data analysis. The number of trials for which the results from the randomly-selected set of SNPs match or exceeded the results actually observed from the SNPs identified in the current study is tabulated. Empirical p values are calculated by dividing the number of trials for which the observed results are matched or exceeded by the total number of Monte Carlo simulation trials performed. This method examines the properties of the actual SNPs contained in each dataset. It is therefore relatively robust despite the uneven distributions of SNP markers across the genome, differences in linkage disequilibrium across the genome in different samples and the different SNPs genotyped using different assays.

c) Identifying the clustered, nominally positive SNPs within the strongest positive support from several “replication” datasets: We next seek convergence between data from several “replicate” samples. We focus on samples that test the same underlying hypothesis (eg that common allelic variants contribute to genetic components of vulnerability to develop substance dependence). Some of these samples and their matched controls differ from each other on other bases (eg racial/ethnic background, primary substances of abuse). We thus use “replication” here in a restricted sense. Such a restricted use of “replication” allows us to reserve use of the term “generalization” to denote identification of genes whose pleiotropic influences are evident in studies of other heritable phenotypes that often co-occur with addictions (see below). Obviously, there is also an aspect of “generalization” when comparing data from a) polysubstance dependent vs control samples collected from individuals of two racial/ethnic backgrounds with b) methamphetamine- dependent vs control samples collected from individuals with a third racial/ethnic background (see below).

Analyses focus on genes that are identified by clustered positive results from several samples. This approach, rather than focus on individual SNPs whose informativeness might differ in different samples, allows for some degree of genetic heterogeneity and for some sample-to-sample differences in the detailed patterns of linkage disequilibrium.

Clustering of positive results in the same gene in each of several independent samples is much less likely to represent purely stochastic effects than observations made in any single sample. Such clustering in multiple samples is more likely to reflect true differences related to the phenotype of interest, eg dependence on addictive substances. However, it is important to emphasize again that these criteria are aimed at identification of genes, rather than precise definition of exact disease-associated haplotypes. We thus allow the phase of association to differ between samples at this level of analysis. Detailed studies of the phase of association can provide a very valuable fine mapping tool to allow identification of the exact pathogenic haplotype [131].

d) Identifying the clustered, nominally positive SNPs within the strongest positive support from several “generalization” datasets: To seek possible generalization of results, we have sought chromosomal locations where the clustered positive data from several substance-dependence genome wide association samples lies near clustered, nominally-positive (and reproducibly positive) results from studies from other “related, heritable” phenotypes.

Baysian approaches to these analyses suggest that the stronger the evidence for coheritabilities of substance dependence and these “related” phenotypes, the higher the likelihood that molecular genetic studies will demonstrate true overlaps [145]. We focus on phenotypes that display good evidence for heritability from classical genetic studies, including evidence that complex genetics plays substantial etiologic roles. We focus first on heritable phenotypes that co-occur with addictions at frequencies much greater than those that we would expect if they were independent of each other. For example, even though substance dependence and bipolar disorder are each common, the product of their population frequencies does not nearly explain the ca. 2/3 of bipolar individuals who report abuse of or dependence on an addictive substance [146, 147].

Twin data that compares co-occurance frequencies in monozygotic vs dizygotic twin pairs provides evidence for shared heritability for some of these phenotypes. For other phenotypes, the magnitude of genetic influences and the frequency of co-occurance with substance dependence each indicate the likelihood of pleiotropic influences of some of the same allelic variants on both phenotypes. Finally, we also present here the idea that “transitive” genetic approaches may also identify evidence for generalization of effects of some pleiotropic influences. If substance dependence and intermediate heritable phenotypes share genetic overlap and co-occur, then a third heritable phenotype that is documented to co-occur with the intermediate phenotype might also share substantial heritability with addiction vulnerability.

Examples of heritable phenotypes for which twin data document shared genetic determinants include frontal lobe brain volume and cognitive abilities [15, 117]. Examples of heritable phenotypes for which co-occurance makes shared genetics highly likely, a priori, include substance dependence and bipolar disorder [148, 149]. A possible “transitive” genetics comes from the shared genetics of substance dependence and cognitive abilities/brain volume on the one hand, and the likely shared genetics of cognitive ability/brain volume and vulnerability to Alzheimer’s disease, on the other hand. Data from cognitive function and frontal brain volume genetics thus provide potential intermediate phenotypes to link the genetics of addiction with that of Alzheimer’s disease. Such links might or might not have been anticipated, based on equivocal evidence from current epidemiologic studies [150].

As we seek to document the extent of “generalizion” of effects of alleles that were initially identified in studies of addiction, we test the null hypothesis that clustered positive results from the genome wide association data from addiction vulnerability do not converge with the chromosomal positions of clustered nominally-positive SNPs in comparisons of other phenotypes, eg bipolar vs control samples. Monte Carlo simulations that test this null hypothesis sample from data within the SNP datasets noted above. 100,000 trials allow estimates of the significance of the generalization of the effects of the alleles identified in studies of addiction vulnerability, as noted above.

e) Controls for the alternative possibilities that results could come from occult racial/ethnic stratification or assay noise. Several alternative hypotheses might explain observed results. To test some of these alternative hypotheses, we compare the clustered- positive SNPs from different samples with SNPs that display the largest allele frequency differences in appropriate control datasets. These comparison datasets include those that contrast allele frequencies in 1) European-American vs African American control individuals from NIDA samples [12]; 2) Japanese vs Han Chinese individuals from HapMap samples (JPT: Japanese from Tokyo and HCB: Han Chinese from Beijing), 3) control individuals sampled in different portions of the United Kingdom [1], and 4) SNPs that display the largest variances from array to array [12]. We can thus compare data from the true comparisons in our experiments to similarly-analyzed data from samples that test alternative hypotheses, providing substantial additional control evidence.

f) Results from alternative approaches: principal components (PCA) and hierarchical clustering. Several alternative approaches to analyses of genome wide association datasets can also provide interesting results that assess the structure of the pool-to-pool variance, based on data from 500 or 600k SNP sets. Principal components analyses (PCA) of the pool-to-pool differences in 500k data from sample sets from European-American, African-American and Asian samples divides the data along these racial/ethnic lines, as we might expect. However, PCA analyses also subdivide data from experiments studying two distinct United States samples of nominally-equivalent genetic background, Sample 1 NIDA European-American samples recruited in Baltimore, Maryland vs Sample 3 COGA European-American samples recruited in St Louis, Bronx New York, San Diego, California and other sites. Similarly, these PCA analyses separate the Asian samples recruited in Japan from those recruited in or near Taipei that are self-characterized as Han Chinese. Each of these results underscores the need for extremely careful matching of the racial/ethnic backgrounds of control and disease samples.

Hierarchical clustering is most conveniently limited to data from individual genes. Large 500k SNP datasets provide substantial limitations based on computer time. When we examine data from several genes using this approach in pools from a single racial/ethnic background, we can identify relatively clear patterns of separation of data from pools containing substance dependent individuals from pools containing control individuals. These hierarchical clustering approaches are independent of the principal analyses noted above. These results reassure us that modest association signals can be identified in many of these addiction-associated genes using a variety of different statistical approaches.

D. Ethical issues in high density genotyping of individuals who are selected due to self-reported illegal behaviors

Individuals who are individually genotyped in relationship to addiction and related phenotypes are subject to a number of potential risks. Some of these risks are shared with individuals who are subjected to high density genotyping in relationship to other disorders and phenotypes. Other risks are more likely to come to the fore in studies of illegal behaviors.

Concerns relating to insurability, employability, paternity determination and providing (or not providing) genotyped individuals with access to their genotypes and/or genetic counseling are shared by individuals with other complex disorders [151153]. Pending legislation in the United States may mitigate several of these concerns, and they are reviewed elsewhere. We therefore will not consider these issues further here.

High density, individual genotyping of DNA from individuals who are addicted to illegal substances raises additional issues. Many of these individuals are likely to have experienced involvement in criminal activities that goes beyond use of illegal substances. Since the risks of high density individual genotyping in this population have not been as generally discussed elsewhere, we provide several lines of information that may inform thinking about thse special ethical issues.

Increasingly-ubiquitous DNA testing related to criminal activities lies at the heart of these concerns. In the United States, each state has a DNA database that collects information from crime scenes and from offenders convicted of particular offenses. A combined DNA index system (CODIS) operates local, State, and national DNA profile databases from convicted offenders, unsolved crime scenes and missing persons. Numerous suspects have been identified through matches between DNA profiles from crime scenes and profiles from convicted offenders. A relevant website reports that the “success of CODIS is demonstrated by the thousands of matches that have linked serial cases to each other and cases that have been solved by matching crime scene evidence to known convicted offenders”. The European Union is just one of the other international entities with a similar system (

“Core” CODIS data comes from genotypes at 13 simple sequence length polymorphic (SSLP) loci. These loci lie near SNP markers that provide information about virtually all of these loci, providing a ready means of translating between SNP and SSLP genotypes. Other mitochondrial, sex chromosome and autosomal markers are also genotyped on substantial numbers of these DNA samples.

A recent, October 2007 analyses of the CODIS-linked DNA index system revealed individually-identifying genotype profiles for more than 5 million convicted offenders, as well as almost 200,000 DNA profiles from crime scenes (

Almost 40% of males and 15% of females in cohorts from the areas of Baltimore from which Sample 1 and Sample 2 research volunteers come have experienced significant adult criminal justice system involvement (eg incarceration as adults) by the time they reach their late 20’s (N. Ialongo, personal communication, 2008). It thus seems reasonable to conclude that several of the >3400 research volunteers from whom DNAs were collected to form Samples 1 and 2 might be at potential risk for matches with crime scene DNA profiles. Similar potential risks might also be incurred through genetic study participation by individuals who report dependence on illegal substances in other parts of the United States. While this problem is not unique to studies of the genetics of illegal behaviors, it appears to be much more likely in this than in most other areas of complex genetics.

Our laboratory, along with most other laboratories that work in this field, has established elaborate means of coding, providing physical protections and providing electronic protections for the electronic and paper records that might identify our research volunteers. Subjects are protected by confidentiality certificates obtained through the Department of Health and Human Services. Data from these studies is analyzed and reported in ways that do not identify individual subjects.

However, the strongest protection for individuals who volunteer for this work comes from development and use of DNA pooling approaches. Since these approaches never generate high densities of genotypes for any individual, it is impossible to abuse or misuse this pooled data for other unintended purposes. Pooling approaches provide these research volunteers with the strongest confidentiality protections that are currently available. Pooling may also merit increasing attention in other settings in which the downside risks of DNA-based personal identification are of significant concern.

IV. Results of tests of clustering within individual samples and tests of convergence between replicate samples for the same phenotypes

A. Polysubstance dependence vs controls (Samples 1 and 2)

NIDA substance dependence samples were analyzed [12] by selecting “nominally positive SNPs” that displayed p values < 0.05 for comparisons between substance dependent vs control samples in both European-American and African-American samples. We assessed the extent to which nominally-positive SNPs that were identified in both samples by SNPs represented on at least two different array types cluster together in small chromosomal regions. Clusters contain at least three SNPs that display p < 0.05 in both samples and lie within 100 kb of each other. In this dataset, 6,666 of the 639,401 tested SNPs displayed reproducible, nominally-significant abuser vs control allele frequency differences (p < 0.05) in both samples. The criterion that the same SNP display nominally-significant association in each of two samples is more stringent than criteria used in other comparisons (see below). This criterion was applied to reduce the number of false positive results, but does not allow as much within-locus heterogeneity. 1,158 of these 6,666 reproducibly-positive SNPs lie in 320 chromosomal clusters. 184 of these clusters identify 244 annotated genes.

Monte Carlo simulation trials that assess the probabilities that these results are due to chance find that none of 100,000 simulation trials identify as many SNPs that display nominally-positive differences between substance dependent vs control samples in both European and African American samples as observed here (thus p < 0.00001) [12]. 2,100 of 100,000 Monte Carlo simulation trials that each began by selecting 6,666 random SNPs provide chromosomal clustering as marked as that observed for the true reproducibly-positive SNPs (p = 0.021).

B. Methamphetamine dependent vs control (Samples 4 and 5)

Nominally-positive SNPs from each of these two samples cluster together with ≤ 25 kb separation between nominally positive SNPs more than anticipated by chance. 846 clusters contain 3,749 of the 15,569 nominally-positive SNPs from Sample 4. 1,787 clusters contain 8,388 of the 25,538 nominally-positive SNPs from Sample 5. Such clustering is found in no Monte Carlo simulation trial (p < 0.0001 for both Sample 4 and Sample 5).

When we evaluated the genes that were identified by clustered, nominally-positive results from both Samples 4 and 5, we obtained evidence for replication and results that could not be expected by chance alone. This criterion for genes to be identified by clustered, nominally positive SNPs from each of two samples is not as stringent as the criterion that the same SNPs produce nominally positive results in each of two samples. It does allow for within-locus heterogeneity. The degree of convergent identification of genes by data from each of these two samples was never observed by chance in any of 100,000 Monte Carlo simulation trials (p < 0.00001).

C. Bipolar disorder vs control (Samples 7, 8 and 9)

WTCCC bipolar vs control 28,192 of the 426,604 SNPs analyzed in the WTCCC bipolar disorder collection displayed χ2 values with p < 0.05 [1]. 12,560 of these SNPs fell into 1,775 clusters in which at least 4 SNPs that each displayed p < 0.05 (and were sampled on at least two array types) lay within 25 kb of each other. Monte Carlo simulation trials that assessed the probabilities that these results were due to chance found that none of 100,000 simulation trials identified as many clusters of SNPs that displayed nominally-positive differences between bipolar vs controls (control and other disease) samples as were actually identifed (thus p < 0.00001) [1].

NIMH bipolar vs control 32,835 of the 536,288 SNPs analyzed in the NIMH bipolar collection [2] displayed t values with p < 0.05. 9,971 of these SNPs fell into 1,770 clusters in which at least 4 SNPs that each displayed t values corresponding to p < 0.05 lay within 25 kb of each other. Monte Carlo simulation trials that assessed the probabilities that these results were due to chance found that none of 100,000 simulation trials identified as many clustered SNPs that displayed nominally-positive differences between bipolar vs control samples as were actually identified from this work (thus p < 0.00001).

German bipolar vs control 27,057 of the 532,835 SNPs analyzed in the German samples displayed t values that corresponded to p < 0.05. 6,110 of these SNPs fell into 1,137 clusters in which at least 4 SNPs that each displayed t values corresponding to p < 0.05 lay within 25 kb of each other [2]. Monte Carlo simulation trials that assessed the probabilities that these results were due to chance found that none of 100,000 simulation trials identified as many clustered SNPs that displayed nominally-positive differences between bipolar vs control samples as observed in this work (thus p < 0.00001).

Convergent data from at least two of the three bipolar vs control comparisons: Simluation trials assessed the likelihood that the clusters of nominally-positive SNPs from at least two of these three bipolar samples identified the same genes. Monte Carlo simulation trials that assessed the probabilities that these results were due to chance found that none of 100,000 simulation trials identified as many clustered SNPs that displayed nominally-positive differences between bipolar vs control samples in the same genes in multiple samples as we actually observe (thus p < 0.00001).

D. Frontal brain volume (Samples 10 and 11)

10,266 SNPs provide nominally-positive results in 500k Affymetrix data from unrelated members of NHLBI twin pairs (Uhl et al, submitted). 583 of these 10,266 nominally-positive SNPs fall into 169 chromosomal clusters that each contain at least three nominally-positive SNPs that lie within 25 kb of each other and come from both the Sty I and Nsp I array types. Monte Carlo trials did not identify such a degree of clustering in any of 100,000 simulation trials (thus p < 0.00001).

5786 SNPs provide nominally-positive results in 100k Affymetrix data from Framingham samples (REF). 2729 of these nominally-positive SNPs fall into 613 chromosomal clusters with at least 3 SNPs that lie within 100 kb of each other. Monte Carlo trials do not identify this degree of clustering in any of 100,000 simluation trials (thus p <0.00001).

Only 212 of 100,000 Monte Carlo trials provided random fits between NHLBI and Framingham datasets that were as strong as those identified from the true datasets. Thus, Monte Carlo p = 0.00212 for the convergence between Sample 10 (500k) and Sample 11(100k) datasets.

E. Success in smoking cessation (Samples 12, 13, 14)

In comparing data from successful vs unsuccessful quitters from samples 12, 13 and 14, we identified 5,411, 4,539 and 4,894 SNPs whose allele frequencies differ between these successful and unsuccessful quitters with nominal p < 0.01 [36]. These nominally-positive SNPs cluster together to extents much greater than expected by chance if their allele frequencies were independent of each other (Monte Carlo p < 0.00001). For Sample 12, 1434 of the 5411 nominally-positive SNPs lay in 308 clusters in which each positive SNP lay within 100 kb of at least two other positive SNPs with representation from SNPs on both array types. For Samples 13 and 14, 2258 of the 4539 nominally-positive SNPs and 2184 of the 4894 nominally-positive SNPs lay in 820 and 861 clusters along with at least one other nominally-positive SNP. We thus observed clustering in each of these independent samples. We would anticipate such clustering if many of these nominally-positive SNPs identified haplotypes that were present in different frequencies in our samples of successful vs unsuccessful quitters, but not if they represented chance independent observations.

Convergent data from at least two of the three quit success comparisons: Nominally-positive clustered SNPs from successful vs unsuccessful quitter comparisons from Samples 12 – 14 also cluster together on small chromosomal regions to extents much greater than chance [36]. The Monte Carlo p values for the replication for samples 12 vs 13, 12 vs 14 and 13 vs 14 are 0.00054, 0.0016 and 0.00063, respectively.

F. Cognitive ability (Samples 15 and 16)

Of the 489,922 autosomal SNPs assessed in Sample 15, 46,361 displayed correlations with cognitive ability assessments with nominal p < 0.05 (Uhl et al, submitted). 19,781 of these nominally-significant SNPs fell into 4,016 clusters in which at least three nominally-positive SNPs sampled on two array types lay within 25 kb of the neighboring nominally-positive SNP. Similarly, of the 489,922 autosomal SNPs that were assessed in Sample 16, 36,689 displayed correlations with cognitive ability with nominal p < 0.05. 11,376 of these nominally-significant SNPs fell into 2,556 clusters. None of 100,000 Monte Carlo simulation trials that sampled at random from the same databases found this degree of clustering of nominally-positive SNPs by chance for either sample (p < 0.00001).

348 genes contained at least three clustered nominally- positive SNPs in data from each of the two cognitive ability samples (Uhl et al, submitted). Monte Carlo simulations never identified convergence of this magnitude by chance (p < 0.00001).

G. Alzheimer’s disease (Samples 17 and 18)

There was significant clustering of the SNPs that displayed nominally-positive data in the Alzheimer’s disease genome wide association studies [4, 5]. For Alzheimer’s disease sample 17, 5,428 of 16,224 nominally-positive SNPs identified 1,125 clusters (reanalysis of [4]). For the Alzheimer’s disease sample 18, 7,469 of the 21,600 nominally-positive SNPs fall into 1,436 chromosomal clusters (reanalysis of [5]). We never observed the extents of clustering identified here in any of 100,000 Monte Carlo simulation trials for either sample (p < 0.00001).

Monte Carlo simulations also document the significant convergence between the results from these two independent studies of Alzheimer’s disease; clustered nominally positive SNPs from both samples identify 99 genes (Uhl et al, submitted). The observed overall convergence between these two datasets was never observed by chance in any of 10,000 simulation trials (thus Monte Carlo p < 0.0001).

V. “Generalization” of selected genome wide association results between related disorders and phenotypes

A. Convergence between human genome wide association studies of dependence on a variety of addictive substances (Samples 1 – 6)

There is a remarkable degree of overall convergence between the clustered, reproducibly positive SNPs in the NIDA samples and those identified by comparisons of alcohol dependent and methamphetamine dependent individuals [32] (Fig 6; Tables I and andIIII).

Figure 6
Distributions of data mapped onto cartoons of human chromosomes
Table I
“Cell adhesion related” genes identified in multiple genome wide association studies of addiction and related disorders
Table II
“Druggable” genes identified in multiple genome wide association studies of addiction related disorders

The “methamphetamine dependence” genes described above display convergence with genes identified by: a) clustered- positive results from genome wide association studies of polysubstance abuse in NIDA-MNB European- and African- American samples [12] b) nominally-positive SNPs from genome wide association studies of alcohol dependence [3, 33] and c) nominally-positive SNPs in comparisons of nicotine-dependent vs nondependent smokers [3]. Data from Samples 5 and 6 converge with these other addiction vulnerability datasets with Monte Carlo p values of a) 0.0412, b) 0.0016 and c) 0.0003, respectively.

B. Convergence between human genome wide association studies of dependence on addictive substances and success in smoking cessation (Samples 5 and 6 vs 12–14)

A number of the reproducibly-positive genes identified by studies of methamphetamine dependence are also identified by clustered- positive results studies of Samples 12, 13 and 14 of European-American smokers who are successful vs unsuccessful in abstaining from smoking during clinical trials for smoking cessation. p values were 0.002 [34] for convergence with sample 13 and < 0.00001 for convergence with samples 12–14 taken together [36, 37] (Figure 5).

C. Bipolar disorder (Samples 7, 8 and 9) vs substance dependence (Samples 1, 2) genes

There was remarkable convergence between the genes identified by clustered, nominally- positive SNPs for addiction and those identified in similar fashion for bipolar disorder (Uhl et al, submitted; reanalysis of [1, 2]). Few of 100,000 Monte Carlo simulation trials identified chance overlaps as significant as those noted in actual data for comparisons between the genes identified by clustered positive SNPs for the NIDA substance dependence vs control comparisons and any of the three bipolar disorder vs control comparison groups. Monte Carlo p values were < 0.00001 for the overlaps between data from samples 1 and 2 vs data from samples 7 and 9. Simulation p value was p = 0.00012 for comparison between data from Samples 1 and 2 vs Sample 8.

D. Neuroticism (Sample 19) vs substance dependence (Samples 1, 2) genes

There was significant convergence between the genes identified by clustered, nominally-positive SNPs for addiction and those identified in generally similar fashion for association with neuroticism (Uhl et al, in preparation, reanalysis of [121]). Of the 31,110 SNPs with nominal p < 0.05 for comparisons of individuals with high vs low neuroticism scores, 4,623 SNPs lie in 903 clusters that each contain ≥ 4 such SNPs.

When we compared these clusters to those identified in Samples 1 and 2, only 30 of 10,000 Monte Carlo simulation trials identified chance overlap that was at least as robust as the true datasets (p = 0.003).

E. Frontal brain volume (Samples 10, 11) vs cognitive function (Samples 15, 16) genes

The frontal brain volume genes identified by both NHLBI and Framingham datasets overlap with those identified in genome wide association studies for cognitive function, whose genetics is linked to frontal lobe function (Uhl et al, submitted).

Nineteen genes identified by clustered nominally positive SNPs for frontal lobe volume in the NHLBI dataset are also identified by clustered nominally-positive SNPs from each of the two genome wide association assessments for cognitive function. Such overlap was identified by chance in only 212 of 100,000 Monte Carlo simulation trials that correct for repeated comparisons (p < 0.00212). One of these genes, CLSTN2, was also identified in a genome wide association study of individual differences in memory function [154].

F. Alzheimer’s disease (Samples 17, 18) vs cognitive function (Samples 15, 16) genes

Each of these overlapping Alzheimer’s disease datasets overlaps significantly with each of datasets for cognitive function. None of 100,000 Monte Carlo simulation trials identifies as many overlapping genes as those identified by these true datasets (Monte Carlo p < 0.00001) (Uhl et al, submitted). These comparisons thus strongly support generalization of the observations that we have made in studying general cognitive ability to vulnerability to AD. Pleiotropic effects of variants in a number of genes are thus likely to influence both cognitive ability and AD.

G. Alzheimer’s disease (Samples 17, 18) vs frontal brain volume (Sample 10) genes

These overlapping AD datasets overlap significantly with the dataset for frontal brain volume (Uhl et al, submitted). None of 100,000 Monte Carlo simulation trials identifies as many overlapping genes as those identified by these true datasets (Monte Carlo p < 0.00001). Pleiotropic effects of variants in a number of genes are thus likely to influence both AD and frontal brain volume.

H. Alzheimer’s disease (Samples 17, 18) vs substance dependence (Samples 1, 2) genes

Data from the overlapping Alzheimer’s disease datasets also overlaps significantly data from the overlapping NIDA addiction vulnerability samples (Samples 1 and 2) [12]. None of 100,000 Monte Carlo simulation trials identifies as many overlapping genes as those identified by these true datasets (Monte Carlo p < 0.00001). These comparisons thus strongly support the “transitive” genetic overlaps between addiction, cognitive function/brain volume and AD based on pleiotropic effects of variants in a number of genes.

I. Failure of control experiments to support alternative hypotheses for the observed genome wide association results

There is also no evidence that many of the clustered, reproducibly positive SNPs identified in these sample sets result from alternative effects of several different classes.

There were no significant overlaps between the SNPs identified in these studies and the SNPs that provided the largest racial/ethnic differences. Comparison groups included: 1) SNPs that provide the largest racial/ethnic differences in comparisons between European- and African-American control individuals from samples 1 and 2) 2) (for Asian samples) SNPs that provide the largest racial/ethnic differences between Japanese andChinese HapMap samples 3) (for samples of European heritage) SNPs that display the largest allele frequency differences between United Kingdom samples of self-reported European heritage [155].

There were no significant overlaps between the SNPs identified in these studies and the SNPs that display the largest variances from array to array. Thus, there is no significant support for either of the alternative hypotheses: 1) that the positive results of any of these studies coming from occult racial/ethnic stratification or 2) that the positive results of these studies come from assay noise alone.

Finally, comparisons with data from a complex phenotype outside the brain provide a useful contrast with the observed congergence identified in the datasets above. Data for type I diabetes from the Welcome trust case control consortium provide such a contrast [1]. Using Monte Carlo simulation approaches identical to those used to identify the highly significant overlaps noted above, we identify no significant overlap between genes that contain clustered, reproducibly nominally-positive SNPs for substance dependence (samples 1 and 2) and genes that are identified in similar fashion for type I diabetes (C J and GRU, unpublished observations, 2007).

VI. Limitations and cautions

It is important to consider several limitations for this convergent, replicated genome wide data. The sizes available for many of the samples from which data is reviewed here provide moderate power, at best, to detect gene variants. False negative results are likely since we require positive data from each of the several samples. The likelihood of false negatives is also increased since we require positive results from several SNPs from at least two array types that cluster within small chromosomal regions, making it easier to miss modest association signals within small genes that contain few SNPs or genes whose SNPs lie on only one array type. We have recently reassessed the relatively large numbers (6,734) of currently-annotated genes for which there is no possibility of detection, using 500k array data and the criteria that a gene must be identified by “at least three clustered (25kb) SNPs from at least two array types lying within the gene’s exons, introns or 10kb flanking sequences”. Genes that do not have the requisite numbers of SNPs with these characteristics are listed in the Supplement, Table SI. This list includes genes for which there is substantial positive association data from candidate gene studies, including the DRD4 dopamine receptor [156158]. A number of these genes could be detected with the addition of 100 + 500k data and even more with more recently available arrays that assess ca. 1 million SNPs; these genes are also indicated in this table.

Differences in allele frequencies in different populations could explain why some genes are strongly associated with substance dependence in European but not in African or Asian samples. Apparent failure to “replicate” in the present analyses could thus derive from the differential informativeness of SNP markers in samples from different racial/ethnic backgrounds.

We focus only on data from autosomal regions. This focus allows us to combine data from male and female samples. However, such analyses will miss potentially important contributions from genes on sex chromosomes.

Many of the subjects of these studies volunteered for demanding clinical protocols, potentially rendering them not totally representative of all individuals in the population with the same phenotypes. Some of the subjects for this work have also come to clinical attention due to specific clinical features, including presentation to emergency rooms with methamphetamine psychosis or presentation to memory clinics with memory complaints. These selection criteria might not allow these individuals to completely represent all individuals who display the phenotype or disorder.

Ethical considerations arise in studying substance abuse, an illegal behavior often associated with other difficult individual and family pathologies (see also above). Such ethical considerations suggest that it is unlikely that any large, truly population-based sample for dependence on illegal substances will be readily obtained. Sampling research volunteers and examining the extent to which the volunteers’ demographic characteristics match or deviate from those of the larger drug abuser communities provides one approach to this issue. We cannot quantitate the selection biases that might be imposed by factors not reflected in the demographic features (eg gender and self-reported racial/ethnic background) that are often used to characterize subgroups.

For studies of genetics of brain volume, it is worth noting that at least some of the genetic influences on frontal cortical volume are likely to provide “pleiotropic” influences on other brain regions. Some gene variants that influence frontal lobes as well as addiction, Alzheimer’s disease and cognitive functions could even provide effects on these behavioral phenotypes through actions outside of the frontal cortex.

For the current analyses, we employ principal analytic approaches that represent only one of many current approaches to analyzing GWA data (see discussion above, also at (RFA-HL-07-010); [6, 85]. Despite this limitation, the replicated positive results obtained here and the failure of control experiments to support alternative hypotheses do provide significant confidence in roles for most of the genes reported.

The “generalization” to results from replicated samples to data from other samples that are likely, a priori, to display genetic overlap also adds to confidence in the results that are obtained. While it is not possible to supply precise estimates for this enhanced confidence, the generalization reported here does add to the overall confidence in the sets of genes listed. Identifying the same gene in multiple studies also enhances confidence that the gene contains relatively common allelic variants that alter its function sufficiently to produce association signals in several GWA datasets.

However, as noted above, it is important to note that, while the analytic approach that we use here seeks to identify genes with variants that are likely to play roles in addiction and in related phenotypes, there are limitations to interpretation of this data that come from our belief that flexible approaches to identification of such genes are likely to benefit analyses such as those that are presented here. If we allow for locus heterogeneity, for sample-to-sample differences in the details (including the phase) of linkage disequilibrium between marker alleles and pathogenic alleles, and for the use of different marker sets with different datasets, then we cannot demand that all samples display the more stringent criterion: significant association of the same SNP with the same phase in all samples. This approach thus seeks to use stepwise analyses to identify and confirm statistically-significant association at the level of individual gene loci, assuming that subsequent followup analyses will use further steps to untangle any locus heterogeneity, to unequivocally identify which individual SNPs are associated, to identify pathological haplotypes and the phases with which they are associated with phenotypes in samples from different racial and ethnic backgrounds. Indeed, our belief that, in many circumstances, molecular biologic, behavioral and other evidence will be required to have confidence in pathological haplotypes allows the analyses that we present here to represent open door invitations for just these sorts of ancillary studies to guide further molecular genetic fine mapping efforts.

For diagnoses of individuals selected due to dependence on a specific substance, eg “methamphetamine dependence”, the specificity of effects of a gene’s variants is likely to be limited by the fact that many of the subjects for these studies also report use of additional addictive substances (eg inhalants for the methamphetamine dependence Samples 4 and 5). These clinical considerations, as well as the overlap between the “methamphetamine dependence” genes and the genes identified in other genome wide association work, support the idea that many, but not all, of the methamphetamine dependence (or nicotine dependence) loci are likely to contain allelic variants that provide a more general vulnerability to addictive substances. When we term some genes “methamphetamine dependence” genes, for example, to denote the fact that variants in these genes are likely to alter vulnerability to developing dependence on this substance, it is likely that many of these allelic variants also predispose individuals to dependence on other addictive substances.

Relatively few of the controls for the studies of substance dependence report any significant use of illegal addictive substances. The genes identified herein thus could influence vulnerabilities to a number of features important for eventually developing dependence including: a) initiation of use, b) persistence of this use, c) failure of attempts at quitting, d) transition from persistent use to dependence, or other steps, in ways that are not completely unraveled by simple comparisons of dependent vs control samples.

The largest limitation to the analyses presented here, however, is likely to come from the modest size of the effects reported for each of these phenotypes (with the exception of the APOE influences in Alzheimer’s disease, of course). In each polygenic disorder, replication of effects of polygenic loci is unlikely to be sufficiently robust that each study will provide nominally-significant observations. Final confidence in true effects requires both replication and generalization, including studies of more and more samples with the phenotypes that we study in the present review. However, we also need to display caution in considering the significance of apparent nonreplications. Recent analyses underscore the role that heterogeneity can play in engendering false-negative results from attempts at replication [126].

VII. Classes of genes that are identified in multiple GWA samples for multiple phenotypes: Focus on cell adhesion related genes

One approach to describing the convergence between the datasets, presented above, relies on the overall convergence between the results obtained in each study. A different approach focuses on convergence of data concerning specific genes and classes of genes, especially when most are expressed in the brain. Many of the genes that we identify in this analysis of convergent genome wide association findings are involved in “cell adhesion” processes whereby neurons recognize and respond to features of their environments that are important for establishing and maintaining proper connections (Table I). Others are involved in enzymatic activities, protein translation, trafficking and degradation; transcriptional regulation, receptor, ion channel and transport processes, disease processes and cell structures. A subset of this latter group is of especial interest since they represent classically “druggable” targets for potential small molecule therapeutics (Table II).

A. Cell adhesion related genes: The genes whose products are involved in cell adhesion processes provide a number of especially interesting results (Table I). Cell adhesion mechanisms are central for properly establishing and regulating neuronal connections during development. Cell adhesion mechanisms can play major roles in mnemonic and other neuroadaptive processes in adults [159, 160]. It is interesting to note that most of the cell adhesion related genes that we identify in these genome wide association studies are expressed in developing and adult brains. Altered expression of several of these genes can alter neurite extension [161163], activate signaling pathways [164169] and alter mnemonic processes [163]. Almost all of these cell adhesion-related genes are expressed in memory-associated brain regions that include hippocampus and cerebral cortex ( [170173]. By contrast, substantial expression in mesolimbic/ mesocortical dopamine “reward system” neurons is not documented for many of them.

“Cell adhesion” related genes identified by these genome wide association studies encode members of several structural cell adhesion molecule subfamilies. Those that are anchored to cell membranes by glycophosphoinositol (GPI) anchors, those that display apparent single-transmembrane topologies, those that display apparent seven transmembrane topologies and those that produce soluble products are each represented.

Here, we discuss several of the cell adhesion related genes that have been identified in multiple sets of replicated genome wide association studies (Table I). Monte Carlo trials that seek to establish the level of significance of identification of these genes in multiple datasets establish levels for this convergent identification that range from 0.006 to < 0.00001. We also discuss follow up data for two cell adhesion molecule genes, NrCAM and NRXN3, which were initially identified by lower density genome wide association studies [10, 11]. Results from these two loci provide examples of the ways in which initial identification in genome wide association studies can lead to subsequent identification of specific haplotypes that alter levels of expression of the gene or levels of expression of a specific set of splice variants.

CAMs with the strongest levels of cumulative support: One of the cell adhesion molecules that achieves the most striking nominal p values in these analyses is an “atypical” member of the cadherin gene family, CDH13. Cadherin 13 is a glycophosphoinositol (GPI)-anchored cell adhesion molecule. CDH13 is expressed in neurons in brain regions that are likely to play roles in addiction, including hippocampus, frontal cortex, and ventral midbrain [171]. CDH13 can inhibit neurite extension from select neuron populations [161, 171] and activate a number of signaling pathways [164167]. It is thus a strong candidate for roles in brain mechanisms important for both developing and quitting addictions.

Other cell adhesion related genes that manifest cumulative p values < 10−6 in these analyses include BAI3, CLSTN2, CNTNAP2, CSMD1, CTNNA2, DAB1, DSCAM, NRXN1, PTPRD and SGCZ. Data from NRXN1 associations in smoking have been recently reviewed. We discuss several of the other genes here.

DSCAM is a single-transmembrane domain cell adhesion molecule with immunoglobulin and fibronectin domains that is expressed strongly in brain [169, 174] and in hippocampus in ways that are required for appropriate neuronal connections to form in memory-associated circuits in model organisms [162, 163]. Different dendritic processes of the same neuron do not often cross each other; this self-avoidance mechanism depends on expression of a large array of tightly-regulated DSCAM isoforms [175, 176]. Simplifying this repertoire substantially disrupts appropriate formation of neuronal networks in vivo [177]. Indeed, flies with altered DSCAM expression display altered memories for both rewarded and punished behaviors [163].

CLSTN2 contains allelic variants that are identified in genome wide association studies of individual differences in memory and executive function as well as the cognitive ability/Alzheimer’s disease vulnerability and frontal brain volume phenotypes reviewed here [178, 179]. CLSTN2 is expressed in frontal cortex and hippocampus [172]. CLSTN2 is well-positioned to provide calcium-dependent cell adhesion functions in the brain regions that include hippocampus and in the postsynaptic densities where it is highly expressed. The structure and expression of CLSTN2 make it a good candidate to function as a single transmembrane domain cell adhesion molecule in which variants could alter the ways in which neuronal and synaptic connections develop, the ways in which they are maintained and reorganized in adult brains or both.

DAB1 interacts with and participates in signaling from several cell adhesion molecules. DAB1 has long been identified with signaling through the cell adhesion molecule reelin in ways that alter formation and maintenance of neuronal processes [180]. More recent evidence also supports roles for DAB1 in signaling through other cell adhesion/cell regulatory mechanisms, including those that utilize the amyloid precursor protein cell adhesion molecule [181]. DAB1 expression in many brain neurons includes those in hippocampus and mid to deep cerebral cortical layers [172] ( Mice with DAB1 disruption display substantial alterations in cerebral cortical development accompanied by gross motor and other behavioral phenotypes [182].

BAI3, a seven transmembrane domain cell adhesion molecule, as well as PTPRM, a single transmembrane receptor tyrosine kinase that mediates homophilic cell recognition and is supported at a more modest level of statistical confidence, are both expressed in vasculature [183, 184]. Identifying these genes fits with the idea that control and regulation of angiogenesis and vascular functions plays important roles in determining the richness of cerebral cortex and other areas of adult brains [184] in ways that have consequences for a variety of interesting brain based phenotypes. In addition, there is substantial neuronal expression of PTPRM in cortical and cerebellar cortical neurons [172, 183].

PTPRD is expressed in brain, and displays prominent hippocampal expression. Its extracellular ligands have not been elucidated, though it can bind to liprin [185]. PTPRD knockout mice display altered hippocampal long-term potentiation and spatial learning [186], which fit well with the human phenotypes related to cognitive function. Mice with deletions of both PTPRD and a related PRP sigma (but not with either knockout alone) die at birth due to failure to innervate appropriately [187]. SCGZ participates in protein complexes with cell adhesion-like [188]. High levels of SGCZ expression in the brain are confirmed by Allen brain atlas images [172]. Biochemical studies identify expression in schwann cells of peripheral nerves [188]. SCGZ can be found in complexes with αδ or with εβδ sarcoglycans, demonstrating specificity of the context of its function in brain [189].

CSMD1 is substantially expressed in adult brain regions that include hippocampus [170]. High levels of CSMD1 expression in growth cones of neurons cultured from developing brain support substantial roles in development as well [170]. Less striking levels of evidence implicate variants in CSMD family members CSMD2 and CSMD3 in several of these brain related phenotypes [190].

CAMs with intermediate levels of cumulative support: Cell adhesion related genes with nominal cumulative significance levels between 0.00007 and 0.006 include ANKS1B, ASTN2, CNTN4, CNTN5, CNTN6, CTNNA3, CTNND2, LRP1B, LRRN6C NRG1, ITGB8, PTPRM, ROR1, TRIO, CSMD2, CNTN5 and SEMA3C. We discuss several of these molecules here.

ASTN family members play substantial roles in migration and organization of neuronal processes along glial fibers in ways that are key to proper development of laminated brain structures [191]. ASTNs is expressed at high levels in the adult hippocampus [172]. ASTNs knockouts slow the development of these structures [192]. ASTNs is upregulated after hippocampal lesions that induce synaptogenesis [193].

CNTN6 is a GPI-anchored cell adhesion molecule that can act as a Notch ligand, triggering nuclear translocation of the Notch intracellular domain. This interaction can promote formation of oligodendroglia from progenitor cells and increase expression of myelin-associated glycoprotein in oligodendrocytes [194]. Neuronal expression in hippocampal, cerebral cortical, cerebellar and thalamic subdivisions in adult brain could contribute to the motor incoordination phenotypes noted in knockout mice [195, 196].

SEMA3C is one of the cell adhesion molecules that has been implicated in development of dopaminergic projections [197]. SEMA3C and its neuropilin 2 receptor are regulated by neuronal injury [198]. Significant levels of SEMA3C expression in hippocampus and connected regions, such as the medial septal nucleus, provide a good rationale for involvement in memory-related phenotypes [199].

LRRN6C displays interesting patterns of brain expression, with dense expression in cells of the dentate gyrus of the hippocampus and entorhinal cortical regions [172]. However, little additional data concerning this gene has appeared in the literature to date.

CTNNA2 is another protein whose strong association with cell adhesion mechanisms, especially cadherin mechanisms, renders it of substantial interest here. Mice with impaired CTNNA2 expression display impairments in prepulse inhibition and fear conditioning, altered dendritic spine morphogenesis in hippocampal neurons, unstable synaptic junctions and defective anterior commissure formation [200, 201].

Potential roles for cell adhesion related genes: The cell adhesion genes identified here provide an attractive way to bridge the gap between 1) the remarkable observed overlap between the molecular genetics of the clinical and cognitive phenotypes reviewed here and 2) the brain differences, especially those that might manifest in the quantity and/or quality of neuronal connections, that might underlie these shared heritable influences.

These cell adhesion related genes also provide an attractive bridge between the genetics of phenotypes that are not accompanied by gross brain pathology and those that are. Some of the cell adhesion genes that we identify in studies of cognitive ability might conceivably be directly involved in Alzheimer’s disease pathological processes [202]. However, the majority of the cognitive ability genes identified here fit with emerging views that relatively subtle differences in brain connections that contribute to individual differences in cognitive abilities also provide individual differences in “cognitive reserves” [203]. If “cognitive reserves” mitigate the cognitive impact from a given density of Alzheimer’s disease senile plaques and neurofibrillary tangles, for example, individuals with greater cognitive reserves might die without dementia despite neuropathological brain burdens that would otherwise produce significant dementia in individuals with lesser cognitive reserves [204].

Cell adhesion molecule genes with substantial follow up information: 1) Neurexin 3 (NRXN3). Neurexins are cell adhesion molecules that help to specify and stabilize synapses and provide receptors for neuroligins, neurexophilins, dystroglycans, and α-latrotoxins [168, 205, 206]. Neurexins function in the nervous system as cell adhesion molecules at excitatory and inhibitory synapses [168, 205214]. The mammalian neurexin genes, NRXN1 –NRXN3, each display multiple promoters from which longer α-neurexins and shorter β-neurexins are transcribed. Differential promoter usage and/or differential splicing events provide many neurexin isoforms [213, 215218]. Neurexin splicing variants also provide opportunities to produce both membrane bound and soluble isoforms [215217].

NRXN3 expression in specific cerebral cortical regions and layers, especially layers 2–3 and 5–6 is documented in rodent brain atlas images [172]. While NRXN3 is also expressed in other brain regions of interest for addiction, its expression in glutamatergic projections that arise from prefrontal cortex to innervate nucleus accumbens and other striatal regions and/or in GABAergic neurons that project from cortical area to cortical area provide sites at which altered expression could readily influence circuits that are key for addictive behaviors. Alterations in the size or strength of synapses in these circuits could readily provide alteration in behavioral features, including vulnerability to addictions.

From 10,000 SNP genome wide association data, we initially reported that SNP rs760288, located in the 3’ region of NRXN3, displayed allele frequencies that distinguished individuals dependent on illegal substances from control individuals in both Sample 1 and Sample 2 [11]. These observations have received support by results from Sample 6 [3] and a linkage study of opioid dependence [219].

We genotyped nine SNPs in the 3’ NRXN3 regions that lie near two splicing sites, termed SS#4 and SS#5, in 144 European-American alcohol dependent and 188 control individuals from Sample 3 [130]. Minor allele frequencies at four SNPs near SS#5 were each higher in alcohol dependent than in control samples, supporting initial observations in other samples. A “T” allele at the rs8019381 (close to the original rs760288 SNP) produced a 2.46 odds ratio for belonging to the alcohol dependent group. Common 3 SNP haplotypes in this area were also strongly associated with alcohol dependence (nominal p= 0.000598, corrected to p= 0.00580 based on permutations).

The rs8019381 SNP was located 23 bp 3’ from the 3’ end of exon 23, near a branch point that could change the splicing of NRXN3 primary transcripts [220, 221]. In mRNAs isolated from postmortem cerebral cortex, individuals with one or two copies of the minor rs8019381 T allele expressed less of the major transmembrane isoform ex22a24b and the minor transmembrane isoform ex22a24a mRNA levels than did CC homozygotes (p= 0.0008 and 0.021 for genotypes by two-tailed Mann Whitney test, respectively). By contrast, expression of the major ex22a23a soluble isoform did not correlate with rs8019381 genotype (p= 0.171 by two-tailed Mann Whitney test). As a consequence, ratios between transmembrane and soluble isoforms were significantly increased in individuals with one or two T alleles in comparison to CC homozygotes (p = 0.018, two-tailed Mann Whitney test).

Standard models for development and alteration of excitatory and inhibitory synapses posit that neurexins provide pre-synaptic cell adhesion molecules that bind heterophilically and with calcium-dependence to postsynaptic cell adhesion molecules to form trans-synaptic complexes that help to stabilize excitatory or inhibitory synapses [205]. Repertoires of diffusible entities enrich this picture. Neurexophilins provide soluble neurexin ligands that are expressed in neurons in a number of brain regions that also express NRXN3. Diffusible NRXN3 isoforms are expressed in brain regions that express several neuroligins and dystroglycan [222]. Conceivably, soluble neurexins and neurexophilins might diffuse to sites at which membrane-bound neuroligins and neurexins were localized. Enhancing ratios between diffusible and membrane bound NRXN3 isoforms in the CT/TT genotypes of rs8019381 might thus provide more diffused signaling in ways that could alter pruning, spreading and/or strength of synaptic contacts [223, 224].

2) NrCAM NrCAM encodes a single-transmembrane-domain cell adhesion molecule with six immunoglobulin domains, four to five fibronectin III repeats, a transmembrane domain and a C-terminal cytoplasmic domain with tyrosine kinase phosphoacceptor sites [225]. Multiple NrCAM isoforms can be identified as the products of differential RNA splicing events which produce NrCAM translation products that contain short inserted peptide sequences. NrCAM mRNA expression is high in hippocampus, mid- to deep layers of the cerebral cortex, cerebellum purkinge cell layers and striatal interneurons. Cells of the substantia nigra and ventral tegmental area (VTA) express hybridization densities that are almost as dense as those identified in hippocampal pyramidal neurons [131]. Many, but not all of the NrCAM immunopositive neurons in the VTA also express dopamine transporter (DAT) immunoreactivity, a marker for dopaminergic neurons [131].

We initially identified NrCAM based on convergence between data from genome wide association and studies of drug regulated gene expression [226]. Our initial 1.5 k SNP genome wide association study identified a reproducibly-nominally-positive SNP that was mapped roughly to the region that contained NrCAM as well as other genes. Follow up studies provided confirmatory observations: 11 of 37 tested mid-chromosome seven simple sequence length polymorphisms (SSLPs) display allele frequency differences between abusers and controls in NIDA European- and/or African-American samples that reach nominal significance. Three displayed nominally-significant differences in both populations; fewer than one would have been expected by chance. These nominally-significant results encompassed markers located between 98–103 Mb of chromosome 7.

Subtracted differential display PCR (SDD) identified NrCAM as a morphine-regulated gene. Sequences that encoded 3’ untranslated regions of NrCAM were among subcloned SDD cDNAs that corresponded to mRNAs whose expression was altered in striata of rats sacrificed 4 hr after treatment with 20 mg/kg morphine. These biochemical results led us to focus on NrCAM. We thus identified 3’ and 5’ blocks of restricted haplotype diversity in NrCAM in European- and Afircan-American samples, and sought association between addiction and allelic frequencies of markers in both of these haplotype blocks. The 3’ haplotype was associated with addiction vulnerability in European American individuals from Samples 1 and 3 (nominal p = 0.0006 and p = 0.003, respectively) and from African-American individuals from Sample 2 (nominal p = 0.0006). However, the phase of the association was opposite in the African-American samples in comparison to the phase in the two European-American samples. Frequencies of the addiction-associated 3’ NrCAM haplotype were thus higher in African American abusers than in controls but lower in two samples of European-American substance abusers than in the corresponding control samples. We interpreted these phase differences as indicating that the 3’ NrCAM haplotype block was close to, but not identical with, the pathogenic haplotype.

NrCAM 5’ flanking region haplotypes, however, displayed association with the same phase in each of these three samples (p = 0.0002, p = 0.06 and p = 0.02, respectively). Further, an additional independent sample that compared haplotype frequencies in 288 alcohol-dependent Japanese subjects vs 472 matched controls also displayed highly-significant association (p < 0.01) with the same phase. These cumulative observations strongly supported the idea that NrCAM variants provide polygenic contributions to human interindividual differences in addiction vulnerability. They contrast with the failure to identify such reproducibly-positive findings at the adjacent genes including LAMB1, LAMB4 and iPLA2.

We sought possible functional effects of these 5’ NrCAM haplotypes by assessing patterns of allele specific expression of NrCAM haplotypes in mRNAs extracted from human postmortem brains. mRNA corresponding to the addiction-associated 5’ NrCAM haplotype was expressed at an average of 26% of the levels of expression of mRNAs encoded by the alternative haplotypes.

When we compared NrCAM mRNA levels in cerebral cortex, midbrain and hippocampal samples from individuals who were heterozygotes for this 5’ NrCAM haplotype to samples from individuals who lacked it, expression was about 40% lower in the brains of individuals who displayed the disease-associated haplotype. None of the adjacent genes’ expression revealed such evidence for haplotype-specific differential expression.

The core 5’ NrCAM haplotype that provided the largest effects on differential expression provided associations that were significant in Sample 1 (p=0.0003), Sample 2 (p=0.02) and Sample 3 (p=0.05).

We then asked if mice with altered levels of NrCAM expression display differences in conditioned place preference, which provides a relatively robust test for alterations in the reward and reward-memories induced by abused substances. Heterozygous and/or homozygous NrCAM knockout mice display striking reductions in their preferences for the places where they received either morphine or methamphetamine, in comparison to wildtype control mice. By contrast, mice of all genotypes displayed similar sensitivities to the acute locomotor stimulant properties of all three drugs.

These data thus provide convergent results from searches for drug- regulated genes and from association-based genome scans for drug abuse vulnerability alleles. The data support the idea that NrCAM and NRXN3 haplotypes contribute to human individual differences in addiction vulnerability in ways that are likely to depend on differences in levels of NrCAM expression and/or regulation and NRXN3 splicing.

VIII. A possible underlying neurobiology: connectivity constellations

Many brain-based phenotypes and disorders are accompanied by gross brain anatomic changes or differences when compared to brains from individuals without such phenotypes. These anatomical differences provide foci for many pathophysiological inquiries. However, in many other disorders and phenotypes, brains do not reveal reproducible gross pathologies. In these circumstances, investigations more often focus on molecular and biochemical pathways. Pharmacological mechanisms can even provide foci for exploration, when drugs that ameliorate disease symptoms are discovered serendipitously. Roles for mechanisms involved in “no gross pathology” phenotypes can be inferred in a number of disorders in which gross brain pathologies have been identified. Cognitive abilities at baseline, accompanied by no gross pathology, can play substantial roles in individual differences in vulnerability to dementing illnesses with gross pathological brain changes, such as Alzheimer’s disease, for example [227].

Most of the common brain phenotypes and disorders that lack gross neuropathological underpinnings have been shown to represent “complex” disorders (or phenotypes) from a genetic perspective [228]. Twin studies support heritability of at least half of total vulnerability to many such disorders or phenotypes. Major psychiatric disorders such as bipolar disorder, major traits such as general cognitive abilities and major brain phenotypes such as the volume of frontal and temporal cerebral lobes each display at least 50% heritabilities in a number of well-performed twin studies (Table III). Linkage studies, which have been good at identifying individual genes whose variants exert substantial effects on phenotypes or diseases, have established a lack of genes of major effect for most of these phenotypes or disorders. Genome-wide association approaches, however, are now revealing more and more of the gene loci that contain variants that contribute to such disorders, and thus more and more of the classes into which genes that contribute to these disorders or traits fall.

Table III
“Connectivity constellation” of phenotypes and disorders, each with sizable heritable influences

It is remarkable that virtually all of the brain-based disorders and traits for which genome wide association data that comes from a variety of laboratories can now be analyzed, as noted above (Table I), appear to receive substantial contributions from variants in cell adhesion related genes.

Could these repeated observations come simply from unexpectedly large representation of such genes in the genome? We have recently performed bioinformatic searches that attempt to enumerate the number of “cell adhesion molecules”. We constructed and performed these searches in ways that were independent of the genome wide association data described here. These searches used a combination of natural-language and keyword-based literature and database searches with DNA sequence motif searches using sequence elements common to cell adhesion molecule families (CYL, QRL and GRU, in preparation). Briefly, we (CYL) integrated Gene Ontology annotations, domain structure information and keyword queries of NCBI Entrez Gene annotations in ways that were independent of the hand-annotated lists of cell adhesion related genes identified by genome wide association. First, we extracted 196 genes that encoded 281 proteins using the gene ontology term “cell adhesion (GO:0007155)” [229]. Second, we extracted detailed domain features for six sub-families of cell adhesion molecules, including cadherins, IgCAMs, integrins, neurexins, catenins and neuroligins [230236]. Using these features and Perl scripts, we identified 218 human genes that encoded 532 proteins based on standardized related InterPro domain architectures and proteins mapped onto these architectures [237]. Fourth, we identified lists of cell adhesion genes for rat and mouse as noted above, and searched for human orthologs using homologene [238]. Finally, we identified 1487 results from searches of Entrez Gene using "adhesion AND Homo sapiens [organism]” [238]. The second through fourth approaches added 136 additional genes, yielding a total of 496 human cell adhesion molecule genes (CYL, QRL and GRU, in preparation).

With this set of genes in hand, we were now able to estimate the fraction of the genome that they represent. These 496 genes represent 1.6% of the 31,227 human genes now annotated by RefSeq. These 496 genes are often large, occupying a total of 73,695,264 bp of the genome. These sequences thus represent 2.4% of the 3,080,436,051 bp currently-elucidated human genome sequence. These genes represent 5.8% of the 1,271,259,295 bp currently annotated as gene sequences (eg exon, intron and 10kb 3’ and 5’ flanking sequence).

We can seek overlap between these “bioinformatic search cell adhesion molecule genes” and the 99 genes that we have 1) identified by clustered, nominally-positive SNPs in one or more of the genome wide association studies described above and 2) independently (GRU) annotated as “cell adhesion related”. Sixty-four of the 99 genes that we identified as “cell adhesion related” in genome wide association datasets are included on this independently-derived list of “bioinformatic search cell adhesion molecules”. These 64 overlapping genes represent 12.9% of all of the “bioinformatic search cell adhesion molecule genes” that we can identify in the genome. These 64 genes total 35,619,485 bp, representing 1.2% of the genome and 2.8% of currently annotated gene sequences.

Despite this relatively modest representation in the genome, then, cell adhesion related genes are substantially overrepresented in several of the genome wide association datasets reviewed here when compared to their genomic representation. Cell adhesion related genes represent 21–26% of the genes identified by the replicated searches for addiction vulnerability genes in Samples 1 and 2 and Samples 4 and 5. They represent 27% of the genes identified for vulnerability to bipolar disorder, 17% of the genes implicated in individual differences in regional cerebral volumes, 20% of genes implicated in cognitive abilities and 20% of the genes implicated in success in smoking cessation.

Further, when we compare the data from the sixty-four genes identified in both bioinformatic and hand searches with the data for the remaining 35 genes, strong cases can be made for inclusion of most of the remaining genes as “cell adhesion related”. CSMD1 and CSMD2, for example, represent two of the three currently-identified cub and sushi multiple domain genes, though they lack literature support for cell adhesion functions that is sufficiently strong to include them on the list of “bioinformatic search cell adhesion molecule genes”. Reviews and individual papers cite receptor protein tyrosine phosphatases as “cell adhesion molecules”, in ways that correspond to our hand annotation but not to their identification in the independent bioinformatic searches as cell adhesion molecules [239243].

B. Quality and/or quantity of connections involved in vulnerabilities

Quantitative morphometric studies have documented the large fraction of volume of cerebral cortical grey matter that is composed of “neuropil”, neuronal processes and their supporting/ensheathing glial elements [244, 245]. By comparison, vasculature and neuronal cell bodies contribute much less to the volume of grey matter. The ways in which neuropil and the neuronal connections that it contains 1) develop 2) change with experience (or disease) and 3) change with aging all provide interesting avenues for functional impact of individual differences in neuronal connections.

Heritability estimates are available for several of the human phenotypes that are likely to be influenced by the quality and/or quantity of such neuronal connections. Regional brain volumes have been among the most studied using classical twin methods [15]. Some of the best data that is currently available comes from studies of the volumes of the cerebrum, frontal lobe, temporal lobe and hippocampus. These twin studies support heritable interindividual differences in these regions that range from 0.4 to 0.6 [15]. Since the majority of the volumes of the grey matter aspects of these regions is comprised of connection-rich neuropil, it seems highly likely that differences in the quantity of connections provides one substantial source of these heritable interindividual differences.

Twin methods also provide data that links these heritable determinants of brain volume to heritable determinants of cognitive functions that include memory and estimates of different aspects of cognitive function [246, 247] In one of the largest available twin datasets, virtually all of the heritable individual differences in brain regional volumes could be attributed to genetic influences shared with influences on cognitive abilities (measured primarily by memory tasks) [247]. In another twin dataset, the overwhelming majority of genetic influences on frontal lobe brain volume were shared with those that influence cognitive ability measured primarily by tests of executive function [117].

These data strongly imply roles for individual differences in the quantity of neuronal connections in heritable determinants of cognitive function. No human data of which we are aware directly address qualitative differences in neuronal connections. However, it seems unlikely that genes that influence quantity would fail to influence quality of connections as well. Interrelations between neurons and glia are also likely to provide significant contributions to the anatomic coherence of white matter pathways. It seems likely that the interindividual differences in diffusion tensor imaging signals that come from white matter differences and that can readily be observed in humans [248] will also receive contributions from “connectivity constellation” genes, to the extent that these individual differences are heritable [249, 250].

Rodent models provide support for quantitative and qualitative effects of variations in connectivity constellation genes. Hints of redundancies in the ways in which these genes might work come from studies of effects of gene × gene interactions in brains of knockout mice. Mice with knockout of the addiction-associated gene NrCAM and another immunoglobulin + fibronectin domain cell adhesion molecule gene, L1, display substantial reductions in brain volume and substantial developmental brain-based phenotypes (NrCAM L1KO). No observable differences between the brains of wildtype mice and knockouts of either NrCAM or L1 alone can be identified, however. While gross brain differences in knockouts of only a few of the “connectivity constellation” genes can be identified, it appears likely that more subtle, more qualitative differences might be induced by variants at many more single loci from this list. Larger quantitative differences that can be grossly observed may require contributions from variants at several of these gene loci.

C. Overlapping effects of “connectivity constellation” gene variants, comorbidities and co-occurance frequencies. One of the predictions that comes from postulates that variants in a limited number of genes contribute to genetic components of susceptibility to a variety of phenotypes is the prediction that these phenotypes would occur together more often than expected by chance. The magnitude of co-occurance, of course, would be expected to differ based on differential penetrance of each locus on each phenotype as well as other factors.

There is relatively strong evidence for co-occurance of vulnerability to a number of the phenotypes listed in Table III. Bipolar disorder and addiction occur together much more frequently than we would expect by chance alone; almost three quarters of individuals with bipolar disorder also manifest substance use disorders in a recent review and elsewhere [146, 147, 251]. This co-occurance of these two heritable phenotypes fits with the substantial overlap that we documented above in comparisons between genome wide association results from addiction and bipolar disorder. Many of the genes that receive substantial support from both of these studies encode cell adhesion molecules and other “connectivity constellation” genes.

As noted above, twin study data supports substantial shared overall genetic influences on cognitive abilities and brain volumes [246, 247]. Comparison of genome wide association datasets for these two traits documents substantial overlaps that are much greater than chance and that again identify “connectivity constellation” genes. One of the strongest risk factors for Alzheimer’s disease that can be identified in the literature is baseline cognitive abilities, as reflected in individual differences in educational attainment, for example [252, 253]. We have identified strong overlap between genome-wide association results for cognitive ability and those for Alzheimer’s disease that includes a number of “connectivity constellation” genes.

Neuroticism is a heritable personality trait [254] that differs most in comparisons of addicted vs control individuals [255, 256]. We have also identified a strong overlap between genome-wide association datasets for neuroticism and addiction [121]; Johnson et al, in preparation). Again, many “connectivity constellation” genes are identified by both of these approaches.

As denser and denser datasets for genome wide association for disorders such as antisocial personality disorder and schizophrenia become available, it will be interesting to seek such overlaps with other “connectivity constellation” disorders.

D. Contributions of “connectivity constellation” genes to morbidity/mortality from brain disorders in the United States One way to assess the impact of complex polygenic genetics on brain disorders is to estimate US costs associated with the disorder, estimate heritabilties from twin study data, estimate Mendelian and oligogenic contributions from family study, segregation, linkage and genome wide association analyses and thus identify the overall impact of polygenic contributions. We have estimated that the polygenic influences on addiction vulnerability are responsible for $212.20 billion in cost to the US in 2004 [257259]. Other “connectivity constellation” disorders and the relevant estimates for costs of polygenic contributions include Alzheimer's disease and dementias, with perhaps 74 billion in such costs, pain and migraine with 58.8 billion in such costs, anxiety disorders with 24 billion in such costs, schizophrenia with 39 billion in such costs, depressive illnesses with almost 19 billion in such costs, developmental disorders with perhaps 5 billion in such costs and stroke, Parkinson’s disease, multiple sclerosis and seizures with 2, 1.4, almost 3 and 0.6 billion, respectively, that can be attributed to complex genetics. If variants in the “connectivity constellation” of genes contribute 20% to the complex genetic impact of these disorders, then the costs attributable to these cell adhesion related gene variants could amount to almost 90 billion in the US annually.

IX. Classes of genes that are identified in multiple GWA samples for multiple phenotypes: focus on “druggable” targets

The emphasis that we have placed on cell adhesion molecule genes here should not obscure the identification of a large number of genes that encode proteins that are more classically “druggable”. Enzymes, receptors and ligands (G-protein coupled receptors, ligand-gated ion channels and peptide ligands), ion channels and transporters, in particular, provide targets for many currently-effective drugs and are thus thought of as more “druggable” than cell adhesion molecules, for which there is less precedent for efficacious small molecule therapeutics. It is therefore instructive to identify the potential targets that come from genes in these families that are supported from results in replicated genome wide association studies of at least three of the phenotypes assessed in this review. It is worth noting that many of these drug targets in fact can be targeted by promising lead compounds that have already been identified (see below).

“Druggable” genes that are most strongly supported by the current analyses: Cumulative nominal p values, based on the strongest clusters on nominally positive SNPs in each of these individual genes in studies of a variety of different phenotypes, provide support for individual genes that is as striking as 10− 3 – < 10− 5 for several of these “druggable” genes.

Enzyme-related genes are most numerous group, with PRKG1, CAMK1D, CHN2, FHIT and SERPINA1 providing th most significant comvergent observations. PRKG1, the cyclic-G dependent protein kinase 1, and FHIT, the fragile histone triad gene that represents a diadenosinase, each display clustered, nominally-positive SNPs in several of these studies in ways that make them especially unlikely to represent chance findings. PRKG1 is expressed in brain and in hippocampal, cerebellar and other neurons [260]. Nitric oxide dramatically modulates brain cGMP systems; PRKG1 thus provides a major target for the products of nitric oxide synthases (NOS). Mnemonic and addictive functions can each be altered by changes in cGMP-dependent protein kinase and/or NOS [261]. Identification of potent imidazopyridine PRKG inhibitors for use in lower species could well provide leads for development of similar compounds for use in humans [262].

FHIT was initially named the fragile histone triad gene based on its location at the chromosome 3p Fra3B locus that provides one of the most common neoplasia-associated human genomic fragile sites. FHIT is highly expressed in brain, with robust expression in and localized in regions that include hippocampus. The high levels of brain expression of FHIT protein and its characterization as the major human enzyme that can hydrolyze diadenosine-polyphosphates provides a strong link to purinergic signaling [263]. Diadenosine polyphosphates are released from synaptic vesicles along with more classical tranasmitters. They can activate P2Y(1), P2Y(2), and P2Y(4) receptors, homomeric P2X(1), P2X(2), P2X(3), P2X(4), and P2X(6) receptors, and even a P4 receptor-operated Ca++ channel [261]. Recent reports have also implicated FHIT in a nonenzymatic function, altering expression of the cell adhesion related protein beta catenin [264]. Robust inhibition of FHIT’s diadenosine triphosphatase activity by suramin provides a starting place for developing more selective inhibitors [265, 266]. However, recent identification of FHIT as a modulator of beta catenin-induced transcriptional regulation may also place this gene in the cell adhesion signaling cascade as well [263].

CAMK1D is expressed in neurons, including those of the hippocampus, at relatively high levels. While little direct information about the functional effects of its brain expression is available, other calcium/calmodulin dependent protein kinases play large roles in memory like processes. Useful reagents to study CAMK1D activities in peripheral systems might well come from studies of mimics of CAMK1D sequences [267269].

LARGE encodes a likely enzyme; this gene also receives much cumulative support from these genome wide association datasets. LARGE encodes a protein with high homology to acetylglucosaminyltransferases. Brain provides many of the LARGE expressed sequence tags, while in situ hybridization documents high levels of expression in apparent neurons in a number of brain regions, including hippocampus and cortex [172]. In muscle, interrelationships between dystroglycans, LARGE and its family members have been so well documented that interactions with other dystroglycans in brain also appear likely [270]. Humans with LARGE mutations can express developmental delay as well as muscular dystrophies; a spontaneously occurring mouse model is also available [271].

The phosphodiesterase PDE4D, that also receives substantial support, is an enzyme expessed in neurons and other cells in brain in a number of regions that include hippocampus. PDE4D knockouts display altered results in “antidepressant” testing [272]. Variants at the PDE4D locus have been associated with a number of additional human phenotypes. PDE4D SNPs have been associated with neuroticism in the initial analyses of data from Sample 19 [9]. A number of studies, but not all, have linked and/or associated PDE4D haplotypes with altered risk of stroke [273]. A PDE4D SNP has been associated with sleepiness in Framingham study subjects in 100,000 SNP genome wide association [274]. Rolipam and other PDE4D inhibitors or stimulators have been extensively characterized, providing starting points for more synthesis of more selective drugs [275]. Memory-enhancing effects of PDE4 inibitors documented in rodent behavioral assays, and provide relatively straightforward links with the genome wide association data that we review here [12].

RYR3 is the channel gene that is most heavily supported by this data. RYR3 is a site at which calcium activated calcium efflux from sequestered intracellular stores elevates free cytoplasmic calcium levels. Brain RYR3 expression is highest in hippocampus [276, 277]. Significant evidence links RYRs to associative learning [278]. RYR3 knockout mice display alterations in several sorts of learning tasks [279]. Ryanodine and a number of other small molecule appear to provide excellent starting points for synthesis of selective RYR3 inhibitors [280].

Other genes on this list of classically-druggable targets display substantial cumulative nominal p values include genes that encode a peptide/growth factor ligand, G protein coupled receptors, and transporters.

FGF14 isoforms are widely expressed in a number of brain regions during development, and expressed in more focal fashion in adult brain [281]. FGF14 lacks signal sequences and may thus play intracellular roles that include interactions with ion channels and intracellular signal transducing proteins [282]. The FGF14 gene is a candidate to play roles in control of midbrain dopamine neurons, based on mouse strain comparison studies [283, 284]. FGF14 expression is upregulated by methylphenidate [285]. FGF14 knockout mice display substantial alterations in learning and aspects of long term potentiation [286]. Humans with FGF14 mutations also display cognitive changes [121]. While we are aware of no small molecule FGF14 agonists or antagonists, such development might well benefit from the substantial progress in producing small molecule peptidomimetics in other fields.

Recent progress in identifying ligands for G protein coupled glutamate receptors (see Caroll et al, this volume) provides substantial hope that mGluR7 ligands will soon be available to test roles for this receptor in substance dependence and other brain functions. The high significance attained by ABCC4 also supports strong consideration of drugs targeting this interesting ABC cassette transporter.

The genes discussed here provide examples of potential therapeutic targets; many of the other genes identified by these studies may also provide equally tractable drug target sites. Indeed, some of the genes that are identified in genome wide data from only one or two phenotypes might conceivably provide targets for drugs with more favorable specificities and therapeutic indices than genes identified by GWA data for many phenotypes.

X. Directions, summary and conclusions

It is an exciting time to be able to summarize and review the rapidly-emerging data on the complex genetics of human addiction vulnerability and of related phenotypes. Genome wide association results for dependence on several different classes of addictive substances converge with each other in striking fashion that is highly unlikely to be due to chance. Studies of dependence phenotypes in samples of individuals from several different racial and ethnic backgrounds support the idea that many of the allelic variants that predispose to these common disorders are so evolutionarily old that they are present in members of each major current human population. These data, combined with the varying results from linkage-based studies, fit a genetic architecture for addiction that is based on polygenic contributions from common allelic variants. Such a genetic architecture is quite consistent with data from family, adoption and twin classical genetic studies.

The identification of genes with markers whose allelic frequencies distinguish addicts of several different ethnicities from matched controls supports “common disease/common allele” genetic architecture [287] for at least much of addiction vulnerability. The convergent data derived from studies of individuals with addictions to substances in several different pharmacological classes supports the idea that “higher order pharmacogenomic/pharmacogenetic” variations enhance vulnerability to many addictions. These results do not exclude additional contributions to addiction vulnerability from genomic variants that influence vulnerability to specific substances or variants that are found only in specific populations. Nevertheless, the findings presented here provide promise for enhancing understanding of features that are common to human addictions in ways that could facilitate efforts to personalize prevention and treatment strategies for debilitating addictive disorders.

Identification of addiction-associated variants in genes that are likely to alter the quality of brain connections provides a first step toward defining a new neurobiology for the underpinnings of specific diseases and phenotypes. For many of these diseases and phenotypes, only little current research focuses on direct study of brain connections. The “connectivity constellation” concepts that we introduce here support studies that develop and use current and novel means for assessing the qualities and quantities of brain connections, especially in contexts in which they assess their functional properties. We have identified contributions of connectivity constellation genes to volumes of the same brain regions in which many of these genes are expressed. This convergence may provide new insights into data that documents individual differences in frontal lobe volume and/or in function, detected by volumetric, deoxyglucose PET and/or fMRI imaging, for virtually all of the “connectivity constellation” phenotypes or disorders noted here [8, 117].

The addiction vulnerability genes identified in this work contribute to the growing body of data that implicates cell adhesion and related memory-like and other cognitive processes in addiction. Studies that alter reconsolidation and other memory-related processes using knockout mice, protein synthesis inhibitors and/or pharmacologic treatments demonstrate powerful influences on addictions [288, 289]. This empirical evidence enriches theoretical work that increasingly recognizes memory-like features for addiction [36] and work that implicates memory-associated brain regions in relapse to addiction. Such work also complements clinical observations which document that addicts’ enhanced vulnerabilities to substance abuse relapse can persist for decades after their last prior use of addictive substances.

There is also substantial evidence for generalization of these results from addiction. This evidence comes from the significant overlaps between the molecular genetics of addiction and the molecular genetics of a number of related phenotypes and disorders. Overlap with bipolar disorder provides one of several likely psychiatric diagnoses for which shared genetic influences are likely a priori, based on the substantial heritabilities of both addiction and the high frequency of addiction/bipolar disorder comorbidity [146, 147]. This same logic suggests that abundant shared genetics may well also underpin the frequent comorbidities between addictions and antisocial personality/conduct disorders [251]. Less compelling evidence points to overlaps with other depressive, anxiety and schizophrenic disorders as well [251].

We have sought evidence for genetic influences that are shared between addiction and 1) frontal lobe brain volumes, 2) cognitive function and 3) Alzheimer’s disease. Hypotheses about such shared genetic influences are based, in part, on initial observtions that so many of the genes that we and others have identified in addiction genome wide association relate to cell connections. These molecularly-based hypotheses were reinforced by the evidence for substantial, complex genetic components to each of these phenotypes. These hypotheses were strengthened by evidence, though often from small samples, that appears to document 1) small frontal lobe volumes in samples of addicts [19, 290], 2) lower performance levels on tests of cognitive and executive function in samples of addicts [291293], and 3) large roles of heritability vs little role for the drug exposure itself in determining the cognitive abilities of twin pair members who are discordant for cannabis use [294]. These hypotheses are further reinforced by 1) twin data that document strong shared genetic influences on frontal brain volumes and cognitive function measures [246, 247], 2) smaller head (and thus likely brain) sizes in individuals who go on to develop Alzheimer’s disease decades later [295], and 3) lower levels of educational attainment (and thus cognitive function) in individuals who go on to develop Alzheimer’s disease many years later [252, 253]. Little convincing prior evidence linked addiction liability directly with Alzheimer’s disease, although there are reasonably strong links between addiction vulnerability and the related, often-dementing neurodegenerative illness, Parkinson’s disease [296] (further reviewed in [297]). Identification of the genes documented here should lead to elucidation of features of addiction vulnerability that might arise preferentially from the “connectivity constellation” gene variants that also alter vulnerability to Alzheimer’s disease.

The focus on genes in the current analyses should not obscure the fact that many of the SNPs and loci identified in each of these genome wide association studies lie between annotated genes. As the biological roles for “intergenic” regions become better understood generally, it should be increasing.

Disease-associated markers both within and between genes can all begin to allow us to assess individual differences in vulnerability to addiction based on profiles of genotypes. In settings in which prevention of addiction is sought, addiction vulnerability genomic profiles could help to target more (or different) prevention resources to individuals at the most (or at different) genetic risk. When a therapeutic opiate is being considered for chronic, noncancer pain, for example, the costs of engendering substance dependence are likely to be sufficient to justify genotyping even if the results provide only partial information about risk assessment and minimization for prescribing physicians. When treatment for an established dependence on nicotine, opiates or alcohol is being contemplated, a number of different therapeutic options with different pharmacological mechanisms of action are now available [298]. Subsets of the SNPs that we have associated with success in quitting smoking appear to provide selective influence success in responding to bupropion, while others appear to provide selective influences on success in response to nicotine replacement. Replication and extension of these observations to treatments for alcohol, opiates and other addictive substances will make it more and more likely that SNP markers will increasingly aid “personalization” of antiaddiction therapies within the near future, in ways that are now impacting the design of clinical trials in this area.

The shared genetic influences on addiction with these other brain based phenotypes and our implication of roles for variants in “cell adhesion” genes in many of these phenotypes underscore the likelihood that the biology of these systems will demand better and better approaches to understanding more of the details of the estimated ca. 1014 synaptic connections in the cerebral cortex, and those in other areas [299]. Gross phenotypes, such as lobar brain volumes, are likely to vastly underrepresent the true complexity of the qualitative differences that are driven by these gene variants, and provide only weak reporters for quantitative differences. The present genetic analyses underscore the urgency of developing better approaches to elucidating individual differences in connectivities in human brain, both living and postmortem.

As we elaborate and extend these molecular genetic approaches, it seems likely that the number of genes with variants that contribute to individual differences in human vulnerabilities to addiction and related phenotypes will continue to grow, and that many of the genes listed here will receive additional support. By the time that this review is printed, more detailed data from arrays that contain markers for almost 1 million SNP and 1 million copy number variant probes will enhance the picture that we present here (Liu et al, in preparation). These studies will add power to detect differences in many of the genes and intragenic regions that are not well studied using the 500 – 600k datasets analyzed in this review, and to allow us to understand potential roles of copy number variation in these addiction and related phenotypes. More and more such studies will provide more and more ability to both better understand, better prevent and better treat these common and debilitating conditions.

This work, taken together, supports the idea that the heritable brain bases for individual differences in addiction vulnerability lie squarely in the midst of the repertoire of common complex determinants of individual differences that are manifest in many heritable complex brain disorders and phenotypes. Such conclusions place the biology of addictions squarely in the midst of important biologies of a number of brain phenotypes and disorders, hopefully in ways that will benefit them all.

Supplementary Material

Supp Doc S1

Supp Table S1


We thank subjects for each of these samples. We thank collaborators who include the H Ujike and the JGIDA methamphetamine investigators, SK Li and the Taiwan methamphetamine investigators, Jed Rose, Caryn Lerman, Ray Niaura, Sean David, Gary Swan, Christina Lessov-Schlaggar. We are grateful to TGen, Glaxo Smith Kline, Framingham study investigators, the NicSNP group and the Wellcome Trust Case-Control Consortium for access to genotype data analyzed here. Rachel Gibson, S Seshadri and J Pollock were of especial help with obtaining access to the GSK, Framingham and NicSNP datasets cited here. We are grateful for dedicated help with clinical characterization of NIDA subjects from Dan Lipstein, Fely Carillo, Carlo Contoreggi, Fred Snyder and other Johns Hopkins-Bayview support staff. We benefited from passionate discussions of statistical issues with Dr Daniel Naiman and from the Baltimore Epidemiology Catechment Area follow up study that was generously provided by Dr. J. Anthony. We thank NHLBI staff and the twin study technicians and investigators, PA Wolf, BL Miller, T Reed, L Epstein, L Hawk, P Shields, F Patterson, A Pinto, M Rukstalis, W Berrettini, R Brown, E Richardson, F M. Behm, P Kukovich, E C Westman and G Samsa for their rigor in overseeing data collection at their research sites. We acknowledge financial support from NIH-IRP (NIDA), DHHS and are also grateful for support for some of the studies discussed in detail here from the Taiwan and Japanese Ministries for Science and Technology, NIH grants P50CA/DA84718, RO1CA 63562, HL32318, DA08511, P50CA84719, 1K08 DA14276-05, HL51429, support from the Welcome Trust (076113), the Pennsylvania Department of Health (which specifically disclaims responsibility for any analyses, interpretations, or conclusions), GlaxoSmithKline, Inc and unrestricted support for studies of adult smoking cessation from Phillip Morris USA. Some human brain tissues were obtained from the Brain and Tissue Bank for Developmental Disorders supported through NIH contract NO1-HD-1-3138.


A priori
Existing in the mind prior to and independent of experiments.
Balancing selection
A natural process that results in the survival and reproductive success of individuals or groups best adjusted to their environment and that leads to the perpetuation of genetic qualities best suited to that particular environment.
Between locus heterogeneity
A single disorder, trait, or pattern of traits caused by mutations in genes at different chromosomal loci.
Common disease and common allele model
The illness results from the cumulative impact of multiple common small-effect, genetic variants, interacting with environmental exposures to exceed a biological threshold.
Complex genetic phenotype (polygenic and multifactorial traits)
Any phenotype that results from the effect of multiple genes at two or more loci, with possible environmental influences too.
Changes in the regulation of the expression of gene activity without alteration of DNA sequence.
A mutation in one gene masks the expression of a different gene.
Genetic heterogeneity
A single disorder, trait, or pattern of traits caused by genetic factors in some cases and non-genetic factors in others.
Genetic selection
Differential and non-random reproduction of different genotypes, operating to alter the gene frequencies within a population.
Genome wide association study
Any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits (such as blood pressure or weight), or the presence or absence of a disease or condition.
The tendency for genes or segments of DNA closely positioned along a chromosome to segregate together at meiosis and therefore be inherited together.
Linkage analysis
Testing DNA sequence polymorphisms that are near or within a gene of interest to track within a family the inheritance of a disease-causing in a given gene.
Linkage disequilibrium
In a population, co-occurrence of a specific DNA marker and a disease at a higher frequency than would be predicted by random chance.
The study of how an individual's genomic system affects the body's response to drugs.
The study focused on specific genes, such as drug-metabolizing enzymes.
Multiple, often seemingly unrelated, physical effects caused by a single altered gene or pair of altered genes.
Segregation analysis
The determination of the number of progeny that have inherited distinct and mutually exclusive phenotypes.
Susceptibility gene
A gene mutation that increases the likelihood that an individual will develop a certain disease or disorder. When such a mutation is inherited, development of symptoms is more likely but not certain.
Passing over to or affecting something else.
Within locus heterogeneity
A single disorder, trait, or pattern of traits influenced by several different variants at a single chromosomal locus


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