Gelernter J, Yu Y, Weiss R, Brady K, Panhuysen C, Yang BZ et al. Haplotype spanning TTC12 and ANKK1, flanked by the DRD2 and NCAM1 loci, is strongly associated to nicotine dependence in two distinct American populations. Hum Mol Genet 15: 3498-3507

Department of Medicine, Boston University, Boston, Massachusetts, United States
Human Molecular Genetics (Impact Factor: 6.39). 01/2007; 15(24):3498-507. DOI: 10.1093/hmg/ddl426
Source: PubMed


Nicotine dependence (ND) is a moderately heritable trait. We ascertained a set of 1615 subjects in 632 families [319 African-American (AA) and 313 European-American (EA)] based on affected sibling pairs with cocaine or opioid dependence. Subjects were interviewed with the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA). Previously, we identified a modest linkage peak (LOD score =1.97) for ND in the EA part of the sample on chromosome 11q23, a region that includes the NCAM1-TTC12-ANKK1-DRD2 gene cluster. DRD2 and NCAM1 are functional candidate genes for substance dependence; the TTC12 and ANKK1 loci are not well characterized. We genotyped a set of 43 single nucleotide polymorphisms (SNPs) spanning this region, and performed family-based association and haplotype analysis. There was relatively weak evidence for association of the flanking DRD2 and NCAM1 markers to ND, but very strong evidence of association of multiple SNPs at TTC12 and ANKK1 in both populations (minimal P=0.0007 in AAs and minimal P=0.00009 in EAs), and in the pooled sample, as well as strong evidence for highly significant association of a single haplotype spanning TTC12 and ANKK1 to ND in the pooled sample (P=0.0000001). We conclude that a risk locus for ND, important both in AAs and EAs, maps to a region that spans TTC12 and ANKK1. Functional studies of these loci are warranted. These results provide additional information useful in evaluating the many earlier discrepant findings regarding association of DRD2 with substance dependence.

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    • "This may represent a possible mechanism underlying findings suggesting the association between MDD susceptibility and polymorphisms in both NCAM1 and DRD2 genes shown in the present study. Taken together, our findings and the overall literature show that variation in the NTAD cluster may also exert a pleiotropic effect since the SNPs and haplotype regions that were associated with internalizing phenotypes in the present study were previously implicated in externalizing characteristics, specially SUD [Gelernter et al., 2006; Yang et al., 2007, 2008; Ducci et al., 2011; Nelson et al., 2013]. Evidence showing that temperament traits, such as Harm Avoidance for example, could underlie the vulnerability to both externalizing [Downey et al., 1997; Jacob et al., 2007; Faraone et al., 2009; Etter et al., 2010; Teh et al., 2012; Milivojevic et al., 2012] and internalizing [Ongur et al., 2005; Purper-Ouakil et al., 2010; Miettunen and Raevuori, 2012] disorders could provide a plausible explanation for such effect. "
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    ABSTRACT: Dysfunctions of the dopaminergic system have been implicated on the etiology of Attention Deficit/Hyperactivity Disorder (ADHD). Meta-analyses addressing the association of the dopamine receptor D2 (DRD2) gene and ADHD were inconclusive due to excessive heterogeneity across studies. Both the great phenotypic heterogeneity of ADHD and the complexity of the genomic region where DRD2 is located could contribute to the inconsistent findings. Most previous DRD2 studies focused on the well-known Taq1A (rs1800497) SNP, which is actually placed in a neighbor gene (ANKK1). These two genes, together with NCAM1 and TTC12, form the NTAD gene cluster on Chr11q22-23. In order to address the reasons for the high heterogeneity previously reported on DRD2 effects on ADHD, this study investigates the role of NTAD variants on ADHD susceptibility in adults and on the modulation of comorbidity and personality profiles in these patients. Functional polymorphisms from NTAD were analyzed, both individually and in haplotypes, on a sample of 520 adults with ADHD and 630 non-ADHD controls. No direct association of NTAD variants with ADHD susceptibility itself was observed. However, different NTAD polymorphisms and haplotypes were associated to various phenotypes relevant to the clinical heterogeneity of ADHD, including Major Depressive Disorder, Generalized Anxiety Disorder, and Harm Avoidance and Persistence temperament scores. Therefore, these findings represent a possible explanation for the multiple conflicting findings regarding polymorphisms in this genomic region in psychiatry. The NTAD cluster may comprise a variety of independent molecular influences on various brain and behavior characteristics eventually associated with ADHD comorbidities and personality traits. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
    American Journal of Medical Genetics Part B Neuropsychiatric Genetics 05/2015; 168(6):433-444. DOI:10.1002/ajmg.b.32317 · 3.42 Impact Factor
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    • "Numerous risk loci have been reported for alcohol dependence by the candidate gene approach. Most of these risk genes implicated have been from (1) classical neurotransmitter signaling systems, including the dopaminergic (e.g., MAOA, COMT, and NCAM1-TTC12-ANKK1-DRD2; Bau et al., 2001; Hutchison et al., 2002; Dick and Foroud, 2003; Olsson et al., 2004; Dahmen et al., 2005; Kohnke et al., 2005; Gelernter et al., 2006; Stapleton et al., 2007; Huang et al., 2008; Yang et al., 2008), serotoninergic (e.g., SLC6A4 and HTR2B), GABAergic (e.g., GABRA2 and GABRG1), and cholinergic systems (e.g., CHRM2 and CHRNA5-CHRNA3-CHRNB4); (2) non-classical neurotransmitter signaling systems (e.g., CRHR1); (3) the ethanol metabolic pathway (e.g., ADH1B, ADH1C, ADH4 and ALDH2; Luo et al., 2006b; Uhl et al., 2008); and (4) the opioidergic signaling pathway (e.g., OPRM1, OPRD1 and OPRK1; Gelernter and Kranzler, 2009; Ray et al., 2006; Zhang et al., 2006). In recent years, genome-wide association studies (GWASs; Treutlein et al., 2009; Bierut et al., 2010; Edenberg et al., 2010; Heath et al., 2011) also reported risk loci for alcohol dependence (summarized previously in Zuo et al., 2012). "
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    ABSTRACT: Objective: We aimed to identify novel, functional, replicable and genome-wide significant risk regions specific for alcohol dependence using genome-wide association studies (GWASs). Methods: A discovery sample (1409 European-American cases with alcohol dependence and 1518 European-American controls) and a replication sample (6438 European-Australian family subjects with 1645 alcohol dependent probands) underwent association analysis. Nineteen other cohorts with 11 different neuropsychiatric disorders served as contrast groups. Additional eight samples underwent expression quantitative locus (eQTL) analysis. Results: A genome-wide significant risk gene region (NKAIN1-SERINC2) was identified in a meta-analysis of the discovery and replication samples. This region was enriched with 74 risk SNPs (unimputed); half of them had significant cis-acting regulatory effects. The distributions of -log(p) values for the SNP-disease associations or SNP-expression associations in this region were consistent throughout eight independent samples. Furthermore, imputing across the NKAIN1-SERINC2 region, we found that among all 795 SNPs in the discovery sample, 471 SNPs were nominally associated with alcohol dependence (1.7×10(-7)≤p≤0.047); 53 survived region- and cohort-wide correction for multiple testing; 92 SNPs were replicated in the replication sample (0.002≤p≤0.050). This region was neither significantly associated with alcohol dependence in African-Americans, nor with other non-alcoholism diseases. Finally, transcript expression of genes in NKAIN1-SERINC2 was significantly (p<3.4×10(-7)) associated with expression of numerous genes in the neurotransmitter systems or metabolic pathways previously associated with alcohol dependence. Conclusion: NKAIN1-SERINC2 may harbor a causal variant(s) for alcohol dependence. It may contribute to the disease risk by way of neurotransmitter systems or metabolic pathways.
    Drug and alcohol dependence 02/2013; 129(3). DOI:10.1016/j.drugalcdep.2013.02.006 · 3.42 Impact Factor
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    • "Furthermore, the GATC haplotype was associated with ND (EAs: P \ 0.00006; AAs: P \ 0.0008; pooled: P \ 0.0000001) showing strong evidence for association of the same haplotype with ND. In these two distinct populations , the AGCT and AGTC haplotypes were associated with decreased risk of ND (EAs: P \ 0.001; AAs: P \ 0.0009) (Gelernter et al. 2006). "
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    ABSTRACT: Here we summarize evidence obtained by our group during the last two decades, and contrasted it with a review of related data from the available literature to show that behavioral syndromes involving attention deficit/hyperactivity disorder (ADHD), externalizing disorders, and substance-use disorder (SUD) share similar signs and symptoms (i.e., have a biological basis as common syndromes), physiopathological and psychopathological mechanisms, and genetic factors. Furthermore, we will show that the same genetic variants harbored in different genes are associated with different syndromes and that non-linear interactions between genetic variants (epistasis) best explain phenotype severity, long-term outcome, and response to treatment. These data have been depicted in our studies by extended pedigrees, where ADHD, externalizing symptoms, and SUD segregate and co-segregate. Finally, we applied here a new formal network analysis using the set of significantly replicated genes that have been shown to be either associated and/or linked to ADHD, disruptive behaviors, and SUD in order to detect significantly enriched gene categories for protein and genetic interactions, pathways, co-expression, co-localization, and protein domain similarity. We found that networks related to pathways involved in axon guidance, regulation of synaptic transmission, and regulation of transmission of nerve impulse are overrepresented. In summary, we provide compiled evidence of complex networks of genotypes underlying a wide phenotype that involves SUD and externalizing disorders.
    Human Genetics 04/2012; 131(6):917-29. DOI:10.1007/s00439-012-1164-4 · 4.82 Impact Factor
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