Smoking Cessation and Variations in Nicotinic Acetylcholine Receptor Subunits α-5, α-3, and β-4 Genes

Breitling, Lutz Philipp; Dahmen, Norbert; Mittelstraß, Kirstin; Illig, Thomas; Rujescu, Dan; Raum, Elke; Winterer, Georg; Brenner, Hermann

doi:10.1016/j.biopsych.2008.10.004

Cited by 39 publications

(28 citation statements)

References 23 publications

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“…However, Thorgeirsson et al [2010] did not find genomewide association with SI. In addition, we recently reported in a cohort of 1,446 heavy smoking subjects no association between smoking cessation success and seven SNPs from the 15q24 locus [Breitling et al, 2009]. Together, these studies may suggest that for some yet unknown reasons the association of the CHRNA5-CHRNA3-CHRNB4 gene cluster with ND could be related to factors that are perhaps indirectly related to SI during adolescence.…”

Section: Introductionmentioning

confidence: 83%

Risk gene variants for nicotine dependence in the CHRNA5–CHRNA3–CHRNB4 cluster are associated with cognitive performance

Winterer

Mittelstraß

Giegling

et al. 2010

American J of Med Genetics Pt B

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Recent studies strongly support an association of the nicotinic acetylcholine receptor gene cluster CHRNA5-CHRNA3-CHRNB4 with nicotine dependence (ND). However, the precise genotype-phenotype relationship is still unknown. Clinical and epidemiological data on smoking behavior raise the possibility that the relevant gene variants may indirectly contribute to the development of ND by affecting cognitive performance in some smokers who consume nicotine for reasons of "cognition enhancement." Here, we tested seven single nucleotide polymorphisms (SNPs) rs684513, rs637137, rs16969968, rs578776, rs1051730, rs3743078, rs3813567 from the CHRNA5-CHRNA3-CHRNB4 gene cluster for association with ND, measures of cognitive performance and gene expression. As expected, we found all SNPs being associated with ND in three independent cohorts (KORA, NCOOP, ESTHER) comprising 5,561 individuals. In an overlapping sample of 2,186 subjects we found three SNPs (rs16969968, rs1051730, rs3743078) being associated with cognitive domains from the Wechsler-Adult-Intelligence Scale (WAIS-R)-most notably in the performance subtest "object assembly" and the verbal subtest "similarities." In a refined analysis of a subsample of 485 subjects, two of these three SNPs (rs16969968, rs1051730) were associated with n-back task performance/Continuous Performance Test. Furthermore, two CHRNA5 risk alleles (rs684513, rs637137) were associated with CHRNA5 mRNA expression levels in whole blood in a subgroup of 190 subjects. We here report for the first time an association of CHRNA5-CHRNA3-CHRNB4 gene variants with cognition possibly mediating in part risk for developing ND. The observed phenotype-genotype associations may depend on altered levels of gene expression. © 2010 Wiley-Liss, Inc.

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Section: Introductionmentioning

confidence: 83%

Risk gene variants for nicotine dependence in the CHRNA5–CHRNA3–CHRNB4 cluster are associated with cognitive performance

Winterer

Mittelstraß

Giegling

et al. 2010

American J of Med Genetics Pt B

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“…90 On the other hand, no association of this gene cluster with smoking cessation was found in a large, well-powered retrospective cohort study from Germany. 91 Seven SNPs which are related to other ND phenotypes were genotyped (rs684513, rs637137, rs16969968, rs578776, rs1051730, rs3743078, rs3813567) in a sample of 1446 heavy smoking individuals ( > 20 CPD). No significant association with smoking cessation probability was found, although the study had enough power to detect even small effects.…”

Section: Chrna5-chrna3-chrnb4 Clustermentioning

confidence: 99%

“…These results suggest that CHRNA5-CHRNA3-CHRNB4 variants contribute to the development of ND, but not to the likelihood of cessation when the ND is already well established. 91 An important reservation is that all the GWAS and case-control studies reviewed above (except the relatively small sample of Sherva et al 89 ) were conducted on European origin samples. Individual SNP associations may be different, or even not exist at all, among diverse ethnic groups.…”

Section: Chrna5-chrna3-chrnb4 Clustermentioning

confidence: 99%

Differential contribution of genetic variation in multiple brain nicotinic cholinergic receptors to nicotine dependence: recent progress and emerging open questions

Greenbaum¹,

Lerer²

2009

Mol Psychiatry

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Nicotine dependence (ND), a major public health challenge, is a complex, multifactorial behavior, in which both genetic and environmental factors have a role. Brain nicotinic acetylcholine receptor (nAChR)-encoding genes are among the most prominent candidate genes studied in the context of ND, because of their biological relevance as binding sites for nicotine. Until recently, most research on the role of nAChRs in ND has focused on two of these genes (encoding the a4-and b2-subunits) and not much attention has been paid to the possible contribution of the other nine brain nAChR subunit genes (a2-a3, a5-a7, a9-a10, b3-b4) to the pathophysiology and genetics of ND. This situation has changed dramatically in the last 2 years during which intensive research had addressed the issue, mainly from the genetics perspective, and has shown the importance of the CHRNA5-CHRNA3-CHRNB4 and CHRNA6-CHRNB3 loci in ND-related phenotypes. In this review, we highlight recent findings regarding the contribution of non-a4/b2-subunit containing nAChRs to ND, based on several lines of evidence: (1) human genetics studies (including linkage analysis, candidate-gene association studies and whole-genome association studies) of several ND-related phenotypes; (2) differential pharmacological and biochemical properties of receptors containing these subunits; (3) evidence from genetically manipulated mice; and (4) the contribution of nAChR genes to ND-related personality traits and neurocognitive profiles. Combining neurobiological genetic and behavioral perspectives, we suggest that genetic susceptibility to ND is not linked to one or two specific nAChR subtype genes but to several. In particular, the a3, a5-6 and b3-4 nAChR subunit-encoding genes may play a much more pivotal role in the neurobiology and genetics of ND than was appreciated earlier. At the functional level, variants in these subunit genes (most likely regulatory) may have independent as well as interactive contributions to the ND phenotype spectrum. We address methodological challenges in the field, highlight open questions and suggest possible pathways for future research.

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“…A detailed comparison of these different algorithms can be found in the study by Dellinger et al [11] . These 3 programs have often helped to find putative disease-related CNVs [18][19][20][21][22] . Moreover, several recent studies have used SNP microarray data to study the characteristics of CNVs [14,23] .…”

Section: Introductionmentioning

confidence: 99%

“…Although laboratory confirmation is necessary to validate CNVs derived from SNP array platforms [2,12,[19][20][21][22] , it is not economically feasible to validate all CNVs in a genome-wide scale, especially for the purpose of estimating measurement accuracy. Here, using duplicates in a GWAS sample, we develop an algorithm to better evaluate the accuracy of CNVs predicted by several CNV calling algorithms for GWAS data.…”

Section: Introductionmentioning

confidence: 99%

Copy Number Variation Accuracy in Genome-Wide Association Studies

et al. 2011

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Background/Aim: Copy number variations (CNVs) are a major source of alterations among individuals and are a potential risk factor in many diseases. Numerous diseases have been linked to deletions and duplications of these chromosomal segments. Data from genome-wide association studies and other microarrays may be used to identify CNVs by several different computer programs, but the reliability of the results has been questioned. Methods: To help researchers reduce the number of false-positive CNVs that need to be followed up with laboratory testing, we evaluated the relative performance of CNVPartition, PennCNV and QuantiSNP, and developed a statistical method for estimating sensitivity and positive predictive values of CNV calls and tested it on 96 duplicate samples in our dataset. Results: We found that the positive predictive rate increases with the number of probes in the CNV and the size of the CNV, with the highest positive predicted rates in CNVs of at least 500 kb and at least 100 probes. Our analysis also indicates that identifying CNVs reported by multiple programs can greatly improve the reproducibility rate and the positive predicted rate. Conclusion: Our methods can be used by investigators to identify CNVs in genome-wide data with greater reliability.

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Smoking Cessation and Variations in Nicotinic Acetylcholine Receptor Subunits α-5, α-3, and β-4 Genes

Cited by 39 publications

References 23 publications

Risk gene variants for nicotine dependence in the CHRNA5–CHRNA3–CHRNB4 cluster are associated with cognitive performance

Risk gene variants for nicotine dependence in the CHRNA5–CHRNA3–CHRNB4 cluster are associated with cognitive performance

Differential contribution of genetic variation in multiple brain nicotinic cholinergic receptors to nicotine dependence: recent progress and emerging open questions

Copy Number Variation Accuracy in Genome-Wide Association Studies

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