Genetic susceptibility to nicotine addiction: Advances and shortcomings in our understanding of the CHRNA5/A3/B4 gene cluster contribution

Icick, Romain; Forget, Benoît; Cloëz‐Tayarani, Isabelle; Pons, Stéphanie; Maskos, Uwe; Besson, Morgane

doi:10.1016/j.neuropharm.2020.108234

Cited by 16 publications

(15 citation statements)

References 218 publications

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“…In our application analysis, Dense-LD identified causal variants from different haplotype blocks in an extended genomic region of 1733 SNPs on chromosome 15. The genes of these causal variants (e.g., IREB2, CHRNA3, CHRNA5, CHRNB4, and HYKK) are related to smoking-related diseases and functionally correlated [8,11,72,73]. Our method showed that the selected SNPs from these genes are strongly correlated, although many are distant.…”

Section: Discussionmentioning

confidence: 90%

“…Thousands of diseases-associated genetic loci have been discovered and replicated [2,3,9,10]; however, even replicated loci may differ from the causal variants due to linkage disequilibrium (LD) among proximal and distant loci. Specifically, non-causal variants can be associated with a trait because of the high correlation with the causal variants due to LD [4,11,12]. Hence, a post-hoc examination of trait associations is required to distinguish likely causal variants from those associated with the trait.…”

Section: Fine-mapping and Existing Methodsmentioning

confidence: 99%

“…An accurate fine-mapping model requires exact knowledge of the LD pattern (i.e., structure) among the SNPs within a GWAS identified region [4,11,12,35,36]. Previous research on multivariate statistics also has shown that the accuracy of variable selection relies on accurate knowledge of the network structure of the dependence between predictors [37,38,39,40,41,42,43].…”

Section: Dense Ld Block Structure and Fine Mappingmentioning

confidence: 99%

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Genetic Fine-Mapping With Dense Linkage Disequilibrium Blocks

Hatch

et al. 2021

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Background: Fine-mapping is an analytical step for causal prioritization of the polymorphic variants in a trait-associated genomic region observed in genome-wide association studies (GWAS). Prioritization of causal variants can be challenging due to linkage disequilibrium (LD) patterns among hundreds to thousands of polymorphisms associated with a trait. Hence, we propose an ℓ0 graph norm shrinkage algorithm to disentangle LD patterns by dense LD blocks consisting of highly correlated single nucleotide polymorphisms (SNPs). We further incorporate the dense LD structure for fine-mapping. Based on graph theory, the concept of "dense" refers to a condition where a block is composed mainly of highly correlated SNPs. We demonstrated the application of our new fine-mapping method using a large UK Biobank (UKBB) sample related to nicotine addiction. We also evaluated and compared its performance with existing fine-mapping algorithms using simulations.Results: Our results suggested that polymorphic variances in both neighboring and distant variants can be consolidated into dense blocks of highly correlated loci. Dense-LD outperformed comparable fine-mapping methods with increased sensitivity and reduced false-positive error rate for causal variant selection. Applying to a UKBB sample, this method replicated the loci reported in previous findings and suggested a strong association with nicotine addiction.Conclusion: We found that the dense LD block structure can guide fine-mapping and accurately determine a parsimonious set of potential causal variants. Our approach is computationally efficient and allows fine-mapping of thousands of polymorphisms.

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Section: Discussionmentioning

confidence: 90%

Section: Fine-mapping and Existing Methodsmentioning

confidence: 99%

Section: Dense Ld Block Structure and Fine Mappingmentioning

confidence: 99%

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Genetic Fine-Mapping With Dense Linkage Disequilibrium Blocks

Hatch

et al. 2021

Preprint

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“…La dépolarisation membranaire qui en résulte entraîne par exemple la contraction de la cellule musculaire, ou la libération de neurotransmetteurs par les neurones du cerveau. Les récepteurs nicotiniques neuronaux sont notamment responsables de l'addiction au tabac, qui implique le circuit dopaminergique de la récompense [7]. Nous avions précédemment décrit l'expression des dif-férentes sous-unités des récepteurs nicotiniques dans le poumon humain [8].…”

Section: Récepteur Nicotinique De L'acétylcholine Et Bronchopneumopat...unclassified

Récepteur nicotinique de l’acétylcholine et bronchopneumopathie chronique obstructive

et al. 2022

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“…Identification of DNA sequences for these genes led to efforts to identify variation in these genes that could be used for linkage and association studies, most commonly single nucleotide polymorphisms (SNPs). The cumulative findings of candidate gene studies of drug dependence have been extensively reviewed many times [Kreek et al, 2005;Gelernter and Kranzler, 2009;Jones and Comer, 2015], including reviews that focus upon particular substances [Stickel et al, 2017;Icick et al, 2020;Thorpe et al, 2020]. Many reviews have tended to indicate that there are greater degrees of concordance between studies than have actually been observed.…”

Section: Candidate Gene Studies: Guessing Which Genes Are Important For Addictionmentioning

confidence: 99%

The Streetlight Effect: Reappraising the Study of Addiction in Light of the Findings of Genome-wide Association Studies

2020

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Drug dependence has long been thought to have a genetic component. Research seeking to identify the genetic basis of addiction has gone through important transitions over its history, in part based upon the emergence of new technologies, but also as the result of changing perspectives. Early research approaches were largely dictated by available technology, with technological advancements having highly transformative effects on genetic research, but the limitations of technology also affected modes of thinking about the genetic causes of disease. This review explores these transitions in thinking about the genetic causes of addiction in terms of the “streetlight effect,” which is a type of observational bias whereby people search for something only where it is easiest to search. In this way, the genes that were initially studied in the field of addiction genetics were chosen because they were the most “obvious,” and formed current understanding of the biological mechanisms underlying the actions of drugs of abuse and drug dependence. The problem with this emphasis is that prior to the genomic era the vast majority of genes and proteins had yet to be identified, much less studied. This review considers how these initial choices, as well as subsequent choices that were also driven by technological limitations, shaped the study of the genetic basis of drug dependence. While genome-wide approaches overcame the initial biases regarding which genes to choose to study inherent in candidate gene studies and other approaches, genome-wide approaches necessitated other assumptions. These included additive genetic causation and limited allelic heterogeneity, which both appear to be incorrect. Thus, the next stage of advancement in this field must overcome these shortcomings through approaches that allow the examination of complex interactive effects, both gene × gene and gene × environment interactions. Techniques for these sorts of studies have recently been developed and represent the next step in our understanding of the genetic basis of drug dependence.

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Genetic susceptibility to nicotine addiction: Advances and shortcomings in our understanding of the CHRNA5/A3/B4 gene cluster contribution

Abstract: Highlights (85 characters including space each)• CHRNA5/A4/B4 and nicotine addiction link on SNPs in high linkage disequilibrium• CHRNA5/A4/B4 cross-regulatory mechanisms should be considered in preclinical models

Cited by 16 publications

References 218 publications

Genetic Fine-Mapping With Dense Linkage Disequilibrium Blocks

Genetic Fine-Mapping With Dense Linkage Disequilibrium Blocks

Récepteur nicotinique de l’acétylcholine et bronchopneumopathie chronique obstructive

The Streetlight Effect: Reappraising the Study of Addiction in Light of the Findings of Genome-wide Association Studies

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