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Cited by 1,896Genetic Associations and EpidemiologySmoking Behavior and CessationSubstance Abuse Treatment and Outcomes

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use

Open access
23andMe Research Team, Liu, Mengzhen, HUNT All-In Psychiatry, Jiang, Yu, Wedow, Robbee, Li, Yue, Brazel, David M., Chen, Fang +117 more·2019·Nature Genetics
DOI 10.1038/s41588-018-0307-5PMC PMC6358542
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Abstract

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6–11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures. Association studies of up to 1.2 million individuals identify 566 genetic variants in 406 loci associated with tobacco use and addiction (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci showing pleiotropic association.

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Written from the paper’s full text.

TL;DR

This large GWAS meta-analysis of up to 1.2 million individuals identified 566 variants in 406 loci for tobacco and alcohol use, with 150 loci showing pleiotropic effects across phenotypes. It provides evidence for nicotinic, dopaminergic, and glutamatergic systems and shows contrasting genetic correlations of smoking versus alcohol use with disease risk.

Key findings

  • Discovered 566 conditionally independent genome-wide significant variants in 406 loci across five phenotypes: smoking initiation (SmkInit, N=1,232,091; 378 variants), age of smoking initiation (AgeSmk, N=341,427; 10), cigarettes per day (CigDay, N=337,334; 55), smoking cessation (SmkCes, N=547,219; 24), and drinks per week (DrnkWk, N=941,280; 99).
  • Found 150 loci with pleiotropic association across multiple substance-use phenotypes; only three loci were significantly associated with all five phenotypes, implicating PDE4B and CUL3. MTAG increased discovery to 1,193 independent genome-wide significant common variants (MAF >1%): 173 AgeSmk, 89 CigDay, 83 SmkCes, 692 SmkInit, and 156 DrnkWk.
  • Initial 566 variants explained 0.1% (SmkCes) to 2.3% (SmkInit) of phenotypic variation; SNP heritability ranged from 4.2% (DrnkWk) to 8.0% (CigDay). Polygenic scores accounted for approximately 1%, 4%, 1%, 4%, and 2.5% of variance in AgeSmk, CigDay, SmkCes, SmkInit, and DrnkWk, respectively.
  • Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with disease risk; DrnkWk was not highly correlated with smoking phenotypes except SmkInit (rg~.34, p=6.7e−63). Replicated CYP2A6 associations with CigDay (p=4.0×10−99) and SmkCes (p=1.6×10−48), and ADH1B with DrnkWk (lowest p<2.2×10−303).

Methods

  • GWAS meta-analysis of five tobacco/alcohol phenotypes in up to 1.2 million individuals; studies were genotyped on genome-wide arrays and mostly imputed to the Haplotype Reference Consortium, with summary statistics generated using RVTESTS and meta-analyzed centrally with rareGWAMA using an imputation-quality-aware fixed-effects approach.
  • Conditional analysis identified independent variants; MTAG leveraged genetic correlations for locus discovery; LD Score Regression estimated SNP heritability and genetic correlations; LDpred polygenic scores were tested in the Health and Retirement Study and Add Health.
  • Functional follow-up included cell/tissue enrichment using LDSC and a mixture model, gene and gene-set tests with PASCAL and DEPICT, and manual review of implicated genes and nonsynonymous variants.

Limitations

  • Stated in the paper: specific mechanisms by which implicated genes influence substance use in humans are largely unknown; the authors caution that strong causal conclusions should not be drawn from the complex causal web; and they call for more precise measurements, functional research, and complementary gene-mapping approaches such as sequencing.
  • Stated in the paper: available alcohol-use measures were simpler than smoking measures, with drinks per week widely available and similarly measured across studies.
  • Evident from the text: the meta-analysis is described in the context of European individuals and single-ancestry groups, and no trans-ancestry analyses are reported, so generalizability to other ancestries is not established.

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Integrity signals

Registry

Crossref · 10.1038/s41588-018-0307-5

Cites

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Statistics

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Claims the paper makes

0 of 58 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    In sample sizes up to 1.2 million individuals, the study discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use and alcohol use.

    “In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association.”
    Quote in the paperSubstantial claim
  • Findingown data

    The initial GWAS identified 10 associated variants for age of initiation of regular smoking in 341,427 individuals.

    “Smoking initiation phenotypes included age of initiation of regular smoking (AgeSmk; N=341,427; 10 associated variants)”
    Quote in the paperNot a substantial claim
  • Findingown data

    The initial GWAS identified 55 associated variants for cigarettes per day in 337,334 individuals.

    “Heaviness of smoking was measured with cigarettes per day (CigDay; N=337,334; 55 associated variants).”
    Quote in the paperSubstantial claim
  • Findingown data

    The initial GWAS identified 378 associated variants for ever having smoked regularly in 1,232,091 individuals.

    “a binary phenotype indicating whether an individual had ever smoked regularly (SmkInit, N=1,232,091; 378 associated variants)”
    Quote in the paperSubstantial claim
  • Findingown data

    150 loci showed pleiotropic association.

    “with 150 loci evidencing pleiotropic association”
    • Also stated in: § Abstract · plain
      “we observed 150 loci that affected multiple substance use phenotypes (Supplementary Table 11), summarized in Figure 2.”
    Quote in the paperSubstantial claim
  • May combine two claims. 33
  • Classified as probably not a substantial claim. 26

  • May combine two claims.

    Findingown data

    Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, so higher genetic risk for alcohol use was associated with lower disease risk.

    “Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The polygenic score accounted for approximately 1%, 4%, 1%, 4%, and 2.5% of variance in AgeSmk, CigDay, SmkCes, SmkInit, and DrnkWk, respectively, about half of estimated SNP heritability.

    “It accounted for approximately 1%, 4%, 1%, 4%, and 2.5% of variance in AgeSmk, CigDay, SmkCes, SmkInit, and DrnkWk, respectively, about half of the estimated SNP heritability of these traits (Figure 3).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    DrnkWk was not highly genetically correlated with the smoking phenotypes (rg~.10) except for SmkInit (rg~.34, p=6.7e−63).

    “DrnkWk was not highly genetically correlated with the smoking phenotypes (rg~.10) except for SmkInit (rg~.34, p=6.7e−63)”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Genetic variation associated with increased alcohol use was associated with greater risky behavior (rg=.20, p=1.8×10−7) and cannabis use (rg=.36, p=6.2×10−10), but with less risk of disease for almost all diseases.

    “Genetic variation associated with increased alcohol use was associated with greater levels of risky behavior (rg=.20, p=1.8×10−7) and cannabis use (rg=.36, p=6.2×10−10), but with less risk of disease, for almost all diseases (Figure 1, Supplementary Table 10).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In Add Health and the Health and Retirement Study, a one SD increase in SmkInit risk score resulted in 12% and 10% increased risk of regularly smoking, respectively.

    “a one SD increase on the SmkInit risk score resulted in a 12% and 10% increased risk of regularly smoking”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Patterns of pleiotropy across phenotypes were highly diverse, with only three loci significantly associated with all five phenotypes.

    “Patterns of pleiotropy across phenotypes were highly diverse, with only three loci significantly associated with all five phenotypes.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Variants near DRD2 were associated with CigDay, SmkCes, and DrnkWk (p=6.5×10−12, 1.1×10−10, and 4.9×10−11, respectively) but not with AgeSmk or SmkInit.

    “We found that variants near the widely studied dopamine receptor D2 (DRD2)28 were associated across phenotypes, including CigDay, SmkCes, and DrnkWk (p=6.5×10−12, 1.1×10−10, and 4.9×10−11, respectively) but not with AgeSmk or SmkInit”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [28] Koob, G. F. & Volkow, N. D. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry 3, 760–773 (2016). source

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    Individual associated genes related to these pathways included GRIN2A (p=3.4×10−11) and HOMER2 (p=3.1×10−14).

    “Individual associated genes related to these pathways included glutamate ionotropic receptor NMDA type subunit 2 (GRIN2A, p=3.4×10−11) and homer scaffolding protein 2 (HOMER2, p=3.1×10−14)”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In Add Health and the Health and Retirement Study, a one SD increase in CigDay risk score resulted in two and three additional daily cigarettes, respectively.

    “a one SD increase in the CigDay risk score resulted in two and three additional daily cigarettes”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The study replicated an association signal in ADH1B associated with DrnkWk, identifying 11 conditionally independently associated variants (lowest p<2.2×10−303).

    “We replicated an association signal in alcohol metabolism gene ADH1B associated with DrnkWk, identifying in that locus 11 conditionally independently associated variants (lowest p<2.2×10−303).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    With the exception of CHRNA7, all CNS-expressed nicotinic receptor genes were significantly associated with one or more smoking phenotypes, many reported for the first time.

    “With the exception of CHRNA7, all CNS-expressed nicotinic receptor genes were significantly associated with one or more smoking phenotypes, many reported here for the first time.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    rs1024323 in GRK4 was associated with CigDay (p=8.7×10−9) and lies within a locus associated with AgeSmk.

    “rs1024323 in G protein-coupled receptor (GPCR) kinase 4 (GRK4) was associated with CigDay (p=8.7×10−9) and lies within a locus associated with AgeSmk.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    One low-frequency variant, rs181508347 (intergenic, MAF=0.0096, p=5×10−10), is associated with smoking initiation and passed the more stringent multiple-testing correction.

    “one low-frequency variant, associated with SmkInit, survived the more stringent multiple testing correction (rs181508347, intergenic, MAF=.0096, p=5×10−10)”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingcitation

    The three techniques yielded an estimate of 9.8-10.1 million independent tests, comparable to other independent estimates.

    “The three techniques estimated the number of independent variants at 9.8–10.1 million independent tests, similar to other independent estimates64.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [64] Wu, Y., Zheng, Z. L., Visscher, P. M. & Yang, J. Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data. Genome Biol. 18, 86 (2017). source

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    In the 406 GWAS loci, 4% of sentinel variants were nonsynonymous, representing significant enrichment (p=2.5×10−10; 0.4% of variants with MAF>0.1% in the imputation panel were nonsynonymous).

    “In the 406 GWAS loci, 4% of sentinel variants were nonsynonymous, representing a significant enrichment (p=2.5×10−10; 0.4% of variants with MAF>0.1% in the imputation panel41 were nonsynonymous).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [41] McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Tobacco and alcohol use are heritable and etiologically related behaviors.

    “They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6–11.”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [2] Hicks, B. M., Schalet, B. D., Malone, S. M., Iacono, W. G. & McGue, M. Psychometric and genetic architecture of substance use disorder and behavioral disinhibition measures for gene association studies. Behav. Genet. 41, 459–475 (2011). source

      Not checked in the free run.

    • cites [3] Polderman, T. J. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015). source

      Not checked in the free run.

    • cites [4] Kendler, K. S., Schmitt, E., Aggen, S. H. & Prescott, C. A. Genetic and environmental influences on alcohol, caffeine, cannabis, and nicotine use from early adolescence to middle adulthood. Arch. Gen. Psychiatry 65, 674–682 (2008). source

      Not checked in the free run.

    • cites [5] Kendler, K. S., Prescott, C. A., Myers, J. & Neale, M. C. The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Arch. Gen. Psychiatry 60, 929–937 (2003). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    These phenotypes are highly polygenic and the majority of heritability is accounted for by variants below standard GWAS thresholds.

    “The results suggest that these phenotypes are highly polygenic and the majority of the heritability is accounted for by variants below standard GWAS thresholds.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    All drugs of abuse activate the mesolimbic dopamine system reward pathway.

    “All drugs of abuse activate the mesolimbic dopamine system reward pathway27, and dopamine-related genes have long been popular candidate genes.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [27] Koob, G. F. & Volkow, N. D. Neurocircuitry of addiction. Neuropsychopharmacology 35, 217–238 (2010). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Neuronal acetylcholine nicotinic receptors are the initial site of nicotine action in the brain and have long been implicated in nicotine use and dependence.

    “Neuronal acetylcholine nicotinic receptors are the initial site of nicotine action in the brain and have long been implicated in nicotine use and dependence32.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Stoker, A. K. & Markou, A. Unraveling the neurobiology of nicotine dependence using genetically engineered mice. Curr. Opin. Neurobiol. 23, 493–499 (2013). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    PDE4B regulates cAMP second messenger availability and is down-regulated by chronic nicotine administration in rats.

    “PDE4B regulates the cAMP second messenger availability and thereby affects signal transduction, and is down-regulated by chronic nicotine administration in rats12.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [12] Polesskaya, O. O., Smith, R. F. & Fryxell, K. J. Chronic nicotine doses down-regulate PDE4 isoforms that are targets of antidepressants in adolescent female rats. Biol. Psychiatry 61, 56–64 (2007). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    PPP1R1B affects synaptic plasticity and reward-based learning in the striatum and contributes to behavioral effects of nicotine in mice.

    “a signal transduction gene that affects synaptic plasticity and reward-based learning in the striatum29,30 and contributes to the behavioral effects of nicotine in mice31.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [29] Fernandez, E., Schiappa, R., Girault, J. A. & Le Novere, N. DARPP-32 is a robust integrator of dopamine and glutamate signals. PLoS Comput. Biol. 2, 1619–1633 (2006). source

      Not checked in the free run.

    • cites [30] Yagishita, S. et al. A critical time window for dopamine actions on the structural plasticity of dendritic spines. Science 345, 1616–1620 (2014). source

      Not checked in the free run.

    • cites [31] Zhu, H. W. et al. DARPP-32 phosphorylation opposes the behavioral effects of nicotine. Biol. Psychiatry 58, 981–989 (2005). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    DLG4 affects NMDA receptors and potassium channel clusters and plays a central role in glutamatergic models of reward-related learning.

    “DLG4 affects NMDA receptors and potassium channel clusters, and plays a central role in glutamatergic models of reward-related learning35.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [35] Kalivas, P. W. The glutamate homeostasis hypothesis of addiction. Nat. Rev. Neurosci. 10, 561–572 (2009). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    CUL3 has wide-ranging effects including on ubiquitination and protein degradation, and de novo mutations in CUL3 are associated with rare diseases affecting response to aldosterone, which is affected by smoking and associated with alcohol use.

    “CUL3 has wide-ranging effects, including on ubiquination and protein degradation, and de novo mutations in CUL3 are associated with rare diseases affecting response to the mineralocorticoid aldosterone13, which itself is affected by smoking14 and associated with alcohol use15.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [13] Boyden, L. M. et al. Mutations in kelch-like 3 and cullin 3 cause hypertension and electrolyte abnormalities. Nature 482, 98–102 (2012). source

      Not checked in the free run.

    • cites [14] Wang, W. et al. Forced expiratory volume in the first second and aldosterone as mediators of smoking effect on stroke in African Americans: the Jackson Heart Study. J. Am. Heart Assoc. 5, e002689 (2016).

      Not checked in the free run.

    • cites [15] Aoun, E. G. et al. A relationship between the aldosterone-mineralocorticoid receptor pathway and alcohol drinking: preliminary translational findings across rats, monkeys and humans. Mol. Psychiatry 23, 1466–1473 (2018). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    HOMER2 affects addictive behavior in mice and regulates GRM1.

    “which affects addictive behavior in mice35,36 and regulates glutamate metabotropic receptor 1 (GRM1).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [35] Kalivas, P. W. The glutamate homeostasis hypothesis of addiction. Nat. Rev. Neurosci. 10, 561–572 (2009). source

      Not checked in the free run.

    • cites [36] Szumlinski, K. K. et al. Methamphetamine addiction vulnerability: the glutamate, the bad, and the ugly. Biol. Psychiatry 81, 959–970 (2017). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    CRH affects hormones involved in the stress response, including cortisol, and has been associated with stress response and relapse to drug taking in animals.

    “CRH affects hormones involved in the stress response, including cortisol, and has been associated with the stress response and relapse to drug taking in animals39,40.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [39] Logrip, M. L., Koob, G. F. & Zorrilla, E. P. Role of corticotropin-releasing factor in drug addiction: potential for pharmacological intervention. CNS Drugs 25, 271–287 (2011). source

      Not checked in the free run.

    • cites [40] Volkow, N. D., Koob, G. F. & McLellan, A. T. Neurobiologic advances from the brain disease model of addiction. N. Engl. J. Med. 374, 363–371 (2016). source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Variants present in only two or fewer studies were excluded.

    “we excluded any variant that was present in only two or fewer studies.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The nonsynonymous variant in SLC39A8 affects zinc and manganese transport, is highly pleiotropic for complex phenotypes, and may impair inflammation, glutamatergic neurotransmission, and regulation of various metals.

    “the nonsynonymous variant in SLC39A8 affects zinc and manganese transport, is highly pleiotropic for complex phenotypes, and may impair inflammation, glutamatergic neurotransmission, and regulation of various metals in the body48.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [48] Costas, J. The highly pleiotropic gene SLC39A8 as an opportunity to gain insight into the molecular pathogenesis of schizophrenia. Am. J. Med. Genet. B Neuropsychiatr. Genet. 177, 274–283 (2018). source

      Not checked in the free run.

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    Study design and conductown data

    The more stringent significance threshold corrects for about 10 million tests, roughly the number of conditionally independent variants when the MAF lower bound is extended from 1% to 0.1%.

    “The more stringent threshold applies a correction for ~10 million tests, which is approximately the number of conditionally independent variants tested once the MAF lower bound was extended from 1% to 0.1%.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Variants with effective sample sizes less than 10% of the total sample size were removed.

    “We removed variants with effective sample sizes < 10% of the total sample size to ensure only well-imputed variants with a modicum of power were included.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Alcohol is known to affect glutamatergic signaling pathways.

    “Alcohol is known to affect glutamatergic signaling pathways37, and over half of the enriched pathways for DrnkWk clustered within the exemplar ‘glutamate ionotropic receptor kainate type subunit 2 (GRIK2) PPI subnetwork’ (Figure 4, Supplementary Table 18).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [37] Gass, J. T. & Olive, M. F. Glutamatergic substrates of drug addiction and alcoholism. Biochem. Pharmacol. 75, 218–265 (2008). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Alcohol consumption may be influenced by individual differences in ability to process calorie-rich alcoholic beverages.

    “perhaps suggesting that alcohol consumption is influenced by individual differences in one’s ability to process calorie-rich alcoholic beverages.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Imputed variants with imputation quality less than 0.3 were removed.

    “We removed imputed variants with imputation quality less than 0.3 (the estimated squared correlation between the imputed dosage and true dosage).”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    Quality control checks were applied to ensure quality of both phenotypes and genotypes.

    “Quality control checks were applied to ensure quality of both the phenotypes and genotypes.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisenone given

    GRK4 is expressed in the midbrain and forebrain, but no research has evaluated its impact on substance use behavior.

    “GRK4 is also expressed in the midbrain and forebrain46,47, but no research has evaluated its impact on substance use behavior.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [46] Yang, J., Villar, V. M., Armando, I., Jose, P. A. & Zeng, C. Y. G. G protein–coupled receptor kinases: crucial regulators of blood pressure. J. Am. Heart Assoc. 5, e003519 (2016). source

      Not checked in the free run.

    • cites [47] GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017). erratum 553, 530 (2018). source

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  • Classified as probably not a substantial claim.

    Study design and conductcitation

    Variants with minor allele frequency less than 0.001 were excluded.

    “We also excluded all variants with minor allele frequency less than 0.001, the lower bound of moderate imputation accuracy with the currently best available imputation reference panel41.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [41] McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation

    Substance use is embedded in a complex web of causal relations.

    “Ultimately, substance use is embedded in a complex web of causal relations49 (e.g., Figure 1), and caution must be exercised in drawing strong causal conclusions.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [49] Kong, A. et al. The nature of nurture: effects of parental genotypes. Science 359, 424–428 (2018). source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data

    Genomic controls and LD Intercept tests of MTAG results were well controlled, and Manhattan and QQ plots were well-behaved.

    “Genomic controls and LD Intercept tests of the MTAG results were well controlled (Supplementary Table 29), and Manhattan and QQ plots well-behaved (Supplementary Figures 16 and 17).”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Post-meta-analysis variant filters were applied to ensure robustness of reported findings.

    “We applied multiple post-meta-analysis variant filters to ensure robustness of reported findings.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    Extensive genetic quality control and filtering was performed on contributed summary statistics from each cohort.

    “Extensive genetic quality control and filtering was performed on the contributed summary statistics from each cohort.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Bonferroni correction within phenotype was used to control multiple testing in the epigenomic enrichment analyses (10 tests for cell-group and 220 for cell-specific).

    “Multiple testing was accounted for through Bonferroni correction within phenotype with 10 tests for the cell-group annotation enrichment analyses and 220 tests for the cell-specific enrichment analyses.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    UCN encodes an endogenous ligand for CRHR1 and CRHR2.

    “UCN encodes an endogenous ligand for CRHR1 and CRHR238.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [38] Vaughan, J. et al. Urocortin, a mammalian neuropeptide related to fish urotensin I and to corticotropin-releasing factor. Nature 378, 287–292 (1995). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    GRK4 is involved in regulation of GPCRs including GRM1, GABAB receptors, and DRD1 and DRD3 in kidneys and cerebellum, and is involved in essential hypertension.

    “GRK4 is involved in the regulation of GPCRs including metabotropic glutamate receptor 1 (GRM1)44, GABAB receptors45, and dopamine receptor D1 (DRD1) and D3 (DRD3) in the kidneys and cerebellum, and is involved in essential hypertension46.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [44] Sallese, M. et al. The G-protein-coupled receptor kinase GRK4 mediates homologous desensitization of metabotropic glutamate receptor 1. FASEB J. 14, 2569–2580 (2000). source

      Not checked in the free run.

    • cites [45] Perroy, J., Adam, L., Qanbar, R., Chenier, S. & Bouvier, M. Phosphorylation-independent desensitization of GABAB receptor by GRK4. EMBO J. 22, 3816–3824 (2003). source

      Not checked in the free run.

    • cites [46] Yang, J., Villar, V. M., Armando, I., Jose, P. A. & Zeng, C. Y. G. G protein–coupled receptor kinases: crucial regulators of blood pressure. J. Am. Heart Assoc. 5, e003519 (2016). source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductown data

    All conditionally independent variants were plotted in LocusZoom and visually inspected; suspicious loci were removed from further consideration.

    “All conditionally independent variants were plotted in LocusZoom and included in Supplementary Figures 1–12. All plots were visually inspected, suspicious loci were identified (see Supplementary Table 27) and removed from further consideration.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given

    Per-study allele labels and frequencies were compared with imputation reference panels, and mismatches were removed or reconciled.

    “We compared the per-study allele labels and allele frequencies with those of the imputation reference panels, and removed or reconciled mismatches.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    A false discovery rate threshold of 0.05 defined significance for genes, gene sets, and tissue/cell enrichments.

    “Genes, gene sets, and tissue/cell enrichments were considered significant when their false discovery rate was below 0.05.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    All loci were plotted with LocusZoom, visually inspected, and suspicious loci were removed from further consideration.

    “All loci were plotted with LocusZoom, visually inspected, with suspicious loci identified (e.g., those without LD support; see Supplementary Table 30) removed from further consideration.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    For quantitative traits, variance of score statistics was plotted against sample size, and trait residuals were tested for proper normalization and same unit across studies.

    “For quantitative traits, we plotted the variance of the score statistics against the sample size, and tested whether the trait residuals in each study were properly normalized and whether the trait analyzed between studies was measured and analyzed using the same unit.”
    Quote in the paperNot a substantial claim
47 of 47

  • The paper relies on this without support.

    The mechanisms by which the implicated genes influence substance use in humans are largely unknown.

    “Specific mechanisms by which implicated genes influence substance use in humans are largely unknown, even for those genes reported above involving systems such as neurotransmission, reward-related learning and memory, and the stress response.”

  • The paper relies on this without support.

    The brain regions showing enrichment have functions previously linked to alcohol and substance use in human imaging research.

    “Structure and function of these regions have been robustly associated with individual differences in frequencies, magnitudes, and clinical characteristics of alcohol use, and substance use/misuse generally, in human imaging research.”

  • The paper relies on this without support.

    Secular trends and policy changes over the past 50 years affect smoking and alcohol use rates.

    “Smoking and alcohol use rates are influenced by secular trends and policy changes over the last half century.”

  • The authors state this as an assumption.

    The prior distribution for effect sizes in the pleiotropic analysis is assumed to be a mixture of a point mass at zero and a normal distribution.

    “The prior distribution for the effect sizes were assumed to follow a mixture distribution: with a point mass at 0 (representing the possibility the locus is not associated with the trait) and a normal distribution (representing the possibility that the locus is associated).”

  • The paper relies on this without support.

    The pleiotropic analysis method is asserted to correctly handle local genetic and residual correlations among phenotypes.

    “The method properly accounts for the local genetic correlation and residual correlation between phenotypes.”

  • The paper relies on this without support.

    Variants with MAF≥1% will be imputed with high confidence.

    “The primary focus was to test variants with MAF≥1%, as these will be imputed with high confidence.”
6 of 6

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Genome-wide association studies have identified genetic variants associated with various SUDs as well as variants that appear to be protective.

    “Genetic studies, including genome- wide association studies (GWAS), have identified genetic variants associated with various SUDs as well as variants that appear to be protective 94 .”

    From Substance use disorders: a comprehensive update of classification, epidemiology, neurobiology, clinical aspects, treatment and prevention (2023) · cites this paper as [94]

    The paper’s Abstract reports GWAS variants associated with tobacco and alcohol use, supporting the genetic-association part of the claim. Its measured outcomes are smoking behaviors and drinks per week, not diagnoses of various substance use disorders; the passages also do not identify variants protective against those disorders. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • A genome-wide association study found no genetic correlation between smoking and bipolar disorder, whereas smoking and schizophrenia appeared genetically correlated.

    “In a recent genome-wide association study (GWAS) of smoking, smoking initiation and severity, and BD no genetic correlation could be established whereas smoking and schizophrenia appeared genetically correlated (20).”

    From The detrimental effects of smoking on the course and outcome in adults with bipolar disorder—A narrative review (2023) · cites this paper as [20]

    The paper’s genetic-correlation passage (page unavailable) says smoking phenotypes were correlated with many psychiatric phenotypes, but does not identify bipolar disorder or give a smoking–schizophrenia correlation. Searches for “bipolar” and “schizophrenia” found no passage establishing the claimed contrast. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

Cites

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Cited by

Sorted by citations

Related

Authors
23andMe Research Team, Liu, M., Psychiatry, H. A.-I., Jiang, Y., Wedow, R., Li, Y., Brazel, D. M., Chen, F., Datta, G., Davila-Velderrain, J., McGuire, D., Tian, C., Zhan, X., Choquet, H., Docherty, A. R., Faul, J. D., Foerster, J. R., Fritsche, L. G., Gabrielsen, M. E., Gordon, S. D., Haessler, J., Hottenga, J.-J., Huang, H., Jang, S.-K., Jansen, P. R., Ling, Y., Mägi, R., Matoba, N., McMahon, G., Mulas, A., Orrù, V., Palviainen, T., Pandit, A., Reginsson, G. W., Skogholt, A. H., Smith, J. A., Taylor, A. E., Turman, C., Willemsen, G., Young, H., Young, K. A., Zajac, G. J. M., Zhao, W., Zhou, W., Bjornsdottir, G., Boardman, J. D., Boehnke, M., Boomsma, D. I., Chen, C., Cucca, F., Davies, G. E., Eaton, C. B., Ehringer, M. A., Esko, T., Fiorillo, E., Gillespie, N. A., Gudbjartsson, D. F., Haller, T., Harris, K. M., Heath, A. C., Hewitt, J. K., Hickie, I. B., Hokanson, J. E., Hopfer, C. J., Hunter, D. J., Iacono, W. G., Johnson, E. O., Kamatani, Y., Kardia, S. L. R., Keller, M. C., Kellis, M., Kooperberg, C., Kraft, P., Krauter, K. S., Laakso, M., Lind, P. A., Loukola, A., Lutz, S. M., Madden, P. A. F., Martin, N. G., McGue, M., McQueen, M. B., Medland, S. E., Metspalu, A., Mohlke, K. L., Nielsen, J. B., Okada, Y., Peters, U., Polderman, T. J. C., Posthuma, D., Reiner, A. P., Rice, J. P., Rimm, E., Rose, R. J., Runarsdottir, V., Stallings, M. C., Stančáková, A., Stefansson, H., Thai, K. K., Tindle, H. A., Tyrfingsson, T., Wall, T. L., Weir, D. R., Weisner, C., Whitfield, J. B., Winsvold, B. S., Yin, J., Zuccolo, L., Bierut, L. J., Hveem, K., Lee, J. J., Munafò, M. R., Saccone, N. L., Willer, C. J., Cornelis, M. C., David, S. P., Hinds, D. A., Jorgenson, E., Kaprio, J., Stitzel, J. A., Stefansson, K., Thorgeirsson, T. E., Abecasis, G., Liu, D. J., & Vrieze, S.
Affiliations
  • University of Minnesota (US)
  • Pennsylvania State University (US)
  • Broad Institute (US)
  • 23andMe (United States) (US)
  • Kaiser Permanente (US)
  • Virginia Commonwealth University (US)
  • University of Michigan (US)
  • Norwegian University of Science and Technology (NO)
  • QIMR Berghofer Medical Research Institute (AU)
  • Fred Hutch Cancer Center (US)
  • Harvard University (US)
  • RIKEN Center for Integrative Medical Sciences (JP)
  • University of Bristol (GB)
  • Institute of Genetic and Biomedical Research (IT)
  • University of Helsinki (FI)
  • deCODE Genetics (Iceland) (IS)
  • University of Colorado Boulder (US)
  • Brown University (US)
  • University of North Carolina at Chapel Hill (US)
  • The University of Sydney (AU)
  • RTI International (US)
  • University of Eastern Finland (FI)
  • Washington University in St. Louis (US)
  • University of Washington (US)
  • Vrije Universiteit Amsterdam (NL)
  • Indiana University Bloomington (US)
  • Vanderbilt University (US)
  • Oslo University Hospital (NO)
  • Northwestern University (US)
Corresponding author
  • Liu, Dajiang J.
  • Vrieze, Scott
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2019-01-14
Volume
51
Issue
2
Pages
237-244
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/s41588-018-0307-5
PMC
PMC6358542
OpenAlex
W2909598358
Semantic Scholar
7f74c7a15b14ebe5862e10426aaf07e9580176fe
Cited by
1,896
References
74
Citations by year
2026: 111 · 2025: 254 · 2024: 377 · 2023: 327 · 2022: 297 · 2021: 296 · 2020: 168 · 2019: 65 · 2018: 1
FWCI
124.08
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Genetics (publishedVersion)
  • PubMed (publishedVersion)
  • Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
  • VU Research Portal (publishedVersion, other-oa)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • Pure Amsterdam UMC (submittedVersion, other-oa)
  • Bristol Research (University of Bristol) (submittedVersion, other-oa)
  • Explore Bristol Research (submittedVersion, other-oa)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
  • eScholarship (California Digital Library) (submittedVersion)
  • NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
  • UnissResearch (Università degli Studi di Sassari) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Funders
  • Vanderbilt University
  • Northwestern University
  • University of Minnesota
  • Washington University in St. Louis
  • University of Michigan
  • Itä-Suomen Yliopisto
  • Helsingin Yliopisto
  • University of Washington
  • RTI International
  • Harvard T.H. Chan School of Public Health
  • Norges Teknisk-Naturvitenskapelige Universitet
  • QIMR Berghofer Medical Research Institute
  • University of Oxford
  • University of Bristol
  • Kuopion Yliopistollinen Sairaala
  • Háskóli Íslands
  • National Institutes of Health (1s10od020069-01, 1r03od032630-01)
  • School of Medicine, Stanford University
  • College of Pharmacy, University of Michigan
  • University of Colorado Boulder
  • University of North Carolina at Chapel Hill
  • University of California, San Diego
  • Feinberg School of Medicine
  • Anschutz Medical Campus, University of Colorado
  • Medical Research Council (MR/K023195/1, MC_UU_00011/7)
  • National Institute on Alcohol Abuse and Alcoholism (R01AA023974, R21AA021223)
  • National Institute on Aging (U01AG009740)
  • National Heart, Lung, and Blood Institute (R01HL119443, K01HL125858, R01HL089897, U01HL089856, U01HL089897)
  • National Institute of Mental Health (R01MH123619, K01MH109765)
  • National Institute on Drug Abuse (U01DA046413, R01DA037904, R21DA040177, R01DA034076, R01DA036583, R01DA042755)
  • National Human Genome Research Institute (R01HG008983, T32HG000040)
  • National Institute on Deafness and Other Communication Disorders (R03DC013373)
  • National Institute of General Medical Sciences (R01GM126479)
  • National Institute of Diabetes and Digestive and Kidney Diseases (R01DK072193, U01DK062370, P30DK020572, T32DK091317, R01DK093757, R01DK062370)
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD066613, R03HD097630, P01HD031921)
  • Carolina Population Center, University of North Carolina at Chapel Hill
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
  • Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
Keywords
tobacco use, alcohol consumption, smoking initiation, smoking cessation, smoking heaviness, genetic loci, genetic variants, pleiotropy, genetic correlation, nicotine, dopamine signaling, glutamatergic neurotransmission
MeSH terms
Alcohol Drinking, Female, Humans, Male, Middle Aged, Phenotype, Risk, Smoking, Nicotiana, Tobacco Use Disorder, Genetic Variation, Genome-Wide Association Study

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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