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1.
Bioinformatics ; 2020 Nov 09.
Artigo em Inglês | MEDLINE | ID: mdl-33165574

RESUMO

MOTIVATION: The wealth of data resources on human phenotypes, risk factors, molecular traits and therapeutic interventions presents new opportunities for population health sciences. These opportunities are paralleled by a growing need for data integration, curation and mining to increase research efficiency, reduce mis-inference and ensure reproducible research. RESULTS: We developed EpiGraphDB (https://epigraphdb.org/), a graph database containing an array of different biomedical and epidemiological relationships and an analytical platform to support their use in human population health data science. In addition, we present three case studies that illustrate the value of this platform. The first uses EpiGraphDB to evaluate potential pleiotropic relationships, addressing mis-inference in systematic causal analysis. In the second case study, we illustrate how protein-protein interaction data offer opportunities to identify new drug targets. The final case study integrates causal inference using Mendelian randomization with relationships mined from the biomedical literature to "triangulate" evidence from different sources. AVAILABILITY: The EpiGraphDB platform is openly available at https://epigraphdb.org. Code for replicating case study results is available at https://github.com/MRCIEU/epigraphdb as Jupyter notebooks using the API, and https://mrcieu.github.io/epigraphdb-r using the R package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

2.
Int J Epidemiol ; 2020 Nov 05.
Artigo em Inglês | MEDLINE | ID: mdl-33150399

RESUMO

BACKGROUND: It is established that Alzheimer's disease (AD) patients experience sleep disruption. However, it remains unknown whether disruption in the quantity, quality or timing of sleep is a risk factor for the onset of AD. METHODS: We used the largest published genome-wide association studies of self-reported and accelerometer-measured sleep traits (chronotype, duration, fragmentation, insomnia, daytime napping and daytime sleepiness), and AD. Mendelian randomization (MR) was used to estimate the causal effect of self-reported and accelerometer-measured sleep parameters on AD risk. RESULTS: Overall, there was little evidence to support a causal effect of sleep traits on AD risk. There was some suggestive evidence that self-reported daytime napping was associated with lower AD risk [odds ratio (OR): 0.70, 95% confidence interval (CI): 0.50-0.99). Some other sleep traits (accelerometer-measured 'eveningness' and sleep duration, and self-reported daytime sleepiness) had ORs of a similar magnitude to daytime napping, but were less precisely estimated. CONCLUSIONS: Overall, we found very limited evidence to support a causal effect of sleep traits on AD risk. Our findings provide tentative evidence that daytime napping may reduce AD risk. Given that this is the first MR study of multiple self-report and objective sleep traits on AD risk, findings should be replicated using independent samples when such data become available.

3.
Nat Commun ; 11(1): 5749, 2020 11 12.
Artigo em Inglês | MEDLINE | ID: mdl-33184277

RESUMO

Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.

5.
Nat Genet ; 52(10): 1122-1131, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32895551

RESUMO

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.

6.
Nat Commun ; 11(1): 3519, 2020 07 14.
Artigo em Inglês | MEDLINE | ID: mdl-32665587

RESUMO

Estimates from Mendelian randomization studies of unrelated individuals can be biased due to uncontrolled confounding from familial effects. Here we describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family-based analyses can reduce such biases. We illustrate empirically how familial effects can affect estimates using data from 61,008 siblings from the Nord-Trøndelag Health Study and UK Biobank and replicated our findings using 222,368 siblings from 23andMe. Both Mendelian randomization estimates using unrelated individuals and within family methods reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while Mendelian randomization estimates from samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects were strongly attenuated in within-family Mendelian randomization analyses. Our findings indicate the necessity of controlling for population structure and familial effects in Mendelian randomization studies.


Assuntos
Análise da Randomização Mendeliana/métodos , Índice de Massa Corporal , Epidemiologia , Feminino , Humanos , Masculino , Polimorfismo de Nucleotídeo Único/genética , Fatores de Risco
7.
Genet Epidemiol ; 44(8): 924-933, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32710482

RESUMO

It has been hypothesised that nonsyndromic cleft lip/palate (nsCL/P) and cancer may share aetiological risk factors. Population studies have found inconsistent evidence for increased incidence of cancer in nsCL/P cases, but several genes (e.g., CDH1, AXIN2) have been implicated in the aetiologies of both phenotypes. We aimed to evaluate shared genetic aetiology between nsCL/P and oral cavity/oropharyngeal cancers (OC/OPC), which affect similar anatomical regions. Using a primary sample of 5,048 OC/OPC cases and 5,450 controls of European ancestry and a replication sample of 750 cases and 336,319 controls from UK Biobank, we estimate genetic overlap using nsCL/P polygenic risk scores (PRS) with Mendelian randomization analyses performed to evaluate potential causal mechanisms. In the primary sample, we found strong evidence for an association between a nsCL/P PRS and increased odds of OC/OPC (per standard deviation increase in score, odds ratio [OR]: 1.09; 95% confidence interval [CI]: 1.04, 1.13; p = .000053). Although confidence intervals overlapped with the primary estimate, we did not find confirmatory evidence of an association between the PRS and OC/OPC in UK Biobank (OR 1.02; 95% CI: 0.95, 1.10; p = .55). Mendelian randomization analyses provided evidence that major nsCL/P risk variants are unlikely to influence OC/OPC. Our findings suggest possible shared genetic influences on nsCL/P and OC/OPC.

8.
Brain Behav Immun ; 89: 43-50, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32473944

RESUMO

BACKGROUND: The causal role of inflammatory markers on self-harm and suicidal risk has been studied using observational data, with conflicting results. Confounding and reverse causation can lead to bias, so we appraised question from a genetic perspective to protect against these biases. We measured associations between genetic liability for high levels of inflammatory markers Interleukin-6 (IL-6) and C-reactive protein (CRP) on self-harm, and conducted a secondary analysis restricted to self-harm with suicidal intent. METHODS: We conducted two sample and multivariable Mendelian randomisation (MR) to assess the effects of IL-6 and CRP on self-harm utilising existing data and conducting new genome wide association studies to instrument IL-6 and CRP, and for the outcome of self-harm. RESULTS: No single nucleotide polymorphisms (SNPs) reached genome-wide significance for self-harm, however 193 SNPs met suggestive significance levels (p < 5 × 10-6). We found no evidence of an association between our instruments for IL-6 and self-harm in the two-sample MR, however we found an inverse association between instruments for CRP and self-harm, indicating that higher levels of circulating CRP may protect against self-harm (inverse variance weighted OR 0.92, 95%CI 0.84, 1.01, p = 0.08; MR Egger OR 0.86, 95% CI 0.74, 1.00, p = 0.05). The direct effect estimate for IL-6 was slightly smaller in the multivariable MR than in the two sample MR, while the CRP effect estimates were consistent with the two sample MR (OR 0.92, SE 1.05, p = 0.09). CONCLUSIONS: Our findings are conflicting and indicate that IL-6 and CRP are not robust etiological markers of increased self-harm or suicide risk.

9.
Nat Commun ; 11(1): 2865, 2020 06 08.
Artigo em Inglês | MEDLINE | ID: mdl-32513961

RESUMO

Linking epigenetic marks to clinical outcomes improves insight into molecular processes, disease prediction, and therapeutic target identification. Here, a statistical approach is presented to infer the epigenetic architecture of complex disease, determine the variation captured by epigenetic effects, and estimate phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe correlations, data structure (cell-count or relatedness), and single-nucleotide polymorphism (SNP) marker effects, improves association estimates and in 9,448 individuals, 75.7% (95% CI 71.70-79.3) of body mass index (BMI) variation and 45.6% (95% CI 37.3-51.9) of cigarette consumption variation was captured by whole blood methylation array data. Pathway-linked probes of blood cholesterol, lipid transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking, showed >1.5 times larger associations with >95% posterior inclusion probability. Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO model, with age-, and tissue-specificity, implying associations are a phenotypic consequence rather than causal.


Assuntos
Epigênese Genética , Característica Quantitativa Herdável , Adulto , Algoritmos , Teorema de Bayes , Biomarcadores/análise , Índice de Massa Corporal , Simulação por Computador , Metilação de DNA/genética , Humanos , Anotação de Sequência Molecular , Especificidade de Órgãos/genética , Reprodutibilidade dos Testes
10.
Sci Adv ; 6(16): eaay0328, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-32426451

RESUMO

Heritability, genetic correlation, and genetic associations estimated from samples of unrelated individuals are often perceived as confirmation that genotype causes the phenotype(s). However, these estimates can arise from indirect mechanisms due to population phenomena including population stratification, dynastic effects, and assortative mating. We introduce these, describe how they can bias or inflate genotype-phenotype associations, and demonstrate methods that can be used to assess their presence. Using data on educational achievement and parental socioeconomic position as an exemplar, we demonstrate that both heritability and genetic correlation may be biased estimates of the causal contribution of genotype. These results highlight the limitations of genotype-phenotype estimates obtained from samples of unrelated individuals. Use of these methods in combination with family-based designs may offer researchers greater opportunities to explore the mechanisms driving genotype-phenotype associations and identify factors underlying bias in estimates.

11.
Clin Epigenetics ; 12(1): 50, 2020 03 30.
Artigo em Inglês | MEDLINE | ID: mdl-32228717

RESUMO

BACKGROUND: DNA methylation is associated with body mass index (BMI), but it is not clear if methylation scores are biomarkers for extant BMI or predictive of future BMI. Here, we explore the causal nature and predictive utility of DNA methylation measured in peripheral blood with BMI and cardiometabolic traits. METHODS: Analyses were conducted across the life course using the ARIES cohort of mothers (n = 792) and children (n = 906), for whom DNA methylation and genetic profiles and BMI at multiple time points (3 in children at birth, in childhood and in adolescence; 2 in mothers during pregnancy and in middle age) were available. Genetic and DNA methylation scores for BMI were derived using published associations between BMI and DNA methylation and genotype. Causal relationships between methylation and BMI were assessed using Mendelian randomisation and cross-lagged models. RESULTS: The DNA methylation scores in adult women explained 10% of extant BMI variance. However, less extant variance was explained by scores generated in the same women during pregnancy (2% BMI variance) and in older children (15-17 years; 3% BMI variance). Similarly, little extant variance was explained in younger children (at birth and at 7 years; 1% and 2%, respectively). These associations remained following adjustment for smoking exposure and education levels. The DNA methylation score was found to be a poor predictor of future BMI using linear and cross-lagged models, suggesting that DNA methylation variation does not cause later variation in BMI. However, there was some evidence to suggest that BMI is predictive of later DNA methylation. Mendelian randomisation analyses also support this direction of effect, although evidence is weak. Finally, we find that DNA methylation scores for BMI are associated with extant cardiometabolic traits independently of BMI and genetic score. CONCLUSION: The age-specific nature of DNA methylation associations with BMI, lack of causal relationship and limited predictive ability of future BMI indicate that DNA methylation is likely influenced by BMI and might more accurately be considered a biomarker of BMI and related outcomes rather than a predictor. Future epigenome-wide association studies may benefit from further examining associations between early DNA methylation and later health outcomes.

12.
J Bone Miner Res ; 35(7): 1224-1235, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32163637

RESUMO

Several epidemiological studies have reported a relationship between statin treatment and increased bone mineral density (BMD) and reduced fracture risk, but the mechanism underlying the purported relationship is unclear. We used Mendelian randomization (MR) to assess whether this relationship is explained by a specific effect in response to statin use or by a general effect of lipid lowering. We utilized 400 single-nucleotide polymorphisms (SNPs) robustly associated with plasma lipid levels as exposure. The outcome results were obtained from a heel estimated BMD (eBMD) genomewide association study (GWAS) from the UK Biobank and dual-energy X-ray absorptiometry (DXA) BMD at four body sites and fracture GWAS from the GEFOS consortium. We performed univariate and multivariable MR analyses of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride levels on BMD and fracture. Univariate MR analyses suggested a causal effect of LDL-C on eBMD (ß = -0.06; standard deviation change in eBMD per standard deviation change in LDL-C, 95% confidence interval [CI] = -0.08 to -0.04; p = 4 × 10-6 ), total body BMD (ß = -0.05, 95% CI = -0.08 to -0.01, p = 6 × 10-3 ) and potentially on lumbar spine BMD. Multivariable MR suggested that the effects of LDL-C on eBMD and total body BMD were independent of HDL-C and triglycerides. Sensitivity MR analyses suggested that the LDL-C results were robust to pleiotropy. MR analyses of LDL-C restricted to SNPs in the HMGCR region showed similar effects on eBMD (ß = -0.083; -0.132 to -0.034; p = .001) to those excluding these SNPs (ß = -0.063; -0.090 to -0.036; p = 8 × 10-6 ). Bidirectional MR analyses provided some evidence for a causal effect of eBMD on plasma LDL-C levels. Our results suggest that effects of statins on eBMD and total body BMD are at least partly due to their LDL-C lowering effect. Further studies are required to examine the potential role of modifying plasma lipid levels in treating osteoporosis. © 2020 American Society for Bone and Mineral Research.

13.
Nat Commun ; 11(1): 1010, 2020 02 21.
Artigo em Inglês | MEDLINE | ID: mdl-32081875

RESUMO

In Mendelian randomization (MR) analysis, variants that exert horizontal pleiotropy are typically treated as a nuisance. However, they could be valuable in identifying alternative pathways to the traits under investigation. Here, we develop MR-TRYX, a framework that exploits horizontal pleiotropy to discover putative risk factors for disease. We begin by detecting outliers in a single exposure-outcome MR analysis, hypothesising they are due to horizontal pleiotropy. We search across hundreds of complete GWAS summary datasets to systematically identify other (candidate) traits that associate with the outliers. We develop a multi-trait pleiotropy model of the heterogeneity in the exposure-outcome analysis due to pathways through candidate traits. Through detailed investigation of several causal relationships, many pleiotropic pathways are uncovered with already established causal effects, validating the approach, but also alternative putative causal pathways. Adjustment for pleiotropic pathways reduces the heterogeneity across the analyses.


Assuntos
Pleiotropia Genética , Análise da Randomização Mendeliana , Modelos Genéticos , Causalidade , Simulação por Computador , Bases de Dados Genéticas , Doença/genética , Variação Genética , Estudo de Associação Genômica Ampla , Humanos , Fenótipo , Polimorfismo de Nucleotídeo Único , Fatores de Risco
14.
Int J Epidemiol ; 49(4): 1163-1172, 2020 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-32003800

RESUMO

OBJECTIVES: To examine whether educational attainment and intelligence have causal effects on risk of Alzheimer's disease (AD), independently of each other. DESIGN: Two-sample univariable and multivariable Mendelian randomization (MR) to estimate the causal effects of education on intelligence and vice versa, and the total and independent causal effects of both education and intelligence on AD risk. PARTICIPANTS: 17 008 AD cases and 37 154 controls from the International Genomics of Alzheimer's Project (IGAP) consortium. MAIN OUTCOME MEASURE: Odds ratio (OR) of AD per standardized deviation increase in years of schooling (SD = 3.6 years) and intelligence (SD = 15 points on intelligence test). RESULTS: There was strong evidence of a causal, bidirectional relationship between intelligence and educational attainment, with the magnitude of effect being similar in both directions [OR for intelligence on education = 0.51 SD units, 95% confidence interval (CI): 0.49, 0.54; OR for education on intelligence = 0.57 SD units, 95% CI: 0.48, 0.66]. Similar overall effects were observed for both educational attainment and intelligence on AD risk in the univariable MR analysis; with each SD increase in years of schooling and intelligence, odds of AD were, on average, 37% (95% CI: 23-49%) and 35% (95% CI: 25-43%) lower, respectively. There was little evidence from the multivariable MR analysis that educational attainment affected AD risk once intelligence was taken into account (OR = 1.15, 95% CI: 0.68-1.93), but intelligence affected AD risk independently of educational attainment to a similar magnitude observed in the univariate analysis (OR = 0.69, 95% CI: 0.44-0.88). CONCLUSIONS: There is robust evidence for an independent, causal effect of intelligence in lowering AD risk. The causal effect of educational attainment on AD risk is likely to be mediated by intelligence.

15.
Nat Commun ; 11(1): 185, 2020 01 10.
Artigo em Inglês | MEDLINE | ID: mdl-31924771

RESUMO

Developing insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. In this study, we apply the principles of Mendelian randomization to systematically evaluate transcriptome-wide associations between gene expression (across 48 different tissue types) and 395 complex traits. Our findings indicate that variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. Moreover, detailed investigations of our results highlight tissue-specific associations, drug validation opportunities, insight into the likely causal pathways for trait-associated variants and also implicate putative associations at loci yet to be implicated in disease susceptibility. Similar evaluations can be conducted at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.


Assuntos
Análise da Randomização Mendeliana , Fenômica , Transcriptoma , Aromatase/genética , Fibrilina-2/genética , Regulação da Expressão Gênica , Estudos de Associação Genética , Predisposição Genética para Doença , Variação Genética , Humanos , Hidroximetilglutaril-CoA Redutases/genética , Herança Multifatorial , Fenótipo , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Proteínas Ribossômicas/genética , Doenças da Glândula Tireoide/genética , Doenças da Glândula Tireoide/metabolismo
16.
Hum Genet ; 139(1): 43-44, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31363835

RESUMO

In the original article publication, there is an incorrect impression that Fig. 1 formed a formal Directed Acyclic Graph (DAG) by describing it as a causal model. However, it was not correct if interpreted in this way.

17.
Trends Mol Med ; 26(2): 232-241, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31718940

RESUMO

Large genome-wide association studies (GWAS) have identified loci that are associated with complex traits and diseases, but index variants are often not causal and reside in non-coding regions of the genome. To gain a better understanding of the relevant biological mechanisms, intermediate traits such as gene expression and protein levels are increasingly being investigated because these are likely mediators between genetic variants and disease outcome. Genetic variants associated with intermediate traits, termed molecular quantitative trait loci (molQTLs), can then be used as instrumental variables in a Mendelian randomization (MR) approach to identify the causal features and mechanisms of complex traits. Challenges such as pleiotropy and the non-specificity of molQTLs remain, and further approaches and methods need to be developed.

18.
Hum Genet ; 139(1): 23-41, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31030318

RESUMO

Replicable genetic association signals have consistently been found through genome-wide association studies in recent years. The recent dramatic expansion of study sizes improves power of estimation of effect sizes, genomic prediction, causal inference, and polygenic selection, but it simultaneously increases susceptibility of these methods to bias due to subtle population structure. Standard methods using genetic principal components to correct for structure might not always be appropriate and we use a simulation study to illustrate when correction might be ineffective for avoiding biases. New methods such as trans-ethnic modeling and chromosome painting allow for a richer understanding of the relationship between traits and population structure. We illustrate the arguments using real examples (stroke and educational attainment) and provide a more nuanced understanding of population structure, which is set to be revisited as a critical aspect of future analyses in genetic epidemiology. We also make simple recommendations for how problems can be avoided in the future. Our results have particular importance for the implementation of GWAS meta-analysis, for prediction of traits, and for causal inference.


Assuntos
Algoritmos , Bancos de Espécimes Biológicos/estatística & dados numéricos , Genética Populacional , Estudo de Associação Genômica Ampla , Herança Multifatorial , Humanos , Polimorfismo de Nucleotídeo Único
19.
Nat Commun ; 10(1): 5039, 2019 11 19.
Artigo em Inglês | MEDLINE | ID: mdl-31745073

RESUMO

Alcohol use is correlated within spouse-pairs, but it is difficult to disentangle effects of alcohol consumption on mate-selection from social factors or the shared spousal environment. We hypothesised that genetic variants related to alcohol consumption may, via their effect on alcohol behaviour, influence mate selection. Here, we find strong evidence that an individual's self-reported alcohol consumption and their genotype at rs1229984, a missense variant in ADH1B, are associated with their partner's self-reported alcohol use. Applying Mendelian randomization, we estimate that a unit increase in an individual's weekly alcohol consumption increases partner's alcohol consumption by 0.26 units (95% C.I. 0.15, 0.38; P = 8.20 × 10-6). Furthermore, we find evidence of spousal genotypic concordance for rs1229984, suggesting that spousal concordance for alcohol consumption existed prior to cohabitation. Although the SNP is strongly associated with ancestry, our results suggest some concordance independent of population stratification. Our findings suggest that alcohol behaviour directly influences mate selection.


Assuntos
Álcool Desidrogenase/genética , Consumo de Bebidas Alcoólicas/genética , Alcoolismo/genética , Bancos de Espécimes Biológicos/estatística & dados numéricos , Casamento , Polimorfismo de Nucleotídeo Único , Adulto , Feminino , Genótipo , Humanos , Masculino , Fenótipo , Reprodução/genética , Autorrelato , Reino Unido
20.
Psychol Med ; : 1-9, 2019 Nov 06.
Artigo em Inglês | MEDLINE | ID: mdl-31689377

RESUMO

BACKGROUND: Smoking prevalence is higher amongst individuals with schizophrenia and depression compared with the general population. Mendelian randomisation (MR) can examine whether this association is causal using genetic variants identified in genome-wide association studies (GWAS). METHODS: We conducted two-sample MR to explore the bi-directional effects of smoking on schizophrenia and depression. For smoking behaviour, we used (1) smoking initiation GWAS from the GSCAN consortium and (2) we conducted our own GWAS of lifetime smoking behaviour (which captures smoking duration, heaviness and cessation) in a sample of 462690 individuals from the UK Biobank. We validated this instrument using positive control outcomes (e.g. lung cancer). For schizophrenia and depression we used GWAS from the PGC consortium. RESULTS: There was strong evidence to suggest smoking is a risk factor for both schizophrenia (odds ratio (OR) 2.27, 95% confidence interval (CI) 1.67-3.08, p < 0.001) and depression (OR 1.99, 95% CI 1.71-2.32, p < 0.001). Results were consistent across both lifetime smoking and smoking initiation. We found some evidence that genetic liability to depression increases smoking (ß = 0.091, 95% CI 0.027-0.155, p = 0.005) but evidence was mixed for schizophrenia (ß = 0.022, 95% CI 0.005-0.038, p = 0.009) with very weak evidence for an effect on smoking initiation. CONCLUSIONS: These findings suggest that the association between smoking, schizophrenia and depression is due, at least in part, to a causal effect of smoking, providing further evidence for the detrimental consequences of smoking on mental health.

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