Mendelian Randomization analysis of the causal effect of cigarette smoking on hospital costs.

INTRODUCTION: Knowledge of the impact of smoking on healthcare costs is important for establishing the external effects of smoking and for evaluating policies intended to modify this behavior. Conventional analysis of this association is difficult because of omitted variable bias, reverse causality, and measurement error. METHODS: We approached these challenges using a Mendelian Randomization study design; genetic variants associated with smoking behaviors were used in instrumental variables models with inpatient hospital costs (calculated from electronic health records) as the outcome. We undertook genome wide association studies to identify genetic variants associated with smoking initiation and a composite smoking index (reflecting cumulative health impacts of smoking) on up to 300,045 individuals (mean age: 57 years at baseline, range 39 to 72 years) in the UK Biobank. We followed individuals up for a mean of six years. RESULTS: Genetic liability to initiate smoking (ever versus never smoking) was estimated to increase mean per-patient annual inpatient hospital costs by £477 (95% confidence interval (CI): £187 to £766). A one-unit change in genetic liability to the composite smoking index (range: 0-4.0) increased inpatient hospital costs by £204 (95% CI: £105 to £303) per unit increase in this index. There was some evidence that the composite smoking index causal models violated the instrumental variable assumptions, and all Mendelian Randomization models were estimated with considerable uncertainty. Models conditioning on risk tolerance were not robust to weak instrument bias. CONCLUSIONS: Our findings have implications for the potential cost-effectiveness of smoking interventions. IMPLICATIONS: We report the first Mendelian Randomization analysis of the causal effect of smoking on healthcare costs. Using two distinct smoking phenotypes, we identified substantial impacts of smoking on inpatient hospital costs, although the causal models were associated with considerable uncertainty. These results could be used alongside other evidence on the impact of smoking to evaluate the cost-effectiveness of anti-smoking interventions and to understand the scale of externalities associated with this behaviour.

Association between inflammation and cognition: Triangulation of evidence using a population-based cohort and Mendelian randomization analyses.

BACKGROUND: Inflammation is associated with cognitive functioning and dementia in older adults, but whether inflammation is related to cognitive functioning in youth and whether these associations are causal remains unclear. METHODS: In a population-based cohort (Avon Longitudinal Study of Parents and Children; ALSPAC), we investigated cross-sectional associations of inflammatory markers (C-reactive protein [CRP], Interleukin-6 [IL-6] and Glycoprotein acetyls [GlycA]) with measures of cold (working memory, response inhibition) and hot (emotion recognition) cognition at age 24 (N = 3,305 in multiple imputation models). Furthermore, we conducted one-sample and two-sample bidirectional Mendelian randomization (MR) analyses to examine potential causal effects of genetically-proxied inflammatory markers (CRP, GlycA, IL-6, IL-6 receptor, soluble IL-6 receptor) on cognitive measures (above) and on general cognitive ability. RESULTS: In the ALSPAC cohort, there was limited evidence of an association between standardised inflammatory markers and standardised cognitive measures at age 24 after adjusting for potential confounders (N = 3,305; beta range, -0.02 [95 % confidence interval (CI) -0.06 to 0.02, p = 0.27] to 0.02 [95 % CI -0.02 to 0.05, p = 0.33]). Similarly, we found limited evidence of potential effects of 1-unit increase in genetically-proxied inflammatory markers on standardised working memory, emotion recognition or response inhibition in one-sample MR using ALSPAC data (beta range, -0.73 [95 % CI -2.47 to 1.01, p = 0.41] to 0.21 [95 % CI -1.42 to 1.84, p = 0.80]; or on standardised general cognitive ability in two-sample MR using the latest Genome-Wide Association Study (GWAS) datasets (inverse-variance weighted beta range, -0.02 [95 % CI -0.05 to 0.01, p = 0.12] to 0.03 [95 % CI -0.01 to 0.07, p = 0.19]). CONCLUSIONS: Our MR findings do not provide strong evidence of a potential causal effect of inflammatory markers (CRP, IL-6, IL-6 receptor, GlycA) on the cognitive functions examined here. Given the large confidence intervals in the one-sample MR, larger GWAS of specific cognitive measures are needed to enable well-powered MR analyses to investigate whether inflammation causally influences specific cognitive domains.

Mendelian randomisation for psychiatry: how does it work, and what can it tell us?

The successful prevention of mental illness relies upon the identification of causal, modifiable risk factors. However, observational evidence exploring such risk factors often produces contradictory results and randomised control trials are often expensive, time-consuming or unethical to conduct. Mendelian randomisation (MR) is a complementary approach that uses naturally occurring genetic variation to identify possible causal effects between a risk factor and an outcome in a time-efficient and low-cost manner. MR utilises genetic variants as instrumental variables for the risk factor of interest. MR studies are becoming more frequent in the field of psychiatry, warranting a reflection upon both the possibilities and the pitfalls. In this Perspective, we consider several limitations of the MR method that are of particular relevance to psychiatry. We also present new MR methods that have exciting applications to questions of mental illness. While we believe that MR can make an important contribution to the field of psychiatry, we also wish to emphasise the importance of clear causal questions, thorough sensitivity analyses, and triangulation with other forms of evidence.

Uncovering the genetic architecture of broad antisocial behavior through a genome-wide association study meta-analysis.

Despite the substantial heritability of antisocial behavior (ASB), specific genetic variants robustly associated with the trait have not been identified. The present study by the Broad Antisocial Behavior Consortium (BroadABC) meta-analyzed data from 28 discovery samples (N = 85,359) and five independent replication samples (N = 8058) with genotypic data and broad measures of ASB. We identified the first significant genetic associations with broad ASB, involving common intronic variants in the forkhead box protein P2 (FOXP2) gene (lead SNP rs12536335, p = 6.32 × 10-10). Furthermore, we observed intronic variation in Foxp2 and one of its targets (Cntnap2) distinguishing a mouse model of pathological aggression (BALB/cJ strain) from controls (BALB/cByJ strain). Polygenic risk score (PRS) analyses in independent samples revealed that the genetic risk for ASB was associated with several antisocial outcomes across the lifespan, including diagnosis of conduct disorder, official criminal convictions, and trajectories of antisocial development. We found substantial genetic correlations of ASB with mental health (depression rg = 0.63, insomnia rg = 0.47), physical health (overweight rg = 0.19, waist-to-hip ratio rg = 0.32), smoking (rg = 0.54), cognitive ability (intelligence rg = -0.40), educational attainment (years of schooling rg = -0.46) and reproductive traits (age at first birth rg = -0.58, father's age at death rg = -0.54). Our findings provide a starting point toward identifying critical biosocial risk mechanisms for the development of ASB.

Exploring the bidirectional causal pathways between smoking behaviours and headache: A Mendelian randomisation study.

INTRODUCTION: Although observational data suggests a relationship between headache and smoking, there remain questions about causality. Smoking may increase headache risk, individuals may smoke to alleviate headaches, or smoking and headache may share common risk factors. Mendelian randomisation (MR) is a method that uses genetic variants as instruments for making causal inferences about an exposure and an outcome. METHODS: First, we conducted logistic regression of observational data in UK Biobank assessing the association between smoking behaviours (smoking status, cigarettes per day amongst daily smokers and lifetime smoking score) on risk of self-reported headache (in the last month and for more than 3 months). Second, we used genetic instruments for smoking behaviours and headache (identified in independent genome-wide association studies) to perform bidirectional MR analysis. RESULTS: Observationally, there is a weak association between smoking behaviour and experiencing headache, with increased cigarettes per day associated with increased headache risk. In the MR analysis, genetic liability to smoking initiation and lifetime smoking increased odds of headache in the last month but not odds of headaches lasting more than three months. In the opposite direction there was weak evidence for higher genetic liability to headaches decreasing the chance of quitting. CONCLUSION: There was weak evidence for a partially bidirectional causal relationship between smoking behaviours and headache in the last month. Given this relationship is distinct from smoking heaviness, it suggests headache and smoking may share common risk factors such as personality traits. IMPLICATIONS: Using Mendelian Randomisation, this study addresses the uncertainty regarding the observed relationship between headache and smoking. There was evidence for weak causal effects of smoking initiation and lifetime smoking (but not smoking heaviness) on likelihood of experiencing headache in the last month, but not over a prolonged period of more than three months. Those at higher genetic liability for headaches were also less likely to successfully stop smoking. This partially bidirectional causal relationship distinct from smoking heaviness, suggests that observed associations are unlikely due to biological effects of tobacco smoke exposure and may be explained by shared personality traits.

Associations between self-reported sleep quality, fatigue severity, factors associated with successful cessation, and cessation beliefs among regular smokers.

BACKGROUND: Smokers report poorer sleep than non-smokers and sleep quality deteriorates further during cessation, increasing risk of smoking relapse. Better understanding of the relationship between sleep and relapse-related outcomes could inform novel approaches to smoking cessation support. The aim of this study was to investigate same day associations of self-reported sleep quality and fatigue severity with factors associated with successful cessation and cessation beliefs, among regular smokers. METHODS: This cross-sectional observational study (n=412) collected self-reported sleep quality, fatigue severity, and factors associated with successful cessation and cessation beliefs among regular smokers via an online survey (60% male). RESULTS: There was evidence of an association between sleep quality (SQ) and reduced 24hr (ß = -0.12, p = 0.05) and lifetime (ß = -0.09, p = 0.04) abstinence self-efficacy. In addition, poorer SQ and higher fatigue severity (FS) were associated with increased smoking urges (SQ: ß = 0.27, p < .001; FS: ß = 0.32, p < .001), increased barriers to cessation (SQ: ß = 0.19, p < .001; FS: ß = 0.32, p < .001), and increased perceived risks to cessation (SQ: ß = 0.18, p < .001; FS: ß = 0.26, p < .001). Fatigue severity was weakly associated with increased perceived benefits to cessation (ß = 0.12, p = .017). CONCLUSIONS: Self-reported sleep quality and fatigue severity were associated with multiple factors associated with successful cessation and cessation beliefs. Further research is needed to extend these findings by using different methods to identify the temporal direction of associations and causality. IMPLICATIONS: This study is the first to examine associations between sleep quality, fatigue severity, and factors associated with successful cessation and cessation beliefs. Findings show that both sleep quality and fatigue severity are associated with multiple factors associated with successful cessation and could be modifiable targets for future smoking cessation interventions. Furthermore, our data suggest that fatigue severity has an independent effect on multiple factors associated with successful cessation when accounting for sleep quality. This indicates that fatigue, independent of sleep quality, could be an important factor in a quit attempt.

Multivariate analyses of molecular genetic associations between childhood psychopathology and adult mood disorders and related traits.

Ubiquitous associations have been detected between different types of childhood psychopathology and polygenic risk scores based on adult psychiatric disorders and related adult outcomes, indicating that genetic factors partly explain the association between childhood psychopathology and adult outcomes. However, these analyses in general do not take into account the correlations between the adult trait and disorder polygenic risk scores. This study aimed to further clarify the influence of genetic factors on associations between childhood psychopathology and adult outcomes by accounting for these correlations. Using a multivariate multivariable regression, we analyzed associations of childhood attention-deficit/hyperactivity disorder (ADHD), internalizing, and social problems, with polygenic scores (PGS) of adult disorders and traits including major depression, bipolar disorder, subjective well-being, neuroticism, insomnia, educational attainment, and body mass index (BMI), derived for 20,539 children aged 8.5-10.5 years. After correcting for correlations between the adult phenotypes, major depression PGS were associated with all three childhood traits, that is, ADHD, internalizing, and social problems. In addition, BMI PGS were associated with ADHD symptoms and social problems, while neuroticism PGS were only associated with internalizing problems and educational attainment PGS were only associated with ADHD symptoms. PGS of bipolar disorder, subjective well-being, and insomnia were not associated with any childhood traits. Our findings suggest that associations between childhood psychopathology and adult traits like insomnia and subjective well-being may be primarily driven by genetic factors that influence adult major depression. Additionally, specific childhood phenotypes are genetically associated with educational attainment, BMI and neuroticism.

Smoking and COVID-19 outcomes: an observational and Mendelian randomisation study using the UK Biobank cohort.

BACKGROUND: Conflicting evidence has emerged regarding the relevance of smoking on risk of COVID-19 and its severity. METHODS: We undertook large-scale observational and Mendelian randomisation (MR) analyses using UK Biobank. Most recent smoking status was determined from primary care records (70.8%) and UK Biobank questionnaire data (29.2%). COVID-19 outcomes were derived from Public Health England SARS-CoV-2 testing data, hospital admissions data, and death certificates (until 18 August 2020). Logistic regression was used to estimate associations between smoking status and confirmed SARS-CoV-2 infection, COVID-19-related hospitalisation, and COVID-19-related death. Inverse variance-weighted MR analyses using established genetic instruments for smoking initiation and smoking heaviness were undertaken (reported per SD increase). RESULTS: There were 421 469 eligible participants, 1649 confirmed infections, 968 COVID-19-related hospitalisations and 444 COVID-19-related deaths. Compared with never-smokers, current smokers had higher risks of hospitalisation (OR 1.80, 95% CI 1.26 to 2.29) and mortality (smoking 1-9/day: OR 2.14, 95% CI 0.87 to 5.24; 10-19/day: OR 5.91, 95% CI 3.66 to 9.54; 20+/day: OR 6.11, 95% CI 3.59 to 10.42). In MR analyses of 281 105 White British participants, genetically predicted propensity to initiate smoking was associated with higher risks of infection (OR 1.45, 95% CI 1.10 to 1.91) and hospitalisation (OR 1.60, 95% CI 1.13 to 2.27). Genetically predicted higher number of cigarettes smoked per day was associated with higher risks of all outcomes (infection OR 2.51, 95% CI 1.20 to 5.24; hospitalisation OR 5.08, 95% CI 2.04 to 12.66; and death OR 10.02, 95% CI 2.53 to 39.72). INTERPRETATION: Congruent results from two analytical approaches support a causal effect of smoking on risk of severe COVID-19.

Does smoking cause lower educational attainment and general cognitive ability? Triangulation of causal evidence using multiple study designs.

BACKGROUND: Observational studies have found associations between smoking and both poorer cognitive ability and lower educational attainment; however, evaluating causality is challenging. We used two complementary methods to explore this. METHODS: We conducted observational analyses of up to 12 004 participants in a cohort study (Study One) and Mendelian randomisation (MR) analyses using summary and cohort data (Study Two). Outcome measures were cognitive ability at age 15 and educational attainment at age 16 (Study One), and educational attainment and fluid intelligence (Study Two). RESULTS: Study One: heaviness of smoking at age 15 was associated with lower cognitive ability at age 15 and lower educational attainment at age 16. Adjustment for potential confounders partially attenuated findings (e.g. fully adjusted cognitive ability ß -0.736, 95% CI -1.238 to -0.233, p = 0.004; fully adjusted educational attainment ß -1.254, 95% CI -1.597 to -0.911, p < 0.001). Study Two: MR indicated that both smoking initiation and lifetime smoking predict lower educational attainment (e.g. smoking initiation to educational attainment inverse-variance weighted MR ß -0.197, 95% CI -0.223 to -0.171, p = 1.78 × 10-49). Educational attainment results were robust to sensitivity analyses, while analyses of general cognitive ability were less so. CONCLUSION: We find some evidence of a causal effect of smoking on lower educational attainment, but not cognitive ability. Triangulation of evidence across observational and MR methods is a strength, but the genetic variants associated with smoking initiation may be pleiotropic, suggesting caution in interpreting these results. The nature of this pleiotropy warrants further study.

Combined polygenic risk scores of different psychiatric traits predict general and specific psychopathology in childhood.

BACKGROUND: Polygenic risk scores (PRSs) operationalize genetic propensity toward a particular mental disorder and hold promise as early predictors of psychopathology, but before a PRS can be used clinically, explanatory power must be increased and the specificity for a psychiatric domain established. To enable early detection, it is crucial to study these psychometric properties in childhood. We examined whether PRSs associate more with general or with specific psychopathology in school-aged children. Additionally, we tested whether psychiatric PRSs can be combined into a multi-PRS score for improved performance. METHODS: We computed 16 PRSs based on GWASs of psychiatric phenotypes, but also neuroticism and cognitive ability, in mostly adult populations. Study participants were 9,247 school-aged children from three population-based cohorts of the DREAM-BIG consortium: ALSPAC (UK), The Generation R Study (Netherlands), and MAVAN (Canada). We associated each PRS with general and specific psychopathology factors, derived from a bifactor model based on self-report and parental, teacher, and observer reports. After fitting each PRS in separate models, we also tested a multi-PRS model, in which all PRSs are entered simultaneously as predictors of the general psychopathology factor. RESULTS: Seven PRSs were associated with the general psychopathology factor after multiple testing adjustment, two with specific externalizing and five with specific internalizing psychopathology. PRSs predicted general psychopathology independently of each other, with the exception of depression and depressive symptom PRSs. Most PRSs associated with a specific psychopathology domain, were also associated with general child psychopathology. CONCLUSIONS: The results suggest that PRSs based on current GWASs of psychiatric phenotypes tend to be associated with general psychopathology, or both general and specific psychiatric domains, but not with one specific psychopathology domain only. Furthermore, PRSs can be combined to improve predictive ability. PRS users should therefore be conscious of nonspecificity and consider using multiple PRSs simultaneously, when predicting psychiatric disorders.

Protecting against researcher bias in secondary data analysis: challenges and potential solutions.

Analysis of secondary data sources (such as cohort studies, survey data, and administrative records) has the potential to provide answers to science and society's most pressing questions. However, researcher biases can lead to questionable research practices in secondary data analysis, which can distort the evidence base. While pre-registration can help to protect against researcher biases, it presents challenges for secondary data analysis. In this article, we describe these challenges and propose novel solutions and alternative approaches. Proposed solutions include approaches to (1) address bias linked to prior knowledge of the data, (2) enable pre-registration of non-hypothesis-driven research, (3) help ensure that pre-registered analyses will be appropriate for the data, and (4) address difficulties arising from reduced analytic flexibility in pre-registration. For each solution, we provide guidance on implementation for researchers and data guardians. The adoption of these practices can help to protect against researcher bias in secondary data analysis, to improve the robustness of research based on existing data.

Association of milk intake with hay fever, asthma, and lung function: a Mendelian randomization analysis.

BACKGROUND: Previous observational studies have indicated a protective effect of drinking milk on asthma and allergy. In Mendelian Randomization, one or more genetic variants are used as unbiased markers of exposure to examine causal effects. We examined the causal effect of milk intake on hay fever, asthma, forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) by using the lactase rs4988235 genotype associated with milk intake. METHODS: We performed a Mendelian Randomization study including 363,961 participants from the UK Biobank. RESULTS: Observational analyses showed that self-reported milk-drinkers vs. non-milk drinkers had an increased risk of hay fever: odds ratio (OR) = 1.36 (95% CI 1.32, 1.40, p < 0.001), asthma: OR = 1.33 (95% CI 1.38, 1.29, p < 0.001), yet a higher FEV1: ß = 0.022 (SE = 0.004, p < 0.001) and FVC: ß = 0.026 (SE = 0.005, p < 0.001). In contrast, genetically determined milk-drinking vs. not drinking milk was associated with a lower risk of hay fever: OR = 0.791 (95% CI 0.636, 0.982, p = 0.033), and asthma: OR = 0.587 (95% CI 0.442, 0.779, p = 0.001), and lower FEV1: ß = - 0.154 (standard error, SE = 0.034, p < 0.001) liter, and FVC: ß = - 0.223 (SE = 0.034, p < 0.001) liter in univariable MR analyses. These results were supported by multivariable Mendelian randomization analyses although not statistically significant. CONCLUSIONS: As opposed to observational results, genetic association findings indicate that drinking milk has a protective effect on hay fever and asthma but may also have a negative effect on lung function. The results should be confirmed in other studies before any recommendations can be made.

Culture and causal inference: The impact of cultural differences on the generalisability of findings from Mendelian randomisation studies.

Cultural effects can influence the results of causal genetic analyses, such as Mendelian randomisation, but the potential influences of culture on genotype-phenotype associations are not currently well understood. Different genetic variants could be associated with different phenotypes in different populations, or culture could confound or influence the direction of the association between genotypes and phenotypes in different populations.

Investigating the causal relationship between allergic disease and mental health.

BACKGROUND: Observational studies have reported an association between allergic disease and mental health, but a causal relationship has not been established. Here, we use Mendelian randomization (MR) to investigate a possible causal relationship between atopic disease and mental health phenotypes. METHODS: The observational relationship between allergic disease and mental health was investigated in UK Biobank. The direction of causality was investigated with bidirectional two-sample MR using summary-level data from published genome-wide association studies. A genetic instrument was derived from associated variants for a broad allergic disease phenotype to test for causal relationships with various mental health outcomes. We also investigated whether these relationships were specific to atopic dermatitis (AD), asthma or hayfever. Given the multiple testing burden, we applied a Bonferroni correction to use an individual test p-value threshold of .0016 (32 tests). RESULTS: We found strong evidence of an observational association between the broad allergic disease phenotype and depression (ORself-report =1.45, 95% CI: 1.41-1.50, p = 3.6 × 10-130 ), anxiety (OR=1.25, 95% CI: 1.18-1.33, p = 6.5 × 10-13 ), bipolar disorder (ORself-report =1.29, 95% CI: 1.12-1.47, p = 2.8 × 10-4 ) and neuroticism (ß = 0.38, 95% CI: 0.36-0.41, p = 6.8 × 10-166 ). Similar associations were found between asthma, AD, hayfever individually with the mental health phenotypes, although the associations between AD and hayfever with bipolar disorder were weaker. There was little evidence of causality in either direction (all p-values>.02). CONCLUSION: Using MR, we were unable to replicate most of the phenotypic associations between allergic disease and mental health. Any causal effects we detected were considerably attenuated compared with the phenotypic association. This suggests that most comorbidity observed clinically is unlikely to be causal.

Smoking and the risk for bipolar disorder: evidence from a bidirectional Mendelian randomisation study.

BACKGROUND: There is increasing evidence that smoking is a risk factor for severe mental illness, including bipolar disorder. Conversely, patients with bipolar disorder might smoke more (often) as a result of the psychiatric disorder. AIMS: We conducted a bidirectional Mendelian randomisation (MR) study to investigate the direction and evidence for a causal nature of the relationship between smoking and bipolar disorder. METHOD: We used publicly available summary statistics from genome-wide association studies on bipolar disorder, smoking initiation, smoking heaviness, smoking cessation and lifetime smoking (i.e. a compound measure of heaviness, duration and cessation). We applied analytical methods with different, orthogonal assumptions to triangulate results, including inverse-variance weighted (IVW), MR-Egger, MR-Egger SIMEX, weighted-median, weighted-mode and Steiger-filtered analyses. RESULTS: Across different methods of MR, consistent evidence was found for a positive effect of smoking on the odds of bipolar disorder (smoking initiation ORIVW = 1.46, 95% CI 1.28-1.66, P = 1.44 × 10-8, lifetime smoking ORIVW = 1.72, 95% CI 1.29-2.28, P = 1.8 × 10-4). The MR analyses of the effect of liability to bipolar disorder on smoking provided no clear evidence of a strong causal effect (smoking heaviness betaIVW = 0.028, 95% CI 0.003-0.053, P = 2.9 × 10-2). CONCLUSIONS: These findings suggest that smoking initiation and lifetime smoking are likely to be a causal risk factor for developing bipolar disorder. We found some evidence that liability to bipolar disorder increased smoking heaviness. Given that smoking is a modifiable risk factor, these findings further support investment into smoking prevention and treatment in order to reduce mental health problems in future generations.

Genetic liability to schizophrenia is associated with exposure to traumatic events in childhood.

BACKGROUND: There is a wealth of literature on the observed association between childhood trauma and psychotic illness. However, the relationship between childhood trauma and psychosis is complex and could be explained, in part, by gene-environment correlation. METHODS: The association between schizophrenia polygenic scores (PGS) and experiencing childhood trauma was investigated using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Norwegian Mother, Father and Child Cohort Study (MoBa). Schizophrenia PGS were derived in each cohort for children, mothers, and fathers where genetic data were available. Measures of trauma exposure were derived based on data collected throughout childhood and adolescence (0-17 years; ALSPAC) and at age 8 years (MoBa). RESULTS: Within ALSPAC, we found a positive association between schizophrenia PGS and exposure to trauma across childhood and adolescence; effect sizes were consistent for both child or maternal PGS. We found evidence of an association between the schizophrenia PGS and the majority of trauma subtypes investigated, with the exception of bullying. These results were comparable with those of MoBa. Within ALSPAC, genetic liability to a range of additional psychiatric traits was also associated with a greater trauma exposure. CONCLUSIONS: Results from two international birth cohorts indicate that genetic liability for a range of psychiatric traits is associated with experiencing childhood trauma. Genome-wide association study of psychiatric phenotypes may also reflect risk factors for these phenotypes. Our findings also suggest that youth at higher genetic risk might require greater resources/support to ensure they grow-up in a healthy environment.

Maternal and offspring genetic risk score analyses of fetal alcohol exposure and attention-deficit hyperactivity disorder risk in offspring.

BACKGROUND: Studies investigating the effects of prenatal alcohol exposure on childhood attention-deficit hyperactivity disorder (ADHD) symptoms using conventional observational designs have reported inconsistent findings, which may be affected by unmeasured confounding and maternal and fetal ability to metabolize alcohol. We used genetic variants from the alcohol metabolizing genes, alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH), as proxies for fetal alcohol exposure to investigate their association with risk of offspring ADHD symptoms around age 7-8 years. METHODS: We used data from 3 longitudinal pregnancy cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC), Generation R study (GenR), and the Norwegian Mother, Father and Child Cohort study (MoBa). Genetic risk scores (GRS) for alcohol use and metabolism using 36 single nucleotide polymorphisms (SNPs) from ADH and ALDH genes were calculated for mothers (NALSPAC  = 8196; NMOBA  = 13,614), fathers (NMOBA  = 13,935), and offspring (NALSPAC =8,237; NMOBA =14,112; NGENR =2,661). Associations between maternal GRS and offspring risk of ADHD symptoms were tested in the full sample to avoid collider bias. Offspring GRS analyses were stratified by maternal drinking status. RESULTS: The pooled estimate in maternal GRS analyses adjusted for offspring GRS in ALSPAC and MoBa was OR = 0.99, 95%CI 0.97-1.02. The pooled estimate in offspring GRS analyses stratified by maternal drinking status across all the cohorts was as follows: ORDRINKING  = 0.98, 95% CI 0.94-1.02; ORNO DRINKING  = 0.99, 95% CI 0.97-1.02. These findings remained similar after accounting for maternal genotype data in ALSPAC and maternal and paternal genotype data in MoBa. CONCLUSIONS: We did not find evidence for a causal effect of fetal alcohol exposure on risk of ADHD symptoms in offspring. The results may be affected by limited power to detect small effects and outcome assessment.

Omega-3 fatty acids for depression in adults.

BACKGROUND: Major depressive disorder (MDD) is highly debilitating, difficult to treat, has a high rate of recurrence, and negatively impacts the individual and society as a whole. One potential treatment for MDD is n-3 polyunsaturated fatty acids (n-3PUFAs), also known as omega-3 oils, naturally found in fatty fish, some other seafood, and some nuts and seeds. Various lines of evidence suggest a role for n-3PUFAs in MDD, but the evidence is far from conclusive. Reviews and meta-analyses clearly demonstrate heterogeneity between studies. Investigations of heterogeneity suggest different effects of n-3PUFAs, depending on the severity of depressive symptoms, where no effects of n-3PUFAs are found in studies of individuals with mild depressive symptomology, but possible benefit may be suggested in studies of individuals with more severe depressive symptomology. Hence it is important to establish their effectiveness in treating MDD. This review updates and incorporates an earlier review with the same research objective (Appleton 2015). OBJECTIVES: To assess the effects of n-3 polyunsaturated fatty acids (also known as omega-3 fatty acids) versus a comparator (e.g. placebo, antidepressant treatment, standard care, no treatment, wait-list control) for major depressive disorder (MDD) in adults. SEARCH METHODS: We searched the Cochrane Central Register of Controlled trials (CENTRAL), Ovid MEDLINE, Embase and PsycINFO together with trial registries and grey literature sources (to 9 January 2021). We checked reference lists and contacted authors of included studies for additional information when necessary. SELECTION CRITERIA: We included studies in the review if they: used a randomised controlled trial design; provided n-3PUFAs as an intervention; used a comparator; measured depressive symptomology as an outcome; and were conducted in adults with MDD. Primary outcomes were depressive symptomology (continuous data collected using a validated rating scale) and adverse events. Secondary outcomes were depressive symptomology (dichotomous data on remission and response), quality of life, and non-completion of studies. DATA COLLECTION AND ANALYSIS: We used standard methodological procedures as expected by Cochrane. We assessed the certainty of the evidence using GRADE criteria. MAIN RESULTS: The review includes 35 relevant studies: 34 studies involving a total of 1924 participants investigated the impact of n-3PUFA supplementation compared to placebo, and one study involving 40 participants investigated the impact of n-3PUFA supplementation compared to antidepressant treatment. For the placebo comparison, n-3PUFA supplementation resulted in a small to modest benefit for depressive symptomology, compared to placebo: standardised mean difference (SMD) (random-effects model) -0.40 (95% confidence interval (CI) -0.64 to -0.16; 33 studies, 1848 participants; very low-certainty evidence), but this effect is unlikely to be clinically meaningful. An SMD of 0.40 represents a difference between groups in scores on the HDRS (17-item) of approximately 2.5 points (95% CI 1.0 to 4.0), where the minimal clinically important change score on this scale is 3.0 points. The confidence intervals include both a possible clinically important effect and a possible negligible effect, and there is considerable heterogeneity between studies. Sensitivity analyses, funnel plot inspection and comparison of our results with those of large well-conducted trials also suggest that this effect estimate may be biased towards a positive finding for n-3PUFAs. Although the numbers of individuals experiencing adverse events were similar in intervention and placebo groups (odds ratio (OR) 1.27, 95% CI 0.99 to 1.64; 24 studies, 1503 participants; very low-certainty evidence), the confidence intervals include a small decrease to a modest increase in adverse events with n-3PUFAs. There was no evidence for a difference between n-3PUFA and placebo groups in remission rates (OR 1.13, 95% CI 0.74 to 1.72; 8 studies, 609 participants, low-certainty evidence), response rates (OR 1.20, 95% CI 0.80 to 1.79; 17 studies, 794 participants; low-certainty evidence), quality of life (SMD -0.38 (95% CI -0.82 to 0.06), 12 studies, 476 participants, very low-certainty evidence), or trial non-completion (OR 0.92, 95% CI 0.70 to 1.22; 29 studies, 1777 participants, very low-certainty evidence). The evidence on which these results are based was also very limited, highly heterogeneous, and potentially biased. Only one study, involving 40 participants, was available for the antidepressant comparison. This study found no differences between treatment with n-3PUFAs and treatment with antidepressants in depressive symptomology (mean difference (MD) -0.70, 95% CI -5.88 to 4.48), rates of response to treatment (OR 1.23, 95% CI 0.35 to 4.31), or trial non-completion (OR 1.00, 95% CI 0.21 to 4.71). Confidence intervals are however very wide in all analyses, and do not rule out important beneficial or detrimental effects of n-3PUFAs compared to antidepressants. Adverse events were not reported in a manner suitable for analysis, and rates of depression remission and quality of life were not reported. AUTHORS' CONCLUSIONS: At present, we do not have sufficient high-certainty evidence to determine the effects of n-3PUFAs as a treatment for MDD. Our primary analyses may suggest a small-to-modest, non-clinically beneficial effect of n-3PUFAs on depressive symptomology compared to placebo; however the estimate is imprecise, and we judged the certainty of the evidence on which this result is based to be low to very low. Our data may also suggest similar rates of adverse events and trial non-completion in n-3PUFA and placebo groups, but again our estimates are very imprecise. Effects of n-3PUFAs compared to antidepressants are very imprecise and uncertain. More complete evidence is required for both the potential positive and negative effects of n-3PUFAs for MDD.

Investigating causality between liability to ADHD and substance use, and liability to substance use and ADHD risk, using Mendelian randomization.

Attention-deficit hyperactivity disorder (ADHD) has consistently been associated with substance use, but the nature of this association is not fully understood. To inform intervention development and public health messages, a vital question is whether there are causal pathways from ADHD to substance use and/or vice versa. We applied bidirectional Mendelian randomization, using summary-level data from the largest available genome-wide association studies (GWAS) on ADHD, smoking (initiation, cigarettes per day, cessation, and a compound measure of lifetime smoking), alcohol use (drinks per week, alcohol problems, and alcohol dependence), cannabis use (initiation), and coffee consumption (cups per day). Genetic variants robustly associated with the "exposure" were selected as instruments and identified in the "outcome" GWAS. Effect estimates from individual genetic variants were combined with inverse-variance weighted regression and five sensitivity analyses (weighted median, weighted mode, MR-Egger, generalized summary data-based MR, and Steiger filtering). We found evidence that liability to ADHD increases likelihood of smoking initiation and heaviness of smoking among smokers, decreases likelihood of smoking cessation, and increases likelihood of cannabis initiation. There was weak evidence that liability to ADHD increases alcohol dependence risk but not drinks per week or alcohol problems. In the other direction, there was weak evidence that smoking initiation increases ADHD risk, but follow-up analyses suggested a high probability of horizontal pleiotropy. There was no clear evidence of causal pathways between ADHD and coffee consumption. Our findings corroborate epidemiological evidence, suggesting causal pathways from liability to ADHD to smoking, cannabis use, and, tentatively, alcohol dependence. Further work is needed to explore the exact mechanisms mediating these causal effects.

Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: a Mendelian randomisation study.

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.