Nongenetic Factors Associated With Psychotic Experiences Among UK Biobank Participants: Exposome-Wide Analysis and Mendelian Randomization Analysis

Bochao Danae Lin; Lotta-Katrin Pries; Halil Suat Sarac; Jim van Os; Bart P F Rutten; Jurjen Luykx; Sinan Guloksuz

doi:10.1001/jamapsychiatry.2022.1655

. 2022 Jul 20;79(9):857–868. doi: 10.1001/jamapsychiatry.2022.1655

Nongenetic Factors Associated With Psychotic Experiences Among UK Biobank Participants

Exposome-Wide Analysis and Mendelian Randomization Analysis

Bochao Danae Lin ^1,^2,³, Lotta-Katrin Pries ¹, Halil Suat Sarac ¹, Jim van Os ^1,^4,⁵, Bart P F Rutten ¹, Jurjen Luykx ^1,^2,^4,^5,⁶, Sinan Guloksuz ^1,^7,^✉

¹Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands

²Brainclinics foundation, Nijmegen, the Netherlands

³Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Basic Medical Sciences, Henan University, Kaifeng, China

⁴Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands

⁵Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom

⁶GGNet Mental Health, Apeldoorn, the Netherlands

⁷Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut

Accepted for Publication: May 10, 2022.

Published Online: July 20, 2022. doi:10.1001/jamapsychiatry.2022.1655

^✉

Corresponding Author: Sinan Guloksuz, MD, PhD, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Vijverdalseweg 1, PO Box 616, 6200 MD, Maastricht, the Netherlands (sinan.guloksuz@maastrichtuniversity.nl).

Author Contributions: Drs Lin and Guloksuz had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Lin and Pries shared contribution.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Lin, Pries, Sarac, Rutten, Luykx, Guloksuz.

Drafting of the manuscript: Lin, Pries, Sarac, Luykx, Guloksuz.

Critical revision of the manuscript for important intellectual content: Lin, Pries, van Os, Rutten, Luykx, Guloksuz.

Statistical analysis: Lin, Pries, Sarac, Guloksuz.

Obtained funding: Rutten, Luykx, Guloksuz.

Administrative, technical, or material support: Luykx, Guloksuz.

Supervision: van Os, Luykx, Guloksuz.

Conflict of Interest Disclosures: None reported.

Funding/Support: Dr Pries is supported by the Kootstra Talent Fellowship of Maastricht University. Drs van Os and Guloksuz are supported by the Ophelia research project, ZonMw grant 636340001. Dr Rutten was funded by a Vidi award (91718336) from the Netherlands Scientific Organisation.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

^✉

Corresponding author.

PMCID: PMC9301596 PMID: 35857297

Key Points

Question

What are the factors associated with psychotic experiences?

Findings

In this cohort study of 155 247 UK Biobank participants, exposome-wide association analysis yielded 148 correlates of psychotic experiences, with 36 independent associations further identified in the fully adjusted multivariable model. Mendelian randomization analyses of these 36 variables indicated a forward association with ever having experienced sexual assault and pleiotropy of risk-taking behavior and a reverse association with ever having experienced a physically violent crime, cannabis use, and worrying too long after embarrassment.

Meaning

The finding that both well-studied and unexplored multiple correlated variables were associated with psychotic experiences underlines the importance of systematic agnostic approaches and triangulation of evidence with genetically informed approaches to probe associations in the big-data era.

This cohort study evaluates nongenetic factors associated with psychotic experiences.

Abstract

Importance

Although hypothesis-driven research has identified several factors associated with psychosis, this one-exposure-to-one-outcome approach fails to embrace the multiplicity of exposures. Systematic approaches, similar to agnostic genome-wide analyses, are needed to identify genuine signals.

Objective

To systematically investigate nongenetic correlates of psychotic experiences through data-driven agnostic analyses and genetically informed approaches to evaluate associations.

Design, Setting, Participants

This cohort study analyzed data from the UK Biobank Mental Health Survey from January 1 to June 1, 2021. An exposome-wide association study was performed in 2 equal-sized split discovery and replication data sets. Variables associated with psychotic experiences in the exposome-wide analysis were tested in a multivariable model. For the variables associated with psychotic experiences in the final multivariable model, the single-nucleotide variant–based heritability and genetic overlap with psychotic experiences using linkage disequilibrium score regression were estimated, and mendelian randomization (MR) approaches were applied to test potential causality. The significant associations observed in 1-sample MR analyses were further tested in multiple sensitivity tests, including collider-correction MR, 2-sample MR, and multivariable MR analyses.

Exposures

After quality control based on a priori criteria, 247 environmental, lifestyle, behavioral, and economic variables.

Main Outcomes and Measures

Psychotic experiences.

Results

The study included 155 247 participants (87 896 [57%] female; mean [SD] age, 55.94 [7.74] years). In the discovery data set, 162 variables (66%) were associated with psychotic experiences. Of these, 148 (91%) were replicated. The multivariable analysis identified 36 variables that were associated with psychotic experiences. Of these, 28 had significant genetic overlap with psychotic experiences. One-sample MR analyses revealed forward associations with 3 variables and reverse associations with 3. Forward associations with ever having experienced sexual assault and pleiotropy of risk-taking behavior and reverse associations without pleiotropy of experiencing a physically violent crime as well as cannabis use and the reverse association with pleiotropy of worrying too long after embarrassment were confirmed in sensitivity tests. Thus, associations with psychotic experiences were found with both well-studied and unexplored multiple correlated variables. For several variables, the direction of the association was reversed in the final multivariable and MR analyses.

Conclusions and Relevance

The findings of this study underscore the need for systematic approaches and triangulation of evidence to build a knowledge base from ever-growing observational data to guide population-level prevention strategies for psychosis.

Introduction

Hypothesis-driven observational studies have identified various nongenetic factors associated with psychosis. These environmental factors include relatively well-studied exposures, such as childhood adversity, immigration, racial or ethnic minority status, urbanicity, cannabis use, and obstetric and pregnancy complications,^1,2 as well as less studied exposures and lifestyle factors, such as physical activity;³ toxins, such as lead poisoning⁴ and nitrogen dioxide air pollution;^5,6 and nutrients, such as caffeine and magnesium.^7,8 Although hypothesis testing is essential and much knowledge on the environmental epidemiology of psychosis has been gained over the years, several limitations of this approach should be acknowledged. First, exposures form highly interconnected clusters.⁹ Therefore, single-exposure analyses are more prone to yield biased and often overestimated effect sizes and type I errors.^9,10 The complexity of associations is also sometimes to the degree that it is difficult to differentiate an exposure from a behavioral outcome in the temporal sequence—for instance, exposure to cannabis vs cannabis use disorder. Second, preconceptions appear to introduce selective reporting and publication bias.¹¹ Third, variation in analytical decisions and variable definitions across studies makes reliable comparison of findings extremely challenging.^10,12 Therefore, systematic and agnostic approaches are needed to dissect strong and consistent signals from selective reporting.¹⁰

Large-scale systematic evaluation offers several advantages over studies on single-candidate exposures. First, the association of exposures that have previously been implicated in hypothesis-driven research (ie, the candidate-exposure approach) can be confirmed. An exposome-wide approach limits sources of bias and decreases the risk of false-positive findings.¹³ Second, large-scale systematic investigation may identify novel correlates that have not been considered thus far. Similar to genome-wide association studies (GWAS), researchers have conducted exposome-wide studies of several phenotypes, such as behavioral problems in children,¹⁴ HIV,¹⁵ and diabetes.¹⁶ Third, mendelian randomization (MR) may help triangulate findings and estimate associations with target variables.¹⁷ We conducted what is to our knowledge the first systematic and agnostic exposome-wide analysis to identify correlates of psychotic experiences and sequentially applied genetically informed approaches to probe potential associations.

Methods

Data were retrieved from the UK Biobank (UKB), a population-based cohort study that included approximately 500 000 participants from the United Kingdom.¹⁸ All participants provided written consent, and ethical approval was given by the National Research Ethics Service Committee North West Multi-Centre Haydock (committee reference: 11/NW/0382).¹⁹ The current study (UKB project number: 55392) analyzed participants with complete data on the mental health questionnaire¹⁹ that assessed psychotic experiences (N = 155 247). The study followed the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) reporting guideline. Guided by previous reports,^20,21 a binary variable of psychotic experiences (n = 7803) was defined as an endorsement of any of the following 4 lifetime experiences: visual hallucination, auditory hallucination, reference delusion, and persecutory delusion (UKB field IDs f20471, f20463, f20474, f20468). After quality control and preprocessing steps (eMethods and eTables 1-3 in Supplements 1, 2, 3, and 4), the final data set included 247 variables (eTable 4 in Supplement 1). Figure 1 provides an overview of the analytical pipeline.

Statistical Analyses

Analyses were performed from January 1 to June 1, 2021, using R version 4.0.4 (R Foundation). There were 3 sequential analytical steps (Figure 1). Guided by previous exposome-wide studies,^15,22,23,24 we split the data into 2 equal-sized discovery and replication data sets by selecting random samples of participants matched in the frequency of psychotic experiences. To conduct the exposome-wide association study (XWAS), logistic regression analyses with psychotic experiences as the outcome were conducted in the discovery and replication data sets. Variables associated with psychotic experiences (threshold for significance, Bonferroni-corrected P < 2.02 × 10⁻⁴) in both discovery and replication data sets were tested in a mutually adjusted multivariable model using complete data (n = 57 702). All analyses were adjusted for age and sex. Variables associated with psychotic experiences in the final multivariable model (threshold for significance, P < .05) were further analyzed using genetically informed approaches to probe potential associations.^25,26 A Bonferroni-corrected significance threshold (.0014) was subsequently applied for genetic analyses based on the multivariable results. Psychotic experiences GWAS summary statistics from the UKB were used to estimate single-nucleotide variant (SNV)–based heritability and genetic overlap with psychotic experiences. One-sample bidirectional MR analyses were conducted, as detailed in the eMethods in Supplement 1. The significant associations identified by the 1-sample MR analyses were further analyzed using sensitivity tests, including 1-sample MR analyses controlling for potential confounders (ie, variables that were significantly associated with allele scores of the variables that were significant in the initial 1-sample MR analyses) and collider-correction (CC) 2-sample MR using individual level data from the UKB to apply 3 models (inverse variance weighted [IVW], MR-Egger, and least absolute deviation [LAD] regression).^27,28 Additionally, statistically significant variables identified in the initial 1-sample MR analyses were tested in 2-sample MR models (eMethods in Supplement 1). For the 2-sample MR, we used GWAS data from an independent adolescent cohort.²⁹ To our knowledge, this adolescent cohort provides the only available GWAS data of psychotic experiences independent of UKB samples. However, the sample size was relatively small for GWAS (N = 8665; minimum P = 1.32 × 10⁻⁶), possibly inflating the risk of type II error. Therefore, as schizophrenia may be considered the severe end of the psychosis spectrum, we also applied 2-sample bidirectional MR using schizophrenia GWAS data³⁰ with the IVW fixed-effect model. We then conducted sensitivity analyses using weighted median (testing associations when up to 50% of SNVs are invalid instruments), MR-Egger (testing associations when all genetic variants are invalid), generalized summary-databased MR (GSMR), and pleiotropy residual sum and outlier (PRESSO). We additionally applied a multivariable MR model to test statistically significant variables identified in the 1-sample forward MR analyses.

Results

Exposome-Wide Analysis

Of the 155 247 individuals included in this study, 87 896 (57%) were female, and the mean (SD) age was 55.94 (7.74) years. Of 162 variables that were associated with psychotic experiences in the discovery data set, 148 (91%) were replicated (eTable 5 and eFigures 1 and 2 in Supplement 1). Figure 2 shows the odds ratios (ORs) and 95% CIs of 148 variables under 13 categories in the whole data set. The multivariable analysis of the 148 replicated variables revealed that 36 (24%) were associated with psychotic experiences (eTable 6 in Supplement 1). The correlation matrix of these 36 variables is provided in eFigure 3 in Supplement 1. The Table reports the ORs and 95% CIs of the 36 variables derived from the discovery, replication, and mutually adjusted multivariable analysis. Compared with the XWAS, the associations of the 5 following variables with psychotic experiences were in the opposite direction (ie, the so-called Janus effect) in the multivariable analysis: frequency of unenthusiasm or disinterest in last 2 weeks; nitrogen dioxide air pollution, annual average 2007; number of operations, self-reported; recent feelings of inadequacy; and worrying too long after embarrassment.

Figure 2. — Odds ratios (ORs) and 95% CIs from the exposome-wide association study (XWAS) of the 148 variables in the total sample. Variables are referred to by field numbers (defined in eTable 5 in Supplement 1). Dots represents ORs and lines represent the 95% CIs.

Table. Associations of Psychotic Experiences With the 36 Variables Identified in the Multivariable Model^a.

Variable	Discovery XWAS		Replication XWAS		Multivariable model
Variable	R² (%)	OR (95% CI)	R² (%)	OR (95% CI)	OR (95% CI)	P value
Ever had period of mania/excitability	5.46	6.54 (5.97-7.17)	4.96	5.96 (5.43-6.53)	2.46 (2.14-2.82)	6.15 × 10⁻³⁸
Ever self-harmed	3.28	4.43 (4.02-4.88)	3.69	4.72 (4.29-5.19)	1.21 (1.03-1.43)	2.31 × 10⁻⁰²
Ever contemplated self-harm	5.63	3.93 (3.66-4.21)	5.41	3.78 (3.52-4.05)	1.26 (1.10-1.43)	5.31 × 10⁻⁰⁴
Ever thought that life is not worth living	5.77	3.47 (3.24-3.71)	5.23	3.22 (3.01-3.44)	1.34 (1.19-1.51)	6.50 × 10⁻⁰⁷
Ever seen a psychiatrist for nerves, anxiety, tension, or depression	3.72	3.38 (3.13-3.64)	4.00	3.48 (3.22-3.75)	1.42 (1.25-1.62)	9.12 × 10⁻⁰⁸
Receipt of attendance/disability/mobility allowance	1.58	3.35 (2.96-3.80)	1.68	3.44 (3.03-3.90)	1.42 (1.11-1.83)	5.59 × 10⁻⁰³
Ever had prolonged feelings of sadness or depression	4.31	3.21 (2.96-3.47)	3.89	2.94 (2.72-3.18)	1.34 (1.19-1.51)	1.63 × 10⁻⁰⁶
Sexual interference by partner or former partner without consent as an adult	1.89	2.87 (2.59-3.17)	1.87	2.76 (2.50-3.05)	1.20 (1.02-1.41)	2.83 × 10⁻⁰²
Felt hated by family member as a child	3.46	2.85 (2.65-3.05)	3.41	2.81 (2.61-3.01)	1.24 (1.11-1.39)	2.15 × 10⁻⁰⁴
Ever felt worried, tense, or anxious for most of a month or longer	4.01	2.80 (2.61-3.00)	4.55	2.98 (2.78-3.19)	1.25 (1.13-1.39)	3.05 × 10⁻⁰⁵
Ever had period extreme irritability	4.00	2.78 (2.60-2.98)	4.18	2.84 (2.65-3.03)	1.36 (1.23-1.50)	3.57 × 10⁻⁰⁹
Ever experienced sexual assault	3.04	2.73 (2.54-2.94)	2.70	2.55 (2.37-2.75)	1.37 (1.21-1.55)	7.98 × 10⁻⁰⁷
Recent restlessness	2.54	2.59 (2.40-2.79)	2.46	2.51 (2.32-2.71)	1.17 (1.02-1.35)	2.26 × 10⁻⁰²
Recent feelings of inadequacy	2.90	2.47 (2.30-2.64)	2.88	2.43 (2.27-2.61)	0.86 (0.76-0.98)	2.82 × 10⁻⁰²
Belittlement by partner or former partner as an adult	2.97	2.44 (2.28-2.61)	3.17	2.49 (2.33-2.66)	1.20 (1.08-1.34)	9.55 × 10⁻⁰⁴
Frequency of unenthusiasm/disinterest in last 2 wk	2.26	2.25 (2.09-2.42)	1.96	2.07 (1.92-2.22)	0.86 (0.75-0.99)	4.01 × 10⁻⁰²
Chest pain or discomfort	1.96	2.21 (2.05-2.39)	1.69	2.04 (1.88-2.20)	1.30 (1.16-1.47)	1.16 × 10⁻⁰⁵
Serious life-threatening event	1.56	2.18 (2.00-2.38)	1.65	2.20 (2.02-2.40)	1.36 (1.20-1.53)	1.30 × 10⁻⁰⁶
Miserableness	2.35	2.08 (1.95-2.23)	2.13	1.96 (1.83-2.09)	1.15 (1.03-1.28)	1.60 × 10⁻⁰²
Ever experienced physically violent crime	1.80	2.01 (1.87-2.16)	1.83	1.99 (1.85-2.14)	1.24 (1.12-1.37)	5.77 × 10⁻⁰⁵
Witnessed sudden violent death	1.29	1.87 (1.72-2.02)	1.46	1.92 (1.77-2.08)	1.24 (1.11-1.39)	2.12 × 10⁻⁰⁴
Any falls in the last year	1.30	1.76 (1.63-1.89)	1.45	1.81 (1.68-1.95)	1.17 (1.05-1.30)	4.42 × 10⁻⁰³
Cannabis use	1.32	1.72 (1.60-1.85)	1.36	1.70 (1.58-1.83)	1.18 (1.06-1.32)	2.32 × 10⁻⁰³
Major dietary changes in the last 5 y	1.21	1.56 (1.46-1.67)	1.21	1.53 (1.44-1.63)	1.13 (1.03-1.24)	7.44 × 10⁻⁰³
Risk-taking behavior	1.10	1.56 (1.46-1.68)	1.31	1.64 (1.53-1.75)	1.17 (1.06-1.28)	2.13 × 10⁻⁰³
Hearing difficulties	0.89	1.47 (1.36-1.58)	1.06	1.54 (1.43-1.66)	1.12 (1.02-1.24)	2.35 × 10⁻⁰²
Participation in leisure/social activities	0.84	1.39 (1.30-1.50)	0.91	1.40 (1.30-1.51)	1.13 (1.02-1.25)	1.66 × 10⁻⁰²
Plays computer games	0.75	1.35 (1.25-1.45)	0.74	1.29 (1.20-1.38)	1.16 (1.05-1.28)	3.93 × 10⁻⁰³
Worrying too long after embarrassment	0.75	1.32 (1.24-1.42)	0.90	1.35 (1.26-1.44)	0.90 (0.81-1.00)	4.87 × 10⁻⁰²
Regular vitamin and mineral supplement intake	0.68	1.26 (1.18-1.35)	0.79	1.29 (1.21-1.38)	1.12 (1.02-1.23)	1.80 × 10⁻⁰²
Townsend Deprivation Index at recruitment	1.24	1.24 (1.20-1.27)	1.20	1.22 (1.18-1.25)	1.06 (1.00-1.11)	3.77 × 10⁻⁰²
No. of operations self-reported	0.88	1.17 (1.14-1.21)	0.85	1.15 (1.11-1.18)	0.94 (0.90-0.99)	1.19 × 10⁻⁰²
Alkaline phosphatase	0.69	1.11 (1.08-1.14)	0.79	1.10 (1.07-1.13)	1.05 (1.00-1.10)	3.91 × 10⁻⁰²
Nitrogen dioxide air pollution, annual average 2007	0.58	1.07 (1.04-1.10)	0.63	1.06 (1.03-1.10)	0.91 (0.83-1.00)	4.77 × 10⁻⁰²
Drives faster than speed limit	0.57	0.82 (0.76-0.88)	0.59	0.87 (0.81-0.94)	0.86 (0.78-0.94)	1.41 × 10⁻⁰³
Hot drink temperature	0.65	0.77 (0.71-0.83)	0.77	0.74 (0.68-0.80)	0.85 (0.76-0.95)	4.08 × 10⁻⁰³

Open in a new tab

Abbreviations: OR, odds ratios; PE, psychotic experiences; R², Nagelkerke R²; XWAS, exposome-wide association study.

^{^a}

The Table shows the results for the 36 variables that were statistically significantly associated with psychotic experiences in the discovery (Bonferroni-corrected P < 2.02 × 10⁻⁴), replication (Bonferroni-corrected P < 2.02 × 10⁻⁴), and final multivariable analyses (P < .05). For ease of comparison, results are provided in descending order of ORs from the analyses in the discovery data set.

Estimating Heritability and Genetic Overlap With Psychotic Experiences

Figure 3 shows the SNV-based heritability and genetic overlap of the 36 variables with psychotic experiences (UKB and adolescent cohort), as detailed in eTable 7 in Supplement 1. The SNV-based heritability of these 36 variables ranged from 0.016 to 0.141 (Figure 3A). Twenty-eight variables were genetically correlated with psychotic experiences in the UKB (Figure 3B). The top hit was chest pain or discomfort (r_g, 0.808; 95% CI, 0.615-1.001; P = 2.5 × 10⁻¹⁶). The following 3 variables showed Janus effects, with a genetic correlation in the opposite direction of the XWAS: receipt of attendance, disability, or mobility allowance; major dietary changes in the last 5 years; and regular vitamin and mineral supplement intake. For the analysis using the adolescent cohort, we only reported the genetic covariance (Figure 3C) and not the genetic correlation, as the SNV-based heritability was out of bounds (ie, negative SNV-based heritability). The top hit was feeling hated by family member as a child (genetic covariance, 0.026; 95% CI, 0.011-0.041). Six variables showed Janus effects compared with the XWAS: major dietary changes in the last 5 years; worrying too long after embarrassment; sexual interference by partner or former partner without consent as an adult; nitrogen dioxide air pollution, annual average 2007; receipt of attendance, disability, or mobility allowance; and regular vitamin and mineral supplement intake.

The 1-Sample Bidirectional MR Analyses

Figure 4 shows the 1-sample bidirectional MR analyses results (eTables 8 and 9 in Supplement 1). Among the 130 363 unrelated participants of European ancestry in the UKB, the allele scores explained fractional variance of the 36 variables ranging from 0.04% to 7.85%. The concordance between the XWAS and the 1-sample MR is shown in eFigure 4 in Supplement 1. The 1-sample forward MR analyses confirmed associations with ever having experienced sexual assault (OR, 1.32; 95% CI, 1.14-1.52; P = 2.67 × 10⁻⁴), ever having experienced a physically violent crime (OR, 1.25; 95% CI, 1.11-1.41; P = 3.28 × 10⁻⁴), and risk-taking behavior (OR, 1.21; 95% CI, 1.08-1.35; P = 1.34 × 10⁻³). The allele scores for these 3 variables explained 0.03% to 0.23% variance of the corresponding variable. F statistics ranged from 21.53 to 181.84, indicating that the results did not suffer from a weak-instrument bias.

The 1-sample reverse MR analyses were conducted using 2 instruments: 1 SNV significantly associated with psychotic experiences in our GWAS in the UKB (rs11792873) and 4 SNVs from a previous study (eTable 10 in Supplement 1). The rs11792873 explained 0.03% variance of psychotic experiences, with an F statistic of 27.34. The 1-sample reverse MR analyses revealed an association with ever having experienced a physically violent crime (OR, 1.17; 95% CI, 1.11-1.24; P = 2.72 × 10⁻⁹) and cannabis use (OR, 1.16; 95% CI, 1.10-1.22; P = 3.96 × 10⁻⁹) (eTable 11 in Supplement 1). We also calculated an instrument based on increasing psychotic experiences risk allele scores using 4 SNVs from a previous study.²⁰ The increasing psychotic experience risk allele scores explained 0.14% variance of psychotic experiences, with an F statistic of 19.26. We validated the abovementioned association with cannabis use (OR, 1.11; 95% CI, 1.06-1.15; P = 2.64 × 10⁻⁶) and ever having experienced a physically violent crime (OR, 1.08; 95% CI, 1.04-1.13; P = 3.92 × 10⁻⁴) (eTable 12 in Supplement 1). Additionally, we detected an association with worrying too long after embarrassment (OR, 1.06; 95% CI, 1.03-1.10; P = 3.96 × 10⁻⁴).

Sensitivity Analyses for 1-Sample MR Analyses

The allele scores of ever having experienced sexual assault, ever having experienced physically violent crime, and risk-taking behavior were correlated with 5, 1, and 14 confounders, respectively (eTable 13 in Supplement 1). The 1-sample forward MR analyses adjusted for these potential confounders confirmed the association with ever having experienced a physically violent crime and ever having experienced sexual assault but not with risk-taking behavior (eTable 14 in Supplement 1). We also validated the forward association with risk-taking behavior in CC-IVW and CC-LAD. However, taking the horizontal pleiotropy effects into account using CC-MR-Egger, the association between risk-taking behavior and psychotic experiences was no longer statistically significant. The I² statistic of CC-MR-Egger was 99.3%, which validated the suitability of the instruments in MR-Egger and confirmed the absence of substantial bias in the association estimates due to uncertainty in the genetic associations. The associations with experiencing physically violent crime and ever having experienced sexual assault could not be tested, as there were not enough independent SNVs (n = 1 at P < 10⁻⁶) to calculate instruments. The reverse associations with having experienced physically violent crime, cannabis use, and worrying too long after embarrassment were confirmed with the CC-MR-Egger and CC-LAD regression models. Of the associations identified in the 1-sample MR analyses, the 2-sample forward MR analyses using schizophrenia GWAS data³⁰ confirmed the associations with having experienced sexual assault and the pleiotropy of risk-taking behavior, while the 2-sample reverse MR analyses using schizophrenia GWAS data³⁰ confirmed the reverse associations with having experienced physically violent crime and cannabis use without pleiotropy, as well as the reverse association with worrying too long after embarrassment with pleiotropy (eMethods, eTables 15 to 20 and eFigures 5 to 8 in Supplements 1 and 5). In the multivariable IVW model, risk-taking behavior and having experienced sexual assault showed significant associations, while neither pleiotropy nor other associations were detected in the multivariable MR-Egger model (eTable 21 in Supplement 1). The consistency of the findings across different MR methods is demonstrated in eFigures 9 and 10 in Supplement 1.

The synopsis of the results from each main analytical step are provided in eTable 22 in Supplement 1. The contingency of the 5 variables identified in the MR analyses is provided in eTable 23 in Supplement 1. The presence of all 5 correlates was associated with increased odds of psychotic experiences (OR, 10.63; 95% CI, 8.27-13.65; P = 1.2 × 10⁻¹¹⁴).

Discussion

This cohort study, to our knowledge constituting the largest systematic investigation of the nongenetic correlates of psychotic experiences, consisted of several sequential analytical steps. Exposome-wide analyses yielded 148 correlates. In line with the literature, environmental exposures, such as traumatic experiences (sexual assault, physical violence, partner abuse, and serious life-threatening event);^2,31,32 hearing difficulties;^2,32 neighborhood, social, and economic deprivation;³³ cannabis use;^32,34 multidimensional psychopathology domains;^35,36 proxies of poor mental health outcome (disability allowance, self-harm, and suicidal ideation);^37,38,39 and physical complaints (chest pain or discomfort or fall during the last year)⁴⁰ were among the top correlates. Psychotic experience was also associated with relatively unexplored factors, including major dietary changes in the last 5 years, driving faster than the speed limit, hot drink temperature, playing computer games,⁴¹ regular vitamin and mineral supplement intake, alkaline phosphatase,⁴² and nitrogen dioxide air pollution.^5,6 Of 36 variables that were significantly associated with psychotic experiences in the multivariable analysis, 28 had significant genetic overlap with psychotic experiences. MR analyses revealed the potential forward association with having experienced sexual assault and pleiotropy of risk-taking behavior and reverse associations with having experienced physically violent crime, cannabis use, and worrying too long after embarrassment.

The forward MR analyses showed an association with having experienced sexual assault, which is in accordance with converging evidence suggesting that psychosis is associated with traumatic events and stress-related mechanisms.^31,43 Sexual assault was 1 of the top associations with the largest odds for psychotic experiences in the World Mental Health Survey.⁴⁴ Although the 1-sample MR analysis suggested an association between experiencing physically violent crime and psychotic experiences, this association could not be confirmed in the 2-sample MR analyses. Our analyses further indicated pleiotropy of risk-taking behavior. Risk-taking behavior is associated with various personality traits and mental disorders, such as schizophrenia, posttraumatic stress disorder, ADHD, and bipolar disorder.^41,45,46,47 Genetic overlap of risk-taking behavior with psychiatric diagnoses, behavioral patterns (smoking, alcohol consumption, and cannabis use), body mass index, and IQ has also been found.^48,49 Recent evidence suggests that the path from genetic predisposition for risk-taking behavior to schizophrenia might be through environmental factors, such as immigration, urbanicity, or drug use.⁴¹ In accordance, we detected 14 possible confounders and, controlling for these, uncovered the pleiotropy of risk-taking behavior.

The reverse MR analyses showed associations between psychotic experiences and having experienced physically violent crime, worrying too long after embarrassment, and cannabis use. The findings support research showing that individuals with mental health problems, particularly psychosis, more frequently experience crimes and that this experience may impact patient trajectories.⁵⁰ These findings highlight the need for population-wide interventions that decrease violence against vulnerable individuals with mental health problems. The finding on worrying too long after embarrassment might be explained by the association of paranoia with rumination and affective regulation.⁵¹ Furthermore, our analyses detected a reverse association between psychotic experiences and cannabis use. These results are in agreement with previous MR studies showing a reverse association between schizophrenia risk and cannabis use.^52,53 There is also evidence that genetic liability to schizophrenia is associated with cannabis use.⁵⁴ However, these results contrast with findings showing that cannabis use is associated with an increase in risk of psychosis in a forward manner.^{55,56,57,58,59} There is an active debate on whether a bidirectional association between cannabis use and risk of psychosis may exist.^52,53,59,60 Longitudinal cohort studies (particularly within-individual designs),⁵⁶ genetically informative approaches,⁶¹ and experimental models⁶² are crucial to understanding the association between psychosis and cannabis use.

Our findings provide support to previous UKB reports showing that polygenic risk score for schizophrenia was associated with several parameters, including risk-taking behavior and psychiatric phenotypes.⁴¹ In accordance with a previous UKB finding²⁰ that showed positive genetic correlations between psychotic experiences and mental disorders, our findings suggest a shared genetic etiology between psychotic experiences and behavioral phenotypes (eg, ever contemplated self-harm; ever had prolonged feelings of sadness or depression; and ever saw a psychiatrist for nerves, anxiety, tension, or depression). Furthermore, we replicated recent UKB findings showing no statistically significant genetic correlation between cannabis use and individual psychotic experience items.⁶³ This is in contrast to several studies suggesting genetic correlation between substance use (eg, smoking, drinking, and cannabis use^{52,54,61,64,65,66}) and psychiatric disorders, including schizophrenia. Other exposures that were previously found to be genetically correlated with schizophrenia in the UKB, such as population density^67,68 and dietary intake,⁶⁹ either failed the quality-control steps or did not reach significance in the XWAS. We also detected Janus effects for several variables across different analytical steps. This finding illustrates how variable selections and analytical modalities may impact study results.^10,12 In accordance with previous studies in the UKB^21,70,71 and other large cohorts,^32,72,73 investigating gene-environment and environment-environment interactions may additionally help explain the variance in psychotic experiences in the UKB.

Limitations

Our systematic approach aimed to overcome biases (eg, selective reporting and data dredging), but it was not without limitations. The sequential replication procedure and stringent multiple-testing correction might have led to type II errors. Contrarily, statistically significant but trivial effects are also likely to emerge in large data analyses. The universally applied data preprocessing steps aim to eliminate confirmation bias and a posteriori decision-making. However, some relevant correlates might have been omitted because of missingness or collinearity. Also, these preprocessing steps might have introduced uninformed categorizations. Although we identified several potential associations in MR analyses, the lack of comparable GWAS data (only available data: adolescent psychotic experiences or schizophrenia), lack of power in the adolescent cohort, and violation of assumptions (eg, weak instruments or pleiotropy effects) posed a challenge for the 2-sample MR analyses. Especially, the associations of psychotic experiences with having experienced sexual assault and having experienced physically violent crime need further validation, as the instruments for these analyses were each based on a single SNV, thereby decreasing statistical power. Furthermore, genetic findings may be biased by a winner’s curse for instrument selection, given that most instruments were calculated based on the discovery UKB results rather than an independent data set.⁷⁴

Conclusions

The findings in this exposome-wide study revealed associations of psychotic experiences with both well-studied and unexplored parameters, some of which were correlated and showed Janus effects. MR analyses revealed an association with having experienced sexual assault and pleiotropy of risk-taking behavior and a reverse association with having experienced physically violent crime, cannabis use, and worrying too long after embarrassment. The findings underline the need for systematic exposome-wide analyses and triangulation of evidence with genetically informed approaches to probe potential causality in the era of big data. To guide public health policies and implementation, future studies aiming for mechanistic understanding are needed.

Supplement 1.

eTable 4. Detailed information on the 247 variables that passed pre-processing and quality control

eTable 5. Associations between the 247 variables and psychotic experiences from the exposome-wide association study

eTable 6. Associations between the 148 variables and psychotic experiences from the final multivariable analysis in the total sample

eTable 7. SNP heritability of the 36 variables and their genetic overlap with psychotic experiences estimated by the linkage disequilibrium score regression

eTable 8. Description of SNPs that were used as instruments in the supplementary eTable 9

eTable 9. Forward causal associations from the one-sample forward Mendelian randomization analyses

eTable 10. Description of SNPs that were used as instruments in the supplementary eTable 11 and 12.

eTable 11. Reverse causal associations from the one-sample reverse Mendelian randomization analyses using rs11792873 from our GWAS

eTable 12. Reverse causal associations from the one-sample reverse Mendelian randomization analyses using the four SNPs from Legge et al.’s study

eTable 13. Potential confounders for the instruments for the one-sample Mendelian randomization analyses

eTable 14. Sensitivity analyses for the one-sample Mendelian randomization analyses

eTable 16. Association of “risk-taking” with psychotic experiences from the leave-one-out sensitivity analyses for the two-sample Mendelian randomization analyses using data from the independent adolescent cohort.

eTable 17. Association of “risk-taking” with schizophrenia from the leave-one-out sensitivity analyses for the two-sample Mendelian randomization analyses using data from the CLOZUK and PGC datasets

eTable 18. Association of “victim of physically violent crime” with schizophrenia from the leave-one-out sensitivity analyses for reverse two-sample Mendelian randomization analyses using data from the CLOZUK and PGC datasets

eTable 19. Association of “ever taken cannabis” with schizophrenia from the leave-one-out sensitivity analyses for the reverse two-sample Mendelian randomization analyses using data from the CLOZUK and PGC datasets

eTable 20. Association of “worry too long after embarrassment” with schizophrenia from the leave-one-out sensitivity analyses for the reverse two-sample Mendelian randomization analyses using data from the CLOZUK and PGC datasets

eTable 21. Multivariable Mendelian randomization analyses of “risk-taking”, “victim of physically violent crime”, and “victim of sexual assault” on adolescent psychotic experience and schizophrenia.

eTable 22. Overview of the associations and effect directions of the phenotypic and genetic analyses

eTable 23. The contingency table of the five variables identified in the Mendelian randomization analyses

eFigure 1. Manhattan plots for the exposome-wide association analyses using the discovery and replication datasets

eFigure 2. Volcano plot for the exposome-wide association analyses using the discovery and replication datasets

eFigure 3. Correlation matrix of the variables included in the final multivariable analysis

eFigure 4. Concordance between XWAS and one-sample MR analysis

eFigure 5. Scatter plots of the association of “risk-taking” on psychotic experiences from the two-sample forward Mendelian randomization analyses

eFigure 6. Association of “risk-taking” with psychotic experiences from the leave-one-out sensitivity analyses for the two-sample Mendelian randomization analyses

eFigure 7. Scatter plots of the candidate variables on schizophrenia from the reverse two-sample Mendelian randomization analyses.

eFigure 8. Leave-one-out sensitivity analyses of the significant associations from the two-sample reverse Mendelian randomization analyses on schizophrenia

eFigure 9. Overview of consistent associations of the forward causal associations from the one- and two-sample Mendelian randomization analyses

eFigure 10. Overview of consistent associations of the reverse causal effects from the one- and two-sample Mendelian randomization analyses

Click here for additional data file.^{(1.3MB, pdf)}

Supplement 2.

eTable 1. Excluded variables: auxiliary, bulk, unrelated information, and follow-up arrays

Click here for additional data file.^{(311.2KB, xlsx)}

Supplement 3.

eTable 2. Missing rates of the 4,678 variables in the initial raw dataset

Click here for additional data file.^{(152.3KB, xlsx)}

Supplement 4.

eTable 3. Collinearity of the 303 variables that passed the missing rate exclusion criteria

Click here for additional data file.^{(1.1MB, xlsx)}

Supplement 5.

eTable 15. Causal associations from the two-sample bidirectional Mendelian randomization analyses using the independent adolescent and schizophrenia cohorts

Click here for additional data file.^{(25KB, xlsx)}

References

1.Belbasis L, Köhler CA, Stefanis N, et al. Risk factors and peripheral biomarkers for schizophrenia spectrum disorders: an umbrella review of meta-analyses. Acta Psychiatr Scand. 2018;137(2):88-97. doi: 10.1111/acps.12847 [DOI] [PubMed] [Google Scholar]
2.Radua J, Ramella-Cravaro V, Ioannidis JPA, et al. What causes psychosis? an umbrella review of risk and protective factors. World Psychiatry. 2018;17(1):49-66. doi: 10.1002/wps.20490 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Firth J, Solmi M, Wootton RE, et al. A meta-review of “lifestyle psychiatry”: the role of exercise, smoking, diet and sleep in the prevention and treatment of mental disorders. World Psychiatry. 2020;19(3):360-380. doi: 10.1002/wps.20773 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Samarghandian S, Shirazi FM, Saeedi F, et al. A systematic review of clinical and laboratory findings of lead poisoning: lessons from case reports. Toxicol Appl Pharmacol. 2021;429:115681. doi: 10.1016/j.taap.2021.115681 [DOI] [PubMed] [Google Scholar]
5.Newbury JB, Arseneault L, Beevers S, et al. Association of air pollution exposure with psychotic experiences during adolescence. JAMA Psychiatry. 2019;76(6):614-623. doi: 10.1001/jamapsychiatry.2019.0056 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Pedersen CB, Raaschou-Nielsen O, Hertel O, Mortensen PB. Air pollution from traffic and schizophrenia risk. Schizophr Res. 2004;66(1):83-85. doi: 10.1016/S0920-9964(03)00062-8 [DOI] [PubMed] [Google Scholar]
7.Topyurek M, Tibbo P, Núñez C, Stephan-Otto C, Good K. Caffeine effects and schizophrenia: is there a need for more research? Schizophr Res. 2019;211:34-35. doi: 10.1016/j.schres.2019.07.026 [DOI] [PubMed] [Google Scholar]
8.Botturi A, Ciappolino V, Delvecchio G, Boscutti A, Viscardi B, Brambilla P. The role and the effect of magnesium in mental disorders: a systematic review. Nutrients. 2020;12(6):1661. doi: 10.3390/nu12061661 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Patel CJ, Ioannidis JP. Studying the elusive environment in large scale. JAMA. 2014;311(21):2173-2174. doi: 10.1001/jama.2014.4129 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Guloksuz S, Rutten BPF, Pries L-K, et al. ; European Network of National Schizophrenia Networks Studying Gene-Environment Interactions Work Package 6 (EU-GEI WP6) Group . The complexities of evaluating the exposome in psychiatry: a data-driven illustration of challenges and some propositions for amendments. Schizophr Bull. 2018;44(6):1175-1179. doi: 10.1093/schbul/sby118 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Ioannidis JP. Why most published research findings are false. PLoS Med. 2005;2(8):e124. doi: 10.1371/journal.pmed.0020124 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Patel CJ, Burford B, Ioannidis JP. Assessment of vibration of effects due to model specification can demonstrate the instability of observational associations. J Clin Epidemiol. 2015;68(9):1046-1058. doi: 10.1016/j.jclinepi.2015.05.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Ioannidis JP, Loy EY, Poulton R, Chia KS. Researching genetic versus nongenetic determinants of disease: a comparison and proposed unification. Sci Transl Med. 2009;1(7):7ps8. doi: 10.1126/scitranslmed.3000247 [DOI] [PubMed] [Google Scholar]
14.Maitre L, Julvez J, López-Vicente M, et al. Early-life environmental exposure determinants of child behavior in Europe: a longitudinal, population-based study. Environ Int. 2021;153:106523. doi: 10.1016/j.envint.2021.106523 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Patel CJ, Bhattacharya J, Ioannidis JPA, Bendavid E. Systematic identification of correlates of HIV infection: an X-wide association study. AIDS. 2018;32(7):933-943. doi: 10.1097/QAD.0000000000001767 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.He Y, Lakhani CM, Rasooly D, Manrai AK, Tzoulaki I, Patel CJ. Comparisons of polyexposure, polygenic, and clinical risk scores in risk prediction of type 2 diabetes. Diabetes Care. 2021;44(4):935-943. doi: 10.2337/dc20-2049 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Choi KW, Stein MB, Nishimi KM, et al. ; 23andMe Research Team; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium . An exposure-wide and mendelian randomization approach to identifying modifiable factors for the prevention of depression. Am J Psychiatry. 2020;177(10):944-954. doi: 10.1176/appi.ajp.2020.19111158 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203-209. doi: 10.1038/s41586-018-0579-z [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Davis KAS, Coleman JRI, Adams M, et al. Mental health in UK Biobank—development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis. BJPsych Open. 2020;6(2):e18. doi: 10.1192/bjo.2019.100 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Legge SE, Jones HJ, Kendall KM, et al. Association of genetic liability to psychotic experiences with neuropsychotic disorders and traits. JAMA Psychiatry. 2019;76(12):1256-1265. doi: 10.1001/jamapsychiatry.2019.2508 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.García-González J, Ramírez J, Howard DM, Brennan CH, Munroe PB, Keers R. The effects of polygenic risk for psychiatric disorders and smoking behaviour on psychotic experiences in UK Biobank. Transl Psychiatry. 2020;10(1):330. doi: 10.1038/s41398-020-01009-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Sohlberg EM, Thomas IC, Yang J, et al. Laboratory-wide association study of survival with prostate cancer. Cancer. 2021;127(7):1102-1113. doi: 10.1002/cncr.33341 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Patel CJ, Cullen MR, Ioannidis JP, Butte AJ. Systematic evaluation of environmental factors: persistent pollutants and nutrients correlated with serum lipid levels. Int J Epidemiol. 2012;41(3):828-843. doi: 10.1093/ije/dys003 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Patel CJ, Bhattacharya J, Butte AJ. An environment-wide association study (EWAS) on type 2 diabetes mellitus. PLoS One. 2010;5(5):e10746. doi: 10.1371/journal.pone.0010746 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Lin BD, Alkema A, Peters T, et al. Assessing causal links between metabolic traits, inflammation and schizophrenia: a univariable and multivariable, bidirectional mendelian-randomization study. Int J Epidemiol. 2019;48(5):1505-1514. doi: 10.1093/ije/dyz176 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Bulik-Sullivan BK, Loh PR, Finucane HK, et al. ; Schizophrenia Working Group of the Psychiatric Genomics Consortium . LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291-295. doi: 10.1038/ng.3211 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Liu J, Richmond RC, Bowden J, et al. Assessing the causal role of sleep traits on glycated haemoglobin: a mendelian randomization study. medRxiv. Posted December 20, 2020. doi: 10.1101/2020.12.18.20224733 [DOI] [PMC free article] [PubMed]
28.Barry C, Liu J, Richmond R, et al. Exploiting collider bias to apply two-sample summary data mendelian randomization methods to one-sample individual level data. PLoS Genet. 2021;17(8):e1009703. doi: 10.1371/journal.pgen.1009703 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Pain O, Dudbridge F, Cardno AG, et al. Genome-wide analysis of adolescent psychotic-like experiences shows genetic overlap with psychiatric disorders. Am J Med Genet B Neuropsychiatr Genet. 2018;177(4):416-425. doi: 10.1002/ajmg.b.32630 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Pardiñas AF, Holmans P, Pocklington AJ, et al. ; GERAD1 Consortium; CRESTAR Consortium . Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet. 2018;50(3):381-389. doi: 10.1038/s41588-018-0059-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Varese F, Smeets F, Drukker M, et al. Childhood adversities increase the risk of psychosis: a meta-analysis of patient-control, prospective- and cross-sectional cohort studies. Schizophr Bull. 2012;38(4):661-671. doi: 10.1093/schbul/sbs050 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Guloksuz S, Pries LK, Delespaul P, et al. ; Genetic Risk and Outcome of Psychosis (GROUP) investigators . Examining the independent and joint effects of molecular genetic liability and environmental exposures in schizophrenia: results from the EUGEI study. World Psychiatry. 2019;18(2):173-182. doi: 10.1002/wps.20629 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Zhu X, Ward J, Cullen B, et al. Polygenic risk for schizophrenia, brain structure, and environmental risk in UK Biobank. Schizophr Bull. 2021;2(1):sgab042. doi: 10.1093/schizbullopen/sgab042 [DOI] [Google Scholar]
34.van Os J, Bak M, Hanssen M, Bijl RV, de Graaf R, Verdoux H. Cannabis use and psychosis: a longitudinal population-based study. Am J Epidemiol. 2002;156(4):319-327. doi: 10.1093/aje/kwf043 [DOI] [PubMed] [Google Scholar]
35.Pries LK, Guloksuz S, Ten Have M, et al. Evidence that environmental and familial risks for psychosis additively impact a multidimensional subthreshold psychosis syndrome. Schizophr Bull. 2018;44(4):710-719. doi: 10.1093/schbul/sby051 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.McGrath JJ, Saha S, Al-Hamzawi A, et al. The bidirectional associations between psychotic experiences and DSM-IV mental disorders. Am J Psychiatry. 2016;173(10):997-1006. doi: 10.1176/appi.ajp.2016.15101293 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Topor A, Stefansson CG, Denhov A, Bülow P, Andersson G. Recovery and economy; salary and allowances: a 10-year follow-up of income for persons diagnosed with first-time psychosis. Soc Psychiatry Psychiatr Epidemiol. 2019;54(8):919-926. doi: 10.1007/s00127-019-01655-4 [DOI] [PubMed] [Google Scholar]
38.Chesney E, Goodwin GM, Fazel S. Risks of all-cause and suicide mortality in mental disorders: a meta-review. World Psychiatry. 2014;13(2):153-160. doi: 10.1002/wps.20128 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Bromet EJ, Nock MK, Saha S, et al. ; World Health Organization World Mental Health Survey Collaborators . Association between psychotic experiences and subsequent suicidal thoughts and behaviors: a cross-national analysis from the World Health Organization World Mental Health Surveys. JAMA Psychiatry. 2017;74(11):1136-1144. doi: 10.1001/jamapsychiatry.2017.2647 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Oh H, Waldman K, Stickley A, DeVylder JE, Koyanagi A. Psychotic experiences and physical health conditions in the United States. Compr Psychiatry. 2019;90:1-6. doi: 10.1016/j.comppsych.2018.12.007 [DOI] [PubMed] [Google Scholar]
41.Socrates A, Maxwell J, Glanville KP, et al. Investigating the effects of genetic risk of schizophrenia on behavioural traits. NPJ Schizophr. 2021;7(1):2. doi: 10.1038/s41537-020-00131-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Du X, Ye F, Li J, et al. Altered levels of BMD, PRL, BAP and TRACP-5b in male chronic patients with schizophrenia. Sci Rep. 2020;10(1):13598. doi: 10.1038/s41598-020-70668-w [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Varchmin L, Montag C, Treusch Y, Kaminski J, Heinz A. Traumatic events, social adversity and discrimination as risk factors for psychosis—an umbrella review. Front Psychiatry. 2021;12:665957. doi: 10.3389/fpsyt.2021.665957 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.McGrath JJ, Saha S, Lim CCW, et al. ; WHO World Mental Health Survey Collaborators . Trauma and psychotic experiences: transnational data from the World Mental Health Survey. Br J Psychiatry. 2017;211(6):373-380. doi: 10.1192/bjp.bp.117.205955 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Zuckerman M, Kuhlman DM. Personality and risk-taking: common biosocial factors. J Pers. 2000;68(6):999-1029. doi: 10.1111/1467-6494.00124 [DOI] [PubMed] [Google Scholar]
46.Ramírez-Martín A, Ramos-Martín J, Mayoral-Cleries F, Moreno-Küstner B, Guzman-Parra J. Impulsivity, decision-making and risk-taking behaviour in bipolar disorder: a systematic review and meta-analysis. Psychol Med. 2020;50(13):2141-2153. doi: 10.1017/S0033291720003086 [DOI] [PubMed] [Google Scholar]
47.Schoenfelder EN, Kollins SH. Topical review: ADHD and health-risk behaviors: toward prevention and health promotion. J Pediatr Psychol. 2016;41(7):735-740. doi: 10.1093/jpepsy/jsv162 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Strawbridge RJ, Ward J, Lyall LM, et al. Genetics of self-reported risk-taking behaviour, trans-ethnic consistency and relevance to brain gene expression. Transl Psychiatry. 2018;8(1):178. doi: 10.1038/s41398-018-0236-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Hindley G, Bahrami S, Steen NE, et al. Characterising the shared genetic determinants of bipolar disorder, schizophrenia and risk-taking. Transl Psychiatry. 2021;11(1):466. doi: 10.1038/s41398-021-01576-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Purcell R, Harrigan S, Glozier N, Amminger GP, Yung AR. Self reported rates of criminal offending and victimization in young people at-risk for psychosis. Schizophr Res. 2015;166(1-3):55-59. doi: 10.1016/j.schres.2015.05.024 [DOI] [PubMed] [Google Scholar]
51.Lebert L, Turkington D, Freeston M, Dudley R. Rumination, intolerance of uncertainty and paranoia in treatment resistant psychosis. Psychosis. 2021;13(1):65-70. doi: 10.1080/17522439.2020.1798489 [DOI] [Google Scholar]
52.Gage SH, Jones HJ, Burgess S, et al. Assessing causality in associations between cannabis use and schizophrenia risk: a two-sample mendelian randomization study. Psychol Med. 2017;47(5):971-980. doi: 10.1017/S0033291716003172 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Pasman JA, Verweij KJH, Gerring Z, et al. ; 23andMe Research Team; Substance Use Disorders Working Group of the Psychiatric Genomics Consortium; International Cannabis Consortium . GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nat Neurosci. 2018;21(9):1161-1170. doi: 10.1038/s41593-018-0206-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Power RA, Verweij KJ, Zuhair M, et al. Genetic predisposition to schizophrenia associated with increased use of cannabis. Mol Psychiatry.2014;19(11):1201-1204. doi: 10.1038/mp.2014.51 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Vaucher J, Keating BJ, Lasserre AM, et al. Cannabis use and risk of schizophrenia: a mendelian randomization study. Mol Psychiatry. 2018;23(5):1287-1292. doi: 10.1038/mp.2016.252 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.van Os J, Pries L-K, Ten Have M, et al. Schizophrenia and the environment: within-person analyses may be required to yield evidence of unconfounded and causal association-the example of cannabis and psychosis. Schizophr Bull. 2021;47(3):594-603. doi: 10.1093/schbul/sbab019 [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Kuepper R, van Os J, Lieb R, Wittchen H-U, Höfler M, Henquet C. Continued cannabis use and risk of incidence and persistence of psychotic symptoms: 10 year follow-up cohort study. BMJ. 2011;342:d738. doi: 10.1136/bmj.d738 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Johnson EC, Hatoum AS, Deak JD, et al. The relationship between cannabis and schizophrenia: a genetically informed perspective. Addiction. 2021;116(11):3227-3234. doi: 10.1111/add.15534 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Ferdinand RF, Sondeijker F, van der Ende J, Selten JP, Huizink A, Verhulst FC. Cannabis use predicts future psychotic symptoms, and vice versa. Addiction. 2005;100(5):612-618. doi: 10.1111/j.1360-0443.2005.01070.x [DOI] [PubMed] [Google Scholar]
60.Khokhar JY, Dwiel LL, Henricks AM, Doucette WT, Green AI. The link between schizophrenia and substance use disorder: a unifying hypothesis. Schizophr Res. 2018;194:78-85. doi: 10.1016/j.schres.2017.04.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Gillespie NA, Kendler KS. Use of genetically informed methods to clarify the nature of the association between cannabis use and risk for schizophrenia. JAMA Psychiatry. 2021;78(5):467-468. doi: 10.1001/jamapsychiatry.2020.3564 [DOI] [PubMed] [Google Scholar]
62.Hindley G, Beck K, Borgan F, et al. Psychiatric symptoms caused by cannabis constituents: a systematic review and meta-analysis. Lancet Psychiatry. 2020;7(4):344-353. doi: 10.1016/S2215-0366(20)30074-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Vaissiere J, Thorp JG, Ong J-S, Ortega-Alonzo A, Derks EM. Exploring phenotypic and genetic overlap between cannabis use and schizotypy. Twin Res Hum Genet. 2020;23(4):221-227. doi: 10.1017/thg.2020.68 [DOI] [PubMed] [Google Scholar]
64.Johnson EC, Demontis D, Thorgeirsson TE, et al. ; Psychiatric Genomics Consortium Substance Use Disorders Workgroup . A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry. 2020;7(12):1032-1045. doi: 10.1016/S2215-0366(20)30339-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Reginsson GW, Ingason A, Euesden J, et al. Polygenic risk scores for schizophrenia and bipolar disorder associate with addiction. Addict Biol. 2018;23(1):485-492. doi: 10.1111/adb.12496 [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Karcher NR, Barch DM, Demers CH, et al. Genetic predisposition vs individual-specific processes in the association between psychotic-like experiences and cannabis use. JAMA Psychiatry. 2019;76(1):87-94. doi: 10.1001/jamapsychiatry.2018.2546 [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Colodro-Conde L, Couvy-Duchesne B, Whitfield JB, et al. Association between population density and genetic risk for schizophrenia. JAMA Psychiatry. 2018;75(9):901-910. doi: 10.1001/jamapsychiatry.2018.1581 [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Maxwell JM, Coleman JRI, Breen G, Vassos E. Association between genetic risk for psychiatric disorders and the probability of living in urban settings. JAMA Psychiatry. 2021;78(12):1355-1364. doi: 10.1001/jamapsychiatry.2021.2983 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Niarchou M, Byrne EM, Trzaskowski M, et al. Genome-wide association study of dietary intake in the UK Biobank study and its associations with schizophrenia and other traits. Transl Psychiatry. 2020;10(1):51. doi: 10.1038/s41398-020-0688-y [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Lehto K, Hägg S, Lu D, Karlsson R, Pedersen NL, Mosing MA. Childhood adoption and mental health in adulthood: the role of gene-environment correlations and interactions in the UK Biobank. Biol Psychiatry. 2020;87(8):708-716. doi: 10.1016/j.biopsych.2019.10.016 [DOI] [PubMed] [Google Scholar]
71.Coleman JRI, Peyrot WJ, Purves KL, et al. ; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium . Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank. Mol Psychiatry. 2020;25(7):1430-1446. doi: 10.1038/s41380-019-0546-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Pries LK, van Os J, Ten Have M, et al. Association of recent stressful life events with mental and physical health in the context of genomic and exposomic liability for schizophrenia. JAMA Psychiatry. 2020;77(12):1296-1304. doi: 10.1001/jamapsychiatry.2020.2304 [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Pries LK, Dal Ferro GA, van Os J, et al. ; Genetic Risk and Outcome of Psychosis (GROUP) investigators . Examining the independent and joint effects of genomic and exposomic liabilities for schizophrenia across the psychosis spectrum. Epidemiol Psychiatr Sci. 2020;29:e182. doi: 10.1017/S2045796020000943 [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Sadreev II, Elsworth BL, Mitchell RE, et al. Navigating sample overlap, winner’s curse and weak instrument bias in mendelian randomization studies using the UK Biobank. medRxiv. Posted July 1, 2021. doi: 10.1101/2021.06.28.21259622 [DOI]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials