Is there a causal relationship between executive function and liability to mental health and substance use? A Mendelian randomisation approach
1School of Psychological Science, University of Bristol, Bristol, UK
2MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
3Centre for Academic Mental Health, Population Health Sciences, University of Bristol, Bristol, UK
4Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
5National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
*Corresponding author; email: zoe.reed@bristol.ac.ukAbstract
Background
Executive function consists of several cognitive control processes that are able to regulate lower level processes. Poorer performance in tasks designed to test executive function is associated with a range of psychopathologies such as schizophrenia, major depressive disorder (MDD) and anxiety, as well as with smoking and alcohol consumption. Despite these well-documented associations, whether they reflect causal relationships, and if so in what direction, remains unclear. We aimed to establish whether there is a causal relationship between a latent factor for performance on multiple executive function tasks – which we refer to as common executive function (cEF) – and liability to schizophrenia, MDD, anxiety, smoking initiation, alcohol consumption, alcohol dependence and cannabis use disorder (CUD), and the directionality of any relationship observed.
Methods
We used a two-sample bidirectional Mendelian randomisation (MR) approach using genome-wide association study (GWAS) summary data from large cohorts (N=17,310 to 848,460) to examine whether causal relationships exist, and if so in which direction.
Results
We found evidence of a causal effect of increased cEF on reduced schizophrenia liability (IVW: OR=0.10; 95% CI 0.05 to 0.19; p-value=3.43×10−12), reduced MDD liability (IVW: OR=0.52; 95% CI 0.38 to 0.72; p-value=5.23×10−05), decreased drinks per week (IVW: β=−0.06; 95% CI −0.10 to −0.02; p-value=0.003), and reduced CUD liability (IVW: OR=0.27; 95% CI 0.12 to 0.61; p-value=1.58×10−03). We also found evidence of a causal effect of increased schizophrenia liability on decreased cEF (IVW: β=−0.04; 95% CI −0.04 to −0.03; p-value=3.25×10−27), as well as smoking initiation on decreased cEF (IVW: β=−0.06; 95%CI −0.09 to −0.03; p-value=6.11×10−05).
Conclusion
Our results indicate a potential bidirectional causal relationship between a latent factor measure of executive function (cEF) and schizophrenia liability, a possible causal effect of increased cEF on reduced MDD liability, CUD liability, and alcohol consumption, and a possible causal effect of smoking initiation on decreased cEF. These results suggest that executive function should be considered as a potential risk factor for some mental health and substance use outcomes, and may also be impacted by mental health (particularly schizophrenia). Further studies are required to improve our understanding of the underlying mechanisms of these effects, but our results suggest that executive function may be a promising intervention target. These results may therefore inform the prioritisation of experimental medicine studies (e.g., of executive function interventions), for both mental health and substance use outcomes, to improve the likelihood of successful translation.
Article notes
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This work was supported by the UK Medical Research Council Integrative Epidemiology Unit at the University of Bristol (Grant ref: MC_UU_00011/7) and the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. HMS is supported by the European Research Council (Grant ref: 758813 MHINT). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. ASH acknowledges funding from that National Institute on Drug Abuse (Grant ref: T32DA007261).
Introduction
The ability to perform nearly all of the activities required for daily living is mediated by executive function (EF)1 – the ability to perform self-directed behaviour toward a goal and to enable self-regulation. The prefrontal cortex is the neural substrate of EF, including cognitive control functions that regulate lower-level processes such as decision making1. There are different aspects of EF, including inhibitory control, working memory and task switching.
EF is thought to be impaired in individuals with a range of mental health problems1, and there is evidence that it is also associated with substance use2. For example, poorer EF has been observed among individuals with schizophrenia1,3–5, major depressive disorder (MDD)6,7, and anxiety8,9, as well as in people who smoke both cigarettes10,11 and cannabis12,13 and consume alcohol10,14. The direction of association between EF and these phenotypes is unclear1, with some studies suggesting EF deficits prior to these2 and others suggesting they occur after3. It is unclear whether these associations represent causal pathways, and if so what the direction of any causal effect might be. EF is potentially modifiable15, and drug repurposing analyses for a common EF factor (cEF) score16 have suggested that this cEF may also be modifiable. Therefore, if we can better understand the relationship between EF and mental health and substance use outcomes, this will help to inform intervention development.
Mendelian randomisation (MR) is a well-established method for causal inference, which relies on approximations of Mendel’s laws of segregation and random assortment17. MR is based on instrumental variable (IV) analysis, with single nucleotide polymorphisms (SNPs) that are robustly associated with the exposure used as IVs. MR is subject to three core assumptions: i) the genetic instrument is robustly associated with the exposure of interest (relevance), ii) there is no confounding of the genetic instrument and the outcome (independence), and iii) the genetic instrument only influences the outcome via the exposure (exclusion restriction). There are different MR methods that test potential violations of these assumptions and therefore a consistent effect estimate across different approaches would provide greater evidence of a truly causal effect, robust to the assumptions of MR. MR minimises the effect of confounding variables as the genetic variants are randomly assigned at conception18. It also overcomes issues around reverse causation as these genetic variants precede any outcomes19.
Summary data from genome-wide association studies (GWAS) can be used as the genetic instruments in MR. A recent GWAS of cEF score, conducted in the UK Biobank (European ancestry), identified 90 genome-wide significant hits16. The factor score was created from five different EF tasks – trail making, symbol-digit substitution, digit span, prospective memory, and pairs memory – using confirmatory factor analysis. Unlike, previous studies focusing on specific EF tasks, the cEF incorporates multiple facets which may better capture the cognitive component of psychopathology. In particular, single executive function tasks are noisy measures of executive functioning, with large method variance components reflecting lower level cognitive processes (the “task impurity problem” 20). By combining multiple tasks, we can create a more “pure” measure of executive functioning. Further, past work has shown that this common component is correlated with – but separable from – IQ, and is genetically associated with psychopathology over and above the genetic influence of other cognitive factors16.
We examined whether there were causal relationships between EF and a range of mental health and substance use phenotypes, and the direction of any effect – for example, does poor mental health lead to poorer EF, or vice versa? We did this by applying a two-sample MR approach, where the SNP-exposure and SNP-outcome estimates are obtained from genome-wide association studies (GWAS) in independent samples and used to estimate causal effects. We focused on schizophrenia, MDD, anxiety, smoking initiation, alcohol consumption (drinks per week), alcohol dependence, and cannabis use disorder (CUD), using a bidirectional approach to determine the causal direction of these relationships.
Methods
Data Sources
We used GWAS data from several studies, shown in Table 1. To minimise sample overlap, we excluded some samples that contributed to the original GWAS in our analyses, as indicated in Table 1.
Executive function
We used summary data from the most recent GWAS of cEF16, which identified 90 independent genome-wide significant (p<5×10−08) SNPs associated with a cEF score, where a higher score reflects increased EF.
Schizophrenia
We used summary data from the most recent Psychiatric Genomics Consortium (PGC) GWAS of schizophrenia21, which identified 294 independent genome-wide significant SNPs. Cases mostly included participants diagnosed with schizophrenia (although diagnoses of other psychotic disorders were also included in some samples).
Major depressive disorder (MDD)
We used summary data from the most recent PGC GWAS of MDD22, which identified 44 independent genome-wide significant SNPs. Cases of MDD were either diagnosed by a clinical professional, or through structured interviews with trained interviewers, using the DSM IV, ICD-9 or ICD-10 criteria.
Anxiety
We used summary data from a GWAS of anxiety23, which identified one independent genome-wide significant SNP. In this meta-analysis, there were up to five different anxiety disorder phenotypes included in the nine samples from seven independent cohorts. Lifetime DSM-based anxiety disorder diagnostic assessments were available for all cohorts except for the Rotterdam study, in which only one-year prevalence was assessed. Each study assessed DSM-based criteria for the following six-lifetime clinical phenotypes: generalised anxiety disorder (GAD), panic disorder, agoraphobia, social phobia, specific phobia and MDD, however, any subject reporting a mood disorder only was removed from analyses.
Smoking initiation
We used summary data from the most recent GWAS of smoking initiation24, which identified 378 conditionally independent genome-wide significant SNPs associated with ever being a regular smoker (current or former). Participants were asked whether they had smoked more than 100 cigarettes in their lifetime and whether they had ever smoked every day for at least a month or ever smoked regularly. To obtain summary statistics for the full sample included in the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) GWAS excluding UK Biobank data, we meta-analysed results from GWAS of 23andMe, Inc. only data and all results excluding UK Biobank and 23andMe. The meta-analysis was conducted using the genome-wide association meta-analysis (GWAMA) software27.
Drinks per week
We used summary data from the most recent GWAS of drinks per week24, which identified 99 conditionally independent genome-wide significant SNPs associated with the average number of drinks a participant reported drinking each week. Participants were asked about the number of alcoholic beverages they had in the past week and the average number of drinks per week than they had in the past year. Data were log-transformed prior to the GWAS. This measure did not account for the type of alcohol consumed and for any study with ranges, the mid-range value was used. Again, to obtain summary statistics for the full GSCAN GWAS excluding UK Biobank data, we meta-analysed results from GWAS of 23andMe only data and all results excluding UK Biobank and 23andMe.
Alcohol Dependence
We used summary data from the PGC substance use disorders working group GWAS of alcohol dependence25, which identified one conditionally independent genome-wide significant SNPs in their European GWAS associated with alcohol dependence. Alcohol dependence cases were those that met criteria for DSM-IV or DSM-III-R alcohol dependence diagnosis.
Cannabis Use Disorder (CUD)
We used summary data from the most recent GWAS of CUD26 from the PGC substance use disorders working group, iPSYCH and deCODE which identified 2 conditionally independent genome-wide significant SNPs associated with CUD. CUD cases were those that met criteria for DSM-5, DSM-IV, DSM-III-R or ICD-10 cannabis abuse or dependence.
Statistical analyses
The analysis plan for this study was pre-registered on the Open Science Framework (https://osf.io/j3tb5). We conducted two-sample MR analyses using R (version 4.0.3)28 and the package TwoSampleMR (version 0.5.0)29,30. In order to assess potential bidirectional pathways, we conducted two-sample MR analyses with cEF as the exposure for one direction, and as the outcome in the other direction (see Figure 1), when assessing causal relationships with liability to schizophrenia, MDD, anxiety, smoking initiation, drinks per week, alcohol dependence and CUD.
We used independent genome-wide significant SNPs for the exposure of interest as instruments in the MR analyses, except when anxiety, alcohol dependence and CUD were the exposures, where we used a p-value threshold of 1×10−05 to select SNPs due to the low number of genome-wide significant SNPs. We excluded all SNPs in linkage disequilibrium (LD) using an r2 of 0.001, a window of 10000 kb and the European 1000 genomes reference panel. Where there were palindromic SNPs, we tried to infer the positive strand based on allele frequencies, but if this was not possible, then these SNPs were also excluded. Where an exposure SNP was not available in the outcome data, we attempted to identify a suitable proxy SNP using the LDproxy tool from LDlink31. We pruned the SNPs extracted to r2≥0.8 and extracted the first SNP that was present in the exposure and outcome data. After exclusions and identifying any proxy SNPs we searched for the remaining cEF SNPs in the outcome GWAS (73 for schizophrenia, 85 for MDD, 83 for anxiety, 82 for smoking initiation, 83 for drinks per week, 73 for alcohol dependence and 73 for CUD) and the remaining mental health and substance use exposure SNPs in the cEF outcome (175 for schizophrenia, 29 for MDD, 17 for anxiety, 187 for smoking initiation, 67 for drinks per week, 19 for alcohol dependence and 37 for CUD). The GWAS summary statistics for the exposure and outcome in each analysis were harmonised so that the SNP allele-exposure and SNP allele-outcome associations were in the same direction.
We used several different MR methods to assess these putative causal relationships: inverse-variance weighted (IVW)32, MR-Egger33, weighted median34, simple mode and weighted mode35, and Steiger filtering36 MR methods. We used the IVW approach as our main method with the other methods used as sensitivity analyses.
The IVW approach constrains the intercept to pass through zero, assuming no horizontal pleiotropy. We tested for heterogeneity between the individual SNPs included in the genetic instrument using Cochran’s test of heterogeneity. The MR-Egger method tests for overall directional pleiotropy by not constraining the intercept to pass through zero. If the intercept is not zero then this is indicative of directional horizontal pleiotropy. We also assessed heterogeneity between the individual SNPs whilst adjusting for any directional pleiotropy for the MR-Egger method using Rucker’s Q test. We used the weighted median method to obtain estimates under the assumption that at least 50% of the SNPs satisfy the MR assumptions and are valid IVs. Finally, we used the mode-based approaches to obtain estimates for the largest cluster of SNPs, where SNPs not in that cluster could be invalid. The weighted method accounts for the largest weights of SNPs. We also conducted single SNP and leave-one-out analyses.
Where we found evidence of a bidirectional causal relationship, we ran Steiger filtering. This allows orientation of the direction of effect where the underlying biology of genetic variants is less clear, by identifying which SNPs explain more variance in the outcome than the exposure and then repeating the MR analyses excluding those SNPs to rule out reverse causation36.
In cases where we found evidence for a causal effect between a given exposure and outcome, we present plots of these results in the Supplementary Material. These plots include scatter plots of the SNP-exposure and SNP-outcome associations with the causal effect estimates from each MR method presented, forest plots for causal effects of each SNP in the instrument (which can indicate if heterogeneity is present), plots presenting the leave-one-out results and funnel plots of each SNP included in the instrument, where a symmetrical plot indicates that the effects of each SNP included are similar to the average effect and asymmetry may indicate horizontal pleiotropy.
We calculated weighted and unweighted regression dilution I-squared statistics for each analysis37, presented in Supplementary Table S1, which give an indication of the amount of bias in the ‘NO Measurement Error’ (NOME) assumption in the MR-Egger estimate34. If the I-squared statistic is 0.9 or above this indicates minimal bias in the MR-Egger estimate and therefore we present the MR-Egger results for these associations. If either the weighted or unweighted I-squared statistics were between 0.6 and 0.9, this may indicate regression dilution bias and therefore we ran simulation extrapolation (SIMEX) corrections, to obtain bias-adjusted point estimates for MR-Egger and we present these results in place of MR-Egger. Anything below 0.6 means that the bias may be too large and therefore we do not report either the SIMEX correction or the MR-Egger results. We also estimated the mean F-statistic for each analysis, indicative of instrument strength, where a value under 10 may indicate a weak instrument37.
Data availability
The data used in this study are publicly-available GWAS data for cEF (available upon request by contacting the authors), schizophrenia (https://www.med.unc.edu/pgc/download-results/scz/), MDD (https://www.med.unc.edu/pgc/download-results/mdd/), anxiety (https://www.med.unc.edu/pgc/download-results/angst/?choice=Other+GWAS+DataAnxiety+Neuro+Genetics+Study+%28ANGST%29), smoking initiation and drinks per week (data with UK Biobank and 23andMe removed can be found here: https://conservancy.umn.edu/handle/11299/201564, and for 23andMe data access needs to be requested (see below), alcohol dependence (https://figshare.com/articles/dataset/sud2018-alc/14672187) and CUD (https://figshare.com/articles/dataset/sud2020-cud/14842692). The full GWAS summary statistics for the 23andMe discovery data set will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Please visit https://research.23andme.com/collaborate/#dataset-access/ for more information and to apply to access the data.
Code availability
The analysis code that forms the basis of the results presented here is available from the University of Bristol’s Research Data Repository (http://data.bris.ac.uk/data/), DOI : To be made available upon publication).
Results
Our two-sample MR results for the causal effects of cEF on mental health and substance use outcomes are presented in Table 2.
Schizophrenia
We found strong evidence of a causal effect of increased cEF on reduced odds of schizophrenia (IVW: OR=0.10; 95% CI 0.05 to 0.19; p-value=3.43×10−12) for all methods except MR Egger, which we were unable to estimate due to violation of the NOME assumption (See Supplementary Table S1). These results were in a consistent direction across the different MR analyses (Supplementary Figure S1). However, we did observe evidence of heterogeneity for the IVW estimate (Supplementary Figure S2) and some asymmetry in the funnel plot (Supplementary Figure S3), although our leave one out analyses did not indicate that a single SNP was driving the association (Supplementary Figure S4). Steiger filtering indicated that only 41% of SNPs instrumenting cEF explained more variance in cEF than schizophrenia, and results were attenuated when repeating analyses with this subset of SNPs. However, these results were in the same direction as the main results (Supplementary Table 2).
Major depressive disorder
We found strong evidence of a causal effect of increased cEF on reduced odds of MDD (IVW: OR=0.52; 95% CI 0.38 to 0.72; p-value=5.23×10−05). These results were in a consistent direction across the different MR analyses (Supplementary Figure S5), and there was evidence of a causal effect for all methods except simple mode and MR Egger, which again we unable to estimate due to violation of the NOME assumption. Here we also found evidence of heterogeneity for the IVW estimate (Supplementary Figure S6), and some asymmetry in the funnel plot (Supplementary Figure S7); however, leave one out analyses did not indicate that a single SNP was driving the association (Supplementary Figure S8). Steiger filtering indicated that 81% of SNPs instrumenting cEF explained more variance in cEF than MDD, but again results were similar to the main results when using this subset of SNPs (Supplementary Table 2).
Anxiety
We did not find evidence of a causal effect of cEF on anxiety liability (IVW: OR=0.49; 95% CI 0.19 to 1.23; p-value=0.13).
Smoking initiation
We did not find clear evidence of a causal effect of cEF on smoking initiation (IVW: OR=0.87; 95% CI 0.74 to 1.02; p-value=0.09) for any of the MR analyses.
Drinks per week
We found some evidence of a causal effect of increased cEF on decreased number of alcoholic drinks per week consumed (IVW: β=−0.06; 95% CI −0.10 to −0.02; p-value=0.003). These results were in a consistent direction across the different MR analyses (Supplementary Figure S9), although there was only evidence of a causal effect for the IVW method. We also found evidence of heterogeneity here (Supplementary Figure S10) and slight asymmetry in the funnel plot (Supplementary Figure S11); however, leave one out analyses did not indicate that a single SNP was driving the association (Supplementary Figure S12).
Alcohol dependence
We did not find clear evidence of a causal effect of cEF on alcohol dependence liability (IVW: OR=0.50; 95% CI 0.23 to 1.28; p-value=0.08) for any of the MR analyses.
Cannabis Use Disorder (CUD)
We found strong evidence of a causal effect of increased cEF on reduced odds of CUD (IVW: OR=0.27; 95% CI 0.12 to 0.61; p-value=1.58×10−03) for all methods except MR Egger, which we were unable to estimate due to violation of the NOME assumption (See Supplementary Table S1). These results were in a consistent direction across the different MR analyses (Supplementary Figure S13). However, we did observe evidence of heterogeneity for the IVW estimate (Supplementary Figure S14) and slight asymmetry in the funnel plot (Supplementary Figure S15), although our leave one out analyses did not indicate that a single SNP was driving the association (Supplementary Figure S16).
Our two-sample MR results for causal effects of the mental health and substance use phenotypes on cEF are presented in Table 3.
Schizophrenia
We found strong evidence of a causal effect of increased odds of schizophrenia on decreased cEF (IVW: β=−0.04; 95% CI −0.04 to −0.03; p-value=3.25×10−27). These results were in a consistent direction across the different MR analyses (Supplementary Figure S17). However, we did observe evidence of heterogeneity for the IVW estimate (Supplementary Figure S18) and some asymmetry in the funnel plot (Supplementary Figure S19), although there was little evidence of directional pleiotropy from the SIMEX estimate and our leave one out analyses did not indicate that a single SNP was driving the association (Supplementary Figure S20). Steiger filtering indicated that all SNPs instrumenting schizophrenia explained more variance in schizophrenia than cEF; therefore these analyses were not repeated (Supplementary Table 2).
MDD
We did not find evidence of a causal effect of MDD liability on cEF (IVW: β=−0.02; 95% CI −0.05 to 0.01; p-value=0.31) using IVW. However, evidence was stronger using other sensitivity methods and the direction of effect was consistent across all approaches i.e., for increased odds of MDD on decreased cEF. Steiger filtering indicated that all SNPs instrumenting schizophrenia explained more variance in MDD than cEF; therefore these analyses were not repeated (Supplementary Table 2).
Anxiety
We did not find evidence of a causal effect of anxiety liability on cEF (IVW: β=8.69×10−04; 95% CI −0.003 to 0.005; p-value=0.68) for any of the MR analyses.
Smoking initiation
We found strong evidence of a causal effect of smoking initiation on decreased cEF (IVW: β=−0.06; 95% CI −0.09 to −0.03; p-value=6.11×10−05) and there was evidence of this causal effect using the weighted median method but not the other MR methods, although the direction of effect was consistent (Supplementary Figure S21). We did find evidence of heterogeneity for the IVW estimate (Supplementary Figure S22) and slight asymmetry in the funnel plot (Supplementary Figure S23); however, our leave one out analyses did not indicate that a single SNP was driving the association (Supplementary Figure S24).
Drinks per week
We did not find clear evidence of a causal effect of cEF on number of alcoholic drinks per week consumed (IVW: β=−0.007; 95% CI −0.17 to 0.16; p-value=0.93).
Alcohol dependence
We did not find clear evidence of a causal effect of alcohol dependence liability on cEF (IVW: β=−3.31×10−03; 95% CI −8.21×10−03 to 1.65×10−03; p-value=0.09) for any of the MR analyses.
Cannabis Use Disorder (CUD)
We did not find clear evidence of a causal effect of CUD liability on cEF (IVW: β=−6.42×10−03; 95% CI −0.01 to 9.56×10−04; p-value=0.09) for any of the MR analyses.
Discussion
We examined whether there was evidence of causal effects of cEF on schizophrenia, MDD, anxiety, smoking initiation, drinks per week, alcohol dependence and CUD. We also examined the reverse direction (i.e., causal effects of mental health and substance use on cEF). Evidence of a causal effect in both directions may be indicative of a bidirectional relationship or some other underlying common risk factor.
Our main findings were evidence of a causal relationship between increased cEF and reduced schizophrenia liability in both directions. Steiger filtering supported the finding of bidirectional effects, despite some attenuation of results for cEF on schizophrenia. This causal effect supports previous observational studies, which have found that people with schizophrenia have poorer EF1,3–5; however, our MR analyses provide evidence that these associations may reflect causal pathways. The fact that we find causal effects between schizophrenia liability and cEF in both directions may point to this association being bidirectional or due to an underlying common risk factor.
The observed causal effect of increased cEF on reduced MDD liability is also interesting, as previous studies have provided mixed evidence for the directionality of this relationship6,7. We did not find strong evidence of a causal effect of MDD on cEF using the IVW approach; however, evidence was stronger when using other MR methods, and the direction of effect we observed was negative and consistent across these. It may also be possible that there is lower power in the MDD instrument than the cEF instrument, due to the lower number of SNPs, which may also be why we do not observe a consistent effect of MDD on cEF. Therefore, we cannot rule out the possibility of a bidirectional relationship and Steiger filtering suggested that the effects could be bidirectional as well. We cannot draw any strong conclusions from our results regarding anxiety and cEF. Whilst we do not find evidence of a causal relationship, this does not mean that there is definitely no effect, but rather that our study may have lacked the power to detect a causal effect here, particularly given that we find some evidence of a possible causal effect of MDD on cEF in our sensitivity analyses and previous studies have reported high genetic correlations between MDD and anxiety38. Thus the association found in previous studies needs further investigation still8,9.
Finally, we also found some evidence of a causal effect of smoking initiation on decreased cEF and increased cEF on decreased drinks per week and reduced CUD liability, all in a consistent direction with previous observational studies10–14. However, evidence for the smoking and drinks per week findings was weak, so further studies examining this would be useful.
Our results are in contrast to a previous study which used latent causal variable (LCV) analyses and did not find evidence of any causal effects of cEF on schizophrenia, MDD, anxiety, alcohol use disorder or other traits examined16. LCV analysis relies on the assumption that there is a latent variable that mediates the genetic correlation between two traits and uses whole genome summary statistics to estimate genetic causality. One trait is partially genetically causal for the other if it is strongly genetically correlated with the LCV. However, LCV aims to capture the overall direction of causality and therefore may be less appropriate for relationships where a bidirectional effect may be present, as we observe in our study. Whereas MR does allow for for bidirectional relationships39. Although, LCV has better control for pleiotropy16, meaning the difference between our results and those in the previous LCV analysis may reflect the presence of pleiotropy, which we were unable to directly test for in several of our analyses. We were able to test this for our MR of schizophrenia on cEF and found no evidence of pleiotropy. However, this should be considered when interpreting our other results.
Limitations
There are a number of limitations to our study that should be considered when interpreting these results. First, there could be low statistical power to detect causal effects for some of the analyses. In particular, where anxiety, alcohol dependence and CUD are the exposures there were a low number of genome-wide significant SNPs. To overcome this issue we lowered the p-value threshold for our MR analyses to 1×10−5, but this means that any interpretation of these results should be approached with caution and revisiting this causal relationship when larger GWAS are available would be valuable. Second, in the majority of our analyses (i.e., in both directions for schizophrenia, MDD, smoking initiation, drinks per week and CUD) evidence of heterogeneity was observed in the IVW estimates, which could suggest that horizontal pleiotropy is present (e.g., that independent pathways are responsible for the influence of SNPs on the exposure and outcome). Therefore, caution should be used when interpreting these results. However, we did test for violations of other MR assumptions using additional MR sensitivity analyses and the direction of the results were consistent with the direction of the main results. Despite this, in a number of our analyses we could not test for directional pleiotropy due to the I-squared estimate being too low.
Finally, MR is also subject to some general limitations40. For example, the ‘Winner’s curse’ can occur when the SNPs used are based on the discovery GWAS only (as opposed to combined discovery and replication results), meaning that SNP-trait effects may be overestimated. This can mean that the MR estimate is biased towards the null. Thus, the focus of our results is on the direction of effect as opposed to the size of any causal effects, although the latter may still be somewhat informative.
Conclusion
Our findings suggest a bidirectional causal relationship between cEF and schizophrenia liability, where increased schizophrenia liability is associated with decreased cEF and vice versa, as well as causal effects of increased cEF on reduced MDD liability and CUD liability and decreased drinks per week, and a causal effect of smoking initiation on decreased cEF. These results require further study to better understand the mechanisms behind these causal effects. Future research would benefit from better powered GWAS for anxiety where a lack of power may explain why we did not detect any causal effects. Our results may inform prioritisation of experimental medicine studies (e.g., of interventions targeting EF) to improve the likelihood of successful translation and suggest that these interventions may be useful prior to the onset of schizophrenia and MDD in particular.
Supporting information
Data Availability
The data used in this study are publicly-available GWAS data for cEF (available upon request by contacting the authors), schizophrenia (https://www.med.unc.edu/pgc/download-results/scz/), MDD (https://www.med.unc.edu/pgc/download-results/mdd/), anxiety (https://www.med.unc.edu/pgc/download-results/angst/?choice=Other+GWAS+DataAnxiety+Neuro+Genetics+Study+%28ANGST%29), smoking initiation and drinks per week (data with UK Biobank and 23andMe removed can be found here: https://conservancy.umn.edu/handle/11299/201564, and for 23andMe data access needs to be requested (see below), alcohol dependence (https://figshare.com/articles/dataset/sud2018-alc/14672187) and CUD (https://figshare.com/articles/dataset/sud2020-cud/14842692). The full GWAS summary statistics for the 23andMe discovery data set will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Please visit https://research.23andme.com/collaborate/#dataset-access/ for more information and to apply to access the data.
Acknowledgements
We thank all the contributors to the consortia we have used GWAS results from in our analyses. We would like the thank the research participants and employees of 23andMe for making this work possible.
Funding
This work was supported by the UK Medical Research Council Integrative Epidemiology Unit at the University of Bristol (Grant ref: MC_UU_00011/7) and the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. HMS is supported by the European Research Council (Grant ref: 758813 MHINT). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. ASH acknowledges funding from that National Institute on Drug Abuse (Grant ref: T32DA007261).
Competing interests
None