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Causal associations of intelligence with schizophrenia and bipolar disorder: A Mendelian randomization analysis

Published online by Cambridge University Press:  13 October 2021

Kazutaka Ohi Open the ORCID record for Kazutaka Ohi [Opens in a new window] ,
Kentaro Takai ,
Ayumi Kuramitsu ,
Shunsuke Sugiyama Open the ORCID record for Shunsuke Sugiyama [Opens in a new window] ,
Midori Soda ,
Kiyoyuki Kitaichi  and
Toshiki Shioiri
Kazutaka Ohi*
Affiliation:
Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan Department of General Internal Medicine, Kanazawa Medical University, Ishikawa, Japan
Kentaro Takai
Affiliation:
Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
Ayumi Kuramitsu
Affiliation:
Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
Shunsuke Sugiyama
Affiliation:
Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
Midori Soda
Affiliation:
Laboratory of Pharmaceutics, Department of Biomedical Pharmaceutics, Gifu Pharmaceutical University, Gifu, Japan
Kiyoyuki Kitaichi
Affiliation:
Laboratory of Pharmaceutics, Department of Biomedical Pharmaceutics, Gifu Pharmaceutical University, Gifu, Japan
Toshiki Shioiri
Affiliation:
Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
*
Author for correspondence:*Kazutaka Ohi, E-mail: [email protected]

Abstract

Background

Intelligence is inversely associated with schizophrenia (SCZ) and bipolar disorder (BD); it remains unclear whether low intelligence is a cause or consequence. We investigated causal associations of intelligence with SCZ or BD risk and a shared risk between SCZ and BD and SCZ-specific risk.

Methods

To estimate putative causal associations, we performed multi-single nucleotide polymorphism (SNP) Mendelian randomization (MR) using generalized summary-data-based MR (GSMR). Summary-level datasets from five GWASs (intelligence, SCZ vs. control [CON], BD vs. CON, SCZ + BD vs. CON, and SCZ vs. BD; sample sizes of up to 269,867) were utilized.

Results

A strong bidirectional association between risks for SCZ and BD was observed (odds ratio; ORSCZ → BD = 1.47, p = 2.89 × 10−41, ORBD → SCZ = 1.44, p = 1.85 × 10−52). Low intelligence was bidirectionally associated with a high risk for SCZ, with a stronger effect of intelligence on SCZ risk (ORlower intelligence → SCZ = 1.62, p = 3.23 × 10−14) than the reverse (ORSCZ → lower intelligence = 1.06, p = 3.70 × 10−23). Furthermore, low intelligence affected a shared risk between SCZ and BD (OR lower intelligence → SCZ + BD = 1.23, p = 3.41 × 10−5) and SCZ-specific risk (ORlower intelligence → SCZvsBD = 1.64, p = 9.72 × 10−10); the shared risk (ORSCZ + BD → lower intelligence = 1.04, p = 3.09 × 10−14) but not SCZ-specific risk (ORSCZvsBD → lower intelligence = 1.00, p = 0.88) weakly affected low intelligence. Conversely, there was no significant causal association between intelligence and BD risk (p > 0.05).

Conclusions

These findings support observational studies showing that patients with SCZ display impairment in premorbid intelligence and intelligence decline. Moreover, a shared factor between SCZ and BD might contribute to impairment in premorbid intelligence and intelligence decline but SCZ-specific factors might be affected by impairment in premorbid intelligence. We suggest that patients with these genetic factors should be categorized as having a cognitive disorder SCZ or BD subtype.

Keywords

Bipolar disorderGWASintelligenceMendelian randomizationschizophreniaSNP
Type
Research Article
Information
European Psychiatry , Volume 64 , Issue 1 , 2021 , e61
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the European Psychiatric Association

Introduction

Schizophrenia (SCZ) and bipolar disorder (BD) are common psychiatric disorders with a lifetime morbidity rate of approximately 1% [Reference Perälä, Suvisaari, Saarni, Kuoppasalmi, Isometsä and Pirkola1,Reference Pini, de Queiroz, Pagnin, Pezawas, Angst and Cassano2]. These psychiatric disorders are the leading cause of years lived with disability worldwide [Reference Whiteford, Degenhardt, Rehm, Baxter, Ferrari and Erskine3]. Both disorders are highly heritable with an estimated heritability of approximately 80% [Reference Sullivan, Kendler and Neale4,Reference Nöthen, Nieratschker, Cichon and Rietschel5]. To find risk genes for these disorders, large-scale genome-wide association studies (GWASs) for SCZ and BD have been performed by the Psychiatric Genomics Consortium (PGC), identifying 108 and 30 distinct genomic loci related to the risk for SCZ and BD, respectively [Reference Ripke, Neale, Corvin, Walters, Farh and Holmans6,Reference Stahl, Breen, Forstner, McQuillin, Ripke and Trubetskoy7]. Substantial overlap between SCZ and BD has been demonstrated with a high genetic correlation (rg = 0.7–0.8) derived from common genetic variants (SNPs) [Reference Stahl, Breen, Forstner, McQuillin, Ripke and Trubetskoy7,Reference Ohi, Shimada, Kataoka, Yasuyama, Kawasaki and Shioiri8]. Despite the shared genetics, the current diagnostic criteria (Diagnostic and Statistical Manual of Mental Disorders, fifth edition [DSM-5]) adhere to the historical distinctions between SCZ and BD since the late 19th century. These disorders were differentiated as independent categorical diagnostic entities based on their clinical presentation with psychotic (positive and negative) symptoms in SCZ and manic symptoms in BD.

Cognitive impairment relatively independent of psychotic and manic symptoms is a core feature of SCZ and BD [Reference Trotta, Murray and MacCabe9–Reference Kataoka, Shimada, Koide, Okubo, Uehara and Shioiri13], although cognitive impairment is not included in the previous and current diagnostic criteria (DSM-IV and 5) for the disorders. Cognitive impairment is a predictor of poor functional outcomes, such as social and occupational dysfunction [Reference Jaeger, Berns, Loftus, Gonzalez and Czobor14–Reference Green, Kern, Braff and Mintz16]. Patients with SCZ and BD show impairment in premorbid intelligence as well as in current intelligence, which involves intelligence decline from the premorbid level [Reference Trotta, Murray and MacCabe9,Reference Ohi, Shimada, Kataoka, Koide, Yasuyama and Uehara12,Reference Vaskinn, Haatveit, Melle, Andreassen, Ueland and Sundet17]. Intelligence is also substantially heritable with an estimated heritability of approximately 50–70% [Reference Swagerman, de Geus, Kan, van Bergen, Nieuwboer and Koenis18,Reference Polderman, Benyamin, de Leeuw, Sullivan, van Bochoven and Visscher19]. Large-scale GWASs using nearly 300,000 healthy individuals of general population-based cohorts have detected more than 100 genome-wide significant loci related to intelligence [Reference Savage, Jansen, Stringer, Watanabe, Bryois and de Leeuw20,Reference Wray, Ripke, Mattheisen, Trzaskowski, Byrne and Abdellaoui21].

The highly heritable disorders SCZ and BD have clinical similarities, such as low intelligence [Reference Ohi, Shimada, Kihara, Yasuyama, Sawai and Matsuda22–Reference Yasuyama, Ohi, Shimada, Uehara and Kawasaki24]. In general, identifying genetic components contributing to these disorders will provide insight into the biology underlying their shared impairments. On the other hand, as SCZ and BD are distinct diagnoses according to DSM-5, SCZ and BD may have disorder-specific genetic factors. To date, the SCZ and BD working groups of the PGC have identified 114 genome-wide significant loci shared between SCZ and BD (SCZ + BD) as well as two genome-wide significant loci differentiating SCZ from BD (SCZ vs. BD) [Reference Ruderfer, Ripke, McQuillin, Boocock, Stahl and Pavlides25]. Although the disorders are associated with impairments in intelligence and there is a high genetic correlation between them, intelligence genetically correlates negatively only with risk for SCZ (rg = 0.2) and not with risk for BD [Reference Ohi, Sumiyoshi, Fujino, Yasuda, Yamamori and Fujimoto26,Reference Toulopoulou, Zhang, Cherny, Dickinson, Berman and Straub27]. Current SCZ diagnosis is considered an aggregation of at least two disorder subtypes: one part is a cognitive disorder that is independent of BD, and the other part resembles high intelligence and BD [Reference Bansal, Mitjans, Burik, Linnér, Okbay and Rietveld28]. It is unclear whether intelligence correlates genetically with a shared genetic factor between SCZ and BD and a disorder-specific genetic factor. We hypothesized that intelligence correlates genetically with both shared genetic factors and SCZ-specific genetic factors.

Several risk factors (exposures), such as low intelligence, are associated with common psychiatric disorders (outcomes), such as SCZ and BD. However, these associations are usually derived from observational studies that cannot distinguish whether the risk factors are “upstream” causal factors, “downstream” consequences of the disorders or confounding factors associated with both exposures and outcomes [Reference Zhu, Zheng, Zhang, Wu, Trzaskowski and Maier29]. Randomized controlled trials (RCTs) are the gold standard approach to assess causality from observational epidemiology, yet RCTs are time-consuming, expensive, or sometimes impractical (e.g., no intervention may exist). As SNPs are present from birth and are unlikely to be confounded by environmental factors under the assumption that there is no other confounding factors, for example, population stratification and assortative mating, methods using SNPs are useful to infer causality. Mendelian randomization (MR) is a method that uses SNPs as instrumental variables to test for causative association between an exposure and an outcome [Reference Zhu, Zheng, Zhang, Wu, Trzaskowski and Maier29,Reference Smith and Ebrahim30]. There are three key assumptions that must hold for a MR study to be valid: (a) relevance assumption (the SNPs associate with the risk factor of interest), (b) independence assumption (there are no unmeasured confounders of the associations between SNPs and outcome), and (c) exclusion restriction assumption (the SNPs affect the outcome only through their effect on the risk factor of interest) [Reference Davies, Holmes and Davey Smith31]. To date, the unidirectional MR analysis found that lower intelligence increases the likelihood of SCZ [Reference Adams32]. However, causal associations among intelligence, SCZ and BD are still unclear. The current study focused on questions about causality: Does a low level of intelligence cause SCZ or BD? Does SCZ or BD cause intelligence decline? Uncovering the nature of these associations would inform interventional strategies.

In this study, we performed a multi-SNP MR analysis (generalized summary-data-based MR; GSMR) to examine potential causal associations of intelligence with risks for SCZ and BD as well as a shared risk between SCZ and BD (SCZ + BD) and SCZ-specific risk (BD vs. SCZ). We used publicly available summary-level datasets from five GWASs ([a] intelligence, [b] SCZ vs. control (CON), [c] BD vs. CON, [d] SCZ + BD vs. CON, and [d] SCZ vs. BD) to investigate putative causal associations among intelligence, SCZ and BD.

Methods

GWAS samples

Five publicly available GWAS summary datasets (intelligence [n = 269,867] [Reference Savage, Jansen, Stringer, Watanabe, Bryois and de Leeuw20], SCZ [n = 33,426] vs. CON [n = 32,541] [Reference Ruderfer, Ripke, McQuillin, Boocock, Stahl and Pavlides25], BD [n = 20,129] vs. CON [n = 21,524] [Reference Ruderfer, Ripke, McQuillin, Boocock, Stahl and Pavlides25], SCZ + BD [n = 53,555] vs. CON [n = 54,065] [Reference Ruderfer, Ripke, McQuillin, Boocock, Stahl and Pavlides25], and SCZ [n = 23,585] vs. BD [n = 15,270] [Reference Ruderfer, Ripke, McQuillin, Boocock, Stahl and Pavlides25]) were utilized through the complex trait genetics (CTG) lab (https://ctg.cncr.nl/software/summary_statistics) and PGC (https://www.med.unc.edu/pgc/results-and-downloads) as MR GWAS samples to identify risk SNPs for each phenotype, the frequency of the effect allele, the effect size (beta or odds ratio [OR]), the standard error, the p value and the sample size. We selected these GWAS summary statistics to avoid any overlapping samples for our analysis because independent GWASs for SCZ versus CON and BD versus CON have been used to identify disorder-specific genetic variants [Reference Ruderfer, Ripke, McQuillin, Boocock, Stahl and Pavlides25]. The sample information and details regarding the sample collection, genotyping, processing, quality control, and imputation procedures applied in each GWAS have been described previously [Reference Savage, Jansen, Stringer, Watanabe, Bryois and de Leeuw20,Reference Ruderfer, Ripke, McQuillin, Boocock, Stahl and Pavlides25].

Mendelian randomization

To estimate credible causal associations among intelligence, SCZ and BD, we performed MR analyses using the GSMR method (https://cnsgenomics.com/software/gcta/#Mendelianrandomisation) [Reference Zhu, Zheng, Zhang, Wu, Trzaskowski and Maier29] in the genome-wide CTA (GCTA) software v1.93.2beta. The GSMR method examines putative causal associations (bxy = bzx/bzy) between a risk factor (bzx) and a disorder (bzy) using summary-level data from GWASs, where z is a genotype of a SNP (coded as 0, 1, or 2), x is the exposure (e.g., intelligence) in standard deviation (SD) units, and y is the outcome (e.g., the liability of a disorder) on the logit scale. bzy is the effect of z on y on the logit scale (logarithm of odds ratio, logOR), bzx is the effect of z on x, and bxy is the effect of x on y free of confounding from nongenetic factors. Near-independent GWAS SNPs (r2 threshold = 0.05, window size = 1 Mb and p value threshold = 5.0 × 10−8) for each phenotype using ABCD Research Consortium data [Reference Garavan, Bartsch, Conway, Decastro, Goldstein and Heeringa33,Reference Uban, Horton, Jacobus, Heyser, Thompson and Tapert34] (n = 4,920 unrelated individuals of European ancestry) as the reference for linkage disequilibrium (LD) estimation were selected after applying the clumping algorithm in PLINK. First, we estimated putative causal associations (bxy) of SCZ GWAS SNPs (p < 5.0 × 10−8) (bzx) on BD (bzy) and vice versa in independent samples. Second, putative causal associations (bxy) of intelligence GWAS SNPs (p < 5.0 × 10−8) (bzx) on SCZ or BD (bzy) were estimated using independent samples, and reverse putative causal associations (bxy) of GWAS SNPs of SCZ or BD (bzx) on intelligence (bzy) were estimated as well. When the phenotype had fewer than 10 independent lead SNPs at the stringent GWAS threshold (p < 5.0 × 10−8), the threshold was relaxed from p < 5.0 × 10−8 to p < 1.0 × 10−5 to obtain a sufficient number of SNPs.

We performed analyses in two ways to test for bidirectionality. First, forward and reverse GSMRs were performed using GWAS SNPs from SCZ and BD as the exposure and outcome variables, respectively. Second, forward GSMR was performed using GWAS SNPs associated with intelligence as the exposure variable and (a) SCZ, (b) BD, (c) a shared factor between SCZ and BD, and (d) a factor differentiating SCZ from BD as the outcomes. In contrast, reverse GSMR was performed using GWAS SNPs from (a) SCZ, (b) BD, (c) a shared factor between SCZ and BD, and (d) a factor differentiating SCZ from BD as exposure variables and intelligence as the outcome. For (b) BD and (d), a factor differentiating SCZ from BD, a threshold of p < 1.0 × 10−5 was used for the selection of lead SNPs because there were < 10 lead SNPs available at the stringent GWAS threshold (p < 5.0 × 10−8). To remove horizontal pleiotropic SNPs for both risk factors and disorders, heterogeneity in dependent instrument (HEIDI)-outlier filtering was applied before the analysis, with the default setting (threshold 0.01) [Reference Zhu, Zheng, Zhang, Wu, Trzaskowski and Maier29]. The HEIDI outlier removal strategy to detect SNPs with a horizontal pleiotropic effect is implemented in the GSMR approach that is utilized in this study. The HEIDI attempts to reduce heterogeneity by removing SNPs that contribute to the heterogeneity disproportionately more than expected given the standard errors of the Wald ratios. To generate the effect size plot, we used R v3.6.1 (http://www.r-project.org/) and an R script (gsmr_plot.r) (https://cnsgenomics.com/software/gcta/#Mendelianrandomisation). A Bonferroni-corrected significance threshold of two-tailed p < 0.0125 (α = 0.05/4 exposure variables) was used to avoid type I error.

Results

Effects of risk for SCZ on risk for BD, and vice versa

We first investigated a bidirectional effect of risk for SCZ on risk for BD and of risk for BD on risk for SCZ by MR (Figure 1 and Table 1). As expected, we found a strong bidirectional effect of risk for SCZ on risk for BD (ORSCZ → BD = 1.47, bxy = 0.387, p = 2.89 × 10−41) and of risk for BD on risk for SCZ (ORBD → SCZ = 1.44, bxy = 0.362, p = 1.85 × 10−52). Both directions of the causation were highly significant, and the degrees of effect sizes were similar. Both risks for these psychotic disorders were associated with each other.

Table 1. Bidirectional causal associations between disorders and between lower intelligence and risks for SCZ, BD, SCZ + BD, or SCZ versus BD.

Abbreviations: BD, bipolar disorder; GSMR, generalized summary-data-based Mendelian randomization; SCZ, schizophrenia; SE, standard error; OR, odds ratio. P values are shown in boldface if p < 0.05.

Figure 1. A bidirectional effect (bxy) of risk for SCZ (bzx) on risk for BD (bzy) (a) and of risk for BD (bzx) on risk for SCZ (bzy) (b). BD, bipolar disorder; SCZ, schizophrenia. We plotted effect sizes of independent lead SNPs from the GWAS of bzx on the x-axis and SNP GWAS effect sizes for bzy on the y-axis. The dotted line shows a line with a slope of bxy and an intercept of 0. Error bars represent 95% confidence intervals for the effect sizes for each disorder.

Effects of lower intelligence on risk levels of SCZ or BD, and vice versa

Next, we investigated causal associations between lower intelligence and risks for SCZ or BD (Figure 2a,b and Table 1) and observed a strong bidirectional effect of lower intelligence on the risk for SCZ (ORlower intelligence→SCZ = 1.62, bxy = 0.482, p = 3.23 × 10−14) and of the risk for SCZ on lower intelligence (ORSCZ → lower intelligence = 1.06, bxy = 0.056, p = 3.70 × 10−23) with a stronger effect of lower intelligence on the risk for SCZ than the reverse. Lower intelligence was strongly associated with a higher risk for SCZ, whereas a higher risk for SCZ was weakly associated with lower intelligence. In contrast, there were no significant effects of lower intelligence on the risk of BD (ORlower intelligence→BD = 1.09, bxy = 0.083, p = 0.27) or of BD on lower intelligence (ORBD → lower intelligence = 0.99, bxy = −0.009, p = 0.065).

Figure 2. A bidirectional effect (bxy) of intelligence (bzx) on risk for SCZ or BD, a shared risk between SCZ and BD (SCZ + BD), or SCZ-specific risk (SCZ vs. BD) (bzy) (a). A bidirectional effect (bxy) of risk for SCZ or BD, SCZ + BD, or SCZ versus BD (bzx) on intelligence (bzy) (b). BD, bipolar disorder; SCZ, schizophrenia.

Effects of lower intelligence on a shared risk between SCZ and BD or SCZ-specific risk, and vice versa

We further tested for bidirectional causal associations between lower intelligence and a shared risk between SCZ and BD (SCZ + BD vs. CON) or a factor differentiating SCZ from BD (SCZ vs. BD), that is, SCZ-specific risk (Figure 2a,b and Table 1). Lower intelligence was associated with higher shared risk between SCZ and BD (ORlower intelligence→SCZ + BD = 1.23, bxy = 0.207, p = 3.41 × 10−5) and SCZ-specific risk (ORlower intelligence → SCZvsBD = 1.64, bxy = 0.493, p = 9.72 × 10−10). Conversely, a shared risk between SCZ and BD was weakly associated with lower intelligence (ORSCZ + BD → lower intelligence = 1.04, bxy = 0.042, p = 3.09 × 10−14), but there was no significant effect of SCZ-specific risk on lower intelligence (ORSCZvsBD → lower intelligence = 1.00, bxy = −0.001, p = 0.88).

Discussion

We, for the first, time investigated causal associations of intelligence with risks for SCZ or BD as well as a shared risk between SCZ and BD and SCZ-specific risk using MR analyses. As expected, risks for SCZ and BD were bidirectionally causally associated with each other, with similar effect sizes: a higher risk for SCZ causes a higher risk for BD, whereas a higher risk for BD increases the risk for SCZ. Furthermore, we found a bidirectional causal association between intelligence and risk for SCZ but not for BD. Lower intelligence was strongly related to risk for SCZ, yet risk for SCZ was only weakly related to lower intelligence. Consistent with the association between intelligence and SCZ, lower intelligence was strongly related to a shared risk between SCZ and BD; the shared risk was weakly related to lower intelligence. Intriguingly, we revealed a unidirectional causal association between intelligence and SCZ-specific risk, whereby lower intelligence was unidirectionally related to higher risk.

We identified a bidirectional causal association between intelligence and SCZ: a protective effect of higher intelligence against risk for SCZ and a harmful effect of SCZ on intelligence. Overall, the impact of lower intelligence on the risk for SCZ (ORlower intelligence→SCZ = 1.62) was stronger than that of the risk for SCZ on lower intelligence (ORSCZ → lower intelligence = 1.06). The OR of 1.62 can be interpreted as follows: individuals whose intelligence scores are 1 SD below the population mean have 1.62-fold higher risk for SCZ compared with the population prevalence. The OR of 1.06 can be interpreted as patients with SCZ compared with general population mean have 1.06-fold higher risk of having intelligence scores 1 SD below the those of the population. The impact of lower intelligence on risk for SCZ would suggest that lower premorbid intelligence causes SCZ onset and that intelligence, as one of the intermediate phenotypes, mediates the influence of genetic risk on SCZ; however, the impact of risk for SCZ on lower intelligence would imply intelligence decline around the onset of SCZ. These causative associations support observational studies in which patients with SCZ show impairment in premorbid intelligence before the onset of the disorder and the intelligence decline from the premorbid level becomes evident around the time of onset [Reference Trotta, Murray and MacCabe9,Reference Ohi, Shimada, Kataoka, Koide, Yasuyama and Uehara12].

Despite no causative association between intelligence and risk for BD, a bidirectional causative association of intelligence with a shared risk between SCZ and BD was identified. Consistent with the causative association between intelligence and risk for SCZ, the bidirectional effect was observed with a stronger one for intelligence on the shared risk between SCZ and BD (ORlower intelligence → SCZ + BD = 1.23) than the reverse (ORSCZ + BD → lower intelligence = 1.04), supporting observational epidemiology that lower intelligence increases risks of SCZ and BD onset [Reference Trotta, Murray and MacCabe9,Reference Ohi, Shimada, Kataoka, Koide, Yasuyama and Uehara12,Reference Vaskinn, Haatveit, Melle, Andreassen, Ueland and Sundet17]. Furthermore, higher SCZ + BD polygenic risk scores (PRSs) are associated with more severe illness, such as psychotic symptoms and a greater number of hospitalizations [Reference Ruderfer, Ripke, McQuillin, Boocock, Stahl and Pavlides25] although the study did not examine association between intelligence and the PRSs. In particular, the shared genetic loci implicate neuronal and synaptic pathways shared between the disorders [Reference Ruderfer, Ripke, McQuillin, Boocock, Stahl and Pavlides25]. These findings suggest that the common risk between SCZ and BD is associated with impairment in premorbid intelligence and intelligence decline via neuronal and synaptic dysfunctions and that SCZ or BD patients with shared genetic risk should be categorized as having a cognitive disorder and symptomatic severity subtype in SCZ and BD.

Lower intelligence was unidirectionally related to a higher factor differentiating SCZ from BD, that is, SCZ-specific risk (ORlower intelligence→SCZvsBD = 1.64), suggesting that the SCZ-specific risk is more strongly associated with impairment in premorbid intelligence compared with intelligence decline. Two genome-wide significant loci differentiating SCZ from BD are DARS2 (aspartyl-tRNA synthetase 2, mitochondrial) and CSE1L (chromosome segregation 1 like). DARS2 is suggested to act as a potential molecular marker of early life stress and vulnerability to psychiatric disorders, and CSE1L plays a role in cellular proliferation and apoptosis [Reference Ruderfer, Ripke, McQuillin, Boocock, Stahl and Pavlides25]. Furthermore, CSE1L is a potential target gene of miR-137 at SCZ risk loci [Reference Ripke, Neale, Corvin, Walters, Farh and Holmans6]. These findings suggest that the factors differentiating SCZ and BD are related to impairment in premorbid intelligence through dysregulation of dopaminergic circuits, synaptic plasticity, and myelination during the developmental stage.

Despite the relationship between decreased intelligence and risk for BD in observational studies [Reference Trotta, Murray and MacCabe9,Reference Vaskinn, Haatveit, Melle, Andreassen, Ueland and Sundet17], our MR analysis suggests that these traits are not causally related. In the MR analysis, we used GWAS summary statistics based on BD (n = 20,129) versus CON (n = 21,524) [Reference Ruderfer, Ripke, McQuillin, Boocock, Stahl and Pavlides25] to avoid overlapping CON samples among GWASs, for example, BD versus CON and SCZ versus CON. In contrast, intelligence may be causally related to risks for bipolar I disorder (BD I) or bipolar II disorder (BD II). Therefore, we further explored causal associations between lower intelligence and risks for BD I and BD II using the other GWAS summary datasets based on BD I (n = 14,879) versus CON (n = 30,992) and BD II (n = 3,421) versus CON (n = 22,155) [Reference Stahl, Breen, Forstner, McQuillin, Ripke and Trubetskoy7] (Supplementary Figure S1). Unexpectedly, a higher risk for BD I was weakly associated with higher intelligence (ORBD I → lower intelligence = 0.98, bxy = −0.025, p = 4.41 × 10−8). Nonetheless, there were no significant effects of intelligence on the risks of BD I and BD II or of the risk of BD II on intelligence (p > 0.05). The population-level correlation between impaired intelligence and risk for BD might be driven by some unobserved confounding factors, such as educational level [Reference Adams32].

There are some limitations to the interpretations of our findings. There are other MR methods other than GSMR, and there are several outlier removal methods that have been used in MR; MR-Egger, Steiger filtering and HEIDI although the detailed concepts were differed among them [Reference Hemani, Bowden and Davey Smith35]. The current study applied the HEIDI outlier removal strategy to detect SNPs with a horizontal pleiotropic effect implemented in the GSMR. However, it is necessary to consider the most appropriate method in future studies. There are mainly three assumptions; (a) relevance assumption, (b) independence assumption, and (c) exclusion restriction assumption made in MR [Reference Davies, Holmes and Davey Smith31], and our putative causal relationships should be treated with caution. Weakly associated genetic variants were used in a few our MR analyses. We included nonoverlapping samples of SCZ, BD, and CON, while a part of samples of the CON and nonpsychiatric participants in GWAS for intelligence might be overlapped. The partial overlapping samples would affect our results of the study. The statistical power to estimate bxy in MR analysis can be greatly improved if bzx and bzy are estimated from independent studies using larger sample sizes [Reference Zhu, Zhang, Hu, Bakshi, Robinson and Powell36]. Compared with the GWAS sample sizes of intelligence, SCZ versus CON and SCZ + BD versus CON, those of BD versus CON and SCZ versus BD were relatively small, potentially resulting in false positive and negative findings. Genetic variants have a direct effect on the causal trait and an indirect effect on the caused trait. That is, genetic variants are assumed to have no influence on confounding factors that influence both causal and caused traits, and affect the caused trait only through their effect on the causal trait. However, it would be difficult to know a priori whether the assumptions are adequate because recent large-scale genetic studies have performed in samples of mainly of European ancestry (confounding factors) and shown that genetic variants often have effects on several traits (horizontal pleiotropy). Therefore, our findings might be biased by a few violating assumptions.

In conclusion, we demonstrate a bidirectional causal association between intelligence and the risk for SCZ but not the risk for BD using MR analyses. The bidirectional causal association was observed with a stronger effect of intelligence on risk for SCZ than the effect of risk for SCZ on intelligence. These findings support observational studies showing that patients with SCZ display impairment in premorbid intelligence and a decline in intelligence around the onset of the disorder. Furthermore, we found that a shared factor between SCZ and BD might contribute to impairment in premorbid intelligence and intelligence decline but that SCZ-specific factors might be affected by impairment in premorbid intelligence. We suggest that SCZ or BD patients with these genetic factors should be categorized as having a cognitive disorder subtype in SCZ and BD. Future personalized studies using these genetic factors to diagnose and treat SCZ or BD patients are required.

Supplementary Materials

To view supplementary material for this article, please visit http://dx.doi.org/10.1192/j.eurpsy.2021.2237.

Data Availability Statement

The summary-level GWAS data from CTG and PGC are available at https://ctg.cncr.nl/software/summary_statistics and https://www.med.unc.edu/pgc/results-and-downloads. The software tools are available at the URLs above.

Acknowledgments

We would like to thank all individuals who participated in this study. This work was supported by Grants-in-Aid for Scientific Research (C) (19K08081) and Young Scientists (B) (16K19784) from the Japan Society for the Promotion of Science (JSPS) and SENSHIN Medical Research Foundation.

Author Contributions

Conceptualization: K.O., S.S., and M.S.; Data curation: K.O., K.T., and A.K.; Formal analysis: K.O.; Funding acquisition: K.O. and S.S.; Investigation: K.O. and K.T.; Methodology: K.O., A.K., K.K., and T.S.; Supervision: K.O., K.K., and T.S.; Project administration: M.S.; Writing – original draft: K.O.; Writing – review & editing: K.T., A.K., S.S., M.S., K.K., and T.S.

Conflict of Interest

There are no conflicts of interest to declare.

References

Perälä, J, Suvisaari, J, Saarni, SI, Kuoppasalmi, K, Isometsä, E, Pirkola, S, et al. Lifetime prevalence of psychotic and bipolar I disorders in a general population. Arch Gen Psychiatry. 2007;64:19–28.CrossRefGoogle Scholar
Pini, S, de Queiroz, V, Pagnin, D, Pezawas, L, Angst, J, Cassano, GB, et al. Prevalence and burden of bipolar disorders in European countries. Eur Neuropsychopharmacol. 2005;15:425–34.CrossRefGoogle ScholarPubMed
Whiteford, HA, Degenhardt, L, Rehm, J, Baxter, AJ, Ferrari, AJ, Erskine, HE, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet (London, England). 2013;382:1575–86.CrossRefGoogle ScholarPubMed
Sullivan, PF, Kendler, KS, Neale, MC. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry. 2003;60:1187–92.CrossRefGoogle ScholarPubMed
Nöthen, MM, Nieratschker, V, Cichon, S, Rietschel, M. New findings in the genetics of major psychoses. Dialog Clin Neurosci. 2010;12:85–93.Google ScholarPubMed
Ripke, S, Neale, BM, Corvin, A, Walters, JT, Farh, KH, Holmans, PA, et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.Google Scholar
Stahl, EA, Breen, G, Forstner, AJ, McQuillin, A, Ripke, S, Trubetskoy, V, et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet. 2019;51:793–803.CrossRefGoogle ScholarPubMed
Ohi, K, Shimada, T, Kataoka, Y, Yasuyama, T, Kawasaki, Y, Shioiri, T, et al. Genetic correlations between subcortical brain volumes and psychiatric disorders. Br J Psychiatry. 2020;216:280–3.CrossRefGoogle ScholarPubMed
Trotta, A, Murray, RM, MacCabe, JH. Do premorbid and post-onset cognitive functioning differ between schizophrenia and bipolar disorder? A systematic review and meta-analysis. Psychol Med. 2015;45:381–94.CrossRefGoogle ScholarPubMed
Solé, B, Jiménez, E, Torrent, C, Del Mar Bonnin, C, Torres, I, Reinares, M, et al. Cognitive variability in bipolar II disorder: who is cognitively impaired and who is preserved. Bipolar Disord. 2016;18:288–99.CrossRefGoogle ScholarPubMed
Schaefer, J, Giangrande, E, Weinberger, DR, Dickinson, D. The global cognitive impairment in schizophrenia: consistent over decades and around the world. Schizophr Res. 2013;150:42–50.CrossRefGoogle ScholarPubMed
Ohi, K, Shimada, T, Kataoka, Y, Koide, Y, Yasuyama, T, Uehara, T, et al. Intelligence decline between present and premorbid IQ in schizophrenia: Schizophrenia Non-Affected Relative Project (SNARP). Eur Neuropsychopharmacol. 2019;29:653–61.CrossRefGoogle Scholar
Kataoka, Y, Shimada, T, Koide, Y, Okubo, H, Uehara, T, Shioiri, T, et al. Differences in executive function among patients with schizophrenia, their unaffected first-degree relatives and healthy participants. Int J Neuropsychopharmacol. 2020;23:731–7.CrossRefGoogle Scholar
Jaeger, J, Berns, S, Loftus, S, Gonzalez, C, Czobor, P. Neurocognitive test performance predicts functional recovery from acute exacerbation leading to hospitalization in bipolar disorder. Bipolar Disord 2007;9:93–102.CrossRefGoogle ScholarPubMed
Kahn, RS, Keefe, RS. Schizophrenia is a cognitive illness: time for a change in focus. JAMA Psychiatry. 2013;70:1107–12.CrossRefGoogle ScholarPubMed
Green, MF, Kern, RS, Braff, DL, Mintz, J. Neurocognitive deficits and functional outcome in schizophrenia: are we measuring the "right stuff"? Schizophr Bull. 2000;26:119–36.CrossRefGoogle ScholarPubMed
Vaskinn, A, Haatveit, B, Melle, I, Andreassen, OA, Ueland, T, Sundet, K. Cognitive heterogeneity across schizophrenia and bipolar disorder: a cluster analysis of intellectual trajectories. J Int Neuropsychol Soc. 2020;26:860–72.CrossRefGoogle ScholarPubMed
Swagerman, SC, de Geus, EJC, Kan, KJ, van Bergen, E, Nieuwboer, HA, Koenis, MMG, et al. The computerized neurocognitive battery: validation, aging effects, and heritability across cognitive domains. Neuropsychology. 2016;30:53–64.CrossRefGoogle ScholarPubMed
Polderman, TJ, Benyamin, B, de Leeuw, CA, Sullivan, PF, van Bochoven, A, Visscher, PM, et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47:702–9.CrossRefGoogle ScholarPubMed
Savage, JE, Jansen, PR, Stringer, S, Watanabe, K, Bryois, J, de Leeuw, CA, et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet. 2018;50:912–9.CrossRefGoogle ScholarPubMed
Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.CrossRefGoogle ScholarPubMed
Ohi, K, Shimada, T, Kihara, H, Yasuyama, T, Sawai, K, Matsuda, Y, et al. Impact of familial loading on prefrontal activation in major psychiatric disorders: a Near-Infrared Spectroscopy (NIRS) Study. Sci Rep. 2017;7:44268.CrossRefGoogle ScholarPubMed
Ohi, K, Takai, K, Sugiyama, S, Kitagawa, H, Kataoka, Y, Soda, M, et al. Intelligence decline across major depressive disorder, bipolar disorder, and schizophrenia. CNS Spectr. 2021;1–7.CrossRefGoogle Scholar
Yasuyama, T, Ohi, K, Shimada, T, Uehara, T, Kawasaki, Y. Differences in social functioning among patients with major psychiatric disorders: Interpersonal communication is impaired in patients with schizophrenia and correlates with an increase in schizotypal traits. Psychiatry Res. 2017;249:30–4.CrossRefGoogle ScholarPubMed
Ruderfer, D, Ripke, S, McQuillin, A, Boocock, J, Stahl, E, Pavlides, J, et al. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell. 2018;173:1705–15 e16.CrossRefGoogle Scholar
Ohi, K, Sumiyoshi, C, Fujino, H, Yasuda, Y, Yamamori, H, Fujimoto, M, et al. Genetic overlap between general cognitive function and schizophrenia: a review of cognitive GWASs. Int J Mol Sci. 2018;19:3822.CrossRefGoogle ScholarPubMed
Toulopoulou, T, Zhang, X, Cherny, S, Dickinson, D, Berman, KF, Straub, RE, et al. Polygenic risk score increases schizophrenia liability through cognition-relevant pathways. Brain. 2019;142:471–85.CrossRefGoogle ScholarPubMed
Bansal, V, Mitjans, M, Burik, CAP, Linnér, RK, Okbay, A, Rietveld, CA, et al. Genome-wide association study results for educational attainment aid in identifying genetic heterogeneity of schizophrenia. Nat Commun. 2018;9:3078.CrossRefGoogle ScholarPubMed
Zhu, Z, Zheng, Z, Zhang, F, Wu, Y, Trzaskowski, M, Maier, R, et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun. 2018;9:224.CrossRefGoogle ScholarPubMed
Smith, GD, Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1–22.CrossRefGoogle ScholarPubMed
Davies, NM, Holmes, MV, Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.CrossRefGoogle ScholarPubMed
Adams, CD. A multivariable Mendelian randomization to appraise the pleiotropy between intelligence, education, and bipolar disorder in relation to schizophrenia. Sci Rep. 2020;10:6018.CrossRefGoogle Scholar
Garavan, H, Bartsch, H, Conway, K, Decastro, A, Goldstein, RZ, Heeringa, S, et al. Recruiting the ABCD sample: design considerations and procedures. Dev Cogn Neurosci. 2018;32:16–22.CrossRefGoogle ScholarPubMed
Uban, KA, Horton, MK, Jacobus, J, Heyser, C, Thompson, WK, Tapert, SF, et al. Biospecimens and the ABCD study: rationale, methods of collection, measurement and early data. Dev Cogn Neurosci. 2018;32:97–106.CrossRefGoogle ScholarPubMed
Hemani, G, Bowden, J, Davey Smith, G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27:R195–R208.CrossRefGoogle ScholarPubMed
Zhu, Z, Zhang, F, Hu, H, Bakshi, A, Robinson, MR, Powell, JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481–7.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Bidirectional causal associations between disorders and between lower intelligence and risks for SCZ, BD, SCZ + BD, or SCZ versus BD.

Figure 1

Figure 1. A bidirectional effect (bxy) of risk for SCZ (bzx) on risk for BD (bzy) (a) and of risk for BD (bzx) on risk for SCZ (bzy) (b). BD, bipolar disorder; SCZ, schizophrenia. We plotted effect sizes of independent lead SNPs from the GWAS of bzx on the x-axis and SNP GWAS effect sizes for bzy on the y-axis. The dotted line shows a line with a slope of bxy and an intercept of 0. Error bars represent 95% confidence intervals for the effect sizes for each disorder.

Figure 2

Figure 2. A bidirectional effect (bxy) of intelligence (bzx) on risk for SCZ or BD, a shared risk between SCZ and BD (SCZ + BD), or SCZ-specific risk (SCZ vs. BD) (bzy) (a). A bidirectional effect (bxy) of risk for SCZ or BD, SCZ + BD, or SCZ versus BD (bzx) on intelligence (bzy) (b). BD, bipolar disorder; SCZ, schizophrenia.

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