Psychiatric disorders are a cluster of complex psychological syndromes in cognition, behavior, or emotion regulation, representing the second leading cause of disability and premature death worldwide (Whiteford et al., 2013). Epidemiological research has shown that the global lifetime incidence of psychiatric disorders in adults ranges between 12.2 and 48.6%, with the overall prevalence varying from 4.3 to 26.4% (Demyttenaere et al., 2004). Certain mental diseases, such as depression, attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and schizophrenia (SCZ), account for approximately 12% of the global disease burden (Ochoa-Reparaz et al., 2020). Because they frequently require long-term treatment, their burden was estimated to be $8.5 trillion in 2010 and continuously raised by 41% between 1990 and 2010 (Patel et al., 2016). Thus, there is an urgent need to identify potential causal risk factors for various psychiatric disorders.

The etiologies of psychiatric disorders are largely multi-factorial, including psychological, genetic, and environmental factors. Recently, growing evidence has suggested that the gut microbiota is closely related to host health and is involved in the etiology of a variety of human complex diseases including psychiatric disorders (Chow et al., 2010; Clemente et al., 2012; Cryan et al., 2019). The gut microbiota is a dynamic and complex community of ecological microbes, inhabiting the human intestine, even called a “forgotten organ” (O’Hara and Shanahan, 2006). The microbiota and central nervous system might communicate with each other via the microbiota–gut–brain (MGB) axis, which includes diverse routes including the immune response, the vagus and enteric nerve, and microbiota-derived molecules or metabolites (Cryan et al., 2019). A variety of observational studies have shown that the gut microbiome differs between healthy controls and psychiatric patients (Wang et al., 2011; Strati et al., 2017; Valles-Colomer et al., 2019; Hua et al., 2020). Altered compositions and function of intestinal microbiota were observed in ASD or depression patients (Strati et al., 2017; Valles-Colomer et al., 2019). Further experimental studies also demonstrated the importance of microbiota in the development of psychiatric disorders. For example, fecal microbiota transplantation (FMT) from human donors with ASD into murine induced exacerbated corresponding symptoms (Sharon et al., 2019). However, the causal association between the gut microbiota and psychiatric disorders remains unclear.

Conventionally, the gold standard for inferring a causal association is randomized controlled trials (RCTs). Whereas a RCT is difficult to implement or sometimes even impossible due to ethic restriction. As an alternative, Mendelian randomization (MR) is an efficient method to statistically assess causality from an exposure to an outcome, utilizing genetic variants as instrumental variables (IVs) (Katan, 1986; Smith and Ebrahim, 2003). Because a random assortment of genetic variants occurs during meiosis yielding according to the Mendel’s second law, the selected genetic variants avoid social economic confounding (Emdin et al., 2017). The MR approach is conceptually similar to the RCT study, with only one difference that patients are allocated according to their DNA genotypes. MR analysis relies on three important assumptions: (i) IV is strongly associated with exposure; (ii) IV should be independent of any observed and unobserved confounders of exposure–outcome association; (iii) IV–outcome association is only mediated via exposure rather than any other pathway. In a recent study, utilizing MR, Sanna et al. (2019) identified that propionate, one type of fecal short-chain fatty acid (SCFA), increases the risk of type 2 diabetes, demonstrating the efficacy of microbiota-oriented causal inference via MR analysis.

Two-sample MR analysis can utilize single-nucleotide polymorphism (SNP)–exposure and SNP–outcome associations from independent GWAS analyses and combine them into a single causal estimate. As the number of genome-wide association studies (GWASs) in gut microbiota and psychiatric disorders has increased rapidly, large-scale summary statistics have become more widely available (Demontis et al., 2019; Grove et al., 2019; Howard et al., 2019; Stahl et al., 2019; Kurilshikov et al., 2021), allowing for two-sample MR analysis with significantly improved statistical power.

In this study, we applied a systematic two-sample MR analysis to comprehensively explore whether gut microbiota components have a causal effect on various psychiatric disorders and to identify specific causal bacterial taxa. Specifically, summary statistics of gut microbiota and 15 common psychiatric disorders/traits were derived from large-scale GWAS or genetic consortia.

After removing palindromic SNPs, we identified 937, 1,576, 1,583, 2,390, 6,525, and 739 SNPs associated with gut microbiota in the phylum, class, order, family, genus, and species levels at the suggestive significance level P < 1.0 × 10^–5, respectively (Supplementary Table 2). After clumping and harmonization, the number of IVs associated with each psychiatric disorder varies from 3 to 28. For instance, a total of 2,411 IVs are associated with SCZ, and these IVs are categorized into nine bacteria phyla (123 SNPs), 15 classes (208 SNPs), 19 orders (251 SNPs), 30 families (397 SNPs), 102 genera (1,285 SNPs), and 15 species (177 SNPs), respectively. For SCZ, the genus with the largest number of SNPs is Bifidobacterium (26 SNPs), followed by Roseburia (24 SNPs) and genus with the least number is Senegalimassilia (four SNPs). There is no feature containing only one SNP at any level.

The horizontal pleiotropy effect was evaluated at each taxonomic level. For SCZ, only one of 16 IVs for the order Coriobacteriales was detected as outlier using the MR-PRESSO outlier test. Similarly, at the family level, two out of 15 IVs in family Desulfovibrionaceae and one out of 16 IVs in family Streptococcaceae were identified as outliers. After removing pleiotropic SNPs identified by the MR-PRESSO outlier test and the MR-Egger regression, there is no evidence of horizontal pleiotropy of the remaining IVs (both MR-PRESSO Global test P > 0.05 and MR-Egger regression P > 0.05) (Supplementary Table 3).

In the current study, we conducted MR analyses to evaluate the potential causality between the gut microbiota and 15 psychiatric disorders/traits. Using large-scale summary statistics from microbiome GWAS and 15 psychiatric disorders/traits GWAS, we identified seven bacterial features that were causally associated with six psychiatric disorders/traits.

The positive association between family Prevotellaceae and ASD is in line with previous findings (Qiao et al., 2018; Dan et al., 2020). Prevotellaceae is characterized as a propionate-producing bacterium. Previous studies have found that propionate, as an enteric metabolite produced by gut microbiota, induced social abnormalities, cognitive impairments, sensorimotor dysfunction, and exacerbated ASD symptoms after intracerebroventricular injection (Shultz et al., 2015). Other studies have also manifested the consistent inference that changes in brain tissue after propionate administration result in conditions similar to ASD patients, such as reactive astrogliosis and oxidative stress (Thomas et al., 2012).

In accordance with the previous studies, class Actinobacteria, as a gram-positive bacterium, showed a positive causal association with SCZ, and class Betaproteobacteria has a positive direction on BD. For instance, patients with SCZ and other psychotic disorders have a higher abundance of Actinobacteria (Zheng et al., 2016; Li et al., 2020; Vindegaard et al., 2020), which is also supported by animal models (Dunphy-Doherty et al., 2018). Members of Betaproteobacteria were found to be more abundant in a mouse model of psychiatric diseases, whereas they were strongly correlated with increased gut permeability and intestinal chronic inflammation in humans, which may affect mental health or brain development via the MGB axis (Bauerl et al., 2018; Chen et al., 2018).

Consistent with previous literature, our result showed that class Bacteroidia and its child taxon, order Bacteroidales, have a negative effect on TS. Both Bacteroidia and Bacteroidales are correlated with the tryptophan hydroxylase-II (TPH2) serotonin pathway (Liu et al., 2020). In animal experiments, deficits and excess of TPH2 activity may induce significant behavioral disturbances and catalepsy, whereas the human TPH2 gene is related to psychiatric disorders (Kulikova and Kulikov, 2019). Moreover, serotonin, one of the main brain neurotransmitters, plays an important role in promoting immunity and reducing inflammation in mucosal infections (Gao J. et al., 2018). Thus, decreased serotonin concentrations in the brain and cerebrospinal fluid in Tourette patients might be implicated in the pathogenesis of TS (Mossner et al., 2007). In a FMT study, the abundance of Bacteroides coprocola was reduced in TS patients, but its restoration could improve tic symptoms (Zhao et al., 2020). Personality traits, such as extroversion and NEU, affect fundamental behavior patterns and have been related to mental disorders. In the present study, phylum Actinobacteria and its child taxon, order Actinobacteria, both have a negative effect direction on extroversion, whereas species Clostridium innocuum has a positive effect on NEU.

In addition to the above causal associations identified by IVW test, several intriguing results were identified by other MR tests, including families Christensenellaceae and Methanobacteriaceae, both of which have negative effects on OCD and SA. These associations are broadly supported by previous studies. For instance, it is accepted that SCFA butyrate might suppress the inflammation and oxidative damages in colon and brain, alleviating cognitive impairments, behavioral disorders, and gastrointestinal disorders (Peruzzotti-Jametti and Pluchino, 2018). Family Christensenellaceae, as a gram-negative, strictly anaerobic, and SCFA-producing taxon (Waters and Ley, 2019), could increase the concentration of butyrate in colon, potentially alleviating colitis-related OCD behaviors by diet in humans (Nagpal et al., 2019). It was also found to be positively correlated with cognitive ability in mice (Gao L. et al., 2018). Whereas Methanobacteriaceae, a dominant methanogenic archaeon, can increase levels of SCFAs in the colon (Samuel and Gordon, 2006) and likely has beneficial psychological effects via SCFAs such as migraine reduction in elderly women, which could partly explain the protective effect of this taxon against suicide (Chen et al., 2019).

There are many population-based observational studies and their meta-analyses for the association of gut microbiota with psychiatric disorders (Strati et al., 2017; Ma et al., 2019; Valles-Colomer et al., 2019; Hua et al., 2020; Iglesias-Vazquez et al., 2020; Nikolova et al., 2021). Among them, Nikolova et al. (2021) meta-analyzed 34 case-control studies in a total of 1,519 psychiatric patients versus 1,429 normal controls and found no difference in the diversity of gut microbiota. The meta-analysis attempts to resolve a controversial scientific question such as if an association between two conditions exists, whereas the causal nature of such association is unknown. Fundamentally different from meta-analysis, MR analysis, on the other hand, is an approach statistically inferring the causal nature of an association observed in a cross-sectional study.

This study has advantages in several aspects. First, the identified causal relationship may provide candidate bacteria for subsequent functional studies. Second, we comprehensively studied up to 15 common psychiatric disorders. In a previous study, Zhuang et al. (2020) studied three psychiatric disorders and revealed that order Enterobacteriales and family Enterobacteriaceae were causally associated with a higher risk of schizophrenia, and increased class Bacilli was causally associated with a higher risk of major depressive disorder.

There are also certain limitations in this study. First, gut microbiota GWAS is still in its infancy in terms of sample size; therefore, the number of associated loci is relatively small compared with that for psychiatric disorders. Second, because of the small sample size and insufficient power for microbiome GWAS, there may not be enough IVs for certain bacterial features at genus or species level. As a compromise, we analyzed features at a higher level (phylum, class, order, or family). When microbiome GWAS will eventually be equipped with sufficient sample size, these more specific features will hopefully be identified at a finer resolution (Thomas, 2019). Third, to maximize sample size and statistical power, GWAS of gut microbiota and psychiatric disorders/traits analyzed in this study might originate from multi-ancestry samples. Thus, the results should be interpreted with caution.

In conclusion, we comprehensively assessed the potential causal association between gut microbiota and a series of psychiatric disorders/traits. Four bacterial features showed positive causal direction with ASD, BD, SCZ, and NEU, whereas another four bacterial features showed a negative causal direction with TS and extroversion. This study may be useful in providing new insights into the development mechanism of microbiota-mediated psychiatric disorders.

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

The studies involving human participants were reviewed and approved by all studies were approved by respective institutional review boards (IRBs). No new IRB approval was required. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

LZ and Y-FP designed the study. LZ, QX, and J-JN collected the data. J-JN and QX analyzed the data. S-SY, B-XH, HZ, X-TW, G-JF, Y-FP, MZ, QX, and J-JN performed the literature search. J-JN drafted the early version of the manuscript. LZ and Y-FP jointly supervised the study. All authors were involved in writing the manuscript and had final approval of the submitted and published versions.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.