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Published in final edited form as: Brain Behav Immun. 2023 Aug 8;113:389–400. doi: 10.1016/j.bbi.2023.08.003

The interactions between host genome and gut microbiome increase the risk of psychiatric disorders: Mendelian randomization and biological annotation

Liling Xiao a,b,c,1, Siyi Liu a,b,c,1, Yulu Wu a,b,c, Yunqi Huang a,b,c, Shiwan Tao a,b,c, Yunjia Liu a,b,c, Yiguo Tang a,b,c, Min Xie a,b,c, Qianshu Ma a,b,c, Yubing Yin a,b,c, Minhan Dai a,b,c, Mengting Zhang a,b,c, Elyse Llamocca d, Hongsheng Gui d,e,*, Qiang Wang a,b,c,*
PMCID: PMC11258998  NIHMSID: NIHMS2006496  PMID: 37557965

Abstract

Background:

The correlation between human gut microbiota and psychiatric diseases has long been recognized. Based on the heritability of the microbiome, genome-wide association studies on human genome and gut microbiome (mbGWAS) have revealed important host-microbiome interactions. However, establishing causal relationships between specific gut microbiome features and psychological conditions remains challenging due to insufficient sample sizes of previous studies of mbGWAS.

Methods:

Cross-cohort meta-analysis (via METAL) and multi-trait analysis (via MTAG) were used to enhance the statistical power of mbGWAS for identifying genetic variants and genes. Using two large mbGWAS studies (7,738 and 5,959 participants respectively) and 12 disease-specific studies from the Psychiatric Genomics Consortium (PGC), we performed bidirectional two-sample mendelian randomization (MR) analyses between microbial features and psychiatric diseases (up to 500,199 individuals). Additionally, we conducted downstream gene- and gene-set-based analyses to investigate the shared biology linking gut microbiota and psychiatric diseases.

Results:

METAL and MTAG conducted in mbGWAS could boost power for gene prioritization and MR analysis. Increases in the number of lead SNPs and mapped genes were witnessed in 13/15 species and 5/10 genera after using METAL, and MTAG analysis gained an increase in sample size equivalent to expanding the original samples from 7% to 63%. Following METAL use, we identified a positive association between Bacteroides faecis and ADHD (OR, 1.09; 95 %CI, 1.02–1.16; P = 0.008). Bacteroides eggerthii and Bacteroides thetaiotaomicron were observed to be positively associated with PTSD (OR, 1.11; 95 %CI, 1.03–1.20; P = 0.007; OR, 1.11; 95 %CI, 1.01–1.23; P = 0.03). These findings remained stable across statistical models and sensitivity analyses. No genetic liabilities to psychiatric diseases may alter the abundance of gut microorganisms. Using biological annotation, we identified that those genes contributing to microbiomes (e.g., GRIN2A and RBFOX1) are expressed and enriched in human brain tissues.

Conclusions:

Our statistical genetics strategy helps to enhance the power of mbGWAS, and our genetic findings offer new insights into biological pleiotropy and causal relationship between microbiota and psychiatric diseases.

Keywords: Psychiatric disease, Genome-wide association study, Gut microbiome, Causal inference, Gut–brain axis, Mendelian randomization study

1. Introduction

Research on the human gut microbiota represents a growing field. Groundbreaking studies have indicated that the gut microbiota exerts significant influence on fundamental neural processes and brain functions. Several of these functions, including neurodevelopment (Carlson et al., 2018), myelination (Hoban et al., 2016), neurogenesis (Ogbonnaya et al., 2015), and microglia maturation (Erny et al., 2015), have been found to be commonly dysregulated across psychiatric diseases (Shoubridge et al., 2022). According to animal experiments, psychiatric diseases are associated with distinct perturbations in the gut microbiota, and past research has consistently demonstrated that fecal microbiota transplantation from patients with diverse psychiatric conditions results in the development of behavioral abnormalities and physiological features of the condition in germ-free mice (Kelly et al., 2016; Sharon et al., 2019; Zhu et al., 2020). Numerous observational studies have also shown that the composition of gut microbiota in individuals with various psychiatric diseases differs from individuals without those conditions (McGuinness et al., 2022; Nikolova et al., 2021).

The microbiome, a complex trait phenotype, is heritable and can be influenced by host genetics (Hughes et al., 2020b; Julia et al., 2014; Kurilshikov et al., 2021). This makes the exploration of host-microbiome interactions a plausible approach to understand etiology of psychiatric diseases. Recent genome-wide association studies between human genotypes and gut microbiome (mbGWAS) have revealed significant host-microbiome interactions (Kurilshikov et al., 2021; Lopera-Maya et al., 2022; Qin et al., 2022). To date, a total of 12 published mbGWASs, including nearly or over 1,000 independent participants (Sanna et al., 2022), have reported hits at the genome-wide significant level. Among these hits, the most consistently observed association was between genetic variants located at the lactase (LCT) genes locus and a highly heritable genus Bifidobacterium, as well as species within this genus (Julia et al., 2016; Kurilshikov et al., 2021; Lopera-Maya et al., 2022; Qin et al., 2022; Turpin et al., 2016).

However, one of the primary challenges of these studies, especially mbGWAS, is the need of sufficient sample size. Compared to human quantitative phenotypes, the need for very large samples is even more pressing in mbGWAS because the majority of taxa are present in<50% of samples, drastically reducing the effective sample size for genetic analyses (Lopera-Maya et al., 2022; Sanna et al., 2022). For taxa present in > 80% individuals, a sample size of around 8,000 is needed to identify typical associations. Only a small portion of taxa in a study sample are present in this proportion of individuals. For microorganisms present in > 20% of the sample, a sample size of over 50,000 is necessary to identify an effect size similar to that of LCT (Lopera-Maya et al., 2022).

Increasing the sample size present is a significant challenge and is unlikely to achieve in a short time. An alternative approach is to use in silico methods to combine studies or traits together to improve statistic power (Turley et al., 2018b; Willer et al., 2010). To increase the sample size, and therefore the statistical power of previous mbGWAS analyses, we selected 25 common microbial features (10 genera and 15 species) present in data from both the Lifelines Dutch Microbial Project (DMP) and the FINRISK 2002 (FR02) study, and performed a meta-analysis of GWASs using these two European cohorts. For genus Bifidobacterium, we employed a multi-trait analysis of GWAS (MTAG) approach, which allowed for a joint analysis of species within this genus, thereby enhancing the power to detect genetic associations for each of them.

In addition, evidence of gut microbiota perturbations has been found in several psychiatric disorders. However, most past studies have often utilized cross-sectional data, leaving the cause of these perturbation unknown (McGuinness et al., 2022; Nikolova et al., 2021). For example, alterations in the microbiota composition in children on the autism spectrum disorder (ASD) might simply reflect dietary preferences related to specific features in ASD patients with restrictive-repetitive behaviors (Yap et al., 2021). Furthermore, the gut microbiota can be influenced by environmental factors such as diet (David et al., 2014) and the use of antibiotics and drugs (Maier et al., 2021; Maier et al., 2018). Observational studies cannot account for these confounding factors, which further limits the ability to establish causal relationships to explain the observed associations between gut microbiota perturbations and psychiatric disorders. One method that may allow exploration of potential causal relationships between them is two-sample mendelian randomization (MR). As a widely used epidemiological method, MR utilizes genetic variants as instruments to infer causality and can control for non-heritable confounders (Pierce and Burgess, 2013; Smith and Ebrahim, 2003).

In this study, we employed bidirectional two-sample MR analyses to evaluate whether causal relationships exist between microbial features (before and after meta-analyses) and psychiatric diseases. Next, we provided biological interpretation of the significant associations between microbial features and psychiatric disorders observed in our meta-analyses and MR analyses.

2. Materials and methods

Fig. 1 presents an overview of the study. This study utilized publicly available data. All original studies have obtained ethical approval and acquired informed consents from its participants.

Fig. 1.

Fig. 1.

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Schematic representation of the study. The schematic representation of our study highlights, for each step, the analysis workflow. We first aimed to increase statistic power by reprocessing the original data (Step 1). We then performed bidirectional two-sample MR (Step 2). The MR procedure consists of three steps: (i) identification of instrumental variables (IVs) using genetic variants; (ii) assessment of causal estimates; (iii) sensitivity analyses. Finally, we investigated downstream gene and gene-set-based analyses (Step 3).

2.1. Data sources

We used summary statistics of 12 psychiatric diseases obtained from the Psychiatric Genomics Consortium (PGC)((International Obsessive Compulsive Disorder Foundation Genetics, C., Studies, O.C.D.C.G.A., 2018) Demontis et al., 2019; Grove et al., 2019; Howard et al., 2019; Mullins et al., 2021; Nievergelt et al., 2019; Otowa et al., 2016; Trubetskoy et al., 2022; Watson et al., 2019; Yu et al., 2019), including attention deficit hyperactivity disorder (ADHD), anorexia nervosa (AN), anxiety disorders (ANX), autism spectrum disorder (ASD), bipolar disorder (BD; also separate for two subtypes, BD I and II), major depressive disorder (MDD), obsessive–compulsive disorder (OCD), posttraumatic stress disorder (PTSD), schizophrenia (SCZ), and Tourette’s syndrome (TS).

To date, a total of 12 mbGWASs that included nearly or over 1,000 independent participants have been published (Sanna et al., 2022). Here we selected two European cohorts with the largest sample size to conduct subsequent analyses, considering the annotation depth of microbial taxa and population consistency. The GWAS summary statistics of microbial abundance in a Dutch cohort came from 7,738 participants of the Lifelines Dutch Microbial Project (DMP) (Lopera-Maya et al., 2022; Weersma et al., 2020). The GWAS summary statistics of microbial abundance in a Finnish cohort came from 5,959 participants of the FINRISK 2002 study (FR02) (Borodulin et al., 2018; Borodulin et al., 2015; Liu et al., 2022; Qin et al., 2022). More information about these two cohorts can be found in the Supplemental Methods in Supplement 1. Detailed descriptions of these GWAS and mbGWAS summary statistics are presented in Table 1.

Table 1.

GWAS summary statistics of 12 psychiatric diseases and mbGWAS summary statistics from two cohorts.

Category Phenotype N
N cases		 N
controls		 Study or population Reported genome-wide statistically significant hits
Psychiatric disease ADHD (Demontis et al., 2019) 20,183 35,191 Meta-analysis of iPSYCH and PGC studies 12 independent loci
AN (Watson et al., 2019) 16,992 55,525 Meta-analysis of ANGI and PGC-ED studies 8 independent loci
ANX (Otowa et al., 2016) 7016 14,745 Seven studies participating in the ANGST consortium 1 locus
ASD (Grove et al., 2019) 18,382 27,969 Meta-analysis of iPSYCH and PGC studies 5 independent loci
BD (Mullins et al., 2021) 41,917 371,549 GWAS meta-analysis of 57 studies of European individuals 64 independent loci
BD I (Mullins et al., 2021) 25,060 449,978 The same as BD 44 independent loci
BD II (Mullins et al., 2021) 6781 364,075 The same as BD 1 locus
MDD (Howard et al., 2019) 170,756 329,443 Meta-analysis of PGC studies and UK Biobank without 23andMe samples 101 independent loci
OCD (International Obsessive Compulsive Disorder Foundation Genetics, C., Studies, O.C.D.C.G.A., 2018) 2688 7037 Meta-analysis of IOCDF-GC and OCGAS studies NA
PTSD (Nievergelt et al., 2019) 23,212 151,447 Meta-analyses of GWAS from the PGC 2 independent loci
SCZ (Trubetskoy et al., 2022) 53,386 77,258 Meta-analyses of European ancestry from the PGC 287 independent loci
TS (Yu et al., 2019) 4819 9488 Meta-analysis of four European ancestry GWAS datasets 1 locus
Cohort DMP (Lopera-Maya et al., 2022) 7,738 Part of the Lifelines cohort study including people from north of the Netherlands 24 independent loci (Study-wide significant: 2 loci)
FR02 (Qin et al., 2022) 5,959 Part of the FINRISK study including people from six geographical areas of Finland 411 independent loci (Study-wide significant:3 loci)
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Abbreviations: ADHD, attention deficit hyperactivity disorder; iPSYCH, the Lundbeck Foundation Initiative for Integrative Psychiatric Research; AN, anorexia nervosa; ANX, anxiety disorders; ANGI, Anorexia Nervosa Genetics Initiative; PGE-ED, Eating Disorder Working Group of the Psychiatric Genomics Consortium; ANGST, Anxiety NeuroGenetics Study; ASD, autism spectrum disorder; BD, bipolar disorder; BD is classified into two main subtypes: bipolar I disorder (BD I), in which manic episodes typically alternate with depressive episodes, and bipolar II disorder (BD II), characterized by the occurrence of at least one hypomanic and depressive episode. MDD, major depressive disorder; OCD, obsessive–compulsive disorder; IOCDF-GC, the International Obsessive-Compulsive Disorder Foundation Genetics Collaborative; OCGAS, the OCD Collaborative Genetics Association Study; PTSD, posttraumatic stress disorder; SCZ, schizophrenia; TS, Tourette’s syndrome. DMP: Lifelines Dutch Microbial Project; FR02: FINRISK 2002 study.

2.2. Data processing

2.2.1. Meta-analysis of genome-wide association studies

We performed a fixed-effect meta-analysis by METAL (Willer et al., 2010), using GWAS summary statistics of 25 microbial features that were measured in both the DMP and FR02 cohorts. Cross-cohort meta-analyses were conducted for each feature separately (see more details from Supplemental Methods in Supplement 1 and Table S1 in Supplement 2).

2.2.2. Multi-trait analysis of GWAS (MTAG)

We used MTAG, a generalization of inverse-variance-weighted (IVW) meta-analysis conducted using summary statistics from single-trait GWASs (Turley et al., 2018b), to perform meta-analysis for species of genus Bifidobacterium collected in both cohorts described above. More specifically, we utilized MTAG to perform a secondary meta-analysis of the four species of Bifidobacterium that went through first-stage meta-analysis using METAL.

To determine whether the statistic power was increased, we performed post-GWAS data mining using FUMA (Watanabe et al., 2017a). We uploaded GWAS summary statistics for each trait from: (1) the DMP cohort, (2) the FR02 cohort, (3) our METAL results, and (4) our MTAG results. More information of data processing can be found in the Supplemental Methods in Supplement 1.

2.3. MR

2.3.1. Selection of instruments

This study was reported according to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) MR guidelines (Skrivankova et al., 2021). The STROBE-MR checklist of recommended items is available in Supplement 3. Two-sample MR analyses were performed using the TwoSampleMR package v.0.5.6 (Hemani et al., 2018). We first selected genetic variants associated with each microbial exposure below a less stringent significance threshold of P < 1 × 10−5, which a previous study identified as an optimal P-value threshold for selecting genetics predictors that explained more variance of the outcome (Sanna et al., 2019). In addition to this, we also employed several different thresholds (5 × 10−6 and 5 × 10−8) to select instruments for microbial exposures (Table S2 in Supplement 2). In an effort to ensure the reliability and persuasiveness of our analysis, we used lenient threshold (1 × 10−5) to explore the potential causal estimates between microbial traits and diseases, and further validated these results by comparing the directional consistency between lenient one and other various thresholds (Supplemental Methods in Supplement 1). We then clumped significant SNPs to ascertain independence between genetic variants using the clump_data () function of the TwoSampleMR package. Following this, we pruned SNPs with linkage disequilibrium r2 > 0.001 in the 1000 Genomes European data within 10-Mb window size. For consistency, we applied the same procedure to select genetic instruments from GWASs of psychiatric diseases using different thresholds (1 × 10−5, 5 × 10−6 and 5 × 10−8) when performing reverse MR analyses. And different from forward analyses, we used the results of 5 × 10−8 as discovery, and other thresholds as validations.

2.3.2. Statistical analysis and MR assumptions

We used the IVW method as the primary approach for MR analysis (Burgess et al., 2013). Moreover, to satisfy three core assumptions (Fig. S1 in Supplement 1), we adopted different quality metrics and sensitivity tests to examine the validity of our instrumental variables and pipeline (Davies et al., 2018b). This included:

  1. Assumption 1 (Variants should be associated with the exposure). F statistics were calculated to measure the strength of instrument variables for each exposure in our study. An F statistic>10 indicates adequate instrument strength (Burgess and Thompson, 2011).

  2. Assumption 2 (Variants should not be associated with the outcome through a confounding pathway). As our two-sample MR were conducted on two independent datasets (i.e., no overlapping sample between exposure and outcome), it is valid to assume confounding was minimized (Davies et al., 2018a). To avoid further confounding from population structure, we used European ancestry populations.

  3. Assumption 3 (Variants should not affect the outcome except through the exposure). By following recommendations of performing and interpreting casual inference (Hughes et al., 2020a), we incorporated the identification of horizontal pleiotropy and heterogeneity among SNPs in our sensitivity analysis. We assessed the presence of horizontal pleiotropy using a global test within MR-PRESSO framework, while heterogeneity among SNPs was examined through Cochran’s Q statistic. Additionally, in order to identify any potential dominance of specific variant in casual estimation, we performed leave-one-out analyses, sequentially excluding each SNP from the assessment and re-performing the causal test (Corbin et al., 2016).

Comparing effect estimates obtained from different approaches can help evaluate the robustness of the primary IVW estimates. Hence we supplemented IVW with a variety of methods that are robust to specific patterns of heterogeneity, including weighted median (Bowden et al., 2016), simple mode (Hartwig et al., 2017), weighted mode (Hartwig et al., 2017), and MR-Egger (Bowden et al., 2015). Finally, we selected potential causal estimates using a type I error rate of α = 0.05 if: 1) the IVW method and other methods confirmed the observed association with respect to heterogeneity statistics and 2) estimates from sensitivity analyses were consistent with the observed association. Estimates were presented as odds ratios (ORs) with 95% confidence intervals (CIs). Details about MR are available in the Supplemental Methods in Supplement 1.

2.4. Biological annotation

We selected positional mapped genes returned from post-GWAS analyses of our METAL results for biological annotation. We first investigated which tissues were significantly enriched for these genes using integrated hypergeometric tests of the FUMA platform (Consortium, 2020; Watanabe et al., 2017b). We then identified protein–protein interaction (PPI) networks constructed with shared positional mapped genes between 12 psychiatric diseases and our METAL results for mbGWAS, using STRING version 11.5 (Szklarczyk et al., 2021). From this, we extracted the largest connected PPI network and visualized it using Cytoscape(Shannon et al., 2003). We identified the top 10 nodes as hub genes using the Maximal Clique Centrality (MCC) method, since gene nodes with multiple interactive connections played a vital role in maintaining the stability of the entire network. (Chin et al., 2014). We examined bulk tissue gene expression for the hub genes using the Genotype-Tissue Expression (GTEx) portal (Consortium, 2020), and evaluated their effect on multiple phenotypes via the phenome-wide association studies (PheWAS) approach by examining the pleiotropy of the hub genes in the summary statistics for 4756 complex traits and diseases across 28 domains using the GWAS ATLAS(Watanabe et al., 2019). We finally annotated the positional mapped genes of instrument variants (IVs) for pairs of microbial features and psychiatric diseases with observed relationships defined as causal in our study using the GWAS catalog(Welter et al., 2014) provided with FUMA, which could provide insight into previously reported associations of the genes with a variety of phenotypes.

3. Results

3.1. Meta-analysis

Heterogeneity was detected in 18 out of the 25 GWAS meta-analyses performed (refer to Table S3 and Table S4 in Supplement 2). For the METAL results, SNPs with heterogeneity (P < 0.05) were excluded from subsequent analyses. Beyond the meta-analysis, we also conducted MTAG(Turley et al., 2018a) to combine species in genus Bifidobacterium to increase our statistical power (Table S5 in Supplement 2). Any trait with mean χ2 over 1.02 were included in the MTAG calculation. Only 2 Bifidobacterium species from each cohort met this criteria (DMP cohort: Bifidobacterium catenulatum and Bifidobacterium longum; FR02 cohort: Bifidobacterium longum and Bifidobacterium ruminantium; after meta-analysis of combined cohort data: Bifidobacterium adolescentis and Bifidobacterium longum).

We compared the number of genomic risk loci, lead SNPs, independent significant SNPs and mapped genes between the GWAS statistics from two individual cohorts and our METAL results. After using METAL, a universal increase in the number of lead SNPs, independent significant SNPs, mapped genes, and genomic risk loci was observed (Table S6 in Supplement 2). We also contrasted the results of the original GWAS with those of MTAG. A comparison of the GWAS and MTAG results is presented in Table S7 in Supplement 2. From GWAS to MTAG, the number of lead SNPs increased from 15 to 33 for Bifidobacterium longum and from 29 to 34 for Bifidobacterium ruminantium in FR02 cohort; from 7 to 8 for Bifidobacterium catenulatum and from 12 to 11 for Bifidobacterium longum in DMP cohort; and from 32 to 46 for Bifidobacterium adolescentis and from 27 to 44 for Bifidobacterium longum after meta-analysis. For each trait, the gain in average power for MTAG relative to the GWAS results was assessed by the increase in the mean χ2 statistic. We found the MTAG analysis in our study yielded gains in sample size equivalent to expanding the original samples from 7% to 63%.

3.2. Bidirectional MR and sensitivity analyses

We performed bidirectional two-sample MR analyses to explore the potential causal relationship between gut microbial features and psychiatric diseases before and after using METAL and MTAG.

As shown in Fig. 2 and Table S8 in Supplement 2, in forward MR analyses, we identified potential causal relationships between 3 microbial features (after using METAL) and 2 psychiatric diseases, including ADHD and PTSD. We observed a positive causal effect of Bacteroides faecis on ADHD (OR, 1.09; 95% CI, 1.02–1.16; P = 0.008). An increase of 1 s.d. in the abundance of Bacteroides faecis was associated with 9% higher risk of ADHD. Bacteroides eggerthii was observed to be positively associated with PTSD (OR, 1.11; 95% CI, 1.03–1.20; P = 0.007). Bacteroides thetaiotaomicron, its relative from the same genus, was also identified to be positively associated with PTSD (OR, 1.11; 95% CI, 1.01–1.23; P = 0.03). The risk of PTSD increased by 11% per 1 s.d. increase in Bacteroides eggerthii and Bacteroides thetaiotaomicron. Effect estimates representing the associations of microbial features on ADHD and PTSD were generally consistent in direction and magnitude across methods, indicating reliable results. Exact genetic instruments for the inference of the causal effects are listed in Table S9 in Supplement 2. The minimum F statistic among instruments was 19, indicating all IVs were strongly associated with the microbiome features. For causal relationships identified in MR, no heterogeneity was observed (PQ > 0.05). Global tests in MR-PRESSO indicated no horizontal pleiotropy (Table S8 in Supplement 2). In the backward MR analyses (analyses with psychiatric diseases as the exposures and microbial features as the outcomes), we did not detect similar causal effects of psychiatric diseases on the three microbial features as detected by the forward MR analyses. And no evidence of associations where psychiatric diseases caused changes in microbial features (Table S10 in Supplement 2). As shown in Fig. 3, leave-one-out analyses found no evidence of high influence variants among instrument SNPs. Apart from using 1 × 10−5 as the threshold for instruments selection for microbial features, we also compared the directional consistency of MR results under different thresholds, including 5 × 10−8. The detailed instrument selection process and MR results using the 5 × 10−8 threshold are comprehensively presented in Table S11 and Table S12 in Supplement 2. We found that MR results had directional consistency for the relationships between species of Bacteroides and psychiatric diseases (Table S8 in Supplement 2).

Fig. 2.

Fig. 2.

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MR analysis. Causal effects were estimated using primary analysis (IVW) and other methods, including weighted median, simple mode, weighted mode and MR-Egger. (A) Results in the forward MR. The forest plot shows the potential causal associations observed between microbial features and psychiatric diseases. Data are expressed as an odds ratio (OR) with corresponding 95% confidence interval (CI). (B) MR scatter plots. Scatterplot of SNP potential effects on microbial features (Bacteroides faecis, Bacteroides eggerthii and Bacteroides thetaiotaomicron) vs psychiatric diseases (ADHD and PTSD), with the slope of each line corresponding to estimated MR effect per method.

Fig. 3.

Fig. 3.

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Leave-one-out plots for MR results. Forest plot of causal estimates omitting each variant in turn.

3.3. Biological annotation

Post-GWAS analyses of 25 METAL results returned 759 positional mapped genes (Table S13 in Supplement 2). We observed that discrepancy of the expression of these genes was higher in the brain compared to all other tissues (Figure S2 in Supplement 1). In addition, positional mapping from 12 psychiatric diseases and meta-analysis results of 25 microbiota features allowed us to map 168 genes connecting psychiatric diseases and microbiota features (Table S14 in Supplement 2). PPI network analysis using the STRING database(Szklarczyk et al., 2021) showed that the set of 168 shared proteins forms a tightly interconnected network (Fig. 4). Enrichment of observed edges was assessed against expected edges to determine a PPI P value of 1.11 × 10−16 for the observed network. To identify molecular mechanisms relevant for gut microbiomes, psychiatric diseases, and their underlying relationship, we also performed gene-set enrichment analysis for common positional mapped genes (Fig. 4 and Table S15 in Supplement 2). We found significant enrichment for synapse organization, a range of channel complex and activity.

Fig. 4.

Fig. 4.

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Biological annotation of 168 common genes between psychiatric diseases and microbial features. (A) Full overview of PPI networks of 168 common genes. Edge colors represent different evidence underlying predicted protein–protein interactions in the STRING database. Line thickness indicates the strength of data support and disconnected nodes are hidden in the network. (B) GO pathways. Gene set enrichment analysis identified 49 significantly enriched Gene Ontology (GO) pathways. BP: biological process, CC: cellular component, MF: molecular function.

We then further investigated the largest connected PPI network, which included 100 proteins. We visualized this network using Cytoscape (Figure S3 in Supplement 1). Using the Genotype-Tissue Expression (GTEx) portal (Consortium, 2020), we found that 9 of 10 hub genes (GRIN2A, RBFOX1, CSMD1, GNB1, GABRG1, DLGAP2, OPCML, ZNF536 and GNG7) are all predominantly expressed in the brain tissues. The final hub gene, CACNB2, is primarily expressed in both the digestive tract and brain (Figure S4 in Supplement 1 and Table S16 in Supplement 2). We found that all hub genes were associated with several phenotypes at a significance threshold of 1.05 × 10−5, which we obtained by dividing the traditional significance threshold of α = 0.05 by 4,756, the number of complex trait and disease phenotypes. Gene-based PheWAS showed a strong link between the hub genes and metabolic traits, and we observed that 8 of 10 hub genes were enriched with genetic signals associated with the metabolic domain or nutritional domain (Figure S5 in Supplement 1 and Table S17 in Supplement 2).

The IVs (59 SNPs in total) for the 3 pairs of microbial features and psychiatric diseases with potential causal relationships in two-sample MR were mapped onto 75 genes (Table S18 in Supplement 2). According to the GWAS catalog, previous studies found that these genes were mainly associated with hippocampal subfield volume (CA1, CA3 and CA4) and total hippocampal volume (Fig. 5 and Table S19 in Supplement 2).

Fig. 5.

Fig. 5.

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Biological annotation of IVs for 3 pairs of microbial features and psychiatric diseases with causal relationships in two-sample MR. Gene sets were obtained from the GWAS-catalog.

4. Discussion

Our research expanded upon the sample size and statistical power of the existing mbGWAS by implementing meta-analysis and multi-trait analysis. We processed the mbGWAS data from two largest European cohorts and the GWAS summary statistics of 12 psychiatric diseases from PGC. This allowed us to investigate potential causal relationships between gut microbiota and diseases. We also explored the possible biological relevance of gut microbiota and psychiatric diseases using gene- and gene-set-based analyses on multi-omics data.

The DMP and FR02 cohorts had no sample overlap, and both used shotgun metagenomic sequencing on feces to identify genome-wide associations between human genotypes and microbial abundances. These two studies robustly replicated associations at LCT locus and ABO locus. Meta-analyses were conducted in 25 microbial features with data available from both cohorts. The meta-analyses results allowed further investigation of the casual relationships between microbial features and psychiatric diseases.

Forward MR analysis results indicated positive casual effects of 3 species from thegenus Bacteroides on ADHD and PTSD. First, we detected an effect of increased abundance of Bacteroides faecis on ADHD. This finding is consistent with a previous cross-sectional study, which found a higher abundance of the family Bacteroidaceae in adolescents with ADHD(Checa-Ros et al., 2021). Wang et al.(Wang et al., 2020) also found an increase of some species of Bacteroides in the ADHD group. Second, we discovered that Bacteroides eggerthii and Bacteroides thetaiotaomicron were both positively correlated with the risk of PTSD. A previous longitudinal study reported that high abundance of Bacteroides eggerthii at Day 0 was determinants of the reappearance of post-traumatic stress symptoms in frontline healthcare workers fighting against COVID-19(Gao et al., 2022). Moreover, previous research has also described increases in Bacteroidetes abundance in the oral microbiome signature of veterans with PTSD(Levert-Levitt et al., 2022).

Although the etiology underlying the association between species of Bacteroides and ADHD or PTSD remains unclear, the neurobiology of brain-derived neurotropic factor (BDNF) can be considered as a possible candidate. BDNF is one of the most prominently studied molecules within psychiatry, and numerous studies have proved that BDNF holds a functional role in fear engrams(Notaras and van den Buuse, 2020) such as extinction, consolidation, and reconsolidation. The intestinal microbiota could increase hippocampal levels of BDNF(Agnihotri and Mohajeri, 2022; Bercik et al., 2011). Simultaneously increased BDNF plays initial role in the consolidation of fear engrams(Lee et al., 2004; Lubin et al., 2008; Mizuno et al., 2012). Then consolidated fear memory could underlie later development of PTSD and other fear-related disorders that manifest in ways that are consistent with an uncontrollable state of fear (Parsons and Ressler, 2013). Different results have been reported regarding BDNF concentrations in ADHD patients. When analyzing males and females separately, a systematic review found peripheral BDNF levels which positively correlated with hippocampal BDNF levels were significantly higher in males with ADHD compared with controls (Klein et al., 2011; Zhang et al., 2018). However, continued study on how the intestinal microbiota alter the neurotrophic factors in brain is necessary.

Biological annotation analyses suggested that expression of genes contributing to microbiomes is enriched in the brain, which supports the existence of the gut–brain axis mediated by the microbiome(Valles-Colomer et al., 2019). We identified hub genes included CACNB2 and ZNF536 connecting diseases and microbiota features. CACNB2 encodes voltage-gated calcium-channel subunit and was identified as a risk gene for bipolar disease(Mullins et al., 2021). ZNF536 has an essential role in development of a small subset of forebrain neurons implicated in stress and social behavior. Moreover, PheWAS analysis identified an overlap between hub genes and broad traits, including metabolic and nutritional traits, in line with previous study(Kurilshikov et al., 2021). We also found that the genes mapped from IVs were associated with hippocampal subfield volume and hippocampal volume(van der Meer et al., 2020; Zhao et al., 2019). The volume of the hippocampal and its subfield are closely associated with psychiatric diseases. Reduced hippocampal volume has been identified as one of risk factors for the development of stress-related psychopathology(Gilbertson et al., 2002; Xie et al., 2018), and significant reduction of hippocampal volume due to atrophy of subfield volume was observed in ADHD patients. Moreover, numerous studies have indicated that BDNF is a significant factor related to hippocampal shrinkage(Erickson et al., 2010). Therefore, a potential mechanism to decipher the causal relationship between species of Bacteroides and ADHD or PTSD may through BDNF-mediated hippocampal volume change.

Despite these insights, several limitations need to be noted regarding the present study. First, as in previous meta-analytic research(Kurilshikov et al., 2021), the heterogeneity of data is still obvious. The heterogeneity may reflect technical variations and demographic differences, such as the use of different DNA isolation and different taxonomic annotations. Second, for some species, it was not appropriate to directly use MTAG due to the lower heritability and smaller sample sizes. Third, for almost all pairs between microbial traits and psychiatric diseases, there were only one IV to conduct MR analyses when selecting IV under a genome-wide level of significance (P < 5 × 10−8). A causal estimate from only one IV will have less precision than multiple genetic variants (Pierce et al., 2011). Therefore, in order to obtain more reliable results, we verified the potential causal relationships explored by the MR in the lenient threshold (1 × 10−5) with the MR results in other thresholds (5 × 10−6 and 5 × 10−8), and then we compared the directional consistency. In the future, we believe with the further increase of sample size, it will be possible to obtain more reliable results directly through MR under stricter thresholds. Finally, although we identified several potential causal associations in MR analyses, the results need further validation. In addition to expanding the sample further, future studies should focus on the development of new methods to increase the statistical power of mbGWAS. Further studies on the biological mechanisms of the brain-gut axis are also needed.

5. Conclusions

In summary, we leveraged the power of meta-analysis to enhance the capabilities of mbGWAS, enabling us to explore the relationship between gut microbial features and psychiatric diseases. We found that several species may have a potential effect on psychiatric diseases. It appears that this influence may be mediated through synapse and channel-related pathways acting predominantly in the brain. These findings may have clinical implications, highlighting that potential manipulation of gut microbiota may be a clinical target for prevention and treatment of psychiatric diseases.

Supplementary Material

Supplement

Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (Grants NO. 82171499 and NO.81771446 to QW), Chinese National Programs for Brain Science and Brian-like Intelligence Technology, China Depression Cohort Study (2021ZD0200700 to QW) and Science and Technology Project of Sichuan Province (2023YFS0030 to QW). HG is supported by Mentored Scientist Grant from Henry Ford Hospital and funding from the National Institute of Mental Health (R03MH135347).

HG and QW conceived and designed the study. HG, QW, LX and SL performed the data analysis. YW, YH, ST, YL, YT, MX, QM, YY, MD, MZ, and LX contributed to manuscript preparation and interpretation of the results. All the authors reviewed and approved the final version of the manuscript.

Footnotes

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbi.2023.08.003.

Data availability

The authors do not have permission to share data.

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