Major depressive disorder (MDD) is clinically documented to co-occur with multiple gastrointestinal disorders (GID), but the potential causal relationship between them remains unclear. We aimed to evaluate the potential causal relationship of MDD with 4 GID [gastroesophageal reflux disease (GERD), irritable bowel syndrome (IBS), peptic ulcer disease (PUD), and non-alcoholic fatty liver disease (NAFLD)] using a two-sample Mendelian randomization (MR) design.

We obtained genome-wide association data for MDD from a meta-analysis (N = 480 359), and for GID from the UK Biobank (N ranges: 332 601–486 601) and FinnGen (N ranges: 187 028–218 792) among individuals of European ancestry. Our primary method was inverse-variance weighted (IVW) MR, with a series of sensitivity analyses to test the hypothesis of MR. Individual study estimates were pooled using fixed-effect meta-analysis.

Meta-analyses IVW MR found evidence that genetically predicted MDD may increase the risk of GERD, IBS, PUD and NAFLD. Additionally, reverse MR found evidence of genetically predicted GERD or IBS may increase the risk of MDD.

Genetically predicted MDD may increase the risk of GERD, IBS, PUD and NAFLD. Genetically predicted GERD or IBS may increase the risk of MDD. The findings may help elucidate the mechanisms underlying the co-morbidity of MDD and GID. Focusing on GID symptoms in patients with MDD and emotional problems in patients with GID is important for the clinical management.

Depression is a debilitating mental disorder that often coexists with anxiety. The genetic mechanisms of depression and anxiety have considerable overlap, and studying depression in non-anxiety samples could help to discover novel gene. We assess the genetic variation of depression in non-anxiety samples, using genome-wide association studies (GWAS) and linkage disequilibrium score regression (LDSC).

The GWAS of depression score and self-reported depression were conducted using the UK Biobank samples, comprising 99,178 non-anxiety participants with anxiety score <5 and 86,503 non-anxiety participants without self-reported anxiety, respectively. Replication analysis was then performed using two large-scale GWAS summary data of depression from Psychiatric Genomics Consortium (PGC). LDSC was finally used to evaluate genetic correlations with 855 health-related traits based on the primary GWAS.

Two genome-wide significant loci for non-anxiety depression were identified: rs139702470 (p = 1.54 × 10−8, OR = 0.29) locate in PIEZO2, and rs6046722 (p = 2.52 × 10−8, OR = 1.09) locate in CFAP61. These associated genes were replicated in two GWAS of depression from PGC, such as rs1040582 (preplication GWAS1 = 0.02, preplication GWAS2 = 2.71 × 10−3) in CFAP61, and rs11661122 (preplication GWAS1 = 8.16 × 10−3, preplication GWAS2 = 8.08 × 10−3) in PIEZO2. LDSC identified 19 traits genetically associated with non-anxiety depression (p < 0.001), such as marital separation/divorce (rg = 0.45, SE = 0.15).

Our findings provide novel clues for understanding of the complex genetic architecture of depression.

The role of neurological proteins in the development of bipolar disorder (BD) and schizophrenia (SCZ) remains elusive now. The current study aims to explore the potential genetic correlations of plasma neurological proteins with BD and SCZ.

By using the latest genome-wide association study (GWAS) summary data of BD and SCZ (including 41,917 BD cases, 11,260 SCZ cases, and 396,091 controls) derived from the Psychiatric GWAS Consortium website (PGC) and a recently released GWAS of neurological proteins (including 750 individuals), we performed a linkage disequilibrium score regression (LDSC) analysis to detect the potential genetic correlations between the two common psychiatric disorders and each of the 92 neurological proteins. Two-sample Mendelian randomisation (MR) analysis was then applied to assess the bidirectional causal relationship between the neurological proteins identified by LDSC, BD and SCZ.

LDSC analysis identified one neurological protein, NEP, which shows suggestive genetic correlation signals for both BD (coefficient = −0.165, p value = 0.035) and SCZ (coefficient = −0.235, p value = 0.020). However, those association did not remain significant after strict Bonferroni correction. Two sample MR analysis found that there was an association between genetically predicted level of NEP protein, BD (odd ratio [OR] = 0.87, p value = 1.61 × 10−6) and SCZ (OR = 0.90, p value = 4.04 × 10−6). However, in the opposite direction, there is no genetically predicted association between BD, SCZ, and NEP protein level.

This study provided novel clues for understanding the genetic effects of neurological proteins on BD and SCZ.

Psychiatric disorders are a group of complex psychological syndromes with high prevalence. Recent studies observed associations between altered plasma proteins and psychiatric disorders. This study aims to systematically explore the potential genetic relationships between five major psychiatric disorders and more than 3,000 plasma proteins.

The genome-wide association study (GWAS) datasets of attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) were driven from the Psychiatric GWAS Consortium. The GWAS datasets of 3,283 human plasma proteins were derived from recently published study, including 3,301 study subjects. Linkage disequilibrium score (LDSC) regression analysis were conducted to evaluate the genetic correlations between psychiatric disorders and each of the 3,283 plasma proteins.

LDSC observed several genetic correlations between plasma proteins and psychiatric disorders, such as ADHD and lysosomal Pro-X carboxypeptidase (p value = 0.015), ASD and extracellular superoxide dismutase (Cu-Zn; p value = 0.023), BD and alpha-N-acetylgalactosaminide alpha-2,6-sialyltransferase 6 (p value = 0.007), MDD and trefoil factor 1 (p value = 0.011), and SCZ and insulin-like growth factor-binding protein 6 (p value = 0.011). Additionally, we detected four common plasma proteins showing correlation evidence with both BD and SCZ, such as tumor necrosis factor receptor superfamily member 1B (p value = 0.012 for BD, p value = 0.011 for SCZ).

This study provided an atlas of genetic correlations between psychiatric disorders and plasma proteome, providing novel clues for pathogenetic and biomarkers, therapeutic studies of psychiatric disorders.

Psychiatric disorders and related intermediate phenotypes are highly heritable and have a complex, overlapping polygenic architecture. A large-scale genome-wide association study (GWAS) of anxiety disorders identified genetic variants that are significant on a genome-wide. The current study investigated the genetic etiological overlaps between anxiety disorders and frequently cooccurring psychiatric disorders and intermediate phenotypes.

Using case–control and factor score models, we investigated the genetic correlations of anxiety disorders with eight psychiatric disorders and intermediate phenotypes [the volumes of seven subcortical brain regions, childhood cognition, general cognitive ability and personality traits (subjective well-being, loneliness, neuroticism and extraversion)] from large-scale GWASs (n = 7556–298 420) by linkage disequilibrium score regression.

Among psychiatric disorders, the risk of anxiety disorders was positively genetically correlated with the risks of major depressive disorder (MDD) (rg ± standard error = 0.83 ± 0.16, p = 1.97 × 10−7), schizophrenia (SCZ) (0.28 ± 0.09, p = 1.10 × 10−3) and attention-deficit/hyperactivity disorder (ADHD) (0.34 ± 0.13, p = 8.40 × 10−3). Among intermediate phenotypes, significant genetic correlations existed between the risk of anxiety disorders and neuroticism (0.81 ± 0.17, p = 1.30 × 10−6), subjective well-being (−0.73 ± 0.18, p = 4.89 × 10−5), general cognitive ability (−0.23 ± 0.08, p = 4.70 × 10−3) and putamen volume (−0.50 ± 0.18, p = 5.00 × 10−3). No other significant genetic correlations between anxiety disorders and psychiatric or intermediate phenotypes were observed (p > 0.05). The case–control model yielded stronger genetic effect sizes than the factor score model.

Our findings suggest that common genetic variants underlying the risk of anxiety disorders contribute to elevated risks of MDD, SCZ, ADHD and neuroticism and reduced quality of life, putamen volume and cognitive performance. We suggest that the comorbidity of anxiety disorders is partly explained by common genetic variants.