Cross-tissue transcriptome-wide association studies identify susceptibility genes shared between schizophrenia and inflammatory bowel disease

Florian Uellendahl-Werth,Oleg Borisov,Eike Matthias Wacker,Isabella Friis Jørgensen,Søren Brunak,André Franke ,Trine Folseraas,Tom H Karlsen ,Peter Krawitz,Franziska Degenhardt,Stefan Schreiber,Markus M Nöthen ,Olaf Wolkenhauer,Carlo Maj,Tim Alexander Steiert,Saptarshi Bej,Simonas Juzėnas ,Per Hoffmann,Christoph Schramm,Karina Banasik,David Ellinghaus

doi:10.1038/s42003-022-03031-6

Abstract

Genetic correlations and an increased incidence of psychiatric disorders in inflammatory-bowel disease have been reported, but shared molecular mechanisms are unknown. We performed cross-tissue and multiple-gene conditioned transcriptome-wide association studies for 23 tissues of the gut-brain-axis using genome-wide association studies data sets (total 180,592 patients) for Crohn’s disease, ulcerative colitis, primary sclerosing cholangitis, schizophrenia, bipolar disorder, major depressive disorder and attention-deficit/hyperactivity disorder. We identified NR5A2, SATB2, and PPP3CA (encoding a target for calcineurin inhibitors in refractory ulcerative colitis) as shared susceptibility genes with transcriptome-wide significance both for Crohn’s disease, ulcerative colitis and schizophrenia, largely explaining fine-mapped association signals at nearby genome-wide association study susceptibility loci. Analysis of bulk and single-cell RNA-sequencing data showed that PPP3CA expression was strongest in neurons and in enteroendocrine and Paneth-like cells of the ileum, colon, and rectum, indicating a possible link to the gut-brain-axis. PPP3CA together with three further suggestive loci can be linked to calcineurin-related signaling pathways such as NFAT activation or Wnt.

Highlights

Genetic correlations and an increased incidence of psychiatric disorders in inflammatorybowel disease have been reported, but shared molecular mechanisms are unknown
The UTMOST framework was used to perform cross-tissue transcriptome-wide association studies (TWAS) for 17,290 genes and 23 selected disease-relevant tissues, which had been identified as disease-relevant for diseases of the GBA in a previous study of Finucane and colleagues[19] (Methods), using transcriptome-wide and genome-wide reference data of 4043 samples provided by consortia projects GTEx20, STARNET21, and BLUEPRINT22 (Fig. 1 and Supplementary Data 2)
We demonstrated the applicability of our approach by examining and replicating established expression quantitative trait loci (eQTL) associations of genome-wide association studies (GWAS) signals as reported in the fine-mapping GWAS studies for Crohn’s disease (CD)/ulcerative colitis (UC)

Summary

Introduction

Genetic correlations and an increased incidence of psychiatric disorders in inflammatorybowel disease have been reported, but shared molecular mechanisms are unknown. We performed cross-tissue and multiple-gene conditioned transcriptome-wide association studies for 23 tissues of the gut-brain-axis using genome-wide association studies data sets (total 180,592 patients) for Crohn’s disease, ulcerative colitis, primary sclerosing cholangitis, schizophrenia, bipolar disorder, major depressive disorder and attention-deficit/hyperactivity disorder. A significant shared proportion of genomic correlation was detected between inflammatory (Crohn’s disease [CD], ulcerative colitis [UC]), and psychiatric traits (SCZ, BD) using LD score regression (LDSC)[11]. It remains unclear, whether the correlation is due to a few loci or is distributed across the genome. TWAS prediction accuracy due to imputation from reference expression panels containing tissues not relevant to disease, (2) a bias in the number of transcriptome-wide significant gene-disease associations due to different sample sizes of reference data sets in expression imputation models[18], (3) an excess of TWAS association signals at loci with multiple TWAS signals due to correlated expression among genes of the same locus, and (4) an inaccurate determination of association boundaries for TWAS signals based on SNP information

Methods

Results

Conclusion