Variants at the MHC Region Associate With Susceptibility to Clostridioides difficile Infection: A Genome-Wide Association Study Using Comprehensive Electronic Health Records

Abstract

Background: Clostridioides difficile is a major cause of healthcare-associated and community-acquired diarrhea. Host genetic susceptibility to Clostridioides difficile infection has not been studied on a large-scale.

Methods: A total of 1,160 Clostridioides difficile infection cases and 15,304 controls were identified by applying the eMERGE Clostridioides difficile infection algorithm to electronic health record data. A genome-wide association study was performed using a linear mixed model, adjusted for significant covariates in the full dataset and the antibiotic subgroup. Colocalization and MetaXcan were performed to identify potential target genes in Clostridioides difficile infection - relevant tissue types.

Results: No significant genome-wide association was found in the meta-analyses of the full Clostridioides difficile infection dataset. One genome-wide significant variant, rs114751021, was identified (OR = 2.42; 95%CI = 1.84-3.11; p=4.50 x 10^-8) at the major histocompatibility complex region associated with Clostridioides difficile infection in the antibiotic group. Colocalization and MetaXcan identified MICA, C4A/C4B, and NOTCH4 as potential target genes. Down-regulation of MICA, upregulation of C4A and NOTCH4 was associated with a higher risk for Clostridioides difficile infection.

Conclusions: Leveraging the EHR and genetic data, genome-wide association, and fine-mapping techniques, this study identified variants and genes associated with Clostridioides difficile infection, provided insights into host immune mechanisms, and described the potential for novel treatment strategies for Clostridioides difficile infection. Future replication and functional validation are needed.

Keywords: C4a; Clostridioides difficile; GWAS; MICA; NOTCH4.

Figure 1

Flowchart of the study. Yellow box: Genotyping and imputation was conducted separately for Phase I and II. Blue box: eMERGE CDI algorithm was applied to identify cases and controls. Clinical information was extracted. Green box: individual GWAS and meta-analyses, followed by a sensitivity analysis and Fine-mapping.

Figure 2

Manhattan and QQ plots for GWAS results associated with CDI in antibiotic treated patients with European ancestry. A linear mixed model regression adjusted for the covariates including sex, cohort, PPI, chemotherapy, T2DM, and Index Age were conducted by BOLT-LMM. Top variants with original p value < 5×10-8 were labeled. The genomic inflation factor, λGC, equals to 1.004, suggesting no evidence for systematic inflation of genome-wide test statistics.

Figure 3

Fine-mapping of the top loci. X-axis represents the tissue types (black indicates GI-tissue). Y-axis represent the genes. Genes that appear in both eCAVIAR and MetaXcan are in blood. (A) Colocalization of the 4 lead SNVs at the MHC region. The dot size represents the maximum CLPP values in the corresponding locus and tissues. The color represents different locus for each lead SNV. (B) MetaXcan associations of the MHC region with CDI in antibiotic subgroup. The size of the dots represents the significance of the association between predicted expression and the CDI in patients exposed to high-risk antibiotics. Red indicates positive correlation while blue negative. Darker color indicates larger genetic component and consequently more active regulation in the tissue (R² is a model performance measure computed as the correlation squared between observed and predicted expression, cross validated in the training set). Only associations with p<0.01 were shown.