Camargo Tavares, Leticia

Background Irritable bowel syndrome (IBS) is a complex disorder of gut-brain interaction, with heterogeneous symptoms, no available biomarkers and limited pathogenetic insight. Objective To identify genetic risk factors and actionable mechanisms for future clinical translation in IBS. Design We conducted a genome-wide association study (GWAS) meta-analysis of IBS in 2 775 539 individuals from 22 biobanks. IBS genetics was studied across multiple ancestries, different case definitions and symptom-related subtypes. Heritability and genetic correlations with other traits were estimated, and Mendelian randomisation was used to test causal relationships. GWAS data were functionally annotated and fine-mapped to prioritise tissues, cell types, pathways, candidate genes, specific mechanisms and druggable targets. Results Significant heritability was only detected in individuals of European ancestry, with near-identical genetic architecture across case definitions. Genetic correlations with GI, psychiatric and cardiometabolic traits were observed, including causal relationships with triglyceride (TG) levels. Functional annotation of IBS risk loci highlighted cell types and pathways relevant to brain, enteric neuro-glial and cardiometabolic domains, as well as actionable targets like GCKR, a regulator of TG metabolism. Druggability analyses converged on cardiometabolic mechanisms, including TG modulation. IBS polygenic risk scores were derived and showed a significant association with case status in an independent case-control dataset, supporting further evaluation in external population-based and clinically ascertained cohorts. Conclusions This study provides the most comprehensive assessment of IBS genetics to date, demonstrating reproducible polygenic inheritance. We link IBS risk to convergent neurogastrointestinal and novel cardiometabolic mechanisms, highlight specific biological pathways and actionable mechanisms and outline translational opportunities emerging from integrated computational analyses.

Abstract Background and Aims Microscopic colitis [MC] is currently regarded as an inflammatory bowel disease that manifests as two subtypes: collagenous colitis [CC] and lymphocytic colitis [LC]. Whether these represent a clinical continuum or distinct entities is, however, an open question. Genetic investigations may contribute important insight into their respective pathophysiologies. Methods We conducted a genome-wide association study [GWAS] meta-analysis in 1498 CC, 373 LC patients, and 13 487 controls from Europe and the USA, combined with publicly available MC GWAS data from UK Biobank and FinnGen [2599 MC cases and 552 343 controls in total]. Human leukocyte antigen [HLA] alleles and polymorphic residues were imputed and tested for association, including conditional analyses for the identification of key causative variants and residues. Genetic correlations with other traits and diagnoses were also studied. Results We detected strong HLA association with CC, and conditional analyses highlighted the DRB1*03:01 allele and its residues Y26, N77, and R74 as key to this association (best p = 1.4 × 10-23, odds ratio [OR] = 1.96). Nominally significant genetic correlations were detected between CC and pneumonia [rg = 0.77; p = 0.048] and oesophageal diseases [rg = 0.45, p = 0.023]. An additional locus was identified in MC GWAS analyses near the CLEC16A and RMI2 genes on chromosome 16 [rs35099084, p = 2.0 × 10-8, OR = 1.31]. No significant association was detected for LC. Conclusion Our results suggest CC and LC have distinct pathophysiological underpinnings, characterised by an HLA predisposing role only in CC. This challenges existing classifications, eventually calling for a re-evaluation of the utility of MC umbrella definitions.

AbstractBackgroundGastroparesis (GP) is characterized by delayed gastric emptying in the absence of mechanical obstruction.ObjectiveGenetic predisposition may play a role; however, investigation at the genome‐wide level has not been performed.MethodsWe carried out a genome‐wide association study (GWAS) meta‐analysis on (i) 478 GP patients from the National Institute of Diabetes and Digestive and Kidney Diseases Gastroparesis Clinical Research Consortium (GpCRC) compared to 9931 population‐based controls from the University of Michigan Health and Retirement Study; and (ii) 402 GP cases compared to 48,340 non‐gastroparesis controls from the Michigan Genomics Initiative. Associations for 5,811,784 high‐quality SNPs were tested on a total of 880 GP patients and 58,271 controls, using logistic mixed models adjusted for age, sex, and principal components. Gene mapping was obtained based on genomic position and expression quantitative trait loci, and a gene‐set network enrichment analysis was performed. Genetic associations with clinical data were tested in GpCRC patients. Protein expression of selected candidate genes was determined in full thickness gastric biopsies from GpCRC patients and controls.ResultsWhile no SNP associations were detected at strict significance (p ≤ 5 × 10−8), nine independent genomic loci were associated at suggestive significance (p ≤ 1 × 10−5), with the strongest signal (rs9273363, odds ratio = 1.4, p = 1 × 10−7) mapped to the human leukocyte antigen region. Computational annotation of suggestive risk loci identified 14 protein‐coding candidate genes. Gene‐set network enrichment analysis revealed pathways potentially involved in immune and motor dysregulation (pFDR ≤ 0.05). The GP risk allele rs6984536A (Peroxidasin‐Like; PXDNL) was associated with increased abdominal pain severity scores (Beta = 0.13, p = 0.03). Gastric muscularis expression of PXDNL also positively correlated with abdominal pain in GP patients (r = 0.8, p = 0.02). Dickkopf WNT Signaling Pathway Inhibitor 1 showed decreased expression in diabetic GP patients (p = 0.005 vs. controls).ConclusionWe report preliminary GWAS findings for GP, which highlight candidate genes and pathways related to immune and sensory‐motor dysregulation. Larger studies are needed to validate and expand these findings in independent datasets.