Multi-ancestry genome-wide association meta-analysis identifies novel associations and informs genetic risk prediction for Hirschsprung disease

Abstract

<strong></strong><p><strong>Background: </strong>Hirschsprung disease (HSCR) is a rare, congenital disease characterized by the absence of enteric ganglia in the hindgut. Common genetic variation contributes substantially to the heritability of the disease yet only three HSCR-associated loci were identified from genome-wide association studies (GWAS) thus far.</p><p><strong>Methods: </strong>We performed the largest multi-ancestry meta-analysis of GWAS to date, totalling 1250 HSCR cases and 7140 controls. Prioritized candidate genes were further characterized using single-cell transcriptomic data of developing human and mouse gut for their roles in development of enteric nervous system (ENS). Functional characterisation using human cells and zebrafish models was performed. Global and ancestry-matched polygenic risk score (PRS) models were derived and evaluated for predicting risk of HSCR.</p><p><strong>Findings: </strong>We identified four HSCR-susceptibility loci, with three loci (JAG1, HAND2 and ZNF25) reaching genome-wide significance and one putative locus (UNC5C) prioritized by functional relevance. Spatiotemporal analysis revealed hotspots of gene dysregulation during ENS development. Functional analyses further demonstrated that knockdown of the candidate genes impaired cell migration and zebrafish knockouts displayed abnormal ENS development. We also demonstrated comparable performance for a PRS model derived from multi-ancestry meta-analysis to those of ancestry-matched PRS models, supporting its potential clinical application in risk prediction of HSCR across populations.</p><p><strong>Interpretation: </strong>Overall, the meta-analysis implicated novel genes, pathways and spatiotemporal developmental hotspots in the genetic aetiology of HSCR. Development of a PRS universally applicable irrespective of ancestries may leverage its clinical utility in risk prediction.</p>