Moving from GWAS signals to rare functional variation in inflammatory bowel disease through application of GenePy2 as a potential DNA biomarker

Objectives: We adopt a weighted variant burden score GenePy2.0 for the UK Biobank phase 2 cohort of inflammatory bowel disease (IBD), to explore potential genomic biomarkers underpinning known associations of IBD. Design: Nucleating from IBD GWAS signals, we identified 794 GWAS loci, including target genes and LD blocks (LDBs) based on linkage disequilibrium (LD) and functional mapping. We calculated GenePy2.0, a burden score of target regions integrating variants with CADDPhred>15 weighted by deleteriousness and zygosity. Collating with other burden based test, GenePy based Mann Whitney U tests on cases/controls with varying extreme scores were used. Significance levels and effect sizes were used for tuning the optimal GenePy thresholds for discriminating patients from controls. Binarized GenePy status (above or below threshold) of candidate regions, was subject to itemset association test via the sparse Apriori algorithm. Results: A tailored IBD cohort was curated (nCrohns\_Disease(CD)=891, nUlcerative\_Colitis(UC)=1409, nControls=60118). Analysing 885 unified target regions (794 GWAS loci and 104 monogenic genes with 13 overlaps), the GenePy approach detected statistical significance in 35 regions of CD and 25 of UC targets exerting risk and protective effects on the disease. Large effect sizes were observed, e.g. CYLD-AS1 (Mann Whitney, theta=0.89[CI:0.78-0.96]) in CD/controls with the top 1% highest scores of the gene. Itemset association learning further highlighted an intriguing signal whereby GenePy status of IL23R and NOD2 were mutually exclusive in CD but always cooccurring in controls. Conclusion: GenePy score per IBD patient detected deleterious variation of large effect underpinning known IBD associations and proved itself a promising tool for genomic biomarker discovery. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by AGENDA EPSRC funding on AI health research (EP/Y01720X/1) and was supported by the National Institute for Health Research (NIHR) Southampton Biomedical Research Centre. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: UK Biobank I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript