Ashton, J. J. | Search bioRxiv

bioRxiv2026

Characterisation of Genetically-Derived Patient Clusters Highlights Genetic Heterogeneity of Inflammatory Bowel Disease

Background and aims. Inflammatory Bowel Disease (IBD) is a complex immune-mediated condition with a strong genetic contribution. The genetics of IBD are highly complex, with inflammation, autoimmunity, and mucosal immunity pathways involved in its pathogenicity. GenePy is a gene-level disease burden metric. The integration of clinical and genetic data and the application of machine learning methods can aid patient stratification for personalised care. Methods. Lists of consensus IBD pathway genes were curated. GenePy scores were computed from whole exome sequencing data. A K-Means algorithm was optimised using clustering quality and stability metrics on the GenePy scores of genes in each IBD pathway, and the results replicated in three independent IBD cohorts (SPARC IBD, MSCCR IBD, and Southampton IBD). Chi-square tests assessed the association between cluster assignment and phenotypic features, and were multiple test corrected via Benjamini-Hochberg FDR. Results. A strong association was replicated for NOD signalling genes driving IBD subtype, Crohns disease behaviour, and IBD surgery across all cohorts, as well as granuloma in Southampton IBD. This was mainly driven by NOD2. Similar genetic clustering patterns were observed across all cohorts, with additional phenotypic enrichment in clusters driven by NLRP1 (surgery in MSCCR IBD), and nominally significant enrichment for disease behaviour linked to BIRC2 (B2 in Southampton IBD and B2B3 in SPARC IBD). Conclusions. This analysis identified a common genetic architecture across NOD signalling genes across IBD cohorts, replicated a signal for NOD2 as a monogenic driver of disease with a strong phenotypic enrichment, and identified other putative genetic drivers of IBD, such as the NLRP1 gene.

bioinformatics↗

bioRxiv2018

GenePy - a score for estimating gene pathogenicity in individuals using next-generation sequencing data

NGS is a revolutionising diagnosis and treatment of rare diseases. However, its relatively modest application in common diseases is limited by analytical approaches.\n\nInstead of variant-level approaches, typical for rare disease or large cohort analyses, contemporary investigation of common polygenic disorders requires the development of tools combining mutational burden and biological impact of a personalised set of mutations into single gene scores. GenePy (https://github.com/UoS-HGIG/GenePy) is a gene score for transforming sequencing data capable of estimating whole-gene pathogenicity on a per-patient basis.\n\nGenePy implements known deleteriousness metrics, incorporates allele frequency and individual zygosity information. Individuals harbouring multiple rare highly deleterious mutations accumulate extreme gene scores while the majority of genes usually achieve very low scores. Following correction for gene length, GenePy intuitively prioritises genes within individuals and affords gene/pathway score comparison between groups of individuals. Herein, we generate GenePy scores from whole-exome sequencing data for [~]15,000 genes across a cohort of 508 individuals. We describe score attributes and model behaviour under various biological conditions.\n\nWe demonstrate proof of concept that GenePy sensitively identifies known causal genes by calculating GenePy scores for NOD2 (an established causal Crohns Disease gene), in a modest cohort of patients for comparison against controls. This test of GenePy using a positive control gene demonstrates markedly more significant results (p=1.37 x 10-4) compared to the most commonly applied tool for combining common and rare variation.\n\nIn addition to increasing the biological information content for each variant, the per gene-per individual nature of GenePy transforms the utility of sequencing data. GenePy scores are intuitive when assessing for individual patients or for comparing between groups. Because GenePy intrinsically reflects pathogenicity at the gene level, this specifically facilitates downstream data integration (e.g. into machine learning, network and topological analyses) with transcriptomic and proteomic data that also report at the gene level.\n\nAuthor SummaryRapid technological advances have made DNA sequencing an effective, economic tool for detecting genomic variation. Detecting rare variation at the individual level is proving very successful in identifying the genetic causes of disease when just single mutations are sufficient to manifest disease. However, interpreting genomic data is much less straightforward for common diseases such as asthma, arthritis or heart disease where many genetic changes across multiple genes combine with the environment to bring about disease symptoms.\n\nWe have developed a new scoring system called GenePy that generates whole gene pathogenicity scores for indiviual patients. The score corrects for the length of the gene and is intuitive to use. Unlike many mutation deleteriousness metrics, GenePy also takes into account the population frequency of the variant and the number of copies of any given mutation and combines data for as many variants as are present in a given gene for any one individual.\n\nIn this paper we apply the GenePy scoring system to a cohort of over 500 individuals for whom we have sequencing data across all genomics regions that code for protein. We descibe how GenePy performs and demonstrate superior sensitivity to detect known causal genes in a common autoimmune condition.

genomics↗