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- W4313406952 abstract "Abstract Prevalence of immune mediated disease (IMD) within N. European populations varies from common (atopic dermatitis AD=10%) to rarer (giant cell arteritis-GCA=0.02%; hidradenitis suppurativa-HS=0.77%). This directly affects statistical power to detect genomewide SNP associations and to identify “causal” genes. Objective Leverage genetic correlations between differently powered IMD cohorts to study the genetic mechanisms underlying multiple immune-mediated diseases. We used UK Biobank clinical data to create 18 IMD cohorts and a control set of individuals without any history of autoimmune disease (N=50,136). Using whole genome sequencing data from ~140K participants of the UKB, we performed SNP-level GWAS with REGENIE v2, and prioritized the associated genes using association statistics, in silico annotation, and eQTL information, as implemented in the Priority Index (Pi) algorithm. We used k-means clustering across relative gene rank distances optimized with the elbow method to group IMDs according to similar genetic architectures. Result IMD cohort size ranged from 196 to 3,411 cases and were each compared with a common set of controls. Optimal clustering yielded 4 groups, each with 6,5,4 or 3 IMD’s respectively. One cluster of interest contained HS, AD, vitiligo, GCA and Sjogren’s syndrome. This cluster reflects similarity in common “pathogenic” genes across IMD and highlights new candidate genes for further investigation that would otherwise go unnoticed in poorly powered studies of GCA and HS. In conclusion, our proposed use of genetic associations and clustering of their relative ranks across multiple disease phenotypes identified pathogenic genes in understudied IMDs that would otherwise not have been elucidated. Supported by Janssen Research & Development" @default.
- W4313406952 created "2023-01-06" @default.
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- W4313406952 date "2022-05-01" @default.
- W4313406952 modified "2023-09-24" @default.
- W4313406952 title "Leveraging genetic correlation across immune-mediated disease to gain insights into underpowered studies of rare conditions" @default.
- W4313406952 doi "https://doi.org/10.4049/jimmunol.208.supp.160.02" @default.
- W4313406952 hasPublicationYear "2022" @default.
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