rs10071051 - CARINH

Magnitude 2.2 · 2 studies on file

Reported associations

• Joint analysis of genome-wide cross-trait and multi-omics reveals molecular mechanisms of inflammatory bowel disease and nominates its novel therapeutic genes. - FASEB journal : official publication of the Federation of American Societies for Experimental Biology (2025) · Zhu Z, Wei R, Li H, Wang X, He G, Du M, Tan S, Cheng L · PubMed 39792054

  Inflammatory bowel disease (IBD) with the two predominant endophenotypes-Crohn's disease (CD) and ulcerative colitis (UC)-represents a group of chronic gastrointestinal inflammatory conditions. Since most genetic associations with IBD are often limited to independent subtypes, we reported a genome-wide association study (GWAS) cross-trait analysis combined with CD and UC to enhance statistical power. Initially, we detected 256 association signals at 54 genomic susceptibility loci and further characterized the functionality of variants within these regions. Subsequently, we revealed tissue and cell-specific heritability enrichment, particularly in whole blood, small intestine terminal ileum, spleen, lung, and colon transverse. Leveraging multi-omics datasets, we adopted a two-pronged approa

• A scalable variational inference approach for increased mixed-model association power - Unknown journal (n.d.) · Unknown authors · PubMed 39789286

  ABSTRACT: The rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational efficiency and statistical power, reducing the benefits of large-scale data collection efforts. We developed Quickdraws, a method that increases association power in quantitative and binary traits without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference and graphics processing unit acceleration. We applied Quickdraws to 79 quantitative and 50 binary traits in 405,088 UK Biobank samples, identifying 4.97% and 3.25% more associations than REGENIE and 22.71%

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