rs10965230 - CDKN2B-AS1

Magnitude 2.2 · 2 studies on file

Reported associations

• Genome‐Wide Assessment of Shared Genetic Architecture Between Rheumatoid Arthritis and Cardiovascular Diseases - Unknown journal (n.d.) · Unknown authors · PubMed 37947095

  ABSTRACT: Background Patients with rheumatoid arthritis (RA) have a 2‐ to 10‐fold increased risk of cardiovascular disease (CVD), but the biological mechanisms and existence of causality underlying such associations remain to be investigated. We aimed to investigate the genetic associations and underlying mechanisms between RA and CVD by leveraging large‐scale genomic data and genetic cross‐trait analytic approaches. Methods and Results Within UK Biobank data, we examined the genetic correlation, shared genetics, and potential causality between RA (Ncases=6754, Ncontrols=452 384) and cardiovascular diseases (CVD, Ncases=44 238, Ncontrols=414 900) using linkage disequilibrium score regression, cross‐trait meta‐analysis, and Mendelian randomization. We observed significant

• Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology - Unknown journal (n.d.) · Unknown authors · PubMed 34077760

  ABSTRACT: Summary Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; p ≤ 5 × 10−8) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 93 novel loci, significantl

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