rs12421799 - RIC8A

Magnitude 2.2 · 2 studies on file

Reported associations

• Genome-wide meta-analysis identifies novel risk loci for uterine fibroids within and across multiple ancestry groups - Unknown journal (n.d.) · Unknown authors · PubMed 40050615

  ABSTRACT: Uterine leiomyomata or fibroids are highly heritable, common, and benign tumors of the uterus with poorly understood etiology. Previous GWAS have reported 72 associated genes but included limited numbers of non-European individuals. Here, we identify 11 novel genes associated with fibroids across multi-ancestry and ancestry-stratified GWAS analyses. We replicate a known fibroid GWAS gene in African ancestry individuals and estimate the SNP-based heritability of fibroids in African ancestry populations as 15.9%. Using genetically predicted gene expression and colocalization analyses, we identify 46 novel genes associated with fibroids. These genes are significantly enriched in cancer, cell death and survival, reproductive system disease, and cellular growth and proliferation netwo

• Genomic and transcriptomic analyses of aortic stenosis enhance therapeutic target discovery and disease prediction - Unknown journal (n.d.) · Unknown authors · PubMed 41419686

  ABSTRACT: Aortic stenosis (AS) is a common valvular heart disease and has no pharmacological therapies. We performed a multi-ancestry genome-wide association meta-analysis of 86,864 AS cases among 2,853,408 individuals, discovering 241 autosomal independent risk loci and 3 X chromosome risk loci. We additionally performed sex-stratified and ancestry-stratified genome-wide association studies (GWASs), identifying an additional 5 sex-specific risk loci, 11 risk loci in European ancestry individuals and 1 risk locus in African ancestry individuals. We also performed a transcriptome-wide association study using expression quantitative trait loci from human aortic valves, discovering 54 new genes for which genetically predicted expression influences the risk of AS. We then generated a new polyg

Auto-generated from study metadata. AI-synthesised commentary is added when this entry is regenerated through content-service's LLM mode.