Expressive

rs10876069 - DIP2B

Magnitude 2.2 · 2 studies on file

Reported associations

  • 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%

  • Investigating the shared genetic architecture between adiposity measures and obesity-related cancers - Unknown journal (n.d.) · Unknown authors · PubMed 40874817

    ABSTRACT: Abstract Fat distribution patterns are increasingly linked to obesity-related cancers; however, their shared genetic determinants remain unclear. To identify shared genetic architecture between adiposity measures and obesity-related cancers. Utilizing large-scale summary statistics from genome-wide association study, we conducted genome-wide cross trait analyses of nine adiposity measures [body mass index (BMI), waist-to-hip (WTH) ratio, waist-to-hip ratio adjusted for BMI, arm fat ratio, trunk fat ratio, leg fat ratio, abdominal subcutaneous adipose tissue, gluteofemoral adipose tissue, and visceral adipose tissue] in five obesity-related cancers (colorectal cancer, esophageal adenocarcinoma, breast cancer, endometrial cancer, and ovarian cancer) to characterize their shared gen

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