rs10761598 - ARID5B

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%

• Genetic Insights into Head-to-Body Ratios Via Deep Learning-Based Image Segmentation and Implications for Common Diseases - Unknown journal (n.d.) · Unknown authors · PubMed 41444482

  ABSTRACT: Head-to-body ratios (HBRs) are important anthropometric traits with direct relevance to human growth, development, and disease risk. However, the role of the proportions between head and body remains understudied, with the genetic basis of HBRs remaining largely unexplored. By applying deep learning models to 38,202 whole-body dual-energy X-ray absorptiometry images from the UK Biobank, we generated 10 distinct HBR phenotypes based on head (length/width) and various body dimensions. Our genome-wide association analyses identify 245 significant loci, with SNP-based heritability estimates ranging from 25% to 43%. Functional annotations show that genes prioritized for HBRs are enriched in chondrocytes in skeletal tissues and oligodendrocytes across multiple brain regions. Polygenic

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