Expressive

rs12401997 - SEMA4A

Magnitude 2.2 · 3 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%

  • Genome-wide association studies and cross-population meta-analyses investigating short and long sleep duration - Unknown journal (n.d.) · Unknown authors · PubMed 37770476

    ABSTRACT: Sleep duration has been linked to a wide range of negative health outcomes and to reduced life expectancy. We present genome-wide association studies of short ( ≤ 5 h) and long ( ≥ 10 h) sleep duration in adults of European (N = 445,966), African (N = 27,785), East Asian (N = 3141), and admixed-American (N = 16,250) ancestry from UK Biobank and the Million Veteran Programme. In a cross-population meta-analysis, we identify 84 independent loci for short sleep and 1 for long sleep. We estimate SNP-based heritability for both sleep traits in each ancestry based on population derived linkage disequilibrium (LD) scores using cov-LDSC. We identify positive genetic correlation between short and long sleep traits (rg = 0.16 ± 0.04; p = 0.0002), as

  • Genome-Wide Association and Mendelian Randomization Analysis Reveal the Causal Relationship Between White Blood Cell Subtypes and Asthma in Africans - Unknown journal (n.d.) · Unknown authors · PubMed 34925446

    ABSTRACT: Background: White blood cell (WBC) traits and their subtypes such as basophil count (Bas), eosinophil count (Eos), lymphocyte count (Lym), monocyte count (Mon), and neutrophil counts (Neu) are known to be associated with diseases such as stroke, peripheral arterial disease, and coronary heart disease. Methods: We meta-analyze summary statistics from genome-wide association studies in 17,802 participants from the African Partnership for Chronic Disease Research (APCDR) and African ancestry individuals from the Blood Cell Consortium (BCX2) using GWAMA. We further carried out a Bayesian fine mapping to identify causal variants driving the association with WBC subtypes. To access the causal relationship between WBC subtypes and asthma, we conducted a two-sample Mendelian randomizatio

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