rs10954732 - POR

Magnitude 4.5 · 2 studies on file

Reported associations

  • Genome-wide meta-analyses of cross substance use disorders in diverse populations. - Molecular psychiatry (2026) · Lai D, Zhang M, Green N, Abreu M, Schwantes-An TH, Parker CC, Zhang S, Jin F, Sun A, Zhang P, Edenberg HJ, Liu Y, Foroud T · PubMed 41057643

    Substance use disorders (SUDs, including alcohol, cannabis, opioids, and tobacco) represent significant public health challenges. The estimated heritability of SUDs is ~50% and many individuals experience multiple SUDs concurrently. Studies have demonstrated the existence of genes shared across multiple SUDs, and identifying these SUD-shared genes is critical to developing novel prevention and treatment strategies. Here, we conducted the largest cross SUD meta-analysis to date to identify SUD-shared genes using samples genetically similar to 1000 Genomes Project European (1kg-EUR-like), African (1kg-AFR-like), and American mixed (1kg-AMR-like) populations. We defined variants that had the same direction of effects across different SUDs (i.e., concordant variants) as SUD-shared. In total, w

  • A genetic map of human metabolism across the allele frequency spectrum. - Nature genetics (2025) · Zoodsma M, Beuchel C, Yasmeen S, Kohleick L, Nepal A, Koprulu M, Kronenberg F, Mayr M, Williamson A, Pietzner M, Langenberg C · PubMed 41044249

    Genetic studies of human metabolism have been limited in scale and allelic breadth. Here we provide a data-driven map of the genetic regulation of circulating small molecules and lipoprotein characteristics (249 traits) measured using proton nuclear magnetic resonance spectroscopy across the allele frequency spectrum in ~450,000 individuals. Trans-ancestral meta-analyses identify 29,824 locus-metabolite associations mapping to 753 regions with effects largely consistent between men and women and large ancestral groups represented in UK Biobank. We observe and classify extreme genetic pleiotropy, identify regulators of lipid metabolism, and assign effector genes at >100 loci through rare-to-common allelic series. We propose roles for genes less established in metabolic control (for example,


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Lifestyle context

Concrete actions anchored to the cited research. We do not prescribe, we describe.

Discuss with your doctor

  • Substance use disorder genetic risk assessment High

    GWAS identifies strong genetic association with substance use disorder (p=9e-10, n=1.7M)

Screening

  • Lipid panel including HDL subclasses High

    GWAS identifies association with elevated triglycerides in medium HDL (p=1e-18, n=450k)

    Periodic lipid panel screening