Expressive

rs10762199 - KRT19P4 - PBLD

Magnitude 2.2 · 3 studies on file

Reported associations

  • Multi-omic spatial effects on high-resolution AI-derived retinal thickness - Unknown journal (n.d.) · Unknown authors · PubMed 39904976

    ABSTRACT: Retinal thickness is a marker of retinal health and more broadly, is seen as a promising biomarker for many systemic diseases. Retinal thickness measurements are procured from optical coherence tomography (OCT) as part of routine clinical eyecare. We processed the UK Biobank OCT images using a convolutional neural network to produce fine-scale retinal thickness measurements across > 29,000 points in the macula, the part of the retina responsible for human central vision. The macula is disproportionately affected by high disease burden retinal disorders such as age-related macular degeneration and diabetic retinopathy, which both involve metabolic dysregulation. Analysis of common genomic variants, metabolomic, blood and immune biomarkers, disease PheCodes and genetic scores a

  • Deciphering tissue-specific protein regulation for insights into cardiometabolic disease - Unknown journal (n.d.) · Unknown authors · PubMed 41456820

    ABSTRACT: Understanding tissue-specific mechanisms of protein regulation gives crucial insights into cardiometabolic disease and informs drug discovery. Most proteomic studies have primarily concentrated on plasma, overlooking tissue-specific effects. Utilizing Olink technology, we assessed relative protein levels across plasma and tissue (aortic wall, mammary artery, liver, and skeletal muscle) from the STARNET cohort: 284 individuals with a high prevalence of coronary artery disease (CAD). We identified 608 cis protein quantitative trait loci (pQTLs), primarily in plasma, reflecting greater protein variability. Of 190 proteins with cis-pQTLs in non-plasma tissues, 50% also had plasma pQTLs, validating Olink technology in these tissues while reinforcing the relevance of plasma data for un

  • Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals - Unknown journal (n.d.) · Unknown authors · PubMed 35361970

    ABSTRACT: We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significan

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