Offsetting origin in crystalmaker5/4/2023 Probing the virtual proteome to identify novel disease biomarkers. A gene-based association method for mapping traits using reference transcriptome data. Integrative approaches for large-scale transcriptome-wide association studies. Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease. Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases. The UK Biobank resource with deep phenotyping and genomic data. The INTERVAL trial to determine whether intervals between blood donations can be safely and acceptably decreased to optimise blood supply: study protocol for a randomised controlled trial. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Finally, we develop a portal ( ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.īarbeira, A. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease for example, JAK–STAT signalling and coronary atherosclerosis. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank 3 to identify disease associations using a phenome-wide scan. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. Here we examine a large cohort (the INTERVAL study 2 n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175 Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing ( n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. ![]() But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics 1. ![]() The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. Nature volume 616, pages 123–131 ( 2023) Cite this article An atlas of genetic scores to predict multi-omic traits
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