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Funder: RCUK | Science and Technology Facilities Council (STFC); doi: https://doi.org/10.13039/501100000271, Funder: Scottish Government Health and Social Care Directorate (SGHSC); doi: https://doi.org/10.13039/100011529, Funder: Wellcome Trust (Wellcome); doi: https://doi.org/10.13039/100010269, Funder: NHS Blood and Transplant; doi: https://doi.org/10.13039/100009033, Funder: Health Data Research UK Department of Health and Social Care (England) Health and Social Care Research and Development Division (Welsh Government) Public Health Agency (Northern Ireland), Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4,312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases., Participants in the INTERVAL trial were recruited with the active collaboration of NHS Blood and Transplant (www.nhsbt.nhs.uk), which has supported fieldwork and other elements of the trial. DNA extraction and genotyping were co-funded by the National Institute for Health and Care Research (NIHR), the NIHR BioResource (http://bioresource.nihr.ac.uk) and the NIHR Cambridge Biomedical Research Centre (BRC) (no. BRC-1215-20014)*. Nightingale Health NMR assays were funded by the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). Metabolon Metabolomics assays were funded by the NIHR BioResource and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014)*. The academic coordinating centre for INTERVAL was supported by core funding from the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (no. NIHR BTRU-2014-10024), NIHR BTRU in Donor Health and Behaviour (NIHR203337), UK Medical Research Council (MRC) (no. MR/L003120/1), British Heart Foundation (nos SP/09/002, RG/13/13/30194 and RG/18/13/33946) and the NIHR Cambridge BRC (no. BRC-1215-20014)*. *The views expressed are those of the author(s) and not necessarily those of the NIHR, NHSBT or the Department of Health and Social Care. A complete list of the investigators and contributors to the INTERVAL trial is provided in ref. 36. The academic coordinating centre would like to thank blood donor centre staff and blood donors for participating in the INTERVAL trial. This work was supported by Health Data Research UK, which is funded by the UK MRC, Engineering and Physical Sciences Research Council (EPSRC), Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. The authors are grateful to UK Biobank for access to data to undertake this study (Projects #7439). This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/P020259/1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk). S.R. is funded by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). S.L. is supported by a Canadian Institutes of Health Research postdoctoral fellowship (MFE-171279). E.P. was funded by the EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart grant 116074 and the NIHR BTRU in Donor Health and Genomics (NIHR BTRU-2014-10024) and is funded by the NIHR BTRU in Donor Health and Behaviour (NIHR203337). J.D. holds a British Heart Foundation Professorship and a NIHR Senior Investigator Award. M.I. is supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). M.I. was also supported by the UK Economic and Social Research 878 Council (ES/T013192/1).

Background Polygenic risk scores (PRSs) can stratify populations into cardiovascular disease (CVD) risk groups. We aimed to quantify the potential advantage of adding information on PRSs to conventional risk factors in the primary prevention of CVD. Methods and findings Using data from UK Biobank on 306,654 individuals without a history of CVD and not on lipid-lowering treatments (mean age [SD]: 56.0 [8.0] years; females: 57%; median follow-up: 8.1 years), we calculated measures of risk discrimination and reclassification upon addition of PRSs to risk factors in a conventional risk prediction model (i.e., age, sex, systolic blood pressure, smoking status, history of diabetes, and total and high-density lipoprotein cholesterol). We then modelled the implications of initiating guideline-recommended statin therapy in a primary care setting using incidence rates from 2.1 million individuals from the Clinical Practice Research Datalink. The C-index, a measure of risk discrimination, was 0.710 (95% CI 0.703–0.717) for a CVD prediction model containing conventional risk predictors alone. Addition of information on PRSs increased the C-index by 0.012 (95% CI 0.009–0.015), and resulted in continuous net reclassification improvements of about 10% and 12% in cases and non-cases, respectively. If a PRS were assessed in the entire UK primary care population aged 40–75 years, assuming that statin therapy would be initiated in accordance with the UK National Institute for Health and Care Excellence guidelines (i.e., for persons with a predicted risk of ≥10% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), then it could help prevent 1 additional CVD event for approximately every 5,750 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5% to, Luanluan Sun and colleagues investigate whether adding polygenic risk scores to conventional risk factors of cardiovascular disease helps predict disease risk., Author summary Why was this study done? Application of polygenic risk scores (PRSs) has opened opportunities to enhance risk stratification and prevention for common diseases. The clinical utility of PRSs in cardiovascular disease (CVD) risk prediction is, however, uncertain. Previous analyses have generally focused only on coronary heart disease (CHD) rather than the composite outcome of CHD and stroke, and have often lacked modelling of clinical implications of initiating guideline-recommended interventions (e.g., statin therapy). What did the researchers do and find? We quantified the incremental predictive gain with PRSs on top of conventional risk factors using data on 306,654 individuals from UK Biobank. We modelled the population health implications of initiating statin therapy as recommended by current guidelines using data from 2.1 million individuals from the Clinical Practice Research Datalink. Addition of information on PRSs to a conventional risk prediction model increased the C-index (a measure of risk discrimination) and improved risk classification of cases and non-cases. We estimated that targeted assessment of PRSs among people at intermediate (i.e., 5% to

Chronic immune-mediated diseases of adulthood often originate in early childhood. To investigate genetic associations between neonatal immunity and disease, we map expression quantitative trait loci (eQTLs) in resting myeloid cells and CD4+ T cells from cord blood samples, as well as in response to lipopolysaccharide (LPS) or phytohemagglutinin (PHA) stimulation, respectively. Cis-eQTLs are largely specific to cell type or stimulation, and 31% and 52% of genes with cis-eQTLs have response eQTLs (reQTLs) in myeloid cells and T cells, respectively. We identified cis regulatory factors acting as mediators of trans effects. There is extensive colocalisation between condition-specific neonatal cis-eQTLs and variants associated with immune-mediated diseases, in particular CTSH had widespread colocalisation across diseases. Mendelian randomisation shows causal neonatal gene expression effects on disease risk for BTN3A2, HLA-C and others. Our study elucidates the genetics of gene expression in neonatal immune cells, and aetiological origins of autoimmune and allergic diseases., Some immune-mediated diseases may originate in early childhood. The authors mapped eQTLs and response eQTLs to various stimuli in neonatal myeloid cells and T cells, and revealed their potential role in immune-mediated diseases using colocalisation and Mendelian randomisation.

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; 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. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. 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 Biobank3 to identify disease associations using a phenome-wide scan. 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. Finally, we develop a portal ( https://www.omicspred.org/ ) 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.

Funder: S.C.R was funded by the National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre (BRC) (BRC-1215-20014) and by the British Heart Foundation (BHF) programme grant (RG/18/13/33946)., Funder: Praveen Surendran was supported by a Rutherford Fund Fellowship from the Medical Research Council grant MR/S003746/1, Funder: Savita Karthikeyan was funded by a British Heart Foundation Programme Grant (RG/18/13/33946), Funder: Samuel A. Lambert was supported by a Canadian Institutes of Health Research postdoctoral fellowship (MFE-171279), Funder: Thomas Bolton was funded by the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024), Funder: Lisa Pennells was funded by a British Heart Foundation Programme Grant (RG/18/13/33946), Funder: John Danesh holds a British Heart Foundation Professorship and a NIHR Senior Investigator Award., Funder: Michael Inouye is supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014), and was also supported by the UK Economic and Social Research 878 Council (ES/T013192/1)., Metabolic biomarker data quantified by nuclear magnetic resonance (NMR) spectroscopy in approximately 121,000 UK Biobank participants has recently been released as a community resource, comprising absolute concentrations and ratios of 249 circulating metabolites, lipids, and lipoprotein sub-fractions. Here we identify and characterise additional sources of unwanted technical variation influencing individual biomarkers in the data available to download from UK Biobank. These included sample preparation time, shipping plate well, spectrometer batch effects, drift over time within spectrometer, and outlier shipping plates. We developed a procedure for removing this unwanted technical variation, and demonstrate that it increases signal for genetic and epidemiological studies of the NMR metabolic biomarker data in UK Biobank. We subsequently developed an R package, ukbnmr, which we make available to the wider research community to enhance the utility of the UK Biobank NMR metabolic biomarker data and to facilitate rapid analysis.

High frequency of depressed mood increases the risk of future cardiometabolic disease above lifestyle factors and genetic susceptibility, raising new questions about risk management in individuals susceptible to depression.

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1–3. Polygenic scores (PGS) aggregate these into a metric representing an individual’s genetic predisposition to disease. PGS have shown promise for early risk prediction4–7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus., Ritchie et al. use polygenic scores to identify plasma proteins with causal roles in cardiometabolic disease.

Polygenic [risk] scores (PGS) can enhance prediction and understanding of common diseases and traits. However, the reproducibility of PGS and their subsequent applications in biological and clinical research have been hindered by several factors, including: inadequate and incomplete reporting of PGS development, heterogeneity in evaluation techniques, and inconsistent access to, and distribution of, the information necessary to calculate the scores themselves. To address this we present the PGS Catalog (www.PGSCatalog.org), an open resource for polygenic scores. The PGS Catalog currently contains 238 published PGS and data from 92 publications for 113 traits, including diabetes, cardiovascular diseases, neurological disorders, cancers, body mass index and blood lipids. Full scoring information for each PGS is available, along with consistently curated metadata required for reproducibility as well as accurate application in independent studies. Using the PGS Catalog, we demonstrate that multiple PGS can be systematically evaluated to generate comparable performance metrics. The PGS Catalog has capabilities for user deposition, expert curation and programmatic access, thus providing the community with an open platform for polygenic score dissemination, research and translation.