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Metabolite levels measured in the human population are endophenotypes for biological processes. We combined sequencing data for 3,924 (whole-exome sequencing, WES, discovery) and 2,805 (whole-genome sequencing, WGS, replication) donors from a prospective cohort of blood donors in England. We used multiple approaches to select and aggregate rare genetic variants (minor allele frequency [MAF] 0.1%) in protein-coding regions and tested their associations with 995 metabolites measured in plasma by using ultra-high-performance liquid chromatography-tandem mass spectrometry. We identified 40 novel associations implicating rare coding variants (27 genes and 38 metabolites), of which 28 (15 genes and 28 metabolites) were replicated. We developed algorithms to prioritize putative driver variants at each locus and used mediation and Mendelian randomization analyses to test directionality at associations of metabolite and protein levels at the ACY1 locus. Overall, 66% of reported associations implicate gene targets of approved drugs or bioactive drug-like compounds, contributing to drug targets' validating efforts.

Sickle cell disease (SCD) remains a major health burden with limited treatment options. Despite promising gene-editing clinical trials, there is an unmet need for cost-effective therapies. As induction of fetal hemoglobin (HbF) is an established therapeutic strategy for SCD, we conducted a genome-wide association study of circulating HbF levels in ~11,000 participants to identify further HbF modulators. We identified associations in 11 genomic regions, including eight novel loci such asABCC1(encoding multidrug resistance-associated protein 1, MRP1). Using gene-editing and pharmacological approaches, we showed that inhibition of MRP1 increases HbF, intracellular glutathione levels, and reduces sickling in erythroid cells from SCD patients. Overall, our findings identify several novel genetically-validated potential therapeutic targets for SCD, including promising proof-of-principle results from small molecule inhibition of MRP1.

BackgroundObservational studies have shown that women with an early menopause are at higher risk of stroke compared to women with a later menopause. However, associations with stroke subtypes are inconsistent and the causality is unclear. Therefore, we conducted a large-scale analysis to investigate the observational association between age at menopause and different types of stroke accompanied by a Mendelian Randomization analysis to evaluate causality.MethodsWe analyzed data of the UK Biobank and EPIC-CVD study. Postmenopausal women without a history of stroke at baseline were eligible for inclusion. The study endpoints were total stroke and stroke subtypes (i.e., ischemic stroke, hemorrhagic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage). We investigated the observational association between age at menopause and risk of stroke using Cox-regression analysis in each study separately before combining effect sizes using random-effects meta-analysis. Cox-regression analyses were progressively adjusted for (1) age, (2) smoking status, body mass index, glycated hemoglobin, total cholesterol, and hypertension, and (3) ever use of hormone replacement therapy and age at menarche. We used two-sample Mendelian Randomization analysis to study whether there is a causal relationship between genetically proxied age at menopause and risk of stroke.ResultsA total of 204,244 women were included (7,883 from EPIC-CVD [5,292 from the sub-cohort]; 196,361 from the UK Biobank). Pooled mean baseline age was 58.9 years (SD 5.8) and pooled mean age at menopause was 47.8 years (SD 6.2). Natural menopause occurred in 77.6% of all women. Over a median follow-up of 12.6 years (IQR 11.8, 13.3), 6,770 women experienced a stroke. In multivariable adjusted observational analyses, the pooled hazard ratios per five years younger age at menopause were 1.09 (95% CI: 1.07, 1.12) for stroke, 1.09 (1.06, 1.13) for ischemic stroke, 1.10 (1.04, 1.16) for hemorrhagic stroke, 1.14 (1.08, 1.20) for intracerebral hemorrhage, and 1.00 (0.84, 1.20) for subarachnoid hemorrhage. The Mendelian Randomization analysis found no evidence for a causal relationship between genetically proxied age at menopause and risk of any type of stroke.ConclusionsEarlier age at menopause is associated with, but not causally related to the risk of stroke.Clinical PerspectiveWhat is new?This analysis involves over 200,000 postmenopausal women and more than 6,000 incident stroke cases and investigates the observational association between age at menopause and various subtypes of stroke. Furthermore, a Mendelian Randomization analysis was conducted to study whether associations are causal or not.Earlier age at menopause was statistically significantly associated with a higher risk of stroke and its subtypes ischemic stroke, hemorrhagic stroke, and intracerebral hemorrhage. We found no statistically significant relationship between earlier or later age at menopause and risk of subarachnoid hemorrhage.The Mendelian Randomization analysis suggested no causal effect of genetically proxied age at menopause and risk of any type of stroke.What are the clinical implications?Women with earlier age at menopause are at higher risk of stroke. The underlying reasons need to be further investigated.Our analysis suggested that earlier menopauseper sedoes not cause stroke. For prevention and adequate treatment of stroke in women, a better understanding of the specific role of menopause and the mechanistic background that leads to higher risk of stroke is needed.

Aims This study aimed to evaluate the association between physical activity and the incidence of coronary heart disease (CHD) in individuals with and without CHD risk factors. Methods and results EPIC-CVD is a case-cohort study of 29 333 participants that included 13 582 incident CHD cases and a randomly selected sub-cohort nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Self-reported physical activity was summarized using the Cambridge physical activity index (inactive, moderately inactive, moderately active, and active). Participants were categorized into sub-groups based on the presence or the absence of the following risk factors: obesity (body mass index ≥30 kg/m2), hypercholesterolaemia (total cholesterol ≥6.2 mmol/L), history of diabetes, hypertension (self-reported or ≥140/90 mmHg), and current smoking. Prentice-weighted Cox regression was used to assess the association between physical activity and incident CHD events (non-fatal and fatal). Compared to inactive participants without the respective CHD risk factor (referent), excess CHD risk was highest in physically inactive and lowest in moderately active participants with CHD risk factors. Corresponding excess CHD risk estimates amongst those with obesity were 47% [95% confidence interval (CI) 32–64%] and 21% (95%CI 2–44%), with hypercholesterolaemia were 80% (95%CI 55–108%) and 48% (95%CI 22–81%), with hypertension were 80% (95%CI 65–96%) and 49% (95%CI 28–74%), with diabetes were 142% (95%CI 63–260%), and 100% (95%CI 32–204%), and amongst smokers were 152% (95%CI 122–186%) and 109% (95%CI 74–150%). Conclusions In people with CHD risk factors, moderate physical activity, equivalent to 40 mins of walking per day, attenuates but does not completely offset CHD risk.

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.

Background: Vasovagal reactions (VVRs) are the most common acute complications of blood donation. Responsible for substantial morbidity, they also reduce the likelihood of repeated donations, and are disruptive and costly for blood services. Although blood establishments worldwide have adopted different strategies to prevent VVRs (including water loading and applied muscle tension [AMT]), robust evidence is limited. The Strategies to Improve Donor Experiences (STRIDES) trial aims to reliably assess the impact of four different interventions to prevent VVRs among blood donors. Methods: STRIDES is a cluster-randomised cross-over/stepped-wedge factorial trial of four interventions to reduce VVRs involving about 1.4 million whole blood donors enrolled from all 73 blood donation sites (mobile teams and donor centres) of National Health Service Blood and Transplant (NHSBT) in England. Each site (“cluster”) has been randomly allocated to receive one or more interventions during a 36-month period, using principles of cross-over, stepped-wedge and factorial trial design to assign the sequence of interventions. Each of the four interventions are compared to NHSBT’s current practices: (i) 500ml isotonic drink before donation vs current 500ml plain water; (ii) 3-minutes rest on donation chair after donation vs current 2 minutes; (iii) new modified AMT vs current practice of AMT; and (iv) psychosocial intervention using preparatory materials vs current practice of nothing. The primary outcome is the number of in-session VVRs with loss of consciousness (i.e., episodes involving loss of consciousness of any duration, with or without additional complications). Secondary outcomes include all in-session VVRs (i.e., with and without loss of consciousness) and delayed VVRs (i.e., those occurring after leaving the venue). Discussion: The STRIDES trial should yield novel information about interventions, singly and in combination, for the prevention of VVRs, with the aim of generating policy-shaping evidence to help inform blood services to improve donor health, donor experience, and service efficiency. Trial registration: ISRCTN: 10412338

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).

We thank all EPIC participants and staff for their contribution to the study. We also thank staff from the EPIC-CVD coordinating centres for sample preparation and data handling. This research has been conducted using the UK Biobank Resource (application number 29916). Data on coronary artery disease have been contributed by CARDIoGRAMplusC4D investigators and have been downloaded from www.CARDIOGRAMPLUSC4D.ORG.I Sluijswas supported by a personal Dr. Dekker postdoctoral grant (2015T019) from the Netherlands Heart Foundation. NGF and FI acknowledge core Medical Research Council Epidemiology Unit support (MC_UU_12015/5) and NGF acknowledges NIHR Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme (IS-BRC-1215-20014). The InterAct project was funded by the EU FP6 programme (grant number LSHM_CT_2006_037197) and provided the biomarker data in the sub-cohort that was used in the current study. These analyses were supported by Cancer Research UK (C8221/A19170). The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF), Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS) PI13/00061 (EPIC-Granada) and PI13/01162 (EPIC-Murcia), Regional Governments of Andalucia, Asturias, Basque Country, Murcia and Navarra, ISCIII Health Research Funds RD12/0036/0018 (cofounded by FEDER funds/European Regional Development Fund ERDF) (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skane and Vasterbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 for EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (UK). EPIC-CVD has been supported by the European Commission Framework Programme 7 (HEALTH-F2-2012-279233), the European Research Council (268834), the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG13/13/30194 and RG/18/13/33946) and the National Institute for Health Research (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust). The MEGASTROKE project received funding from sources specified at http://www.megastroke.org/acknowledgments.html. L.E.T.V.analysed the data and drafted the manuscript. L. E. T. V., I. S. and Y. T. vdS. had access to all data for this study. L. E. T. V., I. S., Y. T. vdS., S. B., N. G. F., H. F., F. I., T. K. N., F. R., E. W., K. A., C. D., A. P. C., M. B. S., T. Y. N. T. and A. S. B. contributed to study conception, design and interpretation of data. All authors contributed to critical revision of the manuscript and approval of version to be published., Higher milk intake has been associated with a lower stroke risk, but not with risk of CHD. Residual confounding or reverse causation cannot be excluded. Therefore, we estimated the causal association of milk consumption with stroke and CHD risk through instrumental variable (IV) and gene-outcome analyses. IV analysis included 29 328 participants (4611 stroke; 9828 CHD) of the European Prospective Investigation into Cancer and Nutrition (EPIC)-CVD (eight European countries) and European Prospective Investigation into Cancer and Nutrition-Netherlands (EPIC-NL) case-cohort studies. rs4988235, a lactase persistence (LP) SNP which enables digestion of lactose in adulthood was used as genetic instrument. Intake of milk was first regressed on rs4988235 in a linear regression model. Next, associations of genetically predicted milk consumption with stroke and CHD were estimated using Prentice-weighted Cox regression. Gene-outcome analysis included 777 024 participants (50 804 cases) from MEGASTROKE (including EPIC-CVD), UK Biobank and EPIC-NL for stroke, and 483 966 participants (61 612 cases) from CARDIoGRAM, UK Biobank, EPIC-CVD and EPIC-NL for CHD. In IV analyses, each additional LP allele was associated with a higher intake of milk in EPIC-CVD (beta = 13 center dot 7 g/d; 95 % CI 8 center dot 4, 19 center dot 1) and EPIC-NL (36 center dot 8 g/d; 95 % CI 20 center dot 0, 53 center dot 5). Genetically predicted milk intake was not associated with stroke (HR per 25 g/d 1 center dot 05; 95 % CI 0 center dot 94, 1 center dot 16) or CHD (1 center dot 02; 95 % CI 0 center dot 96, 1 center dot 08). In gene-outcome analyses, there was no association of rs4988235 with risk of stroke (OR 1 center dot 02; 95 % CI 0 center dot 99, 1 center dot 05) or CHD (OR 0 center dot 99; 95 % CI 0 center dot 95, 1 center dot 03). Current Mendelian randomisation analysis does not provide evidence for a causal inverse relationship between milk consumption and stroke or CHD risk., Netherlands Heart Foundation 2015T019, UK Research & Innovation (UKRI) Medical Research Council UK (MRC), European Commission MC_UU_12015/5, NIHR Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme IS-BRC-1215-20014, European Commission LSHM_CT_2006_037197, Cancer Research UK C8221/A19170, European Commission European Commission Joint Research Centre, International Agency for Research on Cancer, Danish Cancer Society, Deutsche Krebshilfe German Cancer Research Center (DKFZ) (Germany), Federal Ministry of Education & Research (BMBF), Deutsche Krebshilfe Deutsches Krebsforschungszentrum (Germany), Fondazione AIRC per la ricerca sul cancro Consiglio Nazionale delle Ricerche (CNR), Netherlands Government Netherlands Government, World Cancer Research Fund International (WCRF), Netherlands Government, Instituto de Salud Carlos III PI13/00061 PI13/01162, Junta de Andalucia Regional Government of Asturias (Spain) Regional Government of Basque Country (Spain) Regional Government of Murcia (Spain) Regional Government of Navarra (Spain), ISCIII Health Research Funds (FEDER funds/European Regional Development Fund ERDF) (Spain) RD12/0036/0018, Swedish Cancer Society Swedish Research Council County Council of Skane (Sweden) County Council of Vasterbotten (Sweden), European Commission Framework Programme 7 HEALTH-F2-2012-279233 European Research Council (ERC) European Commission 268834, UK Research & Innovation (UKRI) Medical Research Council UK (MRC) MR/L003120/1 British Heart Foundation RG13/13/30194 RG/18/13/33946 National Institute for Health Research (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust) UK Research & Innovation (UKRI) Medical Research Council UK (MRC) 1000143 MR/M012190/1 Cancer Research UK C8221/A19170 14136 C570/A16491

Aims: This study aims to develop the first race‐specific and sex‐specific risk prediction models for heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF). Methods and results: We created a cohort of 1.8 million individuals who had an outpatient clinic visit between 2002 and 2007 within the Veterans Affairs (VA) Healthcare System and obtained information on HFpEF, HFrEF, and several risk factors from electronic health records (EHR). Variables were selected for the risk prediction models in a ‘derivation cohort’ that consisted of individuals with baseline date in 2002, 2003, or 2004 using a forward stepwise selection based on a change in C‐index threshold. Discrimination and calibration were assessed in the remaining participants (internal ‘validation cohort’). A total of 66 831 individuals developed HFpEF, and 92 233 developed HFrEF (52 679 and 71 463 in the derivation cohort) over a median of 11.1 years of follow‐up. The HFpEF risk prediction model included age, diabetes, BMI, COPD, previous MI, antihypertensive treatment, SBP, smoking status, atrial fibrillation, and estimated glomerular filtration rate (eGFR), while the HFrEF model additionally included previous CAD. For the HFpEF model, C‐indices were 0.74 (SE = 0.002) for white men, 0.76 (0.005) for black men, 0.79 (0.015) for white women, and 0.77 (0.026) for black women, compared with 0.72 (0.002), 0.72 (0.004), 0.77 (0.017), and 0.75 (0.028), respectively, for the HFrEF model. These risk prediction models were generally well calibrated in each race‐specific and sex‐specific stratum of the validation cohort. Conclusions: Our race‐specific and sex‐specific risk prediction models, which used easily obtainable clinical variables, can be a useful tool to implement preventive strategies or subtype‐specific prevention trials in the nine million users of the VA healthcare system and the general population after external validation.

Background: A key goal of precision medicine is to disaggregate common, complex diseases into discrete molecular subtypes. Rare coding variants in the low-density lipoprotein receptor gene ( LDLR ) are identified in 1% to 2% of coronary artery disease (CAD) patients, defining a molecular subtype with risk driven by hypercholesterolemia. Methods: To search for additional subtypes, we compared the frequency of rare, predicted loss-of-function and damaging missense variants aggregated within a given gene in 41 081 CAD cases versus 217 115 controls. Results: Rare variants in LDLR were most strongly associated with CAD, present in 1% of cases and associated with 4.4-fold increased CAD risk. A second subtype was characterized by variants in endothelial nitric oxide synthase gene ( NOS3 ), a key enzyme regulating vascular tone, endothelial function, and platelet aggregation. A rare predicted loss-of-function or damaging missense variants in NOS3 was present in 0.6% of cases and associated with 2.42-fold increased risk of CAD (95% CI, 1.80–3.26; P =5.50×10 −9 ). These variants were associated with higher systolic blood pressure (+3.25 mm Hg; [95% CI, 1.86–4.65]; P =5.00×10 −6 ) and increased risk of hypertension (adjusted odds ratio 1.31; [95% CI, 1.14–1.51]; P =2.00×10 −4 ) but not circulating cholesterol concentrations, suggesting that, beyond lipid pathways, nitric oxide synthesis is a key nonlipid driver of CAD risk. Conclusions: Beyond LDLR , we identified an additional nonlipid molecular subtype of CAD characterized by rare variants in the NOS3 gene.

We present the Polygenic Score (PGS) Catalog ( https://www.PGSCatalog.org ), an open resource of published scores (including variants, alleles and weights) and consistently curated metadata required for reproducibility and independent applications. The PGS Catalog has capabilities for user deposition, expert curation and programmatic access, thus providing the community with a platform for PGS dissemination, research and translation.

In cross-platform analyses of 174 metabolites, we identify 499 associations (P

Genetically predicted levels of multi-omic traits can uncover the molecular underpinnings of common phenotypes in a highly efficient manner. Here, we utilised a large cohort (INTERVAL; 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). We used machine learning to train genetic scores for 17,227 molecular traits, including 10,521 which reached Bonferroni-adjusted significance. We evaluated genetic score performances in external validation across European, Asian and African American ancestries, and assessed their longitudinal stability within diverse individuals. We demonstrated 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 UK Biobank to identify disease associations using a phenome-wide scan. Finally, we developed a portal (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.

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in the Trans-Omics for Precision Medicine (TOPMed) program ( n = 63,302) and UK Biobank ( n = 47,080). Bidirectional Mendelian randomization studies were consistent with longer genetically imputed LTL increasing propensity to develop CHIP, but CHIP then, in turn, hastens to shorten measured LTL (mLTL). We also demonstrated evidence of modest mediation between CHIP and CAD by mLTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.

Hypertension is a leading cause of premature death affecting more than a billion individuals worldwide. Here we report on the genetic determinants of blood pressure (BP) traits (systolic, diastolic, and pulse pressure) in the largest single-stage genome-wide analysis to date (N = 1,028,980 European-descent individuals). We identified 2,103 independent genetic signals (P − 8) for BP traits, including 113 novel loci. These associations explain ~ 40% of common SNP heritability of systolic and diastolic BP. Comparison of top versus bottom deciles of polygenic risk scores (PRS) based on these results reveal clinically meaningful differences in BP (12.9 mm Hg for systolic BP, 95% CI 11.5–14.2 mm Hg, p = 9.08×10− 73) and hypertension risk (OR 5.41; 95% CI 4.12 to 7.10; P = 9.71×10− 33) in an independent dataset. Compared with the area under the curve (AUC) for hypertension discrimination for a model with sex, age, BMI, and genetic ancestry, adding systolic and diastolic BP PRS increased discrimination from 0.791 (95% CI = 0.781–0.801) to 0.814 (95% CI = 0.805–0.824, ∆AUC = 0.023, P = 2.27x10− 22). Our transcriptome-wide association study detected 2,793 BP colocalized associations with genetically-predicted expression of 1,070 genes in five cardiovascular tissues, of which 500 are previously unreported for BP traits. These findings represent an advance in our understanding of hypertension and highlight the role of increasingly large genomic studies for development of more accurate PRS, which may inform precision health research.

BACKGROUND: Genome-wide polygenic scores (GPS) integrate information from many common DNA variants into a single number. Because rates of coronary artery disease (CAD) are substantially higher among South Asians, a GPS to identify high-risk individuals may be particularly useful in this population. OBJECTIVES: This analysis used summary statistics from a prior genome-wide association study to derive a new GPSCAD for South Asians. METHODS: This GPSCAD was validated in 7,244 South Asian UK Biobank participants and tested in 491 individuals from a case-control study in Bangladesh. Next, a static ancestry and GPSCAD reference distribution was built using whole-genome sequencing from 1,522 Indian individuals, and a framework was tested for projecting individuals onto this static ancestry and GPSCAD reference distribution using 1,800 CAD cases and 1,163 control subjects newly recruited in India. RESULTS: The GPSCAD, containing 6,630,150 common DNA variants, had an odds ratio (OR) per SD of 1.58 in South Asian UK Biobank participants and 1.60 in the Bangladeshi study (p

Funder: The Healthcare Improvement Studies Institute, IMPORTANCE: A lack of internationally agreed standards for combining available data sources at scale risks inconsistent disease phenotyping limiting research reproducibility. OBJECTIVE: To develop and then evaluate if a rules-based algorithm can identify coronary artery disease (CAD) sub-phenotypes using electronic health records (EHR) and questionnaire data from UK Biobank (UKB). DESIGN: Case-control and cohort study. SETTING: Prospective cohort study of 502K individuals aged 40-69 years recruited between 2006-2010 into the UK Biobank with linked hospitalization and mortality data and genotyping. PARTICIPANTS: We included all individuals for phenotyping into 6 predefined CAD phenotypes using hospital admission and procedure codes, mortality records and baseline survey data. Of these, 408,470 unrelated individuals of European descent had a polygenic risk score (PRS) for CAD estimated. EXPOSURE: CAD Phenotypes. MAIN OUTCOMES AND MEASURES: Association with baseline risk factors, mortality (n = 14,419 over 7.8 years median f/u), and a PRS for CAD. RESULTS: The algorithm classified individuals with CAD into prevalent MI (n = 4,900); incident MI (n = 4,621), prevalent CAD without MI (n = 10,910), incident CAD without MI (n = 8,668), prevalent self-reported MI (n = 2,754); prevalent self-reported CAD without MI (n = 5,623), yielding 37,476 individuals with any type of CAD. Risk factors were similar across the six CAD phenotypes, except for fewer men in the self-reported CAD without MI group (46.7% v 70.1% for the overall group). In age- and sex- adjusted survival analyses, mortality was highest following incident MI (HR 6.66, 95% CI 6.07-7.31) and lowest for prevalent self-reported CAD without MI at baseline (HR 1.31, 95% CI 1.15-1.50) compared to disease-free controls. There were similar graded associations across the six phenotypes per SD increase in PRS, with the strongest association for prevalent MI (OR 1.50, 95% CI 1.46-1.55) and the weakest for prevalent self-reported CAD without MI (OR 1.08, 95% CI 1.05-1.12). The algorithm is available in the open phenotype HDR UK phenotype library (https://portal.caliberresearch.org/). CONCLUSIONS: An algorithmic, EHR-based approach distinguished six phenotypes of CAD with distinct survival and PRS associations, supporting adoption of open approaches to help standardize CAD phenotyping and its wider potential value for reproducible research in other conditions.

Proteins are the effector molecules of biology and are the target of most drugs. To identify proteins and related pathways that may play a causal role in stroke pathogenesis, we used Mendelian randomisation (MR). We tested potential causal effects of 308 plasma proteins (measured in 4,994 blood donors from the INTERVAL study) on stroke outcomes (derived from the MEGASTROKE GWAS) in a two-sample MR framework and assessed whether these associations could be mediated by cardiovascular risk factors. We extended the analysis to identify whether pharmacological targeting of these proteins might have potential adverse side-effects or beneficial effects for other conditions through Phenome-wide MR (Phe-MR) in UK Biobank. MR showed an association between stroke and genetically predicted plasma levels of TFPI, IL6RA, MMP12, CD40, TMPRSS5 and CD6 (P≤1.62⋅10−4). We identified six risk factors (atrial fibrillation, body mass index, smoking, blood pressure, white matter hyperintensities and type 2 diabetes) that were associated with stroke (P≤0.0071) using MR. The association of TFPI, IL6RA and TMPRSS5 with stroke could be mediated by these risk factors, such as body mass index, white matter hyperintensity and atrial fibrillation. Thirty-six additional proteins were potentially causal for one or more of these risk factors. The Phe-MR suggested that targeting TFPI could have potential beneficial effects on other disorders of arteries and hyperlipidaemia in addition to stroke. Our results highlight novel causal pathways and potential therapeutic targets for stroke.

Background Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. Methods In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. Findings Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. Interpretation Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. Funding British Heart Foundation Data Science Centre, led by Health Data Research UK.

Fructosamine is a measure of short-term glycemic control, which has been suggested as a useful complement to glycated hemoglobin (HbA1c) for the diagnosis and monitoring of diabetes. To date, a single genome-wide association study (GWAS) including 8,951 US White and 2,712 US Black individuals without a diabetes diagnosis has been published. Results in Whites and Blacks yielded different association loci, near RCN3 and CNTN5, respectively. Here we performed a GWAS on 20,731 European ancestry blood donors, and meta-analysed our results with previous data from US White participants from The Atherosclerosis Risk in Communities (ARIC) study (Nmeta=29,685). We identified a novel association near GCK (rs3757840, betameta=0.0062, MAF=0.49, pmeta=3.66x10-08) and confirmed the association near RCN3 (rs113886122, betameta=0.0134, MAF=0.17, pmeta= 5.71x10-18). Co-localization analysis with whole blood eQTL data suggested FCGRT as the effector transcript at the RCN3 locus. We further showed that fructosamine has low heritability (h2=7.7%), has no significant genetic correlation with HbA1c and other glycemic traits in individuals without a diabetes diagnosis (p>0.05), but has evidence of shared genetic etiology with some anthropometric traits (Bonferroni corrected pFCGRT for downstream functional studies at the established RCN3 locus.

ObjectiveTo determine the contribution of common genetic variants to risk of early onset ischemic stroke (IS).MethodsWe performed a meta-analysis of genome-wide association studies of early onset IS, ages 18-59, using individual level data or summary statistics in 16,927 cases and 576,353 non-stroke controls from 48 different studies across North America, Europe, and Asia. We further compared effect sizes at our most genome-wide significant loci between early and late onset IS and compared polygenic risk scores for venous thromboembolism between early versus later onset IS.ResultsWe observed an association between early onset IS and ABO, a known stroke locus. The effect size of the peak ABO SNP, rs8176685, was significantly larger in early compared to late onset IS (OR 1.17 (95% C.I.: 1.11-1.22) vs 1.05 (0.99-1.12); p for interaction = 0.008). Analysis of genetically determined ABO blood groups revealed that early onset IS cases were more likely to have blood group A and less likely to have blood group O compared to both non-stroke controls and to late onset IS cases. Using polygenic risk scores, we observed that greater genetic risk for venous thromboembolism, another prothrombotic condition, was more strongly associated with early, compared to late, onset IS (p=0.008).ConclusionThe ABO locus, genetically predicted blood group A, and higher genetic propensity for venous thrombosis are more strongly associated with early onset IS, compared with late onset IS, supporting a stronger role of prothrombotic factors in early onset IS.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections were rising during early summer 2021 in many countries as a result of the Delta variant. We assessed reverse transcription polymerase chain reaction swab positivity in the Real-time Assessment of Community Transmission–1 (REACT-1) study in England. During June and July 2021, we observed sustained exponential growth with an average doubling time of 25 days, driven by complete replacement of the Alpha variant by Delta and by high prevalence at younger, less-vaccinated ages. Prevalence among unvaccinated people [1.21% (95% credible interval 1.03%, 1.41%)] was three times that among double-vaccinated people [0.40% (95% credible interval 0.34%, 0.48%)]. However, after adjusting for age and other variables, vaccine effectiveness for double-vaccinated people was estimated at between ~50% and ~60% during this period in England. Increased social mixing in the presence of Delta had the potential to generate sustained growth in infections, even at high levels of vaccination.

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.

For Alzheimer’s disease–a leading cause of dementia and global morbidity–improved identification of presymptomatic high-risk individuals and identification of new circulating biomarkers are key public health needs. Here, we tested the hypothesis that a polygenic predictor of risk for Alzheimer’s disease would identify a subset of the population with increased risk of clinically diagnosed dementia, subclinical neurocognitive dysfunction, and a differing circulating proteomic profile. Using summary association statistics from a recent genome-wide association study, we first developed a polygenic predictor of Alzheimer’s disease comprised of 7.1 million common DNA variants. We noted a 7.3-fold (95% CI 4.8 to 11.0; p < 0.001) gradient in risk across deciles of the score among 288,289 middle-aged participants of the UK Biobank study. In cross-sectional analyses stratified by age, minimal differences in risk of Alzheimer’s disease and performance on a digit recall test were present according to polygenic score decile at age 50 years, but significant gradients emerged by age 65. Similarly, among 30,541 participants of the Mass General Brigham Biobank, we again noted no significant differences in Alzheimer’s disease diagnosis at younger ages across deciles of the score, but for those over 65 years we noted an odds ratio of 2.0 (95% CI 1.3 to 3.2; p = 0.002) in the top versus bottom decile of the polygenic score. To understand the proteomic signature of inherited risk, we performed aptamer-based profiling in 636 blood donors (mean age 43 years) with very high or low polygenic scores. In addition to the well-known apolipoprotein E biomarker, this analysis identified 27 additional proteins, several of which have known roles related to disease pathogenesis. Differences in protein concentrations were consistent even among the youngest subset of blood donors (mean age 33 years). Of these 28 proteins, 7 of the 8 proteins with concentrations available were similarly associated with the polygenic score in participants of the Multi-Ethnic Study of Atherosclerosis. These data highlight the potential for a DNA-based score to identify high-risk individuals during the prolonged presymptomatic phase of Alzheimer’s disease and to enable biomarker discovery based on profiling of young individuals in the extremes of the score distribution.

Novel drug targets for sustained reduction in body mass index (BMI) are needed to curb the epidemic of obesity, which affects 650 million individuals worldwide and is a causal driver of cardiovascular and metabolic disease and mortality. Previous studies reported that the Arg95Ter nonsense variant of GPR151, an orphan G protein-coupled receptor, is associated with reduced BMI and reduced risk of Type 2 Diabetes (T2D). Here, we follow up on GPR151 with the Pakistan Genome Resource (PGR), which is one of the largest exome biobanks of human homozygous loss-of-function carriers (knockouts) in the world. Among PGR participants, we identify 3 GPR151 putative loss-of-function (plof) variants (Arg95Ter, Tyr99Ter, and Phe175LeufsTer7) with a cumulative allele frequency of 2.2% and present at homozygosity. We confirm these alleles in vitro as loss-of-function. We test if GPR151 plof is associated with BMI, T2D, or other metabolic traits. GPR151 deficiency in complete human knockouts is not associated with a clinically significant difference in BMI. Moreover, loss of GPR151 confers a nominally significant increase in risk of T2D (odds ratio = 1.2, p value = 0.03). Relative to wild-type mice, Gpr151-/- animals exhibit no difference in body weight on normal chow, and higher body weight on a high-fat diet, consistent with the findings in humans. Together, our findings indicate that GPR151 antagonism is not a compelling therapeutic approach for obesity.

Proteins are the effector molecules of biology and are the target of most drugs. To identify proteins and related pathways that may play a causal role in stroke pathogenesis, we used Mendelian randomisation (MR). We tested potential causal effects of 308 plasma proteins (measured in 4,994 blood donors from the INTERVAL study) on stroke outcomes (derived from the MEGASTROKE GWAS) in a two-sample MR framework and assessed whether these associations could be mediated by cardiovascular risk factors. We extended the analysis to identify whether pharmacological targeting of these proteins might have potential adverse side-effects or beneficial effects for other conditions through Phenome-wide MR (Phe-MR) in UK Biobank.MR showed an association between stroke and genetically predicted plasma levels of TFPI, IL6RA, MMP12, CD40, TMPRSS5 and CD6 (P≤1.62×10−4). We identified six risk factors (atrial fibrillation, body mass index, smoking, blood pressure, white matter hyperintensities and type 2 diabetes) that were associated with stroke (P≤0.0071) using MR. The association of TFPI, IL6RA and TMPRSS5 with stroke could be mediated by these risk factors, such as body mass index, white matter hyperintensity and atrial fibrillation. Thirty-six additional proteins were potentially causal for one or more of these risk factors. The Phe-MR suggested that targeting TFPI could have potential beneficial effects on other disorders of arteries and hyperlipidaemia in addition to stroke. Our results highlight novel causal pathways and potential therapeutic targets for stroke.

Funder: Health Data Research UK EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart (11607)., Funder: Health Data Research UK, Telomeres, the end fragments of chromosomes, play key roles in cellular proliferation and senescence. Here we characterize the genetic architecture of naturally occurring variation in leukocyte telomere length (LTL) and identify causal links between LTL and biomedical phenotypes in 472,174 well-characterized UK Biobank participants. We identified 197 independent sentinel variants associated with LTL at 138 genomic loci (108 new). Genetically determined differences in LTL were associated with multiple biological traits, ranging from height to bone marrow function, as well as several diseases spanning neoplastic, vascular and inflammatory pathologies. Finally, we estimated that, at the age of 40 years, people with an LTL >1 s.d. shorter than the population mean had a 2.5-year-lower life expectancy compared with the group with ≥1 s.d. longer LDL. Overall, we furnish new insights into the genetic regulation of LTL, reveal wide-ranging influences of LTL on physiological traits, diseases and longevity, and provide a powerful resource available to the global research community.

Metabolic biomarker data quantified by nuclear magnetic resonance (NMR) spectroscopy for 249 circulating metabolites, lipids, and lipoprotein sub-fractions has recently become available in UK Biobank for approximately 121,657 participants. Here, we describe procedures for quality control and removal of technical variation for this biomarker data. We show that technical and biological effects with linear effects on individual biomarkers can combine in a non-linear fashion on (the 61) composite biomarkers and (81) biomarker ratios, and thus composite biomarkers and ratios should be re-derived after removal of unwanted variation to avoid introducing such cryptic effects. We make available an R package, ukbnmr, for extracting the metabolic biomarker data from UK Biobank and removing the unwanted technical variation. We also make available code for re-deriving the 61 composite biomarkers and 81 ratios, and for further derivation of 76 additional biomarker ratios of potential biological significance. Finally, we demonstrate that our removal of technical variation leads to increased signal for genetic and epidemiological studies of the NMR metabolic biomarkers in UK Biobank.

Background Genetic, lifestyle, and environmental factors can lead to perturbations in circulating lipid levels and increase the risk of cardiovascular and metabolic diseases. However, how changes in individual lipid species contribute to disease risk is often unclear. Moreover, little is known about the role of lipids on cardiovascular disease in Pakistan, a population historically underrepresented in cardiovascular studies. Methods We characterised the genetic architecture of the human blood lipidome in 5662 hospital controls from the Pakistan Risk of Myocardial Infarction Study (PROMIS) and 13,814 healthy British blood donors from the INTERVAL study. We applied a candidate causal gene prioritisation tool to link the genetic variants associated with each lipid to the most likely causal genes, and Gaussian Graphical Modelling network analysis to identify and illustrate relationships between lipids and genetic loci. Results We identified 253 genetic associations with 181 lipids measured using direct infusion high-resolution mass spectrometry in PROMIS, and 502 genetic associations with 244 lipids in INTERVAL. Our analyses revealed new biological insights at genetic loci associated with cardiometabolic diseases, including novel lipid associations at the LPL, MBOAT7, LIPC, APOE-C1-C2-C4, SGPP1, and SPTLC3 loci. Conclusions Our findings, generated using a distinctive lipidomics platform in an understudied South Asian population, strengthen and expand the knowledge base of the genetic determinants of lipids and their association with cardiometabolic disease-related loci.

We performed the largest genome-wide meta-analysis (GWAMA) (Max N=26,494) of the levels of 184 cardiovascular-related plasma protein levels to date and reported 592 independent loci (pQTL) associated with the level of at least one protein (1308 significant associations, median 6 per protein). We estimated that only between 8-37% of testable pQTL overlap with established expression quantitative trait loci (eQTL) using multiple methods, while 132 out of 1064 lead variants show evidence for transcription factor binding, and found that 75% of our pQTL are known DNA methylation QTL. We highlight the variation in genetic architecture between proteins and that proteins share genetic architecture with cardiometabolic complex traits. Using cis-instrument Mendelian randomisation (MR), we infer causal relationships for 11 proteins, recapitulating the previously reported relationship between PCSK9 and LDL cholesterol, replicating previous pQTL MR findings and discovering 16 causal relationships between protein levels and disease. Our MR results highlight IL2-RA as a candidate for drug repurposing for Crohn’s Disease as well as 2 novel therapeutic targets: IL-27 (Crohn’s disease) and TNFRSF14 (Inflammatory bowel disease, Multiple sclerosis and Ulcerative colitis). We have demonstrated the discoveries possible using our pQTL and highlight the potential of this work as a resource for genetic epidemiology.

BACKGROUND Although the burden of premature myocardial infarction (MI) is high in Malaysia, direct evidence on the determinants of MI in this multi-ethnic population remains sparse. OBJECTIVE The Malaysian Acute Vascular Events Risk (MAVERIK) study is a retrospective case-control study established to investigate the genomic, lipid-related, and other determinants of acute MI in Malaysia. In this paper, we report the study protocol and early results. METHODS By June 2019, we had enrolled approximately 2500 patients with their first MI and 2500 controls without cardiovascular disease, who were frequency-matched by age, sex, and ethnicity, from 17 hospitals in Malaysia. For each participant, serum and whole blood have been collected and stored. Clinical, demographic, and behavioral information has been obtained using a 200-item questionnaire. RESULTS Tobacco consumption, a history of diabetes, hypertension, markers of visceral adiposity, indicators of lower socioeconomic status, and a family history of coronary disease were more prevalent in cases than in controls. Adjusted (age and sex) logistic regression models for traditional risk factors indicated that current smoking (odds ratio [OR] 4.11, 95% CI 3.56-4.75; PP=.001), a history of high blood pressure (OR 2.13, 95% CI 1.86-2.44; PPP=.009), and obesity (BMI >30 kg/m2; OR 1.19, 95% CI 1.05-1.34; P=.009) were associated with MI in age- and sex-adjusted models. CONCLUSIONS The MAVERIK study can serve as a useful platform to investigate genetic and other risk factors for MI in an understudied Southeast Asian population. It should help to hasten the discovery of disease-causing pathways and inform regionally appropriate strategies that optimize public health action. INTERNATIONAL REGISTERED REPORT RR1-10.2196/31885

BackgroundLeucocyte telomere length (LTL), a potential marker of biological age, has been associated with risk of many diseases. We investigated whether LTL is associated with risk of frailty, a multidimensional syndrome of decline that affects multiple systems and predisposes to adverse health outcomes.MethodsIn a cross–sectional analysis, we studied 441,781 UK Biobank participants (aged 40–70 years), with complete data on LTL and frailty indicators. We defined frailty as the presence of at least three of five indicators: weaker grip strength, slower walking pace, weight loss in the past year, lower physical activity, and exhaustion in the past two weeks. We evaluated association of LTL with frailty using adjusted (chronological age, sex, deprivation, smoking, alcohol intake, body mass index, multimorbidity) multinomial and ordinal regression models. We used Mendelian randomisation (MR), using 131 genetic variants associated with LTL, to assess if the association of LTL with frailty was causal.FindingsFrail participants (4·6%) were older (median age difference (95% CI): 3 (2·5; 3·5) years), more likely to be female (61%), and had shorter LTL (−0·13SD vs 0·03SD) than non–frail. In adjusted analyses, both age and LTL were associated with frailty (OR=1·03 (95%CI: 1·02–1·04) per year of older chronological age; 1·10 (1·08; 1·11) per SD shorter LTL). Within each age group (40–49, 50–59, 60–69 years) the prevalence of frailty was about 33% higher in participants with shorter (−2SD) versus longer telomeres (+2SD). MR analysis showed an association of LTL with frailty that was directionally consistent with the observational association, but not statistically significant.InterpretationInter–individual variation in LTL is associated with the risk of frailty independently of chronological age and other risk factors. Our findings provide evidence for an additional biological determinant of frailty.FundingThe UK Medical Research Council, the Biotechnology and Biological Sciences Research Council and the British Heart Foundation (MRC grant: MR/M012816/1) funded our measurements of LTL in UK Biobank. The funders had no role in study design; the collection, analysis, and interpretation of data; the writing of the report; and the decision to submit the paper for publication.Research in contextEvidence before this studyTelomere length has been proposed as a biomarker of biological age. Shorter leucocyte telomere length (LTL) is associated with higher incidence of several age–associated diseases, spanning multiple body systems, and with lower life expectancy. However, the association of shorter LTL with frailty, a multidimensional syndrome of decline across multiple systems, is inconclusive. We searched PubMed using the terms “telomere length AND frailty” in the title, abstract, or text with no language restrictions. This search identified 50 papers published before 30 November, 2020. Of these, three systematic reviews and/ or meta–analyses of modest size (five to nine studies, with total nAdded value of this studyOur large–scale population–based study, involving over 441,000 participants, provides strong evidence that shorter LTL is associated with higher risk of both pre–frailty and frailty even after adjustment for chronological age and other established determinants of frailty, namely, age, sex, alcohol intake, smoking, obesity, deprivation and multimorbidity. Within each age group (40–49, 50–59, 60–69 years) the prevalence of frailty was about 33% higher in participants with shorter (−2SD) versus longer telomeres (+2SD). Part of the association of shorter LTL with higher risk of frailty may be due to the association in aggregate of LTL with diseases. Findings using genetic variants associated with LTL as instruments were directionally consistent with the observational association, but not statistically significant.Implications of all the available evidenceShorter LTL is associated with frailty independent of chronological age and several other risk factors. The observation that risk of frailty associated with shorter LTL is proportionately similar across the age range of 40–70 years suggests that shorter LTL acts through a mechanism beyond just accelerating the impact of chronological ageing on risk of frailty.

BackgroundSARS-CoV-2 antibody tests are used for population surveillance and might have a future role in individual risk assessment. Lateral flow immunoassays (LFIAs) can deliver results rapidly and at scale, but have widely varying accuracy.MethodsIn a laboratory setting, we performed head-to-head comparisons of four LFIAs: the Rapid Test Consortium’s AbC-19™Rapid Test, OrientGene COVID IgG/IgM Rapid Test Cassette, SureScreen COVID-19 Rapid Test Cassette, and Biomerica COVID-19 IgG/IgM Rapid Test. We analysed blood samples from 2,847 key workers and 1,995 pre-pandemic blood donors with all four devices.FindingsWe observed a clear trade-off between sensitivity and specificity: the IgG band of the SureScreen device and the AbC-19™device had higher specificities but OrientGene and Biomerica higher sensitivities. Based on analysis of pre-pandemic samples, SureScreen IgG band had the highest specificity (98.9%, 95% confidence interval 98.3 to 99.3%), which translated to the highest positive predictive value across any pre-test probability: for example, 95.1% (95%CI 92.6, 96.8%) at 20% pre-test probability. All four devices showed higher sensitivity at higher antibody concentrations (“spectrum effects”), but the extent of this varied by device.InterpretationThe estimates of sensitivity and specificity can be used to adjust for test error rates when using these devices to estimate the prevalence of antibody. If tests were used to determine whether an individual has SARS-CoV-2 antibodies, in an example scenario in which 20% of individuals have antibodies we estimate around 5% of positive results on the most specific device would be false positives.FundingPublic Health England.Research in contextEvidence before this studyWe searched for evidence on the accuracy of the four devices compared in this study: OrientGene COVID IgG/IgM Rapid Test Cassette, SureScreen COVID-19™ Rapid Test Cassette, Biomerica COVID-19 IgG/IgM Rapid Test and the UK Rapid Test Consortium’s AbC-19™ Rapid Test. We searched Ovid MEDLINE (In-Process & Other Non-Indexed Citations and Daily), PubMed, MedRxiv/BioRxiv and Google Scholar from January 2020 to 16thJanuary 2021. Search terms included device names AND ((SARS-CoV-2) OR (covid)). Of 303 records assessed, data were extracted from 24 studies: 18 reporting on the accuracy of the OrientGene device, 7 SureScreen, 2 AbC-19™ and 1 Biomerica. Only three studies compared the accuracy of two or more of the four devices. With the exception of our previous report on the accuracy of the AbC-19™ device, which the current manuscript builds upon, sample size ranged from 7 to 684. For details, see Supplementary Materials.The largest study compared OrientGene, SureScreen and Biomerica. SureScreen was estimated to have the highest specificity (99.8%, 95% CI 98.9 to 100%) and OrientGene the highest sensitivity (92.6%), but with uncertainty about the latter result due to small sample sizes. The other two comparative studies were small (n = 65, n = 67) and therefore provide very uncertain results.We previously observed spectrum effects for the AbC-19™ device, such that sensitivity is upwardly biased if estimated only from PCR-confirmed cases. The vast majority of previous studies estimated sensitivity in this way.Added value of this studyWe performed a large scale (n = 4,842), head-to-head laboratory-based evaluation and comparison of four lateral flow devices, which were selected for evaluation by the UK Department of Health and Social Care’s New Tests Advisory Group, on the basis of a survey of test and performance data available. We evaluated the performance of diagnosis based on both IgG and IgM bands, and the IgG band alone. We found a clear trade-off between sensitivity and specificity across devices, with the SureScreen and AbC-19™ devices being more specific and OrientGene and Biomerica more sensitive. Based on analysis of 1,995 pre-pandemic blood samples, we are 99% confident that SureScreen (IgG band reading) has the highest specificity of the four devices (98.9%, 95% CI 98.3, 99.3%).We found evidence that all four devices have reduced sensitivity at lower antibody indices, i.e. spectrum effects. However, the extent of this varies by device and appears to be less for other devices than for AbC-19.Our estimates of sensitivity and specificity are likely to be higher than would be observed in real use of these devices, as they were based on majority readings of three trained laboratory personnel.Implications of all the available evidenceWhen used in epidemiological studies of antibody prevalence, the estimates of sensitivity and specificity provided in this study can be used to adjust for test errors. Increased precision in error rates will translate to increased precision in seroprevalence estimates. If lateral flow devices were used for individual risk assessment, devices with maximum specificity would be preferable. However, if, for an example, 20% of the tested population had antibodies, we estimate that around 1 in 20 positive results on the most specific device would be incorrect.

Aims Genetic disposition and lifestyle factors are understood as independent components underlying the risk of multiple diseases. In this study, we aim to investigate the interplay between genetics, educational attainment—an important denominator of lifestyle—and coronary artery disease (CAD) risk. Methods and results Based on the effect sizes of 74 genetic variants associated with educational attainment, we calculated a ‘genetic education score’ in 13 080 cases and 14 471 controls and observed an inverse correlation between the score and risk of CAD [P = 1.52 × 10−8; odds ratio (OR) 0.79, 95% confidence interval (CI) 0.73–0.85 for the higher compared with the lowest score quintile]. We replicated in 146 514 individuals from UK Biobank (P = 1.85 × 10−6) and also found strong associations between the ‘genetic education score’ with ‘modifiable’ risk factors including smoking (P = 5.36 × 10−23), body mass index (BMI) (P = 1.66 × 10−30), and hypertension (P = 3.86 × 10−8). Interestingly, these associations were only modestly attenuated by adjustment for years spent in school. In contrast, a model adjusting for BMI and smoking abolished the association signal between the ‘genetic education score’ and CAD risk suggesting an intermediary role of these two risk factors. Mendelian randomization analyses performed with summary statistics from large genome-wide meta-analyses and sensitivity analysis using 1271 variants affecting educational attainment (OR 0.68 for the higher compared with the lowest score quintile; 95% CI 0.63–0.74; P = 3.99 × 10−21) further strengthened these findings. Conclusion Genetic variants known to affect educational attainment may have implications for a health-conscious lifestyle later in life and subsequently affect the risk of CAD.

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types., Graphical abstract, D’Antonio et al. characterize associations between GWAS signals for COVID-19 disease and eQTLs in 69 human tissues to identify causal variants and their underlying molecular mechanisms. They show that diverse symptoms and disease severity of COVID-19 are associated with variants affecting gene expression in a wide variety of tissues.

A genetic predisposition to higher waist-to-hip ratio adjusted for BMI (WHRadjBMI), a measure of body fat distribution, associates with increased risk for type 2 diabetes. We conducted an exome-wide association study of coding variation in UK Biobank (405,569 individuals) to identify variants that lower WHRadjBMI and protect against type 2 diabetes. We identified four variants in the gene ACVR1C (encoding the activin receptor-like kinase 7 receptor expressed on adipocytes and pancreatic β-cells), which independently associated with reduced WHRadjBMI: Asn150His (-0.09 SD, P = 3.4 × 10-17), Ile195Thr (-0.15 SD, P = 1.0 × 10-9), Ile482Val (-0.019 SD, P = 1.6 × 10-5), and rs72927479 (-0.035 SD, P = 2.6 × 10-12). Carriers of these variants exhibited reduced percent abdominal fat in DEXA imaging. Pooling across all four variants, a 0.2 SD decrease in WHRadjBMI through ACVR1C was associated with a 30% lower risk of type 2 diabetes (odds ratio [OR] 0.70, 95% CI 0.63, 0.77; P = 5.6 × 10-13). In an analysis of exome sequences from 55,516 individuals, carriers of predicted damaging variants in ACVR1C were at 54% lower risk of type 2 diabetes (OR 0.46, 95% CI 0.27, 0.81; P = 0.006). These findings indicate that variants predicted to lead to loss of ACVR1C gene function influence body fat distribution and protect from type 2 diabetes. This work was funded by the National Institutes of Health (R01 HL127564 to S.K.), which had no involvement in the design and conduct of the study; the collection, analysis, and interpretation of the data; or the preparation, review, and approval of the manuscript. This project was also conducted using the Type 2 Diabetes Knowledge Portal resource which is funded by the Accelerating Medicines Partnership. REGICOR study was supported by the Spanish Ministry of Economy and Innovation through the Carlos III Health Institute (Red Investigación Cardiovascular RD12/0042, PI09/90506), European Funds for Development (ERDF-FEDER), and by the Catalan Research andTechnology Innovation Interdepartmental Commission (2014SGR240). Samples for the Leicester cohort were collected as part of projects funded by the British Heart Foundation (British Heart Foundation Family Heart Study, RG2000010; UK Aneurysm Growth Study, CS/14/2/30841) and the National Institute for Health Research (NIHR Leicester Cardiovascular Biomedical Research Unit Biomedical Research Informatics Centre for Cardiovascular Science, IS_BRU_0211_20033). NJS is supported by the British Heart Foundation and is a NIHR Senior Investigator. The Munich MI Study is supported by the German Federal Ministry of Education and Research (BMBF) in the context of the e:Med program (e:AtheroSysMed) and the FP7 European Union project CVgenes@target (261123). Additional grants were received from the Fondation Leducq (CADgenomics: Understanding Coronary Artery Disease Genes, 12CVD02).This study was also supported through the Deutsche Forschungsgemeinschaft cluster of excellence Vascular Biology (ATVB) Study was supported by a grant from RFPS-2007-3-644382 and Programma di ricerca Regione-Università 2010-2012 Area 1–Strategic Programmes– Diabetes Page 30 of 53 30 Regione Emilia-Romagna. Funding for the exome-sequencing project (ESP) was provided by RC2 HL103010 (HeartGO), RC2 HL102923 (LungGO), and RC2 HL102924 (WHISP). Exome sequencing was performed through RC2 HL102925 (BroadGO) and RC2 HL102926 (SeattleGO). The JHS is supported by contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities. Dr. Wilson is supported by U54GM115428 from the National Institute of General Medical Sciences. Exome sequencing in ATVB, PROCARDIS, Ottawa, PROMIS, Southern German Myocardial Infarction Study, and the Jackson Heart Study was supported by 5U54HG003067 (to Dr. Gabriel).

The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR–Cas9, we experimentally validated the effect of an enhancer in MYO9B, which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD.

Severe COVID-19 is characterised by immunopathology and epithelial injury. Proteomic studies have identified circulating proteins that are biomarkers of severe COVID-19, but cannot distinguish correlation from causation. To address this, we performed Mendelian randomisation (MR) to identify proteins that mediate severe COVID-19. Using protein quantitative trait loci (pQTL) data from the SCALLOP consortium, involving meta-analysis of up to 26,494 individuals, and COVID-19 genome-wide association data from the Host Genetics Initiative, we performed MR for 157 COVID-19 severity protein biomarkers. We identified significant MR results for five proteins: FAS, TNFRSF10A, CCL2, EPHB4 and LGALS9. Further evaluation of these candidates using sensitivity analyses and colocalization testing provided strong evidence to implicate the apoptosis-associated cytokine receptor FAS as a causal mediator of severe COVID-19. This effect was specific to severe disease. Using RNA-seq data from 4,778 individuals, we demonstrate that the pQTL at theFASlocus results from genetically influenced alternate splicing causing skipping of exon 6. We show that the risk allele for very severe COVID-19 increases the proportion of transcripts lacking exon 6, and thereby increases soluble FAS. Soluble FAS acts as a decoy receptor for FAS-ligand, inhibiting apoptosis induced through membrane-bound FAS. In summary, we demonstrate a novel genetic mechanism that contributes to risk of severe of COVID-19, highlighting a pathway that may be a promising therapeutic target.

Background Variation in adiposity is associated with cardiometabolic disease outcomes, but mechanisms leading from this exposure to disease are unclear. This study aimed to estimate effects of body mass index (BMI) on an extensive set of circulating proteins. Methods We used SomaLogic proteomic data from up to 2737 healthy participants from the INTERVAL study. Associations between self-reported BMI and 3622 unique plasma proteins were explored using linear regression. These were complemented by Mendelian randomisation (MR) analyses using a genetic risk score (GRS) comprised of 654 BMI-associated polymorphisms from a recent genome-wide association study (GWAS) of adult BMI. A disease enrichment analysis was performed using DAVID Bioinformatics 6.8 for proteins which were altered by BMI. Results Observationally, BMI was associated with 1576 proteins (P −5), with particularly strong evidence for a positive association with leptin and fatty acid-binding protein-4 (FABP4), and a negative association with sex hormone-binding globulin (SHBG). Observational estimates were likely confounded, but the GRS for BMI did not associate with measured confounders. MR analyses provided evidence for a causal relationship between BMI and eight proteins including leptin (0.63 standard deviation (SD) per SD BMI, 95% CI 0.48–0.79, P = 1.6 × 10−15), FABP4 (0.64 SD per SD BMI, 95% CI 0.46–0.83, P = 6.7 × 10−12) and SHBG (−0.45 SD per SD BMI, 95% CI −0.65 to −0.25, P = 1.4 × 10−5). There was agreement in the magnitude of observational and MR estimates (R2 = 0.33) and evidence that proteins most strongly altered by BMI were enriched for genes involved in cardiovascular disease. Conclusions This study provides evidence for a broad impact of adiposity on the human proteome. Proteins strongly altered by BMI include those involved in regulating appetite, sex hormones and inflammation; such proteins are also enriched for cardiovascular disease-related genes. Altogether, results help focus attention onto new proteomic signatures of obesity-related disease.

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

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels. Ten of these: ALB, SRSF2, JAK2, CREB3L3, TMEM136, VARS, NR1H3, PLA2G12A, PPARG and STAB1 have not been implicated for lipid levels using rare coding variation in population-based samples. We prioritize 32 genes identified in array-based genome-wide association study (GWAS) loci based on gene-based associations, of which three: EVI5, SH2B3, and PLIN1, had no prior evidence of rare coding variant associations. Most of the associated genes showed evidence of association in multiple ancestries. Also, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes, and for genes closest to GWAS index single nucleotide polymorphisms (SNP). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization (MR) analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls. We found significant Mendelian randomization results for three proteins (ACE2: P=1.6×10−6, IFNAR2: P=9.8×10−11, and IL-10RB: P=1.9×10−14) using cis-eQTL genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared eQTL signal for IL10RB and IFNAR2, we conducted phenome-wide association scans and pathway enrichment analysis, which suggested that IFNAR2 is more likely to play a role in COVID-19 hospitalization. Our findings prioritize trials of drugs targeting IFNAR2 and ACE2 for early management of COVID-19.

47 p.-11 fig., Mitochondrial DNA (mtDNA) variants influence the risk of late-onset human diseases, but the reasons for this are poorly understood. Undertaking a hypothesis-free analysis of 5,689 blood-derived biomarkers with mtDNA variants in 16,220 healthy donors, here we show that variants defining mtDNA haplogroups Uk and H4 modulate the level of circulating N-formylmethionine (fMet), which initiates mitochondrial protein translation. In human cytoplasmic hybrid (cybrid) lines, fMet modulated both mitochondrial and cytosolic proteins on multiple levels, through transcription, post-translational modification and proteolysis by an N-degron pathway, abolishing known differences between mtDNA haplogroups. In a further 11,966 individuals, fMet levels contributed to all-cause mortality and the disease risk of several common cardiovascular disorders. Together, these findings indicate that fMet plays a key role in common age-related disease through pleiotropic effects on cell proteostasis., This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation and Wellcome. N.C. is supported by an EBI–Sanger Postdoctoral Fellowship. A.G.-D. receives funding from the NIHR Biomedical Research Centre based at Cambridge University Hospitals NHS Foundation Trust. E.Y.-D. was funded by the Isaac Newton Trust/Wellcome Trust ISSF/University of Cambridge Joint Research Grants Scheme. J.M.M.H. was funded by the NIHR Cambridge BRC (BRC-1215-20014)*. P.F.C.is a Wellcome Trust Principal Research Fellow (212219/Z/18/Z) and a UK NIHR Senior Investigator, who receives support from the Medical Research Council Mitochondrial Biology Unit (MC_UU_00015/9), the Evelyn Trust and the NIHR Biomedical Research Centre based at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge. C.H.W.-G. is supported by a RCUK/UKRI Research Innovation Fellowship awarded by the Medical Research Council (MR/R007446/1) and by the Cambridge Centre for Parkinson-Plus. N.S. is supported by the Wellcome Trust(grant number 098051 until 30 September 2016 and 206194 from 1 October 2016),the Cambridge BHF Centre of Research Excellence (RE/18/1/34212) and the NIHR Cambridge Biomedical Research Centre Biomedical Resources Grant, University of Cambridge, Cardiovascular Theme (RG64226). J.D. holds a British Heart Foundation Professorship and an NIHR Senior Investigator Award*. R.A.L. is supported by grants from Parkinson’s UK. Sequencing of INTERVAL samples was supported by the Wellcome Trust (grant number 206194). Metabolon metabolomics assays were funded by the NIHR BioResource, the Wellcome Trust (grant number 206194) and the NIHR Cambridge BRC (BRC-1215-20014)*. Nightingale Health NMR assays were funded by the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). SomaLogic assays were funded by Merck and the NIHR Cambridge BRC (BRC-1215-20014)*. ICICLE-PD was funded by Parkinson’s UK (J-0802, G-1301, G-1507) and the Lockhart Parkinson’s Disease Research Fund and supported by the NIHR Newcastle Biomedical Research Unit and the NIHR Cambridge Biomedical Research Centre (146281). The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 and MC-UU_12015/1) and Cancer Research UK (C864/A14136). Genetic work in the EPIC-Norfolk study was funded by the Medical Research Council (MC_PC_13048). Metabolite measurements in the EPIC-Norfolk study were supported by the MRC Cambridge Initiative in Metabolic Science (MR/L00002/1) and the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372.

BackgroundGenetic, lifestyle, and environmental factors can lead to perturbations in circulating lipid levels and increase risk of cardiovascular and metabolic diseases. However, how changes in individual lipid species contribute to disease risk is often unclear. Moreover, little is known about the role of lipids on cardiovascular disease in Pakistan, a population historically underrepresented in cardiovascular studies.MethodsWe characterised the genetic architecture of the human blood lipidome in 5,662 hospital controls from the Pakistan Risk of Myocardial Infarction Study (PROMIS) and 13,814 healthy British blood donors from the INTERVAL study. We applied a candidate causal gene prioritisation tool to link the genetic variants associated with each lipid to the most likely causal genes, and Gaussian Graphical Modelling network analysis to identify and illustrate relationships between lipids and genetic loci.ResultsWe identified 359 genetic associations with 255 lipids measured using direct infusion high-resolution mass spectrometry in PROMIS, and 616 genetic associations with 326 lipids in INTERVAL. Our analyses revealed new biological insights at genetic loci associated with cardiometabolic diseases, including novel lipid associations at the LPL, MBOAT7, LIPC, APOE-C1-C2-C4, SGPP1, and SPTLC3 loci.ConclusionsOur findings, generated using a distinctive lipidomics platform in an understudied South Asian population, strengthen and expand the knowledge base of the genetic determinants of lipids and their association with cardiometabolic disease-related loci.

ObjectiveTo monitor hospital activity for presentation, diagnosis and treatment of cardiovascular diseases during the COVID-19) pandemic to inform on indirect effects.MethodsRetrospective serial cross-sectional study in nine UK hospitals using hospital activity data from 28 October 2019 (pre-COVID-19) to 10 May 2020 (pre-easing of lockdown) and for the same weeks during 2018–2019. We analysed aggregate data for selected cardiovascular diseases before and during the epidemic. We produced an online visualisation tool to enable near real-time monitoring of trends.ResultsAcross nine hospitals, total admissions and emergency department (ED) attendances decreased after lockdown (23 March 2020) by 57.9% (57.1%–58.6%) and 52.9% (52.2%–53.5%), respectively, compared with the previous year. Activity for cardiac, cerebrovascular and other vascular conditions started to decline 1–2 weeks before lockdown and fell by 31%–88% after lockdown, with the greatest reductions observed for coronary artery bypass grafts, carotid endarterectomy, aortic aneurysm repair and peripheral arterial disease procedures. Compared with before the first UK COVID-19 (31 January 2020), activity declined across diseases and specialties between the first case and lockdown (total ED attendances relative reduction (RR) 0.94, 0.93–0.95; total hospital admissions RR 0.96, 0.95–0.97) and after lockdown (attendances RR 0.63, 0.62–0.64; admissions RR 0.59, 0.57–0.60). There was limited recovery towards usual levels of some activities from mid-April 2020.ConclusionsSubstantial reductions in total and cardiovascular activities are likely to contribute to a major burden of indirect effects of the pandemic, suggesting they should be monitored and mitigated urgently.

Population-scale genetic studies can identify drug targets and allow disease risk to be predicted with resulting benefit for management of individual health risks and system-wide allocation of health care delivery resources. Although population-scale projects are underway in many parts of the world, genetic variation between population groups means that additional projects are warranted. South Asia has a population whose genetics is the least characterized of any of the world’s major populations. Here we describe GenomeAsia studies that characterize population structure in South Asia and that create tools for economical and accurate genotyping at population-scale. Prior work on population structure characterized isolated population groups, the relevance of which to large-scale studies of disease genetics is unclear. For our studies we used whole genome sequence information from 4,807 individuals recruited in the health care delivery systems of Pakistan, India and Bangladesh to ensure relevance to population-scale studies of disease genetics. We combined this with WGS data from 927 individuals from isolated South Asian population groups, and developed a custom SNP array (called SARGAM) that is optimized for future human genetic studies in South Asia. We find evidence for high rates of reproductive isolation, endogamy and consanguinity that vary across the subcontinent and that lead to levels of homozygosity that approach 100 times that seen in outbred populations. We describe founder effects that increase the power to associate functional variants with disease processes and that make South Asia a uniquely powerful place for population-scale genetic studies.

Background:Familial sitosterolemia is a rare Mendelian disorder characterized by hyperabsorption and decreased biliary excretion of dietary sterols. Affected individuals typically have complete genetic deficiency—homozygous loss-of-function (LoF) variants—in theABCG5orABCG8genes and have substantially elevated plasma sitosterol and LDL (low-density lipoprotein) cholesterol (LDL-C) levels. The impact of partial genetic deficiency ofABCG5orABCG8—as occurs in heterozygous carriers of LoF variants—on LDL-C and risk of coronary artery disease (CAD) has remained uncertain.Methods:We first recruited 9 sitosterolemia families, identified causative LoF variants inABCG5orABCG8, and evaluated the associations of theseABCG5orABCG8LoF variants with plasma phytosterols and lipid levels. We next assessed for LoF variants inABCG5orABCG8in CAD cases (n=29 321) versus controls (n=357 326). We tested the association of rare LoF variants inABCG5orABCG8with blood lipids and risk for CAD. Rare LoF variants were defined as protein-truncating variants with minor allele frequency ABCG5orABCG8.Results:In sitosterolemia families, 7 pedigrees harbored causative LoF variants inABCG5and 2 pedigrees inABCG8. Homozygous LoF variants in eitherABCG5orABCG8led to marked elevations in sitosterol and LDL-C. Of those sitosterolemia families, heterozygous carriers ofABCG5LoF variants exhibited increased sitosterol and LDL-C levels compared with noncarriers. Within large-scale CAD case-control cohorts, prevalence of rare LoF variants inABCG5and inABCG8was ≈0.1% each.ABCG5heterozygous LoF variant carriers had significantly elevated LDL-C levels (25 mg/dL [95% CI, 14–35];P=1.1×10−6) and were at 2-fold increased risk of CAD (odds ratio, 2.06 [95% CI, 1.27–3.35];P=0.004). By contrast,ABCG8heterozygous LoF carrier status was not associated with increased LDL-C or risk of CAD.Conclusions:Although familial sitosterolemia is traditionally considered as a recessive disorder, we observed that heterozygous carriers of an LoF variant inABCG5had significantly increased sitosterol and LDL-C levels and a 2-fold increase in risk of CAD.