Search

Understanding the cause of coronary heart diseases relies on the analysis of data from a range of techniques on an epidemiological scale. Lipidomics, the identification and quantification of lipid species in a system, is an omic approach increasingly used in epidemiology. The altered concentration of lipids in plasma is one of the recognised risk factors for these diseases. An important first step in the analysis is to profile lipids in healthy volunteers at an epidemiological level to understand how the geneome influences risk factors; for this reason we made use of the control samples within a bigger case-control sample collection in Pakistan from patients with first acute myocardial infarctions. After extraction, the samples were infused into a Thermo Exactive Orbitrap, without any up-front chromatographic separation. The use of direct infusion allowed fast experiment, facilitating the analysis of large sets of samples. The raw data were processed and analysed using scripts within R, to extract all the meaningful information. The data set originated from this study is a valuable resource to both increase our knowledge in lipid metabolism associated with myocardial infarction, and test new methods and strategy in analysing big lipidomic data sets.

Abstract Blood cells contain functionally important intracellular structures, such as granules, critical to immunity and thrombosis. Quantitative variation in these structures has not been subjected previously to large-scale genetic analysis. We perform genome-wide association studies of 63 flow-cytometry derived cellular phenotypes—including cell-type specific measures of granularity, nucleic acid content and reactivity—in 41,515 participants in the INTERVAL study. We identify 2172 distinct variant-trait associations, including associations near genes coding for proteins in organelles implicated in inflammatory and thrombotic diseases. By integrating with epigenetic data we show that many intracellular structures are likely to be determined in immature precursor cells. By integrating with proteomic data we identify the transcription factor FOG2 as an early regulator of platelet formation and α-granularity. Finally, we show that colocalisation of our associations with disease risk signals can suggest aetiological cell-types—variants in IL2RA and ITGA4 respectively mirror the known effects of daclizumab in multiple sclerosis and vedolizumab in inflammatory bowel disease.

Abstract The benefits of large-scale genetic studies for healthcare of the populations studied are well documented, but these genetic studies have traditionally ignored people from some parts of the world, such as South Asia. Here we describe whole genome sequence (WGS) data from 4806 individuals recruited from the healthcare delivery systems of Pakistan, India and Bangladesh, combined with WGS from 927 individuals from isolated South Asian populations. We characterize population structure in South Asia and describe a genotyping array (SARGAM) and imputation reference panel that are optimized for South Asian genomes. We find evidence for high rates of reproductive isolation, endogamy and consanguinity that vary across the subcontinent and that lead to levels of rare homozygotes that reach 100 times that seen in outbred populations. Founder effects increase the power to associate functional variants with disease processes and make South Asia a uniquely powerful place for population-scale genetic studies.

Abstract 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.

Here, the authors present a method to build genetically personalised metabolic models across tissues to estimate individualised reaction fluxes. A fluxome-wide association study in UK Biobank identifies fluxes associated with metabolites and coronary artery disease.

Mendelian randomization can be used to mimic the effects of protein-targeting drugs in a population of individuals. Here, the authors have identified potential causal proteins for stroke in a two-sample Mendelian randomization framework, providing potential stroke therapeutic targets.

Abstract Background While polygenic risk scores hold significant promise in estimating an individual's risk of developing a complex trait such as obesity, their application in the clinic has, to date, been limited by a lack of data from non‐European populations. As a collaboration model of the International Hundred K+ Cohorts Consortium (IHCC), we endeavored to develop a globally applicable trans‐ethnic PRS for body mass index (BMI) through this relatively new international effort. Methods The polygenic risk score (PRS) model was developed, trained and tested at the Center for Applied Genomics (CAG) of The Children's Hospital of Philadelphia (CHOP) based on a BMI meta‐analysis from the GIANT consortium. The validated PRS models were subsequently disseminated to the participating sites. Scores were generated by each site locally on their cohorts and summary statistics returned to CAG for final analysis. Results We show that in the absence of a well powered trans‐ethnic GWAS from which to derive marker SNPs and effect estimates for PRS, trans‐ethnic scores can be generated from European ancestry GWAS using Bayesian approaches such as LDpred, by adjusting the summary statistics using trans‐ethnic linkage disequilibrium reference panels. The ported trans‐ethnic scores outperform population specific‐PRS across all non‐European ancestry populations investigated including East Asians and three‐way admixed Brazilian cohort. Conclusions Here we show that for a truly polygenic trait such as BMI adjusting the summary statistics of a well powered European ancestry study using trans‐ethnic LD reference results in a score that is predictive across a range of ancestries including East Asians and three‐way admixed Brazilians.

Higher body mass index (BMI) is a risk factor for thrombosis. Platelets are essential for hemostasis but contribute to thrombosis when activated pathologically. We hypothesized that higher BMI leads to changes in platelet characteristics, thereby increasing thrombotic risk. The effect of BMI on platelet traits (measured by Sysmex) was explored in 33 388 UK blood donors (INTERVAL study). Linear regression showed that higher BMI was positively associated with greater plateletcrit (PCT), platelet count (PLT), immature platelet count (IPC), and side fluorescence (SFL, a measure of mRNA content used to derive IPC). Mendelian randomization (MR), applied to estimate a causal effect with BMI proxied by a genetic risk score, provided causal estimates for a positive effect of BMI on both SFL and IPC, but there was little evidence for a causal effect of BMI on PCT or PLT. Follow-up analyses explored the functional relevance of platelet characteristics in a pre-operative cardiac cohort (COPTIC). Linear regression provided observational evidence for a positive association between IPC and agonist-induced whole blood platelet aggregation. Results indicate that higher BMI raises the number of immature platelets, which is associated with greater whole blood platelet aggregation in a cardiac cohort. Higher IPC could therefore contribute to obesity-related thrombosis.

Abstract Background Frailty is a multidimensional syndrome of decline that affects multiple systems and predisposes to adverse health outcomes. Although chronological age is the major risk factor, inter‐individual variation in risk is not fully understood. Leucocyte telomere length (LTL), a proposed marker of biological age, has been associated with risk of many diseases. We sought to determine whether LTL is associated with risk of frailty. Methods We utilized cross‐sectional data from 441 781 UK Biobank participants (aged 40–69 years), with complete data on frailty indicators and LTL. Frailty was defined 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 2 weeks. LTL was measured using a validated qPCR method and reported as a ratio of the telomere repeat number (T) to a single‐copy gene (S) (T/S ratio). Association of LTL with frailty was evaluated using adjusted (chronological age, sex, deprivation, smoking, alcohol intake, body mass index, and multimorbidity) multinomial and ordinal regression models, and results are presented as relative risk (RRR) or odds ratios (OR), respectively, alongside the 95% confidence interval (CI). Mendelian randomization (MR), using 131 genetic variants associated with LTL, was used to assess if the association of LTL with frailty was causal. Results Frail participants (4.6%) were older (median age difference (95% CI): 3 (2.5; 3.5) years, P = 2.73 × 10−33), more likely to be female (61%, P = 1.97 × 10−129), and had shorter LTL (−0.13SD vs. 0.03SD, P = 5.43 × 10−111) than non‐frail. In adjusted analyses, both age and LTL were associated with frailty (RRR = 1.03 (95% CI: 1.02; 1.04) per year of older chronological age, P = 3.99 × 10−12; 1.10 (1.08; 1.11) per SD shorter LTL, P = 1.46 × 10−30). 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 (MR‐Median: OR (95% CI): 1.08 (0.98; 1.19) per SD shorter LTL, P = 0.13). Conclusions Inter‐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.

Background Self-reported adherence to the Mediterranean diet has been modestly inversely associated with incidence of type 2 diabetes (T2D) in cohort studies. There is uncertainty about the validity and magnitude of this association due to subjective reporting of diet. The association has not been evaluated using an objectively measured biomarker of the Mediterranean diet. Methods and findings We derived a biomarker score based on 5 circulating carotenoids and 24 fatty acids that discriminated between the Mediterranean or habitual diet arms of a parallel design, 6-month partial-feeding randomised controlled trial (RCT) conducted between 2013 and 2014, the MedLey trial (128 participants out of 166 randomised). We applied this biomarker score in an observational study, the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study, to assess the association of the score with T2D incidence over an average of 9.7 years of follow-up since the baseline (1991 to 1998). We included 22,202 participants, of whom 9,453 were T2D cases, with relevant biomarkers from an original case-cohort of 27,779 participants sampled from a cohort of 340,234 people. As a secondary measure of the Mediterranean diet, we used a score estimated from dietary-self report. Within the trial, the biomarker score discriminated well between the 2 arms; the cross-validated C-statistic was 0.88 (95% confidence interval (CI) 0.82 to 0.94). The score was inversely associated with incident T2D in EPIC-InterAct: the hazard ratio (HR) per standard deviation of the score was 0.71 (95% CI: 0.65 to 0.77) following adjustment for sociodemographic, lifestyle and medical factors, and adiposity. In comparison, the HR per standard deviation of the self-reported Mediterranean diet was 0.90 (95% CI: 0.86 to 0.95). Assuming the score was causally associated with T2D, higher adherence to the Mediterranean diet in Western European adults by 10 percentiles of the score was estimated to reduce the incidence of T2D by 11% (95% CI: 7% to 14%). The study limitations included potential measurement error in nutritional biomarkers, unclear specificity of the biomarker score to the Mediterranean diet, and possible residual confounding. Conclusions These findings suggest that objectively assessed adherence to the Mediterranean diet is associated with lower risk of T2D and that even modestly higher adherence may have the potential to reduce the population burden of T2D meaningfully. Trial registration Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12613000602729https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=363860. Jakub G Sobiecki and team aim to develop a biomarker score able to discriminate between Mediterranean diet and habitual diet, and test its association with incident type 2 diabetes in a European cohort. Author summary Why was this study done? Epidemiological evidence has indicated that greater self-reported adherence to the Mediterranean diet may be associated with lower risk of new onset type 2 diabetes (T2D), but there has been uncertainty about the magnitude of association due to subjective reporting of diet. A combination of nutritional biomarkers could better assess diet–disease associations, but this approach has rarely been considered for overall diet quality, particularly for the association between the Mediterranean diet and T2D. What did the researchers do and find? Blood carotenoids and fatty acids can be used as an objective measure of adherence to the Mediterranean diet, as indicated by results from a trial (n = 128) of adopting Mediterranean diet with provision of its key foods to study participants. These biomarkers discriminated well between the trial participants under the Mediterranean diet intervention and those randomised to continuation of habitual diet (C-statistic = 0.88). In a study across 8 European countries (n = 22,202), adherence to the Mediterranean diet, estimated using a combination of nutritional biomarkers, was associated with lower risk of new onset T2D, with a stronger relationship compared to that with self-reported Mediterranean diet. The hazard ratios (HRs) (95% confidence interval (CI)) per standard deviation of adherence were 0.71 (0.65 to 0.77) and 0.90 (0.86 to 0.95), respectively. What do these findings mean? Adherence to the Mediterranean diet may be more beneficial for the primary prevention of T2D than previously estimated from observational dietary studies. Even small upward differences in objectively measured Mediterranean diet may be associated with a sizeable reduction of the risk of T2D at the population level. Causal interpretation of these findings is limited by potential measurement error in nutritional biomarkers, unclear specificity of the biomarker score to the Mediterranean diet, and possible residual confounding.

Abstract 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.

Abstract 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.

This retrospective cohort study investigated the incidence and risk factors of major adverse cardiovascular events (MACE) after 1 year of first-documented myocardial infarctions (MIs) in a multi-ethnic Asian population. Secondary MACE were observed in 231 (14.3%) individuals, including 92 (5.7%) cardiovascular-related deaths. Both histories of hypertension and diabetes were associated with secondary MACE after adjustment for age, sex, and ethnicity (HR 1.60 [95%CI 1.22–2.12] and 1.46 [95%CI 1.09–1.97], respectively). With further adjustments for traditional risk factors, individuals with conduction disturbances demonstrated higher risks of MACE: new left-bundle branch block (HR 2.86 [95%CI 1.15–6.55]), right-bundle branch block (HR 2.09 [95%CI 1.02–4.29]), and second-degree heart block (HR 2.45 [95%CI 0.59–10.16]). These associations were broadly similar across different age, sex, and ethnicity groups, although somewhat greater for history of hypertension and BMI among women versus men, for HbA1c control in individuals aged >50 years, and for LVEF ≤ 40% in those with Indian versus Chinese or Bumiputera ethnicities. Several traditional and cardiac risk factors are associated with a higher risk of secondary major adverse cardiovascular events. In addition to hypertension and diabetes, the identification of conduction disturbances in individuals with first-onset MI may be useful for the risk stratification of high-risk individuals.

Bell et al. report 46 new loci associated with biomarkers of iron homeostasis, including ferritin levels, iron binding capacity, and iron saturation, in the Icelandic, Danish and UK populations. The associated loci point to new iron-regulating proteins and important genetic differences between men and women.

Didriksen, Nawaz, et al. identify three novel genetic risk variants for restless legs syndrome and confirm 19 out of 20 previously reported variants through a genome-wide association meta-analysis including nearly half a million individuals. Using expression QTL analysis, they also find that a subset of these loci may have a causal effect on nearby gene expression.

Late-life cognitive dysfunction is common, but the biological substrates are largely unknown. Here, the authors examined a panel of 90 neurology-related protein biomarkers and show that plasma levels of 22 of these proteins are associated with general fluid cognitive ability in later life.

Abstract Background Although lifestyle factors have been studied in relation to individual non-communicable diseases (NCDs), their association with development of a subsequent NCD, defined as multimorbidity, has been scarcely investigated. The aim of this study was to investigate associations between five lifestyle factors and incident multimorbidity of cancer and cardiometabolic diseases. Methods In this prospective cohort study, 291,778 participants (64% women) from seven European countries, mostly aged 43 to 58 years and free of cancer, cardiovascular disease (CVD), and type 2 diabetes (T2D) at recruitment, were included. Incident multimorbidity of cancer and cardiometabolic diseases was defined as developing subsequently two diseases including first cancer at any site, CVD, and T2D in an individual. Multi-state modelling based on Cox regression was used to compute hazard ratios (HR) and 95% confidence intervals (95% CI) of developing cancer, CVD, or T2D, and subsequent transitions to multimorbidity, in relation to body mass index (BMI), smoking status, alcohol intake, physical activity, adherence to the Mediterranean diet, and their combination as a healthy lifestyle index (HLI) score. Cumulative incidence functions (CIFs) were estimated to compute 10-year absolute risks for transitions from healthy to cancer at any site, CVD (both fatal and non-fatal), or T2D, and to subsequent multimorbidity after each of the three NCDs. Results During a median follow-up of 11 years, 1910 men and 1334 women developed multimorbidity of cancer and cardiometabolic diseases. A higher HLI, reflecting healthy lifestyles, was strongly inversely associated with multimorbidity, with hazard ratios per 3-unit increment of 0.75 (95% CI, 0.71 to 0.81), 0.84 (0.79 to 0.90), and 0.82 (0.77 to 0.88) after cancer, CVD, and T2D, respectively. After T2D, the 10-year absolute risks of multimorbidity were 40% and 25% for men and women, respectively, with unhealthy lifestyle, and 30% and 18% for men and women with healthy lifestyles. Conclusion Pre-diagnostic healthy lifestyle behaviours were strongly inversely associated with the risk of cancer and cardiometabolic diseases, and with the prognosis of these diseases by reducing risk of multimorbidity.

Summary: Genetic association studies for blood cell traits, which are key indicators of health and immune function, have identified several hundred associations and defined a complex polygenic architecture. Polygenic scores (PGSs) for blood cell traits have potential clinical utility in disease risk prediction and prevention, but designing PGS remains challenging and the optimal methods are unclear. To address this, we evaluated the relative performance of 6 methods to develop PGS for 26 blood cell traits, including a standard method of pruning and thresholding (P + T) and 5 learning methods: LDpred2, elastic net (EN), Bayesian ridge (BR), multilayer perceptron (MLP) and convolutional neural network (CNN). We evaluated these optimized PGSs on blood cell trait data from UK Biobank and INTERVAL. We find that PGSs designed using common machine learning methods EN and BR show improved prediction of blood cell traits and consistently outperform other methods. Our analyses suggest EN/BR as the top choices for PGS construction, showing improved performance for 25 blood cell traits in the external validation, with correlations with the directly measured traits increasing by 10%–23%. Ten PGSs showed significant statistical interaction with sex, and sex-specific PGS stratification showed that all of them had substantial variation in the trajectories of blood cell traits with age. Genetic correlations between the PGSs for blood cell traits and common human diseases identified well-known as well as new associations. We develop machine learning-optimized PGS for blood cell traits, demonstrate their relationships with sex, age, and disease, and make these publicly available as a resource.

Household income is used as a marker of socioeconomic position, a trait that is associated with better physical and mental health. Here, Hill et al. report a genome-wide association study for household income in the UK and explore its relationship with intelligence in post-GWAS analyses including Mendelian randomization.

Stroke risk is influenced by genetic and lifestyle factors and previously a genomic risk score (GRS) for stroke was proposed, albeit with limited predictive power. Here, Abraham et al. develop a metaGRS that is composed of several stroke-related GRSs and demonstrate improved predictive power compared with individual GRS or classic risk factors.

Background There is controversy about associations between total dietary fatty acids, their classes (saturated fatty acids [SFAs], monounsaturated fatty acids, and polyunsaturated fatty acids), and risk of coronary heart disease (CHD). Specifically, the relevance of food sources of SFAs to CHD associations is uncertain. Methods and Results We conducted a case‐cohort study involving 10 529 incident CHD cases and a random subcohort of 16 730 adults selected from a cohort of 385 747 participants in 9 countries of the EPIC (European Prospective Investigation into Cancer and Nutrition) study. We estimated multivariable adjusted country‐specific hazard ratios (HRs) and 95% CIs per 5% of energy intake from dietary fatty acids, with and without isocaloric macronutrient substitutions, using Prentice‐weighted Cox regression models and pooled results using random‐effects meta‐analysis. We found no evidence for associations of the consumption of total or fatty acid classes with CHD, regardless of macronutrient substitutions. In analyses considering food sources, CHD incidence was lower per 1% higher energy intake of SFAs from yogurt (HR, 0.93 [95% CI, 0.88–0.99]), cheese (HR, 0.98 [95% CI, 0.96–1.00]), and fish (HR, 0.87 [95% CI, 0.75–1.00]), but higher for SFAs from red meat (HR, 1.07 [95% CI, 1.02–1.12]) and butter (HR, 1.02 [95% CI, 1.00–1.04]). Conclusions This observational study found no strong associations of total fatty acids, SFAs, monounsaturated fatty acids, and polyunsaturated fatty acids, with incident CHD. By contrast, we found associations of SFAs with CHD in opposite directions dependent on the food source. These findings should be further confirmed, but support public health recommendations to consider food sources alongside the macronutrients they contain, and suggest the importance of the overall food matrix.

Background Older age is the most powerful risk factor for adverse coronavirus disease-19 (COVID-19) outcomes. It is uncertain whether leucocyte telomere length (LTL), previously proposed as a marker of biological age, is also associated with COVID-19 outcomes.Methods We associated LTL values obtained from participants recruited into UK Biobank (UKB) during 2006–2010 with adverse COVID-19 outcomes recorded by 30 November 2020, defined as a composite of any of the following: hospital admission, need for critical care, respiratory support, or mortality. Using information on 130 LTL-associated genetic variants, we conducted exploratory Mendelian randomisation (MR) analyses in UKB to evaluate whether observational associations might reflect cause-and-effect relationships.Findings Of 6775 participants in UKB who tested positive for infection with SARS-CoV-2 in the community, there were 914 (13.5%) with adverse COVID-19 outcomes. The odds ratio (OR) for adverse COVID-19 outcomes was 1·17 (95% CI 1·05–1·30; P = 0·004) per 1-SD shorter usual LTL, after adjustment for age, sex and ethnicity. Similar ORs were observed in analyses that: adjusted for additional risk factors; disaggregated the composite outcome and reduced the scope for selection or collider bias. In MR analyses, the OR for adverse COVID-19 outcomes was directionally concordant but non-significant.Interpretation Shorter LTL is associated with higher risk of adverse COVID-19 outcomes, independent of several major risk factors for COVID-19 including age. Further data are needed to determine whether this association reflects causality.Funding UK Medical Research Council, Biotechnology and Biological Sciences Research Council and British Heart Foundation.

Urinary albumin-to-creatinine ratio (UCAR) is associated with various clinical outcomes such as kidney disease and cardiovascular disease. Here, the authors report genome-wide meta-analysis in over 500,000 individuals and find 68 UACR loci, followed by statistical fine-mapping, gene prioritization and experimental validation in flies.

Epidemiological studies have associated circulating levels of the amino acid glycine with cardiometabolic outcomes. Here, in a genome-wide meta-analysis of 80,003 individuals, Wittemans et al. identify 22 novel genetic loci for glycine and find a causal relationship with coronary heart disease using MR.

Angiotensin-converting enzyme 2 (ACE2) and serine protease TMPRSS2 have been implicated in cell entry for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19). The expression of ACE2 and TMPRSS2 in the lung epithelium might have implications for the risk of SARS-CoV-2 infection and severity of COVID-19. We use human genetic variants that proxy angiotensin-converting enzyme (ACE) inhibitor drug effects and cardiovascular risk factors to investigate whether these exposures affect lung ACE2 and TMPRSS2 gene expression and circulating ACE2 levels. We observed no consistent evidence of an association of genetically predicted serum ACE levels with any of our outcomes. There was weak evidence for an association of genetically predicted serum ACE levels with ACE2 gene expression in the Lung eQTL Consortium (p = 0.014), but this finding did not replicate. There was evidence of a positive association of genetic liability to type 2 diabetes mellitus with lung ACE2 gene expression in the Gene-Tissue Expression (GTEx) study (p = 4 × 10−4) and with circulating plasma ACE2 levels in the INTERVAL study (p = 0.03), but not with lung ACE2 expression in the Lung eQTL Consortium study (p = 0.68). There were no associations of genetically proxied liability to the other cardiometabolic traits with any outcome. This study does not provide consistent evidence to support an effect of serum ACE levels (as a proxy for ACE inhibitors) or cardiometabolic risk factors on lung ACE2 and TMPRSS2 expression or plasma ACE2 levels.

Genetic variation can influence levels of disease-related plasma proteins and, thus, contribute to the pathogenesis of complex diseases. Here, the authors perform genome-wide QTL analysis for 71 plasma proteins to identify causal proteins for coronary heart disease and provide a molecular QTL browser.

Vitamin D deficiency is associated with multiple human pathologic conditions. In a genome-wide association study of 79,366 individuals, Jiang et al. replicate four and identify two new genetic loci for serum levels of 25-hydroxyvitamin D and find evidence for a shared genetic basis with autoimmune diseases.

Numerous genetic variants, including those located in the non-coding regions of the genome, are known to be associated with blood cells traits. Here, Frontini and colleagues investigate their potential regulatory functions using epigenomic data and promoter long-range interactions.

Abstract Obesity is a genetically heterogeneous disorder. Using targeted and whole-exome sequencing, we studied 32 human and 87 rodent obesity genes in 2,548 severely obese children and 1,117 controls. We identified 52 variants contributing to obesity in 2% of cases including multiple novel variants in GNAS, which were sometimes found with accelerated growth rather than short stature as described previously. Nominally significant associations were found for rare functional variants in BBS1, BBS9, GNAS, MKKS, CLOCK and ANGPTL6. The p.S284X variant in ANGPTL6 drives the association signal (rs201622589, MAF~0.1%, odds ratio = 10.13, p-value = 0.042) and results in complete loss of secretion in cells. Further analysis including additional case-control studies and population controls (N = 260,642) did not support association of this variant with obesity (odds ratio = 2.34, p-value = 2.59 × 10−3), highlighting the challenges of testing rare variant associations and the need for very large sample sizes. Further validation in cohorts with severe obesity and engineering the variants in model organisms will be needed to explore whether human variants in ANGPTL6 and other genes that lead to obesity when deleted in mice, do contribute to obesity. Such studies may yield druggable targets for weight loss therapies.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

In the originally published version of this Article, financial support was not fully acknowledged. The sentence “KS was supported by the ‘Biomedical Research Program’ funds at Weill Cornell Medicine in Qatar, a program funded by the Qatar Foundation” has been added to the acknowledgement section in both the PDF and HTML versions of the Article.

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.

Growth differentiation factor-15 (GDF15) is a stress response cytokine that is elevated in several cardiometabolic diseases and has attracted interest as a potential therapeutic target. To further explore the association of GDF15 with human disease, we conducted a broad study into the phenotypic and genetic correlates of GDF15 concentration in up to 14,099 individuals. Assessment of 772 traits across 6610 participants in FINRISK identified associations of GDF15 concentration with a range of phenotypes including all-cause mortality, cardiometabolic disease, respiratory diseases and psychiatric disorders, as well as inflammatory markers. A meta-analysis of genome-wide association studies (GWAS) of GDF15 concentration across three different assay platforms (n=14,099) confirmed significant heterogeneity due to a common missense variant (rs1058587; p.H202D) in GDF15, potentially due to epitope-binding artefacts. After conditioning on rs1058587, statistical fine mapping identified four independent putative causal signals at the locus. Mendelian randomisation (MR) analysis found evidence of a causal relationship between GDF15 concentration and high-density lipoprotein (HDL) but not body mass index (BMI). Using reverse MR, we identified a potential causal association of BMI on GDF15 (IVW pFDR = 0.0040). Taken together, our data derived from human population cohorts do not support a role for moderately elevated GDF15 concentrations as a causal factor in human cardiometabolic disease but support its role as a biomarker of metabolic stress.