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Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p-value < 5x10-8), which validated 12 previously reported loci for proinsulin and 10 additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher vs. lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait loci (eQTL) data, suggesting candidate genes including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal, but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms predisposing to disease.

OBJECTIVE To investigate the association between accelerometer-derived physical activity energy expenditure (PAEE) and incident type 2 diabetes (T2D) in a cohort of middle-aged adults and within subgroups. RESEARCH DESIGN AND METHODS Data were from 90,096 UK Biobank participants without prevalent diabetes (mean 62 years of age; 57% women) who wore a wrist accelerometer for 7 days. PAEE was derived from wrist acceleration using a population-specific method validated against doubly labeled water. Logistic regressions were used to assess associations between PAEE, its underlying intensity, and incident T2D, ascertained using hospital episode and mortality data up to November 2020. Models were progressively adjusted for demographic, lifestyle factors, and BMI. RESULTS The association between PAEE and T2D was approximately linear (n = 2,018 events). We observed 19% (95% CI 17–21) lower odds of T2D per 5 kJ · kg−1 · day−1 in PAEE without adjustment for BMI and 11% (9–13) with BMI adjustment. The association was stronger in men than women and weaker in those with obesity and higher genetic susceptibility to obesity. There was no evidence of effect modification by genetic susceptibility to T2D or insulin resistance. For a given level of PAEE, odds of T2D were lower among those engaging in more moderate-to-vigorous activity. CONCLUSIONS There was a strong linear relationship between PAEE and incident T2D. A difference in PAEE equivalent to an additional daily 20-min brisk walk was associated with 19% lower odds of T2D. The association was broadly similar across population subgroups, supporting physical activity for diabetes prevention in the whole population.

Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci; and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian Randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, alpha-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.

BACKGROUND: Self-reported meat consumption is associated with disease risk but objective assessment of different dimensions of this heterogeneous dietary exposure in observational and interventional studies remains challenging. OBJECTIVES: We aimed to derive and validate scores based on plasma metabolites for types of meat consumption. For the most predictive score, we aimed to test whether the included metabolites varied with change in meat consumption, and whether the score was associated with incidence of type 2 diabetes (T2D) and other noncommunicable diseases. METHODS: We derived scores based on 781 plasma metabolites for red meat, processed meat, and poultry consumption assessed with 7-d food records among 11,432 participants in the EPIC-Norfolk (European Prospective Investigation into Cancer and Nutrition-Norfolk) cohort. The scores were then tested for internal validity in an independent subset (n = 853) of the same cohort. In focused analysis on the red meat metabolite score, we examined whether the metabolites constituting the score were also associated with meat intake in a randomized crossover dietary intervention trial of meat (n = 12, Lyon, France). In the EPIC-Norfolk study, we assessed the association of the red meat metabolite score with T2D incidence (n = 1478) and other health endpoints. RESULTS: The best-performing score was for red meat, comprising 139 metabolites which accounted for 17% of the explained variance of red meat consumption in the validation set. In the intervention, 11 top-ranked metabolites in the red meat metabolite score increased significantly after red meat consumption. In the EPIC-Norfolk study, the red meat metabolite score was associated with T2D incidence (adjusted HR per SD: 1.17; 95% CI: 1.10, 1.24). CONCLUSIONS: The red meat metabolite score derived and validated in this study contains metabolites directly derived from meat consumption and is associated with T2D risk. These findings suggest the potential for objective assessment of dietary components and their application for understanding diet-disease associations.The trial in Lyon, France, was registered at clinicaltrials.gov as NCT03354130.

Many diseases with significant health burden and healthcare costs have been neglected in biomedical research, partly due to a lack of data. Here, we systematically harmonized >12 million primary care and hospitalisation health records from ∼440,000 UK Biobank participants into 1445 collated disease terms for genetic analyses. This included ∼200 diseases with >10,000 cases that are predominantly managed in primary care, like skin and non-serious infectious diseases. We quantify the heritability for these common diseases, identify novel loci with extreme effect sizes, and highlight novel roles of poorly characterised genes, e.g.PNLIPRP3as a sebaceous gland cell-specific gene for rosacea. We characterise the substantial regional pleiotropy with more than 300 independent genomic regions associated with multiple, often seemingly unrelated diseases and use these insights to generate a pan-genome disease network of shared disease loci to prioritise potential pathways contributing to multiple diseases and onset of multimorbidity. We demonstrate the value of primary care data to improve and guide genetic approaches for drug selection, repurposing, and adverse event prediction, e.g.,IGFR1as a putative multi-disease target for, among others, gout and atrial fibrillation, through network augmentation and integration of molecular quantitative trait loci. We make all results publicly available via an interactive webserver (https://www.omicscience.org/apps/phecodes). Our results provide new insights for diseases across diverse clinical specialties and provide a resource for drug discovery and mechanistic understanding.

BackgroundUnderstanding the role of circulating proteins in cancer risk can reveal key biological pathways and identify novel therapeutic targets for cancer prevention.MethodsWe investigated the associations of 2,094 circulating proteins with risk of nine common cancers (bladder, breast, endometrium, head and neck, lung, ovary, pancreas, kidney, and malignant non-melanoma) usingcispQTL Mendelian randomisation (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using an independent cancer GWAS. Additionally, MR and colocalization phenome-wide association analyses (PHEWAS) were conducted to identify potential adverse side-effects of altering risk proteins. Finally, we mapped cancer risk proteins to drug and ongoing clinical trials targets.ResultsWe identified 40 proteins associated with cancer risk, of which a majority replicated and were novel. Among these were proteins associated with common cancers, such as PLAUR and risk of breast cancer [odds ratio per standard deviation increment (OR): 2.27, 95% CI: 1.88 to 2.74], and with high-mortality cancers, such as CTRB1 and pancreatic cancer [OR: 0.79, 95% CI: 0.73 to 0.85]. PHEWAS highlighted multiple links between proteins and potential adverse effects of protein-altering interventions. Additionally, 18 proteins associated with cancer risk mapped to existing therapeutic interventions, while 15 were not currently known to be under clinical investigation, such as GAS1 and triple negative breast cancer [OR: 1.88, 95% CI: 1.42 to 2.47].ConclusionOur findings emphasize the importance of proteomics for improving our understanding of cancer aetiology. Additionally, we demonstrate the benefit of in-depth protein PHEWAS analyses on risk proteins to identify potential adverse side-effects of protein-altering interventions. Using these methods, we identify a subset of risk proteins as potential drug targets for the prevention and treatment of cancer as well as opportunities for drug repurposing.

Genome-wide association studies (GWAS) have found few common variants that influence fasting measures of insulin sensitivity. We hypothesized that a GWAS of an integrated assessment of fasting and dynamic measures of insulin sensitivity would detect novel common variants. We performed a GWAS of the modified Stumvoll Insulin Sensitivity Index (ISI) within the Meta-Analyses of Glucose and Insulin-Related Traits Consortium. Discovery for genetic association was performed in 16,753 individuals, and replication was attempted for the 23 most significant novel loci in 13,354 independent individuals. Association with ISI was tested in models adjusted for age, sex, and BMI and in a model analyzing the combined influence of the genotype effect adjusted for BMI and the interaction effect between the genotype and BMI on ISI (model 3). In model 3, three variants reached genome-wide significance: rs13422522 (NYAP2; P = 8.87 × 10−11), rs12454712 (BCL2; P = 2.7 × 10−8), and rs10506418 (FAM19A2; P = 1.9 × 10−8). The association at NYAP2 was eliminated by conditioning on the known IRS1 insulin sensitivity locus; the BCL2 and FAM19A2 associations were independent of known cardiometabolic loci. In conclusion, we identified two novel loci and replicated known variants associated with insulin sensitivity. Further studies are needed to clarify the causal variant and function at the BCL2 and FAM19A2 loci.

Obesity strongly increases the risk of cardiometabolic diseases, yet the underlying mediators of this relationship are not fully understood. Given that obesity has broad effects on circulating protein levels, we investigated circulating proteins that mediate the effects of obesity on coronary artery disease (CAD), stroke, and type 2 diabetes—since doing so may prioritize targets for therapeutic intervention. By integrating proteome-wide Mendelian randomization (MR) screening 4,907 plasma proteins, colocalization, and mediation analyses, we identified seven plasma proteins, including collagen type VI α3 (COL6A3). COL6A3 was strongly increased by body mass index (BMI) (β= 0.32, 95% CI: 0.26–0.38,P= 3.7 × 10-8per s.d. increase in BMI) and increased the risk of CAD (OR = 1.47, 95% CI:1.26–1.70,P= 4.5 × 10-7per s.d. increase in COL6A3). Notably, COL6A3 is cleaved at its C-terminus to produce endotrophin, which was found to mediate this effect on CAD. In single-cell RNA sequencing of adipose tissues and coronary arteries,COL6A3was highly expressed in cell types involved in metabolic dysfunction and fibrosis. Finally, we found that body fat reduction can reduce plasma levels of COL6A3-derived endotrophin, thereby highlighting a tractable way to modify endotrophin levels. In summary, we provide actionable insights into how circulating proteins mediate the effect of obesity on cardiometabolic diseases and prioritize endotrophin as a potential therapeutic target.

Sclerostin inhibition is a new therapeutic approach for increasing bone mineral density (BMD) but its cardiovascular safety is unclear. We conducted a genome-wide association study (GWAS) meta-analysis of circulating sclerostin in 33,961 Europeans followed by Mendelian randomization (MR) to estimate the causal effects of sclerostin on 15 atherosclerosis-related diseases and risk factors. GWAS meta-analysis identified 18 variants independently associated with sclerostin, which including a novel cis signal in the SOST region and three trans signals in B4GALNT3, RIN3 and SERPINA1 regions that were associated with opposite effects on circulating sclerostin and eBMD. MR combining these four SNPs suggested lower sclerostin increased hypertension risk (odds ratio [OR]=1.09, 95%CI=1.04 to 1.15), whereas bi-directional analyses revealed little evidence for an effect of genetic liability to hypertension on sclerostin levels. MR restricted to cis (SOST) SNPs additionally suggested sclerostin inhibition increased risk of type 2 diabetes (T2DM) (OR=1.26; 95%CI=1.08 to 1.48) and myocardial infarction (MI) (OR=1.31, 95% CI=1.183 to 1.45). Furthermore, these analyses suggested sclerostin inhibition increased coronary artery calcification (CAC) (β=0.74, 95%CI=0.33 to 1.15), levels of apoB (β=0.07; 95%CI=0.04 to 0.10; this result was driven by rs4793023) and triglycerides (β=0.18; 95%CI=0.13 to 0.24), and reduced HDL-C (β=-0.14; 95%CI=-0.17 to -0.10). This study provides genetic evidence to support a causal effect of sclerostin inhibition on increased hypertension risk. Cis-only analyses suggested that sclerostin inhibition additionally increases the risk of T2DM, MI, CAC, and an atherogenic lipid profile. Together, our findings reinforce the requirement for strategies to mitigate against adverse effects of sclerostin inhibitors like romosozumab on atherosclerosis and its related risk factors.

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.

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.

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioral traits and the disease etiology of neuropsychiatric disorders. Here, the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,500 individuals for 184 neuro-related proteins in human plasma. The analysis identified 117 cis-regulatory protein quantitative trait loci (cis-pQTL) and 166 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. Mendelian randomization analyses revealed multiple proteins showing potential causal effects on neuro-related traits such as sleeping, smoking, feelings, alcohol intake, mental health, and psychiatric disorders. Integrating with established drug information, we validated 13 out of 13 matched combinations of protein targets and diseases or side effects with available drugs, while suggesting hundreds of re-purposing and new therapeutic targets. This consortium effort provides a large-scale proteogenomic resource for biomedical research on human behaviors and other neuro-related phenotypes.

Background: Human plasma contains a wide variety of circulating proteins. These proteins can be important clinical biomarkers in disease and also possible drug targets. Large scale genomics studies of circulating proteins can identify genetic variants that lead to relative protein abundance. Methods: We conducted a meta-analysis on genome-wide association studies of autosomal chromosomes in 22,997 individuals of primarily European ancestry across 12 cohorts to identify protein quantitative trait loci (pQTL) for 92 cardiometabolic associated plasma proteins. Results: We identified 503 (337 cis and 166 trans) conditionally independent pQTLs, including several novel variants not reported in the literature. We conducted a sex-stratified analysis and found that 118 (23.5%) of pQTLs demonstrated heterogeneity between sexes. The direction of effect was preserved but there were differences in effect size and significance. Additionally, we annotate trans-pQTLs with nearest genes and report plausible biological relationships. Using Mendelian randomization, we identified causal associations for 18 proteins across 19 phenotypes, of which 10 have additional genetic colocalization evidence. We highlight proteins associated with a constellation of cardiometabolic traits including angiopoietin-related protein 7 (ANGPTL7) and Semaphorin 3F (SEMA3F). Conclusion: Through large-scale analysis of protein quantitative trait loci, we provide a comprehensive overview of common variants associated with plasma proteins. We highlight possible biological relationships which may serve as a basis for further investigation into possible causal roles in cardiometabolic diseases.

Understanding the genetic basis of neuro-related proteins is essential for dissecting the disease etiology of neuropsychiatric disorders and other complex traits and diseases. Here, the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,500 individuals for 184 neuro-related proteins in human plasma. The analysis identified 117 cis-regulatory protein quantitative trait loci (cis-pQTL) and 166 trans-pQTL. The mapped pQTL capture on average 50% of each protein’s heritability. Mendelian randomization analyses revealed multiple proteins showing potential causal effects on neuro-related traits as well as complex diseases such as hypertension, high cholesterol, immune-related disorders, and psychiatric disorders. Integrating with established drug information, we validated 13 combinations of protein targets and diseases or side effects with available drugs, while suggesting hundreds of re-purposing and new therapeutic targets for diseases and comorbidities. This consortium effort provides a large-scale proteogenomic resource for biomedical research.

ContextHumans respond profoundly to changes in diet, while nutrition and environment have a great impact on population health. It is therefore important to deeply characterize the human nutritional responses.ObjectiveEndocrine parameters and the metabolome of human plasma are rapidly responding to acute nutritional interventions such as caloric restriction or a glucose challenge. It is less well understood whether the plasma proteome would be equally dynamic, and whether it could be a source of corresponding biomarkers.MethodsWe used high-throughput mass spectrometry to determine changes in the plasma proteome of i) 10 healthy, young, male individuals in response to 2 days of acute caloric restriction followed by refeeding; ii) 200 individuals of the Ely epidemiological study before and after a glucose tolerance test at 4 time points (0, 30, 60, 120 minutes); and iii) 200 random individuals from the Generation Scotland study. We compared the proteomic changes detected with metabolome data and endocrine parameters.ResultsBoth caloric restriction and the glucose challenge substantially impacted the plasma proteome. Proteins responded across individuals or in an individual-specific manner. We identified nutrient-responsive plasma proteins that correlate with changes in the metabolome, as well as with endocrine parameters. In particular, our study highlights the role of apolipoprotein C1 (APOC1), a small, understudied apolipoprotein that was affected by caloric restriction and dominated the response to glucose consumption and differed in abundance between individuals with and without type 2 diabetes.ConclusionOur study identifies APOC1 as a dominant nutritional responder in humans and highlights the interdependency of acute nutritional response proteins and the endocrine system.

The state of somatic energy stores in metazoans is communicated to the brain, which regulates key aspects of behaviour, growth, nutrient partitioning and development1. The central melanocortin system acts through melanocortin 4 receptor (MC4R) to control appetite, food intake and energy expenditure2. Here we present evidence that MC3R regulates the timing of sexual maturation, the rate of linear growth and the accrual of lean mass, which are all energy-sensitive processes. We found that humans who carry loss-of-function mutations in MC3R, including a rare homozygote individual, have a later onset of puberty. Consistent with previous findings in mice, they also had reduced linear growth, lean mass and circulating levels of IGF1. Mice lacking Mc3r had delayed sexual maturation and an insensitivity of reproductive cycle length to nutritional perturbation. The expression of Mc3r is enriched in hypothalamic neurons that control reproduction and growth, and expression increases during postnatal development in a manner that is consistent with a role in the regulation of sexual maturation. These findings suggest a bifurcating model of nutrient sensing by the central melanocortin pathway with signalling through MC4R controlling the acquisition and retention of calories, whereas signalling through MC3R primarily regulates the disposition of calories into growth, lean mass and the timing of sexual maturation. MC3R deficiency is associated with a delay in the onset of puberty, and a reduction in growth and lean mass.

BACKGROUND: Globally, there is a paucity of multimorbidity and comorbidity data, especially for minority ethnic groups and younger people. We estimated the frequency of common disease combinations and identified non-random disease associations for all ages in a multiethnic population. METHODS: In this population-based study, we examined multimorbidity and comorbidity patterns stratified by ethnicity or race, sex, and age for 308 health conditions using electronic health records from individuals included on the Clinical Practice Research Datalink linked with the Hospital Episode Statistics admitted patient care dataset in England. We included individuals who were older than 1 year and who had been registered for at least 1 year in a participating general practice during the study period (between April 1, 2010, and March 31, 2015). We identified the most common combinations of conditions and comorbidities for index conditions. We defined comorbidity as the accumulation of additional conditions to an index condition over an individual's lifetime. We used network analysis to identify conditions that co-occurred more often than expected by chance. We developed online interactive tools to explore multimorbidity and comorbidity patterns overall and by subgroup based on ethnicity, sex, and age. FINDINGS: We collected data for 3 872 451 eligible patients, of whom 1 955 700 (50.5%) were women and girls, 1 916 751 (49.5%) were men and boys, 2 666 234 (68.9%) were White, 155 435 (4.0%) were south Asian, and 98 815 (2.6%) were Black. We found that a higher proportion of boys aged 1-9 years (132 506 [47.8%] of 277 158) had two or more diagnosed conditions than did girls in the same age group (106 982 [40.3%] of 265 179), but more women and girls were diagnosed with multimorbidity than were boys aged 10 years and older and men (1 361 232 [80.5%] of 1 690 521 vs 1 161 308 [70.8%] of 1 639 593). White individuals (2 097 536 [78.7%] of 2 666 234) were more likely to be diagnosed with two or more conditions than were Black (59 339 [60.1%] of 98 815) or south Asian individuals (93 617 [60.2%] of 155 435). Depression commonly co-occurred with anxiety, migraine, obesity, atopic conditions, deafness, soft-tissue disorders, and gastrointestinal disorders across all subgroups. Heart failure often co-occurred with hypertension, atrial fibrillation, osteoarthritis, stable angina, myocardial infarction, chronic kidney disease, type 2 diabetes, and chronic obstructive pulmonary disease. Spinal fractures were most strongly non-randomly associated with malignancy in Black individuals, but with osteoporosis in White individuals. Hypertension was most strongly associated with kidney disorders in those aged 20-29 years, but with dyslipidaemia, obesity, and type 2 diabetes in individuals aged 40 years and older. Breast cancer was associated with different comorbidities in individuals from different ethnic groups. Asthma was associated with different comorbidities between males and females. Bipolar disorder was associated with different comorbidities in younger age groups compared with older age groups. INTERPRETATION: Our findings and interactive online tools are a resource for: patients and their clinicians, to prevent and detect comorbid conditions; research funders and policy makers, to redesign service provision, training priorities, and guideline development; and biomedical researchers and manufacturers of medicines, to provide leads for research into common or sequential pathways of disease and inform the design of clinical trials. FUNDING: UK Research and Innovation, Medical Research Council, National Institute for Health and Care Research, Department of Health and Social Care, Wellcome Trust, British Heart Foundation, and The Alan Turing Institute.

BackgroundRaynaud’s phenomenon (RP) is a common vasospastic disorder that causes severe pain and ulcers in fingers and toes triggered by cold or emotional stress. Despite its high reported heritability, no causal genes have been robustly identified, limiting mechanistic understanding and treatment options.AimTo investigate the genetic architecture of RP to better understand its aetiology and identify new potential therapeutic targets.MethodsWe conducted a genome-wide association study including 9,084 RP cases and 435,357 controls, based on diagnoses from electronic health records, among participants of the UK Biobank study. We identified candidate causal variants and genes using Bayesian fine-mapping and colocalization with gene expression across 49 tissues. We performed phenome-wide association analyses for all significant RP loci and computed genetic correlations followed by latent causal variable analyses between RP and a total of 205 selected phenotypes.ResultsWe identified eight unreported genomic regions associated with the risk of RP atp<5×10−8and assignedADRA2A(rs7090046, odds ratio (OR) per allele: 1.27; 95%-CI: 1.23-1.31; p−47) andIRX1(rs11748327, OR: 1.20; 95%-CI: 1.16-1.23, p−28) as the candidate causal genes at the two strongest loci. Higher expression ofADRA2Ain tibial artery andIRX1in skeletal muscle was thereby associated with a higher RP risk. We identified a likely causal detrimental effect of low fasting glucose levels on RP risk (rG=-0.12; p-value=0.01), while significant positive genetic correlations with reported comorbidities like migraine, depression, or peripheral artery disease are likely explained by shared risk factors.ConclusionOur results provide the first robust evidence for a strong genetic contribution to RP, highlighting a so far underrated role of α2A-adrenoreceptor signalling, encoded atADRA2A, as an important mechanism for hypersensitivity to catecholamine-induced vasospasms even at thermoneutral conditions.

Type 2 diabetes (T2D) is a heritable metabolic disorder. While population studies have identified hundreds of common genetic variants associated with T2D, the role of rare (frequency < 0.1%) protein-coding variation is less clear. We performed exome sequence analysis in 418,436 (n = 32,374 T2D cases) individuals in the UK Biobank. We identified previously reported genes (GCK, GIGYF1, HNF1A) in addition to missense variants in ZEB2 (n = 31 carriers; odds ratio [OR] = 5.5 [95% confidence interval = 2.5-12.0]; p = 6.4 × 10-7), MLXIPL (n = 245; OR = 2.3 [1.6-3.2]; p = 3.2 × 10-7), and IGF1R (n = 394; OR = 2.4 [1.8-3.2]; p = 1.3 × 10-10). Carriers of damaging missense variants within IGF1R were also shorter (-2.2 cm [-1.8 to -2.7]; p = 1.2 × 10-19) and had higher circulating insulin-like growth factor-1 (IGF-1) protein levels (2.3 nmol/L [1.7-2.9]; p = 2.8 × 10-14), indicating relative IGF-1 resistance. A likely causal role of IGF-1 resistance was supported by Mendelian randomization analyses using common variants. These results increase understanding of the genetic architecture of T2D and highlight the growth hormone/IGF-1 axis as a potential therapeutic target.

There is considerable interest in GIPR agonism to enhance the insulinotropic and extrapancreatic effects of GIP, thereby improving glycemic and weight control in type 2 diabetes (T2D) and obesity. Recent genetic epidemiological evidence has implicated higher GIPR-mediated GIP levels in raising coronary artery disease (CAD) risk, a potential safety concern for GIPR agonism. We therefore aimed to quantitatively assess whether the association between higher GIPR-mediated fasting GIP levels and CAD risk is mediated via GIPR or is instead the result of linkage disequilibrium (LD) confounding between variants at the GIPR locus. Using Bayesian multitrait colocalization, we identified a GIPR missense variant, rs1800437 (G allele; E354), as the putatively causal variant shared among fasting GIP levels, glycemic traits, and adiposity-related traits (posterior probability for colocalization [PPcoloc] > 0.97; PP explained by the candidate variant [PPexplained] = 1) that was independent from a cluster of CAD and lipid traits driven by a known missense variant in APOE (rs7412; distance to E354 ∼770 Kb; R2 with E354 = 0.004; PPcoloc > 0.99; PPexplained = 1). Further, conditioning the association between E354 and CAD on the residual LD with rs7412, we observed slight attenuation in association, but it remained significant (odds ratio [OR] per copy of E354 after adjustment 1.03; 95% CI 1.02, 1.04; P = 0.003). Instead, E354’s association with CAD was completely attenuated when conditioning on an additional established CAD signal, rs1964272 (R2 with E354 = 0.27), an intronic variant in SNRPD2 (OR for E354 after adjustment for rs1964272: 1.01; 95% CI 0.99, 1.03; P = 0.06). We demonstrate that associations with GIP and anthropometric and glycemic traits are driven by genetic signals distinct from those driving CAD and lipid traits in the GIPR region and that higher E354-mediated fasting GIP levels are not associated with CAD risk. These findings provide evidence that the inclusion of GIPR agonism in dual GIPR/GLP1R agonists could potentiate the protective effect of GLP-1 agonists on diabetes without undue CAD risk, an aspect that has yet to be assessed in clinical trials.

Purpose: The study aimed to systematically ascertain male sex chromosome abnormalities, 47,XXY (Klinefelter syndrome [KS]) and 47,XYY, and characterize their risks of adverse health outcomes. Methods: We analyzed genotyping array or exome sequence data in 207,067 men of European ancestry aged 40 to 70 years from the UK Biobank and related these to extensive routine health record data. Results: Only 49 of 213 (23%) of men whom we identified with KS and only 1 of 143 (0.7%) with 47,XYY had a diagnosis of abnormal karyotype on their medical records or self-report. We observed expected associations for KS with reproductive dysfunction (late puberty: risk ratio [RR] = 2.7; childlessness: RR = 4.2; testosterone concentration: RR = -3.8 nmol/L, all P < 2 x 10(-8)), whereas XYY men appeared to have normal reproductive function. Despite this difference, we identified several higher disease risks shared across both KS and 47,XYY, including type 2 diabetes (RR = 3.0 and 2.6, respectively), venous thrombosis (RR = 6.4 and 7.4, respectively), pulmonary embolism (RR = 3.3 and 3.7, respectively), and chronic obstructive pulmonary disease (RR = 4.4 and 4.6, respectively) (all P Conclusion: KS and 47,XYY were mostly unrecognized but conferred substantially higher risks for metabolic, vascular, and respiratory diseases, which were only partially explained by higher levels of body mass index, deprivation, and smoking. (C) 2022 The Authors. Published by Elsevier Inc. on behalf of American College of Medical Genetics and Genomics.

Funder: Wellcome Trust, Funder: National Institute for Health Research (NIHR), Despite two years of intense global research activity, host genetic factors that predispose to a poorer prognosis of COVID-19 infection remain poorly understood. Here, we prioritise eight robust (e.g., ELF5) or suggestive but unreported (e.g., RAB2A) candidate protein mediators of COVID-19 outcomes by integrating results from the COVID-19 Host Genetics Initiative with population-based plasma proteomics using statistical colocalisation. The transcription factor ELF5 (ELF5) shows robust and directionally consistent associations across different outcome definitions, including a >4-fold higher risk (odds ratio: 4.88; 95%-CI: 2.47-9.63; p-value < 5.0 × 10-6) for severe COVID-19 per 1 s.d. higher genetically predicted plasma ELF5. We show that ELF5 is specifically expressed in epithelial cells of the respiratory system, such as secretory and alveolar type 2 cells, using single-cell RNA sequencing and immunohistochemistry. These cells are also likely targets of SARS-CoV-2 by colocalisation with key host factors, including ACE2 and TMPRSS2. In summary, large-scale human genetic studies together with gene expression at single-cell resolution highlight ELF5 as a risk gene for severe COVID-19, supporting a role of epithelial cells of the respiratory system in the adverse host response to SARS-CoV-2.

The EPIC-CVD project was supported by the European Union Framework 7 (HEALTH-F2-2012-279233), the European Research Council (268834), the UK Medical Research Council (G0800270, MR/L003120/1), the British Heart Foundation (SP/09/002, RG/08/014, RG13/13/30194), and the UK National Institute of Health Research (to EPIC-CVD). The establishment of the subcohort was supported by the EU Sixth Framework Programme (FP6) (grant LSHM_CT_2006_037197 to the InterAct project) and the Medical Research Council Epidemiology Unit (grants MC_ UU_12015/1 and MC_UU_12015/5). The national EPIC cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Generale de l'Education Nationale, Institut National de la Sante et de la Recherche Medicale (INSERM) (France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); Ministry of Health and Social Solidarity, Stavros Niarchos Foundation and Hellenic Health Foundation (Greece); Italian Association for Research on Cancer (AIRC) and National Research Council (Italy) and MIUR "Dipartimenti di Eccellenza"(Project D15D18000410001) to the Department of Medical Sciences; Dutch Ministry of Public Health, Welfare and Sports (VWS), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF); ERC-2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), Regional Governments of Andalucia, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Scientific Council and Regional Government of Skane and Vasterbotten (Sweden); and Cancer Research UK, Medical Research Council (United Kingdom). LIFE-Heart is funded by the Leipzig Research Center for Civilization Diseases (LIFE). LIFE is an organizational unit affiliated to the Medical Faculty of the University of Leipzig. LIFE is funded by means of the European Union, by the European Regional Development Fund (ERDF), and by funds of the Free State of Saxony within the framework of the excellence initiative. This work is supported by the Dutch Heart Foundation (2013T083 to V.D.), by a UK Medical Research Council Skills Development Fellowship (MR/P014550/1 to S.A.E.P.), and by a Sir Henry Dale Fellowship jointly funded by the Welcome Trust and the Royal Society (204623/Z/16/Z to S.B.). None of the funding sources had a role in the collection, analysis, and interpretation of the data, nor in the decision to submit the article for publication., Background: Accelerated reproductive aging, in women indicated by early natural menopause, is associated with increased coronary heart disease (CHD) risk in observational studies. Conversely, an adverse CHD risk profile has been suggested to accelerate menopause. Objectives: To study the direction and evidence for causality of the relationship between reproductive aging and (non-)fatal CHD and CHD risk factors in a bidirectional Mendelian randomization (MR) approach, using age at natural menopause (ANM) genetic variants as a measure for genetically determined reproductive aging in women. We also studied the association of these variants with CHD risk (factors) in men. Design: Two-sample MR, using both cohort data as well as summary statistics, with 4 methods: simple and weighted median-based, standard inverse-variance weighted (IVW) regression, and MR-Egger regression. Participants: Data from EPIC-CVD and summary statistics from UK Biobank and publicly available genome-wide association studies were pooled for the different analyses. Main Outcome Measures: CHD, CHD risk factors, and ANM. Results: Across different methods of MR, no association was found between genetically determined reproductive aging and CHD risk in women (relative risk estimateIVW = 0.99; 95% confidence interval (CI), 0.97-1.01), or any of the CHD risk factors. Similarly, no associations were found in men. Neither did the reversed analyses show evidence for an association between CHD (risk factors) and reproductive aging. Conclusion: Genetically determined reproductive aging is not causally associated with CHD risk (factors) in women, nor were the genetic variants associated in men. We found no evidence for a reverse association in a combined sample of women and men., European Commission HEALTH-F2-2012-279233 European Research Council (ERC) European Commission 268834, UK Research & Innovation (UKRI) Medical Research Council UK (MRC) G0800270 MR/L003120/1 British Heart Foundation SP/09/002 RG/08/014 RG13/13/30194, National Institute for Health Research (NIHR), EU Sixth Framework Programme (FP6) LSHM_CT_2006_037197, UK Research & Innovation (UKRI), Medical Research Council UK (MRC) MC_ UU_12015/1 MC_UU_12015/5, Danish Cancer Society, Ligue Contre le Cancer (France), Institut Gustave Roussy (France), Mutuelle Generale de l'Education Nationale (France), Institut National de la Sante et de la Recherche Medicale (Inserm), Deutsche Krebshilfe Federal Ministry of Education & Research (BMBF) Deutsches Krebsforschungszentrum (Germany), Ministry of Health and Social Solidarity (Greece) Stavros Niarchos Foundation (Greece) Hellenic Health Foundation (Greece), Fondazione AIRC per la ricerca sul cancro Consiglio Nazionale delle Ricerche (CNR) MIUR "Dipartimenti di Eccellenza" D15D18000410001, Dutch Ministry of Public Health, Welfare and Sports, LK Research Funds, Dutch Prevention Funds Netherlands Organization for Scientific Research (NWO), World Cancer Research Fund International (WCRF) ERC-2009-AdG 232997, Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway), Instituto de Salud Carlos III, Junta de Andalucia, Principality of Asturias, Regional Government of Basque Country (Spain) Regional Government of Murcia (Spain) Regional Government of Navarra (Spain), Instituto de Salud Carlos III RD06/0020, Swedish Cancer Society Swedish Scientific Council (Sweden) Regional Government of Skane and Vasterbotten (Sweden), Cancer Research UK UK Research & Innovation (UKRI) Medical Research Council UK (MRC), Leipzig Research Center for Civilization Diseases (LIFE), European Commission, Free State of Saxony, Netherlands Heart Foundation 2013T083, Medical Research Council UK (MRC) MR/P014550/1, Sir Henry Dale Fellowship - Welcome Trust 204623/Z/16/Z, Sir Henry Dale Fellowship - Royal Society 204623/Z/16/Z

Lung function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry GWAS meta-analysis of lung function to date, comprising 580,869 participants, 1020 independent association signals identified 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score (GRS) showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies (PheWAS) for selected associated variants, and trait and pathway-specific GRS to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies.

BACKGROUND AND AIMS UMOD is a major risk gene for monogenic and complex forms of kidney disease. The encoded kidney-specific protein uromodulin is the most abundant protein in urine and related to chronic kidney disease, hypertension and pathogen defense. Through basolateral release from kidney epithelial cells, uromodulin also reaches the blood, where its function is largely unknown. To gain insights into potential systemic roles, we performed genome-wide screens of circulating uromodulin in seven cohorts using two complementary assays. METHOD Separate genome-wide association study meta-analyses for circulating uromodulin were conducted for the antibody-based assay (five cohorts, N = 13 985) and the aptamer-based SOMAscan assay (two cohorts, N = 18 070). Genome-wide significant loci were placed into their functional genomic context using RNA-seq, ATAC-seq and Hi-C data generated from primary human kidney tissue. An array of downstream genetic analyses was then performed for significant loci, including fine-mapping, colocalization analyses and gene-by-gene interaction analyses. The B4GALNT2 p.Cys466Arg allele was expressed in MDCK cells and studied by immunofluorescence and Western blotting analyses. RESULTS We detected and replicated 13 genome-wide significant loci (P CONCLUSION This study provides human genetic evidence of new pathway members of uromodulin and delivers novel insights into its determinants and systemic role in the circulation.

Amino acids are key to protein synthesis, energy metabolism, cell signaling and gene expression; however, the contribution of specific maternal amino acids to fetal growth is unclear. We explored the effect of maternal circulating amino acids on fetal growth, proxied by birthweight, using two-sample Mendelian randomization and summary data from a genome-wide association study (GWAS) of serum amino acids levels (sample 1, n = 86,507) and a maternal GWAS of offspring birthweight, adjusting for fetal genotype effects (sample 2, n = 406,063 with maternal and/or fetal genotype effect estimates). A total of 106 independent single nucleotide polymorphisms (SNPs) robustly associated with 19 amino acids (p < 4.9 × 10−10) were used as genetic instrumental variables. Our results provide evidence that maternal circulating glutamine (59 g offspring birthweight increase per SD increase in maternal amino acid level, 95% CI: 7, 110) and serine (27 g, 95% CI: 9, 46) raise, while leucine (−59 g, 95% CI: -106, -11) and phenylalanine (−25 g, 95% CI: -47, -4) lower offspring birthweight. Our findings strengthen evidence for key roles of maternal circulating amino acids in healthy fetal growth.

We conduct a large-scale meta-analysis of heart failure genome-wide association studies (GWAS) consisting of over 90,000 heart failure cases and more than 1 million control individuals of European ancestry to uncover novel genetic determinants for heart failure. Using the GWAS results and blood protein quantitative loci, we perform Mendelian randomization and colocalization analyses on human proteins to provide putative causal evidence for the role of druggable proteins in the genesis of heart failure. We identify 39 genome-wide significant heart failure risk variants, of which 18 are previously unreported. Using a combination of Mendelian randomization proteomics and genetic cis-only colocalization analyses, we identify 10 additional putatively causal genes for heart failure. Findings from GWAS and Mendelian randomization-proteomics identify seven (CAMK2D,PRKD1,PRKD3,MAPK3,TNFSF12,APOC3andNAE1) proteins as potential targets for interventions to be used in primary prevention of heart failure.

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.

Type 2 diabetes (T2D) is a heritable metabolic disorder. While population studies have identified hundreds of common genetic variants associated with T2D, the role of rare (frequency 0.1%) protein-coding variation is less clear. We performed exome sequence analysis in 418,436 (n = 32,374 T2D cases) individuals in the UK Biobank. We identified previously reported genes (

The application of large-scale metabolomic profiling provides new opportunities for realizing the potential of omics-based precision medicine for asthma. By leveraging data from over 14,000 individuals in four distinct cohorts, this study identifies and independently replicates 17 steroid metabolites whose levels were significantly reduced in individuals with prevalent asthma. Although steroid levels were reduced among all asthma cases regardless of medication use, the largest reductions were associated with inhaled corticosteroid (ICS) treatment, as confirmed in a 4-year low-dose ICS clinical trial. Effects of ICS treatment on steroid levels were dose dependent; however, significant reductions also occurred with low-dose ICS treatment. Using information from electronic medical records, we found that cortisol levels were substantially reduced throughout the entire 24-hour daily period in patients with asthma who were treated with ICS compared to those who were untreated and to patients without asthma. Moreover, patients with asthma who were treated with ICS showed significant increases in fatigue and anemia as compared to those without ICS treatment. Adrenal suppression in patients with asthma treated with ICS might, therefore, represent a larger public health problem than previously recognized. Regular cortisol monitoring of patients with asthma treated with ICS is needed to provide the optimal balance between minimizing adverse effects of adrenal suppression while capitalizing on the established benefits of ICS treatment.

To identify circulating proteins influencing Coronavirus Disease 2019 (COVID-19) susceptibility and severity, we undertook a two-sample Mendelian randomization (MR) study, rapidly scanning hundreds of circulating proteins while reducing bias due to reverse causation and confounding. In up to 14,134 cases and 1.2 million controls, we found that an s.d. increase in OAS1 levels was associated with reduced COVID-19 death or ventilation (odds ratio (OR) = 0.54, P = 7 × 10−8), hospitalization (OR = 0.61, P = 8 × 10−8) and susceptibility (OR = 0.78, P = 8 × 10−6). Measuring OAS1 levels in 504 individuals, we found that higher plasma OAS1 levels in a non-infectious state were associated with reduced COVID-19 susceptibility and severity. Further analyses suggested that a Neanderthal isoform of OAS1 in individuals of European ancestry affords this protection. Thus, evidence from MR and a case–control study support a protective role for OAS1 in COVID-19 adverse outcomes. Available pharmacological agents that increase OAS1 levels could be prioritized for drug development. A variant of the OAS1 gene, which encodes an enzyme that is critical for the innate immune response to viral infections, is associated with decreased risk of death in patients with COVID-19.

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

Type 2 diabetes (T2D) is a chronic metabolic disorder with a significant genetic component. While large-scale population studies have identified hundreds of common genetic variants associated with T2D susceptibility, the role of rare (minor allele frequency < 0.1%) protein coding variation is less clear. To this end, we performed a gene burden analysis of 18,691 genes in 418,436 (n=32,374 T2D cases) individuals sequenced by the UK Biobank (UKBB) study to assess the impact of rare genetic variants on T2D risk. Our analysis identified T2D associations at exome-wide significance (P < 6.9×10-7) with rare, damaging variants within previously identified genes including GCK, GIGYF1, HNF1A, and TNRC6B. In addition, individuals with rare, damaging missense variants in the genes ZEB2 (N=31 carriers; OR=5.5 [95% CI=2.5-12.0]; p=6.4×10-7), MLXIPL (N=245; OR=2.3 [1.6-3.2]; p=3.2×10-7), and IGF1R (N=394; OR=2.4 [1.8-3.2]; p=1.3×10-10) have higher risk of T2D. Carriers of damaging missense variants within IGF1R were also shorter (-2.2cm [-1.8-2.7]; p=1.2×10-19) and had higher circulating protein levels of insulin-like growth factor-1 (IGF-1; 2.3 nmol/L [1.7-2.9] p=2.8×10-14), indicating relative IGF-1 resistance. A likely causal role of IGF-1 resistance on T2D was further supported by Mendelian randomisation analyses using common variants. Our results increase our understanding of the genetic architecture of T2D and highlight a potential therapeutic benefit of targeting the Growth Hormone/IGF-1 axis.

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.

BackgroundIt is plausible that maternal pregnancy metabolism influences risk of offspring congenital heart disease (CHD). We sought to explore this through a systematic approach using different methods and data.MethodsWe undertook multivariable logistic regression of the odds of CHD for 923 Mass Spectrometry (MS)-derived metabolites in a sub-sample of a UK birth cohort (Born in Bradford (BiB); N = 2,605, 46 CHD cases). We considered metabolites reaching a p-value threshold ResultsIn the main multivariable analyses, we identified 44 metabolites suggestively associated with CHD, including those from the following super pathways: amino acids, lipids, co-factors and vitamins, xenobiotics, nucleotides, energy, and several unknown molecules. Of these 44, isoleucine and leucine were available in the larger BiB cohort (NMR), and for these the results were validated. MR analyses were possible for 27/44 metabolites and for 11 there was consistency with multivariable regression results.ConclusionsIn summary, we have used complimentary data sources and statistical techniques to construct layers of evidence. We found that amino acid metabolism during pregnancy, several lipids (more specifically androgenic steroids), and levels of succinylcarnitine could be important contributing factors for CHD.

Common SNPs are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here we show, using GWAS data from 5.4 million individuals of diverse ancestries, that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a median size of ~90 kb, covering ~21% of the genome. The density of independent associations varies across the genome and the regions of elevated density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs account for 40% of phenotypic variance in European ancestry populations but only ~10%-20% in other ancestries. Effect sizes, associated regions, and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely explained by linkage disequilibrium and allele frequency differences within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than needed to implicate causal genes and variants. Overall, this study, the largest GWAS to date, provides an unprecedented saturated map of specific genomic regions containing the vast majority of common height-associated variants.

UMOD is a major risk gene for monogenic and complex forms of kidney disease. The encoded kidney-specific protein uromodulin is highly abundant in urine and related to chronic kidney disease, hypertension, and pathogen defense. To gain insights into potential systemic roles, we performed genome-wide screens of circulating uromodulin using complementary antibody-based (N=13,985) and aptamer-based (N=18,070) assays. We detected 3 and 10 distinct significant (p

Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3–10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12–16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check. Large-scale aptamer-based scanning of plasma proteins coupled with machine learning demonstrates proof-of-concept and feasibility of an individualized health check using a single blood sample.

Aims/hypothesis Type 1 and type 2 diabetes differ with respect to pathophysiological factors such as beta cell function, insulin resistance and phenotypic appearance, but there may be overlap between the two forms of diabetes. However, there are relatively few prospective studies that have characterised the relationship between autoimmunity and incident diabetes. We investigated associations of antibodies against the 65 kDa isoform of GAD (GAD65) with type 1 diabetes and type 2 diabetes genetic risk scores and incident diabetes in adults in European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct, a case-cohort study nested in the EPIC cohort. Methods GAD65 antibodies were analysed in EPIC participants (over 40 years of age and free of known diabetes at baseline) by radioligand binding assay in a random subcohort (n = 15,802) and in incident diabetes cases (n = 11,981). Type 1 diabetes and type 2 diabetes genetic risk scores were calculated. Associations between GAD65 antibodies and incident diabetes were estimated using Prentice-weighted Cox regression. Results GAD65 antibody positivity at baseline was associated with development of diabetes during a median follow-up time of 10.9 years (HR for GAD65 antibody positive vs negative 1.78; 95% CI 1.43, 2.20) after adjustment for sex, centre, physical activity, smoking status and education. The genetic risk score for type 1 diabetes but not type 2 diabetes was associated with GAD65 antibody positivity in both the subcohort (OR per SD genetic risk 1.24; 95% CI 1.03, 1.50) and incident cases (OR 1.97; 95% CI 1.72, 2.26) after adjusting for age and sex. The risk of incident diabetes in those in the top tertile of the type 1 diabetes genetic risk score who were also GAD65 antibody positive was 3.23 (95% CI 2.10, 4.97) compared with all other individuals, suggesting that 1.8% of incident diabetes in adults was attributable to this combination of risk factors. Conclusions/interpretation Our study indicates that incident diabetes in adults has an element of autoimmune aetiology. Thus, there might be a reason to re-evaluate the present subclassification of diabetes in adulthood.

Even though physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Here, we combine data for up to 674,980 individuals from 51 studies in a trans-ancestry meta-analysis of genome-wide association studies for self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA); leisure screen time (LST); sedentary commuting; and sedentary behavior at work. We identify 99 loci that associate with at least one trait. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. Molecular dynamics simulations suggest that the Glu to Ala substitution encoded by rs2229456 (ACTN3) – associated with more MVPA – disrupts salt bridge interactions and makes the alpha actinin 3 filaments more flexible. In isolated type IIA muscle fibers, the Ala-encoding allele is associated with lower maximal force and power during an isometric contraction, suggesting protection from exercise-induced muscle damage. Finally, Mendelian Randomization analyses show that the causal effect of LST on BMI is 2-3 times larger than the effect of body mass index (BMI) on LST, and that beneficial effects of LST and MVPA on several risk factors and diseases are mediated or confounded by BMI. Taken together, our results provide mechanistic insights into the regulation of MVPA and into the role of LST and MVPA in disease prevention. These insights may facilitate the development of tailored physical activity interventions.

A major challenge of genome-wide association studies (GWAS) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations, and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels, and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. Two prioritized genes, CREBRF and RRBP1, show convergent evidence across functional datasets supporting their roles in lipid biology.

Affinity-based proteomics has enabled scalable quantification of thousands of protein targets in blood enhancing biomarker discovery, understanding of disease mechanisms, and genetic evaluation of drug targets in humans through protein quantitative trait loci (pQTLs). Here, we integrate two partly complementary techniques-the aptamer-based SomaScan�� v4 assay and the antibody-based Olink assays-to systematically assess phenotypic consequences of hundreds of pQTLs discovered for 871 protein targets across both platforms. We create a genetically anchored cross-platform proteome-phenome network comprising 547 protein-phenotype connections, 36.3% of which were only seen with one of the two platforms suggesting that both techniques capture distinct aspects of protein biology. We further highlight discordance of genetically predicted effect directions between assays, such as for PILRA and Alzheimer's disease. Our results showcase the synergistic nature of these technologies to better understand and identify disease mechanisms and provide a benchmark for future cross-platform discoveries., The Fenland Study (10.22025/2017.10.101.00001) is funded by the Medical Research Council (MC_UU_12015/1). We are grateful to all the volunteers and to the General Practitioners and practice staff for assistance with recruitment. We thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams. We further acknowledge support for genomics from the Medical Research Council (MC_PC_13046). Proteomic measurements were supported and governed by a collaboration agreement between the University of Cambridge and Somalogic. JCZ is supported by a 4-year Wellcome Trust PhD Studentship and the Cambridge Trust, CL, EW, and NJW are funded by the Medical Research Council (MC_UU_12015/1). NJW is a NIHR Senior Investigator. ADH is an NIHR Senior Investigator and supported by the UCL Hospitals NIHR Biomedical Research Centre and the UCL BHF Research Accelerator (AA/18/6/34223). We thank Philippa Pettingill, Ida Grundberg, Klev Diamanti, and Andrea Ballagi for advice and comments on an earlier draft of this manuscript. We thank Vladimir Saudek for generating a 3D-model of variant GDF-15 protein.

Detangling gene-disease connections Many diseases are at least partially due to genetic causes that are not always understood or targetable with specific treatments. To provide insight into the biology of various human diseases as well as potential leads for therapeutic development, Pietzner et al . undertook detailed, genome-wide proteogenomic mapping. The authors analyzed thousands of connections between potential disease-associated mutations, specific proteins, and medical conditions, thereby providing a detailed map for use by future researchers. They also supplied some examples in which they applied their approach to medical contexts as varied as connective tissue disorders, gallstones, and COVID-19 infections, sometimes even identifying single genes that play roles in multiple clinical scenarios. —YN

Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4,701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict adverse COVID-19 outcomes in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4,701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different adverse COVID-19 outcomes were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of adverse COVID-19 outcomes. Further research is needed to understand how to incorporate protein measurement into clinical care.

Background: Current lipid guidelines suggest measurement of Lp(a) (lipoprotein[a]) and ApoB (apolipoprotein B) for atherosclerotic cardiovascular disease risk assessment. Polygenic risk scores (PRSs) for Lp(a) and ApoB may identify individuals unlikely to have elevated Lp(a) or ApoB and thus reduce such suggested testing. Methods: PRSs were developed using least absolute shrinkage and selection operator regression among 273 222 and 356 958 UK Biobank participants of white British ancestry for Lp(a) and ApoB, respectively, and validated in separate sets of 60 771 UK Biobank and 15 050 European Prospective Investigation into Cancer and Nutrition-Norfolk participants. We then assessed the proportion of participants who, based on these PRSs, were unlikely to benefit from Lp(a) or ApoB measurements, according to current lipid guidelines. Results: In the UK Biobank and European Prospective Investigation into Cancer and Nutrition-Norfolk cohorts, the area under the receiver operating curve for the PRS-predicted Lp(a) and ApoB to identify individuals with elevated Lp(a) and ApoB was at least 0.91 (95% CI, 0.90–0.92) and 0.74 (95% CI, 0.73–0.75), respectively. The Lp(a) PRS and measured Lp(a) showed comparable association with atherosclerotic cardiovascular disease incidence, whereas the ApoB PRS was in general less predictive of atherosclerotic cardiovascular disease risk than measured ApoB. In the context of the European Society of Cardiology/European Atherosclerosis Society lipid guidelines, at a 95% sensitivity to identify individuals with elevated Lp(a) and ApoB levels, at least 54% of Lp(a) and 24% of ApoB testing could be reduced by prescreening with a PRS while maintaining a low false-negative rate. Conclusions: A substantial proportion of suggested testing for elevated Lp(a) and a modest proportion of testing for elevated ApoB could potentially be reduced by prescreening individuals with PRSs.

Context Biological and translational insights from large-scale, array-based genetic studies of fat distribution, a key determinant of metabolic health, have been limited by the difficulty in linking predominantly noncoding variants to specific gene targets. Rare coding variant analyses provide greater confidence that a specific gene is involved, but do not necessarily indicate whether gain or loss of function (LoF) would be of most therapeutic benefit. Objective This work aimed to identify genes/proteins involved in determining fat distribution. Methods We combined the power of genome-wide analysis of array-based rare, nonsynonymous variants in 450 562 individuals in the UK Biobank with exome-sequence-based rare LoF gene burden testing in 184 246 individuals. Results The data indicate that the LoF of 4 genes (PLIN1 [LoF variants, P = 5.86 × 10–7], INSR [LoF variants, P = 6.21 × 10–7], ACVR1C [LoF + moderate impact variants, P = 1.68 × 10–7; moderate impact variants, P = 4.57 × 10–7], and PDE3B [LoF variants, P = 1.41 × 10–6]) is associated with a beneficial effect on body mass index–adjusted waist-to-hip ratio and increased gluteofemoral fat mass, whereas LoF of PLIN4 (LoF variants, P = 5.86 × 10–7 adversely affects these parameters. Phenotypic follow-up suggests that LoF of PLIN1, PDE3B, and ACVR1C favorably affects metabolic phenotypes (eg, triglycerides [TGs] and high-density lipoprotein [HDL] cholesterol concentrations) and reduces the risk of cardiovascular disease, whereas PLIN4 LoF has adverse health consequences. INSR LoF is associated with lower TG and HDL levels but may increase the risk of type 2 diabetes. Conclusion This study robustly implicates these genes in the regulation of fat distribution, providing new and in some cases somewhat counterintuitive insight into the potential consequences of targeting these molecules therapeutically.