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BACKGROUND: Adverse pregnancy outcomes (APO) contribute to higher risk of maternal cerebrovascular disease, but longitudinal data that include APO and stroke timing are lacking. We hypothesized that APO are associated with younger age at first stroke, with a stronger relationship in those with >1 pregnancy with APO. METHODS: We analyzed longitudinal Finnish nationwide health registry data from the FinnGen Study. We included women who gave birth after 1969 when the hospital discharge registry was established. We defined APO as a pregnancy affected by gestational hypertension, preeclampsia, eclampsia, preterm birth, small for gestational age infant, or placental abruption. We defined stroke as first hospital admission for ischemic stroke or nontraumatic intracerebral or subarachnoid hemorrhage, excluding stroke during pregnancy or within 1 year postpartum. We used Kaplan-Meier survival curves and multivariable-adjusted Cox and generalized linear models to assess the relationship between APO and future stroke. RESULTS: We included 144 306 women with a total of 316 789 births in the analysis sample, of whom 17.9% had at least 1 pregnancy with an APO and 2.9% experienced an APO in ≥2 pregnancies. Women with APO had more comorbidities including obesity, hypertension, heart disease, and migraine. Median age at first stroke was 58.3 years in those with no APO, 54.8 years in those with 1 APO, and 51.6 years in those with recurrent APO. In models adjusted for sociodemographic characteristics and stroke risk factors, risk of stroke was greater in women with 1 APO (adjusted hazard ratio, 1.3 [95% CI, 1.2–1.4]) and recurrent APO (adjusted hazard ratio, 1.4 [95% CI, 1.2–1.7]) compared with those with no APO. Women with recurrent APO had more than twice the stroke risk before age 45 (adjusted odds ratio, 2.1 [95% CI, 1.5–3.1]) compared with those without APO. CONCLUSIONS: Women who experience APO have earlier onset of cerebrovascular disease, with the earliest onset in those with more than 1 affected pregnancy.

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.

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.

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

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.

Studies of the genetic regulation of cerebrospinal fluid (CSF) proteins may reveal pathways for treatment of neurological diseases. 398 proteins in CSF were measured in 1,591 participants from the BioFINDER study. Protein quantitative trait loci (pQTL) were identified as associations between genetic variants and proteins, with 176 pQTLs for 145 CSF proteins (P 1.25 × 10

The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data. Funding Agencies|SciLifeLab; Swedish Research Council (Vetenskapsradet)Swedish Research Council [D0886501]; US National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [MH077139, MH1095320]; European UnionEuropean Commission [610307, 733161, 825843]; Swedish Cancer SocietySwedish Cancer Society; Swedish Research CouncilSwedish Research CouncilEuropean Commission [2018-02837, 2014-03352, 2009-1039, 2018-03307]; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation; Karolinska Institutet, Karolinska University HospitalKarolinska Institutet; Radiumhemmets Forskningsfonder; European Research CouncilEuropean Research Council (ERC)European Commission [CoG-2015_681742_NASCENT]; Swedish Heart-Lung FoundationSwedish Heart-Lung Foundation [20160872]; Swedish Foundation for Strategic Research (IRC Center); Novo Nordisk FoundationNovo Nordisk FoundationNovocure Limited; ALF; Strategic Research Area Epidemiology at Karolinska Institutet; Swedish Rheumatism Association; Vth 80-year Foundation; Swedish Society of Medicine; Clinical Research Support (Avtal om Lakarutbildning och Forskning); Swedish government; county councils; ALF-agreement; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research [15-0067]; IMI2 Joint Undertaking [115974]; European Federation of Pharmaceutical Industries and Associations with JDRF; H2020 Program ERA PerMed JTC 2018 Call (VR) [2018-05619]

Objective: To identify causal pathophysiological mechanisms for atherosclerosis and incident cardiovascular events using protein measurements. Approach and Results: Carotid artery atherosclerosis was assessed by ultrasound, and 86 cardiovascular-related proteins were measured using the Olink CVD-I panel in 7 Swedish prospective studies (11 754 individuals). The proteins were analyzed in relation to intima-media thickness in the common carotid artery (IMT-CCA), plaque occurrence, and incident cardiovascular events (composite end point of myocardial infarction or ischemic stroke) using a discovery/replication approach in different studies. After adjustments for traditional cardiovascular risk factors, 11 proteins remained significantly associated with IMT-CCA in the replication stage, whereas 9 proteins were replicated for plaque occurrence and 17 proteins for incident cardiovascular events. NT-proBNP (N-terminal pro-B-type natriuretic peptide) and MMP (matrix metalloproteinase)-12 were associated with both IMT-CCA and incident events, but the overlap was considerably larger between plaque occurrence and incident events, including MMP-12, TIM-1 (T-cell immunoglobulin and mucin domain 1), GDF (growth/differentiation factor)-15, IL (interleukin)-6, U-PAR (urokinase plasminogen activator surface receptor), LOX-1 (lectin-like oxidized LDL [low-density lipoprotein] receptor 1), and TRAIL-R2 (TNF [tumor necrosis factor]-related apoptosis-inducing ligand receptor 2). Only MMP-12 was associated with IMT-CCA, plaque, and incident events with a positive and concordant direction of effect. However, a 2-sample Mendelian randomization analysis suggested that increased MMP-12 may be protective against ischemic stroke ( P =5.5×10 −7 ), which is in the opposite direction of the observational analyses. Conclusions: The present meta-analysis discovered several proteins related to carotid atherosclerosis that partly differed in their association with IMT-CCA, plaque, and incident atherosclerotic disease. Mendelian randomization analysis for the top finding, MMP-12, suggests that the increased levels of MMP-12 could be a consequence of atherosclerotic burden rather than the opposite chain of events.

Introduction: PCSK6 is a protease that activates cytokines and growth factors and strongly enriched in healthy human liver, however its function in this context has not been explored. We have previously shown that PCSK6 is induced in atherosclerotic plaques from patients with symptoms of stroke and important for regulating several cell types in this context. Here, we aimed to investigate the role of PCSK6 in lipid metabolism in liver, particularly in the context of atherosclerosis and non-alcoholic fatty liver disease (NAFLD). Methods: We used publically available datasets and several atherosclerosis biobanks to investigate the expression of PCSK6 in healthy and diseased human tissues. In addition, we used full Pcsk6 -/- mice as well as liver specific conditional Pcsk6 -/- knockout mice compared to littermate controls, to investigate the effects of PCSK6 ablation on lipid metabolism. Results: Genetic analyses of the PCSK6 locus identified a variant, rs7181043, that was significantly associated with PCSK6 mRNA expression in healthy human adipose tissue, liver and in atherosclerotic plaques. The same variant was associated specifically with plaque fat content and atherosclerotic patient’s plasma LDL levels. In addition, PCSK6 mRNA expression in plaques was positively correlated with total plasma cholesterol and LDL levels in atherosclerotic patients as well as lipid metabolism associated pathways within the carotid plaque. Microarray comparison of the livers from Pcsk6 -/- mice and controls showed that VLDL particle assembly was one of the upregulated processes. I n vivo studies showed that Pcsk6 -/- mice have higher plasma cholesterol and LPL levels at baseline compared to controls, and lower levels of LDLR in their liver. These findings were further confirmed in liver specific conditional knockouts. Preliminary results show that liver specific knockout mice develop increased liver steatosis and fibrosis on a modified western diet. Conclusions: Our data suggests that PCSK6 is involved in cholesterol and metabolic control in liver. Breeding of liver specific Pcsk6 knockout mice on an ApoE -/- background is currently ongoing and will provide insight into the role of liver PCSK6 in atherosclerosis and NAFLD development.

Fibroblast growth factor 23 (FGF-23) is associated with a range of cardiovascular and non-cardiovascular diseases in conventional epidemiological studies, but substantial residual confounding may exist. Mendelian randomisation approaches can help control for such confounding. SCALLOP consortium data on 19,195 participants were used to generate an FGF-23 genetic score. Data from 337,448 UK Biobank participants were used to estimate associations between higher genetically-predicted FGF-23 concentration and the odds of any atherosclerotic cardiovascular disease (n=26,266 events), of any non-atherosclerotic cardiovascular disease (n=12,652), and of non-cardiovascular diseases previously linked to FGF-23. Measurements of carotid intima-media thickness (CIMT) and left ventricular mass (LVM) were available in a subset. Associations with cardiovascular outcomes were also tested in three large case-control consortia: CARDIOGRAMplusC4D (coronary artery disease, n=181,249 cases), MEGASTROKE (stroke, n=34,217), and HERMES (heart failure, n=47,309). We identified 34 independent variants for circulating FGF-23 which formed a validated genetic score. There were no associations between genetically-predicted FGF-23 and any of the cardiovascular or non-cardiovascular outcomes. In UK Biobank, the odds ratio for any atherosclerotic cardiovascular disease per 1-SD higher genetically-predicted logFGF-23 was 1.03 (95% confidence interval [CI] 0.98-1.08), and for any non-atherosclerotic cardiovascular disease was 1.01 (0.94-1.09). The odds ratios in the case-control consortia were 1.00 (0.97-1.03) for coronary artery disease, 1.01 (0.95-1.07) for stroke, and 1.00 (0.95-1.05) for heart failure. In those with imaging, logFGF-23 was not associated with CIMT or LVM index. This suggests that previously reported observational associations of FGF-23 with risk of atherosclerotic and non-atherosclerotic cardiovascular diseases are unlikely to be causal.

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.

Genetic loci associated with CHD show different relationships with intima-media thickness in the common carotid artery (IMT-CCA) and in the bulb (IMT-bulb). We evaluated if IMT-CCA and IMT-bulb differ also with respect to circulating protein profiles and risk of incident atherosclerotic disease.In three Swedish cohorts (MDC, IMPROVE, PIVUS, total n 7000), IMT-CCA and IMT-bulb were assessed by ultrasound at baseline, and 86 cardiovascular-related proteins were analyzed. In the PIVUS study only, IMT-CCA and IMT-bulb were investigated in relation to incident atherosclerotic disease over 10 years of follow-up.In a meta-analysis of the analysis performed separately in the cohorts, three proteins, matrix metalloproteinase-12 (MMP-12), hepatocyte growth factor (HGF) and N-terminal pro-B-type natriuretic peptide (NT-proBNP), were associated with IMT-CCA when adjusted for traditional cardiovascular risk factors. Five proteins were associated with IMT-bulb (MMP-12, growth/differentiation factor 15 (GDF-15), osteoprotegerin, growth hormone and renin). Following adjustment for cardiovascular risk factors, IMT-bulb was significantly more closely related to incident stroke or myocardial infarction (total number of cases, 111) than IMT-CCA in the PIVUS study (HR 1.51 for 1 SD, 95%CI 1.21-1.87, p 0.001 vs HR 1.17, 95%CI 0.93-1.47, p = 0.16). MMP-12 levels were related to this combined end-point (HR 1.30, 95%CI 1.08-1.56, p = 0.0061).Elevated levels of MMP-12 were associated with both IMT-CCA and IMT-bulb, but other proteins were significantly related to IMT in only one of these locations. The finding that IMT-bulb was more closely related to incident atherosclerotic disease than IMT-CCA emphasizes a difference between these measurements of IMT.

ObjectiveHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome. We aimed to derive HFpEF phenotype-based groups ('phenogroups') based on clinical and echocardiogram data using machine learning, and to compare clinical characteristics, proteomics and outcomes across the phenogroups.MethodsWe applied model-based clustering to 32 echocardiogram and 11 clinical and laboratory variables collected in stable condition from 320 HFpEF outpatients in the Karolinska-Rennes cohort study (56% female, median 78 years (IQR: 71–83)). Baseline proteomics and the composite end point of all-cause mortality or heart failure (HF) hospitalisation were used in secondary analyses.ResultsWe identified six phenogroups, for which significant differences in the prevalence of concomitant atrial fibrillation (AF), anaemia and kidney disease were observed (pConclusionsUsing machine learning we identified distinct HFpEF phenogroups with differential characteristics and outcomes, as well as differential levels of inflammatory and cardiovascular proteins.

Despite the increasing global burden of neurological disorders, there is a lack of effective diagnostic and therapeutic biomarkers. Proteins are often dysregulated in disease and have a strong genetic component. Here, we carry out a protein quantitative trait locus analysis of 184 neurologically-relevant proteins, using whole genome sequencing data from two isolated population-based cohorts (N = 2893). In doing so, we elucidate the genetic landscape of the circulating proteome and its connection to neurological disorders. We detect 214 independently-associated variants for 107 proteins, the majority of which (76%) are cis-acting, including 114 variants that have not been previously identified. Using two-sample Mendelian randomisation, we identify causal associations between serum CD33 and Alzheimer’s disease, GPNMB and Parkinson’s disease, and MSR1 and schizophrenia, describing their clinical potential and highlighting drug repurposing opportunities., Serum proteins are easily accessible biomarkers and drug targets. Here, the authors use whole genome sequencing data to describe the genetic architecture of neurologically-relevant serum proteins and establish causal protein-neurological disease relationships.

Aims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targets) consortium aims to identify the genomic and molecular basis of heart failure.Methods and results The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34-90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of >1.10 for common variants (allele frequency > 0.05) and >1.20 for low-frequency variants (allele frequency 0.01-0.05) at P < 5 x 10(-8) under an additive genetic model.Conclusions HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.

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

To date, the development of disease-modifying therapies for Alzheimer’s disease (AD) has largely focused on the removal of amyloid beta Aβ fragments from the CNS. Proteomic profiling of patient fluids may help identify novel therapeutic targets and biomarkers associated with AD pathology. Here, we applied the Olink™ ProSeek immunoassay to measure 270 CSF and plasma proteins across 415 Aβ- negative cognitively normal individuals (Aβ- CN), 142 Aβ-positive CN (Aβ+ CN), 50 Aβ- mild cognitive impairment (MCI) patients, 75 Aβ+ MCI patients, and 161 Aβ+ AD patients from the Swedish BioFINDER study. A validation cohort included 59 Aβ- CN, 23 Aβ- + CN, 44 Aβ- MCI and 53 Aβ+ MCI. To compare protein concentrations in patients versus controls, we applied multiple linear regressions adjusting for age, gender, medications, smoking and mean subject-level protein concentration, and corrected findings for false discovery rate (FDR, q d q d q q

Highlights • Current risk prediction models use a variety of factors to identify women at risk of developing breast cancer. • Proteins circulating in blood represent an attractive but currently still underrepresented source of candidates serving as molecular risk factors. • Plasma samples from women participating in a prospective breast cancer cohort study were studied for proteomic risk factors related to a future breast cancer diagnosis. • Applying data-driven approaches on the levels of circulating proteins, women with future breast cancers and previous use of menopausal hormone therapy were identified. • Menopausal hormone therapy was found to alter components of the circulating proteomes even years after the treatment ended., Accessible risk predictors are crucial for improving the early detection and prognosis of breast cancer. Blood samples are widely available and contain proteins that provide important information about human health and disease, however, little is still known about the contribution of circulating proteins to breast cancer risk prediction. We profiled EDTA plasma samples collected before diagnosis from the Swedish KARMA breast cancer cohort to evaluate circulating proteins as molecular predictors. A data-driven analysis strategy was applied to the molecular phenotypes built on 700 circulating proteins to identify and annotate clusters of women. The unsupervised analysis of 183 future breast cancer cases and 366 age-matched controls revealed five stable clusters with distinct proteomic plasma profiles. Among these women, those in the most stable cluster (N = 19; mean Jaccard index: 0.70 ± 0.29) were significantly more likely to have used menopausal hormonal therapy (MHT), get a breast cancer diagnosis, and were older compared to the remaining clusters. The circulating proteins associated with this cluster (FDR < 0.001) represented physiological processes related to cell junctions (F11R, CLDN15, ITGAL), DNA repair (RBBP8), cell replication (TJP3), and included proteins found in female reproductive tissue (PTCH1, ZP4). Using a data-driven approach on plasma proteomics data revealed the potential long-lasting molecular effects of menopausal hormonal therapy (MHT) on the circulating proteome, even after women had ended their treatment. This provides valuable insights concerning proteomics efforts to identify molecular markers for breast cancer risk prediction.

Obesity-related inflammation is associated with cardiovascular, metabolic, and pulmonary diseases. The aim of this study was to demonstrate associations between adiposity measurements and levels of inflammation-related plasma proteins in a population of young adults. Subjects from a population-based birth cohort with a mean age of 22.5 years were included in the study population (n = 2074). Protein levels were analyzed using the Olink Proseek Multiplex Inflammation panel. Percentage body fat (%BF) and visceral fat rating (VFR) measurements were collected using Tanita MC 780 body composition monitor. Linear regression of standardized values was used to investigate associations. Potential effect modifications by sex and BMI category were assessed. Of 71 investigated proteins, 54 were significantly associated with all adiposity measurements [%BF, body mass index (BMI), VFR and waist circumference]. Among proteins associated with %BF, seven showed a larger or unique association in overweight/obese subjects and three showed a significant effect modification by sex. Fourteen proteins more strongly associated with VFR in females compared to males. Adipose-associated systemic inflammation was observed in this young adult population. Sex and adiposity localization influenced some of the associations. Our results highlight specific proteins as suitable biomarkers related to adiposity.

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

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. By mapping and replicating protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, we identified 467 pQTLs for 85 proteins. The pQTLs were used in combination with other sources of information to evaluate known drug targets, and suggest new target candidates or repositioning opportunities, underpinned by a) causality assessment using Mendelian randomization, b) pathway mapping usingtrans-pQTL gene assignments, and c) protein-centric polygenic risk scores enabling matching of plausible target mechanisms to sub-groups of individuals enabling precision medicine.

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health.

Many Alzheimer’s disease (AD) genes including Apolipoprotein E (APOE) are found to be expressed in blood-derived macrophages and thus may alter blood protein levels. We measured 91 neuro-proteins in plasma from 316 participants of the Rotterdam Study (incident AD = 161) using Proximity Extension Ligation assay. We studied the association of plasma proteins with AD in the overall sample and stratified by APOE. Findings from the Rotterdam study were replicated in 186 AD patients of the BioFINDER study. We further evaluated the correlation of these protein biomarkers with total tau (t-tau), phosphorylated tau (p-tau) and amyloid-beta (Aβ) 42 levels in cerebrospinal fluid (CSF) in the Amsterdam Dementia Cohort (N = 441). Finally, we conducted a genome-wide association study (GWAS) to identify the genetic variants determining the blood levels of AD-associated proteins. Plasma levels of the proteins, CDH6 (β = 0.638, P = 3.33 × 10−4) and HAGH (β = 0.481, P = 7.20 × 10−4), were significantly elevated in APOE ε4 carrier AD patients. The findings in the Rotterdam Study were replicated in the BioFINDER study for both CDH6 (β = 1.365, P = 3.97 × 10−3) and HAGH proteins (β = 0.506, P = 9.31 × 10−7) when comparing cases and controls in APOE ε4 carriers. In the CSF, CDH6 levels were positively correlated with t-tau and p-tau in the total sample as well as in APOE ε4 stratum (P −3). The HAGH protein was not detected in CSF. GWAS of plasma CDH6 protein levels showed significant association with a cis-regulatory locus (rs111283466, P = 1.92 × 10−9). CDH6 protein is implicated in cell adhesion and synaptogenesis while HAGH protein is related to the oxidative stress pathway. Our findings suggest that these pathways may be altered during presymptomatic AD and that CDH6 and HAGH may be new blood-based biomarkers.

Objective— Revealing patterns of associations between circulating protein and lipid levels could improve biological understanding of cardiovascular disease (CVD). In this study, we investigated the associations between proteins related to CVD and triglyceride (TG), total cholesterol, LDL (low-density lipoprotein), and HDL (high-density lipoprotein) cholesterol levels in individuals from the general population. Approach and Results— We measured plasma protein levels using the Olink ProSeek CVD I or II+III arrays and analyzed 57 proteins available in 3 population-based cohorts: EpiHealth (n=2029; 52% women; median age, 61 years), PIVUS (Prospective Study of the Vasculature in Uppsala Seniors; n=790; 51% women; all aged 70 years), and ULSAM (Uppsala Longitudinal Study of Adult Men; n=551; all men aged 77 years). A discovery analysis was performed in EpiHealth in a regression framework (adjusted for sex, age, body mass index, smoking, glucose levels, systolic blood pressure, blood pressure medication, diabetes mellitus medication, and CVD history), and associations with false discovery rate P value of 0.05 was considered a successful replication (validation false discovery rate of 0.1%). We used summary statistics from a genome-wide association study on each protein biomarker (meta-analysis of EpiHealth, PIVUS, ULSAM, and IMPROVE [Carotid Intima–Media Thickness and IMT-Progression as Predictors of Vascular Events in a High-Risk European Population]) and publicly available data from Global Lipids Genetics Consortium to perform Mendelian randomization analyses to address possible causality of protein levels. Of 57 tested proteins, 42 demonstrated an association with at least 1 lipid fraction; 35 were associated with TG, 15 with total cholesterol, 9 with LDL cholesterol, and 24 with HDL cholesterol. Among these associations, we found KIM-1 (kidney injury molecule-1), TNFR (TNF [tumor necrosis factor] receptor) 1 and 2, TRAIL-R2 (TRAIL [TNF-related apoptosis-inducing ligand] receptor 2), and RETN (resistin) to be associated with all 4 lipid fractions. Further, 15 proteins were related to both TG and HDL cholesterol in a consistent and biologically expected manner, that is, higher TG and lower HDL cholesterol or vice versa. Another common pattern of associations was concomitantly higher TG, total cholesterol, and LDL cholesterol, which is associated with higher CVD risk. We did not find evidence of causal links for protein levels. Conclusions— Our comprehensive analysis of plasma proteins and lipid fractions of 3370 individuals from the general population provides new information about lipid metabolism.

BACKGROUND: Genome-wide association studies have identified numerous genetic regions that influence cross-sectional lung function. Longitudinal decline in lung function also includes a heritable component but the genetic determinants have yet to be defined.OBJECTIVES: We aimed to determine whether regions associated with cross-sectional lung function were also associated with longitudinal decline and to seek novel variants which influence decline.METHODS: We analysed genome-wide data from 4167 individuals from the Busselton Health Study cohort, who had undergone spirometry (12 695 observations across eight time points). A mixed model was fitted and weighted risk scores were calculated for the joint effect of 26 known regions on baseline and longitudinal changes in FEV1 and FEV1/FVC. Potential additional regions of interest were identified and followed up in two independent cohorts.RESULTS: The 26 regions previously associated with cross-sectional lung function jointly showed a strong effect on baseline lung function (p=4.44×10-16 for FEV1/FVC) but no effect on longitudinal decline (p=0.160 for FEV1/FVC). This was replicated in an independent cohort. 39 additional regions of interest (48 variants) were identified; these associations were not replicated in two further cohorts.CONCLUSIONS: Previously identified genetic variants jointly have a strong effect on cross-sectional lung function in adults but little or no effect on the rate of decline of lung function. It is possible that they influence COPD risk through lung development. Although no genetic variants have yet been associated with lung function decline at stringent genome-wide significance, longitudinal change in lung function is heritable suggesting that there is scope for future discoveries.

Heart failure (HF) is a leading cause of morbidity and mortality worldwide1. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained2–4. We report the largest GWAS meta-analysis of HF to-date, comprising 47,309 cases and 930,014 controls. We identify 12 independent variant associations with HF at 11 genomic loci, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function suggesting shared genetic aetiology. Expression quantitative trait analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homeostasis (BAG3), and cellular senescence (CDKN1A). Using Mendelian randomisation analysis we provide new evidence supporting previously equivocal causal roles for several HF risk factors identified in observational studies, and demonstrate CAD-independent effects for atrial fibrillation, body mass index, hypertension and triglycerides. These findings extend our knowledge of the genes and pathways underlying HF and may inform the development of new therapeutic approaches.

BackgroundSmoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk.MethodsWe analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci.ResultsMeta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals.ConclusionsRare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.