Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease

Effect of Cheese Intake on Cardiovascular Diseases and Cardiovascular Biomarkers

Background: A growing number of cohort studies revealed an inverse association between cheese intake and cardiovascular diseases, yet the causal relationship is unclear. Objective: To assess the causal relationship between cheese intake, and cardiovascular diseases and cardiovascular biomarkers. Methods: A two-sample Mendelian randomization (MR) analysis based on publicly available genome-wide association studies was employed to infer the causal relationship. The effect estimates were calculated using the random-effects inverse-variance-weighted method. Results: Cheese intake per standard deviation increase causally reduced the risks of type 2 diabetes (odds ratio (OR) = 0.46; 95% confidence interval (CI), 0.34–0.63; p = 1.02 × 10⁻⁶), heart failure (OR = 0.62; 95% CI, 0.49–0.79; p = 0.0001), coronary heart disease (OR = 0.65; 95% CI, 0.53–0.79; p = 2.01 × 10⁻⁵), hypertension (OR = 0.67; 95% CI, 0.53–0.84; p = 0.001), and ischemic stroke (OR = 0.76; 95% CI, 0.63–0.91; p = 0.003). Suggestive evidence of an inverse association between cheese intake and peripheral artery disease was also observed. No associations were observed for atrial fibrillation, cardiac death, pulmonary embolism, or transient ischemic attack. The better prognosis associated with cheese intake may be explained by lower body mass index (BMI; effect estimate = −0.58; 95% CI, from −0.88 to −0.27; p = 0.0002), waist circumference (effect estimate = −0.49; 95% CI, from −0.76 to −0.23; p = 0.0003), triglycerides (effect estimate = −0.33; 95% CI, from −0.50 to −0.17; p = 4.91 × 10⁻⁵), and fasting glucose (effect estimate = −0.20; 95% CI, from −0.33 to −0.07; p = 0.0003). There was suggestive evidence of a positive association between cheese intake and high-density lipoprotein. No influences were observed for blood pressure or inflammation biomarkers. Conclusions: This two-sample MR analysis found causally inverse associations between cheese intake and type 2 diabetes, heart failure, coronary heart disease, hypertension, and ischemic stroke.

Gene-based whole genome sequencing meta-analysis of 250 circulating proteins in three isolated European populations

OBJECTIVE

Deep sequencing offers unparalleled access to rare variants in human populations. Understanding their role in disease is a priority, yet prohibitive sequencing costs mean that many cohorts lack the sample size to discover these effects on their own. Meta-analysis of individual variant scores allows the combination of rare variants across cohorts and study of their aggregated effect at the gene level, boosting discovery power. However, the methods involved have largely not been field-tested. In this study, we aim to perform the first meta-analysis of gene-based rare variant aggregation optimal tests, applied to the human cardiometabolic proteome.

METHODS

Here, we carry out this analysis across MANOLIS, Pomak and ORCADES, three isolated European cohorts with whole-genome sequencing (total N = 4,422). We examine the genetic architecture of 250 proteomic traits of cardiometabolic relevance. We use a containerised pipeline to harmonise variant lists across cohorts and define four sets of qualifying variants. For every gene, we interrogate protein-damaging variants, exonic variants, exonic and regulatory variants, and regulatory only variants, using the CADD and Eigen scores to weigh variants according to their predicted functional consequence. We perform single-cohort rare variant analysis and meta-analyse variant scores using the SMMAT package.

RESULTS

We describe 5 rare variant pQTLs (RV-pQTL) which pass our stringent significance threshold (7.45 × 10-11) and quality control procedure. These were split between four cis signals for MARCO, TEK, MMP2 and MPO, and one trans association for GDF2 in the SERPINA11 gene. We show that the cis-MPO association, which was not detectable using the single-point data alone, is driven by 5 missense and frameshift variants. These include rs140636390 and rs119468010, which are specific to MANOLIS and ORCADES, respectively. We show how this kind of signal could improve the predictive accuracy of genetic factors in common complex disease such as stroke and cardiovascular disease.

CONCLUSIONS

Our proof-of-concept study demonstrates the power of gene-based meta-analyses for discovering disease-relevant associations complementing common-variant signals by incorporating population-specific rare variation.

The genetic architecture of pneumonia susceptibility implicates mucin biology and a relationship with psychiatric illness

Pneumonia remains one of the leading causes of death worldwide. In this study, we use genome-wide meta-analysis of lifetime pneumonia diagnosis (N = 391,044) to identify four association signals outside of the previously implicated major histocompatibility complex region. Integrative analyses and finemapping of these signals support clinically tractable targets, including the mucin MUC5AC and tumour necrosis factor receptor superfamily member TNFRSF1A. Moreover, we demonstrate widespread evidence of genetic overlap with pneumonia susceptibility across the human phenome, including particularly significant correlations with psychiatric phenotypes that remain significant after testing differing phenotype definitions for pneumonia or genetically conditioning on smoking behaviour. Finally, we show how polygenic risk could be utilised for precision treatment formulation or drug repurposing through pneumonia risk scores constructed using variants mapped to pathways with known drug targets. In summary, we provide insights into the genetic architecture of pneumonia susceptibility and genetics informed targets for drug development or repositioning.

Genetically regulated gene expression and proteins revealed discordant effects

Background

Although gene-expression (GE) and protein levels are typically strongly genetically regulated, their correlation is known to be low. Here we investigate this phenomenon by focusing on the genetic background of this correlation in order to understand the similarities and differences in the genetic regulation of these omics layers.

Methods and results

We performed locus-wide association studies of 92 protein levels measured in whole blood for 2,014 samples of European ancestry and found that 66 are genetically regulated. Three female- and one male-specific effects were detected. We estimated the genetically regulated GE for all significant genes in 49 GTEx v8 tissues. A total of 7 proteins showed negative correlations with their respective GE across multiple tissues. Finally, we tested for causal links of GE on protein expression via Mendelian Randomization, and confirmed a negative causal effect of GE on protein level for five of these genes in a total of 63 gene-tissue pairs: BLMH, CASP3, CXCL16, IL6R, and SFTPD. For IL6R, we replicated the negative causal effect on coronary-artery disease (CAD), while its GE was positively linked to CAD.

Conclusion

While total GE and protein levels are only weakly correlated, we found high correlations between their genetically regulated components across multiple tissues. Of note, strong negative causal effects of tissue-specific GE on five protein levels were detected. Causal network analyses revealed that GE effects on CAD risks was in general mediated by protein levels.

Genetic analyses identify pleiotropy and causality for blood proteins and highlight Wnt/β-catenin signalling in migraine

Migraine is a common complex disorder with a significant polygenic SNP heritability (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{{SNP}}^{2}$$\end{document}hSNP2). Here we utilise genome-wide association study (GWAS) summary statistics to study pleiotropy between blood proteins and migraine under the polygenic model. We estimate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{{SNP}}^{2}$$\end{document}hSNP2 for 4625 blood protein GWASs and identify 325 unique proteins with a significant \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{{SNP}}^{2}$$\end{document}hSNP2 for use in subsequent genetic analyses. Pleiotropy analyses link 58 blood proteins to migraine risk at genome-wide, gene and/or single-nucleotide polymorphism levels—suggesting shared genetic influences or causal relationships. Notably, the identified proteins are largely distinct from migraine GWAS loci. We show that higher levels of DKK1 and PDGFB, and lower levels of FARS2, GSTA4 and CHIC2 proteins have a significant causal effect on migraine. The risk-increasing effect of DKK1 is particularly interesting—indicating a role for downregulation of β-catenin-dependent Wnt signalling in migraine risk, suggesting Wnt activators that restore Wnt/β-catenin signalling in brain could represent therapeutic tools against migraine.

Understanding of the causes and treatment of migraine is incomplete. Here, the authors detect pleiotropic genetic effects and causal relationships between migraine and 58 proteins that are largely distinct from migraine-associated loci identified by GWAS.

An optimal variant to gene distance window derived from an empirical definition of cis and trans protein QTLs

Background

A genome-wide association study (GWAS) correlates variation in the genotype with variation in the phenotype across a cohort, but the causal gene mediating that impact is often unclear. When the phenotype is protein abundance, a reasonable hypothesis is that the gene encoding that protein is the causal gene. However, as variants impacting protein levels can occur thousands or even millions of base pairs from the gene encoding the protein, it is unclear at what distance this simple hypothesis breaks down.

Results

By making the simple assumption that cis-pQTLs should be distance dependent while trans-pQTLs are distance independent, we arrive at a simple and empirical distance cutoff separating cis- and trans-pQTLs. Analyzing a recent large-scale pQTL study (Pietzner in Science 374:eabj1541, 2021) we arrive at an estimated distance cutoff of 944 kilobasepairs (95% confidence interval: 767–1,161) separating the cis and trans regimes.

Conclusions

We demonstrate that this simple model can be applied to other molecular GWAS traits. Since much of biology is built on molecular traits like protein, transcript and metabolite abundance, we posit that the mathematical models for cis and trans distance distributions derived here will also apply to more complex phenotypes and traits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04706-x.

Genome-Wide Identification of Associations of Circulating Molecules With Spontaneous Coronary Artery Dissection and Aortic Aneurysm and Dissection

Circulating proteins play functional roles in various biological processes and disease pathogenesis. The aim of this study was to highlight circulating proteins associated with aortic aneurysm and dissection (AAD) and spontaneous coronary artery dissection (SCAD). We examined the associations of circulating molecule levels with SCAD by integrating data from a genome-wide association study (GWAS) of CanSCAD and 7 pQTL studies. Mendelian randomization (MR) analysis was applied to examine the associations between circulating molecule levels and AAD by using data from UK Biobank GWAS and pQTL studies. The SCAD-associated SNPs in 1q21.2 were strongly associated with circulating levels of extracellular matrix protein 1 (ECM1) and 25 other proteins (encoded by CTSS, CAT, CNDP1, KNG1, SLAMF7, TIE1, CXCL1, MBL2, ESD, CXCL16, CCL14, KCNE5, CST7, PSME1, GPC3, MAP2K4, SPOCK3, LRPPRC, CLEC4M, NOG, C1QTNF9, CX3CL1, SCP2D1, SERPINF2, and FN1). These proteins were enriched in biological processes such as regulation of peptidase activity and regulation of cellular protein metabolic processes. Proteins (FGF6, FGF9, HGF, BCL2L1, and VEGFA) involved in the Ras signaling pathway were identified to be related to AAD. In addition, SCAD- and AAD-associated SNPs were associated with cytokine and lipid levels. MR analysis showed that circulating ECM1, SPOCK3 and IL1b levels were associated with AAD. Circulating levels of low-density lipoprotein cholesterol and small very-low-density lipoprotein particles were strongly associated with AAD. The present study found associations between circulating proteins and lipids and SCAD and AAD. Circulating ECM1 and low-density lipoprotein cholesterol may play a role in the pathology of SCAD and AAD.

Mendelian Randomization Analysis Reveals Causal Effects of Plasma Proteome on Body Composition Traits

CONTEXT

Observational studies have demonstrated associations between plasma proteins and obesity, but evidence of causal relationship remains to be studied.

OBJECTIVE

We aimed to evaluate the causal relationship between plasma proteins and body composition.

METHODS

We conducted a 2-sample Mendelian randomization (MR) analysis based on the genome-wide association study (GWAS) summary statistics of 23 body composition traits and 2656 plasma proteins. We then performed hierarchical cluster analysis to evaluate the structure and pattern of the identified causal associations, and we performed gene ontology enrichment analysis to explore the functional relevance of the identified proteins.

RESULTS

We identified 430 putatively causal effects of 96 plasma proteins on 22 body composition traits (except obesity status) with strong MR evidence (P < 2.53 × 10 - 6, at a Bonferroni-corrected threshold). The top 3 causal associations are follistatin (FST) on trunk fat-free mass (Beta = -0.63, SE = 0.04, P = 2.00 × 10-63), insulin-like growth factor-binding protein 1 (IGFBP1) on trunk fat-free mass (Beta = -0.54, SE = 0.03, P = 1.79 × 10-57) and r-spondin-3 (RSPO3) on WHR (waist circumference/hip circumference) (Beta = 0.01, SE = 4.47 × 10-4, P = 5.45 × 10-60), respectively. Further clustering analysis and pathway analysis demonstrated that the pattern of causal effect to fat mass and fat-free mass may be different.

CONCLUSION

Our findings may provide evidence for causal relationships from plasma proteins to various body composition traits and provide basis for further targeted functional studies.

Genetic estimates of correlation and causality between blood-based biomarkers and psychiatric disorders

There is a long-standing interest in exploring the relationship between blood-based biomarkers and psychiatric disorders, despite their causal role being difficult to resolve in observational studies. In this study, we leverage genome-wide association study data for a large panel of heritable serum biochemical traits to refine our understanding of causal effect in biochemical-psychiatric trait pairings. We observed widespread positive and negative genetic correlation between psychiatric disorders and biochemical traits. Causal inference was then implemented to distinguish causation from correlation, with strong evidence that C-reactive protein (CRP) exerts a causal effect on psychiatric disorders. Notably, CRP demonstrated both protective and risk-increasing effects on different disorders. Multivariable models that conditioned CRP effects on interleukin-6 signaling and body mass index supported that the CRP-schizophrenia relationship was not driven by these factors. Collectively, these data suggest that there are shared pathways that influence both biochemical traits and psychiatric illness.

Association of Circulating Cathepsin B Levels With Blood Pressure and Aortic Dilation

Hypertension is a key risk factor for spontaneous coronary artery dissection (SCAD) and aortic dilation. Circulating proteins play key roles in a range of biological processes and represent a major source of druggable targets. The aim of this study was to identify circulating proteins that were associated with blood pressure (BP), SCAD and aortic dilation. We identified shared genetic variants of BP and SCAD in genome-wide association studies, searched for circulating protein affected by these variants and examined the association of circulating protein levels with BP, aortic aneurysm and dissection (AAD) and aortic diameters by integrating data from circulating protein quantitative trait loci (pQTL) studies and genome wide association study (GWAS) in individuals from the UK Biobank using two-sample Mendelian randomization analysis methods. Single nucleotide polymorphisms (SNPs) in JAG1, ERI1, ULK4, THSD4, CMIP, COL4A2, FBN1, FAM76B, FGGY, NUS1, and HNF4G, which were related to extracellular matrix components, were associated with both BP and SCAD. We found 49 significant pQTL signals among these SNPs. The regulated proteins were encoded by MMP10, IL6R, FIGF, MMP1, CTSB, IGHG1, DSG2, TTC17, RETN, POMC, SCARF2, RELT, and GALNT16, which were enriched in biological processes such as collagen metabolic process and multicellular organism metabolic process. Causal associations between BP and AAD and aortic diameters were detected. Significant associations between circulating levels of cathepsin B, a well-known prorenin processing enzyme, and BP and aortic diameters were identified by using several Mendelian randomization analysis methods and were validated by independent data.

Proteome-wide Mendelian randomization identifies causal links between blood proteins and severe COVID-19

In November 2021, the COVID-19 pandemic death toll surpassed five million individuals. We applied Mendelian randomization including >3,000 blood proteins as exposures to identify potential biomarkers that may indicate risk for hospitalization or need for respiratory support or death due to COVID-19, respectively. After multiple testing correction, using genetic instruments and under the assumptions of Mendelian Randomization, our results were consistent with higher blood levels of five proteins GCNT4, CD207, RAB14, C1GALT1C1, and ABO being causally associated with an increased risk of hospitalization or respiratory support/death due to COVID-19 (ORs = 1.12-1.35). Higher levels of FAAH2 were solely associated with an increased risk of hospitalization (OR = 1.19). On the contrary, higher levels of SELL, SELE, and PECAM-1 decrease risk of hospitalization or need for respiratory support/death (ORs = 0.80-0.91). Higher levels of LCTL, SFTPD, KEL, and ATP2A3 were solely associated with a decreased risk of hospitalization (ORs = 0.86-0.93), whilst higher levels of ICAM-1 were solely associated with a decreased risk of respiratory support/death of COVID-19 (OR = 0.84). Our findings implicate blood group markers and binding proteins in both hospitalization and need for respiratory support/death. They, additionally, suggest that higher levels of endocannabinoid enzymes may increase the risk of hospitalization. Our research replicates findings of blood markers previously associated with COVID-19 and prioritises additional blood markers for risk prediction of severe forms of COVID-19. Furthermore, we pinpoint druggable targets potentially implicated in disease pathology.

Single Nucleotide Variant in FAS Associates With Organ Failure and Soluble Fas Cell Surface Death Receptor in Critical Illness

OBJECTIVES

Multiple organ failure in critically ill patients is associated with poor prognosis, but biomarkers contributory to pathogenesis are unknown. Previous studies support a role for Fas cell surface death receptor (Fas)-mediated apoptosis in organ dysfunction. Our objectives were to test for associations between soluble Fas and multiple organ failure, identify protein quantitative trait loci, and determine associations between genetic variants and multiple organ failure.

DESIGN

Retrospective observational cohort study.

SETTING

Four academic ICUs at U.S. hospitals.

PATIENTS

Genetic analyses were completed in a discovery (n = 1,589) and validation set (n = 863). Fas gene expression and flow cytometry studies were completed in outpatient research participants (n = 250).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

In discovery and validation sets of critically ill patients, we tested for associations between enrollment plasma soluble Fas concentrations and Sequential Organ Failure Assessment score on day 3. We conducted a genome-wide association study of plasma soluble Fas (discovery n = 1,042) and carried forward a single nucleotide variant in the FAS gene, rs982764, for validation (n = 863). We further tested whether the single nucleotide variant in FAS (rs982764) was associated with Sequential Organ Failure Assessment score, FAS transcriptional isoforms, and Fas cell surface expression. Higher plasma soluble Fas was associated with higher day 3 Sequential Organ Failure Assessment scores in both the discovery (β = 4.07; p < 0.001) and validation (β = 6.96; p < 0.001) sets. A single nucleotide variant in FAS (rs982764G) was associated with lower plasma soluble Fas concentrations and lower day 3 Sequential Organ Failure Assessment score in meta-analysis (-0.21; p = 0.02). Single nucleotide variant rs982764G was also associated with a lower relative expression of the transcript for soluble as opposed to transmembrane Fas and higher cell surface expression of Fas on CD4+ T cells.

CONCLUSIONS

We found that single nucleotide variant rs982764G was associated with lower plasma soluble Fas concentrations in a discovery and validation population, and single nucleotide variant rs982764G was also associated with lower organ dysfunction on day 3. These findings support further study of the Fas pathway as a potential mediator of organ dysfunction in critically ill patients.

Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease

BACKGROUND

Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants.

METHODS

Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics).

RESULTS

We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10-11. These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, β=0.61±0.05, P=3.27×10-30) and MMP-3 (β=-0.60±0.05, P=1.67×10-32), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, β=0.34±0.04, P=1.34×10-17) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure.

CONCLUSIONS

Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.

Causal effects of inflammatory protein biomarkers on inflammatory diseases

Many circulating proteins are associated with the presence or severity of disease. However, whether these protein biomarkers are causal for disease development is usually unknown. We investigated the causal effect of 21 well-known or exploratory protein biomarkers of inflammation on 18 inflammatory diseases using two-sample Mendelian randomization. We identified six proteins to have causal effects on any of 11 inflammatory diseases (FDR < 0.05, corresponding to P < 1.4 × 10^–3). IL-12B protects against psoriasis and psoriatic arthropathy, LAP-TGF-β-1 protects against osteoarthritis, TWEAK protects against asthma, VEGF-A protects against ulcerative colitis, and LT-α protects against both type 1 diabetes and rheumatoid arthritis. In contrast, IL-18R1 increases the risk of developing allergy, hay fever, and eczema. Most proteins showed protective effects against development of disease rather than increasing disease risk, which indicates that many disease-related biomarkers are expressed to protect from tissue damage. These proteins represent potential intervention points for disease prevention and treatment.

Large-scale integration of the plasma proteome with genetics and disease

The plasma proteome can help bridge the gap between the genome and diseases. Here we describe genome-wide association studies (GWASs) of plasma protein levels measured with 4,907 aptamers in 35,559 Icelanders. We found 18,084 associations between sequence variants and levels of proteins in plasma (protein quantitative trait loci; pQTL), of which 19% were with rare variants (minor allele frequency (MAF) < 1%). We tested plasma protein levels for association with 373 diseases and other traits and identified 257,490 associations. We integrated pQTL and genetic associations with diseases and other traits and found that 12% of 45,334 lead associations in the GWAS Catalog are with variants in high linkage disequilibrium with pQTL. We identified 938 genes encoding potential drug targets with variants that influence levels of possible biomarkers. Combining proteomics, genomics and transcriptomics, we provide a valuable resource that can be used to improve understanding of disease pathogenesis and to assist with drug discovery and development.

Genome-wide association study of hospitalized COVID-19 patients in the United Arab Emirates

BACKGROUND

The heterogeneity in symptomatology and phenotypic profile attributable to COVID-19 is widely unknown. The objective of this manuscript is to conduct a trans-ancestry genome wide association study (GWAS) meta-analysis of COVID-19 severity to improve the understanding of potentially causal targets for SARS-CoV-2.

METHODS

This cross-sectional study recruited 646 participants in the UAE that were divided into two phenotypic groups based on the severity of COVID-19 phenotypes, hospitalized (n=482) and non-hospitalized (n=164) participants. Hospitalized participants were COVID-19 patients that developed acute respiratory distress syndrome (ARDS), pneumonia or progression to respiratory failure that required supplemental oxygen therapy or mechanical ventilation support or had severe complications such as septic shock or multi-organ failure. We conducted a trans-ancestry meta-analysis GWAS of European (n=302), American (n=102), South Asian (n=99), and East Asian (n=107) ancestry populations. We also carried out comprehensive post-GWAS analysis, including enrichment of SNP associations in tissues and cell-types, expression quantitative trait loci and differential expression analysis.

FINDINGS

Eight genes demonstrated a strong association signal: VWA8 gene in locus 13p14·11 (SNP rs10507497; p=9·54 x10-7), PDE8B gene in locus 5q13·3 (SNP rs7715119; p=2·19 x10-6), CTSC gene in locus 11q14·2 (rs72953026; p=2·38 x10-6), THSD7B gene in locus 2q22·1 (rs7605851; p=3·07x10-6), STK39 gene in locus 2q24·3 (rs7595310; p=4·55 x10-6), FBXO34 gene in locus 14q22·3 (rs10140801; p=8·26 x10-6), RPL6P27 gene in locus 18p11·31 (rs11659676; p=8·88 x10-6), and METTL21C gene in locus 13q33·1 (rs599976; p=8·95 x10-6). The genes are expressed in the lung, associated to tumour progression, emphysema, airway obstruction, and surface tension within the lung, as well as an association to T-cell-mediated inflammation and the production of inflammatory cytokines.

INTERPRETATION

We have discovered eight highly plausible genetic association with hospitalized cases in COVID-19. Further studies must be conducted on worldwide population genetics to facilitate the development of population specific therapeutics to mitigate this worldwide challenge.

FUNDING

This review was commissioned as part of a project to study the host cell receptors of coronaviruses funded by Khalifa University's CPRA grant (Reference number 2020-004).

Synergistic insights into human health from aptamer- and antibody-based proteomic profiling

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.

Mapping the proteo-genomic convergence of human diseases

Characterization of the genetic regulation of proteins is essential for understanding disease etiology and developing therapies. We identified 10,674 genetic associations for 3892 plasma proteins to create a cis-anchored gene-protein-disease map of 1859 connections that highlights strong cross-disease biological convergence. This proteo-genomic map provides a framework to connect etiologically related diseases, to provide biological context for new or emerging disorders, and to integrate different biological domains to establish mechanisms for known gene-disease links. Our results identify proteo-genomic connections within and between diseases and establish the value of cis-protein variants for annotation of likely causal disease genes at loci identified in genome-wide association studies, thereby addressing a major barrier to experimental validation and clinical translation of genetic discoveries.

Placental Gene Co-expression Network for Maternal Plasma Lipids Revealed Enrichment of Inflammatory Response Pathways

Maternal dyslipidemia during pregnancy has been associated with suboptimal fetal growth and increased cardiometabolic diseasse risk in offspring. Altered placental function driven by placental gene expression is a hypothesized mechanism underlying these associations. We tested the relationship between maternal plasma lipid concentrations and placental gene expression. Among 64 pregnant women from the NICHD Fetal Growth Studies–Singleton cohort with maternal first trimester plasma lipids we extracted RNA-Seq on placental samples obtained at birth. Placental gene co-expression networks were validated by regulatory network analysis that integrated transcription factors and gene expression, and genome-wide transcriptome analysis. Network analysis detected 24 gene co-expression modules in placenta, of which one module was correlated with total cholesterol (r = 0.27, P-value = 0.03) and LDL-C (r = 0.31, P-value = 0.01). Genes in the module (n = 39 genes) were enriched in inflammatory response pathways. Out of the 39 genes in the module, three known lipid-related genes (MPO, PGLYRP1 and LTF) and MAGEC2 were validated by the regulatory network analysis, and one known lipid-related gene (ALX4) and two germ-cell development-related genes (MAGEC2 and LUZP4) were validated by genome-wide transcriptome analysis. Placental gene expression signatures associated with unfavorable maternal lipid concentrations may be potential pathways underlying later life offspring cardiometabolic traits.

Clinical Trial Registration:ClinicalTrials.gov, identifier NCT00912132.

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.

Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders

Understanding the tissue-specific genetic controls of protein levels is essential to uncover mechanisms of post-transcriptional gene regulation. In this study, we generated a genomic atlas of protein levels in three tissues relevant to neurological disorders (brain, cerebrospinal fluid and plasma) by profiling thousands of proteins from participants with and without Alzheimer's disease. We identified 274, 127 and 32 protein quantitative trait loci (pQTLs) for cerebrospinal fluid, plasma and brain, respectively. cis-pQTLs were more likely to be tissue shared, but trans-pQTLs tended to be tissue specific. Between 48.0% and 76.6% of pQTLs did not co-localize with expression, splicing, DNA methylation or histone acetylation QTLs. Using Mendelian randomization, we nominated proteins implicated in neurological diseases, including Alzheimer's disease, Parkinson's disease and stroke. This first multi-tissue study will be instrumental to map signals from genome-wide association studies onto functional genes, to discover pathways and to identify drug targets for neurological diseases.

Association of IL1R1 Coding Variant With Plasma-Level Soluble ST2 and Risk of Aortic Dissection

Objective: Aortic dissection (AD) is characterized by an acute onset, rapid progress, and high mortality. Levels of soluble ST2 (sST2) on presentation are elevated in patients with acute AD, which can be used to discriminate AD patients from patients with chest pain. sST2 concentrations were found to be highly heritable in the general population. The aim of this study was to investigate the associations of variations in ST2-related gene expression with sST2 concentrations and AD risk. Methods: This case-control study involving a total of 2,277 participants were conducted, including 435 AD patients and age- and sex-matched 435 controls in the discovery stage, and 464 patients and 943 controls in the validation stage. Eight ST2-related genes were selected by systematic review. Tag single-nucleotide polymorphisms (SNPs) were screened out from the Chinese population of the 1,000 Genomes Database. Twenty-one ST2-related SNPs were genotyped, and plasma sST2 concentrations were measured. Results: In the discovery stage, rs13019803 located in IL1R1 was significantly associated with AD after Bonferroni correction (p = 0.0009) and was correlated with circulating sST2 levels in patients with type A AD(AAD) [log-sST2 per C allele increased by 0.180 (95%) CI: 0.002 - 0.357] but not in type B. Combining the two stages together, rs13019803C was associated with plasma sST2 level in AAD patients [log-sST2 increased by 0.141 (95% CI: 0.055-0.227) for per C allele]. Odds ratio of rs13019803 on the risk of AAD is 1.67 (95% CI: 1.33-2.09). Conclusions: The IL1R1 SNP rs13019803C is associated with higher sST2 levels and increased risk of AAD.

Identification of ROBO2 as a Potential Locus Associated with Inhaled Corticosteroid Response in Childhood Asthma

Inhaled corticosteroids (ICS) are the most common asthma controller medication. An important contribution of genetic factors in ICS response has been evidenced. Here, we aimed to identify novel genetic markers involved in ICS response in asthma. A genome-wide association study (GWAS) of the change in lung function after 6 weeks of ICS treatment was performed in 166 asthma patients from the SLOVENIA study. Patients with an improvement in lung function ≥8% were considered as ICS responders. Suggestively associated variants (p-value ≤ 5 × 10⁻⁶) were evaluated in an independent study (n = 175). Validation of the association with asthma exacerbations despite ICS use was attempted in European (n = 2681) and admixed (n = 1347) populations. Variants previously associated with ICS response were also assessed for replication. As a result, the SNP rs1166980 from the ROBO2 gene was suggestively associated with the change in lung function (OR for G allele: 7.01, 95% CI: 3.29–14.93, p = 4.61 × 10⁻⁷), although this was not validated in CAMP. ROBO2 showed gene-level evidence of replication with asthma exacerbations despite ICS use in Europeans (minimum p-value = 1.44 × 10⁻⁵), but not in admixed individuals. The association of PDE10A-T with ICS response described by a previous study was validated. This study suggests that ROBO2 could be a potential novel locus for ICS response in Europeans.

Safety and efficacy of itepekimab in patients with moderate-to-severe COPD: a genetic association study and randomised, double-blind, phase 2a trial

BACKGROUND

Genetic data implicate IL-33 in asthma susceptibility. Itepekimab, a monoclonal antibody targeting IL-33, demonstrated clinical activity in asthma, with potential in chronic obstructive pulmonary disease (COPD). In this study we first aimed to test the hypothesis that genetic variants in the IL-33 pathway were also associated with COPD. On the basis of the strong association of IL-33 pathway genes with pulmonary diseases like asthma and COPD, we conducted this phase 2a trial to assess the safety and efficacy of itepekimab in patients with moderate-to-severe COPD on a stable regimen of triple-inhaled or double-inhaled background maintenance therapy.

METHODS

In this two-part study, genetic analyses of loss-of-function and gain-of-function variants in the IL-33 pathway, previously associated with asthma risk, were initially characterised for COPD. We then did a double-blind, phase 2a trial comparing itepekimab with placebo in patients with moderate-to-severe COPD despite standard therapy, at 83 study sites in ten countries. Patients aged 40-75 years who were current or former smokers, had been diagnosed with COPD for at least 1 year, and were on a stable regimen of triple-inhaled or double-inhaled background maintenance therapy, were randomly assigned (1:1) to receive itepekimab 300 mg or placebo, administered as two subcutaneous injections every 2 weeks for 24-52 weeks. The primary endpoint of the phase 2a trial was annualised rate of moderate-to-severe acute exacerbations of COPD during the treatment period. The key secondary outcome was change in prebronchodilator FEV₁ from baseline to weeks 16-24. Prespecified subgroup analyses were done for each of the endpoints, including by smoking status. Efficacy and safety analyses were done in all participants who received at least one dose of assigned treatment (modified intention-to-treat population). This trial is registered at ClinicalTrials.gov (NCT03546907).

FINDINGS

Genetic analyses demonstrated association of loss of function in IL33 with reduced COPD risk, and gain of function in IL33 and IL1RL1 variants with increased risk. Subsequent to this, in the phase 2 trial, 343 patients were randomly assigned to placebo (n=171) or itepekimab (n=172) from July 16, 2018, to Feb 19, 2020. Annualised rates of acute exacerbations of COPD were 1·61 (95% CI 1·32-1·97) in the placebo group and 1·30 (1·05-1·61) in the itepekimab group (relative risk [RR] 0·81 [95% CI 0·61-1·07], p=0·13), and least squares mean prebronchodilator FEV₁ change from baseline to weeks 16-24 was 0·0 L (SD 0·02) and 0·06 L (0·02; difference 0·06 L [95% CI 0·01-0·10], p=0·024). When analysis was restricted to former smokers, treatment with itepekimab was associated with nominally significant reductions in acute exacerbations of COPD (RR 0·58 [95% CI 0·39-0·85], p=0·0061) and FEV₁ improvement (least squares mean difference 0·09 L [0·02-0·15], p=0·0076) compared with placebo. Current smokers treated with itepekimab showed no treatment benefit versus placebo for exacerbations (RR 1·09 [0·74-1·61], p=0·65) or FEV₁ (least squares mean difference 0·02 [-0·05 to 0·09], p=0·54). Treatment-emergent adverse events (TEAEs) occurred in 135 (78%) patients in the itepekimab group and 136 (80%) in the placebo group. The most common TEAEs were nasopharyngitis (28 [16%] in the itepekimab group vs 29 [17%] in the placebo group), bronchitis (18 [10%] vs 14 [8%]), headache (14 [8%] vs 23 [13%]), and upper respiratory tract infection (13 [8%] vs 15 [9%]).

INTERPRETATION

The primary endpoint in the overall population was not met, subgroup analysis showed that itepekimab reduced exacerbation rate and improved lung function in former smokers with COPD. Two phase 3 clinical studies are ongoing to confirm the efficacy and safety profile of itepekimab in former smokers with COPD.

FUNDING

Sanofi and Regeneron Pharmaceuticals.

Circulating Galectin-3 Levels Are Not Associated With Nonalcoholic Fatty Liver Disease: A Mendelian Randomization Study

CONTEXT

The impact of galectin-3 inhibitors on nonalcoholic fatty liver diseases (NAFLD)-related outcomes is currently under investigation in randomized clinical trials. Whether there is a causal association between plasma galectin-3 levels and NAFLD is unknown.

OBJECTIVE

To evaluate the causal effect of circulating galectin-3 levels on NAFLD as well as >800 other human diseases.

DESIGN

Inverse variance-weighted (IVW) Mendelian randomization (MR) and phenome-wide MR.

SETTING

Summary statistics of genome-wide association studies.

PATIENTS

Participants of the UK Biobank, Electronic Medical Records and Genomics (eMERGE), FinnGen, Prevention of Renal and Vascular End-Stage Disease (PREVEND), and IMPROVE cohorts.

INTERVENTION

Identification of independent single-nucleotide polymorphisms (SNPs) associated with galectin-3 levels (P < 5 × 10-8) in the PREVEND (14 SNPs) and IMPROVE (3 SNPs) cohorts.

MAIN OUTCOME MEASURES

Presence of NAFLD in a meta-analysis of genome-wide association study of the eMERGE, UK Biobank, and FinnGen cohorts (3042 NAFLD cases and 504 853 controls), as well as >800 other human diseases in the UK Biobank and FinnGen.

RESULTS

Using IVW-MR, we found no causal association between galectin-3 levels and NAFLD in the meta-analysis of the 3 cohorts or in each individual cohort. After correction for multiple testing, we found no causal association between galectin-3 levels and >800 human disease-related traits.

CONCLUSIONS

This MR study revealed no causal associations between circulating galectin-3 levels and NAFLD or any other disease traits, suggesting that plasma galectin-3 levels may not be directly implicated in the pathogenesis of NAFLD or other human diseases.

Genetic evidence for a causative effect of airflow obstruction on left ventricular filling: a Mendelian randomisation study

Background

Observational studies on the general population have suggested that airflow obstruction associates with left ventricular (LV) filling. To limit the influence of environmental risk factors/exposures, we used a Mendelian randomisation (MR) approach based on common genetic variations and tested whether a causative relation between airflow obstruction and LV filling can be detected.

Methods

We used summary statistics from large genome-wide association studies (GWAS) on the ratio of forced expiratory volume in 1 s to forced vital capacity (FEV1/FVC) measured by spirometry and the LV end-diastolic volume (LVEDV) as assessed by cardiac magnetic resonance imaging. The primary MR was based on an inverse variance weighted regression. Various complementary MR methods and subsets of the instrument variables were used to assess the plausibility of the findings.

Results

We obtained consistent evidence in our primary MR analysis and subsequent sensitivity analyses that reducing airflow obstruction leads to increased inflow to the LV (odds ratio [OR] from inverse variance weighted regression 1.05, 95% confidence interval [CI] 1.01–1.09, P = 0.0172). Sensitivity analyses indicated a certain extent of negative horizontal pleiotropy and the estimate from biased-corrected MR-Egger was adjusted upward (OR 1.2, 95% CI 1.09–1.31, P < 0.001). Prioritisation of single genetic variants revealed rs995758, rs2070600 and rs7733410 as major contributors to the MR result.

Conclusion

Our findings indicate a causal relationship between airflow obstruction and LV filling in the general population providing genetic context to observational associations. The results suggest that targeting (even subclinical) airflow obstruction can lead to direct cardiac improvements, demonstrated by an increase in LVEDV. Functional annotation of single genetic variants contributing most to the causal effect estimate could help to prioritise biological underpinnings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01795-9.

Genome-Wide Identification of RNA Modifications for Spontaneous Coronary Aortic Dissection

RNA modification plays important roles in many biological processes such as gene expression control. Genetic variants that affect RNA modification may have functional roles in aortic dissection. The aim of this study was to identify RNA modifications related to spontaneous coronary artery dissection (SCAD). We examined the association of RNA modification-associated single-nucleotide polymorphisms (RNAm-SNPs) with SCAD in summary data from a genome-wide association study (GWAS) of European descent (270 SCAD cases and 5,263 controls). Furthermore, we performed expression quantitative loci (eQTL) and protein quantitative loci (pQTL) analyses for the RNAm-SNPs using publicly available data. Functional enrichment and protein–protein interaction analyses were performed for the identified proteins. We found 11,464 unique RNAm-SNPs in the SCAD GWAS dataset, and 519 were nominally associated with SCAD. Nine RNAm-SNPs were associated with SCAD at p < 0.001, and among them, seven were N⁶-methyladenosine (m⁶A) methylation-related SNPs, one (rs113664950 in HLA-DQB1) was m⁷G-associated SNP, and one [rs580060 in the 3′-UTR of Mitochondrial Ribosomal Protein S21 (MRPS21)] was A-to-I modification SNP. The genome-wide significant SNP rs3818978 (SCAD association p = 5.74 × 10^–10) in the 5′-UTR of MRPS21 was related to m⁶A modification. These nine SNPs all showed eQTL effects, and six of them were associated with circulating protein or metabolite levels. The related protein-coding genes were enriched in specific Gene Ontology (GO) terms such as extracellular space, extracellular region, defense response, lymphocyte migration, receptor binding and cytokine receptor binding, and so on. The present study found the associations between RNAm-SNPs and SCAD. The findings suggested that RNA modification may play functional roles in SCAD.

Depression and interleukin-6 signaling: A Mendelian Randomization study

BACKGROUND

A large body of research has reported associations between depression and elevated interleukin-6 (IL-6), a cytokine with several roles including pro-inflammatory signaling. The nature and directionality of this relationship are not yet clear. In this study we use Mendelian Randomization to examine the possibility of a causal relationship between IL-6 and depressive symptoms, and to explore multiple signaling pathways that could serve as mechanisms for this relationship.

METHODS

This study uses a two-sample Mendelian Randomization design. Data come from the UK Biobank (n = 89,119) and published summary statistics from six existing GWAS analyses. The primary analysis focuses on the soluble interleukin-6 receptor (sIL-6R), which is involved in multiple signaling pathways. Exploratory analyses use C-reactive protein (CRP) and soluble glycoprotein 130 (sgp130) to further examine potential underlying mechanisms.

RESULTS

Results are consistent with a causal effect of sIL-6R on depression (PCA-IVW Odds Ratio: 1.023 (95% Confidence Interval: 1.006-1.039), p = 0.006). Exploratory analyses demonstrate that the relationship could be consistent with either decreased classical signaling or increased trans signaling as the underlying mechanism.

DISCUSSION

These results strengthen the body evidence implicating IL-6 signaling in depression. When compared with existing observational and animal findings, the direction of these results suggests involvement of IL-6 trans signaling. Further study is needed to examine whether IL6R genetic variants might influence IL-6 trans signaling in the brain, as well as to explore other potential pathways linking depression and inflammation.

Plasma Protein Profile of Incident Myocardial Infarction, Ischemic Stroke, and Heart Failure in 2 Cohorts

Background The aim is to study common etiological pathways for 3 major cardiovascular diseases (CVD), as reflected in multiple proteins. Methods and Results Eighty-four proteins were measured using the proximity extension technique in 870 participants in the PIVUS (Prospective Investigation of Uppsala Seniors Study) cohort on 3 occasions (age 70, 75, and 80 years). The sample was followed for incident myocardial infarction, ischemic stroke or heart failure. The same proteins were measured in an independent validation sample, the ULSAM (Uppsala Longitudinal Study of Adult Men) cohort in 595 participants at age 77. During a follow-up of up to 15 years in PIVUS and 9 years in ULSAM, 222 and 167 individuals experienced a CVD. Examining associations with the 3 outcomes separately in a meta-analysis of the 2 cohorts, 6 proteins were related to incident myocardial infarction, 25 to heart failure, and 8 proteins to ischemic stroke following adjustment for traditional risk factors. Growth differentiation factor 15 and tumor necrosis factor-related apoptosis-inducing ligand receptor 2 were related to all 3 CVDs. Including estimated glomerular filtration rate in the models attenuated some of these relationships. Fifteen proteins were related to a composite of all 3 CVDs using a discovery/validation approach when adjusting for traditional risk factors. A selection of 7 proteins by lasso in PIVUS improved discrimination of incident CVD by 7.3% compared with traditional risk factors in ULSAM. Conclusions We discovered and validated associations of multiple proteins with incident CVD. Only a few proteins were associated with all 3 diseases: myocardial infarction, ischemic stroke, and heart failure.

Plasma Proteomic Profile Predicts Survival in Heart Failure With Reduced Ejection Fraction

BACKGROUND

It remains unclear whether the plasma proteome adds value to established predictors in heart failure (HF) with reduced ejection fraction (HFrEF). We sought to derive and validate a plasma proteomic risk score (PRS) for survival in patients with HFrEF (HFrEF-PRS).

METHODS

Patients meeting Framingham criteria for HF with EF<50% were enrolled (N=1017) and plasma underwent SOMAscan profiling (4453 targets). Patients were randomly divided 2:1 into derivation and validation cohorts. The HFrEF-PRS was derived using Cox regression of all-cause mortality adjusted for clinical score and NT-proBNP (N-terminal pro-B-type natriuretic peptide), then was tested in the validation cohort. Risk stratification improvement was evaluated by C statistic, integrated discrimination index, continuous net reclassification index, and median improvement in risk score for 1-year and 3-year mortality.

RESULTS

Participants' mean age was 68 years, 48% identified as Black, 35% were female, and 296 deaths occurred. In derivation (n=681), 128 proteins associated with mortality, 8 comprising the optimized HFrEF-PRS. In validation (n=336) the HFrEF-PRS associated with mortality (hazard ratio, 2.27 [95% CI, 1.84-2.82], P=6.3×10^-14), Kaplan-Meier curves differed significantly between HFrEF-PRS quartiles (P=2.2×10^-6), and it remained significant after adjustment for clinical score and NT-proBNP (hazard ratio, 1.37 [95% CI, 1.05-1.79], P=0.021). The HFrEF-PRS improved metrics of risk stratification (C statistic change, 0.009, P=0.612; integrated discrimination index, 0.041, P=0.010; net reclassification index=0.391, P=0.078; median improvement in risk score=0.039, P=0.016) and associated with cardiovascular death and HF phenotypes (eg, 6-minute walk distance, EF change). Most HFrEF-PRS proteins had little known connection to HFrEF.

CONCLUSIONS

A plasma multiprotein score improved risk stratification in patients with HFrEF and identified novel candidates.

Next generation plasma proteome profiling to monitor health and disease

The need for precision medicine approaches to monitor health and disease makes it important to develop sensitive and accurate assays for proteome profiles in blood. Here, we describe an approach for plasma profiling based on proximity extension assay combined with next generation sequencing. First, we analyze the variability of plasma profiles between and within healthy individuals in a longitudinal wellness study, including the influence of genetic variations on plasma levels. Second, we follow patients newly diagnosed with type 2 diabetes before and during therapeutic intervention using plasma proteome profiling. The studies show that healthy individuals have a unique and stable proteome profile and indicate that a panel of proteins could potentially be used for early diagnosis of diabetes, including stratification of patients with regards to response to metformin treatment. Although validation in larger cohorts is needed, the analysis demonstrates the usefulness of comprehensive plasma profiling for precision medicine efforts.

Mendelian randomisation identifies alternative splicing of the FAS death receptor as a mediator of severe COVID-19

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 the FAS locus 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.

Associations of Adiposity, Circulating Protein Biomarkers, and Risk of Major Vascular Diseases

Question

Is adiposity associated with differences in circulating protein concentrations, and might these proteins potentially explain the associations of adiposity with risk of cardiovascular disease?

Findings

In a cohort study of 628 individuals in China, there was evidence of genetic associations of body mass index with protein biomarkers consistent with observational associations, particularly for interleukin-6, interleukin-18, monocyte chemoattractant protein–1, monocyte chemotactic protein–3, TNF-related apoptosis-inducing ligand, and hepatocyte growth factor. Several of these proteins were observationally associated with risk of incident cardiovascular disease.

Meaning

In this study of Chinese adults, adiposity was associated both cross-sectionally and through genetic analyses with a range of protein biomarkers, which might partly explain the association between adiposity and cardiovascular disease.

Importance

Obesity is associated with a higher risk of cardiovascular disease (CVD), but little is known about the role that circulating protein biomarkers play in this association.

Objective

To examine the observational and genetic associations of adiposity with circulating protein biomarkers and the observational associations of proteins with incident CVD.

Design, Setting, and Participants

This subcohort study included 628 participants from the prospective China Kadoorie Biobank who did not have a history of cancer at baseline. The Olink platform measured 92 protein markers in baseline plasma samples. Data were collected from June 2004 to January 2016 and analyzed from January 2019 to June 2020.

Exposures

Measured body mass index (BMI) obtained during the baseline survey and genetically instrumented BMI derived using 571 externally weighted single-nucleotide variants.

Main Outcomes and Measures

Cross-sectional associations of adiposity with biomarkers were examined using linear regression. Associations of biomarkers with CVD risk were assessed using Cox regression among those without prior cancer or CVD at baseline. Mendelian randomization was conducted to derive genetically estimated associations of BMI with biomarkers.

Findings

In observational analyses of 628 individuals (mean [SD] age, 52.2 [10.5] years; 385 women [61.3%]), BMI (mean [SD], 23.9 [3.6]) was positively associated with 27 proteins (per 1-SD higher BMI; eg, interleukin-6: 0.21 [95% CI, 0.12-0.29] SD; interleukin-18: 0.13 [95% CI, 0.05-0.21] SD; monocyte chemoattractant protein–1: 0.12 [95% CI, 0.04-0.20] SD; hepatocyte growth factor: 0.31 [95% CI, 0.24-0.39] SD), and inversely with 3 proteins (Fas ligand: −0.11 [95% CI, −0.19 to −0.03] SD; TNF-related weak inducer of apoptosis, −0.14 [95% CI, −0.23 to −0.06] SD; and carbonic anhydrase 9: (−0.14 [95% CI, −0.22 to −0.05] SD), with similar associations identified for other adiposity traits (eg, waist circumference [r = 0.96]). In mendelian randomization, the associations of genetically elevated BMI with specific proteins were directionally consistent with the observational associations. In meta-analyses of genetically elevated BMI with 8 proteins, combining present estimates with previous studies, the most robust associations were shown for interleukin-6 (per 1-SD higher BMI; 0.21 [95% CI, 0.13-0.29] SD), interleukin-18 (0.16 [95% CI, 0.06-0.26] SD), monocyte chemoattractant protein–1 (0.21 [95% CI, 0.11-0.30] SD), monocyte chemotactic protein–3 (0.12 [95% CI, 0.03-0.21] SD), TNF-related apoptosis-inducing ligand (0.23 [95% CI, 0.13-0.32] SD), and hepatocyte growth factor (0.14 [95% CI, 0.06-0.22] SD). Of the 30 BMI-associated biomarkers, 10 (including interleukin-6, interleukin-18, and hepatocyte growth factor) were nominally associated with incident CVD.

Conclusions and Relevance

Mendelian randomization shows adiposity to be associated with a range of protein biomarkers, with some biomarkers also showing association with CVD risk. Future studies are warranted to validate these findings and assess whether proteins may be mediators between adiposity and CVD.

A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity

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.

Revealing the role of the human blood plasma proteome in obesity using genetic drivers

Blood circulating proteins are confounded readouts of the biological processes that occur in different tissues and organs. Many proteins have been linked to complex disorders and are also under substantial genetic control. Here, we investigate the associations between over 1000 blood circulating proteins and body mass index (BMI) in three studies including over 4600 participants. We show that BMI is associated with widespread changes in the plasma proteome. We observe 152 replicated protein associations with BMI. 24 proteins also associate with a genome-wide polygenic score (GPS) for BMI. These proteins are involved in lipid metabolism and inflammatory pathways impacting clinically relevant pathways of adiposity. Mendelian randomization suggests a bi-directional causal relationship of BMI with LEPR/LEP, IGFBP1, and WFIKKN2, a protein-to-BMI relationship for AGER, DPT, and CTSA, and a BMI-to-protein relationship for another 21 proteins. Combined with animal model and tissue-specific gene expression data, our findings suggest potential therapeutic targets further elucidating the role of these proteins in obesity associated pathologies.

Long-lasting Imprint in the Soluble Inflammatory Milieu despite Early Treatment of Acute Symptomatic Hepatitis C

Background

Treatment with direct-acting antivirals (DAAs) in patients with chronic hepatitis C infection leads to partial restoration of soluble inflammatory mediators (SIMs). In contrast, we hypothesized that early DAA treatment of acute hepatitis C virus (HCV) with DAAs may normalize most SIMs.

Methods

In this study, we made use of a unique cohort of acute symptomatic hepatitis C patients who cleared HCV with a 6-week course of ledipasvir/sofosbuvir. Plasma samples were used for proximity extension assay measuring 92 proteins.

Results

Profound SIM alterations were observed in acute HCV patients, with marked upregulation of interleukin (IL)-6 and CXCL-10, whereas certain mediators were downregulated (eg, monocyte chemoattractant protein-4, IL-7). During treatment and follow-up, the majority of SIMs decreased but not all normalized (eg, CDCP1, IL-18). Of note, SIMs that were downregulated before DAA treatment remained suppressed, whereas others that were initially unchanged declined to lower values during treatment and follow-up (eg, CD244).

Conclusions

Acute hepatitis C was associated with marked changes in the soluble inflammatory milieu compared with both chronic hepatitis patients and healthy controls. Whereas early DAA treatment partly normalized this altered signature, long-lasting imprints of HCV remained.

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

It's in Our Blood: A Glimpse of Personalized Medicine

Recent advances in protein profiling technology has facilitated simultaneous measurement of thousands of proteins in large population studies, exposing the depth and complexity of the plasma and serum proteomes. This revealed that proteins in circulation were organized into regulatory modules under genetic control and closely associated with current and future common diseases. Unlike networks in solid tissues, serum protein networks comprise members synthesized across different tissues of the body. Genetic analysis reveals that this cross-tissue regulation of the serum proteome participates in systemic homeostasis and mirrors the global disease state of individuals. Here, we discuss how application of this information in routine clinical evaluations may transform the future practice of medicine.

The GWAS-MAP platform for aggregation of results of genome-wide association studies and the GWAS-MAP|homo database of 70 billion genetic associations of human traits

Hundreds of genome-wide association studies (GWAS) of human traits are performed each year. The results of GWAS are often published in the form of summary statistics. Information from summary statistics can be used for multiple purposes – from fundamental research in biology and genetics to the search for potential biomarkers and therapeutic targets. While the amount of GWAS summary statistics collected by the scientific community is rapidly increasing, the use of this data is limited by the lack of generally accepted standards. In particular, the researchers who would like to use GWAS summary statistics in their studies have to become aware that the data are scattered across multiple websites, are presented in a variety of formats, and, often, were not quality controlled. Moreover, each available summary statistics analysis tools will ask for data to be presented in their own internal format. To address these issues, we developed GWAS-MAP, a high-throughput platform for aggregating, storing, analyzing, visualizing and providing access to a database of big data that result from region- and genome-wide association studies. The database currently contains information on more than 70 billion associations between genetic variants and human diseases, quantitative traits, and “omics” traits. The GWAS-MAP platform and database can be used for studying the etiology of human diseases, building predictive risk models and finding potential biomarkers and therapeutic interventions. In order to demonstrate a typical application of the platform as an approach for extracting new biological knowledge and establishing mechanistic hypotheses, we analyzed varicose veins, a disease affecting on average every third adult in Russia. The results of analysis confirmed known epidemiologic associations for this disease and led us to propose a hypothesis that increased levels of MICB and CD209 proteins in human plasma may increase susceptibility to varicose veins.

Genetic architecture of host proteins involved in SARS-CoV-2 infection

Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).

Whole-genome sequencing analysis of the cardiometabolic proteome

The human proteome is a crucial intermediate between complex diseases and their genetic and environmental components, and an important source of drug development targets and biomarkers. Here, we comprehensively assess the genetic architecture of 257 circulating protein biomarkers of cardiometabolic relevance through high-depth (22.5×) whole-genome sequencing (WGS) in 1328 individuals. We discover 131 independent sequence variant associations (P < 7.45 × 10-11) across the allele frequency spectrum, all of which replicate in an independent cohort (n = 1605, 18.4x WGS). We identify for the first time replicating evidence for rare-variant cis-acting protein quantitative trait loci for five genes, involving both coding and noncoding variation. We construct and validate polygenic scores that explain up to 45% of protein level variation. We find causal links between protein levels and disease risk, identifying high-value biomarkers and drug development targets.

Genetic Variants Associated with Non-Alcoholic Fatty Liver Disease Do Not Associate with Measures of Sub-Clinical Atherosclerosis: Results from the IMPROVE Study

Non-alcoholic fatty liver disease (NAFLD) and atherosclerosis-related cardiovascular diseases (CVD) share common metabolic pathways. We explored the association between three NAFLD-associated single nucleotide polymorphisms (SNPs) rs738409, rs10401969, and rs1260326 with sub-clinical atherosclerosis estimated by the carotid intima-media thickness (c-IMT) and the inter-adventitia common carotid artery diameter (ICCAD) in patients free from clinically overt NAFLD and CVD. The study population is the IMPROVE, a multicenter European study (n = 3711). C-IMT measures and ICCAD were recorded using a standardized protocol. Linear regression with an additive genetic model was used to test for association of the three SNPs with c-IMT and ICCAD. In secondary analyses, the association of the three SNPs with c-IMT and ICCAD was tested after stratification by alanine aminotransferase levels (ALT). No associations were found between rs738409, rs1260326, rs10401969, and c-IMT or ICCAD. Rs738409-G and rs10401969-C were associated with ALT levels (p < 0.001). In patients with ALT levels above 28 U/L (highest quartile), we observed an association between rs10401969-C and c-IMT measures of c-IMT_max and c-IMT_mean-max (p = 0.018 and 0.021, respectively). In conclusion, NAFLD-associated SNPs do not associate with sub-clinical atherosclerosis measures. However, our results suggest a possible mediating function of impaired liver function on atherosclerosis development.

Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals

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.

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.

Characterization of the cytokine storm reflects hyperinflammatory endothelial dysfunction in COVID-19

BACKGROUND

Physicians treating patients with coronavirus disease 2019 (COVID-19) increasingly believe that the hyperinflammatory acute stage of COVID-19 results in a cytokine storm. The circulating biomarkers seen across the spectrum of COVID-19 have not been characterized compared with healthy controls, but such analyses are likely to yield insights into the pursuit of interventions that adequately reduce the burden of these cytokine storms.

OBJECTIVE

To identify and characterize the host inflammatory response to severe acute respiratory syndrome coronavirus 2 infection, we assessed levels of proteins related to immune responses and cardiovascular disease in patients stratified as mild, moderate, and severe versus matched healthy controls.

METHODS

Blood samples from adult patients hospitalized with COVID-19 were analyzed using high-throughput and ultrasensitive proteomic platforms and compared with age- and sex-matched healthy controls to provide insights into differential regulation of 185 markers.

RESULTS

Results indicate a dominant hyperinflammatory milieu in the circulation and vascular endothelial damage markers within patients with COVID-19, and strong biomarker association with patient response as measured by Ordinal Scale. As patients progress, we observe statistically significant dysregulation of IFN-γ, IL-1RA, IL-6, IL-10, IL-19, monocyte chemoattractant protein (MCP)-1, MCP-2, MCP-3, CXCL9, CXCL10, CXCL5, ENRAGE, and poly (ADP-ribose) polymerase 1. Furthermore, in a limited series of patients who were sampled frequently, confirming reliability and reproducibility of our assays, we demonstrate that intervention with baricitinib attenuates these circulating biomarkers associated with the cytokine storm.

CONCLUSIONS

These wide-ranging circulating biomarkers show an association with increased disease severity and may help stratify patients and selection of therapeutic options. They also provide insights into mechanisms of severe acute respiratory syndrome coronavirus 2 pathogenesis and the host response.

Genetics meets proteomics: perspectives for large population-based studies

Proteomic analysis of cells, tissues and body fluids has generated valuable insights into the complex processes influencing human biology. Proteins represent intermediate phenotypes for disease and provide insight into how genetic and non-genetic risk factors are mechanistically linked to clinical outcomes. Associations between protein levels and DNA sequence variants that colocalize with risk alleles for common diseases can expose disease-associated pathways, revealing novel drug targets and translational biomarkers. However, genome-wide, population-scale analyses of proteomic data are only now emerging. Here, we review current findings from studies of the plasma proteome and discuss their potential for advancing biomedical translation through the interpretation of genome-wide association analyses. We highlight the challenges faced by currently available technologies and provide perspectives relevant to their future application in large-scale biobank studies.

Multiple functional variants in the IL1RL1 region are pretransplant markers for risk of GVHD and infection deaths

Graft-versus-host disease (GVHD) and infections are the 2 main causes of death without relapse after allogeneic hematopoietic cell transplantation (HCT). Elevated soluble serum simulation-2 (sST2), the product of IL1RL1 in plasma/serum post-HCT, is a validated GVHD biomarker. Hundreds of SNPs at 2q12.1 have been shown to be strongly associated with sST2 concentrations in healthy populations. We therefore hypothesized that the donor genetic variants in IL1RL1 correlate with sST2 protein levels associated with patient survival outcomes after HCT. We used DISCOVeRY-BMT (Determining the Influence of Susceptibility Conveying Variants Related to 1-Year Mortality after Blood and Marrow Transplantation), a genomic study of >3000 donor-recipient pairs, to inform our hypothesis. We first measured pre-HCT plasma/serum sST2 levels in a subset of DISCOVeRY-BMT donors (n = 757) and tested the association of donor sST2 levels with donor single nucleotide polymorphisms (SNPs) in the 2q12.1 region. Donor SNPs associated with sST2 levels were then tested for association with recipient death caused by acute GVHD (aGVHD)-, infection-, and transplant-related mortality in cohorts 1 and 2. Meta-analyses of cohorts 1 and 2 were performed using fixed-effects inverse variance weighting, and P values were corrected for multiple comparisons. Donor risk alleles in rs22441131 (P _meta = .00026) and rs2310241 (P _meta = .00033) increased the cumulative incidence of aGVHD death up to fourfold and were associated with high sST2 levels. Donor risk alleles at rs4851601 (P _meta = 9.7 × 10^-7), rs13019803 (P _meta = 8.9 × 10^-6), and rs13015714 (P _meta = 5.3 × 10^-4) increased cumulative incidence of infection death to almost sevenfold and were associated with low sST2 levels. These functional variants are biomarkers of infection or aGVHD death and could facilitate donor selection, prophylaxis, and a conditioning regimen to reduce post-HCT mortality.

Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults

BACKGROUND

The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets.

METHODS

In this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink® inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches).

RESULTS

We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified 3 CpG sites spread across 3 proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to 1 polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease and between IL12B and Crohn's disease.

CONCLUSIONS

Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease.

Exploring Diseases/Traits and Blood Proteins Causally Related to Expression of ACE2, the Putative Receptor of SARS-CoV-2: A Mendelian Randomization Analysis Highlights Tentative Relevance of Diabetes-Related Traits

OBJECTIVE

COVID-19 has become a major public health problem. There is good evidence that ACE2 is a receptor for SARS-CoV-2, and high expression of ACE2 may increase susceptibility to infection. We aimed to explore risk factors affecting susceptibility to infection and prioritize drug repositioning candidates, based on Mendelian randomization (MR) studies on ACE2 lung expression.

RESEARCH DESIGN AND METHODS

We conducted a phenome-wide MR study to prioritize diseases/traits and blood proteins causally linked to ACE2 lung expression in GTEx. We also explored drug candidates whose targets overlapped with the top-ranked proteins in MR, as these drugs may alter ACE2 expression and may be clinically relevant.

RESULTS

The most consistent finding was tentative evidence of an association between diabetes-related traits and increased ACE2 expression. Based on one of the largest genome-wide association studies on type 2 diabetes mellitus (T2DM) to date (N = 898,130), T2DM was causally linked to raised ACE2 expression (P = 2.91E-03; MR-IVW). Significant associations (at nominal level; P < 0.05) with ACE2 expression were observed across multiple diabetes data sets and analytic methods for T1DM, T2DM, and related traits including early start of insulin. Other diseases/traits having nominal significant associations with increased expression included inflammatory bowel disease, (estrogen receptor-positive) breast cancer, lung cancer, asthma, smoking, and elevated alanine aminotransferase. We also identified drugs that may target the top-ranked proteins in MR, such as fostamatinib and zinc.

CONCLUSIONS

Our analysis suggested that diabetes and related traits may increase ACE2 expression, which may influence susceptibility to infection (or more severe infection). However, none of these findings withstood rigorous multiple testing corrections (at false discovery rate <0.05). Proteome-wide MR analyses might help uncover mechanisms underlying ACE2 expression and guide drug repositioning. Further studies are required to verify our findings.