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Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis - and trans -expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis -eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans -eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans -eQTL. Trans -eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes. Analyses of expression profiles from whole blood of 31,684 individuals identify cis -expression quantitative trait loci (eQTL) effects for 88% of genes and trans -eQTL effects for 37% of trait-associated variants.

More than a half of plasma proteins are N-glycosylated. Most of them are synthesized, glycosylated, and secreted to the bloodstream by liver and lymphoid tissues. While associations with N-glycosylation are implicated in the rising number of liver, cardiometabolic, and immune diseases, little is known about the genetic regulation of this process. Here, we performed the largest genome-wide association study of N-glycosylation of the blood plasma proteome in 10,000 individuals. We doubled the number of genetic loci known to be associated with blood N-glycosylation by identifying 16 novel loci and prioritizing 13 novel genes contributing to N-glycosylation. Among these were the GCKR , TRIB1 , HP, SERPINA1 and CFH genes. These genes are predominantly expressed in the liver and show a previously unknown genetic link between plasma protein N-glycosylation, metabolic and liver diseases, and inflammatory response. By integrating glycomics, proteomics, transcriptomics, and genomics, we provide a resource that facilitates deeper exploration of disease pathogenesis and supports the discovery of glycan-based biomarkers. Proteins are often modified by complex carbohydrates (N-glycans). Here, authors identified gene regulators of this process and uncovered links between plasma protein Nglycosylation, metabolic and liver diseases, and anti-inflammatory proteins.

Lipoprotein lipase (LPL) has a central role in lipoprotein metabolism to maintain normal lipoprotein levels in blood and, through tissue specific regulation of its activity, to determine when and in what tissues triglycerides are unloaded. Recent data indicate that angiopoietin-like protein (Angptl)-4 inhibits LPL and retards lipoprotein catabolism. We demonstrate here that the N-terminal coiled-coil domain of Angptl-4 binds transiently to LPL and that the interaction results in conversion of the enzyme from catalytically active dimers to inactive, but still folded, monomers with decreased affinity for heparin. Inactivation occurred with less than equimolar ratios of Angptl-4 to LPL, was strongly temperature-dependent, and did not consume the Angptl-4. Furthermore, we show that Angptl-4 mRNA in rat adipose tissue turns over rapidly and that changes in the Angptl-4 mRNA abundance are inversely correlated to LPL activity, both during the fed-to-fasted and fasted-to-fed transitions. We conclude that Angptl-4 is a fasting-induced controller of LPL in adipose tissue, acting extracellularly on the native conformation in an unusual fashion, like an unfolding molecular chaperone.

Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development. A genetic atlas of the human plasma proteome, comprising 1,927 genetic associations with 1,478 proteins, identifies causes of disease and potential drug targets.

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. A genome-wide association study of plasma protein levels measured with 4,907 aptamers in 35,559 Icelanders highlights links with over 370 disease endpoints and other traits.

Understanding the function of human blood serum proteins in disease has been limited by difficulties in monitoring their production, accumulation, and distribution. Emilsson et al. investigated human serum proteins of more than 5000 Icelanders over the age of 65. The composition of blood serum includes a complex regulatory network of proteins that are globally coordinated across most or all tissues. The authors identified modules and functional groups associated with disease and health outcomes and were able to link genetic variants to complex diseases. Science , this issue p. 769 A deep proteome analysis of human serum reveals the relationship between disease and genetics. Proteins circulating in the blood are critical for age-related disease processes; however, the serum proteome has remained largely unexplored. To this end, 4137 proteins covering most predicted extracellular proteins were measured in the serum of 5457 Icelanders over 65 years of age. Pairwise correlation between proteins as they varied across individuals revealed 27 different network modules of serum proteins, many of which were associated with cardiovascular and metabolic disease states, as well as overall survival. The protein modules were controlled by cis- and trans-acting genetic variants, which in many cases were also associated with complex disease. This revealed co-regulated groups of circulating proteins that incorporated regulatory control between tissues and demonstrated close relationships to past, current, and future disease states.

The innate immune system can immediately respond to microorganism intrusion by helping to prevent further invasion. Bactericidal/permeability-increasing protein (BPI) is a major constituent of neutrophils that possesses anti-inflammatory properties. Inflammation is increasingly recognized as a component of the metabolic syndrome. We hypothesized that the production of BPI could be linked to insulin sensitivity and glucose tolerance. We studied circulating BPI across categories of glucose tolerance. We also studied whether these cross-sectional associations were of functional importance. For this reason, we investigated circulating bioactive lipopolysaccharide and the effects of changing insulin action-after treatment with an insulin sensitizer (metformin)-on circulating BPI in subjects with glucose intolerance. Finally, we tested whether a 3'-untranslated region (UTR) BPI polymorphism led to differences in BPI and insulin action among nondiabetic subjects. Age- and BMI-adjusted circulating BPI was significantly lower among patients with type 2 diabetes. Circulating BPI correlated negatively with fasting and postload glucose and insulin concentrations. In subjects with glucose intolerance, BPI was also linked to BMI, waist-to-hip ratio, and age- and BMI-adjusted insulin sensitivity. Bioactive lipopolysaccharide was negatively correlated with circulating BPI (r = -0.57, P < 0.0001) and positively with plasma lipopolysaccharide-binding protein (r = 0.54, P = 0.002). In parallel to improved insulin sensitivity, plasma BPI significantly increased in the metformin group but not in the placebo group. A 3'-UTR BPI polymorphism was simultaneously associated with plasma BPI concentration, waist-to-hip ratio, fasting and postload insulin concentration, fasting plasma triglycerides, and insulin sensitivity. These findings suggest that this component of the innate immune system is associated with metabolic pathways.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand–receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public–private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics 1 . The Pharma Proteomics Project generates the largest open-access plasma proteomics dataset to date, offering insights into trans protein quantitative trait loci across multiple biological domains, and highlighting genetic influences on ligand–receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks.

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. Mendelian randomization (MR) and colocalization analyses are used to estimate causal effects of 1,002 plasma proteins on 225 phenotypes. Evidence from drug developmental programs shows that target-indication pairs with MR and colocalization support were more likely to be approved, highlighting the value of this approach for prioritizing therapeutic targets.