Bram Peter Prins

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

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

Stroke is the second leading cause of death with substantial unmet therapeutic needs. To identify potential stroke therapeutic targets, we estimate the causal effects of 308 plasma proteins on stroke outcomes in a two-sample Mendelian randomization framework and assess mediation effects by stroke risk factors. We find associations between genetically predicted plasma levels of six proteins and stroke (P ≤ 1.62 × 10⁻⁴). The genetic associations with stroke colocalize (Posterior Probability >0.7) with the genetic associations of four proteins (TFPI, TMPRSS5, CD6, CD40). Mendelian randomization supports atrial fibrillation, body mass index, smoking, blood pressure, white matter hyperintensities and type 2 diabetes as stroke risk factors (P ≤ 0.0071). Body mass index, white matter hyperintensity and atrial fibrillation appear to mediate the TFPI, IL6RA, TMPRSS5 associations with stroke. Furthermore, thirty-six proteins are associated with one or more of these risk factors using Mendelian randomization. Our results highlight causal pathways and potential therapeutic targets for stroke.

Combining human genomics with proteomics is becoming a powerful tool for drug discovery. Associations between genetic variants and protein levels can uncover disease mechanisms, clinical biomarkers, and candidate drug targets. To date, most population-level proteogenomic studies have focused on common alleles through genome-wide association studies (GWAS). Here, we studied the contribution of rare protein-coding variants to 1,472 plasma proteins abundances measured via the Olink Explore 1536 assay in 50,829 UK Biobank human exomes. Through a variant-level exome-wide association study (ExWAS), we identified 3,674 rare and significant protein quantitative trait loci (pQTLs), of which 76% were undetected in a prior GWAS performed on the same cohort, and we found that rare pQTLs are less likely to be random in their variant effect annotation. In gene-based collapsing analyses, we identified an additional 166 significant gene-protein pQTL signals that were undetected through single-variant analyses. Of the total 456 protein-truncating variant (PTV)-driven cis -pQTLs in the gene-based collapsing analysis, 99.3% were associated with decreased protein levels. We demonstrate how this resource can identify allelic series and propose biomarkers for several candidate therapeutic targets, including GRN, HSD17B13, NLRC4 , and others. Finally, we introduce a new collapsing analysis framework that combines PTVs with missense cis -pQTLs that are associated with decreased protein abundance to bolster genetic discovery statistical power. Our results collectively highlight a considerable role for rare variation in plasma protein abundance and demonstrate the utility of plasma proteomics in gene discovery and unravelling mechanisms of action.

The UK Biobank Pharma Proteomics Project (UKB-PPP) is a collaboration between the UK Biobank (UKB) and thirteen biopharmaceutical companies characterising the plasma proteomic profiles of 54,306 UKB participants. Here, we describe results from the first phase of UKB-PPP, including protein quantitative trait loci (pQTL) mapping of 1,463 proteins that identifies 10,248 primary genetic associations, of which 85% are newly discovered. We also identify independent secondary associations in 92% of cis and 29% of trans loci, expanding the catalogue of genetic instruments for downstream analyses. The study provides an updated characterisation of the genetic architecture of the plasma proteome, leveraging population-scale proteomics to provide novel, extensive insights into trans pQTLs across multiple biological domains. We highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement proteins, 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 target discovery by extending the genetic proxied effect of PCSK9 levels on lipid concentrations, cardio- and cerebro-vascular diseases, and additionally disentangle specific genes and proteins perturbed at COVID-19 susceptibility loci. This public-private partnership provides the scientific community with an open-access proteomics resource of unprecedented breadth and depth to help elucidate biological mechanisms underlying genetic discoveries and accelerate the development of novel biomarkers and therapeutics.

Background: SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood. Methods: We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics–based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data. Results: We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis–protein quantitative trait loci–based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10–2.42]; P =0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05–2.21]; P =0.03), and infection (odds ratio, 1.60 [95% CI, 1.08–2.37]; P =0.02). Tissue- and cell type–specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells. Conclusions: Human plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.

Proteins are the effector molecules of biology and are the target of most drugs. To identify proteins and related pathways that may play a causal role in stroke pathogenesis, we used Mendelian randomisation (MR). We tested potential causal effects of 308 plasma proteins (measured in 4,994 blood donors from the INTERVAL study) on stroke outcomes (derived from the MEGASTROKE GWAS) in a two-sample MR framework and assessed whether these associations could be mediated by cardiovascular risk factors. We extended the analysis to identify whether pharmacological targeting of these proteins might have potential adverse side-effects or beneficial effects for other conditions through Phenome-wide MR (Phe-MR) in UK Biobank. MR showed an association between stroke and genetically predicted plasma levels of TFPI, IL6RA, MMP12, CD40, TMPRSS5 and CD6 (P≤1.62⋅10⁻⁴). We identified six risk factors (atrial fibrillation, body mass index, smoking, blood pressure, white matter hyperintensities and type 2 diabetes) that were associated with stroke (P≤0.0071) using MR. The association of TFPI, IL6RA and TMPRSS5 with stroke could be mediated by these risk factors, such as body mass index, white matter hyperintensity and atrial fibrillation. Thirty-six additional proteins were potentially causal for one or more of these risk factors. The Phe-MR suggested that targeting TFPI could have potential beneficial effects on other disorders of arteries and hyperlipidaemia in addition to stroke. Our results highlight novel causal pathways and potential therapeutic targets for stroke.

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

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer.

Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls. We found significant Mendelian randomization results for three proteins (ACE2, P = 1.6 × 10⁻⁶; IFNAR2, P = 9.8 × 10⁻¹¹ and IL-10RB, P = 2.3 × 10⁻¹⁴) using cis-expression quantitative trait loci genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared expression quantitative trait loci signal for IL10RB and IFNAR2, we conducted phenome-wide association scans and pathway enrichment analysis, which suggested that IFNAR2 is more likely to play a role in COVID-19 hospitalization. Our findings prioritize trials of drugs targeting IFNAR2 and ACE2 for early management of COVID-19.