Proteogenomic links to human metabolic diseases

Genetics of circulating proteins in newborn babies at high risk of type 1 diabetes

Type 1 diabetes is a chronic, autoimmune disease characterized by the destruction of insulin-producing β-cells in the pancreas. Early detection can facilitate timely intervention, potentially delaying or preventing disease onset. Circulating proteins reflect dysregulated biological processes and offer insights into early disease mechanisms. Here, we construct a genome-wide pQTL map of 1985 proteins in 695 newborn babies (median age 2 days) at increased genetic risk of developing Type 1 diabetes. We identify 535 pQTLs (352 cis-pQTLs, 183 trans-pQTLs), 62 of which characteristic of newborns. We show colocalization of pQTLs for CTRB1, APOBR, IL7R, CPA1, and PNLIPRP1 with Type 1 diabetes GWAS signals, and Mendelian randomization causally implicates each of these five proteins in the aetiology of Type 1 diabetes. Our study illustrates the utility of newborn molecular profiles for discovering potential drug targets for childhood diseases of significant concern.

Discovering genetically-supported drug targets for multisite chronic pain through multi-omics Mendelian randomization and single-cell RNA-sequencing

Multisite chronic pain (MCP) is a highly prevalent disorder with substantial unmet therapeutic needs.We conducted multi-omics Mendelian randomization and Bayesian colocalization to identify potential therapeutic targets for MCP. Summary-level data of gene expressions and protein abundance levels were obtained from corresponding quantitative trait loci studies, respectively. Summary-level data for MCP was leveraged from the UK Biobank. The transcriptome-wide association study (TWAS), Mendelian randomization, and Bayesian colocalization approaches were applied to investigate the potential causal effects of gene expressions and protein levels on MCP in both blood and brain tissues. Phenome-wide Mendelian randomization analysis (MR-PheWAS), single-cell sequencing data, protein-protein interaction (PPI), and reaction pathway analysis were further conducted to digging the underlying mechanisms. Our analysis identified and validated two plasma targets for MCP, namely KLC1 and LANCL1, at both gene expression levels and protein levels across multi-methodologies. Moreover, MR-PheWAS observed additional benefits associated with these two targets. Through analyses based on single-cell sequencing data, we identified critical cell types for KLC1, primarily megakaryocytes, and neurons, notably linked to the axon guidance pathway, while LANCL1 showed associations with B lymphocytes, neurons, and the electron transport pathway. In dorsal root ganglions, we identified enrichments of both LANCL1 and KLC1 in putative silent nociceptors. The effects are possibly mediated through axonal transport and the activation of NMDARs, supported by PPI and reaction pathway analysis. Our multi-dimensional analysis suggests that genetically determined KLC1 and LANCL1 are causally linked to MCP risk, holding promise as appealing drug targets for MCP.

Multi-omic studies on the pathogenesis of Sepsis

BACKGROUND

Sepsis is a life-threatening inflammatory condition, and its underlying genetic mechanisms are not yet fully elucidated. We applied methods such as Mendelian randomization (MR), genetic correlation analysis, and colocalization analysis to integrate multi-omics data and explore the relationship between genetically associated genes and sepsis, as well as sepsis-related mortality, with the goal of identifying key genetic factors and their potential mechanistic pathways.

METHODS

To identify therapeutic targets for sepsis and sepsis-related mortality, we conducted an MR analysis on 11,643 sepsis cases and 1,896 cases of 28-day sepsis mortality from the UK Biobank cohort. The exposure data consisted of 15,944 potential druggable genes (expression quantitative trait loci, eQTL) and 4,907 plasma proteins (protein quantitative trait loci, pQTL). We then performed sensitivity analysis, SMR analysis, reverse MR analysis, genetic correlation analysis, colocalization analysis, enrichment analysis, and protein-protein interaction network analysis on the overlapping genes. Validation was conducted using 17,133 sepsis cases from FinnGen R12. Drug prediction and molecular docking were subsequently used to further assess the therapeutic potential of the identified drug targets, while PheWAS was used to evaluate potential side effects. Finally, mediation analysis was conducted to identify the mediating role of related metabolites.

RESULTS

The MR analysis results identified a significant causal relationship between 24 genes and sepsis. The robustness of these causal associations was further strengthened by SMR analysis, sensitivity analysis, and reverse MR analysis. Genetic correlation analysis revealed that only two of these genes were genetically correlated with sepsis. Colocalization analysis showed that only one gene was closely associated with sepsis, while validation using the FinnGen dataset identified three genes. In the MR analysis of 28-day sepsis mortality, seven genes were found to have significant associations, with reverse MR analysis excluding one gene. The remaining genes passed sensitivity analysis, with no significant genes identified in genetic correlation and colocalization analyses. Molecular docking demonstrated excellent binding affinity between drugs and proteins with available structural data. PheWAS at the gene level did not reveal any potential side effects of the related drugs.

CONCLUSIONS

The identified drug targets, associated pathways, and metabolites have enhanced our understanding of the complex relationships between genes and sepsis. These genes and metabolites can serve as effective targets for sepsis treatment, paving new pathways in this field and laying a foundation for future research.

Comparative studies of 2168 plasma proteins measured by two affinity-based platforms in 4000 Chinese adults

Proteomics offers unique insights into human biology and drug development, but few studies have directly compared the utility of different proteomics platforms. We measured plasma levels of 2168 proteins in 3976 Chinese adults using both Olink Explore and SomaScan platforms. The correlation of protein levels between platforms was modest (median rho = 0.29), with protein abundance and data quality parameters being key factors influencing correlation. For 1694 proteins with one-to-one matched reagents, 765 Olink and 513 SomaScan proteins had cis-pQTLs, including 400 with colocalising cis-pQTLs. Moreover, 1096 Olink and 1429 SomaScan proteins were associated with BMI, while 279 and 154 proteins were associated with risk of ischaemic heart disease, respectively. Addition of Olink and SomaScan proteins to conventional risk factors for ischaemic heart disease improved C-statistics from 0.845 to 0.862 (NRI: 12.2%) and 0.863 (NRI: 16.4%), respectively. These results demonstrate the utility of these platforms and could inform the design and interpretation of future studies.

Metagenome-informed metaproteomics of the human gut microbiome, host, and dietary exposome uncovers signatures of health and inflammatory bowel disease

Host-microbiome-dietary interactions play crucial roles in regulating human health, yet their direct functional assessment remains challenging. We adopted metagenome-informed metaproteomics (MIM), in mice and humans, to non-invasively explore species-level microbiome-host interactions during commensal and pathogen colonization, nutritional modification, and antibiotic-induced perturbation. Simultaneously, fecal MIM accurately characterized the nutritional exposure landscape in multiple clinical and dietary contexts. Implementation of MIM in murine auto-inflammation and in human inflammatory bowel disease (IBD) characterized a "compositional dysbiosis" and a concomitant species-specific "functional dysbiosis" driven by suppressed commensal responses to inflammatory host signals. Microbiome transfers unraveled early-onset kinetics of these host-commensal cross-responsive patterns, while predictive analyses identified candidate fecal host-microbiome IBD biomarker protein pairs outperforming S100A8/S100A9 (calprotectin). Importantly, a simultaneous fecal nutritional MIM assessment enabled the determination of IBD-related consumption patterns, dietary treatment compliance, and small intestinal digestive aberrations. Collectively, a parallelized dietary-bacterial-host MIM assessment functionally uncovers trans-kingdom interactomes shaping gastrointestinal ecology while offering personalized diagnostic and therapeutic insights into microbiome-associated disease.

Atlas of the plasma proteome in health and disease in 53,026 adults

Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine. Here, we provide a detailed atlas of 2,920 plasma proteins linking to diseases (406 prevalent and 660 incident) and 986 health-related traits in 53,026 individuals (median follow-up: 14.8 years) from the UK Biobank, representing the most comprehensive proteome profiles to date. This atlas revealed 168,100 protein-disease associations and 554,488 protein-trait associations. Over 650 proteins were shared among at least 50 diseases, and over 1,000 showed sex and age heterogeneity. Furthermore, proteins demonstrated promising potential in disease discrimination (area under the curve [AUC] > 0.80 in 183 diseases). Finally, integrating protein quantitative trait locus data determined 474 causal proteins, providing 37 drug-repurposing opportunities and 26 promising targets with favorable safety profiles. These results provide an open-access comprehensive proteome-phenome resource (https://proteome-phenome-atlas.com/) to help elucidate the biological mechanisms of diseases and accelerate the development of disease biomarkers, prediction models, and therapeutic targets.

Bayesian Effect Size Ranking to Prioritise Genetic Risk Variants in Common Diseases for Follow-Up Studies

Biological datasets often consist of thousands or millions of variables, e.g. genetic variants or biomarkers, and when sample sizes are large it is common to find many associated with an outcome of interest, for example, disease risk in a GWAS, at high levels of statistical significance, but with very small effects. The False Discovery Rate (FDR) is used to identify effects of interest based on ranking variables according to their statistical significance. Here, we develop a complementary measure to the FDR, the priorityFDR, that ranks variables by a combination of effect size and significance, allowing further prioritisation among a set of variables that pass a significance or FDR threshold. Applying to the largest GWAS of type 1 diabetes to date (15,573 cases and 158,408 controls), we identified 26 independent genetic associations, including two newly-reported loci, with qualitatively lower priorityFDRs than the remaining 175 signals. We detected putatively causal type 1 diabetes risk genes using Mendelian Randomisation, and found that these were located disproportionately close to low priorityFDR signals (p = 0.005), as were genes in the IL-2 pathway (p = 0.003). Selecting variables on both effect size and significance can lead to improved prioritisation for mechanistic follow-up studies from genetic and other large biological datasets.

The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.

ABO gene polymorphism and COVID-19 severity: The impact on haematological complications, inflammatory markers, and lung lesions

PURPOSE

The study aimed to investigate the connection between an intronic variant in the ABO gene (rs657152) and the severity of COVID-19 in terms of clinical symptoms, haematological complications, inflammatory markers, and lung lesions.

METHODS

After applying exclusion criteria, the study included 240 patients divided into 3 groups: 88 Outpatients, 84 Ward-hospitalized, and 68 ICU-admitted/failed patients. The tetra-ARMS PCR method was used to genotype ABO polymorphism in the patient. Paraclinical tests of patients at the time of admission (before receiving conventional treatments) included levels of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), as well as a complete blood count (CBC). Also, the severity of lung lesions was evaluated based on the results of spiral computed tomography (CT) of the chest during admission.

RESULTS

The statistical analysis using the ANOVA test revealed significant differences in the mean values of allele frequencies (p-value = 0.0020) and genotype proportions (p-value = 0.0017) among clinical groups. The study also found a notable difference in ABO polymorphism across different levels of the inflammatory marker CRP, but not with the ESR levels. Furthermore, the study showed a significant difference in the distribution of lung lesion severity and ABO polymorphism among different clinical groups.

CONCLUSION

To conclude, our findings supported the substantial impact of ABO polymorphism rs657152 on the severity of COVID-19 in Iranian patients, specifically concerning haematological complications, inflammatory markers, and lung lesions. The study underscored the protective effect of the AC genotype and the detrimental impact of the CC genotype on clinical manifestations.

Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance

Studying the genetic regulation of protein expression (through protein quantitative trait loci (pQTLs)) offers a deeper understanding of regulatory variants uncharacterized by mRNA expression regulation (expression QTLs (eQTLs)) studies. Here we report cis-eQTL and cis-pQTL statistical fine-mapping from 1,405 genotyped samples with blood mRNA and 2,932 plasma samples of protein expression, as part of the Japan COVID-19 Task Force (JCTF). Fine-mapped eQTLs (n = 3,464) were enriched for 932 variants validated with a massively parallel reporter assay. Fine-mapped pQTLs (n = 582) were enriched for missense variations on structured and extracellular domains, although the possibility of epitope-binding artifacts remains. Trans-eQTL and trans-pQTL analysis highlighted associations of class I HLA allele variation with KIR genes. We contrast the multi-tissue origin of plasma protein with blood mRNA, contributing to the limited colocalization level, distinct regulatory mechanisms and trait relevance of eQTLs and pQTLs. We report a negative correlation between ABO mRNA and protein expression because of linkage disequilibrium between distinct nearby eQTLs and pQTLs.

Ancestrally diverse genome-wide association analysis highlights ancestry-specific differences in genetic regulation of plasma protein levels

Fully characterizing the genetic architecture of circulating proteins in multi-ancestry populations provides an unprecedented opportunity to gain insights into the etiology of complex diseases. We characterized and contrasted the genetic associations of plasma proteomes in 9,455 participants of European and African (19.8%) ancestry from the Atherosclerosis Risk in Communities Study. Of 4,651 proteins, 1,408 and 2,565 proteins had protein-quantitative trait loci (pQTLs) identified in African and European ancestry respectively, and twelve unreported potentially causal protein-disease relationships were identified. Shared pQTLs across the two ancestries were detected in 1,113 aptamer-region pairs pQTLs, where 53 of them were not previously reported (all trans pQTLs). Sixteen unique protein-cardiovascular trait pairs were colocalized in both European and African ancestry with the same candidate causal variants. Our systematic cross-ancestry comparison provided a reliable set of pQTLs, highlighted the shared and distinct genetic architecture of proteome in two ancestries, and demonstrated possible biological mechanisms underlying complex diseases.

Proteogenomic analysis integrated with electronic health records data reveals disease-associated variants in Black Americans

BACKGROUNDMost GWAS of plasma proteomics have focused on White individuals of European ancestry, limiting biological insight from other ancestry-enriched protein quantitative loci (pQTL).METHODSWe conducted a discovery GWAS of approximately 3,000 plasma proteins measured by the antibody-based Olink platform in 1,054 Black adults from the Jackson Heart Study (JHS) and validated our findings in the Multi-Ethnic Study of Atherosclerosis (MESA). The genetic architecture of identified pQTLs was further explored through fine mapping and admixture association analysis. Finally, using our pQTL findings, we performed a phenome-wide association study (PheWAS) across 2 large multiethnic electronic health record (EHR) systems in All of Us and BioMe.RESULTSWe identified 1,002 pQTLs for 925 protein assays. Fine mapping and admixture analyses suggested allelic heterogeneity of the plasma proteome across diverse populations. We identified associations for variants enriched in African ancestry, many in diseases that lack precise biomarkers, including cis-pQTLs for cathepsin L (CTSL) and Siglec-9, which were linked with sarcoidosis and non-Hodgkin's lymphoma, respectively. We found concordant associations across clinical diagnoses and laboratory measurements, elucidating disease pathways, including a cis-pQTL associated with circulating CD58, WBC count, and multiple sclerosis.CONCLUSIONSOur findings emphasize the value of leveraging diverse populations to enhance biological insights from proteomics GWAS, and we have made this resource readily available as an interactive web portal.FUNDINGNIH K08 HL161445-01A1; 5T32HL160522-03; HHSN268201600034I; HL133870.

Proteomics profiling and association with cardiorenal complications in type 2 diabetes subtypes in Asian population

AIM

Among multi-ethnic Asians, type 2 diabetes (T2D) clustered in three subtypes; mild obesity-related diabetes (MOD), mild age-related diabetes with insulin insufficiency (MARD-II) and severe insulin-resistant diabetes with relative insulin insufficiency (SIRD-RII) had differential cardio-renal complication risk. We assessed the proteomic profiles to identify subtype specific biomarkers and its association with diabetes complications.

METHODS

1448 plasma proteins at baseline were measured and compared across the T2D subtypes. Multivariable cox regression was used to assess associations between significant proteomics features and cardio-renal complications.

RESULTS

Among 645 T2D participants (SIRD-RII [19%], MOD [45%], MARD-II [36%]), 295 proteins expression differed significantly across the groups. These proteins were enriched in cell adhesion, neurogenesis and inflammatory response processes. In SIRD-RII group, ADH4, ACY1, THOP1, IGFBP2, NEFL, ENTPD2, CALB1, HAO1, CTSV, ITGAV, SCLY, EDA2R, ERBB2 proteins significantly associated with progressive CKD and LILRA5 protein with incident heart failure (HF). In MOD group, TAFA5, RSPO3, EDA2R proteins significantly associated with incident HF. In MARD-II group, FABP4 protein significantly associated with progressive CKD and PTPRN2 protein with major adverse cardiovascular events. Genetically determined NEFL and CALB1 were associated with kidney function decline.

CONCLUSIONS

Each T2D subtype has unique proteomics signature and association with clinical outcomes and underlying mechanisms.

Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

Proteomic prediction of diverse incident diseases: a machine learning-guided biomarker discovery study using data from a prospective cohort study

BACKGROUND

Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far been limited to very few selected diseases and have not evaluated predictive performance across multiple conditions. We aimed to evaluate the potential of serum proteins to improve risk prediction over and above health-derived information and polygenic risk scores across a diverse set of 24 outcomes.

METHODS

We designed multiple case-cohorts nested in the EPIC-Norfolk prospective study, from participants with available serum samples and genome-wide genotype data, with more than 32 974 person-years of follow-up. Participants were middle-aged individuals (aged 40-79 years at baseline) of European ancestry who were recruited from the general population of Norfolk, England, between March, 1993 and December, 1997. We selected participants who developed one of ten less common diseases within 10 years of follow-up; we also subsampled a randomly drawn control subcohort, which also served to investigate 14 more common outcomes (n>70), including all-cause premature mortality (death before the age of 75 years; case numbers 71-437; controls 608-1556). Individuals were excluded from the current study owing to failed genotyping or proteomic quality control, relatedness, or missing information on age, sex, BMI, or smoking status. We used a machine learning framework to derive sparse predictive protein models for the onset of the the 23 individual diseases and all-cause premature mortality, and to derive a single common sparse multimorbidity signature that was predictive across multiple diseases from 2923 serum proteins.

FINDINGS

Participants who developed one of ten less common diseases within 10 years of follow-up included 482 women and 507 men, with a mean age at baseline of 64·56 years (8·08). The random subcohort included 990 women and 769 men, with a mean age of 58·79 years (9·31). As few as five proteins alone outperformed polygenic risk scores for 17 of 23 outcomes (median dfference in concordance index [C-index] 0·13 [0·10-0·17]) and improved predictive performance when added over basic patient-derived information models for seven outcomes, achieving a median C-index of 0·82 (IQR 0·77-0·82). This included diseases with poor prognosis such as lung cancer (C-index 0·85 [+/- cross-validation error 0·83-0·87]), for which we identified unreported biomarkers such as C-X-C motif chemokine ligand 17. A sparse multimorbidity signature of ten proteins improved prediction across seven outcomes over patient-derived information models, achieving performances (median C-index 0·81 [IQR 0·80-0·82]) similar to those of disease-specific signatures.

INTERPRETATION

We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and to benchmark these against clinical assays, which are required to understand the translational potential of these findings.

FUNDING

Medical Research Council, Health Data Research UK, UK Research and Innovation-National Institute for Health and Care Research, Cancer Research UK, and Wellcome Trust.

Systemic proteome adaptions to 7-day complete caloric restriction in humans

Surviving long periods without food has shaped human evolution. In ancient and modern societies, prolonged fasting was/is practiced by billions of people globally for religious purposes, used to treat diseases such as epilepsy, and recently gained popularity as weight loss intervention, but we still have a very limited understanding of the systemic adaptions in humans to extreme caloric restriction of different durations. Here we show that a 7-day water-only fast leads to an average weight loss of 5.7 kg (±0.8 kg) among 12 volunteers (5 women, 7 men). We demonstrate nine distinct proteomic response profiles, with systemic changes evident only after 3 days of complete calorie restriction based on in-depth characterization of the temporal trajectories of ~3,000 plasma proteins measured before, daily during, and after fasting. The multi-organ response to complete caloric restriction shows distinct effects of fasting duration and weight loss and is remarkably conserved across volunteers with >1,000 significantly responding proteins. The fasting signature is strongly enriched for extracellular matrix proteins from various body sites, demonstrating profound non-metabolic adaptions, including extreme changes in the brain-specific extracellular matrix protein tenascin-R. Using proteogenomic approaches, we estimate the health consequences for 212 proteins that change during fasting across ~500 outcomes and identified putative beneficial (SWAP70 and rheumatoid arthritis or HYOU1 and heart disease), as well as adverse effects. Our results advance our understanding of prolonged fasting in humans beyond a merely energy-centric adaptions towards a systemic response that can inform targeted therapeutic modulation.

Smoking changes adaptive immunity with persistent effects

Individuals differ widely in their immune responses, with age, sex and genetic factors having major roles in this inherent variability1-6. However, the variables that drive such differences in cytokine secretion-a crucial component of the host response to immune challenges-remain poorly defined. Here we investigated 136 variables and identified smoking, cytomegalovirus latent infection and body mass index as major contributors to variability in cytokine response, with effects of comparable magnitudes with age, sex and genetics. We find that smoking influences both innate and adaptive immune responses. Notably, its effect on innate responses is quickly lost after smoking cessation and is specifically associated with plasma levels of CEACAM6, whereas its effect on adaptive responses persists long after individuals quit smoking and is associated with epigenetic memory. This is supported by the association of the past smoking effect on cytokine responses with DNA methylation at specific signal trans-activators and regulators of metabolism. Our findings identify three novel variables associated with cytokine secretion variability and reveal roles for smoking in the short- and long-term regulation of immune responses. These results have potential clinical implications for the risk of developing infections, cancers or autoimmune diseases.

Deep proteomic analysis of obstetric antiphospholipid syndrome by DIA-MS of extracellular vesicle enriched fractions

Proteins in the plasma/serum mirror an individual's physiology. Circulating extracellular vesicles (EVs) proteins constitute a large portion of the plasma/serum proteome. Thus, deep and unbiased proteomic analysis of circulating plasma/serum extracellular vesicles holds promise for discovering disease biomarkers as well as revealing disease mechanisms. We established a workflow for simple, deep, and reproducible proteome analysis of both serum large and small EVs enriched fractions by ultracentrifugation plus 4D-data-independent acquisition mass spectrometry (4D-DIA-MS). In our cohort study of obstetric antiphospholipid syndrome (OAPS), 4270 and 3328 proteins were identified from large and small EVs enriched fractions respectively. Both of them revealed known or new pathways related to OAPS. Increased levels of von Willebrand factor (VWF) and insulin receptor (INSR) were identified as candidate biomarkers, which shed light on hypercoagulability and abnormal insulin signaling in disease progression. Our workflow will significantly promote our understanding of plasma/serum-based disease mechanisms and generate new biomarkers.

Large-scale plasma proteomics comparisons through genetics and disease associations

High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project1 on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people2, for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.

Plasma proteomic associations with genetics and health in the UK Biobank

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 therapeutics1.

Biological and functional multimorbidity-from mechanisms to management

Globally, the number of people with multiple co-occurring diseases will increase substantially over the coming decades, with important consequences for patients, carers, healthcare systems and society. Addressing this challenge requires a shift in the prevailing clinical, educational and scientific thinking and organization-with a strong emphasis on the maintenance of generalist skills to balance the specialization trends of medical education and research. Multimorbidity is not a single entity but differs quantitively and qualitatively across life stages, ethnicities, sexes, socioeconomic groups and geographies. Data-driven science that quantifies the impact of disease co-occurrence-beyond the small number of currently well-studied long-term conditions (such as cardiometabolic diseases)-can help illuminate the pathological diversity of multimorbidity and identify common, mechanistically related, and prognostically relevant clusters. Broader access to data opportunities across modalities and disciplines will catalyze vertical and horizontal integration of multimorbidity research, to enable reconfiguring of medical services, clinical trials, guidelines and research in a way that accounts for the complexity of multimorbidity-and provides efficient, joined-up services for patients.