File content:

processed_gwas_region_files.1.tgz : Full summary statistics for +/- 500kb regional refinements around 8,462 rQTL lead SNPs using imputed genotype data in the discovery cohort (tab-separated, text).

processed_gwas_region_files.2.tgz : Full summary statistics for +/- 500kb regional refinements around 8,462 rQTL lead SNPs using imputed genotype data in the replication cohort (tab-separated, text).

OLINK_analyze_gwas_regions.pdf : Regional association plots for local refinements (multi-page PDF)

MANHATTAN_PLOTS.tgz : Manhattan plots for 2,821 GWAS with ratios (multiple PDF files)

Summary statistics for significant sex-differential pQTLs for Olink Explore 3072 measurements in UK Biobank and Somalogic measurements in Fenland study. The summary statistics contain approaximately 1 Mb region surrounding (±500 Kb on either side) the significant sex-differential pQTL. The regions were merged if there were multiple variants passing the significance threshold within close proximity. A proteome and genome-wide Bonferroni corrected significance threshold (phet<1.01x10-11 and phet<1.71x10-11 respectively for aptamer- [i.e. Somalogic] and antibody-based platforms [i.e. Olink Explore 3072]) for heterogeneity p-value was used to define sex-differential protein quantitative trait loci (i.e. sd-pQTLs).

The file names for the files are as follows: {protein_target}_{chr}_{region_start}_{region_end}_{cohort}_proteomics_{sex_included_in_the_analysis}.txt

Corresponding author: Claudia Langenberg (claudia.langenberg@qmul.cam.ac.uk)

Assessing how much variability in blood plasma proteins is due to genetic or environmental factors is essential for advancing personalized medicine. While large-scale studies have established SNP-based heritability (SNP-h2) estimates for plasma proteins, less is known about the proportion of total genetic effects on protein variability. We applied quantitative genetic twin models to estimate the heritability of 2321 plasma proteins and to assess the proportion of heritability accounted for by SNP-h2 estimates. Olink proteomics data were generated for 401 twins aged 56–70, including 196 complete same-sex twin pairs. On average, 40% of protein variability was attributable to genetic effects. Twin-based heritability estimates were highly correlated with published SNP-h2 estimates from the UK Biobank (Spearman coefficient: ρ = 0.80). However, on average, only half of the total heritability was covered by SNP-h2, and the other half, representing one-fifth of the total protein phenotypic variability, remains missing.

Colorectal cancer is one of the leading causes of cancer-related mortality in the world. Incidence and mortality are predicted to rise globally during the next several decades. When detected early, colorectal cancer is treatable with surgery and medications. This leads to the requirement for prognostic and diagnostic biomarker development. Our study integrates machine learning models and protein network analysis to identify protein biomarkers for colorectal cancer. Our methodology leverages an extensive collection of proteome profiles from both healthy and colorectal cancer individuals. To identify a potential biomarker with high predictive ability, we used three machine learning models. To enhance the interpretability of our models, we quantify each protein’s contribution to the model’s predictions using SHapley Additive exPlanations values. Three classifiers—LASSO, XGBoost, and LightGBM were evaluated for predictive performance along with hyperparameter tuning of each model using grid search, with LASSO achieving the highest AUC of 75% in the UK Biobank dataset and the AUCs for LightGBM and XGBoost are 69.61% and 71.42%, respectively. Using SHapley Additive exPlanations values, TFF3, LCN2, and CEACAM5 were found to be key biomarkers associated with cell adhesion and inflammation. Protein quantitative trait loci analyze studies provided further evidence for the involvement of TFF1, CEACAM5, and SELE in colorectal cancer, with possible connections to the PI3K/Akt and MAPK signaling pathways. By offering insights into colorectal cancer diagnostics and targeted therapeutics, our findings set the stage for further biomarker validation.

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been nine attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137) and the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under End User Licence (EUL) (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

National Child Development Study: Proteomics: Special Licence, 2002-2004
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004. This will substantially enhance NCDS and catalyse a step change in our understanding of the relationship between exposures from birth to midlife and their consequences for multiple physical and mental health disorders. It will provide high-dimensional biological information on these individuals during early midlife (aged 42 to 44), prior to the onset of most chronic disease, and at an age that is underrepresented in most cohorts, including UK Biobank (UKB).

Embedding this technology within NCDS with linkage to existing genetics and biomarker data, repeat measures of social and biomedical exposures, and pre-clinical and clinical disease outcomes will drive a major uptake in NCDS data use, including by large-scale international academic consortia aiming to understand the determinants of healthy ageing.

BackgroundSepsis is an inflammatory disease that leads to severe mortality, highlighting the urgent need to identify new therapeutic strategies for sepsis. Proteomic research serves as a primary source for drug target identification. We employed proteome-wide Mendelian randomization (MR), genetic correlation analysis, and colocalization analysis to identify potential targets for sepsis and sepsis-related death.MethodsGenetic data for plasma proteomics were obtained from 35,559 Icelandic individuals and an initial MR analysis was conducted using 13,531 sepsis cases from the FinnGen R10 cohort to identify associations between plasma proteins and sepsis. Subsequently, significant proteins underwent genetic correlation analysis, followed by replication in 54,306 participants from the UK Biobank Pharma Proteomics Project and validation in 11,643 sepsis cases from the UK Biobank. The identified proteins were then subjected to colocalization analysis, enrichment analysis, and protein-protein interaction network analysis. Additionally, we also investigated a MR analysis using plasma proteins on 1,896 sepsis cases with 28-day mortality from the UK Biobank.ResultsAfter FDR correction, MR analysis results showed a significant causal relationship between 113 plasma proteins and sepsis. Genetic correlation analysis revealed that only 8 proteins had genetic correlations with sepsis. In the UKB-PPP replication analysis, only 4 proteins were found to be closely associated with sepsis, while validation in the UK Biobank sepsis cases found overlaps for 21 proteins. In total, 30 proteins were identified in the aforementioned analyses, and colocalization analysis revealed that only 2 of these proteins were closely associated with sepsis. Additionally, in the 28-day mortality MR analysis of sepsis, we also found that only 2 proteins were significant.ConclusionsThe identified plasma proteins and their associated metabolic pathways have enhanced our understanding of the complex relationship between proteins and sepsis. This provides new avenues for the development of drug targets and paves the way for further research in this field.

In over 100 years of blood donation practice, INTERVAL is the first randomised controlled trial to assess the impact of varying the frequency of blood donation on donor health and the blood supply. It provided policy-makers with evidence that collecting blood more frequently than current intervals can be implemented over two years without impacting on donor health, allowing better management of the supply to the NHS of units of blood with in-demand blood groups. INTERVAL was designed to deliver a multi-purpose strategy: an initial purpose related to blood donation research aiming to improve NHS Blood and Transplant’s core services and a longer-term purpose related to the creation of a comprehensive resource that will enable detailed studies of health-related questions.

Approximately 50,000 generally healthy blood donors were recruited between June 2012 and June 2014 from 25 NHS Blood Donation centres across England. Approximately equal numbers of men and women; aged from 18-80; ~93% white ancestry. All participants completed brief online questionnaires at baseline and gave blood samples for research purposes. Participants were randomised to giving blood every 8/10/12 weeks (for men) and 12/14/16 weeks (for women) over a 2-year period. ~30,000 participants returned after 2 years and completed a brief online questionnaire and gave further blood samples for research purposes.

The baseline questionnaire includes brief lifestyle information (smoking, alcohol consumption, etc), iron-related questions (e.g., red meat consumption), self-reported height and weight, etc. The SF-36 questionnaire was completed online at baseline and 2-years, with a 6-monthly SF-12 questionnaire between baseline and 2-years.

All participants have had the Affymetrix Axiom UK Biobank genotyping array assayed and then imputed to 1000G+UK10K combined reference panel (80M variants in total). 4,000 participants have 50X whole-exome sequencing and 12,000 participants have 15X whole-genome sequencing. Whole-blood RNA sequencing has commenced in ~5,000 participants.

The dataset also contains data on clinical chemistry biomarkers, blood cell traits, >200 lipoproteins, metabolomics (Metabolon HD4), lipidomics, and proteomics (SomaLogic, Olink), either cohort-wide or is large sub-sets of the cohort.

Osteoarthritis (OA) is characterised by loss of articular cartilage, synovial membrane dysfunction and subchondral sclerosis. Few studies have used a global approach to stratify equine synovial fluid (SF) molecular profiles according to OA severity. SF was collected from 58 metacarpophalangeal (MCP) and metatarsophalangeal joints of racing Thoroughbred horses (Hong Kong Jockey Club; HKJC) and 83 MCP joints of mixed breed horses from an abattoir and equine hospital (biobank). Joints were histologically and macroscopically assessed for OA severity. For proteomic analysis, native SF and SF loaded onto ProteoMiner™ equalisation columns, to deplete high abundant proteins, were analysed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and label-free quantification. Validation of selected differentially expressed proteins was undertaken using clinical SF collected during diagnostic investigations. Native SF metabolites were analysed using 1D 1H Nuclear Magnetic Resonance (NMR). 1,834 proteins and 40 metabolites were identified in equine SF. Afamin levels decreased with synovitis severity and four uncharacterised proteins decreased with OA severity. Gelsolin and lipoprotein binding protein decreased with OA severity and apolipoprotein A1 levels increased for mild and moderate OA. Within the biobank, glutamate levels decreased with OA severity and for the HKJC, 2-aminobutyrate, alanine and creatine increased with severity. Proteomic and metabolomic integration was undertaken using linear regression via Lasso penalisation modelling, incorporating 29 variables (R2=0.82) with principal component 2 able to discriminate advanced OA from earlier stages, predominantly driven by H9GZQ9, F6ZR63 and alanine. Combining biobank and HKJC datasets, discriminant analysis of principal components modelling prediction was good for mild OA (90%). This study has stratified equine OA using both metabolomic and proteomic SF profiles and identified a panel of markers of interest which may be applicable to grading OA severity. This is also the first study to undertake computational integration of NMR metabolomic and LC-MS/MS proteomic datasets of any biological system.

BackgroundThe current landscape of acne therapeutics is notably lacking in targeted treatments, highlighting a critical need for the discovery of new drug targets to improve treatment outcomes.ObjectivesThis study aims to investigate the connections between proteomics and genetics in relation to acne across extensive population cohorts, aspiring to identify innovative preventive and therapeutic approaches.MethodsEmploying a longitudinal cohort of 54,306 participants from the UK Biobank Pharmacological Proteomics Project (UKB-PPP), we performed an exhaustive evaluation of the associations between 2,923 serum proteins and acne risk. Initial multivariate Cox regression analyses assessed the relationship between protein expression levels and acne onset, followed by two-sample Mendelian Randomization (TSMR), Summary-data-based Mendelian Randomization (SMR), and colocalization to identify genetic correlations with potential protein targets.ResultsWithin the UKB cohort, we identified 19 proteins significantly associated with the risk of acne. Subsequent analysis using Two-Sample Mendelian Randomization (TSMR) refined this to two specific proteins: FSTL1 and ANXA5. Each one-standard deviation increase in the expression levels of FSTL1 and ANXA5 was associated with a 24% and 32% increase in acne incidence, respectively. These results were further validated by additional Summary-data-based Mendelian Randomization (SMR) and differential expression analyses.ConclusionsOur comprehensive analysis of proteomic and genetic data from a European adult cohort provides compelling causal evidence that several proteins are promising targets for novel acne treatments.

Association of High Polygenic Score with Alzheimer’s Disease in the UK Biobank.

BackgroundEndometriosis (EM) is a chronic painful condition that predominantly affects women of reproductive age. Currently, surgery or medication can only provide limited symptom relief. This study used a comprehensive genetic analytical approach to explore potential drug targets for EM in the plasma proteome.MethodsIn this study, 2,923 plasma proteins were selected as exposure and EM as outcome for two-sample Mendelian randomization (MR) analyses. The plasma proteomic data were derived from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP), while the EM dataset from the FinnGen consortium R10 release data. Several sensitivity analyses were performed, including summary-data-based MR (SMR) analyses, heterogeneity in dependent instruments (HEIDI) test, reverse MR analyses, steiger detection test, and bayesian co-localization analyses. Furthermore, proteome-wide association study (PWAS) and single-cell transcriptomic analyses were also conducted to validate the findings.ResultsSix significant (p < 3.06 × 10-5) plasma protein-EM pairs were identified by MR analyses. These included EPHB4 (OR = 1.40, 95% CI: 1.20 - 1.63), FSHB (OR = 3.91, 95% CI: 3.13 - 4.87), RSPO3 (OR = 1.60, 95% CI: 1.38 - 1.86), SEZ6L2 (OR = 1.44, 95% CI: 1.23 - 1.68) and WASHC3 (OR = 2.00, 95% CI: 1.54 - 2.59) were identified as risk factors, whereas KDR (OR = 0.80, 95% CI: 0.75 - 0.90) was found to be a protective factor. All six plasma proteins passed the SMR test (P < 8.33 × 10-3), but only four plasma proteins passed the HEIDI heterogeneity test (PHEIDI > 0.05), namely FSHB, RSPO3, SEZ6L2 and EPHB4. These four proteins showed strong evidence of co-localization (PPH4 > 0.7). In particular, RSPO3 and EPHB4 were replicated in the validated PWAS. Single-cell analyses revealed high expression of SEZ6L2 and EPHB4 in stromal and epithelial cells within EM lesions, while RSPO3 exhibited elevated expression in stromal cells and fibroblasts.ConclusionOur study identified FSHB, RSPO3, SEZ6L2, and EPHB4 as potential drug targets for EM and highlighted the critical role of stromal and epithelial cells in disease development. These findings provide new insights into the diagnosis and treatment of EM.

Statistical code

Table S1. Characteristics of participants by cohort

Table S2. List of hallmark-related diseases

Table S3. Associations between indicators of social disadvantage and specific ARDs in UK Biobank and FPS

Table S4. Associations of indicators of social disadvantage with hallmark-specific ARDs at follow-up in participants without hallmark-related diseases at baseline in UK Biobank and FPS

Table S5. Associations of indicators of social disadvantage with hallmark-specific ARDs at follow-up in participants without hallmark-related diseases at baseline in UK Biobank and FPS by sex

Table S6. Associations of indicators of social disadvantage with hallmark-specific ARDs at follow-up in participants without hallmark-related diseases at baseline in UK Biobank and FPS – A Fine & Gray analysis corrected for competing risk of death

Table S7. Number of hallmark-specific ARDs per 100 by ages 55 and 70 by level of social disadvantage (absolute risk) in UK Biobank and FPS

Table S8. Rate of hallmark-related diseases by ages 55 and 70 by level of social disadvantage (relative risk) in UK Biobank and FPS

Table S9. The rate per 100 per 10 years for health transitions in hallmark-specific ARDs by level of social disadvantage in UK Biobank

Table S10. The rate per 100 per 10 years for health transitions in hallmark-specific ARDs by level of social disadvantage in FPS

Table S11. A multivariate model for the independent associations of education and adult SES with hallmark-specific ARDs in UK Biobank

Table S12. Test of health-related selection (reverse causality) hypothesis in FPS

Table S13. Associations of social disadvantage with hallmark-specific ARDs at follow-up in participants without hallmark-specific ARDs at baseline in the Whitehall study

Table S14. List of proteins included signatures of organ-specific and non-organ-specific (organismal) ageing (Source: Oh, H. et al Nat 2023;624:164-172)

Table S15. Associations of social disadvantage with proteomic signatures of organ-specific and organismal ageing in the Whitehall study

Table S16. Proteomic organ-specific age gaps as mediators for social disadvantage-hallmark-specific ARD associations in the Whitehall study

Table S17. Search terms for proteins related to hallmarks of ageing in SomaScan assay (Human Protein Atlas)

Table S18. List of single age-related proteins in the Whitehall study

Table S19. Associations of social disadvantage with 14 proteins (A) and hallmark-specific ARDs (B) in the Whitehall study

Table S20. Associations of social disadvantage with all-cause mortality in the Whitehall study

Table S21. Association of 14 plasma proteins with hallmark-specific ARDs in the Whitehall study

Table S22. Association of 14 plasma proteins with hallmark-specific ARDs by sex in the Whitehall study

Table S23. 14 socially-patterned proteins as mediators for the education-hallmark-specific ARD associations in the Whitehall study

Table S24. 14 socially-patterned proteins as mediators for the adult SES-hallmark-specific ARD associations in the Whitehall study

Table S25. Levels of proteins by change in social disadvantage between early to later life in the Whitehall study

Table S26. Levels of proteins by life course social standing score in the Whitehall study

Table S27: Characteristics of ARIC participants at visit 2 (1990-1992)

Table S28: Characteristics of ARIC participants at visit 5 (2011-2013)

Table S29. Associations of social disadvantage with age-related proteins and mortality in middle-aged ARIC participants

Table S30. Associations of social disadvantage with age-related proteins and mortality in older ARIC participants

Table S31. Age-, sex-, ethnicity and genetic score-adjusted associations of social disadvantage with hallmark-specific ARDs at follow-up in participants without hallmark-specific ARD at baseline in UK Biobank

Table S32. Age-, sex-, ethnicity and genetic score-adjusted associations of social disadvantage with hallmark-specific ARDs at follow-up in participants without hallmark-specific ARD at baseline in the Whitehall study

Table S33. Age-, sex-, ethnicity and genetic score-adjusted associations between social disadvantage and 14 proteins in the Whitehall study

Table S34. Age-, sex-, ethnicity and genetic score-adjusted association between 14 plasma proteins and hallmark-specific ARDs in the Whitehall study

BackgroundAnkylosing Spondylitis (AS) is a chronic inflammatory disorder which can lead to considerable pain and disability. Mendelian randomization (MR) has been extensively applied for repurposing licensed drugs and uncovering new therapeutic targets. Our objective is to pinpoint innovative therapeutic protein targets for AS and assess the potential adverse effects of druggable proteins.MethodsWe conducted a comprehensive proteome-wide MR study to assess the causal relationships between plasma proteins and the risk of AS. The plasma proteins were sourced from the UK Biobank Pharma Proteomics Project (UKB-PPP) database, encompassing GWAS data for 2,940 plasma proteins. Additionally, GWAS data for AS were extracted from the R9 version of the Finnish database, including 2,860 patients and 270,964 controls. The colocalization analysis was executed to identify shared causal variants between plasma proteins and AS. Finally, we examined the potential adverse effects of druggable proteins for AS therapy by conducting a phenome-wide association study (PheWAS) utilizing the extensive Finnish database in version R9, encompassing 2,272 phenotypes categorized into 46 groups.ResultsThe findings revealed a positive genetic association between the predicted plasma levels of six proteins and an elevated risk of AS, while two proteins exhibited an inverse association with AS risk (Pfdr < 0.05). Among these eight plasma proteins, colocalization analysis identified AIF1, TNF, FKBPL, AGER, ALDH5A1, and ACOT13 as shared variation with AS(PPH3+PPH4>0.8), suggesting that they represent potential direct targets for AS intervention. Further phenotype-wide association studies have shown some potential side effects of these six targets (Pfdr < 0.05).ConclusionOur investigation examined the causal connections between six plasma proteins and AS, providing a comprehensive understanding of potential therapeutic targets.

BackgroundAnkylosing Spondylitis (AS) is a chronic inflammatory disorder which can lead to considerable pain and disability. Mendelian randomization (MR) has been extensively applied for repurposing licensed drugs and uncovering new therapeutic targets. Our objective is to pinpoint innovative therapeutic protein targets for AS and assess the potential adverse effects of druggable proteins.MethodsWe conducted a comprehensive proteome-wide MR study to assess the causal relationships between plasma proteins and the risk of AS. The plasma proteins were sourced from the UK Biobank Pharma Proteomics Project (UKB-PPP) database, encompassing GWAS data for 2,940 plasma proteins. Additionally, GWAS data for AS were extracted from the R9 version of the Finnish database, including 2,860 patients and 270,964 controls. The colocalization analysis was executed to identify shared causal variants between plasma proteins and AS. Finally, we examined the potential adverse effects of druggable proteins for AS therapy by conducting a phenome-wide association study (PheWAS) utilizing the extensive Finnish database in version R9, encompassing 2,272 phenotypes categorized into 46 groups.ResultsThe findings revealed a positive genetic association between the predicted plasma levels of six proteins and an elevated risk of AS, while two proteins exhibited an inverse association with AS risk (Pfdr < 0.05). Among these eight plasma proteins, colocalization analysis identified AIF1, TNF, FKBPL, AGER, ALDH5A1, and ACOT13 as shared variation with AS(PPH3+PPH4>0.8), suggesting that they represent potential direct targets for AS intervention. Further phenotype-wide association studies have shown some potential side effects of these six targets (Pfdr < 0.05).ConclusionOur investigation examined the causal connections between six plasma proteins and AS, providing a comprehensive understanding of potential therapeutic targets.

To select the global tuning parameter, six candidate scores were assessed in a validation set of 119,248 randomly-selected participants of European ancestry from the UK Biobank of whom 279 (0.2%) had been diagnosed with Alzheimer’s disease. Each candidate score was associated with disease in logistic regression models that included age, sex, and principal components of ancestry as covariates and odds ratio (OR) per standard deviation (SD) of polygenic score and area under the receiver operator curve (AUROC) was calculated. The tuning parameter refers to the LDpred ρ parameter used to control the proportion of variants assumed to be causal. Bold indicates polygenic score with maximal AUROC carried forward to the testing datasets. The calibration curves and intercepts were derived by fitting a linear regression model with observed Alzheimer’s prevalence as the outcome variable and predicted prevalence as the independent variable. (XLSX)

BackgroundAnkylosing Spondylitis (AS) is a chronic inflammatory disorder which can lead to considerable pain and disability. Mendelian randomization (MR) has been extensively applied for repurposing licensed drugs and uncovering new therapeutic targets. Our objective is to pinpoint innovative therapeutic protein targets for AS and assess the potential adverse effects of druggable proteins.MethodsWe conducted a comprehensive proteome-wide MR study to assess the causal relationships between plasma proteins and the risk of AS. The plasma proteins were sourced from the UK Biobank Pharma Proteomics Project (UKB-PPP) database, encompassing GWAS data for 2,940 plasma proteins. Additionally, GWAS data for AS were extracted from the R9 version of the Finnish database, including 2,860 patients and 270,964 controls. The colocalization analysis was executed to identify shared causal variants between plasma proteins and AS. Finally, we examined the potential adverse effects of druggable proteins for AS therapy by conducting a phenome-wide association study (PheWAS) utilizing the extensive Finnish database in version R9, encompassing 2,272 phenotypes categorized into 46 groups.ResultsThe findings revealed a positive genetic association between the predicted plasma levels of six proteins and an elevated risk of AS, while two proteins exhibited an inverse association with AS risk (Pfdr < 0.05). Among these eight plasma proteins, colocalization analysis identified AIF1, TNF, FKBPL, AGER, ALDH5A1, and ACOT13 as shared variation with AS(PPH3+PPH4>0.8), suggesting that they represent potential direct targets for AS intervention. Further phenotype-wide association studies have shown some potential side effects of these six targets (Pfdr < 0.05).ConclusionOur investigation examined the causal connections between six plasma proteins and AS, providing a comprehensive understanding of potential therapeutic targets.

BackgroundThis study aimed to identify novel therapeutic targets for primary open-angle glaucoma (POAG).MethodsThe summary-data-based Mendelian randomization (SMR) method was used to evaluate the genetic association between plasma proteins and POAG. Two sets of plasma protein quantitative trait loci (pQTLs) data considered exposures were obtained from the Icelandic Decoding Genetics Study and UK Biobank Pharma Proteomics Project. The summary-level genome-wide association studies data for POAG were extracted from the latest Round 10 release of the FinnGen consortium (8,530 cases and 391,275 controls) and the UK Biobank (4,737 cases and 458,196 controls). Colocalization analysis was used to screen out pQTLs that share the same variant with POAG as drug targets identified. The two-sample Mendelian randomization, reverse causality testing and phenotype scanning were performed to further validate the main findings. Protein-protein interaction, pathway enrichment analysis and druggability assessment were conducted to determine whether the identified plasma proteins have potential as drug targets.ResultsAfter systematic analysis, this study identified eight circulating proteins as potential therapeutic targets for POAG. Three causal proteins with strong evidence of colocalization, ROBO1 (OR = 1.38, p = 1.48 × 10−4, PPH4 = 0.865), FOXO3 (OR = 0.35, p = 4.34 × 10−3, PPH4 = 0.796), ITIH3 (OR = 0.89, p = 2.76 × 10−4, PPH4 = 0.767), were considered tier one targets. Five proteins with medium support evidence of colocalization, NCR1 (OR = 1.25, p = 4.18 × 10−4, PPH4 = 0.682), NID1 (OR = 1.38, p = 1.54 × 10−3, PPH4 = 0.664), TIMP3 (OR = 0.91, p = 4.01 × 10−5, PPH4 = 0.659), SERPINF1 (OR = 0.81, p = 2.77 × 10−4, PPH4 = 0.59), OXT (OR = 1.17, p = 9.51 × 10−4, PPH4 = 0.526), were classified as tier two targets. Additional sensitivity analyses further validated the robustness and directionality of these findings. According to druggability assessment, Pimagedine, Resveratrol, Syringaresinol and Clozapine may potentially be important in the development of new anti-glaucoma agents.ConclusionOur integrated study identified eight potential associated proteins for POAG. These proteins play important roles in neuroprotection, extracellular matrix regulation and oxidative stress. Therefore, they have promising potential as therapeutic targets to combat POAG.

IntroductionFemale infertility is defined as the absence of clinical pregnancy after 12 months of regular unprotected sexual intercourse.MethodsThis study employed metabolomics and proteomics approaches to investigate the relationship between metabolites and proteins and female infertility. The study used metabolomics and proteomics data from the UK Biobank to identify metabolites and proteins linked to infertility.ResultsThe results showed that GRAM domain-containing protein 1C and metabolites fibrinogen cleavage peptides ADpSGEGDFXAEGGGVR and 3-Hydroxybutyrate had a positive correlation with infertility, whereas proteins such as Interleukin-3 receptor subunit alpha, Thrombospondin type-1 domain-containing protein 1, Intestinal-type alkaline phosphatase, and platelet and endothelial cell adhesion molecule 1 exhibited a negative correlation. These findings provide new clues and targets for infertility diagnosis and treatment. However, further research is required to validate these results and gain a deeper understanding of the specific roles of these metabolites and proteins in infertility pathogenesis.DiscussionIn conclusion, metabolomics and proteomics techniques have significant application value in the study of infertility, allowing for a better understanding of the biological mechanisms underlying infertility and providing new insights and strategies for its diagnosis and treatment. These research findings provide a crucial biological mechanistic basis for early infertility screening, prevention, and treatment.

The early detection of liver cirrhosis and its complications is a conundrum in clinical practice. We aim to address this conundrum using proteomic features of plasma. A total of 52,891 participants without cirrhosis or its complications were recruited from the UK Biobank longitudinal population cohort. We identified GDF15, CDCP1, ADGRG1, GGT1, HGF, MFAP4, and THBS2 from 2923 plasma proteins and developed proteomic models to predict early cirrhosis and its complications occurring at 5, 10, and over 10 years. These protein markers were validated to be associated with liver fibrosis in an external liver biopsy cohort. High levels of GDF15 and GGT1 were associated with an increased risk of developing cirrhosis and its complications. The two proteins began to change at least 13 years before the diagnosis of cirrhosis and its complications. Transcriptomic analysis delineated the cellular localization of these proteins in the liver and demonstrated their expression changes during fibrosis progression across different etiologies. Mendelian randomization analyses further supported a potential causal effect of GGT1 on cirrhosis.

Beta represents average change in protein level among individuals in 90% AD PRS compared to those in the 10%. (XLSX)