Whole-genome sequencing analysis of the cardiometabolic proteome

Causal association between mitochondrial function and psychiatric disorders: Insights from a bidirectional two-sample Mendelian randomization study

BACKGROUND

Previous observational studies have suggested that there appears to be a close association between mitochondrial function and psychiatric disorders, but whether a causal role exists remains unclear.

METHODS

We extracted genetic instruments for 67 mitochondrial-related proteins and 10 psychiatric disorders from publicly available genome-wide association studies, and employed five distinct MR methods and false discovery rate correction to detect causal associations between them. Additionally, we conducted a series of sensitivity tests and additional model analysis to ensure the robustness of the results. For potential causal associations, we further performed reverse MR analyses to assess the impact of reverse causality.

RESULTS

We identified a total of 2 significant causal associations and 24 suggestive causal associations. Specifically, Phenylalanine-tRNA ligase was found to increase the risk of Alzheimer's disease, while Mitochondrial glutamate carrier 2 decreased the risk of autism spectrum disorder. Furthermore, there was no evidence of significant pleiotropy, heterogeneity, or reverse causality.

LIMITATIONS

This study was limited to individuals of European ancestry, and the conclusions drawn are merely revelatory.

CONCLUSION

This study provides novel insights into the relationship between mitochondria and psychiatric disorders, as well as the pathogenesis and treatment strategies for psychiatric disorders.

Novel targets for the treatment and prevention of Alzheimer's disease in the European population, inspiration from amyloid beta and tau protein

Alzheimer's disease (AD) is a gradual neurodegenerative ailment that lacks any disease-modifying intervention. Our objective was to pinpoint pharmacological targets with a focus on amyloid beta (Aβ) and tau to treat and prevent AD in the European population. A proteome-wide Mendelian randomization (MR) analysis was carried out to estimate the associations between proteins and cerebrospinal fluid (CSF) Aβ-42 and phosphorylated Tau (p-Tau). We utilized colocalization and MR analysis to investigate whether the identified proteins were associated with the risk of AD. Additionally, we expanded our investigation to include non-AD phenotypes by conducting a phenome-wide MR analysis of 1646 disease traits based on the FinnGen and UK Biobank databases to explore potential side effects. We identified 11 proteins that were genetically associated with both CSF Aβ-42 and p-Tau levels. The genetically predicted levels of three proteins, GAL3ST2, POLR1C, and BIN1, were found to be associated with an increased risk of AD with high colocalization. In the phenome-wide MR analysis, two out of the three biomarkers were associated with at least one disease, except for GAL3ST2, which was not associated with any disease under the threshold of FDR <0.1. POLR1C was found to be associated with the most disease traits, and all disease associations with genetically inhibited BIN1 were protective. The proteome-wide MR investigation revealed 11 proteins that were associated with the level of CSF Aβ-42 and p-Tau. Among them, GAL3ST2, POLR1C, and BIN1 were identified as potential therapeutic targets for AD and warrant further investigation.

Integrating metabolomics and proteomics to identify novel drug targets for heart failure and atrial fibrillation

BACKGROUND

Altered metabolism plays a role in the pathophysiology of cardiac diseases, such as atrial fibrillation (AF) and heart failure (HF). We aimed to identify novel plasma metabolites and proteins associating with cardiac disease.

METHODS

Mendelian randomisation (MR) was used to assess the association of 174 metabolites measured in up to 86,507 participants with AF, HF, dilated cardiomyopathy (DCM), and non-ischemic cardiomyopathy (NICM). Subsequently, we sourced data on 1567 plasma proteins and performed cis MR to identify proteins affecting the identified metabolites as well as the cardiac diseases. Proteins were prioritised on cardiac expression and druggability, and mapped to biological pathways.

RESULTS

We identified 35 metabolites associating with cardiac disease. AF was affected by seventeen metabolites, HF by nineteen, DCM by four, and NCIM by taurine. HF was particularly enriched for phosphatidylcholines (p = 0.029) and DCM for acylcarnitines (p = 0.001). Metabolite involvement with AF was more uniform, spanning for example phosphatidylcholines, amino acids, and acylcarnitines. We identified 38 druggable proteins expressed in cardiac tissue, with a directionally concordant effect on metabolites and cardiac disease. We recapitulated known associations, for example between the drug target of digoxin (AT1B2), taurine and NICM risk. Additionally, we identified numerous novel findings, such as higher RET values associating with phosphatidylcholines and decreasing AF and HF. RET is targeted by drugs such as regorafenib which has known cardiotoxic side-effects. Pathway analysis implicated involvement of GDF15 signalling through RET, and ghrelin regulation of energy homeostasis in cardiac pathogenesis.

CONCLUSIONS

This study identified 35 plasma metabolites involved with cardiac diseases and linked these to 38 druggable proteins, providing actionable leads for drug development.

Variance quantitative trait loci reveal gene-gene interactions which alter blood traits

Gene-gene (GxG) interactions play an important role in human genetics, potentially explaining part of the "missing heritability" of polygenic traits and the variable expressivity of monogenic traits. Many GxG interactions have been identified in model organisms through experimental breeding studies, but they have been difficult to identify in human populations. To address this challenge, we applied two complementary variance QTL (vQTL)-based approaches to identify GxG interactions that contribute to human blood traits and blood-related disease risk. First, we used the previously validated genome-wide scale test for each trait in ~450,000 people in the UK Biobank and identified 4 vQTLs. Genome-wide GxG interaction testing of these vQTLs enabled discovery of novel interactions between (1) CCL24 and CCL26 for eosinophil count and plasma CCL24 and CCL26 protein levels and (2) HLA-DQA1 and HLA-DQB1 for lymphocyte count and risk of celiac disease, both of which replicated in ~140,000 NIH All of Us and ~70,000 Vanderbilt BioVU participants. Second, we used a biologically informed approach to search for vQTL in disease-relevant genes. This approach identified (1) a known interaction for hemoglobin between two pathogenic variants in HFE which cause hereditary hemochromatosis and alters risk of cirrhosis and (2) a novel interaction between the JAK2 46/1 haplotype and a variant on chromosome 14 which modifies platelet count, JAK2 V617F clonal hematopoiesis, and risk of polycythemia vera. This work identifies novel disease-relevant GxG interactions and demonstrates the utility of vQTL-based approaches in identifying GxG interactions relevant to human health at scale.

Exploring the relationship between the interleukin family and lung adenocarcinoma through Mendelian randomization and RNA sequencing analysis

BACKGROUND

Lung adenocarcinoma (LUAD) is still one of the most prevalent malignancies. Interleukin factors are closely associated with the initiation and progression of cancer. However, the relationship between interleukin factors and LUAD has not been fully elucidated. This study aimed to use Mendelian randomization (MR) and RNA sequencing (RNA-seq) analyses to identify the interleukin factors associated with the onset and progression of LUAD.

METHODS

Exposure-related instrumental variables were selected from interleukin factor summary datasets. The LUAD summary dataset from FINGENE served as the outcome. MR and sensitivity analyses were conducted to screen for interleukin factors associated with LUAD occurrence. Transcriptome analyses revealed the role of interleukin factors in lung tissues. The results were validated through Western blotting and further confirmed with driver gene-negative patients from multiple centers. Potential mechanisms influencing LUAD occurrence and development were explored using bulk RNA-seq and single-cell RNA-seq data.

RESULTS

MR analysis indicated that elevated plasma levels of IL6RB, IL27RA, IL22RA1, and IL16 are causally associated with increased LUAD risk, while IL18R1 and IL11RA exhibit the opposite effect. Transcriptome analyses revealed that IL11RA, IL18R1, and IL16 were downregulated in tumor tissues compared with normal lung tissue, but only higher expression of IL11RA correlated with improved prognosis in patients with LUAD from different centers and persisted even in driver-gene negative patients. The IL11RA protein level was lower in various LUAD cell lines than in human bronchial epithelial cells. The genes co-expressed with IL11RA were enriched in the Ras signaling pathway and glycosylation processes. Fibroblasts were the primary IL11RA-expressing cell population, with IL11RA+fibroblasts exhibiting a more immature state. The genes differentially expressed between IL11RA+and IL11RA- fibroblasts were involved in the PI3K-Akt/TNF signaling pathway.

CONCLUSION

According to the MR and transcriptome analyses, the downregulation of IL11RA was closely related to the occurrence and development of LUAD.

The role of serum interleukins in Cancer: A Multi-center Mendelian Randomization study

The occurrence of cancer is often accompanied by immune evasion and tumor-promoting inflammation, with interleukins (IL) playing a pivotal role in the immune-inflammatory mechanism. However, the precise contribution of serum interleukins in cancer remains elusive. We obtained GWAS summary data for 35 interleukins from eight independent large-scale serum proteome studies of European ancestry populations and for 23 common cancers from the FinnGen Consortium. We then conducted a multicenter Mendelian Randomization (MR) study to explore the relationship between systemic inflammatory status and cancers. 24 causal associations between interleukins and cancers were supported by multicenter data, 18 of which were reported for the first time. Our results indicated that IL-1α (Hodgkin lymphoma), IL-5 (bladder cancer), IL-7 (prostate cancer), IL-11 (bone malignant tumor), IL-16 (lung cancer), IL-17A (pancreatic cancer), IL-20 (bladder cancer), IL-22 (lymphocytic leukemia), IL-34 (breast cancer), IL-36β (prostate cancer), and IL-36γ (liver cancer) were risk factors for related cancers. Conversely, IL-9 (malignant neoplasms of the corpus uteri), IL-17C (liver cancer), and IL-31 (colorectal cancer, bladder cancer, pancreatic cancer, and cutaneous melanoma) exhibited protective effects against related cancers. Notably, the dual effects of serum interleukins were also observed. IL-18 acted as a risk factor for prostate cancer, however, was a protective factor against laryngeal cancer. Similarly, IL-19 promoted the development of lung cancer and myeloid leukemia, while conferring protection against Breast, cervical, and thyroid cancers. Our study confirmed the genetic association between multiple serum interleukins and cancers. Immune and anti-inflammatory strategies targeting these associations provide opportunities for prevention and treatment.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.

Causality between various cytokines and asthma: a bidirectional two-sample Mendelian randomization analysis

Background

Many studies have shown that cytokines play an important role in the pathogenesis of asthma, but their biological effects on asthma remain unclear. The Mendelian randomization (MR) method was used to evaluate the causal relationship between various cytokines [such as interleukins (ILs), interferons (IFNs), tumor necrosis factors (TNFs), colony-stimulating factors (CSFs), transforming growth factor (TGF), etc.,] and asthma.

Methods

In this study, inverse variance weighting was used to evaluate the causal relationship between asthma and cytokines. In addition, the reliability of the results is ensured by multiple methods such as MR-Egger, weighted median, MR-Raps, MR-Presso, and RadialMR, as well as sensitivity analysis.

Results

The results showed that none of the 11 cytokines was associated with the risk of asthma. In contrast, asthma can increase levels of IL-5 [odds ratio (OR) = 1.112, 95% confidence interval (CI): 1.009-1.224, P = 0.032] and IL-9 (OR = 1.111, 95% CI: 1.013-1.219, P = 0.025).

Conclusion

Genetically predicted asthma was positively associated with elevated levels of IL-5 and IL-9, indicating the downstream effects of IL-5 and IL-9 on asthma. Medical treatments can thus be designed to target IL-5 and IL-9 to prevent asthma exacerbations.

Circulating Proteins and IgA Nephropathy: A Multiancestry Proteome-Wide Mendelian Randomization Study

Key Points

Background

The therapeutic options for IgA nephropathy are rapidly evolving, but early diagnosis and targeted treatment remain challenging. We aimed to identify circulating plasma proteins associated with IgA nephropathy by proteome-wide Mendelian randomization studies across multiple ancestry populations.

Methods

In this study, we applied Mendelian randomization and colocalization analyses to estimate the putative causal effects of 2615 proteins on IgA nephropathy in Europeans and 235 proteins in East Asians. Following two-stage network Mendelian randomization, multitrait colocalization analysis and protein-altering variant annotation were performed to strengthen the reliability of the results. A protein–protein interaction network was constructed to investigate the interactions between the identified proteins and the targets of existing medications.

Results

Putative causal effects of 184 and 13 protein–disease pairs in European and East Asian ancestries were identified, respectively. Two protein–disease pairs showed shared causal effects across them (CFHR1 and FCRL2). Supported by the evidence from colocalization analysis, potential therapeutic targets were prioritized and four drug-repurposing opportunities were suggested. The protein–protein interaction network further provided strong evidence for existing medications and pathways that are known to be therapeutically important.

Conclusions

Our study identified a number of circulating proteins associated with IgA nephropathy and prioritized several potential drug targets that require further investigation.

Matrix Metalloproteinase and Aortic Aneurysm: A Two-sample Mendelian Randomization Study

BACKGROUND

Studies have linked matrix metalloproteinases (MMPs) to both thoracic aortic aneurysm and abdominal aortic aneurysm (TAA and AAA). The precise MMPs entailed in this procedure, however, were still unknown. This study used a two-sample Mendelian randomization (MR) analysis to look into the causal relationship between MMPs and the risk of TAA and AAA.

METHODS

Eight MMPs, including MMP-1, MMP-2, MMP-3, MMP-8, MMP-9, MMP-10, MMP-12, and MMP-13, were found among people of European ancestry with accessible Genome-Wide Association Studies (GWAS). We employed the findings from Genome-Wide Association Studies (GWAS) for 8 MMPs, and TAA and AAA from the FinnGen consortiums (3,201 cases and 317,899 controls, respectively) were used in a two-sample MR analysis. The primary method of analysis for MR was the inverse variance weighted (IVW) method, along with analyses of heterogeneity and horizontal pleiotropy. 31 single-nucleotide polymorphisms connected to MMP were retrieved.

RESULTS

IVW demonstrated a negative causal association between TAA and AAA and serum MMP-12 levels. The incidence of TAA decreased by 1.031% for every 1 ng/mL increase in serum MMP-12 [odds ratio (OR) = 0.897, 95% confidence interval (CI): 0.831-0.968, P = 0.005]. The incidence of AAA fell by 1.653% (OR = 0.835, 95% CI: 0.752-0.926, P = 0.001) for every 1 ng/mL increase in serum MMP-12. There was no horizontal pleiotropy or heterogeneity in the MR data (P > 0.05).

CONCLUSIONS

The levels of TAA and AAA and serum MMP-12 are causally related. MMP-12 is a factor that reduces the risk of AAA and TTA. Our study suggested that MMP-12 level is causally associated with a decreased risk of TAA and AAA.

Evaluating the causal effect of circulating proteome on the risk of inflammatory bowel disease-related traits using Mendelian randomization

Objective

This study sought to identify circulating proteins causally linked to Inflammatory Bowel Disease (IBD) traits through a Mendelian Randomization (MR) analytical framework.

Methods

Using a large-scale, two-sample MR approach, we estimated the genetic links of numerous plasma proteins with IBD and its subtypes, leveraging information from the Inflammatory Bowel Disease Genetics Consortium. To assess the robustness of MR findings, methods like Bayesian colocalization, and Steiger filtering analysis, evaluation of protein-altering variants. Further insights into IBD's underlying mechanisms and therapeutic targets were gleaned from single-cell sequencing analyses, protein-protein interaction assessments, pathway enrichment analyses, and evaluation of drug targets.

Results

By cis-only MR analysis, we identified 83 protein-phenotype associations involving 27 different proteins associated with at least one IBD subtype. Among these proteins, DAG1, IL10, IL12B, IL23R, MST1, STAT3 and TNFRSF6B showed overlapping positive or negative associations in all IBD phenotypes. Extending to cis + trans MR analysis, we further identified 117 protein-feature associations, including 44 unique proteins, most of which were not detected in the cis-only analysis. In addition, by performing co-localization analysis and Steiger filtering analysis on the prioritized associations, we further confirmed the causal relationship between these proteins and the IBD phenotype and verified the exact causal direction from the protein to the IBD-related feature.

Conclusion

MR analysis facilitated the identification of numerous circulating proteins associated with IBD traits, unveiling protein-mediated mechanisms and promising therapeutic targets.

Causal relationship between gut microbiota and ankylosing spondylitis and potential mediating role of inflammatory cytokines: A mendelian randomization study

Associations between gut microbiota and ankylosing spondylitis have been discovered in previous studies, but whether these associations reflect a causal relationship remains inconclusive. Aiming to reveal the bidirectional causal associations between gut microbiota and ankylosing spondylitis, we utilized publicly available genome wide association study summary data for 211 gut microbiota (GM) taxa and ankylosing spondylitis (AS) to conduct two sample mendelian randomization analyses. Mediation analysis was performed to explore mediating inflammatory cytokines. We found that genetically predicted higher abundance of Lactobacillaceae family, Rikenellaceae family and Howardella genus had suggestive associations with decreased risk of ankylosing spondylitis while genetic proxied higher abundance of Actinobacteria class and Ruminococcaceae_NK4A214_group genus was associated with increased risk of ankylosing spondylitis. IL23 and IFN-γ were potential mediating cytokines for GM dysbiosis, especially for Actinobacteria class, leading to AS. Our study provided a new exploration direction for the treatment of AS. Lactobacillaceae family, Rikenellaceae family, Howardella genus, Actinobacteria class and Ruminococcaceae_NK4A214_group genus are expected to become new therapeutic targets and monitoring indicators for AS.

The interplay between cytokines and stroke: a bi-directional Mendelian randomization study

Stroke, the second leading cause of death and disability, causes massive cell death in the brain followed by secondary inflammatory injury initiated by disease associated molecular patterns released from dead cells. Nonetheless, the evidence regarding the causal relationship between inflammatory cytokines and stroke subtypes is obscure. To leverage large scale genetic association data to investigate the interplay between circulating cytokines and stroke, we adopted a two-sample bi-directional Mendelian randomization (MR) analysis. Firstly, we performed a forward MR analysis to examine the associations of genetically determined 31 cytokines with 6 stroke subtypes. Secondly, we conducted a reverse MR analysis to check the associations of 6 stroke subtypes with 31 cytokines. In the forward MR analysis, genetic evidence suggests that 21 cytokines were significantly associated with certain stroke subtype risk with |β| ranging from 1.90 × 10-4 to 0.74. In the reverse MR analysis, our results found that five stroke subtypes (intracerebral hemorrhage (ICH), large artery atherosclerosis ischemic stroke (LAAS), lacunar stroke (LS), cardioembolic ischemic stroke (CEI), small-vessel ischemic stroke (SV)) caused significantly changes in 16 cytokines with |β| ranging from 1.08 × 10-4 to 0.69. In particular, those five stroke subtypes were statistically significantly associated with C-reactive protein (CRP). In addition, ICH, LAAS, LS and SV were significantly correlated with vascular endothelial growth factor (VEGF), while LAAS, LS, CEI and SV were significantly related to fibroblast growth factor (FGF). Moreover, integrated bi-directional MR analysis, these factors (IL-3Rα, IL-6R, IL-6Rα, IL-1Ra, insulin-like growth factor-1(IGF-1), IL-12Rβ2) can be used as predictors of some specific stroke subtypes. As well as, IL-16 and C-C motif chemokine receptor 7 (CCR7) can be used as prognostic factors of stroke. Our findings prognostic identify potential pharmacological opportunities, including perturbation of circulating cytokines for both predicting stroke risk and post stroke treatment effects. As we conducted a comprehensive search and analysis of stroke subtype and cytokines in the existing publicly available GWAS database, the results have good population-generalizability.

Causal linkage between angiotensin-converting enzyme 2 and risk of lung cancer: a bidirectional two-sample Mendelian randomization study

Background

Observational studies suggest a connection between ACE2 (angiotensin-converting enzyme 2) and lung cancer. However, it's not apparent if confounding variables are interfering with the link. Therefore, we aimed to define the relationships between ACE2 and the risk of lung cancer.

Methods

With the aim of developing genetic tools, we selected SNPs substantially associated with ACE2 using a statistically significant criterion. The relevant SNPs were then taken from the lung cancer GWAS dataset for additional research. After that, we used two-sample Mendelian randomization (MR) to ascertain if ACE2 is causally linked to the risk of developing lung cancer. To investigate the causal links' directions, we also performed a reverse MR analysis.

Results

According to our findings, there is strong evidence that ACE2 is linked to a decreased chance of developing lung cancer (odds ratio: 0.94; 95% confidence interval: 0.90-0.98; P = 0.0016). The IVW method, the major MR analysis, was not impacted by heterogeneity in any of the analyses, according to Cochrane's Q test (P Cochran e ' sQ = 0.207). The MR-Egger intercept (P intercept = 0.622) showed no indication of horizontal pleiotropy in any of the investigations. Outlier SNPs were not detected by the MR-PRESSO global test (P globaltest = 0.191). The leave-one-out analysis was performed, and the results showed a steady outcome. Nonsignificant causal estimates between lung cancer and ACE2 were produced by reverse MR analysis.

Conclusion

MR investigation revealed a significant causal link between ACE2 and the risk of getting lung cancer. These findings may have implications for public health measures aimed at reducing the incidence of lung cancer.

A causal relationship between particulate matter 2.5 and obesity and its related indicators: a Mendelian randomization study of European ancestry

Background

In recent years, the prevalence of obesity has continued to increase as a global health concern. Numerous epidemiological studies have confirmed the long-term effects of exposure to ambient air pollutant particulate matter 2.5 (PM2.5) on obesity, but their relationship remains ambiguous.

Methods

Utilizing large-scale publicly available genome-wide association studies (GWAS), we conducted univariate and multivariate Mendelian randomization (MR) analyses to assess the causal effect of PM2.5 exposure on obesity and its related indicators. The primary outcome given for both univariate MR (UVMR) and multivariate MR (MVMR) is the estimation utilizing the inverse variance weighted (IVW) method. The weighted median, MR-Egger, and maximum likelihood techniques were employed for UVMR, while the MVMR-Lasso method was applied for MVMR in the supplementary analyses. In addition, we conducted a series of thorough sensitivity studies to determine the accuracy of our MR findings.

Results

The UVMR analysis demonstrated a significant association between PM2.5 exposure and an increased risk of obesity, as indicated by the IVW model (odds ratio [OR]: 6.427; 95% confidence interval [CI]: 1.881-21.968; P FDR = 0.005). Additionally, PM2.5 concentrations were positively associated with fat distribution metrics, including visceral adipose tissue (VAT) (OR: 1.861; 95% CI: 1.244-2.776; P FDR = 0.004), particularly pancreatic fat (OR: 3.499; 95% CI: 2.092-5.855; PFDR =1.28E-05), and abdominal subcutaneous adipose tissue (ASAT) volume (OR: 1.773; 95% CI: 1.106-2.841; P FDR = 0.019). Furthermore, PM2.5 exposure correlated positively with markers of glucose and lipid metabolism, specifically triglycerides (TG) (OR: 19.959; 95% CI: 1.269-3.022; P FDR = 0.004) and glycated hemoglobin (HbA1c) (OR: 2.462; 95% CI: 1.34-4.649; P FDR = 0.007). Finally, a significant negative association was observed between PM2.5 concentrations and levels of the novel obesity-related biomarker fibroblast growth factor 21 (FGF-21) (OR: 0.148; 95% CI: 0.025-0.89; P FDR = 0.037). After adjusting for confounding factors, including external smoke exposure, physical activity, educational attainment (EA), participation in sports clubs or gym leisure activities, and Townsend deprivation index at recruitment (TDI), the MVMR analysis revealed that PM2.5 levels maintained significant associations with pancreatic fat, HbA1c, and FGF-21.

Conclusion

Our MR study demonstrates conclusively that higher PM2.5 concentrations are associated with an increased risk of obesity-related indicators such as pancreatic fat content, HbA1c, and FGF-21. The potential mechanisms require additional investigation.

Evaluating the Causal Effects of Circulating Proteome on the Risk of Sepsis and Related Outcomes

The current investigation deployed Mendelian randomization (MR) to elucidate the causal relationship between circulating proteins and sepsis. A rigorous two-sample MR analysis evaluated the effect of plasma proteins on the sepsis susceptibility. To affirm the integrity of MR findings, a suite of supplementary analyses, including Bayesian colocalization, Steiger filtering, the assessment of protein-altering polymorphisms, and the correlation between expression quantitative trait loci and protein quantitative trait loci (pQTLs), was employed. The study further integrated the examination of protein-protein interactions and pathway enrichment, along with the identification of pharmacologically actionable targets, to advance our comprehension and outline potential sepsis therapies. Subsequent analyses leveraging cis-pQTLs within MR studies unveiled noteworthy relationships: 94 specific proteins exhibited significant links with sepsis-related 28 day mortality, while 96 distinct proteins correlated with survival outcomes in sepsis. Furthermore, incorporating both cis- and trans-pQTLs in MR investigations revealed more comprehensive findings, associating 201 unique proteins with sepsis-related 28 day mortality and 199 distinct proteins with survival outcomes in sepsis. Markedly, colocalization analyses confirmed that eight of these proteins exhibited prominent evidence for colocalization, emphasizing their potential criticality in sepsis pathophysiology. Further in silico analyses were conducted to delineate putative regulatory networks and to highlight prospective drug targets among these proteins. Employing the MR methodology has shed light on plasma proteins implicated in the etiopathogenesis of sepsis. This novel approach unveiled numerous biomarkers and targets, providing a scientific rationale for the development of new therapeutic strategies and prophylactic measures against sepsis.

Leveraging pQTL-based Mendelian randomization to identify new treatment prospects for primary biliary cholangitis and primary sclerosing cholangitis

Primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC) are autoimmune disorders characterized by progressive and chronic damage to the bile ducts, presenting clinicians with significant challenges. The objective of this study is to identify potential druggable targets to offer new avenues for treatment. A Mendelian randomization analysis was performed to identify druggable targets for PBC and PSC. This involved obtaining Cis-protein quantitative trait loci (Cis-pQTL) data from the deCODE database to serve as exposure. Outcome data for PBC (557 cases and 281,127 controls) and PSC (1,715 cases and 330,903 controls) were obtained from the FINNGEN database. Colocalization analysis was conducted to determine whether these features share the same associated SNPs. Validation of the expression level of druggable targets was done using the GSE119600 dataset and immunohistochemistry for clinical samples. Lastly, the DRUGBANK database was used to predict potential drugs. The MR analysis identified eight druggable targets each for PBC and PSC. Subsequent summary-data-based MR and colocalization analyses showed that LEFTY2 had strong evidence as a therapeutic candidate for PBC, while HSPB1 had moderate evidence. For PSC, only FCGR3B showed strong evidence as a therapeutic candidate. Additionally, upregulated expression of these genes was validated in PBC and PSC groups by GEO dataset and clinical samples. This study identifies two novel druggable targets with strong evidence for therapeutic candidates for PBC (LEFTY2 and HSPB1) and one for PSC (FCGR3B). These targets offer new therapeutic opportunities to address the challenging nature of PBC and PSC treatment.

Identification of early coagulation changes associated with survival outcomes post severe burns from multiple perspectives

Coagulation alterations manifest early after severe burns and are closely linked to mortality outcomes. Nevertheless, the precise characterization of coagulation changes associated with early mortality remains elusive. We examined alterations in indicators linked to mortality outcomes at both the transcriptomic and clinical characteristic levels. At the transcriptomic level, we pinpointed 28 differentially expressed coagulation-related genes (DECRGs) following burn injuries and endeavored to validate their causal relationships through Mendelian randomization. DECRGs tied to survival exhibit a significant association with neutrophil function, wherein the expression of CYP4F2 and P2RX1 serves as robust predictors of fatal outcomes. In terms of clinical indicators, early levels of D-dimer and alterations in serum calcium show a strong correlation with mortality outcomes. Coagulation depletion and fibrinolytic activation, stemming from the hyperactivation of coagulation pathways post-severe burns, are strongly linked to patient mortality. Monitoring these early coagulation markers with predictive value can effectively identify individuals necessitating priority critical care.

Association Between Neuroinflammation and Parkinson's Disease: A Comprehensive Mendelian Randomization Study

The objective of the study is to determine the causal relationship and potential mechanisms between Parkinson's disease (PD) and neuroinflammatory and neurotoxic mediators. We conducted two-sample Mendelian randomization (2SMR) study and multivariable Mendelian randomization (MVMR) analysis to investigate the causality between PD and neuroinflammatory and neurotoxic mediators. The mediation analysis with MR was also conducted to determine the potential mediating effect of neuroinflammatory and neurotoxic mediators between asthma and PD. Genetically predicted levels of nine neuroinflammation were associated with changes in PD risk. The associations of PD with CCL24, galectin-3 levels, haptoglobin, and Holo-Transcobalamin-2 remained significant in multivariable analyses. The mediation analysis with MR revealed that asthma affects PD through CCL24 and galectin-3. The results showed neuroinflammation could affect the pathogenesis of PD. In the combined analysis of these nine variables, CCL24, galectin-3 levels, HP, and Holo-Transcobalamin-2 alone were found to be significant. Asthma plays an intermediary role through CCL24 and galectin-3 levels.

Genetic determinants of plasma protein levels in the Estonian population

The proteome holds great potential as an intermediate layer between the genome and phenome. Previous protein quantitative trait locus studies have focused mainly on describing the effects of common genetic variations on the proteome. Here, we assessed the impact of the common and rare genetic variations as well as the copy number variants (CNVs) on 326 plasma proteins measured in up to 500 individuals. We identified 184 cis and 94 trans signals for 157 protein traits, which were further fine-mapped to credible sets for 101 cis and 87 trans signals for 151 proteins. Rare genetic variation contributed to the levels of 7 proteins, with 5 cis and 14 trans associations. CNVs were associated with the levels of 11 proteins (7 cis and 5 trans), examples including a 3q12.1 deletion acting as a hub for multiple trans associations; and a CNV overlapping NAIP, a sensor component of the NAIP-NLRC4 inflammasome which is affecting pro-inflammatory cytokine interleukin 18 levels. In summary, this work presents a comprehensive resource of genetic variation affecting the plasma protein levels and provides the interpretation of identified effects.

The causal effect of oxidative stress on the risk of glaucoma

Glaucoma is a complex multifactorial disease. Oxidative stress has been implicated in its pathogenesis. However, establishing a causal relationship between oxidative stress and glaucoma is challenging due to confounding and reverse causality. In this study, we performed bidirectional two-sample Mendelian randomization (MR) analyses based on genetic instrumental variables as proxies for 11 biomarkers of oxidative stress injury to investigate the causal relationship between oxidative stress and glaucoma. Eight significant associations were identified. Increased circulating levels of catalase (OR = 0.915, 95 % CI: 0.848-0.987, P = 0.022), retinol (OR = 0.481, 95 % CI: 0.248-0.932, P = 0.044) and superoxide dismutase (OR = 0.779, 95 % CI: 0. 616-0.986, P = 0.038) are associated with a decreased risk of glaucoma, whereas an increased myeloperoxidase level (OR = 2.145, 95 % CI: 1.119-4.111, P = 0.029) is associated with an increased risk of glaucoma. Glaucoma was causally associated with lower levels of total bilirubin (OR = 0.961, 95 % CI: 0.927-0.997, P = 0.039), glutathione peroxidase (OR = 0. 934, 95 % CI: 0.890-0.981, P = 0.006), paraoxonase (OR = 0.883, 95 % CI: 0.810-0.963, P = 0.005) and albumin (OR = 0.988, 95 % CI: 0.978-0.998, P = 0.014). The bidirectional MR analysis revealed a causal relationship between oxidative stress and glaucoma. These findings provide a greater understanding of the underlying mechanisms of glaucomatous neurodegeneration and imply a potential therapeutic approach for glaucoma through targeting oxidative stress pathways.

Association between circulating inflammatory markers and adult cancer risk: a Mendelian randomization analysis

BACKGROUND

Tumour-promoting inflammation is a "hallmark" of cancer and conventional epidemiological studies have reported links between various inflammatory markers and cancer risk. The causal nature of these relationships and, thus, the suitability of these markers as intervention targets for cancer prevention is unclear.

METHODS

We meta-analysed 6 genome-wide association studies of circulating inflammatory markers comprising 59,969 participants of European ancestry. We then used combined cis-Mendelian randomization and colocalisation analysis to evaluate the causal role of 66 circulating inflammatory markers in risk of 30 adult cancers in 338,294 cancer cases and up to 1,238,345 controls. Genetic instruments for inflammatory markers were constructed using genome-wide significant (P < 5.0 × 10-8) cis-acting SNPs (i.e., in or ±250 kb from the gene encoding the relevant protein) in weak linkage disequilibrium (LD, r2 < 0.10). Effect estimates were generated using inverse-variance weighted random-effects models and standard errors were inflated to account for weak LD between variants with reference to the 1000 Genomes Phase 3 CEU panel. A false discovery rate (FDR)-corrected P-value ("q-value") <0.05 was used as a threshold to define "strong evidence" to support associations and 0.05 ≤ q-value < 0.20 to define "suggestive evidence". A colocalisation posterior probability (PPH4) >70% was employed to indicate support for shared causal variants across inflammatory markers and cancer outcomes. Findings were replicated in the FinnGen study and then pooled using meta-analysis.

FINDINGS

We found strong evidence to support an association of genetically-proxied circulating pro-adrenomedullin concentrations with increased breast cancer risk (OR: 1.19, 95% CI: 1.10-1.29, q-value = 0.033, PPH4 = 84.3%) and suggestive evidence to support associations of interleukin-23 receptor concentrations with increased pancreatic cancer risk (OR: 1.42, 95% CI: 1.20-1.69, q-value = 0.055, PPH4 = 73.9%), prothrombin concentrations with decreased basal cell carcinoma risk (OR: 0.66, 95% CI: 0.53-0.81, q-value = 0.067, PPH4 = 81.8%), and interleukin-1 receptor-like 1 concentrations with decreased triple-negative breast cancer risk (OR: 0.92, 95% CI: 0.88-0.97, q-value = 0.15, PPH4 = 85.6%). These findings were replicated in pooled analyses with the FinnGen study. Though suggestive evidence was found to support an association of macrophage migration inhibitory factor concentrations with increased bladder cancer risk (OR: 2.46, 95% CI: 1.48-4.10, q-value = 0.072, PPH4 = 76.1%), this finding was not replicated when pooled with the FinnGen study. For 22 of 30 cancer outcomes examined, there was little evidence (q-value ≥0.20) that any of the 66 circulating inflammatory markers examined were associated with cancer risk.

INTERPRETATION

Our comprehensive joint Mendelian randomization and colocalisation analysis of the role of circulating inflammatory markers in cancer risk identified potential roles for 4 circulating inflammatory markers in risk of 4 site-specific cancers. Contrary to reports from some prior conventional epidemiological studies, we found little evidence of association of circulating inflammatory markers with the majority of site-specific cancers evaluated.

FUNDING

Cancer Research UK (C68933/A28534, C18281/A29019, PPRCPJT∖100005), World Cancer Research Fund (IIG_FULL_2020_022), National Institute for Health Research (NIHR202411, BRC-1215-20011), Medical Research Council (MC_UU_00011/1, MC_UU_00011/3, MC_UU_00011/6, and MC_UU_00011/4), Academy of Finland Project 326291, European Union's Horizon 2020 grant agreement no. 848158 (EarlyCause), French National Cancer Institute (INCa SHSESP20, 2020-076), Versus Arthritis (21173, 21754, 21755), National Institutes of Health (U19 CA203654), National Cancer Institute (U19CA203654).

Copy number variations and their effect on the plasma proteome

Structural variations, including copy number variations (CNVs), affect around 20 million bases in the human genome and are common causes of rare conditions. CNVs are rarely investigated in complex disease research because most CNVs are not targeted on the genotyping arrays or the reference panels for genetic imputation. In this study, we characterize CNVs in a Swedish cohort (N = 1,021) using short-read whole-genome sequencing (WGS) and use long-read WGS for validation in a subcohort (N = 15), and explore their effect on 438 plasma proteins. We detected 184,182 polymorphic CNVs and identified 15 CNVs to be associated with 16 proteins (P < 8.22×10-10). Of these, 5 CNVs could be perfectly validated using long-read sequencing, including a CNV which was associated with measurements of the osteoclast-associated immunoglobulin-like receptor (OSCAR) and located upstream of OSCAR, a gene important for bone health. Two other CNVs were identified to be clusters of many short repetitive elements and another represented a complex rearrangement including an inversion. Our findings provide insights into the structure of common CNVs and their effects on the plasma proteome, and highlights the importance of investigating common CNVs, also in relation to complex diseases.

Genome-wide meta-analysis of 92 cardiometabolic protein serum levels

OBJECTIVES

Global cardiometabolic disease prevalence has grown rapidly over the years, making it the leading cause of death worldwide. Proteins are crucial components in biological pathways dysregulated in disease states. Identifying genetic components that influence circulating protein levels may lead to the discovery of biomarkers for early stages of disease or offer opportunities as therapeutic targets.

METHODS

Here, we carry out a genome-wide association study (GWAS) utilising whole genome sequencing data in 3,005 individuals from the HELIC founder populations cohort, across 92 proteins of cardiometabolic relevance.

RESULTS

We report 322 protein quantitative trait loci (pQTL) signals across 92 proteins, of which 76 are located in or near the coding gene (cis-pQTL). We link those association signals with changes in protein expression and cardiometabolic disease risk using colocalisation and Mendelian randomisation (MR) analyses.

CONCLUSIONS

The majority of previously unknown signals we describe point to proteins or protein interactions involved in inflammation and immune response, providing genetic evidence for the contributing role of inflammation in cardiometabolic disease processes.

Evaluating the causal effect of circulating proteome on the risk of osteoarthritis-related traits

OBJECTIVES

This study aims to identify circulating proteins that are causally associated with osteoarthritis (OA)-related traits through Mendelian randomisation (MR)-based analytical framework.

METHODS

Large-scale two-sample MR was employed to estimate the effects of thousands of plasma proteins on 12 OA-related traits. Additional analyses including Bayesian colocalisation, Steiger filtering analysis, assessment of protein-altering variants and mapping expression quantitative trait loci to protein quantitative trait loci were performed to investigate the reliability of the MR findings; protein-protein interaction, pathway enrichment analysis and evaluation of drug targets were conducted to deepen the understanding and identify potential therapeutic targets of OA.

RESULTS

Dozens of circulating proteins were identified to have putatively causal effects on OA-related traits, and a majority of these proteins were either drug targets or considered druggable.

CONCLUSIONS

Through MR analysis, we have identified numerous plasma proteins associated with OA-related traits, shedding light on protein-mediated mechanisms and offering promising therapeutic targets for OA.

Evidence based on Mendelian randomization: Causal relationship between mitochondrial biological function and lung cancer and its subtypes

OBJECTIVE

This study aimed to investigate the causal relationship between mitochondrial biological function and lung cancer, including its subtypes, via MR.

METHODS

SNPs significantly associated with lung cancer and its subtypes were employed as instrumental variables. MR-Egger regression, simple mode, weighted mode, simple median, and weighted median, were utilized to determine the causal relationship between the exposure factor and the occurrence of lung cancer and its subtypes.

RESULTS

NADH dehydrogenase (ubiquinone) flavoprotein 2 and transmembrane protein 70 were found to have a causal relationship with lung adenocarcinoma, acting as protective factors. The causal relationship between mitochondrial import inner membrane translocase subunit and NADH dehydrogenase (ubiquinone) iron-sulfur protein 4 and small-cell lung cancer was established as a risk factor. NADH dehydrogenase (ubiquinone) 1 beta subcomplex subunit 8 exhibited a causal relationship with small-cell lung cancer, acting as a protective factor. Furthermore, NAD-dependent protein deacylase sirtuin-5 was causally linked to lung squamous cell carcinoma, serving as a protective factor. A funnel plot demonstrated the symmetrical distribution of the SNPs. Thew pleiotroy test (P > 0.05) and "leave-one-out" test validated the relative stability of the results.

CONCLUSION

This study established a causal relationship between mitochondrial biological function and lung cancer, including its subtypes.

The Pregnancy-Associated Plasma Protein-A (PAPP-A) Story

Pregnancy-associated plasma protein-A (PAPP-A) was first identified in the early 1970s as a placental protein of unknown function, present at high concentrations in the circulation of pregnant women. In the mid-to-late 1990s, PAPP-A was discovered to be a metzincin metalloproteinase, expressed by many nonplacental cells, that regulates local insulin-like growth factor (IGF) activity through cleavage of high-affinity IGF binding proteins (IGFBPs), in particular IGFBP-4. With PAPP-A as a cell surface-associated enzyme, the reduced affinity of the cleavage fragments results in increased IGF available to bind and activate IGF receptors in the pericellular environment. This proteolytic regulation of IGF activity is important, since the IGFs promote proliferation, differentiation, migration, and survival in various normal and cancer cells. Thus, there has been a steady growth in investigation of PAPP-A structure and function outside of pregnancy. This review provides historical perspective on the discovery of PAPP-A and its structure and cellular function, highlights key studies of the first 50 years in PAPP-A research, and introduces new findings from recent years.

The influence of gut microbiota on circulating inflammatory cytokines and host: A Mendelian randomization study with meta-analysis

AIMS

The gut microbiota has been found to be altered in different inflammatory disorders, but its involvement in the regulation of inflammatory cytokines remains unclear. Therefore, this study aimed to investigate the impacts of gut microbiota on circulating inflammatory cytokines and their potential roles in host diseases.

MAIN METHODS

Two-sample Mendelian randomization (MR) analyses were conducted using summary-level data from genome-wide association studies (GWAS) to identify significant causal associations between 196 gut microbiota and 41 inflammatory cytokines. Meta-analysis was applied to test the robustness of the results. Enrichment analyses of identified cytokines were further utilized to infer the effects of gut microbiota on the host.

KEY FINDINGS

The MR analyses and meta-analyses identified the following significant causal associations: phylum Euryarchaeota on interleukin-2 (IL-2) (βIVW = 0.085, P = 1.5 × 10-2) and interleukin-8 (IL-8) (βIVW = 0.065, P = 4.1 × 10-2), phylum Tenericutes and class Mollicutes on macrophage inflammatory protein 1a (MIP1a) (βIVW = -0.142, P = 7.0 × 10-3), class Bacilli on hepatocyte growth factor (HGF) (βIVW = -0.106, P = 2.5 × 10-2), order Enterobacteriales on monocyte chemoattractant protein-1 (MCP1) (βIVW = 0.182, P = 1.8 × 10-2), and genus Lachnospiraceae NC2004 group on TNF-related apoptosis-inducing ligand (TRAIL) (βIVW = -0.207, P = 6.0 × 10-4). Enrichment analyses suggested that phylum Euryarchaeota and order Enterobacteriales might be risk factors for certain autoimmune diseases and neoplasms, while the phylum Tenericutes may have a protective effect.

SIGNIFICANCE

This study represents the first evidence confirming the causal effect of specific gut microbial taxa on circulating inflammatory cytokines and sheds light on their potential roles in the development and progression of various host diseases.

Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With de novo Chest Pain

BACKGROUND

Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS).

METHODS

Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRSCAD) was calculated. The prediction was performed using combinations of PRSCAD, proteins, and PMRS as features in models using stability selection and machine learning.

RESULTS

Prediction of absence of CAD yielded an area under the curve of PRSCAD-model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, -0.04; P=0.13). Optimal predictive ability was achieved by the full model (PRSCAD+protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone (P<0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of >15% pretest probability patients.

CONCLUSIONS

For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT02264717.

Integrating GWAS summary statistics, individual-level genotypic and omic data to enhance the performance for large-scale trait imputation

Recently, a non-parametric method has been proposed to impute the genetic component of a trait for a large set of genotyped individuals based on a separate genome-wide association study (GWAS) summary dataset of the same trait (from the same population). The imputed trait may contain linear, non-linear and epistatic effects of genetic variants, thus can be used for downstream linear or non-linear association analyses and machine learning tasks. Here, we propose an extension of the method to impute both genetic and environmental components of a trait using both single nucleotide polymorphism (SNP)-trait and omics-trait association summary data. We illustrate an application to a UK Biobank subset of individuals (n ≈ 80K) with both body mass index (BMI) GWAS data and metabolomic data. We divided the whole dataset into two equally sized and non-overlapping training and test datasets; we used the training data to build SNP- and metabolite-BMI association summary data and impute BMI on the test data. We compared the performance of the original and new imputation methods. As by the original method, the imputed BMI values by the new method largely retained SNP-BMI association information; however, the latter retained more information about BMI-environment associations and were more highly correlated with the original observed BMI values.

Inflammatory biomarkers and delirium: a Mendelian randomization study

Background

The association between inflammatory biomarkers and individual delirium symptoms remains controversial in observational studies. We investigated the relationship between inflammatory biomarkers and the risk of developing delirium.

Methods

A bidirectional two-sample Mendelian randomization (MR) was performed. Genetic instruments associated with peripheral tumor necrosis factor-a (TNF-a) C-reactive protein (CRP), interleukin (IL)-1α, IL-1β, IL-2, IL-8, IL-6, soluble IL-6 receptor alpha (sIL-6Rα), and soluble gp130 were identified in three different large summary genome-wide association studies (GWAS) conducted in the European population. Summary-level statistics for delirium not induced by alcohol and other psychoactive substances were obtained from the FinnGen consortium (2,612 cases and 325,306 controls). The estimated causal effects were performed using instruments' variants at the genome-wide significant level (P < 5e-8 and P < 5e-6), applying a linkage disequilibrium clumping approach with a threshold of r2 < 0.001 for each of the exposures. Reverse causation was also performed. The inverse-variance weighted method (IVW), MR-Egger method, weighted median method, MR-Egger regression, and MR Pleiotropy RESidual Sum were used for MR analyses.

Results

At the genome-wide significant level (P < 5e-8, r2 < 0.001), genetically predicted sIL-6Rα was significantly associated with a decreased risk of delirium with less than three single-nucleotide polymorphisms (SNPs) in all three GWAS data sources (ORWaldratio = 0.89, 95% CI: 0.79-0.96, PWaldratio = 0.0016; ORIVW = 0.88, 95% CI: 0.79-0.97, PIVW = 0.008; ORIVW = 0.88, 95% CI: 0.80-0.96, PIVW = 0.004). The causal relationship between sIL-6Rα and delirium became non-significant when a more liberal threshold of P of < 5e-6 was applied (all PIVW > 0.05). At the two genome-wide significance levels (P < 5e-8 and P < 5e-6), we found no evidence for the causal effects of peripheral TNF-α, CRP, IL-1α, IL-1β, IL-2, IL-6, IL-8, and soluble gp130 on delirium (all P > 0.05). The MR-Egger intercept and MR-PRESSO results indicated that no SNP had possible pleiotropy (all P > 0.05). Regarding the reverse, no evidence for an effect of delirium on these inflammatory biomarkers could be found (all P > 0.05).

Conclusion

The results of this MR analysis did not support that peripheral TNF-α, CRP, IL-1α, IL-1β, IL-2, IL-6, sIL-6Rα, soluble gp130, and IL-8 were causally associated with delirium. More research is needed to explore the role of inflammatory factors in the pathogenesis of delirium.

Association between air pollution and primary liver cancer in European and east Asian populations: a Mendelian randomization study

Purpose

The incidence of primary liver cancer is increasing year by year, with environmental factors playing a non-negligible role. At present, many studies are still disputing whether air pollution is associated with primary liver cancer incidence, and it is difficult to draw causal inferences. Therefore, in this study, we used two-sample Mendelian randomization (MR) to assess the causal relationship between air pollution (including PM2.5, PM2.5-10, PM10, nitrogen dioxide and nitrogen oxides) and primary liver cancer risk and its related biomarkers (Alpha-fetoprotein, Osteopontin, Glypican-3 and Arginase-1).

Patients and methods

We used large-scale publicly available genome-wide association studies (GWAS) summary data to conduct MR analyses of European and East Asian populations. Inverse variance weighted (IVW) method was used as the main analysis method, and weighted median model, MR-Egger, simple model and weighted model methods were selected for quality control. Heterogeneity was checked by the Cochran's Q test. The MR-Egger regression and the MR-PRESSO global test detect pleiotropy. The sensitivity analysis was performed using the leave-one-out method.

Results

Between air pollution and primary liver cancer in either European (PM2.5: p = 0.993; PM2.5-10: p = 0.833; PM10: p = 0.257; nitrogen dioxide: p = 0.215; nitrogen oxides: p = 0.614) or East Asian (PM2.5: p = 0.718; PM2.5-10: p = 0.362; PM10: p = 0.720; nitrogen dioxide: p = 0.101; nitrogen oxides: p = 0.760) populations were found no statistical association. Notably, there was a causal relationship between nitrogen oxides and Arginase-1, a biomarker associated with hepatocellular differentiation, statistically significant associations remained after deletion for single nucleotide polymorphisms (SNPs) associated with alcohol intake frequency, Body mass index (BMI) and cancers (Beta: 4.46; 95%CI: 0.83-8.08; p = 0.015). There was no heterogeneity or pleiotropy in the results.

Conclusion

This MR study found no evidence to support a causality between air pollution and primary liver cancer in European and East Asian populations, but nitrogen oxides may affect hepatocellular differentiation.

Association between circulating inflammatory markers and adult cancer risk: a Mendelian randomization analysis

Background

Tumour-promoting inflammation is a "hallmark" of cancer and conventional epidemiological studies have reported links between various inflammatory markers and cancer risk. The causal nature of these relationships and, thus, the suitability of these markers as intervention targets for cancer prevention is unclear.

Methods

We meta-analysed 6 genome-wide association studies of circulating inflammatory markers comprising 59,969 participants of European ancestry. We then used combined cis-Mendelian randomization and colocalisation analysis to evaluate the causal role of 66 circulating inflammatory markers in risk of 30 adult cancers in 338,162 cancer cases and up to 824,556 controls. Genetic instruments for inflammatory markers were constructed using genome-wide significant (P < 5.0 x 10-8) cis-acting SNPs (i.e. in or ±250 kb from the gene encoding the relevant protein) in weak linkage disequilibrium (LD, r2 < 0.10). Effect estimates were generated using inverse-variance weighted random-effects models and standard errors were inflated to account for weak LD between variants with reference to the 1000 Genomes Phase 3 CEU panel. A false discovery rate (FDR)-corrected P-value ("q-value") < 0.05 was used as a threshold to define "strong evidence" to support associations and 0.05 ≤ q-value < 0.20 to define "suggestive evidence". A colocalisation posterior probability (PPH4) > 70% was employed to indicate support for shared causal variants across inflammatory markers and cancer outcomes.

Results

We found strong evidence to support an association of genetically-proxied circulating pro-adrenomedullin concentrations with increased breast cancer risk (OR 1.19, 95% CI 1.10-1.29, q-value=0.033, PPH4=84.3%) and suggestive evidence to support associations of interleukin-23 receptor concentrations with increased pancreatic cancer risk (OR 1.42, 95% CI 1.20-1.69, q-value=0.055, PPH4=73.9%), prothrombin concentrations with decreased basal cell carcinoma risk (OR 0.66, 95% CI 0.53-0.81, q-value=0.067, PPH4=81.8%), macrophage migration inhibitory factor concentrations with increased bladder cancer risk (OR 1.14, 95% CI 1.05-1.23, q-value=0.072, PPH4=76.1%), and interleukin-1 receptor-like 1 concentrations with decreased triple-negative breast cancer risk (OR 0.92, 95% CI 0.88-0.97, q-value=0.15), PPH4=85.6%). For 22 of 30 cancer outcomes examined, there was little evidence (q-value ≥ 0.20) that any of the 66 circulating inflammatory markers examined were associated with cancer risk.

Conclusion

Our comprehensive joint Mendelian randomization and colocalisation analysis of the role of circulating inflammatory markers in cancer risk identified potential roles for 5 circulating inflammatory markers in risk of 5 site-specific cancers. Contrary to reports from some prior conventional epidemiological studies, we found little evidence of association of circulating inflammatory markers with the majority of site-specific cancers evaluated.

Non-steroidal anti-inflammatory drug target gene associations with major depressive disorders: a Mendelian randomisation study integrating GWAS, eQTL and mQTL Data

Previous observational studies reported associations between non-steroidal anti-inflammatory drugs (NSAIDs) and major depressive disorder (MDD), however, these associations are often inconsistent and underlying biological mechanisms are still poorly understood. We conducted a two-sample Mendelian randomisation (MR) study to examine relationships between genetic variants and NSAID target gene expression or DNA methylation (DNAm) using publicly available expression, methylation quantitative trait loci (eQTL or mQTL) data and genetic variant-disease associations from genome-wide association studies (GWAS of MDD). We also assessed drug exposure using gene expression and DNAm levels of NSAID targets as proxies. Genetic variants were robustly adjusted for multiple comparisons related to gene expression, DNAm was used as MR instrumental variables and GWAS statistics of MDD as the outcome. A 1-standard deviation (SD) lower expression of NEU1 in blood was related to lower C-reactive protein (CRP) levels of -0.215 mg/L (95% confidence interval (CI): 0.128-0.426) and a decreased risk of MDD (odds ratio [OR] = 0.806; 95% CI: 0.735-0.885; p = 5.36 × 10^-6). A concordant direction of association was also observed for NEU1 DNAm levels in blood and a risk of MDD (OR = 0.886; 95% CI: 0.836-0.939; p = 4.71 × 10^-5). Further, the genetic variants associated with MDD were mediated by NEU1 expression via DNAm (β = -0.519; 95% CI: -0.717 to -0.320256; p = 3.16 × 10^-7). We did not observe causal relationships between inflammatory genetic marker estimations and MDD risk. Yet, we identified a concordant association of NEU1 messenger RNA and an adverse direction of association of higher NEU1 DNAm with MDD risk. These results warrant increased pharmacovigilance and further in vivo or in vitro studies to investigate NEU1 inhibitors or supplements for MDD.

Mendelian Randomization Analysis Provides Insights into the Pathogenesis of Serum Levels of Branched-Chain Amino Acids in Cardiovascular Disease

Several observational studies have indicated an association between high serum levels of branched-chain amino acids (BCAAs) and an increased risk of cardiovascular disease (CVD). To assess whether theses associations reflect causality, we carried out two-sample Mendelian randomization (MR). Single-nucleotide polymorphisms (SNPs) associated with BCAA were evaluated in 10 studies, including 24,925 participants. The association between SNPs and coronary artery disease (CAD) were assessed using summary estimates from the CARDIoGRAMplusC4D consortium. Further MR analysis of BCAAs and seven CVD outcomes was performed. The BCAA-raising gene functions were also analyzed. MR analyses revealed a risk-increasing causal relationship between serum BCAA concentrations and CAD (odds ratio 1.08; 95% confidence interval (CI) 1.02–1.14), which was partly mediated by blood pressure and type 2 diabetes. BCAA also demonstrated a causal relationship with ischemic CVD events induced by plaque rupture and thrombosis (false discovery rate <0.05). Two BCAA-raising genes (MRL33 and CBLN1) were preferentially associated with myocardial infarction risk in the presence of atherosclerosis (p < 0.003). Functional analysis of the BCAA-raising genes suggested the causal involvement of two pathophysiological pathways, including glucose metabolism (PPM1K and TRMT61A) related to plaque progression, and the newly discovered neuroendocrine disorders regulating blood pressure (MRPL33, CBLN1, and C2orf16) related to plaque rupture and thrombosis. This comprehensive MR analysis provided insights into the potential causal mechanisms linking BCAA with CVD risk and suggested targeting neuroendocrine disorders as a potential strategy for the prevention of CVD. These results warrant further studies to elucidate the mechanisms underlying these reported causal associations.

Proteogenomic links to human metabolic diseases

Studying the plasma proteome as the intermediate layer between the genome and the phenome has the potential to identify new disease processes. Here, we conducted a cis-focused proteogenomic analysis of 2,923 plasma proteins measured in 1,180 individuals using antibody-based assays. We (1) identify 256 unreported protein quantitative trait loci (pQTL); (2) demonstrate shared genetic regulation of 224 cis-pQTLs with 575 specific health outcomes, revealing examples for notable metabolic diseases (such as gastrin-releasing peptide as a potential therapeutic target for type 2 diabetes); (3) improve causal gene assignment at 40% (n = 192) of overlapping risk loci; and (4) observe convergence of phenotypic consequences of cis-pQTLs and rare loss-of-function gene burden for 12 proteins, such as TIMD4 for lipoprotein metabolism. Our findings demonstrate the value of integrating complementary proteomic technologies with genomics even at moderate scale to identify new mediators of metabolic diseases with the potential for therapeutic interventions.

Potential therapeutic targets for sarcopenia identified by Mendelian randomisation

BACKGROUND

Identifying sarcopenia's causally associated plasma proteins would provide potential therapeutic targets.

METHODS

We screened out sarcopenia-related proteins with genome-wide association studies (GWAS) summary data and cis-protein loci genetic instruments. Summary data of sarcopenia were obtained from a GWAS of 256,523 Europeans aged 60 years and over. The causal effects of the proteins were investigated by cis-Mendelian Randomisation (MR) and multiverse sensitivity analysis. We also explored the robust proteins' causal associations with appendicular lean mass (ALM) and surveyed their druggability and clinical development activities.

RESULTS

In sum, 60 proteins from plasma proteome analysis studies and 12 from other studies were enrolled for MR analysis. In the whole population, four proteins (HPT, AT1B2, ISLR2 and TNF12) showed causal associations with the risk of sarcopenia according to the European Working Group on Sarcopenia in Older People (EWGSOP) criterion. In the female population, AT1B2 and TNFSF12 revealed causal associations with sarcopenia risk according to the EWGSOP criterion; HGF revealed a negative association according to the National Institutes of Health criterion. All of them were druggable, and the inhibitors of TNF12 and HGF were evaluated in clinical trials for other diseases. TNF12 also revealed a negative causal association with ALM, whereas HGF was positively causally associated with ALM.

CONCLUSIONS

Five druggable plasma proteins revealed causal associations with sarcopenia in the whole or female populations. TNF12 and HGF were the targets of therapeutic agents evaluated in clinical trials, and they were also causally associated with ALM. Our study suggested the potential mechanisms and therapeutic targets for sarcopenia.

Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics

Insulin is amongst the human genome's most well-studied genes/proteins due to its connection to metabolic health. Within this article, we review literature and data to build a knowledge base of Insulin (INS) genetics that influence transcription, transcript processing, translation, hormone maturation, secretion, receptor binding, and metabolism while highlighting the future needs of insulin research. The INS gene region has 2076 unique variants from population genetics. Several variants are found near the transcriptional start site, enhancers, and following the INS transcripts that might influence the readthrough fusion transcript INS-IGF2. This INS-IGF2 transcript splice site was confirmed within hundreds of pancreatic RNAseq samples, lacks drift based on human genome sequencing, and has possible elevated expression due to viral regulation within the liver. Moreover, a rare, poorly characterized African population-enriched variant of INS-IGF2 results in a loss of the stop codon. INS transcript UTR variants rs689 and rs3842753, associated with type 1 diabetes, are found in many pancreatic RNAseq datasets with an elevation of the 3'UTR alternatively spliced INS transcript. Finally, by combining literature, evolutionary profiling, and structural biology, we map rare missense variants that influence preproinsulin translation, proinsulin processing, dimer/hexamer secretory storage, receptor activation, and C-peptide detection for quasi-insulin blood measurements.

Novel Causal Plasma Proteins for Hypothyroidism: A Large-scale Plasma Proteome Mendelian Randomization Analysis

CONTEXT

Although several risk proteins for hypothyroidism have been reported in recent years, many more plasma proteins have not been tested.

OBJECTIVE

To determine potential mechanisms and novel causal plasma proteins for hypothyroidism using Mendelian randomization (MR).

METHODS

A large-scale plasma proteome MR analysis was conducted using protein quantitative trait loci (pQTLs) for 2297 plasma proteins. We classified pQTLs into 4 different groups. MR analyses were conducted within the 4 groups simultaneously. Significant proteins were discovered and validated in 2 different cohorts. Colocalization analysis and enrichment analysis were conducted using proteins found with MR.

RESULTS

Thirty-one proteins were identified in the discovery cohort. Among them, 13 were validated in the validation cohort. Nine of the 13 proteins are risk factors (ISG15, Fc receptor-like protein 2, tumor necrosis factor ligand superfamily member 14, Rab-2A, FcRL3, thrombomodulin, interferon [IFN]-lambda-1, platelet glycoprotein Ib alpha chain, IL-7RA) for hypothyroidism, whereas others are protective proteins (protein O-glucosyltransferase 1 [POGLUT1], tumor necrosis factor ligand superfamily, 3-hydroxyisobutyryl-CoA hydrolase, transferrin receptor protein 1). Among the significant proteins, POGLUT1 strongly colocalized with expression quantitative trait loci from whole blood (posterior probability of colocalization [PP4] = 0.978) and the thyroid (PP4 = 0.978). Two different trans-pQTLs (rs2111485 PP4 = 0.998; rs35103715 PP4 = 0.998) for IFN-lambda-1 strongly colocalized with hypothyroidism in different chromosomes.

CONCLUSION

Thirteen various proteins were identified and validated to be associated with hypothyroidism using univariable MR. We reinforced and expanded the effect of IFN on hypothyroidism. Several proteins identified in this study could explain part of the association between the coagulation system and hypothyroidism. Our study broadens the causal proteins for hypothyroidism and provides the relationships between plasma proteins and hypothyroidism. The proteins identified in this study can be used as early screening biomarkers for hypothyroidism.

A large-scale plasma proteome Mendelian randomization study identifies novel causal plasma proteins related to primary biliary cholangitis

Background and aims

Primary biliary cholangitis (PBC) is a progressive chronic autoimmune cholestatic liver disease characterized by the destruction of small intrahepatic bile ducts leading to biliary cirrhosis. Liver biopsy is required in the diagnosis of Antimitochondrial antibody-negative patients. Therefore, novel biomarkers are needed for the non-invasive diagnosis of PBC. To identify novel biomarkers for PBC, we conducted large-scale plasma proteome Mendelian randomization (MR).

Methods

A total of 21,593 protein quantitative trait loci (pQTLs) for 2297 circulating proteins were used and classified into four different groups. MR analyses were conducted in the four groups separately. Furthermore, the results were discovered and replicated in two different cohorts of PBC. Colocalization analysis and enrichment analysis were also conducted.

Results

Three plasma proteins (ficolin-1, CD40 and protein FAM177A1) were identified and replicated as being associated with PBC. All of them showed significant protective effects against PBC. An increase in ficolin-1 (OR=0.890 [0.843-0.941], p=3.50×10^-5), CD40 (OR=0.814 [0.741-0.895], p=1.96×10^-5) and protein FAM177A1 (OR=0.822 [0.754-0.897], p=9.75×10^-6) reduced the incidence of PBC. Ficolin-1 (PP4 = 0.994) and protein FAM177A1 (PP4 = 0.995) colocalized with the expression of the genes FCN1 and FAM177A1 in whole blood, respectively. Furthermore, CD40 (PP4 = 0.977) and protein FAM177A1 (PP4 = 0.897) strongly colocalized with PBC.

Conclusions

We expand the current biomarkers for PBC. In total, three (ficolin-1, CD40, and protein FAM177A1) plasma proteins were identified and replicated as being associated with PBC in MR analysis. All of them showed significant protective effects against PBC. These proteins can be potential biomarkers or drug targets for PBC.

Cardiometabolic Traits in Adult Twins: Heritability and BMI Impact with Age

Background: The prevalence of obesity and cardiometabolic diseases continues to rise globally and obesity is a significant risk factor for cardiometabolic diseases. However, to our knowledge, evidence of the relative roles of genes and the environment underlying obesity and cardiometabolic disease traits and the correlations between them are still lacking, as is how they change with age. Method: Data were obtained from the Chinese National Twin Registry (CNTR). A total of 1421 twin pairs were included. Univariate structural equation models (SEMs) were performed to evaluate the heritability of BMI and cardiometabolic traits, which included blood hemoglobin A1c (HbA1c), fasting blood glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TGs), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C). Bivariate SEMs were used to assess the genetic/environmental correlations between them. The study population was divided into three groups for analysis: ≤50, 51−60, and >60 years old to assess the changes in heritability and genetic/environmental correlations with ageing. Results: Univariate SEMs showed a high heritability of BMI (72%) and cardiometabolic traits, which ranged from 30% (HbA1c) to 69% (HDL-C). With age increasing, the heritability of all phenotypes has different degrees of declining trends. Among these, BMI, SBP, and DBP presented significant monotonous declining trends. The bivariate SEMs indicated that BMI correlated with all cardiometabolic traits. The genetic correlations were estimated to range from 0.14 (BMI and LDL-C) to 0.39 (BMI and DBP), while the environmental correlations ranged from 0.13 (BMI and TC/LDL-C) to 0.31 (BMI and TG). The genetic contributions underlying the correlations between BMI and SBP and DBP, TC, TG, and HDL-C showed a progressive decrease as age groups increased. In contrast, environmental correlations displayed a significant increasing trend for HbA1c, SBP, and DBP. Conclusions: The findings suggest that genetic and environmental factors have essential effects on BMI and all cardiometabolic traits. However, as age groups increased, genetic influences presented varying degrees of decrement for BMI and most cardiometabolic traits, suggesting the increasing importance of environments. Genetic factors played a consistently larger role than environmental factors in the phenotypic correlations between BMI and cardiometabolic traits. Nevertheless, the relative magnitudes of genetic and environmental factors may change over time.

Identifying causal serum protein-cardiometabolic trait relationships using whole genome sequencing

Cardiometabolic diseases, such as type 2 diabetes and cardiovascular disease, have a high public health burden. Understanding the genetically-determined regulation of proteins that are dysregulated in disease can help to dissect the complex biology underpinning them. Here, we perform a protein quantitative trait locus (pQTL) analysis of 255 serum proteins relevant to cardiometabolic processes in 2893 individuals. Meta-analysing whole-genome sequencing (WGS) data from two Greek cohorts, MANOLIS (n = 1356; 22.5x WGS) and Pomak (n = 1537; 18.4x WGS), we detect 302 independently-associated pQTL variants for 171 proteins, including 12 rare variants (minor allele frequency [MAF] < 1%). We additionally find 15 pQTL variants that are rare in non-Finnish European populations, but have drifted up in frequency in the discovery cohorts here. We identify proteins causally associated with cardiometabolic traits, including MEP1B for high-density lipoprotein levels; and describe a knock-out Mep1b mouse model. Our findings furnish insights into the genetic architecture of the serum proteome, identify new protein-disease relationships, and demonstrate the importance of isolated populations in pQTL analysis.

Greater Adherence to Cardioprotective Diet Can Reduce Inflammatory Bowel Disease Risk: A Longitudinal Cohort Study

Background: The cardioprotective diet was reported to be associated with several chronic cardiometabolic diseases through an anti-inflammation effect. However, the association between the cardioprotective diet and the risk of inflammatory bowel disease (IBD) was unclear and deserved to be further explored. Methods: We calculated the cardioprotective diet score based on the consumptions of seven common food groups using the validated food frequency questionnaire data in the UK Biobank. Incident IBD was ascertained from primary care data, inpatient data, and the death registry. Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between the cardioprotective diet score and the risk of IBD. Results: During a mean follow-up of 12.1 years, we documented 2717 incident IBD cases, including 851 cases of Crohn’s disease and 1866 cases of ulcerative colitis. Compared to participants with a cardioprotective diet score of 0−1, we observed a decreased risk of IBD among participants with cardioprotective diet scores of 3 (HR 0.85, 95% CI 0.73−0.99), 4 (HR 0.84, 95% CI 0.72−0.98), and 5−7 (HR 0.77, 95% CI 0.66−0.89) (p-trend < 0.001). Conclusions: A greater adherence to the cardioprotective diet was associated with a lower risk of IBD. Our finding highlighted the importance of focusing on the cardioprotective diet to prevent IBD.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Advances and Trends in Omics Technology Development

The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of “omics” and methodological advancement in systematically interrogating a cellular system. Yet, the ever-growing types of molecules and regulatory mechanisms being discovered have been persistently transforming our understandings on the cellular machinery. This renders cell omics seemingly, like the universe, expand with no limit and our goal toward the complete harness of the cellular system merely impossible. Therefore, it is imperative to review what has been done and is being done to predict what can be done toward the translation of omics information to disease control with minimal cell perturbation. With a focus on the “four big omics,” i.e., genomics, transcriptomics, proteomics, metabolomics, we delineate hierarchies of these omics together with their epiomics and interactomics, and review technologies developed for interrogation. We predict, among others, redoxomics as an emerging omics layer that views cell decision toward the physiological or pathological state as a fine-tuned redox balance.

Connecting Genomics and Proteomics to Identify Protein Biomarkers for Adult and Youth-Onset Type 2 Diabetes: A Two-Sample Mendelian Randomization Study

Type 2 diabetes shows an increasing prevalence in both adults and children. Identification of biomarkers for both youth and adult-onset type 2 diabetes is crucial for development of screening tools or drug targets. In this study, using two-sample Mendelian randomization (MR), we identified 22 circulating proteins causally linked to adult type 2 diabetes and 11 proteins with suggestive evidence for association with youth-onset type 2 diabetes. Among these, colocalization analysis further supported a role in type 2 diabetes for C-type mannose receptor 2 (MR odds ratio [OR] 0.85 [95% CI 0.79-0.92] per genetically predicted SD increase in protein level), MANS domain containing 4 (MR OR 0.90 [95% CI 0.88-0.92]), sodium/potassium-transporting ATPase subunit β2 (MR OR 1.10 [95% CI 1.06-1.15]), endoplasmic reticulum oxidoreductase 1β (MR OR 1.09 [95% CI 1.05-1.14]), spermatogenesis-associated protein 20 (MR OR 1.12 [95% CI 1.06-1.18]), haptoglobin (MR OR 0.96 [95% CI 0.94-0.98]), and α1-3-N-acetylgalactosaminyltransferase and α1-3-galactosyltransferase (MR OR 1.04 [95% CI 1.03-1.05]). Our findings support a causal role in type 2 diabetes for a set of circulating proteins, which represent promising type 2 diabetes drug targets.

Mapping the proteo-genomic convergence of human diseases

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

Estimating colocalization probability from limited summary statistics

BACKGROUND

Colocalization is a statistical method used in genetics to determine whether the same variant is causal for multiple phenotypes, for example, complex traits and gene expression. It provides stronger mechanistic evidence than shared significance, which can be produced through separate causal variants in linkage disequilibrium. Current colocalization methods require full summary statistics for both traits, limiting their use with the majority of reported GWAS associations (e.g. GWAS Catalog). We propose a new approximation to the popular coloc method that can be applied when limited summary statistics are available. Our method (POint EstiMation of Colocalization, POEMColoc) imputes missing summary statistics for one or both traits using LD structure in a reference panel, and performs colocalization using the imputed summary statistics.

RESULTS

We evaluate the performance of POEMColoc using real (UK Biobank phenotypes and GTEx eQTL) and simulated datasets. We show good correlation between posterior probabilities of colocalization computed from imputed and observed datasets and similar accuracy in simulation. We evaluate scenarios that might reduce performance and show that multiple independent causal variants in a region and imputation from a limited subset of typed variants have a larger effect while mismatched ancestry in the reference panel has a modest effect. Further, we find that POEMColoc is a better approximation of coloc when the imputed association statistics are from a well powered study (e.g., relatively larger sample size or effect size). Applying POEMColoc to estimate colocalization of GWAS Catalog entries and GTEx eQTL, we find evidence for colocalization of 150,000 trait-gene-tissue triplets.

CONCLUSIONS

We find that colocalization analysis performed with full summary statistics can be closely approximated when only the summary statistics of the top SNP are available for one or both traits. When applied to the full GWAS Catalog and GTEx eQTL, we find that colocalized trait-gene pairs are enriched in tissues relevant to disease etiology and for matches to approved drug mechanisms. POEMColoc R package is available at https://github.com/AbbVie-ComputationalGenomics/POEMColoc .

Sortilin as a Biomarker for Cardiovascular Disease Revisited

Genetic variants in the genomic region containing SORT1 (encoding the protein sortilin) are strongly associated with cholesterol levels and the risk of coronary artery disease (CAD). Circulating sortilin has therefore been proposed as a potential biomarker for cardiovascular disease. Multiple studies have reported association between plasma sortilin levels and cardiovascular outcomes. However, the findings are not consistent across studies, and most studies have small sample sizes. The aim of this study was to evaluate sortilin as a biomarker for CAD in a well-characterized cohort with symptoms suggestive of CAD. In total, we enrolled 1,173 patients with suspected stable CAD referred to coronary computed tomography angiography. Sortilin was measured in plasma using two different technologies for quantifying circulating sortilin: a custom-made enzyme-linked immunosorbent assay (ELISA) and OLINK Cardiovascular Panel II. We found a relative poor correlation between the two methods (correlation coefficient = 0.21). In addition, genotyping and whole-genome sequencing were performed on all patients. By whole-genome regression analysis of sortilin levels measured with ELISA and OLINK, two independent cis protein quantitative trait loci (pQTL) on chromosome 1p13.3 were identified, with one of them being a well-established risk locus for CAD. Incorporating rare genetic variants from whole-genome sequence data did not identify any additional pQTLs for plasma sortilin. None of the traditional CAD risk factors, such as sex, age, smoking, and statin use, were associated with plasma sortilin levels. Furthermore, there was no association between circulating sortilin levels and coronary artery calcium score (CACS) or disease severity. Sortilin did not improve discrimination of obstructive CAD, when added to a clinical pretest probability (PTP) model for CAD. Overall, our results indicate that studies using different methodologies for measuring circulating sortilin should be compared with caution. In conclusion, the well-known SORT1 risk locus for CAD is linked to lower sortilin levels in circulation, measured with ELISA; however, the effect sizes are too small for sortilin to be a useful biomarker for CAD in a clinical setting of low- to intermediate-risk chest-pain patients.