Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases.
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ABSTRACT:
SUBMITTER: Zheng J
PROVIDER: S-EPMC7610464 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCausality between plasma triglyceride (TG) levels and atherosclerotic cardiovascular disease (ASCVD) risk remains controversial despite more than four decades of study and two recent landmark trials, STRENGTH, and REDUCE-IT. Further unclear is the association between TG levels and non-atherosclerotic diseases across organ systems.MethodsHere, we conducted a phenome-wide, two-sample Mendelian randomization (MR) analysis using inverse-variance weighted (IVW) regression to systematically infer the causal effects of plasma TG levels on 2600 disease traits in the European ancestry population of UK Biobank. For replication, we externally tested 221 nominally significant associations (p<0.05) in an independent cohort from FinnGen. To account for potential horizontal pleiotropy and the influence of invalid instrumental variables, we performed sensitivity analyses using MR-Egger regression, weighted median estimator, and MR-PRESSO. Finally, we used multivariable MR (MVMR) controlling for correlated lipid fractions to distinguish the independent effect of plasma TG levels.ResultsOur results identified seven disease traits reaching Bonferroni-corrected significance in both the discovery (p<1.92 × 10-5) and replication analyses (p<2.26 × 10-4), suggesting a causal relationship between plasma TG levels and ASCVDs, including coronary artery disease (OR 1.33, 95% CI 1.24-1.43, p=2.47 × 10-13). We also identified 12 disease traits that were Bonferroni-significant in the discovery or replication analysis and at least nominally significant in the other analysis (p<0.05), identifying plasma TG levels as a novel potential risk factor for nine non-ASCVD diseases, including uterine leiomyoma (OR 1.19, 95% CI 1.10-1.29, p=1.17 × 10-5).ConclusionsTaking a phenome-wide, two-sample MR approach, we identified causal associations between plasma TG levels and 19 disease traits across organ systems. Our findings suggest unrealized drug repurposing opportunities or adverse effects related to approved and emerging TG-lowering agents, as well as mechanistic insights for future studies.FundingRD is supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) (R35-GM124836) and the National Heart, Lung, and Blood Institute of the NIH (R01-HL139865 and R01-HL155915).
| S-EPMC10079290 | biostudies-literature
Project description:BackgroundCaffeine is one of the most utilized drugs in the world, yet its clinical effects are not fully understood. Circulating caffeine levels are influenced by the interplay between consumption behaviour and metabolism. This study aimed to investigate the effects of circulating caffeine levels by considering genetically predicted variation in caffeine metabolism.MethodsLeveraging genetic variants related to caffeine metabolism that affect its circulating levels, we investigated the clinical effects of plasma caffeine in a phenome-wide association study (PheWAS). We validated novel findings using a two-sample Mendelian randomization framework and explored the potential mechanisms underlying these effects in proteome-wide and metabolome-wide Mendelian randomization.ResultsHigher levels of genetically predicted circulating caffeine among caffeine consumers were associated with a lower risk of obesity (odds ratio (OR) per standard deviation increase in caffeine = 0.97, 95% confidence interval (CI) CI: 0.95-0.98, p = 2.47 × 10-4), osteoarthrosis (OR = 0.97, 95% CI: 0.96-0.98, P=1.10 × 10-8) and osteoarthritis (OR: 0.97, 95% CI: 0.96 to 0.98, P = 1.09 × 10-6). Approximately one third of the protective effect of plasma caffeine on osteoarthritis risk was estimated to be mediated through lower bodyweight. Proteomic and metabolomic perturbations indicated lower chronic inflammation, improved lipid profiles, and altered protein and glycogen metabolism as potential biological mechanisms underlying these effects.ConclusionsWe report novel evidence suggesting that long-term increases in circulating caffeine may reduce bodyweight and the risk of osteoarthrosis and osteoarthritis. We confirm prior genetic evidence of a protective effect of plasma caffeine on risk of overweight and obesity. Further clinical study is warranted to understand the translational relevance of these findings before clinical practice or lifestyle interventions related to caffeine consumption are introduced.
| S-EPMC10880284 | biostudies-literature
Project description:BackgroundThe prevalence of atrial fibrillation (AF) is increasing with an aging worldwide population, yet a comprehensive understanding of its causes and consequences remains limited. We aim to assess the causes and consequences of AF via a bidirectional Mendelian randomization (MR) analysis.MethodsWe used publicly available genome-wide association study (GWAS) summary data, centralized and harmonized by an open GWAS database. We assessed the genetically predicted effects of 5048 exposures on risk of AF, and the genetically predicted effects of genetic liability to AF, on 10 308 outcomes via two-sample MR analysis. Multivariable MR analysis was further conducted to explore the comparative roles of identified risk factors.ResultsMR analysis suggested that 55 out of 5048 exposure traits, including four proteins, play a causal role in AF (P <1e-5 allowing for multiple comparisons). Multivariable analysis suggested that higher body mass index, height and systolic blood pressure as well as genetic liability to coronary artery diseases independently cause AF. Three out of the four proteins (DUSP13, TNFSF12 and IL6R) had a drug prioritizing score for atrial fibrillation of 0.26, 0.38 and 0.88, respectively (values closer to 1 indicating stronger evidence of the protein as a potential drug target). Genetic liability to AF was linked to a higher risk of cardio-embolic ischaemic stroke.ConclusionsOur results suggest body mass index, height, systolic blood pressure and genetic liability to coronary artery disease are independent causal risk factors for AF. Several proteins, including DUSP13, IL-6R and TNFSF12, may have therapeutic potential for AF.
| S-EPMC9365635 | biostudies-literature
Project description:BackgroundWe performed phenome-wide Mendelian randomization analysis (MR-PheWAS), two-sample MR analysis, and systemic review to comprehensively explore the health effects of milk consumption in the European population.MethodsRs4988235 located upstream of the LCT gene was used as the instrumental variable for milk consumption. MR-PheWAS analysis was conducted to map the association of genetically predicted milk consumption with 1081 phenotypes in the UK Biobank study (n=339,197). The associations identified in MR-PheWAS were examined by two-sample MR analysis using data from the FinnGen study (n=260,405) and international consortia. A systematic review of MR studies on milk consumption was further performed.ResultsPheWAS and two-sample MR analyses found robust evidence in support of inverse associations of genetically predicted milk consumption with risk of cataract (odds ratio (OR) per 50 g/day increase in milk consumption, 0.89, 95% confidence interval (CI), 0.84-0.94; p=3.81×10-5), hypercholesterolemia (OR, 0.91, 95% CI 0.86-0.96; p=2.97×10-4), and anal and rectal polyps (OR, 0.85, 95% CI, 0.77-0.94; p=0.001). An inverse association for type 2 diabetes risk (OR, 0.92, 95% CI, 0.86-0.97; p=0.003) was observed in MR analysis based on genetic data with body mass index adjustment but not in the corresponding data without body mass index adjustment. The systematic review additionally found evidence that genetically predicted milk consumption was inversely associated with asthma, hay fever, multiple sclerosis, colorectal cancer, and Alzheimer's disease, and positively associated with Parkinson's disease, renal cell carcinoma, metabolic syndrome, overweight, and obesity.ConclusionsThis study suggests several health effects of milk consumption in the European population.
| S-EPMC9694907 | biostudies-literature
Project description:BackgroundPrevious studies have explored the role of plasma proteins on osteonecrosis. This Mendelian randomization (MR) study further assessed plasma proteins on osteonecrosis whether a causal relationship exists and provides some evidence of causality.MethodsSummary-level data of 4,907 circulating protein levels were extracted from a large-scale protein quantitative trait loci study including 35,559 individuals by the deCODE Genetics Consortium. The outcome data for osteonecrosis were sourced from the FinnGen study, comprising 1,543 cases and 391,037 controls. MR analysis was conducted to estimate the associations between protein and osteonecrosis risk. Additionally, Phenome-wide MR analysis, and candidate drug prediction were employed to identify potential causal circulating proteins and novel drug targets.ResultsWe totally assessed the effect of 1,676 plasma proteins on osteonecrosis risk, of which 71 plasma proteins had a suggestive association with outcome risk (P < 0.05). Notably, Heme-binding protein 1 (HEBP1) was significant positively associated with osteonecrosis risk with convening evidence (OR, 1.40, 95% CI, 1.19 to 1.65, P = 3.96 × 10-5, P FDR = 0.044). This association was further confirmed in other MR analysis methods and did not detect heterogeneity and pleiotropy (all P > 0.05). To comprehensively explore the health effect of HEBP1, the phenome-wide MR analysis found it was associated with 136 phenotypes excluding osteonecrosis (P < 0.05). However, no significant association was observed after the false discovery rate adjustment.ConclusionThis comprehensive MR study identifies 71 plasma proteins associated with osteonecrosis, with HEBP1, ITIH1, SMOC1, and CREG1 showing potential as biomarkers of osteonecrosis. Nonetheless, further studies are needed to validate this candidate plasma protein.
| S-EPMC11306153 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the predominant histological subtype of lung cancer and the leading cause of cancer-related mortality. Identifying effective drug targets is crucial for advancing LUAD treatment strategies.MethodsThis study employed proteome-wide Mendelian randomization (MR) and colocalization analyses. We collected data on 1394 plasma proteins from a protein quantitative trait loci (pQTL) study involving 4907 individuals. Genetic associations with LUAD were derived from the Transdisciplinary Research in Cancer of the Lung (TRICL) study, including 11,245 cases and 54,619 controls. We integrated pQTL and LUAD genome-wide association studies (GWASs) data to identify candidate proteins. MR utilizes single nucleotide polymorphisms (SNPs) as genetic instruments to estimate the causal effect of exposure on outcome, while Bayesian colocalization analysis determines the probability of shared causal genetic variants between traits. Our study applied these methods to assess causality between plasma proteins and LUAD. Furthermore, we employed a two-step MR to quantify the proportion of risk factors mediated by proteins on LUAD. Finally, protein-protein interaction (PPI) analysis elucidated potential links between proteins and current LUAD medications.ResultsWe identified nine plasma proteins significantly associated with LUAD. Increased levels of ALAD, FLT1, ICAM5, and VWC2 exhibited protective effects, with odds ratios of 0.79 (95% CI 0.72-0.87), 0.39 (95% CI 0.28-0.55), 0.91 (95% CI 0.72-0.87), and 0.85 (95% CI 0.79-0.92), respectively. Conversely, MDGA2 (OR, 1.13; 95% CI 1.08-1.19), NTM (OR, 1.12; 95% CI 1.09-1.16), PMM2 (OR, 1.35; 95% CI 1.18-1.53), RNASET2 (OR, 1.15; 95% CI 1.08-1.21), and TFPI (OR, 4.58; 95% CI 3.02-6.94) increased LUAD risk. Notably, none of the nine proteins showed evidence of reverse causality. Bayesian colocalization indicated that RNASET2, TFPI, and VWC2 shared the same variant with LUAD. Furthermore, NTM and FLT1 demonstrated interactions with targets of current LUAD medications. Additionally, FLT1 and TFPI are currently under evaluation as therapeutic targets, while NTM, RNASET2, and VWC2 are potentially druggable. These findings shed light on LUAD pathogenesis, highlighting the tumor-promoting effects of RNASET2, TFPI, and NTM, along with the protective effects of VWC2 and FLT1, providing a significant biological foundation for future LUAD therapeutic targets.ConclusionsOur proteome-wide MR analysis highlighted RNASET2, TFPI, VWC2, NTM, and FLT1 as potential drug targets for further clinical investigation in LUAD. However, the specific mechanisms by which these proteins influence LUAD remain elusive. Targeting these proteins in drug development holds the potential for successful clinical trials, providing a pathway to prioritize and reduce costs in LUAD therapeutics.
| S-EPMC10993587 | biostudies-literature
Project description:Blood levels of iron and copper, even within their normal ranges, have been associated with a wide range of clinical outcomes. The available epidemiological evidence for these associations is often inconsistent and suffers from confounding and reverse causation. This study aims to examine the causal clinical effects of blood iron and copper with Mendelian randomization (MR) analyses. Genetic instruments for the blood levels of iron and copper were curated from existing genome-wide association studies. Candidate clinical outcomes were identified based on a phenome-wide association study (PheWAS) between these genetic instruments and a wide range of phenotypes in 310,999 unrelated individuals of European ancestry from the UK Biobank. All signals passing stringent correction for multiple testing were followed by MR analyses, with replication in independent data sources where possible. We found that genetically predicted higher blood levels of iron and copper are both associated with lower risks of iron deficiency anemia (odds ratio (OR) = 0.75, 95% confidence interval (CI): 0.67-0.85, p = 1.90 × 10-6 for iron; OR = 0.88, 95% CI: 0.78-0.98, p = 0.032 for copper), lipid metabolism disorders, and its two subcategories, hyperlipidemia (OR = 0.90, 95% CI: 0.85-0.96, p = 6.44 × 10-4; OR = 0.92, 95% CI: 0.87-0.98, p = 5.51 × 10-3) and hypercholesterolemia (OR = 0.90, 95% CI: 0.84-0.95, p = 5.34 × 10-4; OR = 0.93, 95% CI: 0.89-0.99, p = 0.022). Consistently, they are also associated with lower blood levels of total cholesterol and low-density lipoprotein cholesterol. Multiple sensitivity tests were applied to assess the presence of pleiotropy and the robustness of causal estimates. Regardless of the approaches, consistent evidence was obtained. Moreover, the unique clinical effects of each blood mineral were identified. Notably, genetically predicated higher blood iron is associated with an enhanced risk of varicose veins (OR = 1.28, 95% CI: 1.15-1.42, p = 4.34 × 10-6), while blood copper is positively associated with the risk of osteoarthrosis (OR = 1.07, 95% CI: 1.02-1.13, p = 0.010). Sex-stratified MR analysis further revealed some degree of sex differences in their clinical effects. Our comparative PheWAS-MR study of iron and copper comprehensively characterized their shared and unique clinical effects, highlighting their potential causal roles in hyperlipidemia and hypercholesterolemia. Given the modifiable nature of blood mineral status and the potential for clinical intervention, these findings warrant further investigation.
| S-EPMC7603077 | biostudies-literature
Project description:BACKGROUND:The role of urate in cardiovascular diseases (CVDs) has been extensively investigated in observational studies; however, the extent of any causal effect remains unclear, making it difficult to evaluate its clinical relevance. METHODS AND FINDINGS:A phenome-wide association study (PheWAS) together with a Bayesian analysis of tree-structured phenotypic model (TreeWAS) was performed to examine disease outcomes related to genetically determined serum urate levels in 339,256 unrelated White British individuals (54% female) in the UK Biobank who were aged 40-69 years (mean age, 56.87; SD, 7.99) when recruited from 2006 to 2010. Mendelian randomization (MR) analyses were performed to replicate significant findings using various genome-wide association study (GWAS) consortia data. Sensitivity analyses were conducted to examine possible pleiotropic effects on metabolic traits of the genetic variants used as instruments for urate. PheWAS analysis, examining the association with 1,431 disease outcomes, identified 13 distinct phecodes representing 4 disease groups (inflammatory polyarthropathies, hypertensive disease, circulatory disease, and metabolic disorders) and 9 disease outcomes (gout, gouty arthropathy, pyogenic arthritis, essential hypertension, coronary atherosclerosis, ischemic heart disease, chronic ischemic heart disease, myocardial infarction, and hypercholesterolemia) that were associated with genetically determined serum urate levels after multiple testing correction (p < 3.35 × 10-4). TreeWAS analysis, examining 10,750 ICD-10 diagnostic terms, identified more sub-phenotypes of cardiovascular and cerebrovascular diseases (e.g., angina pectoris, heart failure, cerebral infarction). MR analysis successfully replicated the association with gout, hypertension, heart diseases, and blood lipid levels but indicated the existence of genetic pleiotropy. Sensitivity analyses support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observational associations with CVDs. The main limitations of this study relate to possible bias from pleiotropic effects of the considered genetic variants and possible misclassification of cases for mild disease that did not require hospitalization. CONCLUSION:In this study, high serum urate levels were found to be associated with increased risk of different types of cardiac events. The finding of genetic pleiotropy indicates the existence of common upstream pathological elements influencing both urate and metabolic traits, and this may suggest new opportunities and challenges for developing drugs targeting a common mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular comorbidities.
| S-EPMC6799886 | biostudies-literature
Project description:BackgroundAge at menarche has been associated with various health outcomes. We aimed to identify potential causal effects of age at menarche on health-related traits in a hypothesis-free manner.MethodsWe conducted a Mendelian randomization phenome-wide association study (MR-pheWAS) of age at menarche with 17,893 health-related traits in UK Biobank (n = 181,318) using PHESANT. The exposure of interest was the genetic risk score for age at menarche. We conducted a second MR-pheWAS after excluding SNPs associated with BMI from the genetic risk score, to examine whether results might be due to the genetic overlap between age at menarche and BMI. We followed up a subset of health-related traits to investigate MR assumptions and seek replication in independent study populations.ResultsOf the 17,893 tests performed in our MR-pheWAS, we identified 619 associations with the genetic risk score for age at menarche at a 5% false discovery rate threshold, of which 295 were below a Bonferroni-corrected P value threshold. These included potential effects of younger age at menarche on lower lung function, higher heel bone-mineral density, greater burden of psychosocial/mental health problems, younger age at first birth, higher risk of childhood sexual abuse, poorer cardiometabolic health, and lower physical activity. After exclusion of variants associated with BMI, the genetic risk score for age at menarche was related to 37 traits at a 5% false discovery rate, of which 29 were below a Bonferroni-corrected P value threshold. We attempted to replicate findings for bone-mineral density, lung function, neuroticism, and childhood sexual abuse using 5 independent cohorts/consortia. While estimates for lung function, higher bone-mineral density, neuroticism, and childhood sexual abuse in replication cohorts were consistent with UK Biobank estimates, confidence intervals were wide and often included the null.ConclusionsThe genetic risk score for age at menarche was related to a broad range of health-related traits. Follow-up analyses indicated imprecise evidence of an effect of younger age at menarche on greater bone-mineral density, lower lung function, higher neuroticism score, and greater risk of childhood sexual abuse in the smaller replication samples available; hence, these findings need further exploration when larger independent samples become available.
| S-EPMC7087394 | biostudies-literature
Project description:BackgroundThe interest in targeted cancer therapies has been growing rapidly. While numerous cancer biomarkers and targeted treatment strategies have been developed and employed, there are still significant limitations and challenges in the early diagnosis and targeted treatment of cancers. Accordingly, there is an urgent need to identify novel targets and develop new targeted drugs.MethodsThe study was conducted using combined cis-Mendelian randomization (cis-MR) and colocalization analysis. We analyzed data from 732 plasma proteins to identify potential drug targets associated with eight site-specific cancers. These findings were further validated using the UK Biobank dataset. Then, a protein-protein interaction network was also constructed to examine the interplay between the identified proteins and the targets of existing cancer medications.ResultsThis MR analysis revealed associations between five plasma proteins and prostate cancer, five with breast cancer, and three with lung cancer. Subsequently, these proteins were classified into four distinct target groups, with a focus on tier 1 and 2 targets due to their higher potential to become drug targets. Our study indicatied that genetically predicted KDELC2 (OR: 0.89, 95% CI 0.86-0.93) and TNFRSF10B (OR: 0.74, 95% CI 0.65-0.83) are inversely associated with prostate cancer. Furthermore, we observed an inverse association between CPNE1 (OR: 0.96, 95% CI 0.94-0.98) and breast cancer, while PDIA3 (OR: 1.19, 95% CI 1.10-1.30) were found to be associated with the risk of breast cancer. In addition, we also propose that SPINT2 (OR: 1.05, 95% CI 1.03-1.06), GSTP1 (OR: 0.82, 95% CI 0.74-0.90), and CTSS (OR: 0.91, 95% CI 0.88-0.95) may serve as potential therapeutic targets in prostate cancer. Similarly, GDI2 (OR: 0.85, 95% CI 0.80-0.91), ISLR2 (OR: 0.87, 95% CI 0.82-0.93), and CTSF (OR: 1.14, 95% CI 1.08-1.21) could potentially be targets for breast cancer. Additionally, we identified SFTPB (OR: 0.93, 95% CI 0.91-0.95), ICAM5 (OR: 0.95, 95% CI 0.93-0.97), and FLRT3 (OR: 1.10, 95% CI 1.05-1.15) as potential targets for lung cancer. Notably, TNFRSF10B, GSTP1, and PDIA3 were found to interact with the target proteins of current medications used in prostate or breast cancer treatment.ConclusionsThis comprehensive analysis has highlighted thirteen plasma proteins with potential roles in three site-specific cancers. Continued research in this area may reveal their therapeutic potential, particularly KDELC2, TNFRSF10B, CPNE1, and PDIA3, paving the way for more effective cancer treatments.
| S-EPMC10512580 | biostudies-literature