Individuals Of European Descent Research Articles

Associations between common genetic variants and income provide insights about the socio-economic health gradient.

We conducted a genome-wide association study on income among individuals of European descent (N = 668,288) to investigate the relationship between socio-economic status and health disparities. We identified 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes (the Income Factor). Our polygenic index captures 1-5% of income variance, with only one fourth due to direct genetic effects. A phenome-wide association study using this index showed reduced risks for diseases including hypertension, obesity, type 2 diabetes, depression, asthma and back pain. The Income Factor had a substantial genetic correlation (0.92, s.e. = 0.006) with educational attainment. Accounting for the genetic overlap of educational attainment with income revealed that the remaining genetic signal was linked to better mental health but reduced physical health and increased risky behaviours such as drinking and smoking. These findings highlight the complex genetic influences on income and health.

Frailty and social contact with dementia risk: A prospective cohort study.

Frailty and social contact are significant factors influencing dementia risk. While previous studies have separately examined these factors, their combined impact on dementia remains underexplored. This study included 338,567 UK biobank participants from 2006 to 2010, with follow-up until December 2022. Additionally, 30,408 participants with brain magnetic resonance imaging data were analyzed for hippocampal volume. Cox proportional hazards regression and linear regression models were used to assess associations. The study followed 338,567 participants (mean [SD] age, 60.4 [5.2] years; 54.1 % men) for a median of 13.7 years, documenting 7362 cases of all-cause dementia. Both frailty and lower social contact independently increased the risk of all-cause dementia, Alzheimer's disease (AD), and vascular dementia (VaD). Compared to individuals with non-frailty and high social contact, those with lower social contact and higher frailty had a significantly increased risk of all-cause dementia, with the highest risk observed in individuals with frailty and low social contact (HR = 2.65, 95 % CI: 2.27-3.11). Similar patterns were found for AD and VaD. Furthermore, hippocampal volume was significantly reduced in individuals with frailty and low social contact (β = -0.24, 95 % CI: -0.43 to -0.06) compared to those with non-frailty and high social contact. The study predominantly included European descent individuals, with most frailty and social contact data based on baseline self-reports. The combination of frailty and low social contact is associated with the highest risk of dementia. These findings suggest that both physiological and social factors should be simultaneously considered in dementia prevention strategies.

Serum uric acid levels and intracerebral hemorrhage: A two-sample Mendelian randomization study.

Previous observational studies have generated controversy regarding the correlation between serum uric acid (UA) levels and intracerebral hemorrhage (ICH), with the causal relationship remaining uncertain. To assess the potential causal relationship between serum UA levels and ICH, two-sample Mendelian randomization analysis was applied. Single-nucleotide polymorphisms (SNPs) closely associated with serum UA were retrieved from the genome-wide association study (GWAS) database, including 580,505 individuals of European descent. A total of 27 and 251 SNPs were chosen as instrumental variables. Summary data for ICH included 1935 cases and 471,578 controls. Two-sample MR analyses, including inverse-variance weighted (IVW), MR-Egger, weighted median, and weighted mode methods, were employed to assess the potential causal relationship between serum UA levels and ICH, with odds ratios (ORs) as effect estimates. Heterogeneity was evaluated using Cochran's Q test, and sensitivity analyses were conducted using the leave-one-out method. The IVW analysis revealed that a 1 mg/dL increase in serum UA was associated with a 16.5 % higher risk of ICH (OR 1.165, 95 % CI 1.01-1.34, P = 0.034), while a 1 quantile increase in serum UA was associated with a 25.9 % higher risk (OR 1.259, 95 % CI 1.091-1.46, P = 0.002). Cochran's Q test showed no evidence of heterogeneity. No horizontal pleiotropy was detected. The sensitivity analysis using the leave-one-out method supported the robustness and reliability of our results. The study reveals that elevated serum UA levels are causally linked to ICH, suggesting the potential applicability of serum UA as a biomarker for the occurrence of ICH.

Genetic association and drug target exploration of inflammation-related proteins with risk of major depressive disorder

BackgroundIn numerous observational studies, circulating inflammation-related proteins have been linked with major depressive disorder (MDD), yet the precise causal direction of this relationship remains unclear. This study aims to investigate the potential causal link between inflammation-related proteins and the risk of developing MDD. MethodsWe utilized summary data from a genome-wide association study (GWAS) of 91 circulating inflammation-associated proteins in 14,824 individuals of European descent. Additionally, we incorporated findings from a substantial GWAS on MDD, which included 294,322 cases and 741,438 controls. Our analysis employed a two-sample bidirectional Mendelian randomization (MR) approach, with inverse variance weighting (IVW) as the primary method. We augmented this with two supplementary techniques (MR-Egger and weighted median approaches) to detect and address potential pleiotropy. Furthermore, to identify and evaluate possible drug targets, we conducted a thorough search within the Drug-Gene Interaction Database (DGIdb). ResultsAnalysis using MR unveiled significant and causative associations between genetically determined CASP-8 (odds ratio (OR): 0.97), CD40 (OR: 0.96), IL-18 (OR: 0.98), SLAMF1 (OR: 0.97), and uPA (OR: 0.98) with MDD. Conversely, reverse MR analysis indicated causal associations between MDD and CCL19 (OR: 1.15), HGF (OR: 1.15), IL-8 (OR: 1.10), IL-18 (OR: 1.11), IL20RA (OR: 1.12), TGFA (OR: 1.12) and TNFSF14 (OR: 1.16). Notably, a significant bidirectional causal link was observed between IL-18 and MDD. Gene-drug analysis identified CD40, HGF, IL-8, IL-18, SLAMF1, and TGFA as potential therapeutic targets. ConclusionsWe've pinpointed causal links between inflammation-related proteins and MDD, offering compelling and innovative evidence to enhance our understanding of the inflammatory mechanisms involved in MDD and to investigate potential targets for anti-MDD medications.

Genetic evidence of circulating metabolites linking to gastric cancers: a two-sample Mendelian randomization study

Observational studies have implicated that blood metabolites were correlated with gastric cancer. This study was designed to reveal the potential causal link between gastric cancer and the levels of circulating metabolites. Single-nucleotide polymorphisms associated with lipid traits in univariate analysis were obtained from the Genome Wide Association Study (GWAS) repositories, involving 8299 European individuals, respectively. Blood metabolites in multivariate analysis were obtained from the latest GWAS Catalog, covering 850 known and 241 unknown metabolites, plus 309 metabolite ratios. Data on gastric cancer were obtained from the FinnGen Consortium, including 279 444 individuals of European descent, 1037 cases of which were gastric cancer. Subsequently, MR analysis was performed using conventional methods, including inverse variance-weighted, weighted median, weighted-mode, simple-mode and MREgger regression to demonstrate the causal relationship between circulating metabolites and gastric cancer. Two-sample Mendelian randomization method showed that seven circulating metabolites (X-24588, SAH to ribothymidine ratio, histidine to asparagine ratio, 1-dihomo-linolenylglycerol, ribothymidine, 2-linoleoylglycerol levels, and 2ʹ-o-methyluridine) were causally associated with the risk of gastric cancer. After verification of MR results and meta-analysis, levels of histidine to asparagine ratio, SAH to ribothymidine ratio, and 2ʹ-o-methyluridine consistently achieved statistical significance. Furthermore, histidine metabolism was identified as the most significant metabolic pathway. It is indicated that three circulating metabolites (histidine to asparagine ratio, SAH to ribothymidine ratio, and 2ʹ-o-methyluridine) achieved statistical significance in the casual relationship with the occurrence of gastric cancer, indicating that histidine metabolism may serve as the crucial metabolic pathway and therapeutic targets for gastric cancer.

Liver enzyme and risk of vascular dementia: A univariable and multivariable Mendelian randomization of European descent

AbstractBackgroundObservational studies have indicated a link between liver enzymes and dementia, but the causal relationship remains uncertain. We conducted a two‐sample Mendelian randomization (MR) study to investigate potential causal links between liver function markers (alanine aminotransferase [ALT], aspartate aminotransferase [AST], alkaline phosphatase [ALP], and γ‐glutamyltransferase [GGT]) and various forms of dementia (all‐cause dementia, Alzheimer's disease [AD], vascular dementia [VaD], and frontotemporal dementia [FTD]).MethodsGenome‐wide association study (GWAS) data of liver enzyme levels with 517 single nucleotide polymorphisms from 315,572 individuals of European descent were considered as exposures. Additional GWAS data on dementia from the FinnGen consortium and the UK Biobank were used as outcomes. The causal relationship was evaluated using univariable MR (UVMR) and multivariable MR (MVMR) methods. UVMR approaches such as inverse variance weighting (IVW), MR‐Egger, weighted median, simple mode, and weighted mode were used, with IVW as primary. MVMR techniques, such as extended versions of IVW, MR‐Egger, and Q‐minimization methods, were used to assess causal effects. The robustness of the MR analysis findings was verified through heterogeneity, horizontal pleiotropy, and leave‐one‐out analyses.ResultsMVMR analysis demonstrated that a genetically determined one standard deviation rise in blood GGT levels was associated with an increased risk of VaD (IVW: odds ratio = 1.007, 95% confidence interval = 1.002–1.011, p = 0.010). These findings remained consistent after adjusting for confounding variables in MVMR analysis. Sensitivity analyses further supported the causal relationship. However, no significant links were observed between ALT, AST, ALP, and all‐cause dementia, VaD, AD, or FTD.ConclusionsOur study suggests clinical implications, demonstrating that high blood GGT concentrations are potential causal risk factors for VaD in European populations. Further research is needed to uncover the underlying biological mechanisms between GGT and VaD and validate the clinical relevance of early prevention and intervention strategies.

FC27 Cardiovascular genetic risk factors associated with increased psoriasis risk without a reciprocal effect

Abstract The epidemiological link between immune-mediated diseases (IMIDs) and cardiovascular disease has often been attributed to systemic inflammation. However, the direction of causality and the biological mechanisms linking cardiovascular disease with IMIDs are incompletely understood. Given the robust epidemiological association and the growing body of supportive mechanistic evidence, psoriasis is an exemplary IMID model for exploring this relationship. Mendelian randomization (MR), a powerful genetic tool to help distinguish causation from associations observed in epidemiological studies, was used to provide supportive evidence for causality between traits. The bidirectional relationships between genetic predictors of psoriasis and the two major forms of cardiovascular disease, coronary artery disease (CAD) and stroke were first assessed. The association of genetic predictors of cardiovascular disease with nine other IMIDs was then evaluated. The study conducted two-sample MR analyses using summary-level data from large-scale genome-wide association meta-analysis studies (GWAS) for each trait. The analysis focused on individuals of European descent from GWAS meta-analyses, involving CAD (181 249 cases; 1 165 690 controls), stroke (110 182 cases; 1 503 898 controls), psoriasis (36 466 cases; 458 078 controls), and nine other IMIDs. The primary outcomes were the associations of genetic predictors of CAD and stroke on the risk of psoriasis and nine other IMIDs, determined using Inverse-variance weighted (IVW) MR estimates. In contrast to previous assumptions, we found that genetic predictors of psoriasis had no association with CAD (P = 0.12) or stroke (P = 0.77). In the reverse direction, we found that genetic predictors of both CAD [MR estimate: odds ratio (OR), 1.07; 95% confidence interval, CI (1.04–1.10); P = 0.003] and stroke [IVW OR, 1.22; 95% CI (1.05–1.41), P = 0.01] had risk-increasing associations with psoriasis. Adjusting for stroke rendered the associations of genetically predicted CAD on psoriasis risk non-significant (and vice versa), suggesting that a shared effect underlying genetic risk of CAD and stroke associated with increased psoriasis risk. Adjusting for nine confounding factors, the effects remained statistically significant, supporting the independence of the observed cardiovascular MR effects from key confounding factors. No risk-increasing associations were observed for cardiovascular genetic risk factors with other common IMIDs including rheumatoid arthritis and inflammatory bowel disease. Findings from this study indicate that cardiovascular genetic risk factors associated with increased psoriasis risk with no reciprocal effect or association with other IMIDs. This supports the notion that pathways influencing cardiovascular disease risk also contribute to the causal biological pathways of psoriasis. Risk-increasing associations were not observed between cardiovascular disease and other common IMIDs, indicating that these associations may be specific to the psoriatic disease context. Elucidating mechanisms underpinning this MR effect could lead to novel therapeutic approaches in both cardiovascular disease and psoriasis.

Novel candidate plasma proteins for the pathogenesis and treatment of atopic dermatitis revealed by proteome-wide association study

Atopic dermatitis (AD) is an immune-related skin disease with a genetic background. Numerous loci have been identified associated with AD to better comprehend its complicated genetic mechanisms by genome‑wide association studies (GWASs). However, current studies reveal the underlying mechanisms of these loci in the pathogenesis of AD inadequately. Therefore, we integrated the GWAS statistics of AD with plasma proteins to explore candidate proteins correlated with the pathogenesis of AD based on protein-centered omics studies. Herein, we adopted the updated AD GWAS statistics (N = 864,982) and the dataset of plasma protein quantitative trait loci (pQTLs), comprising 1,348 proteins from individuals of European descent. We first conducted the AD-related proteome-wide association studies (PWASs) (N = 7,213) by integrating pQTLs with the AD GWAS statistics and identified twenty-six significant plasma proteins by PWAS (FDR < 0.05). Then, the potential causal proteins of AD were identified via Mendelian randomization (MR), and seventeen causal proteins of AD were discovered afterward. Following this, Bayesian colocalization analysis was then utilized to explore proteins sharing the same causal variants. Five causal proteins strongly associated with the pathogenesis of AD were eventually pinpointed. Finally, we discovered drugs that could be repurposed for AD with the plasma proteins that might contribute to the pathogenesis of AD in the Drug Gene Interaction Database.

TEMR: Trans-ethnic mendelian randomization method using large-scale GWAS summary datasets.

Available large-scale genome-wide association study (GWAS) summary datasets predominantly stem from European populations, whilesample sizes for other ethnicities, notably Central/South Asian, East Asian, African, Hispanic, etc., remain comparatively limited, resulting in low precision of causal effect estimations within these ethnicities when using Mendelian randomization (MR). In this paper, we propose a trans-ethnic MR method, TEMR, to improve the statistical power and estimation precision of MR in a target population that is underrepresented, using trans-ethnic large-scale GWAS summary datasets. TEMR incorporates trans-ethnic genetic correlation coefficients through a conditional likelihood-based inference framework, producing calibrated p values with substantially improved MR power. In the simulation study, compared with other existing MR methods, TEMR exhibited superior precision and statistical power in causal effect estimation within the target populations. Finally, we applied TEMR to infer causal relationships between concentrations of 16 blood biomarkers and the risk of developing five diseases (hypertension, ischemic stroke, type 2 diabetes, schizophrenia, and major depression disorder) in East Asian, African, and Hispanic/Latino populations, leveraging biobank-scale GWAS summary data obtained from individuals of European descent. We found that the causal biomarkers were mostly validated by previous MR methods, and we also discovered 17 causal relationships that were not identified using previously published MR methods.

The associations among platelet count, mean platelet volume, and erectile dysfunction: an observational and Mendelian randomization study.

While previous studies have explored the associations and causalities among platelet count (PC), mean platelet volume (MPV), and erectile dysfunction (ED), further investigations are needed to clarify these relationships using advanced methodologies and analyzing specific populations. To investigate the associations and causalities among PC, MPV, and ED using observational study and Mendelian randomization (MR) analysis. A total of 114 patients with ED and 158 healthy control participants underwent a fasting blood draw to test for PC and MPV along with a comprehensive laboratory examination. The International Erectile Function Index was used to diagnose ED. Genetic variants of ED were obtained from individuals of European ancestry including 6175 ED cases and 217 630 controls. PC and MPV values were obtained from the UK Biobank and Investigating the effect of varying the whole blood inter-donation interval (INTERVAL) studies, encompassing a cohort of 173 480 individuals of European descent. Inverse-variant weighted (IVW), weighted median (WM), and MR-Egger methods were employed in MR analysis to explore the causal effects between variables to assess the impact of PC and MPV on ED. Various sensitivity analyses were employed to ensure the reliability of the results. Both observational study results and MR results revealed that elevated PC levels were associated with a heightened risk of ED, whereas reductions in MPV were linked to a decreased risk. Logistic regression analysis indicated that an increased PC was associated with a greater risk of ED, with an odds ratio (OR) of 1.14 (95% CI: 1.08, 1.22; P = .005), whereas decreased MPV was linked to an increased risk for ED, with an OR of 0.65 (95% CI: 0.48, 0.88; P = .003). Our MR analysis also revealed that genetically predicted PC was associated with a 1.09-fold increased risk of ED (95% CI: 1.01, 1.18; P = .016). Conversely, genetically predicted MPV was linked to a 0.93-fold increased risk of ED (95% CI: 0.88, 0.99; P = .014). The absence of heterogeneity (P > .05) and pleiotropy (P > .05) was confirmed through Cochran's Q tests and MR-Egger regression. Exclusion of individual single-nucleotide polymorphisms (SNPs) did not alter the robustness of the results. In clinical work, it is an important guide for the prevention, diagnosis, and treatment of ED. Our study employed a combination of observational studies and MR studies to strengthen our evidence. The observational study's sample size was relatively small, and MR was limited to individuals of European ancestry. A high PC and a low MPV are associated with an increased risk of ED, highlighting the importance of addressing platelet parameters in ED management.

Causal association of serum vitamin D levels with urolithiasis: a bidirectional two-sample Mendelian randomization study.

In light of inconsistent evidence from previous observational studies regarding the correlation between serum vitamin D levels and urolithiasis, this study aimed to investigate the genome-wide causal association between genetically predicted serum 25(OH)D levels and urolithiasis using the Mendelian randomization (MR) approach. In this study, we utilized genome-wide association studies (GWAS) summary statistics from the UK Biobank and SUNLIGHT consortium for serum vitamin D levels, as well as urolithiasis data from FinnGen. We employed bidirectional two-sample MR analysis to evaluate potential causal relationships. The primary MR analysis relied on the inverse variance weighted (IVW) method, supplemented by MR-Egger, weighted median, and weighted mode approaches. Sensitivity analyses were conducted to ensure result robustness, including Cochran's Q test, MR-Egger intercept test, leave-one-out tests, and MR pleiotropy residual sum and outlier (MR-PRESSO) test. The MR analysis indicated no significant causal effects of serum 25(OH)D levels on urolithiasis [IVW method: (kidney and ureteral stones: OR = 1.134;95% CI, 0.953 to 1.350, p = 0.155; lower urinary tract stones: OR = 1.158; 95% CI, 0.806 to 1.666, p = 0.428)]. However, according to the IVW results, genetically predicted kidney and ureteral stones were associated with decreased serum 25(OH)D levels (beta = -0.025; 95% CI, -0.048 to -0.003; p = 0.028), while they did not indicate a causal effect of lower urinary tract stones on serum 25(OH)D levels (beta = -0.002; 95% CI, -0.013 to -0.008; p = 0.662). A sensitivity analysis suggested the robustness of these causal associations. Our MR study did not provide evidence supporting a causal association between serum 25(OH)D levels and urolithiasis among individuals of European descent. However, there might exist a negative causal association between kidney and ureteral stones and serum 25(OH)D levels.

Causality between autoimmune diseases and schizophrenia: a bidirectional Mendelian randomization study

BackgroundObservational studies have shown a link between autoimmune diseases and schizophrenia, with conflicting conclusions. Due to the existence of confounding factors, the causal link between autoimmune diseases and schizophrenia is still unknown.MethodWe conducted a comprehensive Mendelian randomization (MR) analysis of schizophrenia and ten common autoimmune diseases in individuals of European descent using genome-wide association studies (GWASs). To evaluate the relationships between autoimmune diseases and schizophrenia, inverse variance weighted, MR-RAPS, Bayesian weighted MR, constrained maximum likelihood, debiased IVW, MR-Egger, and weighted median were utilized. Several sensitivity analyses were performed to ensure the reliability of the study's results.ResultsOur findings reveal that genetically predicted ankylosing spondylitis is related to an increased risk of schizophrenia, whereas celiac disease, type 1 diabetes, and systemic lupus erythematosus are associated with a lower risk of schizophrenia. In the reverse MR analysis, our study indicated that genetically predicted schizophrenia is linked to higher risks of ankylosing spondylitis, Crohn's disease, ulcerative colitis, inflammatory bowel disease, and psoriasis. Neither multiple sclerosis nor rheumatoid arthritis have been linked to schizophrenia, and vice versa.ConclusionDespite contradicting some other observational reports, this study showed support for a causal link between autoimmune diseases and schizophrenia. To gain a better understanding of the mechanisms underlying the development of immune-mediated schizophrenia, additional research is required to identify potential mechanisms identified in observational studies.

Identifying the impact of ARHGAP and MAP gene families on autism spectrum disorders.

The rising incidence of Autism Spectrum Disorder (ASD) has become a major concern, affecting children's psychological well-being and placing a significant strain on healthcare systems. Despite its impact, the etiological mechanisms underpinning ASD remain elusive. This study leveraged dorsolateral prefrontal cortex gene data from 452 individuals of European descent, sourced from the CommonMindConsortium, and examined ASD-related gene expression data from the Gene Expression Omnibus (GEO) database (GSE18123), along with Genome-Wide Association Studies (GWAS) data from the Lundbeck Foundation Integrated Psychiatric Research and Psychiatric Genomics Consortium. Expression quantitative trait loci data were sourced from the GTExv8 database. We employed Transcriptome-Wide Association Studies (TWAS) and Weighted Gene Co-expression Network Analysis (WGCNA) to pinpoint genes within ASD-associated susceptibility gene families (ARHGAP, MAP). Four genes-ARHGAP27, MAPT, ARHGAP19, and MAP1B-were scrutinized, and their biological implications were elucidated through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein-Protein Interaction (PPI) analysis and conditional analysis within the TWAS framework helped identify pivotal genes (ARHGAP27, MAPT). A subsequent verification phase involving Mendelian Randomization (MR) evaluated the potential causal links between the identified genes and ASD. The findings revealed no causal association between ARHGAP19, MAP1B, and ASD. In contrast, significant causal relationships were established for ARHGAP27 and MAPT, suggesting that ARHGAP27 may elevate ASD risk as a susceptibility gene, whereas MAPT appears to reduce the risk as a protective gene.

Association of coffee consumption and caffeine metabolism with arrhythmias and cardiac morphology: an observational, genetic and Mendelian Randomization study

The association of coffee intake and caffeine metabolism with arrhythmias and cardiac structure is not fully understood. We aim to explore the associations of coffee intake and caffeine metabolism with the risk of certain types of arrhythmias, a broad range of cardiovascular imaging phenotypes, and a potential gene-coffee interaction. On the basis of the UK Biobank imaging study, heart metrics were extracted via cardiovascular magnetic resonance using machine learning. Caffeine metabolism was determined by cytochrome P4501A2 gene (CYP1A2) or a polygenetic score of different genetic variants influencing caffeine metabolism. Genome-wide association data for 2-sample Mendelian randomization originated predominantly from individuals of European descent. A total of 34,992 individuals (mean age 63.5 ± 7.5 years; 52.9% female) were assessed in the principal imaging analysis. Higher daily consumption of coffee was independently associated with higher left ventricular mass, larger global ventricular volume, higher ventricular stroke volume, and larger atrial volume. The results were further supported by Mendelian randomization analysis. CYP1A2 and genetic score for caffeine metabolism were not associated with cardiovascular measurements (P>.053 for all tests). Coffee consumption was associated with a lower risk of sinus rhythm with short PR (per cup increase: odds ratio 0.90; 95% confidence interval 0.82-0.99) and atrial fibrillation (odds ratio 0.89; 95% confidence interval 0.84-0.93). Coffee consumption was associated with a lower risk of atrial fibrillation, increased left ventricular mass, elevated ventricular volume, and larger atrial volume. Caffeine metabolism did not significantly alter the association. Our results suggest that customary caffeine consumption limitations aimed at arrhythmia risk reduction might be unnecessary.

Association between antithrombotic agents use and hepatocellular carcinoma risk: a two-sample mendelian randomization analysis

BackgroundHepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide. Multiple observational studies demonstrated a negative association between the use of antithrombotic agents and the risk of HCC. However, the precise causal relationship between these factors remains uncertain. Therefore, our study used a two-sample Mendelian randomization (MR) analysis to assess the causal link between these two factors.MethodThe summary statistics of single nucleotide polymorphisms (SNPs) associated with the use of antithrombotic agents were acquired from a genome-wide association study (GWAS) performed on individuals of European descent. A two-sample MR analysis was performed using the inverse variance weighting (IVW), the weighted median estimate, the MR-Egger regression, and the weighted-mode estimate. Sensitivity analysis of the primary findings was performed using MR-PRESSO, MR-Egger regression, Cochran’s Q test, and Leave-one-out analysis.ResultsTen SNPs associated with the use of antithrombotic agents were selected as instrumental variables. The MR analysis performed using the four methods mentioned above revealed a negative causal association between the use of antithrombotic agents and HCC. Univariate MR estimates based on the inverse variance weighting (IVW) method suggested a negative causal association between the use of antithrombotic agents and HCC [odds ratio (OR) 0.444, 95% confidence interval (CI) 0.279 to 0.707, P = 0.001]. The other methods also produced similar results. No heterogeneity and horizontal pleiotropy were found.ConclusionOur findings suggested an inverse causal association of antithrombotic agents with the risk of HCC.

Genetic Insights into Sepsis: Mendelian Randomization Analysis of Cerebrospinal Fluid Metabolites.

Sepsis, a life-threatening response to infection leading to systemic inflammation and organ dysfunction, has been hypothesized to be influenced by metabolic alterations in cerebrospinal fluid (CSF). Despite extensive research, the specific metabolic pathways contributing to sepsis remain unclear. This study aims to elucidate the causal relationships between CSF metabolites and sepsis risk using Mendelian Randomization (MR), offering insights that could lead to novel therapeutic strategies. We conducted a two-sample MR analysis using genetic variants as instrumental variables (IVs) to investigate 338 CSF metabolites identified through a genome-wide association study. Data on sepsis-related outcomes were extracted from the genome-wide association study (GWAS) catalog encompassing 486,484 individuals of European descent. IVs were rigorously selected based on stringent genetic association and linkage disequilibrium criteria. Statistical analyses, including inverse variance weighting (IVW) and weighted median methods, were performed using the 'TwoSampleMR' package in R software, supplemented by comprehensive sensitivity analyses to ensure the robustness of our findings. Our analysis identified 19 CSF metabolites causally associated with sepsis risk. Notably, metabolites such as 1-palmitoyl-2-stearoyl-gpc (16:0/18:0) and 2-hydroxyglutarate showed significant negative correlations with sepsis risk. The reverse MR analysis further revealed that sepsis could negatively impact certain CSF metabolite levels, particularly Ribonate, suggesting a bidirectional relationship. These relationships were substantiated by rigorous statistical testing and sensitivity analyses confirming the absence of horizontal pleiotropy and the stability of our results across various MR methods. This study demonstrates significant causal associations between specific CSF metabolites and the risk of developing sepsis, highlighting the potential for these metabolites to serve as biomarkers or therapeutic targets. The bidirectional nature of these findings also suggests that sepsis itself may alter metabolic profiles, offering further avenues for intervention.

Using genetics to explore complement C5 as a druggable protein in periodontitis.

An excessively activated or dysregulated complement system has been proven to be a vital contributor to the pathogenesis of periodontitis. It has been previously hypothesized that inhibiting the activity of complement component C5 by targeting the C5a receptor is a powerful candidate for treating periodontitis. Here, we apply the drug target instrumental variable (IV) approach to investigate the therapeutic effect of genetically proxied inhibition of C5 on periodontitis. In our primary analysis, we used 26 independent 'cis' single nucleotide polymorphisms as IVs from the vicinity of the encoding locus of C5 that are associated with plasma C5 levels. In a secondary analysis, we assess the validity of our primary findings, exploring the involvement of alternative downstream biomarkers, interleukin 17 (IL-17), interleukin 1β (IL-1β), and tumor necrosis factor (TNF). Summary statistics of plasma levels (C5, IL-17, IL-1β, and TNF) were obtained from a genome-wide association study (GWAS) of 35,559 European descent individuals. We extracted association statistics from a GWAS of 17,353 clinical periodontitis cases and 28,210 European controls. Wald ratios were combined using inverse-variance weighted meta-analysis. In our primary approach, inhibiting C5 reduced the risk of periodontitis (Odds ratio 0.89 per 1 standard deviation reduction in C5; 95% confidence Interval 0.80-0.98, p value=0.022). Our secondary analysis suggests an involvement of IL-17 within the potential causal pathway, but was inconclusive for other biomarkers. The findings from our study suggest that C5 inhibition may reduce the risk of periodontitis, prioritizing C5 inhibitors as a potential adjunctive therapeutic intervention in this disease.

Development of a novel prediction model for carriage of BRCA1/2 pathogenic variant in Japanese patients with breast cancer based on Japanese organization of hereditary breast and ovarian cancer registry data.

With the increasing demand for BRCA genetic testing, most existing prediction models were developed using data from individuals of European descent. This study aimed to identify clinicopathological factors of hereditary breast and ovarian cancer (HBOC) syndrome and develop the first Japanese-specific prediction model for BRCA pathogenic variant carriers in Japan. We utilized data from 3072 Japanese patients with breast cancer aggregated by the Japanese Organization of Hereditary Breast and Ovarian Cancer registry. Prediction models were developed using 70% of the overall dataset and validated using the remaining 30%. Factors associated with the BRCA pathogenic variant status were identified using logistic univariate analysis, and significant factors were further analyzed using logistic multivariate analysis to develop prediction models for BRCA1/2 (BRCA1 and/or BRCA2), BRCA1, and BRCA2 pathogenic variants. BRCA1 showed associations with aggressive clinicopathological factors such as triple-negative breast cancer and nuclear grade 3. Moreover, the prediction model showed a high area under the curve (AUC) of 0.879. By contrast, BRCA2 exhibited fewer characteristic associated factors, and the AUC of the model was 0.669. Common factors shared by BRCA1/2, BRCA1, and BRCA2 were the age at diagnosis of breast cancer and the youngest age of relatives with breast cancer. Consistent with previous research, early-onset breast cancer appeared to be strongly associated with HBOC. We successfully developed prediction models for BRCA1/2, BRCA1, and BRCA2 pathogenic variants. By accurately stratifying patients' risk and guiding targeted screening and preventative interventions, these models will contribute to improved management and outcomes of HBOC.

Genetic architecture of oral glucose-stimulated insulin release provides biological insights into type 2 diabetes aetiology

The genetics of β-cell function (BCF) offer valuable insights into the aetiology of type 2 diabetes (T2D)1,2. Previous studies have expanded the catalogue of BCF genetic associations through candidate gene studies3–7, large-scale genome-wide association studies (GWAS) of fasting BCF8,9 or functional islet studies on T2D risk variants10–14. Nonetheless, GWAS focused on BCF traits derived from oral glucose tolerance test (OGTT) data have been limited in sample size15,16 and have often overlooked the potential for related traits to capture distinct genetic features of insulin-producing β-cells17,18. We reasoned that investigating the genetic basis of multiple BCF estimates could provide a broader understanding of β-cell physiology. Here, we aggregate GWAS data of eight OGTT-based BCF traits from ~26,000 individuals of European descent, identifying 55 independent genetic associations at 44 loci. By examining the effects of BCF genetic signals on related phenotypes, we uncover diverse disease mechanisms whereby genetic regulation of BCF may influence T2D risk. Integrating BCF-GWAS data with pancreatic islet transcriptomic and epigenomic datasets reveals 92 candidate effector genes. Gene silencing in β-cell models highlights ACSL1 and FAM46C as key regulators of insulin secretion. Overall, our findings yield insights into the biology of insulin release and the molecular processes linking BCF to T2D risk, shedding light on the heterogeneity of T2D pathophysiology.

Abstract B012: Prioritization of ancestry-specific variations in disease data helps to alleviate bias in genomic data

Abstract 95% of sequencing data is from individuals of European descent. Underrepresentation of non- European individuals in genomic resources has limited our understanding of the effects of population-specific variants on complex traits and disease. Human populations have undergone selection for variants beneficial to survival in their environment, resulting in individuals with genetic variants unique to their ancestral populations. While these variants can impact disease occurrence and treatment outcome, genetic ancestry is often unaccounted for in disease modeling. Lack of ancestral population representation in data has led to disparate disease outcomes and treatment. While the primary proposed solution to this problem is more diverse sequencing of patients, these efforts will take years of effort and continual work to maintain the diversity balance. Additionally, they will not solve the issue; clinical trials cannot expect to capture human global diversity at every stage. Here we show we can mitigate data bias by utilizing readily available genomic population databases to target genes with ancestrally relevant variations. Our AI model, PhyloFrame, creates equitable gene signatures from transcriptomic input data. PhyloFrame corrects for ancestral bias in data by utilizing genomic population databases and functional interaction networks to identify genes both relevant to disease and associated with a variant unique to a single ancestry. PhyloFrame uses gnomAD data to quantify genome-wide ancestry-specific allele frequencies from a large cohort of healthy individuals, which PhyloFrame uses to create ancestry-agnostic signatures when modeling disease data. Our model demonstrates the utility of including readily available population-level data from healthy individuals in AI methods to overcome existing genomic data bias. PhyloFrame serves also as a demonstration of how equitable AI methods create gene signatures applicable across ancestries without requiring sample-specific ancestry information. Our model demonstrates how AI can be used to help mitigate ancestral bias in genomic data using preexisting data and contribute to equitable representation in medical research. Citation Format: Leslie A. Smith, Kiley Graim, James A. Cahill. Prioritization of ancestry-specific variations in disease data helps to alleviate bias in genomic data [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B012.