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Background: Previous research has demonstrated correlations between the complex types and functions of brain cells and the etiology of glioma. However, the causal relationship between gene expression regulation in specific brain cell types and glioma risk, along with its therapeutic implications, remains underexplored. Methods: Utilizing brain cell type‐specific cis‐expression quantitative trait loci (cis‐eQTLs) and glioma genome‐wide association study (GWAS) datasets in conjunction with Mendelian randomization (MR) and colocalization analyses, we conducted a systematic investigation to determine whether an association exists between the gene expression of specific brain cell types and the susceptibility to glioma, including its subtypes. Additionally, the potential pathogenicity was explored utilizing mediation and bioinformatics analyses. This exploration ultimately led to the identification of a series of brain cell‐specific therapeutic targets. Results: A total of 110 statistically significant and robust associations were identified through MR analysis, with most genes exhibiting causal effects exclusively in specific brain cell types or glioma subtypes. Bayesian colocalization analysis validated 36 associations involving 26 genes as potential brain cell‐specific therapeutic targets. Mediation analysis revealed genes indirectly influencing glioma risk via telomere length. Bioinformatics analysis highlighted the involvement of these genes in glioma pathogenesis pathways and supported their enrichment in specific brain cell types. Conclusions: This study, employing an integrated approach, demonstrated the genetic susceptibility between brain cell‐specific gene expression and the risk of glioma and its subtypes. Its findings offer novel insights into glioma etiology and underscore potential therapeutic targets specific to brain cell types. [ABSTRACT FROM AUTHOR]

Background: Blood and urine biomarkers have been associated with urologic tumors, but their causal relationship with urologic tumors is unclear. Methods: We performed a bidirectional Mendelian randomization (MR) analysis of the association between 35 blood and urine biomarkers and urological tumors in our discovery cohort. A Bayesian weighting approach was used to validate the positive results identified in the discovery cohort, and steiger filtering analysis was used to distinguish causality from reverse causality. Bayesian colocalization analysis was used to analyze which single nucleotide polymorphisms (SNPs) specifically co-located between the positive blood and urine biomarkers and the disease phenotypes were driven, and MR-positive results from the discovery cohort and the validation cohort were combined using the MR-meta method. Results: Several blood and urine biomarkers were found to be significantly and causally associated with urologic cancers. Notably, calcium (OR: 1.34, 95%CI 1.10–1.63, P = 0.0040) and sex hormone-binding globulin (SHBG) (OR: 0.81, 95%CI 0.70–0.95, P = 0.0092) were associated with bladder cancer; gamma glutamyl transferase (GGT) (OR: 0.91, 95%CI 0.83–0.99, P = 0.0209), lipoprotein A (Lp(a)) (OR: 1.12, 95%CI 1.01–1.24, P = 0.0399), and insulinlike growth factor 1 (IGF 1) (OR: 1.10, 95%CI 1.01–1.20, P = 0.0220) were linked to prostate cancer (PCa); non albumin protein (OR: 0.78, 95%CI 0.65–0.93, P = 0.0065) and total protein (OR: 0.80, 95%CI 0.64–0.99, P = 0.0380) were linked to renal cancer; and apolipoprotein A (Apo-A) (OR: 0.56, 95%CI 0.32–0.98, P = 0.0426) and urate (OR:1.89, 95%CI 1.03–3.47, P = 0.0399) were associated with renal pelvis cancer. These associations were validated in an independent cohort, with GGT, IGF 1, and Lp(a) being consistently linked to PCa. Conclusion: This study identified significant biomarkers associated with urological cancers in blood and urine. These include GGT, IGF 1, and Lp(a), which are strong biomarkers for PCa. In addition, the findings of this study provide evidence for a handful of risk and protective factors for the development of urologic cancers. [ABSTRACT FROM AUTHOR]

Abstract 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

Abstract Background Blood and urine biomarkers have been associated with urologic tumors, but their causal relationship with urologic tumors is unclear. Methods We performed a bidirectional Mendelian randomization (MR) analysis of the association between 35 blood and urine biomarkers and urological tumors in our discovery cohort. A Bayesian weighting approach was used to validate the positive results identified in the discovery cohort, and steiger filtering analysis was used to distinguish causality from reverse causality. Bayesian colocalization analysis was used to analyze which single nucleotide polymorphisms (SNPs) specifically co-located between the positive blood and urine biomarkers and the disease phenotypes were driven, and MR-positive results from the discovery cohort and the validation cohort were combined using the MR-meta method. Results Several blood and urine biomarkers were found to be significantly and causally associated with urologic cancers. Notably, calcium (OR: 1.34, 95%CI 1.10–1.63, P = 0.0040) and sex hormone-binding globulin (SHBG) (OR: 0.81, 95%CI 0.70–0.95, P = 0.0092) were associated with bladder cancer; gamma glutamyl transferase (GGT) (OR: 0.91, 95%CI 0.83–0.99, P = 0.0209), lipoprotein A (Lp(a)) (OR: 1.12, 95%CI 1.01–1.24, P = 0.0399), and insulinlike growth factor 1 (IGF 1) (OR: 1.10, 95%CI 1.01–1.20, P = 0.0220) were linked to prostate cancer (PCa); non albumin protein (OR: 0.78, 95%CI 0.65–0.93, P = 0.0065) and total protein (OR: 0.80, 95%CI 0.64–0.99, P = 0.0380) were linked to renal cancer; and apolipoprotein A (Apo-A) (OR: 0.56, 95%CI 0.32–0.98, P = 0.0426) and urate (OR:1.89, 95%CI 1.03–3.47, P = 0.0399) were associated with renal pelvis cancer. These associations were validated in an independent cohort, with GGT, IGF 1, and Lp(a) being consistently linked to PCa. Conclusion This study identified significant biomarkers associated with urological cancers in blood and urine. These include GGT, IGF 1, and Lp(a), which are strong biomarkers for PCa. In addition, the findings of this study provide evidence for a handful of risk and protective factors for the development of urologic cancers.

Abstract Numerous clinical studies speculated the association between multiple myeloma (MM) and inflammatory diseases; however, there is limited validation of these claims via establishing a causal relationship and revealing the underlying mechanism. This exploratory study employed bidirectional Mendelian randomization (MR) analysis to investigate the causal relationships between MM and inflammatory diseases, including atherosclerosis, asthma, ankylosing spondylitis, Alzheimer's disease (AD), Parkinson's disease (PD), sarcoidosis, inflammatory bowel disease, nonalcoholic fatty liver disease, type II diabetes, and schizophrenia (SZ). Transcriptomic and genome‐wide Bayesian colocalization analyses were further applied to reveal the underlying mechanism. A significant and previously unrecognized positive association was identified between genetic predisposition to MM and the risk of SZ. Two independent case reports showed that treatment‐resistant psychosis is due to underlying MM and is resolved by treating MM. From our MR analyses, various statistical methods confirmed this association without detecting heterogeneity or pleiotropy effects. Transcriptomic analysis revealed shared inflammation‐relevant pathways in MM and SZ patients, suggesting inflammation as a potential pathophysiological mediator of MM's causal effect on SZ. Bayesian colocalization analysis identified rs9273086, which maps to the protein‐coding region of HLA‐DRB1, as a common risk variant for both MM and SZ. Polymorphism of the HLA‐DRB1 allele has been implicated in AD and PD, further highlighting the impact of our results. Additionally, we confirmed that interleukin‐6 (IL‐6) is a risk factor for SZ through secondary MR, reinforcing the role of neuroinflammation in SZ etiology. Overall, our findings showed that genetic predisposition to MM, HLA‐DRB1 polymorphism, and enhanced IL‐6 signaling are associated with the increased risk of SZ, providing evidence for a causal role for neuroinflammation in SZ etiology.

Background: Genome-wide association studies (GWAS) have revealed numerous loci associated with multiple sclerosis (MS). However, the challenge lies in deciphering the mechanisms by which these loci influence the target traits. Here, we employed an integrative analytical pipeline to efficiently transform genetic associations to identify novel proteins for MS. Methods: We systematically integrated MS GWAS data (N = 115,803) with human plasma proteome data (N = 7213) and conducted proteome-wide association studies (PWAS) to identify MS-associated pathogenic proteins. Following this, we employed Mendelian randomization and Bayesian colocalization analyses to verify the causal relationship between these significant plasma proteins and MS. Lastly, we utilized the Drug-Gene Interaction Database (DGIdb) to identify potential drug targets for MS. Results: The PWAS identified 25 statistically significant cis-regulated plasma proteins associated with MS at a false discovery rate of P

Observational studies have suggested an association between inflammatory bowel disease [IBD] and psoriasis. However, the detailed genetic basis, causality, and direction of this association remain unclear. Bidirectional two-sample Mendelian Randomization [MR] analysis was conducted using summary statistics from published genome-wide association studies. Bayesian Colocalization and multivariable MR [MVMR] analyses were performed to identify candidate variants and risk genes involved in the shared genetic basis between IBD, psoriasis, and their subtypes. Genetically predicted IBD and Crohn's disease [CD] were associated with an increased risk of psoriasis, psoriasis vulgaris [PsV], and psoriatic arthritis [PsA] (IBD on psoriasis: pooled odds ratio [OR] 1.09, 95% confidence interval [CI] 1.04–1.14, p =.0001; CD on psoriasis: pooled OR 1.10, 95% CI 1.06–1.15, p <.0001) and vice versa (psoriasis on IBD: pooled OR 1.11, 95%CI 1.02–1.21), whereas CD only exhibited a unidirectional association with psoriasis. Colocalization analysis revealed eight candidate genetic variants and risk genes (including LINC00824, CDKAL1, IL10, IL23R, DNAJC27, LPP, RUNX3, and RGS14) associated with a shared genetic basis. Among these, IL23R, DNAJC27, LPP, and RGS14 were further validated by MVMR analysis. Our findings indicated bidirectional causal associations between IBD and psoriasis (including PsV and PsA), which were attributed primarily to CD rather than Ulcerative colitis [UC]. Furthermore, we identified several candidate variants and risk genes involved in the shared genetic basis of IBD and psoriasis. Acquiring a better understanding of the shared genetic architecture underlying IBD and psoriasis would help improve clinical strategies. [ABSTRACT FROM AUTHOR]

AbstractBackground Observational studies have suggested an association between inflammatory bowel disease [IBD] and psoriasis. However, the detailed genetic basis, causality, and direction of this association remain unclear.Methods Bidirectional two-sample Mendelian Randomization [MR] analysis was conducted using summary statistics from published genome-wide association studies. Bayesian Colocalization and multivariable MR [MVMR] analyses were performed to identify candidate variants and risk genes involved in the shared genetic basis between IBD, psoriasis, and their subtypes.Results Genetically predicted IBD and Crohn’s disease [CD] were associated with an increased risk of psoriasis, psoriasis vulgaris [PsV], and psoriatic arthritis [PsA] (IBD on psoriasis: pooled odds ratio [OR] 1.09, 95% confidence interval [CI] 1.04–1.14, p = .0001; CD on psoriasis: pooled OR 1.10, 95% CI 1.06–1.15, p

Background: Nasal polyps (NP) are benign inflammatory growths of nasal and paranasal sinus mucosa that can substantially impair patients' quality of life by various symptoms such as nasal obstruction, insomnia, and anosmia. NP often relapse even after surgical treatment, and the curative therapy would be challenging without understanding the underlying mechanisms. Genome wide association studies (GWASs) on NP have been conducted; however, few genes that are causally associated with NP have been identified. Methods: We aimed to prioritize NP associated genes for functional follow-up studies using the summary data-based Mendelian Randomization (SMR) and Bayesian colocalization (COLOC) methods to integrate the summary-level data of the GWAS on NP and the expression quantitative trait locus (eQTL) study in blood. We utilized the GWAS data including 5,554 NP cases and 258,553 controls with 34 genome-wide significant loci from the FinnGen consortium (data freeze 8) and the eQTL data from 31,684 participants of predominantly European ancestry from the eQTLGen consortium. Results: The SMR analysis identified several genes including TNFRSF18, CTSK, and IRF1 that were associated with NP due to not linkage but pleiotropy or causality. The COLOC analysis strongly suggested that these genes and the trait of NP were affected by shared causal variants, and thus were colocalized. An enrichment analysis by Metascape suggested that these genes might be involved in the biological process of cellular response to cytokine stimulus. Conclusion: We could prioritize several NP associated genes including TNFRSF18, CTSK, and IRF1 for follow-up functional studies in future to elucidate the underlying disease mechanisms. [ABSTRACT FROM AUTHOR]

How genome-wide associated loci confer risk for Parkinson's disease is unclear. We aim to reveal causal genes through effects on brain proteins to provide new pathogenesis insights for Parkinson's disease. Proteome-wide and transcriptome-wide associations were determined by functional summary-based imputation leveraging data from genome-wide association summary (56 306 Europeans, 1.4 million controls), brain proteomes (528 cases from 2 separate data sets), and transcriptome (452 cases), followed by Mendelian randomization, Bayesian colocalization, cell-type-specific and brain regional expression, and drug–gene interaction analyses. As a result, genetically regulated protein abundances of 11 genes were associated with Parkinson's disease. Five genes (CD38 , GPNMB , TMEM175 , RAB7L1 , and HIP1R) were colocalized. Four genes (GPNMB , SEC23IP , CD38 , and DGKQ) demonstrated Mendelian randomized correlations (p < 8.10 × 10−5). Higher GPNMB level (1.47, 1.28–1.68) and lower CD38 level (0.319, 0.24–0.43) were causally associated with higher risk of Parkinson's disease, consistent with transcriptomic evaluations. CD38 and GPNMB were preferentially enriched in astrocytes and oligodendrocyte precursor cells, respectively. And CD38 and GPNMB were suggested to be the targets of many oncological drugs from Drug–Gene Interaction database. In conclusion, utilizing multidimensional data, GPNMB and CD38 were prioritized as the causal genes of Parkinson's disease, crucial for mechanistic and therapeutic investigations. [ABSTRACT FROM AUTHOR]

Abstract Background Genome-wide association studies (GWAS) have revealed numerous loci associated with stroke. However, the underlying mechanisms at these loci in the pathogenesis of stroke and effective stroke drug targets are elusive. Therefore, we aimed to identify causal genes in the pathogenesis of stroke and its subtypes. Methods Utilizing multidimensional high-throughput data generated, we integrated proteome-wide association study (PWAS), transcriptome-wide association study (TWAS), Mendelian randomization (MR), and Bayesian colocalization analysis to prioritize genes that contribute to stroke and its subtypes risk via affecting their expression and protein abundance in brain and blood. Results Our integrative analysis revealed that ICA1L was associated with small-vessel stroke (SVS), according to robust evidence at both protein and transcriptional levels based on brain-derived data. We also identified NBEAL1 that was causally related to SVS via its cis-regulated brain expression level. In blood, we identified 5 genes (MMP12, SCARF1, ABO, F11, and CKAP2) that had causal relationships with stroke and stroke subtypes. Conclusions Together, via using an integrative analysis to deal with multidimensional data, we prioritized causal genes in the pathogenesis of SVS, which offered hints for future biological and therapeutic studies.

Identification of shared causal genes between dementia and its related clinical outcomes can help understand shared aetiology and multimorbidity surrounding dementia. We performed the HyPrColoc colocalization analysis to detect possible shared causal genes between dementia or Alzheimer's disease (AD) and 5 selected traits: stroke, diabetes, atherosclerosis, cholesterol level, and alcohol consumption within 601 dementia or AD associated genetic regions using summary results of the UK Biobank genome-wide association studies. Functional analysis was performed on the candidate causal genes to explore potential biological pathways. Rs150562240 in the LPIN3 gene was identified as a candidate shared causal variant across dementia, AD and atherosclerosis. Evidence for pairwise colocalization between dementia and stroke, dementia (or AD) and atherosclerosis, and dementia (or AD) and diabetes was found in 2, 6 and 2 genetic regions respectively. Colocalization signals between diabetes and the other 3 non-dementia/AD traits were detected in 5 regions. The colocalization evidence shown in our study suggested shared aetiology between dementia and related diseases such as stroke, atherosclerosis, and diabetes. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies (GWAS) have revealed numerous loci associated with stroke. However, the underlying mechanisms at these loci in the pathogenesis of stroke and effective stroke drug targets are elusive. Therefore, we aimed to identify causal genes in the pathogenesis of stroke and its subtypes.Methods: Utilizing multidimensional high-throughput data generated, we integrated proteome-wide association study (PWAS), transcriptome-wide association study (TWAS), Mendelian randomization (MR), and Bayesian colocalization analysis to prioritize genes that contribute to stroke and its subtypes risk via affecting their expression and protein abundance in brain and blood.Results: Our integrative analysis revealed that ICA1L was associated with small-vessel stroke (SVS), according to robust evidence at both protein and transcriptional levels based on brain-derived data. We also identified NBEAL1 that was causally related to SVS via its cis-regulated brain expression level. In blood, we identified 5 genes (MMP12, SCARF1, ABO, F11, and CKAP2) that had causal relationships with stroke and stroke subtypes.Conclusions: Together, via using an integrative analysis to deal with multidimensional data, we prioritized causal genes in the pathogenesis of SVS, which offered hints for future biological and therapeutic studies. [ABSTRACT FROM AUTHOR]

• MR analysis revealed 196 immune cell signatures were associated with types of OA. • Reverse MR showed the impact of OA on the levels of 140 immune cell signatures. • OA shares genetic variants with some immune cell characteristics. Previous investigations have explored the associations between immune cell signatures and osteoarthritis (OA); however, causality remains unclear. This study employs an integrated analysis, combining bidirectional Mendelian randomization (MR) and Bayesian colocalization (Coloc), to investigate causal relationships between 731 immune cells signatures and OA, identifying shared causal variants. Utilizing publicly available summary data, this study primarily employs inverse variance weighting (IVW). Supplementary methods include MR-Egger regression, weighted median, weight mode, and simple mode. Various sensitivity tests, including Cochran's Q test, MR pleiotropy Residual Sum and Outlier, and leave-one-out tests, were conducted to assess the robustness of the analysis results. Coloc was employed to identify shared causal genetic variants among potential associations. IVW analysis revealed 196 immune cell signatures potentially linked to OA across diverse subtypes. Reverse MR analyses indicated the causal impact of OA on the levels of 140 immune cell signatures, with subtype-specific variations. Notably, several specific associations, including CD64 on CD14-CD16 + monocyte for Hip OA (OR = 1.0593, 95 % CI: 1.0260–1.0938, P = 0.0004), HLA-DR on CD14 + CD16- monocyte (OR = 0.9664, 95 % CI: 0.9497–0.9834, P = 0.0001), HLA-DR on CD14 + monocyte (OR = 0.9680, 95 % CI: 0.9509–0.9853, P = 0.0003) in the Knee or Hip OA, PDL-1 on CD14-CD16 + monocyte by All OA (OR = 1.7091, 95 %CI:1.2494–2.3378, P = 0.0008), and herpesvirus entry mediator on effector memory CD4 + T cell by Spine OA (OR = 0.5200, 95 %CI:0.3577–0.7561, P = 0.0006) remained significant post-Bonferroni correction. Sensitivity tests validated the credibility of the IVW analysis. Additionally, Coloc revealed several potential associations among shared genetic variants, including rs115328872, rs1800973, and rs317667. Our findings provide evidence for the potential involvement of immune cell signatures in OA development, revealing avenues for early prevention and innovative therapeutic strategies. [ABSTRACT FROM AUTHOR]