Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

The GWAS candidate far upstream element binding protein 3 (FUBP3) is required for normal skeletal growth, and adult bone mass and strength in mice

Bone mineral density (BMD) and height are highly heritable traits for which hundreds of genetic loci have been linked through genome wide association studies (GWAS). FUBP3 is a DNA and RNA binding protein best characterised as a transcriptional regulator of c-Myc, but little is known about its role in vivo. Single nucleotide polymorphisms in FUBP3 at the 9q34.11 locus have been associated with BMD, fracture and height in multiple GWAS, but FUBP3 has no previously established role in the skeleton. We analysed Fubp3-deficient mice to determine the consequence of FUBP3 deficiency in vivo. Mice lacking Fubp3 had reduced survival to adulthood and impaired skeletal growth. Bone mass was decreased, most strikingly in the vertebrae, with altered trabecular micro-architecture. Fubp3 deficient bones were also weak. These data provide the first functional demonstration that Fubp3 is required for normal skeletal growth and development and maintenance of adult bone structure and strength, indicating that FUBP3 contributes to the GWAS association of 9q34.11 with variation in height, BMD and fracture.

Separating the effects of childhood and adult obesity on depression, subjective well-being, and suicide attempt: a Mendelian randomization study

Observational studies have linked obesity, both in childhood and adulthood, with higher risks of depression, reduced subjective well-being (SWB), and suicide attempts (SA). However, the causality remains unclear. This study aimed to investigate the causal effects of childhood and adult obesity on depression, SWB, and SA. A bidirectional two-sample Mendelian randomization (MR) was performed using genome-wide association study (GWAS) data to examine the causal effects of body mass index (BMI) on depression, SWB, and SA. The inverse variance weighted method was used for primary analysis. Univariable and multivariable MR were employed to assess the total and independent effects of early life and adult body size. Cochran's Q test and MR-Egger intercept were applied to evaluate heterogeneity and pleiotropy. Genetically predicted BMI was significantly associated with an increased risk of major depressive disorder (MDD: OR = 1.13, 95%CI = 1.06-1.22, p = 6.1 × 10⁻⁴), SA-ISGC (OR = 1.17, 95%CI = 1.08-1.27, p = 1.9 × 10⁻⁴), and SA-iPSYCH (OR = 1.31, 95%CI = 1.12-1.54, p = 6.2 × 10⁻⁴). No significant causal effects of MDD, SWB, or SA on BMI were found. Early-life body size showed no direct effect on MDD or SA. However, adult body size was directly linked to increased risks of MDD (OR = 1.32, 95%CI = 1.13-1.55, p = 4.7 × 10⁻⁴), SA-ISGC (OR = 1.24, 95%CI = 1.03-1.47, p = 0.022), and SA-iPSYCH (OR = 1.80, 95%CI = 1.29-2.50, p = 5.6 × 10⁻⁴). This study provides robust evidence supporting a causal link between obesity and an increased risk of both depression and SA, with adult body size exerting a more direct impact on these outcomes than early-life body size.

Association Between Inflammatory Cytokines and Refractive Errors: A Bidirectional Mendelian Randomization Study

Purpose

This investigation aimed to elucidate the causal role of inflammatory cytokines in the risk of developing refractive errors.

Methods

Genetic variants previously associated with inflammatory cytokines served as instrumental variables in genome-wide association studies (GWASs) of European ancestry. Bidirectional two-sample Mendelian randomization (MR) analyses were conducted using summary data from GWAS meta-analyses. Rigorous sensitivity analyses were performed to validate the reliability of the MR results.

Results

We found that, for every unit increase in interleukin 1 receptor antagonist (IL1RA) and interleukin 2 (IL2), there was a corresponding decrease in the prevalence of myopic refractive errors by 0.235 (95% confidence interval [CI], 0.050-0.419 for fixed effects; 95% CI, 0.125-0.345 for random effects) and 0.132 (95% CI, 0.032-0.231 for fixed effects; 95% CI, 0.044-0.220 for random effects), respectively. No substantial causal associations were observed for IL1α, IL1β, IL12p70, or monocyte chemoattractant protein 1 (MCP1) with refractive errors. Conversely, reverse MR analyses failed to indicate a causal influence of refractive errors on IL1RA and IL2.

Conclusions

The present study offers evidence for a causal link between inflammatory cytokines and refractive errors, which could have significant implications for the early detection, surveillance, and management of refractive errors.

Translational Relevance

Our study underscores the importance of IL1RA and IL2 in the prevention and management of refractive errors, suggesting the feasibility of strategies for early identification, continuous surveillance, and the deployment of focused therapeutic approaches.

Trans-ancestry transcriptome-wide association and functional studies to uncover novel susceptibility genes and therapeutic targets for colorectal cancer

By integrating findings from large-scale omics analyses with experimental tests, this study aims to decipher susceptibility genes and the underlying biological mechanisms involved in the development of colorectal cancer (CRC). We first conducted a trans-ancestry transcriptome-wide association study (TWAS) among 57,402 CRC cases and 119,110 controls, aiming to examine how altered gene expression influences CRC risk in European and Asian populations. Then, functional experiments in (i) CRC cell lines and (ii) tumor xenografts were conducted to examine potential underlying mechanisms involved in colorectal carcinogenesis. Further, a drug sensitivity test was employed to explore possible clinical implications for CRC treatment. The TWAS identified 67 genes highly associated with CRC risk, 23 of which were novel findings. Functional annotation of variants within TWAS-identified loci revealed that the majority (93.6%) showed evidence of transcriptional regulatory mechanisms via proximal promoter or distal enhancer-promoter interactions. Among the identified susceptibility genes, splicing factor 3a subunit 3 (SF3A3) may act as an oncogene on the basis that overexpression of this gene was significantly associated with increased risk of CRC (P = 5.75 × 10-11). Further cell and animal experiments confirmed that SF3A3 plays an oncogenic role in CRC development, and the underlying biological mechanism is likely to be related to its anti-apoptosis effect. The drug sensitivity test suggested that phenethyl isothiocyanate (PEITC) targeting SF3A3 can inhibit CRC progression. This study identified novel CRC susceptibility genes and potential biological mechanisms of SF3A3 involved in CRC development, providing important insight into the etiology and potential leads to the treatment of CRC.

Glutathione-specific gamma-glutamylcyclotransferase 1 (CHAC1) increases kidney disease risk by modulating ferroptosis

Genome-wide association studies (GWASs) have identified more than 1000 loci where genetic variants correlate with kidney function. However, the specific genes, cell types, and mechanisms influenced by these genetic variants remain largely uncharted. Here, we identified glutathione-specific gamma-glutamylcyclotransferase 1 (CHAC1) on chromosome 15 as affected by GWAS variants by analyzing human kidney gene expression and methylation information. Both CHAC1 RNA and protein were expressed in the loop of Henle region in mouse and human kidneys, and CHAC1 expression was higher in patients carrying disease risk variants. Using CRISPR technology, we created mice with a single functional copy of the Chac1 gene (Chac1+/-) that displayed no baseline phenotypic alterations in kidney structure or function. These mice demonstrated resilience to kidney disease in multiple models, including folic acid-induced nephropathy, adenine-induced chronic kidney disease, and uninephrectomy-streptozotocin-induced diabetic nephropathy. We further showed that CHAC1 plays a critical role in degrading the cellular antioxidant glutathione. Tubule cells isolated from Chac1+/- mice showed increased glutathione, decreased lipid peroxidation, improved cell viability, and protection against ferroptosis. Expression of ferroptosis-associated genes was also lower in mice with only one copy of Chac1. Higher CHAC1 protein also correlated with ferroptosis-related protein abundance in kidney biopsies from patients with kidney disease. This study positions CHAC1 as an important mediator of kidney disease that influences glutathione concentrations and ferroptosis, suggesting potential avenues to explore for the treatment of kidney diseases.

HORNET: tools to find genes with causal evidence and their regulatory networks using eQTLs

Motivation

Nearly two decades of genome-wide association studies (GWAS) have identify thousands of disease-associated genetic variants, but very few genes with evidence of causality. Recent methodological advances demonstrate that Mendelian randomization (MR) using expression quantitative loci (eQTLs) as instrumental variables can detect potential causal genes. However, existing MR approaches are not well suited to handle the complexity of eQTL GWAS data structure and so they are subject to bias, inflation, and incorrect inference.

Results

We present a whole-genome regulatory network analysis tool (HORNET), which is a comprehensive set of statistical and computational tools to perform genome-wide searches for causal genes using summary level GWAS data, i.e. robust to biases from multiple sources. Applying HORNET to schizophrenia, eQTL effects in the cerebellum were spread throughout the genome, and in the cortex were more localized to select loci.

Availability and implementation

Freely available at https://github.com/noahlorinczcomi/HORNET or Mac, Windows, and Linux users.

Novel insight of critical genes involved in breast cancer brain metastasis: evidence from a cross-tissue transcriptome association study and validation through external clinical cohorts

BACKGROUND

Breast cancer represents the most prevalent form of tumors among females and is characterized by a significant genetic component. The brain is a frequent site of metastasis for breast cancer. Although numerous loci associated with breast cancer brain metastasis (BCBM) have been identified, the critical regulatory genes underlying BCBM remain largely unclear.

METHODS

The FinnGen R11 dataset was combined with Genotype-Tissue Expression Project (GTEx) for Transcriptome-wide Association Study (TWAS). The Unified Test for Molecular Signatures (UTMOST), Multimarker Analysis of Genomic Annotation (MAGMA), and Functional Summary-based Imputation (FUSION) were used to identify candidate genes. Summary-data-based mendelian randomization (SMR) and co-localization were performed further to elucidate the association between key genes and BCBM. Finally, multiple external cohorts were obtained to validate the findings.

RESULT

In our study, 12 new genes associated with breast cancer were identified with TWAS. Subsequently, both SMR and co-localization have shown that CAPS8 was only expressed in brain tissues including frontal cortex and cerebellar hemispheres associated with breast cancer. Potential regulation of CASP8 could occur in BCBM. Finally, the findings were ultimately validated by external clinical cohorts.

CONCLUSION

Our study identified key gene CASP8, which was associated with BCBM, providing new insights into the occurrence of BCBM.

Multi-omics analysis identifies diagnostic circulating biomarkers and potential therapeutic targets, revealing IQGAP1 as an oncogene in gastric cancer

This study employed a multi-omics integration approach to identify circulating biomarkers for gastric cancer (GC). We analyzed plasma and tumor tissue single-cell RNA sequencing data, along with gene and protein quantitative trait loci analyses. Leveraging data from UK Biobank and FinnGen, we investigated genetic associations with GC. Through colocalization, Mendelian Randomization, and various filtering analyses, we identified four genes (IQGAP1, KRTCAP2, PARP1, MLF2) and four proteins (EGFL9 [DLK2], ECM1, PDIA5, TIMP4) as potential GC biomarkers. These were selected based on significant genetic colocation probabilities and significant associations with GC. Seven of these biomarkers demonstrated predictive capability for GC occurrence, with AUC ranging from 0.61 to 0.99. Drug prediction analysis identified seven protein biomarkers as potential targets for immunotherapy, targeted therapies, and tumor chemotherapy. Further scRNA-seq analysis revealed significant expression differences between gastric tumor and normal tissues, particularly the upregulation of IQGAP1, which highlights its role in tumor growth.

Causal Links Between Corneal Biomechanics and Myopia: Evidence from Bidirectional Mendelian Randomization in the UK Biobank

BACKGROUND

Myopia is a leading cause of visual impairment worldwide, and accumulating evidence suggests that biomechanics may be closely linked to its development. Understanding this relationship may help clarify the underlying mechanisms of myopia and guide treatment strategies. The aim of the study is to investigate the causal relationship between myopia and corneal biomechanics using the UK Biobank (UKB) database.

METHODS

Data from 11,064 eyes in the UKB, including refraction results and Ocular Response Analyzer (ORA) measurements, were analyzed. Eyes were categorized by spherical equivalent (SE) into emmetropia, mild myopia, moderate myopia, and high myopia. One-way ANOVA assessed differences in corneal biomechanical parameters across the varying myopia groups, while Quantile Regression (QR) explored the relationship between these parameters and myopia severity across the different quantiles. A Mendelian randomization (MR) analysis was employed to explore the causal relationships.

RESULTS

Significant differences in corneal biomechanical parameters and intraocular pressure (IOP) were observed across the myopia levels (p < 0.001). High myopia was associated with lower corneal hysteresis (CH), a lower corneal resistance factor (CRF), and increased IOP. The QR analysis demonstrated that lower corneal biomechanics were associated with higher degrees of myopia, with the impact of corneal biomechanics becoming more pronounced as the myopia severity increased. The MR analysis indicated that low CH (OR = 0.9943, p = 0.004) and CRF (OR = 0.9946, p = 0.002) values were risk factors for myopia, while no causal effect was found when the myopia was treated as the exposure and corneal biomechanics as the outcome.

CONCLUSIONS

This study establishes a causal relationship where reduced corneal biomechanics contribute to myopia, while myopia itself does not directly affect biomechanics. Corneal biomechanics could serve as a biomarker for assessing high myopia risk. These findings offer new insights into high myopia's pathological mechanisms and targeted prevention.

Investigating the causal impact of gut microbiota on trigeminal neuralgia: a bidirectional Mendelian randomization study

Background

The etiology and pathogenesis of trigeminal neuralgia remain unclear. This study examines the connection between gut microbiota and trigeminal neuralgia using Mendelian randomization analysis to provide insights into the disorder's origin and propose potential therapies based on our findings.

Methods

We used data from the MiBioGen consortium (13,266 participants) for gut microbiota and the IEU OpenGWAS project (800 cases, 195,047 controls) for trigeminal neuralgia. We checked for heterogeneity and horizontal pleiotropy and used the inverse variance weighting method as our main approach to study the causal link between gut bacteria and trigeminal neuralgia, MR-Egger, simple mode, weighted median, and weighted mode as supplementary methods, with a sensitivity test using leave-one-out analysis. If a bacteria-trigeminal neuralgia link was found, we conducted a reverse analysis for confirmation.

Results

According to the final results, these groups include Butyricimonas (Genus, id = 945, p-value = 0.007, OR = 1.742, 95% CI: 1.165-2.604), unknowngenus (Genus, id = 1000005479, p-value = 0.005, OR = 1.774, 95% CI: 1.187-2.651) and Bacteroidales (Family, p-value = 0.005, OR = 1.774, 95% CI: 1.187-2.651) were causally associated with trigeminal neuralgia. No significant results according to reverse Mendelian randomization analysis.

Conclusion

In our study, we identified specific gut bacteria linked to trigeminal neuralgia. To comprehensively understand their impact and mechanisms, additional randomized trials are necessary.

Beyond guilty by association at scale: searching for causal variants on the basis of genome-wide summary statistics

Understanding the causal genetic architecture of complex phenotypes will fuel future research into disease mechanisms and potential therapies. Here, we illustrate the power of a novel framework: it detects, starting from summary statistics, and across the entire genome, sets of variants that carry non-redundant information on the phenotypes and are therefore more likely to be causal in a biological sense. The approach, implemented in open-source software, is also computationally efficient, requiring less than 15 minutes on a single CPU to perform genome-wide analysis. Through extensive genome-wide simulation studies, we show that the method can substantially outperform existing methods in false discovery rate control, statistical power and various fine-mapping criteria. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD), we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline. Massively parallel reporter assays and CRISPR-Cas9 experiments have confirmed the functionality of the putative causal variants our method points to. Finally, we retrospectively analyzed summary statistics from 67 large-scale GWAS for a variety of phenotypes. Results reveal the method's capacity to robustly discover additional loci for polygenic traits and pinpoint potential causal variants underpinning each locus beyond conventional GWAS pipeline, contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses.

Genetic evidence for amlodipine's protective role in gastroesophageal reflux disease: A focus on CACNB2

OBJECTIVE

This study aims to elucidate the causal relationship between genetically predicted amlodipine use and the risk of gastroesophageal reflux disease (GERD) using a bidirectional Mendelian Randomization (MR) approach and to explore the underlying genetic and molecular mechanisms through functional enrichment analysis and the construction of a competing endogenous RNA (ceRNA) network.

METHODS

Publicly available GWAS datasets from the Neale Lab consortium were used, including data on amlodipine (13,693 cases, 323,466 controls) and GERD (14,316 cases, 322,843 controls). Genome-wide significant SNPs were selected as instrumental variables and clustered by linkage disequilibrium. MR analysis was conducted using R software with all five methods. Sensitivity analyses assessed pleiotropy and heterogeneity. Drug target genes were analyzed using GO and KEGG pathways. GeneMANIA was used for network visualization, and a ceRNA network was constructed with Cytoscape. Differential gene expression analysis on GERD-related datasets from GEO validated the findings.

RESULTS

The MR analysis indicated a significant negative association between genetically predicted amlodipine use and GERD risk (IVW OR = 0.872, 95% CI = 0.812-0.937, P = 0.0002). Sensitivity analyses confirmed the robustness of these findings, showing no evidence of pleiotropy or heterogeneity. The enrichment analysis identified key biological processes and pathways involving calcium ion transport and signaling. The ceRNA network highlighted core targets such as CACNB2, which were further validated by differential expression analysis intersecting drug target genes with GERD-related gene expression changes.

CONCLUSION

This study provides robust evidence of a protective effect of amlodipine against GERD, supported by genetic and molecular analyses. The findings suggest that calcium channel blockers like amlodipine could be repurposed for GERD treatment. The identification of CACNB2 and other core targets in the ceRNA network offers novel insights into the pathophysiology of GERD and potential therapeutic targets, paving the way for personalized medicine approaches to improve patient outcomes.

Leveraging pleiotropy for the improved treatment of psychiatric disorders

Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and one billion US dollars to develop a single successful drug. Drug development is more challenging for psychiatric disorders, where disease comorbidity and complex symptom profiles obscure the identification of causal mechanisms for therapeutic intervention. One promising approach for determining more suitable drug candidates in clinical trials is integrating human genetic data into the selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. In this article, we argue that leveraging pleiotropic effects will provide opportunities to discover novel drug targets and identify more effective treatments for psychiatric disorders by targeting a common mechanism rather than treating each disease separately.

A Linear Mixed Model with Measurement Error Correction (LMM-MEC): A Method for Summary-data-based Multivariable Mendelian Randomization

Summary-data-based multivariable Mendelian randomization (MVMR) methods, such as MVMR-Egger, MVMR-IVW, MVMR median-based, and MVMR-PRESSO, assess the causal effects of multiple risk factors on disease. However, accounting for variances in summary statistics related to risk factors remains a challenge. We propose a linear mixed model with measurement error correction (LMM-MEC) that accounts for the variance of summary statistics for both disease outcomes and risk factors. In step I, a linear mixed model is applied to account for the variance in disease summary statistics. Specifically, if heterogeneity is present in disease summary statistics, we treat it as a random effect and adopt an iteratively re-weighted least squares algorithm to estimate causal effects. In step II, we treat the variance in the summary statistics of risk factors as multiple measurement errors and apply a regression calibration method for simultaneous multiple measurement error correction. In a simulation study, when using independent genetic variants as instrumental variables (IV), our method showed comparable performance to existing MVMR methods under conditions of no pleiotropy or balanced pleiotropy with the outcome, and it exhibited higher coverage rates and power under directional pleiotropy. Similar findings were observed when using genetic variants with low to moderate linkage disequilibrium (LD) (0 < ρ 2 ≤ 0.3) as IVs, although coverage rates reduced for all methods compared to using independent genetic variants as IVs. In the application study, we examined causal associations between correlated cholesterol biomarkers and longevity. By including 739 genetic variants selected based on P values <5×10 -5 from GWAS and allowing for low LD ( ρ 2 ≤ 0.1), our method identified that large LDL-c were causally associated with lower likelihood of achieving longevity.

Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy

BACKGROUND

Gene signatures derived from transcriptomic-causal networks offer potential for tailoring clinical care in cancer treatment by identifying predictive and prognostic biomarkers. This study aimed to uncover such signatures in metastatic colorectal cancer (CRC) patients to aid treatment decisions.

METHODS

We constructed transcriptomic-causal networks and integrated gene interconnectivity into overall survival (OS) analysis to control for confounding genes. This integrative approach involved germline genotype and tumor RNA-seq data from 1165 metastatic CRC patients. The patients were enrolled in a randomized clinical trial receiving either cetuximab or bevacizumab in combination with chemotherapy. An external cohort of paired CRC normal and tumor samples, along with protein-protein interaction databases, was used for replication.

RESULTS

We identify promising predictive and prognostic gene signatures from pre-treatment gene expression profiles. Our study discerns sets of genes, each forming a signature that collectively contribute to define patient subgroups with different prognosis and response to the therapies. Using an external cohort, we show that the genes influencing OS within the signatures, such as FANCI and PRC1, are upregulated in CRC tumor vs. normal tissue. These signatures are highly associated with immune features, including macrophages, cytotoxicity, and wound healing. Furthermore, the corresponding proteins encoded by the genes within the signatures interact with each other and are functionally related.

CONCLUSIONS

This study underscores the utility of gene signatures derived from transcriptomic-causal networks in patient stratification for effective therapies. The interpretability of the findings, supported by replication, highlights the potential of these signatures to identify patients likely to benefit from cetuximab or bevacizumab.

Decoding the Therapeutic Target SVEP1: Harnessing Molecular Trait GWASs to Unravel Mechanisms of Human Disease

Although human genetics has substantial potential to illuminate novel disease pathways and facilitate drug development, identifying causal variants and deciphering their mechanisms remain challenging. We believe these challenges can be addressed, in part, by creatively repurposing the results of molecular trait genome-wide association studies (GWASs). In this review, we introduce techniques related to molecular GWASs and unconventionally apply them to understanding SVEP1, a human coronary artery disease risk locus. Our analyses highlight SVEP1's causal link to cardiometabolic disease and glaucoma, as well as the surprising discovery of SVEP1 as the first known physiologic ligand for PEAR1, a critical receptor governing platelet reactivity. We further employ these techniques to dissect the interactions between SVEP1, PEAR1, and the Ang/Tie pathway, with therapeutic implications for a constellation of diseases. This review underscores the potential of molecular GWASs to guide drug discovery and unravel the complexities of human health and disease by demonstrating an integrative approach that grounds mechanistic research in human biology.

Integrated analyses of Mendelian randomization, eQTL, and single-cell transcriptome identify CCN3 as a potential biomarker in aortic dissection

Plasma secretory proteins are associated with various diseases, including aortic dissection (AD). However, current research on the correlation between AD and plasma protein levels is scarce or lacks specificity. This study aimed to explore plasma secretory proteins as potential biomarkers for AD. Through genome-wide association studies, expression quantitative trait locus (eQTL) analysis, and human plasma protein profiling, we identified DBNL, NPC2, SUMF2, and TFPI as high-risk genes and CCN3, PRKCSH, TEX264, and TGFBR3 as low-risk genes for AD. Further cell localization and differential expression analysis of these eight genes were conducted using single-cell data. We also examined their expression in three Gene Expression Omnibus datasets, measured their mRNA levels in AD versus normal tissues using qPCR, and assessed their protein levels in patients' blood versus healthy individuals using enzyme-linked immunosorbent assay. Our findings suggest that CCN3, consistently downregulated in both mRNA and plasma levels during AD, may have a protective role. Initial enrichment analyses of differentially expressed CCN3 cells suggested their involvement in focal adhesion, actin cytoskeleton regulation, and the PI3K-Akt signaling pathway.

Lipid Profiles, Telomere Length, and the Risk of Malignant Tumors: A Mendelian Randomization and Mediation Analysis

Background/Objectives: The relationship between lipid profiles, telomere length (TL), and cancer risk remains unclear. Methods: This study employed two-sample Mendelian randomization (MR) with mediation analysis to investigate their causal relationships, examining lipid profiles as exposure, TL as mediator, and nine cancer types as outcomes. We conducted our analysis using two-stage least squares (2SLS) regression integrated with inverse variance weighted (IVW) methods to address potential endogeneity and strengthen our causal inference. Results: we found that unfavorable lipid profiles were causally linked to increased TL (p < 0.05). TL showed positive causal associations with lung and hematologic cancers (OR > 1, p < 0.05). Direct associations were observed between total and low-density lipoprotein (LDL) cholesterol and gastric cancer (OR < 1, p < 0.05), and between remnant cholesterol and colorectal cancer (OR > 1, p < 0.05). Mediation analysis revealed TL as a significant mediator in the pathway from lipid profiles to cancer development (p < 0.05). No horizontal pleiotropy was detected. Conclusions: Our findings suggest that lipid metabolism disorders may influence cancer development through telomere regulation, particularly in lung and hematologic cancers. This emphasizes the importance of lipid management in cancer prevention and treatment, especially for these cancer types.

Association between hypothyroidism and obstructive sleep apnea: a bidirectional Mendelian randomization study combined with the geo database

Background

The causal relationship between hypothyroidism and obstructive sleep apnea (OSA) remains controversial. Therefore, our research used a bidirectional Mendelian randomization (MR) method in an attempt to determine the causal relationship between hypothyroidism and OSA.

Methods

From the publicly accessible genome-wide association analysis (GWAS) summary database, we obtained single nucleotide polymorphism (SNPs) data pertaining to hypothyroidism and OSA. Inverse variance weighting (IVW) was the principal method of analysis utilized, with validation also conducted via weighted median, MR-Egger, simple model, and weighted model approaches. To further evaluate the robustness of the results, heterogeneity testing, pleiotropy testing, and the "leave-one-out" sensitivity analysis were performed. Differentially expressed genes (DEGs) from the OSA dataset (GSE135917) and hypothyroidism dataset (GSE176153) derived from the Gene Expression Omnibus (GEO) database were screened using the "limma" package. The "clusterProfiler" and "GO plot" packages were used for further enrichment analysis in order to validate the findings of the MR study. The Cytoscape software was utilized to build a protein-protein interaction (PPI) network of DEGs and to screen for hub genes.

Results

The MR analysis showed that genetically predicted hypothyroidism was associated with an increased risk of OSA [IVW odds ratio (OR) = 1.734; 95% confidence interval (CI) = 1.073-2.801; p = 0.025]. The trend of the outcomes of the other approaches is consistent with the trend of the IVW outcome. However, the reverse MR analysis suggested no evidence for the causal effect of OSA on hypothyroidism (IVW OR = 1.002, 95% CI: 0.996-1.009, p = 0.454). The robustness of the results was confirmed by the sensitivity analysis. Bioinformatics analysis revealed that there were DEGs that hypothyroidism and OSA have in common.

Conclusion

Our findings suggested that hypothyroidism may increase the risk of OSA, while the effect of OSA on hypothyroidism was not found in this MR study. Thus, patients with hypothyroidism should be enhanced with screening for OSA for early diagnosis and appropriate treatment.

Multiome-wide Association Studies: Novel Approaches for Understanding Diseases

The rapid development of multiome (transcriptome, proteome, cistrome, imaging, and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases. Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective. This review provides a detailed categorization and summary of the statistical models, use cases, and advantages of recent multiome-wide association studies. In addition, to illustrate gene-disease association studies based on transcriptome-wide association study (TWAS), we collected 478 disease entries across 22 categories from 235 manually reviewed publications. Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS, indicating its potential to deepen our understanding of the genetic architecture of complex diseases. In summary, this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.

Long Noncoding RNA TRIBAL Links the 8q24.13 Locus to Hepatic Lipid Metabolism and Coronary Artery Disease

BACKGROUND

Genome-wide association studies identified a 20-Kb region of chromosome 8 (8q24.13) associated with plasma lipids, hepatic steatosis, and risk for coronary artery disease. The region is proximal to TRIB1, and given its well-established role in lipid regulation in animal models, TRIB1 has been proposed to mediate the contribution of the 8q24.13 locus to these traits. This region overlaps a gene encoding the primate-specific long noncoding RNA transcript TRIBAL/TRIB1AL (TRIB1-associated locus), but the contribution of TRIBAL to coronary artery disease risk remains untested.

METHODS

Using recently available expression quantitative trait loci data and hepatocyte models, we further investigated this locus by Mendelian randomization analysis. Following antisense oligonucleotide targeting of TRIBAL, transcription array, quantitative reverse transcription polymerase chain reaction, and enrichment analyses were performed and effects on apoB and triglyceride secretion were determined.

RESULTS

Mendelian randomization analysis supports a causal relationship between genetically determined hepatic TRIBAL expression and markers of hepatic steatosis and coronary artery disease risk. By contrast, expression data sets did not support expression quantitative trait loci relationships between coronary artery disease-associated variants and TRIB1. TRIBAL suppression reduced the expression of key regulators of triglyceride metabolism and bile acid synthesis. Enrichment analyses identified patterns consistent with impaired metabolic functions, including reduced triglyceride and cholesterol handling ability. Furthermore, TRIBAL suppression was associated with reduced hepatocyte secretion of triglycerides.

CONCLUSIONS

This work identifies TRIBAL as a gene bridging the genotype-phenotype relationship at the 8q24.13 locus with effects on genes regulating hepatocyte lipid metabolism and triglyceride secretion.

The importance of functional genomics studies in precision rheumatology

Rheumatic diseases, those that affect the musculoskeletal system, cause significant morbidity. Among risk factors of these diseases is a significant genetic component. Recent advances in high-throughput omics techniques now allow a comprehensive profiling of patients at a genetic level through genome-wide association studies. Without functional interpretation of variants identified through these studies, clinical insight remains limited. Strategies include statistical fine-mapping that refine the list of variants in loci associated with disease, whilst colocalization techniques attempt to attribute function to variants that overlap a genetically active chromatin annotation. Functional validation using genome editing techniques can be used to further refine genetic signals and identify key pathways in cell types relevant to rheumatic disease biology. Insight gained from the combination of genetic studies and functional validation can be used to improve precision medicine in rheumatic diseases by allowing risk prediction and drug repositioning.

Plasma pQTL and brain eQTL integration identifies PNKP as a therapeutic target and reveals mechanistic insights into migraine pathophysiology

BACKGROUND

Migraine is a prevalent neurological disorder affecting 14.1% of the global population. Despite advances in genetic research, further investigation is needed to identify therapeutic targets and better understand its mechanisms. In this study, we aimed to identify drug targets and explore the relationships between gene expression, protein levels, and migraine pathophysiology.

METHODS

We utilized cis-pQTL data from deCODE Genetics, combined with migraine GWAS data from the GERA + UKB cohort as the discovery cohort and the FinnGen R10 cohort as the replication cohort. SMR and MR analyses identified migraine-associated protein loci. Brain eQTL data from GTEx v8 and BrainMeta v2 were used to explore causal relationships between gene expression, protein levels, and migraine risk. Mediation analysis assessed the role of metabolites, and PheWAS evaluated potential side effects.

RESULTS

Four loci were identified: PNKP, MRVI1, CALCB, and INPP5B. PNKP and MRVI1 showed a high level of evidence and opposing effects at the gene and protein levels. PNKP gene expression in certain brain regions was protective against migraine, while its plasma protein levels were positively associated with migraine risk. MRVI1 showed protective effects at the protein level but had the opposite effect at the gene expression level. Mediation analysis revealed that the glutamate to pyruvate ratio and 3-CMPFP mediated PNKP's effects on migraine. PheWAS indicated associations between PNKP and body composition traits, suggesting drug safety considerations.

CONCLUSION

PNKP and MRVI1 exhibit dual mechanisms of action at the gene and protein levels, potentially involving distinct mechanistic pathways. Among them, PNKP emerges as a promising drug target for migraine treatment, supported by multi-layered validation.

Disentangling mechanisms behind the pleiotropic effects of proximal 16p11.2 BP4-5 CNVs

Whereas 16p11.2 BP4-5 copy-number variants (CNVs) represent one of the most pleiotropic etiologies of genomic syndromes in both clinical and population cohorts, the mechanisms leading to such pleiotropy remain understudied. Identifying 73 deletion and 89 duplication carrier individuals among unrelated White British UK Biobank participants, we performed a phenome-wide association study (PheWAS) between the region's copy number and 117 complex traits and diseases, mimicking four dosage models. Forty-six phenotypes (39%) were affected by 16p11.2 BP4-5 CNVs, with the deletion-only, mirror, U-shape, and duplication-only models being the best fit for 30, 10, 4, and 2 phenotypes, respectively, aligning with the stronger deleteriousness of the deletion. Upon individually adjusting CNV effects for either body mass index (BMI), height, or educational attainment (EA), we found that sixteen testable deletion-driven associations-primarily with cardiovascular and metabolic traits-were BMI dependent, with EA playing a more subtle role and no association depending on height. Bidirectional Mendelian randomization supported that 13 out of these 16 associations were secondary consequences of the CNV's impact on BMI. For the 23 traits that remained significantly associated upon individual adjustment for mediators, matched-control analyses found that 10 phenotypes, including musculoskeletal traits, liver enzymes, fluid intelligence, platelet count, and pneumonia and acute kidney injury risk, remained associated under strict Bonferroni correction, with 10 additional nominally significant associations. These results paint a complex picture of 16p11.2 BP4-5's pleiotropic pattern that involves direct effects on multiple physiological systems and indirect co-morbidities consequential to the CNV's impact on BMI and EA, acting through trait-specific dosage mechanisms.

Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables

Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene regulatory effects, that is, loci where the same genetic variants are associated to multiple nearby genes, MVMR can potentially be used to predict candidate causal genes. However, consensus in the field dictates that the genetic instruments in MVMR must be independent (not in linkage disequilibrium), which is usually not possible when considering a group of candidate genes from the same locus. Here we used causal inference theory to show that MVMR with correlated instruments satisfies the instrumental set condition. This is a classical result by Brito and Pearl (2002) for structural equation models that guarantees the identifiability of individual causal effects in situations where multiple exposures collectively, but not individually, separate a set of instrumental variables from an outcome variable. Extensive simulations confirmed the validity and usefulness of these theoretical results. Importantly, the causal effect estimates remained unbiased and their variance small even when instruments are highly correlated, while bias introduced by horizontal pleiotropy or LD matrix sampling error was comparable to standard MR. We applied MVMR with correlated instrumental variable sets at genome-wide significant loci for coronary artery disease (CAD) risk using expression Quantitative Trait Loci (eQTL) data from seven vascular and metabolic tissues in the STARNET study. Our method predicts causal genes at twelve loci, each associated with multiple colocated genes in multiple tissues. We confirm causal roles for PHACTR1 and ADAMTS7 in arterial tissues, among others. However, the extensive degree of regulatory pleiotropy across tissues and the limited number of causal variants in each locus still require that MVMR is run on a tissue-by-tissue basis, and testing all gene-tissue pairs with cis-eQTL associations at a given locus in a single model to predict causal gene-tissue combinations remains infeasible. Our results show that within tissues, MVMR with dependent, as opposed to independent, sets of instrumental variables significantly expands the scope for predicting causal genes in disease risk loci with pleiotropic regulatory effects. However, considering risk loci with regulatory pleiotropy that also spans across tissues remains an unsolved problem.

GENETIC PREDICTION OF CAUSAL RELATIONSHIPS BETWEEN OSTEOPOROSIS AND SEPSIS: EVIDENCE FROM MENDELIAN RANDOMIZATION WITH TWO-SAMPLE DESIGNS

ABSTRACT

Background: Recent observational studies have suggested that osteoporosis may be a risk factor for sepsis. To mitigate confounding factors and establish the causal relationship between sepsis and osteoporosis, we conducted a two-sample Mendelian randomization analysis using publicly available summary statistics. Methods: Utilizing summary data from FinnGen Biobank, we employed a two-sample Mendelian randomization (MR) analysis to predict the causal relationship between osteoporosis and sepsis. The MR analysis primarily utilized the inverse variance weighted (IVW) method, supplemented by MR-Egger, weighted median, weighted mode, and simple mode analyses, with Bayesian weighted MR (BWMR) analysis employed for result validation. Sensitivity analyses included MR-PRESSO, "leave-one-out" analysis, MR-Egger regression, and Cochran Q test. Results: In the European population, an increase of one standard deviation in osteoporosis was associated with an 11% increased risk of sepsis, with an odds ratio (OR) of 1.11 (95% CI, 1.06-1.16; P = 3.75E-06). BWMR yielded an OR of 1.11 (95% CI, 1.06-1.67; P = 1.21E-05), suggesting osteoporosis as a risk factor for sepsis. Conversely, an increase of one standard deviation in sepsis was associated with a 26% increased risk of osteoporosis, with an OR of 1.26 (95% CI, 1.11-1.16; P = 0.45E-03). BWMR yielded an OR of 1.26 (95% CI, 1.09-1.45; P = 1.45E-03), supporting sepsis as a risk factor for osteoporosis. Conclusion: There is an association between osteoporosis and sepsis, with osteoporosis serving as a risk factor for the development of sepsis, while sepsis may also promote the progression of osteoporosis.

MR Corge: sensitivity analysis of Mendelian randomization based on the core gene hypothesis for polygenic exposures

SUMMARY

Mendelian randomization is being utilized to assess causal effects of polygenic exposures, where many genetic instruments are subject to horizontal pleiotropy. Existing methods for detecting and correcting for horizontal pleiotropy have important assumptions that may not be fulfilled. Built upon the core gene hypothesis, we developed MR Corge for performing sensitivity analysis of Mendelian randomization. MR Corge identifies a small number of putative core instruments that are more likely to affect genes with a direct biological role in an exposure and obtains causal effect estimates based on these instruments, thereby reducing the risk of horizontal pleiotropy. Using positive and negative controls, we demonstrated that MR Corge estimates aligned with established biomedical knowledge and the results of randomized controlled trials. MR Corge may be widely applied to investigate polygenic exposure-outcome relationships.

AVAILABILITY AND IMPLEMENTATION

An open-sourced R package is available at https://github.com/zhwm/MRCorge.

HORNET: Tools to find genes with causal evidence and their regulatory networks using eQTLs

Motivation

Nearly two decades of genome-wide association studies (GWAS) have identify thousands of disease-associated genetic variants, but very few genes with evidence of causality. Recent methodological advances demonstrate that Mendelian Randomization (MR) using expression quantitative loci (eQTLs) as instrumental variables can detect potential causal genes. However, existing MR approaches are not well suited to handle the complexity of eQTL GWAS data structure and so they are subject to bias, inflation, and incorrect inference.

Results

We present a whole-genome regulatory network analysis tool (HORNET), which is a comprehensive set of statistical and computational tools to perform genome-wide searches for causal genes using summary level GWAS data that is robust to biases from multiple sources. Applying HORNET to schizophrenia, we identified differential magnitudes of gene expression causality. Applying HORNET to schizophrenia, we identified differential magnitudes of gene expression causality across different brain tissues.

Availability and Implementation

Freely available at https://github.com/noahlorinczcomi/HORNETor Mac, Windows, and Linux users.

Contact

njl96@case.edu .

The causal relationship between trace element status and upper gastrointestinal ulcers: a Mendelian randomization study

Background

This study aimed to investigate the bidirectional causal relationships between trace elements (such as zinc, magnesium, phosphate, and folate) and upper gastrointestinal ulcers (including gastric and duodenal ulcers). We utilized a two-sample Mendelian randomization (MR) analysis to achieve this.

Methods

We conducted a two-sample MR analysis using summary-level data from genome-wide association studies (GWAS) obtained from public genomics repositories. We utilized a range of MR methods, including inverse-variance weighted (IVW), MR-Egger, and weighted median methods, and conducted a meta-analysis to synthesize results across different datasets. To ensure the robustness of our findings, we performed extensive sensitivity analyses, including pleiotropy assessment, heterogeneity tests, and leave-one-out analysis.

Results

Our findings are significant, indicating a positive causal relationship between increased zinc levels and the risk of gastric ulcers. Moreover, magnesium and folate appear to offer potential protective effects against gastroduodenal ulcers (p < 0.05). The meta-analysis further supports the causal relationship between zinc and gastric ulcers (p < 0.05), confirming zinc's significant causal impact on this condition.

Conclusion

The study confirms a positive causal relationship between zinc and gastric ulcers and highlights the complexity of how trace elements regulate the progression of upper gastrointestinal ulcers. These results provide a scientific basis for dietary recommendations regarding trace element intake in clinical and public health practices. They also offer new insights into effective prevention and treatment strategies for gastric and duodenal ulcers.

Quantitative omnigenic model discovers interpretable genome-wide associations

As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the "quantitative omnigenic model" (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans-, break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of nontranscriptional (including environmental) effects.

Causal Relationship Between Childhood Obesity and Sleep Apnea Syndrome: Bidirectional Two-Sample Mendelian Randomization Analysis

Background

Childhood obesity has become a global pandemic, leading to a range of diseases. Childhood obesity appears to be associated with an increased prevalence of sleep apnea syndrome. Sleep apnea is an inestimable risk factor for thrombosis, hypertension, cardiomyopathy and many other diseases. Therefore, exploring the relationship between childhood obesity and sleep apnea syndrome will help to understand the potential link between the two and provide research directions for future disease prevention and treatment. However, no studies have confirmed whether there is a causal relationship between childhood obesity and sleep apnea syndrome.

Methods

The IEU OpenGWAS project provided the GWAS-aggregated data for childhood obesity and sleep apnea syndrome. Inverse-variance weighted (IVW) was used as the main method to evaluate the causal relationship between childhood obesity and sleep apnea syndrome. Single nucleotide polymorphisms (SNPs) were regarded as instrumental variables, and the screening threshold was P <5.0×10-6. Leave-one-out method was performed to confirm the robustness of the results.

Results

IVW analysis confirmed a causal relationship between genetic susceptibility to childhood obesity and an increased risk of sleep apnea syndrome [odds ratio (OR)=1.12, 95% confidence interval (CI): 1.02-1.23, P=0.016]. However, two-sample MR results also showed no causal relationship between genetic susceptibility to sleep apnea syndrome and an increased risk of childhood obesity (OR=1.50, 95% CI: 0.95-2.38, P=0.083). The intercept of MR-Egger regression was close to 0, which implies that there are no confounding factors in the analysis to affect the results of two-sample MR analysis. The leave-one-out results show that the bidirectional two-sample MR analysis results were robust.

Conclusion

There is a causal relationship between genetic susceptibility to childhood obesity and increased risk of sleep apnea syndrome. People with a history of childhood obesity should pay more attention to physical examination to early prevention and management of sleep apnea syndrome.

Exploring Pathogenic Genes in Frozen Shoulder through weighted gene co-expression network analysis and Mendelian Randomization

Background: Frozen shoulder (FS) is characterized by the thickening and fibrosis of the joint capsule, leading to joint contracture and a reduction in joint volume. The precise etiology responsible for these pathological changes remains elusive. Therefore, the primary aim of this study was to explore the potential involvement of pathogenic genes in FS and analyze their underlying roles in the disease progression. Methods: Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to investigate co-expressed genes potentially associated with FS. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted to elucidate the potential roles of these co-expressed genes. Subsequently, Mendelian randomization (MR) analysis was performed using expression quantitative trait loci datasets for the co-expressed genes, combined with summary statistics from the genome-wide association study of FS, aiming to identify key genes causally associated with FS. The identified key genes were further validated through reverse transcription-quantitative PCR (RT-qPCR). Additionally, a nomogram model and receiver operating characteristic (ROC) curves were established to assess the diagnostic value of the hub genes. Furthermore, the infiltration of immune cells was evaluated using the CIBERSORT algorithm and the relationship between key genes and immune-infiltrating cells was analyzed. Results: 295 overlapping co-expressed genes were identified by intersecting the differentially expressed genes with the hub genes obtained from associated modules identified through WGCNA. Utilizing MR analysis, four key genes, namely ADAMTS1, NR4A2, PARD6G and SMKR1, were found to exhibit positive causal relationships with FS, which were subsequently validated through RT-qPCR analysis. Moreover, the diagnostic value of these four key genes was demonstrated through the development of a nomogram model and the construction of ROC curves. Notably, a causal relationship between ADAMTS1 and immune cell infiltration in FS was observed. Conclusion: Our study suggested genetic predisposition to higher expression levels of ADAMTS1, NR4A2, PARD6G and SMKR1, was associated with an increased risk of FS. Further investigations elucidating the functional roles of these genes will enhance our understanding of the pathogenesis of FS and may facilitate the development of targeted treatment strategies.

Extensive co-regulation of neighboring genes complicates the use of eQTLs in target gene prioritization

Identifying causal genes underlying genome-wide association studies (GWASs) is a fundamental problem in human genetics. Although colocalization with gene expression quantitative trait loci (eQTLs) is often used to prioritize GWAS target genes, systematic benchmarking has been limited due to unavailability of large ground truth datasets. Here, we re-analyzed plasma protein QTL data from 3,301 individuals of the INTERVAL cohort together with 131 eQTL Catalog datasets. Focusing on variants located within or close to the affected protein identified 793 proteins with at least one cis-pQTL where we could assume that the most likely causal gene was the gene coding for the protein. We then benchmarked the ability of cis-eQTLs to recover these causal genes by comparing three Bayesian colocalization methods (coloc.susie, coloc.abf, and CLPP) and five Mendelian randomization (MR) approaches (three varieties of inverse-variance weighted MR, MR-RAPS, and MRLocus). We found that assigning fine-mapped pQTLs to their closest protein coding genes outperformed all colocalization methods regarding both precision (71.9%) and recall (76.9%). Furthermore, the colocalization method with the highest recall (coloc.susie - 46.3%) also had the lowest precision (45.1%). Combining evidence from multiple conditionally distinct colocalizing QTLs with MR increased precision to 81%, but this was accompanied by a large reduction in recall to 7.1%. Furthermore, the choice of the MR method greatly affected performance, with the standard inverse-variance-weighted MR often producing many false positives. Our results highlight that linking GWAS variants to target genes remains challenging with eQTL evidence alone, and prioritizing novel targets requires triangulation of evidence from multiple sources.

Translation of genome-wide association study: from genomic signals to biological insights

Since the turn of the 21st century, genome-wide association study (GWAS) have successfully identified genetic signals associated with a myriad of common complex traits and diseases. As we transition from establishing robust genetic associations with diverse phenotypes, the central challenge is now focused on characterizing the underlying functional mechanisms driving these signals. Previous GWAS efforts have revealed multiple variants, each conferring relatively subtle susceptibility, collectively contributing to the pathogenesis of various common diseases. Such variants can further exhibit associations with multiple other traits and differ across ancestries, plus disentangling causal variants from non-causal due to linkage disequilibrium complexities can lead to challenges in drawing direct biological conclusions. Combined with cellular context considerations, such challenges can reduce the capacity to definitively elucidate the biological significance of GWAS signals, limiting the potential to define mechanistic insights. This review will detail current and anticipated approaches for functional interpretation of GWAS signals, both in terms of characterizing the underlying causal variants and the corresponding effector genes.

Unraveling the significance of AGPAT4 for the pathogenesis of endometriosis via a multi-omics approach

Endometriosis is characterized by the ectopic proliferation of endometrial cells, posing considerable diagnostic and therapeutic challenges. Our study investigates AGPAT4's involvement in endometriosis pathogenesis, aiming to unveil new therapeutic targets. Our investigation by analyzing eQTL data from GWAS for preliminary screening. Subsequently, within the GEO dataset, we utilized four machine learning algorithms to precisely identify risk-associated genes. Gene validity was confirmed through five Mendelian Randomization methods. AGPAT4 expression was measured by Single-Cell Analysis, ELISA and immunohistochemistry. We investigated AGPAT4's effect on endometrial stromal cells using RNA interference, assessing cell proliferation, invasion, and migration with CCK8, wound-healing, and transwell assays. Protein expression was analyzed by western blot, and AGPAT4 interactions were explored using AutoDock. Our investigation identified 11 genes associated with endometriosis risk, with AGPAT4 and COMT emerging as pivotal biomarkers through machine learning analysis. AGPAT4 exhibited significant upregulation in both ectopic tissues and serum samples from patients with endometriosis. Reduced expression of AGPAT4 was observed to detrimentally impact the proliferation, invasion, and migration capabilities of endometrial stromal cells, concomitant with diminished expression of key signaling molecules such as Wnt3a, β-Catenin, MMP-9, and SNAI2. Molecular docking analyses further underscored a substantive interaction between AGPAT4 and Wnt3a.Our study highlights AGPAT4's key role in endometriosis, influencing endometrial stromal cell behavior, and identifies AGPAT4 pathways as promising therapeutic targets for this condition.

Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables

Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene regulatory effects, that is, loci where the same genetic variants are associated to multiple nearby genes, MVMR can potentially be used to predict candidate causal genes. However, consensus in the field dictates that the genetic instruments in MVMR must be independent (not in linkage disequilibrium, which is usually not possible when considering a group of candidate genes from the same locus. Here we used causal inference theory to show that MVMR with correlated instruments satisfies the instrumental set condition. This is a classical result by Brito and Pearl (2002) for structural equation models that guarantees the identifiability of individual causal effects in situations where multiple exposures collectively, but not individually, separate a set of instrumental variables from an outcome variable. Extensive simulations confirmed the validity and usefulness of these theoretical results. Importantly, the causal effect estimates remained unbiased and their variance small even when instruments are highly correlated, while bias introduced by horizontal pleiotropy or LD matrix sampling error was comparable to standard MR. We applied MVMR with correlated instrumental variable sets at genome-wide significant loci for coronary artery disease (CAD) risk using expression Quantitative Trait Loci (eQTL) data from seven vascular and metabolic tissues in the STARNET study. Our method predicts causal genes at twelve loci, each associated with multiple colocated genes in multiple tissues. We confirm causal roles for PHACTR 1 and ADAMTS 7 in arterial tissues, among others. However, the extensive degree of regulatory pleiotropy across tissues and the limited number of causal variants in each locus still require that MVMR is run on a tissue-by-tissue basis, and testing all gene-tissue pairs with cis-eQTL associations at a given locus in a single model to predict causal gene-tissue combinations remains infeasible. Our results show that within tissues, MVMR with dependent, as opposed to independent, sets of instrumental variables significantly expands the scope for predicting causal genes in disease risk loci with pleiotropic regulatory effects. However, considering risk loci with regulatory pleiotropy that also spans across tissues remains an unsolved problem.

Widespread natural selection on metabolite levels in humans

Natural selection acts ubiquitously on complex human traits, predominantly constraining the occurrence of extreme phenotypes (stabilizing selection). These constraints propagate to DNA sequence variants associated with traits under selection. The genetic signatures of such evolutionary events can thus be detected via combining effect size estimates from genetic association studies and the corresponding allele frequencies. Although this approach has been successfully applied to high-level traits, the prevalence and mode of selection acting on molecular traits remain poorly understood. Here, we estimate the action of natural selection on genetic variants associated with metabolite levels, an important layer of molecular traits. By leveraging summary statistics of published genome-wide association studies with large sample sizes, we find strong evidence of stabilizing selection for 15 out of 97 plasma metabolites, with nonessential amino acids displaying especially strong selection signatures. Mendelian randomization analysis reveals that metabolites under stronger stabilizing selection display larger effects on a range of clinically relevant complex traits, suggesting that maintaining a disease-free profile may be an important source of selective constraints on the metabolome. Metabolites under strong stabilizing selection in humans are also more conserved in their concentrations among diverse mammalian species, suggesting shared selective forces across micro- and macroevolutionary timescales. Overall, this study demonstrates that variation in metabolite levels among humans is frequently shaped by natural selection and this may act through their causal impact on disease susceptibility.

The causal relationship between triglycerides and myocardial infarction: A two-sample Mendelian randomization

The causal relationship between triglycerides and myocardial infarction (MI) was investigated using Mendelian randomization (MR) studies. Triglycerides were the exposure factor, and MI served as the outcome variable. Inverse variance weighting was used as the main analysis method, MR-Egger, and weight median as other analysis methods for MR analysis. In addition, heterogeneity test, level multivariate analysis, and sensitivity analysis were carried out. Inverse variance weighting results showed that the increase in triglyceride level affected the incidence of MI (OR = 1.287; 95% CI = 1.185-1.398; P = 1.988 × 10-9). Consistently, the results from all 3 methods indicated a statistically significant increase in the risk of MI with higher triglyceride levels (P < .05). The results showed that patients with high triglyceride levels had a higher incidence of MI, suggesting that MI should be prevented in the high triglyceride population.

Host genetics and gut microbiota synergistically regulate feed utilization in egg-type chickens

BACKGROUND

Feed efficiency is a crucial economic trait in poultry industry. Both host genetics and gut microbiota influence feed efficiency. However, the associations between gut microbiota and host genetics, as well as their combined contributions to feed efficiency in laying hens during the late laying period, remain largely unclear.

METHODS

In total, 686 laying hens were used for whole-genome resequencing and liver transcriptome sequencing. 16S rRNA gene sequencing was conducted on gut chyme (duodenum, jejunum, ileum, and cecum) and fecal samples from 705 individuals. Bioinformatic analysis was performed by integrating the genome, transcriptome, and microbiome to screen for key genetic variations, genes, and gut microbiota associated with feed efficiency.

RESULTS

The heritability of feed conversion ratio (FCR) and residual feed intake (RFI) was determined to be 0.28 and 0.48, respectively. The ileal and fecal microbiota accounted for 15% and 10% of the FCR variance, while the jejunal, cecal, and fecal microbiota accounted for 20%, 11%, and 10% of the RFI variance. Through SMR analysis based on summary data from liver eQTL mapping and GWAS, we further identified four protein-coding genes, SUCLA2, TNFSF13B, SERTM1, and MARVELD3, that influence feed efficiency in laying hens. The SUCLA2 and TNFSF13B genes were significantly associated with SNP 1:25664581 and SNP rs312433097, respectively. SERTM1 showed significant associations with rs730958360 and 1:33542680 and is a potential causal gene associated with the abundance of Corynebacteriaceae in feces. MARVELD3 was significantly associated with the 1:135348198 and was significantly correlated with the abundance of Enterococcus in ileum. Specifically, a lower abundance of Enterococcus in ileum and a higher abundance of Corynebacteriaceae in feces were associated with better feed efficiency.

CONCLUSIONS

This study confirms that both host genetics and gut microbiota can drive variations in feed efficiency. A small portion of the gut microbiota often interacts with host genes, collectively enhancing feed efficiency. Therefore, targeting both the gut microbiota and host genetic variation by supporting more efficient taxa and selective breeding could improve feed efficiency in laying hens during the late laying period.

Dissecting the causal relationship between moderate to vigorous physical activity levels and cognitive performance: a bidirectional two-sample Mendelian randomization study

Introduction

Recent studies increasingly suggest that moderate-to-vigorous physical activity (MVPA) impacts cognitive risk. However, the bidirectional nature of this relationship warrants further exploration. To address this, we employed a Mendelian randomization (MR) approach, analyzing two distinct samples.

Methods

These analyses utilized published genome-wide association study (GWAS) summary statistics for MVPA (n = 377,234) and cognitive performance (n = 257,841). Our primary method was the inverse variance weighted (IVW) model with random effects, aiming to deduce potential causal links. Additionally, we employed supplementary methods, including MR Egger regression, Weighted median, Weighted mode, and Simple mode. For sensitivity analysis, tools like the MR Egger test, Cochran's Q, MR PRESSO, and leave-one-out (LOO) were utilized.

Results

Our findings indicate a decrease in cognitive risk with increased MVPA (Odds Ratio [OR] = 0.577, 95% Confidence Interval [CI]: 0.460-0.723, p = 1.930 × 10-6). Furthermore, enhanced cognitive levels corresponded to a reduced risk of inadequate MVPA (OR = 0.866, 95% CI: 0.839-0.895, p = 1.200 × 10-18).

Discussion

In summary, our study demonstrates that MVPA lowers cognitive risk, while poor cognitive health may impede participation in MVPA. Overall, these findings provide valuable insights for developing personalized prevention and intervention strategies in health and sports sciences.

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

Crosstalk between epitranscriptomic and epigenomic modifications and its implication in human diseases

Crosstalk between N6-methyladenosine (m6A) and epigenomes is crucial for gene regulation, but its regulatory directionality and disease significance remain unclear. Here, we utilize quantitative trait loci (QTLs) as genetic instruments to delineate directional maps of crosstalk between m6A and two epigenomic traits, DNA methylation (DNAme) and H3K27ac. We identify 47 m6A-to-H3K27ac and 4,733 m6A-to-DNAme and, in the reverse direction, 106 H3K27ac-to-m6A and 61,775 DNAme-to-m6A regulatory loci, with differential genomic location preference observed for different regulatory directions. Integrating these maps with complex diseases, we prioritize 20 genome-wide association study (GWAS) loci for neuroticism, depression, and narcolepsy in brain; 1,767 variants for asthma and expiratory flow traits in lung; and 249 for coronary artery disease, blood pressure, and pulse rate in muscle. This study establishes disease regulatory paths, such as rs3768410-DNAme-m6A-asthma and rs56104944-m6A-DNAme-hypertension, uncovering locus-specific crosstalk between m6A and epigenomic layers and offering insights into regulatory circuits underlying human diseases.

Investigating the causal relationship between gut microbiota and gastroenteropancreatic neuroendocrine neoplasms: a bidirectional Mendelian randomization study

Background

The interaction between the intestinal flora and gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) remains poorly understood, despite the known effect of the gut microbiota on gastrointestinal adenocarcinomas. Hence, the present research aimed to determine the potential causal correlation between the intestinal flora and GEP-NENs by conducting a bidirectional Mendelian randomization (MR) analysis.

Methods

Two-sample MR analysis was conducted using the summary statistics of the gut microbiota from the MiBioGen consortium and those of GEP-NENs from the FinnGen research project. The inverse-variance weighted approach was utilized as the primary analytical method. To enhance the robustness of our findings, multiple sensitivity tests were performed, including Cochran's Q test for evaluating heterogeneity, the MR-Egger intercept test to detect horizontal pleiotropy, and the MR-PRESSO test to identify outliers and assess pleiotropy bias. Additionally, a leave-one-out analysis was performed to validate the consistency of our findings. The MR-Steiger test was also utilized to determine the causal direction in the correlation between the gut microbiota and GEP-NENs. Finally, a reverse MR analysis was performed to assess reverse causality between the intestinal flora and GEP-NENs.

Results

We identified 42 taxa of the gut microbiota that were potentially causally associated with GEP-NENs; of these taxa, 7, 8, 11, and 16 taxa were causally associated with pancreatic NENs, colorectal NENs, small intestinal NENs, and gastric NENs, respectively. After adjusting for false discovery rate (FDR) correction, we found significant causal links of Euryarchaeota with small intestinal NENs and Family XIII UCG-001 with gastric NENs. The sensitivity analyses confirmed the stability of these correlations. In the reverse MR analysis, colorectal NENs and small intestinal NENs were found to be associated with variations in 8 and 6 different taxa of the gut microbiota, respectively. After adjusting for FDR correction, no significant causal links were detected between GEP-NENs and the intestinal flora.

Conclusion

The present study reveals a potential causal association between certain taxa of the intestinal flora and GEP-NENs, thus providing new perspectives regarding the role of the intestinal flora in the development of these tumors. These insights could provide innovative approaches to screen and prevent these diseases.

A transcriptomic atlas of the human brain reveals genetically determined aspects of neuropsychiatric health

Regulation of gene expression is a vital component of neurological homeostasis. Cataloging the consequences of endogenous gene expression on the physical structure and connectivity of the brain offers a means of unifying trait-associated genetic variation with trait-associated neurological features. We perform tissue-specific transcriptome-wide association studies (TWASs) on over 3,400 neuroimaging phenotypes in the UK Biobank (N = 33,224) using our joint-tissue imputation (JTI)-TWAS method. We identify highly significant associations between predicted expression for 7,192 genes and a wide variety of measures of the brain derived from magnetic resonance imaging (MRI). Our approach generates reproducible results in internal and external replication datasets. Genetically determined expression alone is sufficient for high-fidelity reconstruction of brain structure and organization. We demonstrate complementary benefits of cross-tissue and single-tissue analyses toward an integrated neurobiology and provide evidence that gene expression outside the central nervous system provides unique insights into brain health. As an application, we provide evidence suggesting that the genetically regulated expression of schizophrenia risk genes causally affects over 73% of neurological phenotypes that are altered in individuals with schizophrenia (as identified by neuroimaging studies). Imaging features associated with neuropsychiatric traits can provide valuable insights into underlying pathophysiology. By linking neuroimaging-derived phenotypes with expression levels of specific genes, this resource represents a powerful gene prioritization schema that can improve our understanding of brain function, development, and disease. The use of multiple different cortical and subcortical atlases in the resource facilitates direct integration of these data with findings from a diverse range of clinical neuroimaging studies.

pyTWMR: transcriptome-wide Mendelian randomization in python

MOTIVATION

Mendelian randomization (MR) is a widely used approach to estimate causal effect of variation in gene expression on complex traits. Among several MR-based algorithms, transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) enables the uses of multiple SNPs as instruments and multiple gene expression traits as exposures to facilitate causal inference in observational studies.

RESULTS

Here we present a Python-based implementation of TWMR and revTWMR. Our implementation offers GPU computational support for faster computations and robust computation mode resilient to highly correlated gene expressions and genetic variants.

AVAILABILITY AND IMPLEMENTATION

pyTWMR is available at github.com/soreshkov/pyTWMR.

Metabolic gene function discovery platform GeneMAP identifies SLC25A48 as necessary for mitochondrial choline import

Organisms maintain metabolic homeostasis through the combined functions of small-molecule transporters and enzymes. While many metabolic components have been well established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene-Metabolite Association Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions and even pinpoint genes that are distant from the variants implicated by GWAS. In particular, our analysis identified solute carrier family 25 member 48 (SLC25A48) as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite betaine. Integrative rare variant and polygenic score analyses in UK Biobank provide strong evidence that the SLC25A48 causal effects on human disease may in part be mediated by the effects of choline. Altogether, our study provides a discovery platform for metabolic gene function and proposes SLC25A48 as a mitochondrial choline transporter.

Single nucleus RNA-sequencing integrated into risk variant colocalization discovers 17 cell-type-specific abdominal obesity genes for metabolic dysfunction-associated steatotic liver disease

BACKGROUND

Abdominal obesity increases the risk for non-alcoholic fatty liver disease (NAFLD), now known as metabolic dysfunction-associated steatotic liver disease (MASLD).

METHODS

To elucidate the directional cell-type level biological mechanisms underlying the association between abdominal obesity and MASLD, we integrated adipose and liver single nucleus RNA-sequencing and bulk cis-expression quantitative trait locus (eQTL) data with the UK Biobank genome-wide association study (GWAS) data using colocalization. Then we used colocalized cis-eQTL variants as instrumental variables in Mendelian randomization (MR) analyses, followed by functional validation experiments on the target genes of the cis-eQTL variants.

FINDINGS

We identified 17 colocalized abdominal obesity GWAS variants, regulating 17 adipose cell-type marker genes. Incorporating these 17 variants into MR discovers a putative tissue-of-origin, cell-type-aware causal effect of abdominal obesity on MASLD consistently with multiple MR methods without significant evidence for pleiotropy or heterogeneity. Single cell data confirm the adipocyte-enriched mean expression of the 17 genes. Our cellular experiments across human adipogenesis identify risk variant -specific epigenetic and transcriptional mechanisms. Knocking down two of the 17 genes, PPP2R5A and SH3PXD2B, shows a marked decrease in adipocyte lipidation and significantly alters adipocyte function and adipogenesis regulator genes, including DGAT2, LPL, ADIPOQ, PPARG, and SREBF1. Furthermore, the 17 genes capture a characteristic MASLD expression signature in subcutaneous adipose tissue.

INTERPRETATION

Overall, we discover a significant cell-type level effect of abdominal obesity on MASLD and trace its biological effect to adipogenesis.

FUNDING

NIH grants R01HG010505, R01DK132775, and R01HL170604; the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant No. 802825), Academy of Finland (Grants Nos. 333021), the Finnish Foundation for Cardiovascular Research the Sigrid Jusélius Foundation and the Jane and Aatos Erkko Foundation; American Association for the Study of Liver Diseases (AASLD) Advanced Transplant Hepatology award and NIH/NIDDK (P30DK41301) Pilot and Feasibility award; NIH/NIEHS F32 award (F32ES034668); Finnish Diabetes Research Foundation, Kuopio University Hospital Project grant (EVO/VTR grants 2005-2021), the Academy of Finland grant (Contract no. 138006); Academy of Finland (Grant Nos 335443, 314383, 272376 and 266286), Sigrid Jusélius Foundation, Finnish Medical Foundation, Finnish Diabetes Research Foundation, Novo Nordisk Foundation (#NNF20OC0060547, NNF17OC0027232, NNF10OC1013354) and Government Research Funds to Helsinki University Hospital; Orion Research Foundation, Maud Kuistila Foundation, Finish Medical Foundation, and University of Helsinki.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Identifying dysregulated regions in amyotrophic lateral sclerosis through chromatin accessibility outliers

The high heritability of amyotrophic lateral sclerosis (ALS) contrasts with its low molecular diagnosis rate post-genetic testing, pointing to potential undiscovered genetic factors. To aid the exploration of these factors, we introduced EpiOut, an algorithm to identify chromatin accessibility outliers that are regions exhibiting divergent accessibility from the population baseline in a single or few samples. Annotation of accessible regions with histone chromatin immunoprecipitation sequencing and Hi-C indicates that outliers are concentrated in functional loci, especially among promoters interacting with active enhancers. Across different omics levels, outliers are robustly replicated, and chromatin accessibility outliers are reliable predictors of gene expression outliers and aberrant protein levels. When promoter accessibility does not align with gene expression, our results indicate that molecular aberrations are more likely to be linked to post-transcriptional regulation rather than transcriptional regulation. Our findings demonstrate that the outlier detection paradigm can uncover dysregulated regions in rare diseases. EpiOut is available at github.com/uci-cbcl/EpiOut.

A systems biology-based identification and in vivo functional screening of Alzheimer's disease risk genes reveal modulators of memory function

Genome-wide association studies (GWASs) have uncovered over 75 genomic loci associated with risk for late-onset Alzheimer's disease (LOAD), but identification of the underlying causal genes remains challenging. Studies of induced pluripotent stem cell (iPSC)-derived neurons from LOAD patients have demonstrated the existence of neuronal cell-intrinsic functional defects. Here, we searched for genetic contributions to neuronal dysfunction in LOAD using an integrative systems approach that incorporated multi-evidence-based gene mapping and network-analysis-based prioritization. A systematic perturbation screening of candidate risk genes in Caenorhabditis elegans (C. elegans) revealed that neuronal knockdown of the LOAD risk gene orthologs vha-10 (ATP6V1G2), cmd-1 (CALM3), amph-1 (BIN1), ephx-1 (NGEF), and pho-5 (ACP2) alters short-/intermediate-term memory function, the cognitive domain affected earliest during LOAD progression. These results highlight the impact of LOAD risk genes on evolutionarily conserved memory function, as mediated through neuronal endosomal dysfunction, and identify new targets for further mechanistic interrogation.