Circulating Proteins Influencing Psychiatric Disease: A Mendelian Randomization Study

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

Key Points

Background

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

Methods

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

Results

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

Conclusions

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

The gut-facial aging axis: A two-sample Mendelian randomization and mediation analysis of gut microbiota, gut microbiota metabolic pathways, and blood metabolites

BACKGROUND

Facial aging (FA) is a complex process influenced by both genetic and environmental factors. Gut microbiota (GM), gut microbiota metabolic pathways (GMMPs), and blood metabolites (BMs) have been implicated in the regulation of FA, but the causal and mediating effects of these factors remain unclear.

METHODS

We used summary-level data from genome-wide association studies (GWAS) of 16S rRNA gene sequencing data for GM (n = 18 340), GWAS of GMMPs (n = 7738), BMs (n = 24 925), and GWAS of FA (n = 423 999). We applied Mendelian randomization (MR) methods to estimate the causal effects of GM, GMMPs, and BMs on FA. We performed mediation analysis to quantify the proportion of the effects mediated by blood metabolites.

RESULTS

We identified nine genus, two phylum, two families of GM, nine GM metabolic pathways, and 73 BMs that showed potential causal effects on FA. After Bonferroni correction, three BMs remained causally associated with FA, including average number of methylene groups per double bond (β, -0.023; 95% CI, -0.032∼-0.014; p = 3.120×10-7) and average number of methylene groups in a fatty acid chain (β, -0.031; 95% CI, -0.045∼-0.016; p = 2.062×10-5), which had strong negative causal effects on FA, and ratio of bisallylic groups to total fatty acids (β, 0.023; 95% CI, 0.017∼-0.029; p = 8.441×10-15), which had a strong positive causal effect on FA. Mediation analysis revealed that histidine, average number of methylene groups in a fatty acid chain, and triglycerides in chylomicrons and largest VLDL particles mediated the effects of anaerofilum and/ or superpathway of Laspartate and Lasparagine biosynthesis on FA.

CONCLUSION

Our study provides novel insights into the causal and mediating effects of GM, GMMPs, and BMs on FA. These findings may have implications for the development of new strategies for preventing or delaying FA.

ENPP2/Autotaxin: The potential drug target for alcoholic liver disease identified through Mendelian randomization analysis

BACKGROUND AND AIMS

At present, there is still a lack of radical drug targets for intervention in alcoholic liver disease (ALD), and drug discovery through randomized controlled trials is a lengthy, risky, and expensive undertaking, so we aimed to identify effective drug targets based on human genetics.

METHODS

We used Mendelian randomization (MR) and Bayesian colocalization analysis to investigate 2639 genes encoding druggable proteins and examined the causal effects on ALD (PMID 34737426: 456348 European with 451 cases and 455 897 controls). In addition, we conducted the mediation analysis to explore the potential mechanism using the genome-wide association study (GWAS) data of blood biomarkers as mediators.

RESULTS

We finally identified the drug target: ENPP2/Autotaxin and genetically proxied ENPP2/Autotaxin was causally associated with the risk of ALD (OR = 2.28, 95% CI: 1.64 to 3.16, p = 7.49E-7). In addition, we found that the effect of ENPP2/Autotaxin on ALD may be partly mediated by effector memory CD8+ T cell (the proportion of mediation effect: 8.49%).

CONCLUSIONS

Our integrative analysis suggested that genetically determined levels of circulating ENPP2/Autotaxin have a causal effect on ALD risk and are a promising drug target.

Causal Association Between Type 2 Diabetes Mellitus and Alzheimer's Disease: A Two-Sample Mendelian Randomization Study

Background

There is now increasing evidence that type 2 diabetes mellitus (T2DM) is associated with Alzheimer's disease (AD). However, it is unclear whether the two are causally related.

Objective

To reveal the causal association between T2DM and AD, we performed a bidirectional Mendelian randomization (MR) analysis.

Methods

Genetic instrumental variables were systematically screened, and inverse-variance weighting, MR-Egger regression, weighted median, simple mode, and weighted mode were applied to assess the pathogenic associations between the two diseases, and sensitivity analyses were used to further validate the robustness of the results.

Results

The results of forward MR analysis with T2DM as the exposure were [OR = 0.998, 95% CI (0.975∼1.021), p = 0.857], and the results of reverse MR analysis with AD as the exposure were [OR = 0.966, 95% CI (0.934∼0.999), p = 0.043]. The results showed no significant association between T2DM and AD at the gene level (p < 0.025). Sensitivity analyses were consistent with the results of the main analysis, confirming the robustness of the study.

Conclusions

T2DM and AD may not be genetically causally associated.

Genetic and phenotypic associations of frailty with cardiovascular indicators and behavioral characteristics

INTRODUCTION

Frailty Index (FI) is a common measure of frailty, which has been advocated as a routine clinical test by many guidelines. The genetic and phenotypic relationships of FI with cardiovascular indicators (CIs) and behavioral characteristics (BCs) are unclear, which has hampered ability to monitor FI using easily collected data.

OBJECTIVES

This study is designed to investigate the genetic and phenotypic associations of frailty with CIs and BCs, and further to construct a model to predict FI.

METHOD

Genetic relationships of FI with 288 CIs and 90 BCs were assessed by the cross-trait LD score regression (LDSC) and Mendelian randomization (MR). The phenotypic data of these CIs and BCs were integrated with a machine-learning model to predict FI of individuals in UK-biobank. The relationships of the predicted FI with risks of type 2 diabetes (T2D) and neurodegenerative diseases were tested by the Kaplan-Meier estimator and Cox proportional hazards model.

RESULTS

MR revealed putative causal effects of seven CIs and eight BCs on FI. These CIs and BCs were integrated to establish a model for predicting FI. The predicted FI is significantly correlated with the observed FI (Pearson correlation coefficient = 0.660, P-value = 4.96 × 10-62). The prediction model indicated "usual walking pace" contributes the most to prediction. Patients who were predicted with high FI are in significantly higher risk of T2D (HR = 2.635, P < 2 × 10-16) and neurodegenerative diseases (HR = 2.307, P = 1.62 × 10-3) than other patients.

CONCLUSION

This study supports associations of FI with CIs and BCs from genetic and phenotypic perspectives. The model that is developed by integrating easily collected CIs and BCs data in predicting FI has the potential to monitor disease risk.

Circulating Metabolite Abundances Associated With Risks of Bipolar Disorder, Schizophrenia, and Depression: A Mendelian Randomization Study

BACKGROUND

Preventive measures and treatments for psychiatric disorders are limited. Circulating metabolites are potential candidates for biomarker and therapeutic target identification, given their measurability and essential roles in biological processes.

METHODS

Leveraging large-scale genome-wide association studies, we conducted Mendelian randomization analyses to assess the associations between circulating metabolite abundances and the risks of bipolar disorder, schizophrenia, and depression. Genetic instruments were selected for 94 metabolites measured in the Canadian Longitudinal Study on Aging cohort (N = 8299). We repeated Mendelian randomization analyses based on the UK Biobank, INTERVAL, and EPIC (European Prospective Investigation into Cancer)-Norfolk studies.

RESULTS

After validating Mendelian randomization assumptions and colocalization evidence, we found that a 1 SD increase in genetically predicted circulating abundances of eicosapentaenoate and docosapentaenoate was associated with odds ratios of 0.72 (95% CI, 0.65-0.79) and 0.63 (95% CI, 0.55-0.72), respectively, for bipolar disorder. Genetically increased Ω-3 unsaturated fatty acids abundance and Ω-3-to-total fatty acids ratio, as well as genetically decreased Ω-6-to-Ω-3 ratio, were negatively associated with the risk of bipolar disorder in the UK Biobank. Genetically increased circulating abundances of 3 N-acetyl-amino acids were associated with an increased risk of schizophrenia with a maximum odds ratio of 1.31 (95% CI, 1.18-1.44) per 1 SD increase. Furthermore, a 1 SD increase in genetically predicted circulating abundance of hypotaurine was associated with an odds ratio of 0.85 (95% CI, 0.78-0.93) for depression.

CONCLUSIONS

The biological mechanisms that underlie Ω-3 unsaturated fatty acids, NAT8-catalyzed N-acetyl-amino acids, and hypotaurine warrant exploration to identify new biomarkers and potential therapeutic targets.

SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals

MOTIVATION

Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus.

RESULTS

We propose SharePro to extend the COLOC framework for colocalization analysis. SharePro integrates linkage disequilibrium (LD) modeling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Compared to existing methods, SharePro provided stronger and more consistent colocalization evidence for known lipid-lowering drug target proteins and their corresponding lipid traits. Through an additional challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals.

AVAILABILITY AND IMPLEMENTATION

SharePro for colocalization analysis is written in Python and openly available at https://github.com/zhwm/SharePro_coloc.

Causal effects of various types of physical activities on psychiatric disorders: a Mendelian randomization study

Background

Psychiatric disorders (PD) pose a significant burden, with vast prevalence and mortality, inflicting substantial costs on individuals and society. Despite its widespread prevalence, the complex pathogenesis of PD remains elusive, leading to limited and challenging therapeutic development. An emerging risk factor for chronic diseases, prolonged sedentary behavior, contrasts with the therapeutic potential of exercise, regardless of its intensity, for various ailments, including PD. Yet, the diversity in exercise modalities and intensities may offer varied impacts on health. This study, leveraging Mendelian Randomization (MR), seeks to investigate the causal relationship between exercise and PD, aiming to elucidate the optimal exercise modality and intensity for PD mitigation while addressing potential confounders.

Methods

This study employed a Mendelian randomization analysis using the genome-wide association study (GWAS) database to investigate the causal relationship between types of physical activity and psychiatric disorders. Sensitivity analysis was conducted to demonstrate the reliability and robustness of the results.

Results

In the past 4 weeks, engaging in a substantial amount of DIY physical activity was found to have a causal relationship with psychiatric disorders (IVW: OR = 0.228, 95% CI: 0.113-0.461, P = 0.000038). As for the types of exercises, there may be a potential causal association between aerobic training (including swimming, cycling, fitness, and bowling) and psychiatric disorders (IVW: OR = 0.322, 95% CI = 0.148-0.704, P = 0.004). However, there was no causal relationship found between mild DIY physical activity and psychiatric disorders (IVW: OR = 0.918, 95% CI = 0.417-2.021, P = 0.831). Furthermore, it seems that there is no causal relationship between vigorous exercise and psychiatric disorders (IVW: OR = 2.705, 95% CI = 0.081-3.419, P = 0.578).

Conclusion

Our study confirms that only a certain level of training activity can have a protective effect on psychiatric disorders, while mild physical activity or vigorous training does not have an impact on psychiatric disorders.

Cerebrospinal and Brain Proteins Implicated in Neuropsychiatric and Risk Factor Traits: Evidence from Mendelian Randomization

Neuropsychiatric disorders present a global health challenge, necessitating an understanding of their molecular mechanisms for therapeutic development. Using Mendelian randomization (MR) analysis, this study explored associations between genetically predicted levels of 173 proteins in cerebrospinal fluid (CSF) and 25 in the brain with 14 neuropsychiatric disorders and risk factors. Follow-up analyses assessed consistency across plasma protein levels and gene expression in various brain regions. Proteins were instrumented using tissue-specific genetic variants, and colocalization analysis confirmed unbiased gene variants. Consistent MR and colocalization evidence revealed that lower cortical expression of low-density lipoprotein receptor-related protein 8, coupled higher abundance in the CSF and plasma, associated with lower fluid intelligence scores and decreased bipolar disorder risk. Additionally, elevated apolipoprotein-E2 and hepatocyte growth factor-like protein in the CSF and brain were related to reduced leisure screen time and lower odds of physical activity, respectively. Furthermore, elevated CSF soluble tyrosine-protein kinase receptor 1 level increased liability to attention deficit hyperactivity disorder and schizophrenia alongside lower fluid intelligence scores. This research provides genetic evidence supporting novel tissue-specific proteomic targets for neuropsychiatric disorders and their risk factors. Further exploration is necessary to understand the underlying biological mechanisms and assess their potential for therapeutic intervention.

Mendelian randomization study reveals the relationship between dietary factors and respiratory diseases

The existence of causal relationship between dietary factors and respiratory diseases is uncertain. We comprehensively investigated the association between dietary factors and respiratory diseases by using Mendelian randomization (MR). Genetic variants linked to dietary factors were selected as instrumental variables with genome-wide significance. These instrumental variables were obtained from large GWAS databases. These databases include Biobank, the FinnGen study, and other large consortia. We used multivariate MR analyses to control the effects of smoking and education. Median analysis was conducted to evaluate whether body mass index (BMI) played a role in dietary factors in respiratory diseases. Dried fruit intake was found to be associated with a decreased risk of chronic obstructive pulmonary disease (COPD) (OR: 0.211; 95% CI 0.117-0.378; P < 0.001) and asthma (OR: 0.539; 95% CI 0.357-0.815; P = 0.003). Conversely, pork intake was associated with an increased risk of international pharmaceutical federation (IPF) (OR: 1.051*102, 95% CI 4.354-2.56*103, P = 0.004). However, no significant associations were observed between the 20 dietary factors and obstructive sleep apnea (OSA). In addition, multivariate MR analyses showed that the above results were unchanged in smoking and nonsmoking populations, while the effect of dried fruit intake on asthma was significantly attenuated after corrective education. The results of the mediator variable analysis indicated that BMI could serve as a mediator of the above results. This study found that dried fruits slowed the progression of COPD and asthma, while pork promoted IPF. However, no effect of dietary factors on OSA was found. Meanwhile, we showed that the above results were unchanged in smoking and non-smoking populations. In contrast, education could influence the role of diet on asthma, and BMI could be used as a mediating variable to influence the above results.

Development of risk prediction models for depression combining genetic and early life risk factors

Background

Both genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated.

Methods

Leveraging a meta-analysis of major depressive disorder genome-wide association studies (N = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank (N = 130,092 European ancestry individuals). In a UK Biobank test dataset (N = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression.

Results

In the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14-1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05-1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively.

Conclusion

We have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression.