The brain-heart axis: Integrative analysis of the shared genetic etiology between neuropsychiatric disorders and cardiovascular disease

BACKGROUND

Multiple observational studies have reported substantial comorbidity between neuropsychiatric disorders and cardiovascular disease (CVD), but the underlying mechanisms remain largely unknown.

METHODS

Using GWAS summary datasets of 8 neuropsychiatric disorders and 6 cardiovascular diseases, an integrative analysis incorporating linkage-disequilibrium-score-regression (LDSC), Mendelian randomization (MR), functional mapping and annotation (FUMA), and functional enrichment analysis, was conducted to investigate shared genetic etiology of the brain-heart axis from the whole genome level, single-nucleotide polymorphism (SNP) level, gene level, and biological pathway level.

RESULTS

In LDSC analysis, 18 pairwise traits between neuropsychiatric disorders and CVD were identified with significant genetic overlaps, revealing extensive genome-wide genetic correlations. In bidirectional MR analysis, 19 pairwise traits were identified with significant causal relationships. Genetic liabilities to neuropsychiatric disorders, particularly attention-deficit hyperactivity disorder and major depressive disorder, conferred extensive significant causal effects on the risk of CVD, while hypertension seemed to be a risk factor for multiple neuropsychiatric disorders, with no significant heterogeneity or pleiotropy. In FUMA analysis, 13 shared independent significant SNPs and 887 overlapping protein-coding genes were detected between neuropsychiatric disorders and CVD. With GO and KEEG functional enrichment analysis, biological pathways of the brain-heart axis were highly concentrated in neurotransmitter synaptic transmission, lipid metabolism, aldosterone synthesis and secretion, glutathione metabolism, and MAPK signaling pathway.

CONCLUSION

Extensive genetic correlations and genetic overlaps between neuropsychiatric disorders and CVD were identified in this study, which might provide some new insights into the brain-heart axis and the therapeutic targets in clinical practice.

Genetic influences on testosterone and PTSD

Females are twice as likely to experience PTSD as compared to males. Although sex differences in prevalence are well-established, little is known about why such sex differences occur. Biological factors that vary with sex, including sex hormone production, may contribute to these differences. Considerable evidence links sex hormones, such as testosterone, to PTSD risk though less is known about the shared genetic underpinnings. The objective of the present study was to test for genetic relationships between testosterone and PTSD. To do so, we used summary statistics from large, publicly available genetic consortia to conduct linkage disequilibrium score regression to estimate the genetic correlations between PTSD and testosterone in males and females, and two-sample, bi-directional Mendelian randomization to examine potential causal relationships of testosterone on PTSD and the reverse. Heritability estimates of testosterone were significantly higher in males (0.17, SE = 0.02) than females (0.11, SE = 0.01; z = 2.46, p = 00.01). The correlation between testosterone and PTSD was negative in males (rg = -0.11, SE = 0.02, p = 6.7 x 10-6), but not significant in females (rg = 0.002, SE = 0.03, p = 0.95). MR analyses found no evidence of a causal effect of testosterone on PTSD or the reverse. Findings are consistent with phenotypic literature suggesting a relationship between testosterone and PTSD that may be sex-specific. This work provides early evidence of a relationship between testosterone and PTSD genotypically and suggests an avenue for future research that will enable a better understanding of disparities in PTSD.

The causal relationship between human blood metabolites and the risk of visceral obesity: a mendelian randomization analysis

BACKGROUND

We aimed to explore the causal relationship between blood metabolites and the risk of visceral obesity, as measured by visceral adipose tissue (VAT).

METHODS

Summary statistics for 486 blood metabolites and total, as well as sex-stratified, MRI-derived VAT measurements, adjusted for body mass index (BMI) and height, were collected from previous genome-wide association studies (GWAS). A two-sample Mendelian Randomization (MR) design was used. Comprehensive evaluation was further conducted, including sensitivity analysis, linkage disequilibrium score (LDSC) regression, Steiger test, and metabolic pathway analysis.

RESULTS

After multiple testing correction, arachidonate (20:4n6) has been implicated in VAT accumulation (β = 0.35, 95%CI:0.18-0.52, P < 0.001; FDR = 0.025). Additionally, several blood metabolites were identified as potentially having causal relationship (FDR < 0.10). Among them, lysine (β = 0.67, 95%CI: 0.28-1.06, P < 0.001; FDR = 0.074), proline (β = 0.30, 95%CI:0.13-0.48, P < 0.001; FDR = 0.082), valerate (β = 0.50, 95%CI:0.23-0.78, P < 0.001, FDR = 0.091) are associated with an increased risk of VAT accumulation. On the other hand, glycine (β=-0.21, 95%CI: -0.33-0.09), P < 0.001, FDR = 0.076) have a protective effect against VAT accumulation. Most blood metabolites showed consistent trends between different sexes. Multivariable MR analysis demonstrated the effect of genetically predicted arachidonate (20:4n6) and proline on VAT remained after accounting for BMI and glycated hemoglobin (HbA1c). There is no evidence of heterogeneity, pleiotropy, and reverse causality.

CONCLUSION

Our MR findings suggest that these metabolites may serve as biomarkers, as well as for future mechanistic exploration and drug target selection of visceral obesity.

Genetically Determined Metabolites in Graves Disease: Insight From a Mendelian Randomization Study

Context

Graves disease (GD) is a prevalent autoimmune disorder with a complex etiology. The association between serum metabolites and GD remains partially understood.

Objective

This study aimed to elucidate the causal connections between serum metabolites and predisposition to GD, examining potential genetic interplay.

Methods

A 1-sample Mendelian randomization (MR) study was conducted on the GD analysis that included 2836 cases and 374 441 controls. We utilized genome-wide association study summary data from the FinnGen project, analyzing the causal impact of 486 serum metabolites on GD. Approaches used were the inverse variance weighted methodology, Cochran's Q test, MR-Egger regression, MR-PRESSO, Steiger test, and linkage disequilibrium score regression analyses to assess genetic influence on metabolites and GD.

Results

19 metabolites were identified as having a pronounced association with GD risk, of which 10 maintained noteworthy correlations after stringent sensitivity assessments. Three metabolites exhibited significant heritability: kynurenine (OR 3.851, P = 6.09 × 10-4), a risk factor; glycerol 2-phosphate (OR 0.549, P = 3.58 × 10-2) and 4-androsten-3beta,17beta-diol disulfate 2 (OR 0.461, P = 1.34 × 10-2) were recognized as protective factors against GD. Crucially, all 3 exhibited no shared genetic interrelation with GD, further substantiating their potential causal significance in the disease.

Conclusion

This study unveils pivotal insights into the intricate relationships between serum metabolites and GD risk. By identifying specific risk and protective factors, it opens avenues for more precise disease understanding and management. The findings underline the importance of integrating genomics with metabolomics to fathom the multifaceted nature of GD.

Robustness of quantifying mediating effects of genetically regulated expression on complex traits with mediated expression score regression

Genetic association signals have been mostly found in noncoding regions through genome-wide association studies (GWAS), suggesting the roles of gene expression regulation in human diseases and traits. However, there has been limited success in colocalizing expression quantitative trait locus (eQTL) with disease-associated variants. Mediated expression score regression (MESC) is a recently proposed method to quantify the proportion of trait heritability mediated by genetically regulated gene expressions (GReX). Applications of MESC to GWAS results have yielded low estimation of mediated heritability for many traits. As MESC relies on stringent independence assumptions between cis-eQTL effects, gene effects, and nonmediated SNP effects, it may fail to characterize the true relationships between those effect sizes, which leads to biased results. Here, we consider the robustness of MESC to investigate whether the low fraction of mediated heritability inferred by MESC reflects biological reality for complex traits or is an underestimation caused by model misspecifications. Our results suggest that MESC may lead to biased estimates of mediated heritability with misspecification of gene annotations leading to underestimation, whereas misspecification of SNP annotations may lead to overestimation. Furthermore, errors in eQTL effect estimates may lead to underestimation of mediated heritability.

Understanding the relationship between asthma and autism spectrum disorder: a population-based family and twin study

BACKGROUND

There is some evidence that autism spectrum disorder (ASD) frequently co-occurs with immune-mediated conditions including asthma. We aimed to explore the familial co-aggregation of ASD and asthma using different genetically informed designs.

METHODS

We first examined familial co-aggregation of asthma and ASD in individuals born in Sweden from 1992 to 2007 (n = 1 569 944), including their full- and half-siblings (n = 1 704 388 and 356 544 pairs) and full cousins (n = 3 921 890 pairs), identified using Swedish register data. We then applied quantitative genetic modeling to siblings (n = 620 994 pairs) and twins who participated in the Child and Adolescent Twin Study in Sweden (n = 15 963 pairs) to estimate the contribution of genetic and environmental factors to the co-aggregation. Finally, we estimated genetic correlations between traits using linkage disequilibrium score regression (LDSC).

RESULTS

We observed a within-individual association [adjusted odds ratio (OR) 1.33, 95% confidence interval (CI) 1.28-1.37] and familial co-aggregation between asthma and ASD, and the magnitude of the associations decreased as the degree of relatedness decreased (full-siblings: OR 1.44, 95% CI 1.38-1.50, maternal half-siblings: OR 1.28, 95% CI 1.18-1.39, paternal half-siblings: OR 1.05, 95% CI 0.96-1.15, full cousins: OR 1.06, 95% CI 1.03-1.09), suggesting shared familial liability. Quantitative genetic models estimated statistically significant genetic correlations between ASD traits and asthma. Using the LDSC approach, we did not find statistically significant genetic correlations between asthma and ASD (coefficients between -0.09 and 0.12).

CONCLUSIONS

Using different genetically informed designs, we found some evidence of familial co-aggregation between asthma and ASD, suggesting the weak association between these disorders was influenced by shared genetics.

Genome- and transcriptome-wide association studies show that pulmonary embolism is associated with bone-forming proteins

BACKGROUND

Pulmonary embolism (PE) is a leading cause of death in stroke patients and a severe health burden worldwide. There is a pressing need to understand the mechanisms by which it occurs and to identify at-risk patients efficiently and accurately.

OBJECTIVES

The aim of this paper was to analyze the genetic correlation between PE and human plasma proteins through genome-wide association study (GWAS) with transcriptome-wide association study (TWAS), in combination with mRNA expression profiling at three levels: DNA, RNA, and protein.

METHODS

First, based on data from GWAS in European populations, we performed a linkage disequilibrium score regression (LDSC) analysis of plasma proteins and PE in 3,283 individuals and additionally analyzed the genetic association between PE and fracture. Then, we performed a TWAS on PE GWAS data using skeletal muscle and blood for gene expression references. Finally, we validated the genetic correlation between PE and human plasma proteins by co-matching the genes encoding the identified proteins and those identified using TWAS with the differentially expressed genes obtained from mRNA expression profiling of PE (Figure1).

RESULTS

We identified five plasma proteins associated with PE, including hydroxycarboxylic acid receptor 2, defensin 118, and bone morphogenetic protein (BMP) 7, as well as a relationship between PE and fracture. Comparison of genes encoding these proteins with genes obtained from TWAS and then with differentially expressed genes obtained from PE mRNA expression profiling revealed that PE was highly correlated with the BMP family of genes.

GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data

Genetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.

Molecular genetic overlap between posttraumatic stress disorder and sleep phenotypes

STUDY OBJECTIVES

Sleep problems are common, serving as both a predictor and symptom of posttraumatic stress disorder (PTSD), with these bidirectional relationships well established in the literature. While both sleep phenotypes and PTSD are moderately heritable, there has been a paucity of investigation into potential genetic overlap between sleep and PTSD. Here, we estimate genetic correlations between multiple sleep phenotypes (including insomnia symptoms, sleep duration, daytime sleepiness, and chronotype) and PTSD, using results from the largest genome-wide association study (GWAS) to date of PTSD, as well as publicly available GWAS results for sleep phenotypes within UK Biobank data (23 variations, encompassing four main phenotypes).

METHODS

Genetic correlations were estimated utilizing linkage disequilibrium score regression (LDSC), an approach that uses GWAS summary statistics to compute genetic correlations across traits, and Mendelian randomization (MR) analyses were conducted to follow up on significant correlations.

RESULTS

Significant, moderate genetic correlations were found between insomnia symptoms (rg range 0.36-0.49), oversleeping (rg range 0.32-0.44), undersleeping (rg range 0.48-0.49), and PTSD. In contrast, there were mixed results for continuous sleep duration and daytime sleepiness phenotypes, and chronotype was not correlated with PTSD. MR analyses did not provide evidence for casual effects of sleep phenotypes on PTSD.

CONCLUSION

Sleep phenotypes, particularly insomnia symptoms and extremes of sleep duration, have shared genetic etiology with PTSD, but causal relationships were not identified. This highlights the importance of further investigation into the overlapping influences on these phenotypes as sample sizes increase and new methods to investigate directionality and causality become available.

Exploring the genetic overlap between psychiatric illness and epilepsy: A review

There is a long-documented epidemiological link between epilepsy and psychiatric disorders. People with epilepsy are at an increased risk for a variety of psychiatric illnesses, as are their family members, and people with epilepsy may experience psychiatric side effects because of their antiepileptic drugs (AEDs). In recent years, large-scale, collaborative international studies have begun to shed light on the role of genetic variation in both epilepsy and psychiatric illnesses, such as schizophrenia, depression, and anxiety. But so far, finding shared genetic links between epilepsy and psychiatric illness has proven surprisingly difficult. This review will discuss the prevalence of psychiatric comorbidities in epilepsy, recent advances in genetic research into both epilepsy and psychiatric illness, and the extent of our current knowledge of the genetic overlap between these two important neurobiological conditions.