Causal associations and genetic overlap between COVID-19 and intelligence

Causal effect of COVID-19 on longitudinal volumetric changes in subcortical structures: A mendelian randomization study

A few observational neuroimaging investigations have reported subcortical structural changes in the individuals who recovered from the coronavirus disease-2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the causal relationships between COVID-19 and longitudinal changes of subcortical structures remain unclear. We performed two-sample Mendelian randomization (MR) analyses to estimate putative causal relationships between three COVID-19 phenotypes (susceptibility, hospitalization, and severity) and longitudinal volumetric changes of seven subcortical structures derived from MRI. Our findings demonstrated that genetic liability to SARS-CoV-2 infection had a great long-term impact on the volumetric reduction of subcortical structures, especially caudate. Our investigation may contribute in part to the understanding of the neural mechanisms underlying COVID-19-related neurological and neuropsychiatric sequelae.

Exploring the causal effects of depression and antidepressants on COVID-19

BACKGROUND

While existing studies have suggested an increased risk of COVID-19 in patients with depression, the causal impact of MDD on the severity of COVID-19 remains to be validated. Additionally, the potential impact of antidepressant medication on the risk of COVID-19 is not known.

METHODS

In our study, we applied a Mendelian Randomization (MR) method, leveraging summary data from GWAS, to evaluate the potential causal effects of depression on three COVID-19 outcomes. Furthermore, we investigated the causal effects of antidepressants on COVID-19 outcomes. The COVID-19 datasets contain information on various stages of the disease, including SARS-CoV-2 infection (N = 2,597,856), hospitalized COVID-19 (N = 2,095,324), and critical COVID-19 (N = 1,086,211). Datasets for depression and antidepressants were comprised of 1,349,887 and 106,785 participants, respectively.

RESULTS

Employing the inverse variance-weighted (IVW) method, we show a causal association between depression and three COVID-19 outcomes. Specifically, we found that genetic liability to depression is linked to critical COVID-19 (OR: 1.28, 95 % CI: 1.13-1.46), hospitalized COVID-19 (OR: 1.23, 95 % CI: 1.13-1.34), and SARS-CoV-2 infection (OR: 1.06, 95 % CI: 1.02-1.10). Interestingly, the use of antidepressants was not associated with COVID-19, with the odds ratios for critical COVID-19 (OR: 1.05, 95 % CI: 0.88-1.26), hospitalization (OR: 1.01, 95 % CI: 0.90-1.13), and SARS-CoV-2 infection (OR: 1.03, 95 % CI: 0.99-1.08) indicating no causal impact.

CONCLUSION

Our study indicates that genetic liability to depression may increase the susceptibility to COVID-19 and its severe forms. The lack of causal effect of antidepressant use on COVID-19 implies antidepressant medication may counteract the detrimental effect of depression on COVID-19.

Causal association of attention-deficit/hyperactivity disorder and autism spectrum disorder with post-traumatic stress disorder

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are two neurodevelopmental disorders that often result in individuals experiencing traumatic events. However, little is known about the connection between ADHD/ASD and post-traumatic stress disorder (PTSD). This study aimed to investigate the genetic associations between these disorders.

METHODS

Genetic correlation analysis was used to examine the genetic components shared between ADHD (38 691 cases and 275 986 controls), ASD (18 381 cases and 27 969 controls) and PTSD (23 212 cases and 151 447 controls). Two-sample Mendelian randomization analyses were employed to explore the bidirectional causal relationships between ADHD/ASD and PTSD.

RESULTS

The results of the genetic correlation analysis revealed significant positive correlations of PTSD with ADHD(r g  = 0.70) and ASD (r g  = 0.34). Furthermore, the Mendelian randomization analysis revealed that genetic liabilities to ADHD [odds ratio (OR) = 1.14; 95% confidence interval (CI), 1.06-1.24; P  = 7.88 × 10 -4 ] and ASD (OR = 1.04; CI, 1.01-1.08; P  = 0.014) were associated with an increased risk of developing PTSD later in life. However, no evidence supported that genetic liability to PTSD could elevate the risk of ADHD or ASD.

CONCLUSION

The findings of this study supported that ADHD and ASD may increase the risk of PTSD, but not vice versa.

Exploring the influences of education, intelligence and income on mental disorders

Background

Previous studies have shown that educational attainment (EA), intelligence and income are key factors associated with mental disorders. However, the direct effects of each factor on major mental disorders are unclear.

Aims

We aimed to evaluate the overall and independent causal effects of the three psychosocial factors on common mental disorders.

Methods

Using genome-wide association study summary datasets, we performed Mendelian randomisation (MR) and multivariable MR (MVMR) analyses to assess potential associations between the 3 factors (EA, N=766 345; household income, N=392 422; intelligence, N=146 808) and 13 common mental disorders, with sample sizes ranging from 9907 to 807 553. Inverse-variance weighting was employed as the main method in the MR analysis.

Results

Our MR analysis showed that (1) higher EA was a protective factor for eight mental disorders but contributed to anorexia nervosa, obsessive-compulsive disorder (OCD), bipolar disorder (BD) and autism spectrum disorder (ASD); (2) higher intelligence was a protective factor for five mental disorders but a risk factor for OCD and ASD; (3) higher household income protected against 10 mental disorders but confers risk for anorexia nervosa. Our MVMR analysis showed that (1) higher EA was a direct protective factor for attention-deficit/hyperactivity disorder (ADHD) and insomnia but a direct risk factor for schizophrenia, BD and ASD; (2) higher intelligence was a direct protective factor for schizophrenia but a direct risk factor for major depressive disorder (MDD) and ASD; (3) higher income was a direct protective factor for seven mental disorders, including schizophrenia, BD, MDD, ASD, post-traumatic stress disorder, ADHD and anxiety disorder.

Conclusions

Our study reveals that education, intelligence and income intertwine with each other. For each factor, its independent effects on mental disorders present a more complex picture than its overall effects.

Causal influences of neuropsychiatric disorders on Alzheimer's disease

Previous studies have observed a significant comorbidity between Alzheimer's disease (AD) and some other neuropsychiatric disorders. However, the mechanistic connections between neuropsychiatric disorders and AD are not well understood. We conducted a Mendelian randomization analysis to appraise the potential influences of 18 neurodegenerative and neuropsychiatric disorders on AD. We found that four disorders are causally associated with increased risk for AD, including bipolar disorder (BD) (OR: 1.09), migraine (OR: 1.09), schizophrenia (OR: 1.05), and Parkinson's disease (PD) (OR: 1.07), while attention-deficit/hyperactivity disorder (ADHD) was associated with a decreased risk for AD (OR: 0.80). In case of amyotrophic lateral sclerosis (OR: 1.04) and Tourette's syndrome (OR: 1.05), there was suggestive evidence of their causal effects of on AD. Our study shows that genetic components predisposing to BD, migraine, schizophrenia, and PD may promote the development of AD, while ADHD may be associated with a reduced risk of AD. The treatments aimed at alleviating neuropsychiatric diseases with earlier onset may also influence the risk of AD-related cognitive decline, which is typically observed later in life.

Causal associations between COVID-19 and childhood mental disorders

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can invade both the peripheral and central nervous systems and impact the function of the brain. Therefore, it is necessary to evaluate the mutual influences between COVID-19 outcomes and childhood mental disorders.

METHODS

We examined genetic correlations and potential causalities between three childhood mental disorders and three COVID-19 phenotypes by genetically proxied analyses. The three mental disorders included attention-deficit/hyperactivity disorder (ADHD, N = 292,548), Tourette's syndrome (TS, N = 14,307), and autism spectrum disorder (ASD, N = 46,350). The three COVID-19 traits included SARS-CoV-2 infection (N = 2,597,856), hospitalized COVID-19 (N = 2,095,324), and critical COVID-19 (N = 1,086,211). Literature-based analysis was used to build gene-based pathways connecting ADHD and COVID-19.

RESULTS

ADHD was positively correlated with the three COVID-19 outcomes (Rg: 0.22 ~ 0.30). Our Mendelian randomization (MR) analyses found that ADHD confers a causal effect on hospitalized COVID-19 (odds ratio (OR): 1.36, 95% confidence interval (CI): 1.10-1.69). TS confers a causal effect on critical COVID-19 (OR: 1.14, 95% CI: 1.04-1.25). Genetic liability to the COVID-19 outcomes may not increase the risk for the childhood mental disorders. Pathway analysis identified several immunity-related genes that may link ADHD to COVID-19, including CRP, OXT, IL6, PON1, AR, TNFSF12, and IL10.

CONCLUSIONS

Our study suggests that both ADHD and TS may augment the severity of COVID-19 through immunity-related pathways. However, our results did not support a causal role of COVID-19 in the risk for the childhood mental disorders.

Causal association of COVID-19 with brain structure changes: Findings from a non-overlapping 2-sample Mendelian randomization study

Recent cohort studies suggested that SARS-CoV-2 infection is associated with changes in brain structure. However, the potential causal relationship remains unclear. We performed a two-sample Mendelian randomization analysis to determine whether genetic susceptibility of COVID-19 is causally associated with changes in cortical and subcortical areas of the brain. This 2-sample MR (Mendelian Randomization) study is an instrumental variable analysis of data from the COVID-19 Host Genetics Initiative (HGI) meta-analyses round 5 excluding UK Biobank participants (COVID-19 infection, N = 1,348,701; COVID-19 severity, N = 1,557,411), the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) Global and regional cortical measures, N = 33,709; combined hemispheric subcortical volumes, N = 38,851), and UK Biobank (left/right subcortical volumes, N = 19,629). A replication analysis was performed on summary statistics from different COVID-19 GWAS study (COVID-19 infection, N = 80,932; COVID-19 severity, N = 72,733). We found that the genetic susceptibility of COVID-19 was not significantly associated with changes in brain structures, including cortical and subcortical brain structure. Similar results were observed for different (1) MR estimates, (2) COVID-19 GWAS summary statistics, and (3) definitions of COVID-19 infection and severity. This study suggests that the genetic susceptibility of COVID-19 is not causally associated with changes in cortical and subcortical brain structure.

Causal influences of osteoarthritis on COVID-19: a Mendelian randomization study

Objective

Although observational and genetic studies have indicated a correlation between OA and COVID-19, it remains uncertain whether osteoarthritis (OA) contributes to the severity of COVID-19. Here, we aimed to investigate the potential causal links between the two.

Methods

In this study, we conducted Mendelian randomization (MR) analysis to investigate whether there is a potential causal connection between OA and COVID-19 outcomes. The analysis utilized publicly available GWAS summary datasets, incorporating data on OA (N = 455,221), SARS-CoV-2 infection (N = 2,597,856), hospitalized COVID-19 (N = 2,095,324), and critical COVID-19 (N = 1,086,211). Additionally, we performed a literature analysis to establish a molecular network connecting OA and COVID-19.

Results

The MR analysis showed causal effects of OA on hospitalized COVID-19 (OR: 1.21, 95% CI: 1.02-1.43, p = 0.026) and critical COVID-19 (OR: 1.35, 95% CI: 1.09-1.68, p = 0.006) but not on SARS-CoV-2 infection as such (OR: 1.00, 95% CI: 0.92-1.08, p = 0.969). Moreover, the literature-based pathway analysis uncovered a set of specific genes, such as CALCA, ACE, SIRT1, TNF, IL6, CCL2, and others, that were found to mediate the association between OA and COVID-19.

Conclusion

Our findings indicate that OA elevates the risk of severe COVID-19. Therefore, larger efforts should be made in the prevention of COVID-19 in OA patients.

Integrating population-level and cell-based signatures for drug repositioning

Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Furthermore, drugs with genetic evidence are more likely to progress successfully through clinical trials towards FDA approval. Exploiting these developments, single gene-based drug repositioning methods have been implemented, but approaches leveraging the entire spectrum of molecular signatures are critically underexplored. Most multi-gene-based approaches rely on differential gene expression (DGE) analysis, which is prone to identify the molecular consequence of disease and renders causal inference challenging. We propose a framework TReD (Transcriptome-informed Reversal Distance) that integrates population-level disease signatures robust to reverse causality and cell-based drug-induced transcriptome response profiles. TReD embeds the disease signature and drug profile in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell screen assay. The robustness is ensured by evaluation in additional cell screens. For an application, we implement the framework to identify potential drugs against COVID-19. Taking transcriptome-wide association study (TWAS) results from four relevant tissues and three DGE results as disease features, we identify 37 drugs showing potential reversal roles in at least four of the seven disease signatures. Notably, over 70% (27/37) of the drugs have been linked to COVID-19 from other studies, and among them, eight drugs are supported by ongoing/completed clinical trials. For example, TReD identifies the well-studied JAK1/JAK2 inhibitor baricitinib, the first FDA-approved immunomodulatory treatment for COVID-19. Novel potential candidates, including enzastaurin, a selective inhibitor of PKC-beta which can be activated by SARS-CoV-2, are also identified. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screens that can accelerate the search for new therapeutic strategies.

Mendelian Randomization Analyses Accounting for Causal Effect of COVID-19 on Brain Imaging-Derived Phenotypes

BACKGROUND

The coronavirus disease 2019 (COVID-19) has been a major challenge to global health and a financial burden. Little is known regarding the possible causal effects of COVID-19 on the macro- and micro-structures of the human brain.

OBJECTIVE

To determine the causal links between susceptibility, hospitalization, and the severity of COVID-19 and brain imaging-derived phenotypes (IDPs).

METHODS

Mendelian randomization (MR) analyses were performed to investigate the causal effect of three COVID-19 exposures (SARS-CoV-2 infection, hospitalized COVID-19, and critical COVID-19) on brain structure employing summary datasets of genome-wide association studies.

RESULTS

In terms of cortical phenotypes, hospitalization due to COVID-19 was associated with a global decrease in the surface area (SA) of the cortex structure (β= -624.77, 95% CI: -1227.88 to -21.66, p = 0.042). At the regional level, SARS-CoV-2 infection was found to have a nominally causal effect on the thickness (TH) of the postcentral region (β= -0.004, 95% CI: -0.007 to -0.001, p = 0.01), as well as eight other IDPs. Hospitalized COVID-19 has a nominally causal relationship with TH of postcentral (β= -0.004, 95% CI: -0.007 to -0.001, p = 0.01) and other 6 IDPs. The nominally causal effects of critical COVID-19 on TH of medial orbitofrontal (β=0.004, 95% CI: 0.001to 0.007, p = 0.004) and other 7 IDPs were revealed.

CONCLUSIONS

Our study provides compelling genetic evidence supporting causal relationships between three COVID-19 traits and brain IDPs. This discovery holds promise for enhancing predictions and interventions in brain imaging.