A framework to decipher the genetic architecture of combinations of complex diseases: applications in cardiovascular medicine.

MOTIVATION: Currently, most genome-wide association studies (GWAS) are studies of a single disease against controls. However, an individual is often affected by more than one condition. For example, coronary artery disease (CAD) is often comorbid with type 2 diabetes mellitus (T2DM). Similarly, it is clinically meaningful to study patients with one disease but without a related comorbidity. For example, obese T2DM may have different pathophysiology from nonobese T2DM. RESULTS: We developed a statistical framework (CombGWAS) to uncover susceptibility variants for comorbid disorders (or a disorder without comorbidity), using GWAS summary statistics only. In essence, we mimicked a case-control GWAS in which the cases are affected with comorbidities or a disease without comorbidity. We extended our methodology to analyze continuous traits with clinically meaningful categories (e.g. lipids), and combination of more than two traits. We verified the feasibility and validity of our method by applying it to simulated scenarios and four cardiometabolic (CM) traits. In total, we identified 384 and 587 genomic risk loci respectively for 6 comorbidities and 12 CM disease 'subtypes' without a relevant comorbidity. Genetic correlation analysis revealed that some subtypes may be biologically distinct from others. Further Mendelian randomization analysis showed differential causal effects of different subtypes to relevant complications. For example, we found that obese T2DM is causally related to increased risk of CAD (P = 2.62E-11). AVAILABILITY AND IMPLEMENTATION: R code is available at: https://github.com/LiangyingYin/CombGWAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Causal relationships between blood lipids and depression phenotypes: a Mendelian randomisation analysis.

BACKGROUND: The etiology of depression remains poorly understood. Changes in blood lipid levels were reported to be associated with depression and suicide, however study findings were mixed. METHODS: We performed a two-sample Mendelian randomisation (MR) analysis to investigate the causal relationship between blood lipids and depression phenotypes, based on large-scale GWAS summary statistics (N = 188 577/480 359 for lipid/depression traits respectively). Five depression-related phenotypes were included, namely major depression (MD; from PGC), depressive symptoms (DS; from SSGAC), longest duration and number of episodes of low mood, and history of deliberate self-harm (DSH)/suicide (from UK Biobank). MR was conducted with inverse-variance weighted (MR-IVW), Egger and Generalised Summary-data-based MR (GSMR) methods. RESULTS: There was consistent evidence that triglyceride (TG) is causally associated with DS (MR-IVW ß for one-s.d. increase in TG = 0.0346, 95% CI 0.0114-0.0578), supported by MR-IVW and GSMR and multiple r2 clumping thresholds. We also observed relatively consistent associations of TG with DSH/suicide (MR-Egger OR = 2.514, CI 1.579-4.003). There was moderate evidence for positive associations of TG with MD and the number of episodes of low mood. For HDL-c, we observed moderate evidence for causal associations with DS and MD. LDL-c and TC did not show robust causal relationships with depression phenotypes, except for weak evidence that LDL-c is inversely related to DSH/suicide. We did not detect significant associations when depression phenotypes were treated as exposures. CONCLUSIONS: This study provides evidence to a causal relationship between TG, and to a lesser extent, altered cholesterol levels with depression phenotypes. Further studies on its mechanistic basis and the effects of lipid-lowering therapies are warranted.

Translating GWAS findings into therapies for depression and anxiety disorders: gene-set analyses reveal enrichment of psychiatric drug classes and implications for drug repositioning.

BACKGROUND: Depression and anxiety disorders (AD) are the first and sixth leading causes of disability worldwide. Despite their high prevalence and significant disability resulted, there are limited advances in new drug development. Recently, genome-wide association studies (GWAS) have greatly advanced our understanding of the genetic basis underlying psychiatric disorders. METHODS: Here we employed gene-set analyses of GWAS summary statistics for drug repositioning. We explored five related GWAS datasets, including two on major depressive disorder (MDD2018 and MDD-CONVERGE, with the latter focusing on severe melancholic depression), one on AD, and two on depressive symptoms and neuroticism in the population. We extracted gene-sets associated with each drug from DSigDB and examined their association with each GWAS phenotype. We also performed repositioning analyses on meta-analyzed GWAS data, integrating evidence from all related phenotypes. RESULTS: Importantly, we showed that the repositioning hits are generally enriched for known psychiatric medications or those considered in clinical trials. Enrichment was seen for antidepressants and anxiolytics but also for antipsychotics. We also revealed new candidates or drug classes for repositioning, some of which were supported by experimental or clinical studies. For example, the top repositioning hit using meta-analyzed p values was fendiline, which was shown to produce antidepressant-like effects in mouse models by inhibition of acid sphingomyelinase. CONCLUSION: Taken together, our findings suggest that human genomic data such as GWAS are useful in guiding drug discoveries for depression and AD.

Differential associations of depression-related phenotypes with cardiometabolic risks: Polygenic analyses and exploring shared genetic variants and pathways.

BACKGROUND: Numerous studies have suggested associations between depression and cardiometabolic (CM) diseases. However, little is known about the mechanism underlying this comorbidity, and whether the relationship differs by depression subtypes. METHODS: Using polygenic risk scores (PRS) and linkage disequilibrium (LD) score regression, we investigated the genetic overlap of various depression-related phenotypes with a comprehensive panel of 20 CM traits. GWAS results for major depressive disorder (MDD) were taken from the PGC and CONVERGE studies, with the latter focusing on severe melancholic depression. GWAS results on general depressive symptoms (DS) and neuroticism were also included. We identified the shared genetic variants and inferred enriched pathways. We also looked for drugs over-represented among the top-shared genes, with an aim to finding repositioning opportunities for comorbidities. RESULTS: We found significant genetic overlap between MDD, DS, and neuroticism with cardiometabolic traits. In general, positive polygenic associations with CM abnormalities were observed except for MDD-CONVERGE. Counterintuitively, PRS representing severe melancholic depression was associated with reduced CM risks. Enrichment analyses of shared SNPs revealed many interesting pathways such as those related to inflammation that underlie the comorbidity of depressive and CM traits. Using a gene-set analysis approach, we also revealed several repositioning candidates with literature support (e.g., bupropion). CONCLUSIONS: Our study highlights shared genetic bases of depression with CM traits, and suggests the associations vary by depression subtypes, which may have implications in targeted prevention of cardiovascular events for patients. Identification of shared genetic factors may also guide drug discovery for the comorbidities.

Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry.

Knowledge of psychiatric disease genetics has advanced rapidly during the past decade with the advent of genome-wide association studies (GWAS). However, less progress has been made in harnessing these data to reveal new therapies. Here we propose a framework for drug repositioning by comparing transcriptomes imputed from GWAS data with drug-induced gene expression profiles from the Connectivity Map database and apply this approach to seven psychiatric disorders. We found a number of repositioning candidates, many supported by preclinical or clinical evidence. Repositioning candidates for a number of disorders were also significantly enriched for known psychiatric medications or therapies considered in clinical trials. For example, candidates for schizophrenia were enriched for antipsychotics, while those for bipolar disorder were enriched for both antipsychotics and antidepressants. These findings provide support for the usefulness of GWAS data in guiding drug discovery.