Biology subjects

Derks, E. M.

Publications and source records attributed to Derks, E. M..

7 recordsLinked to original sources

bioRxiv2026

Computational drug repurposing identifies N-acetylglucosamine as a potential therapeutic compound for CLN3 Batten disease

Batten disease, also known as neuronal ceroid lipofuscinoses, is one of the most common causes of childhood dementia. It is characterized by the accumulation of lipofuscin in lysosomes, leading to loss of brain cell function, onset of dementia-like symptoms, vision loss and seizures and has extremely limited treatment options. Here, we performed computational drug repurposing analysis to identify existing compounds that may target Batten disease risk genes. A total of 81 candidate compounds were identified, 6 of which were selected based on clinical tractability for downstream testing in Batten disease (CLN3) iPSC-derived models. After confirming disease phenotype and drug candidate safety, CLN3 brain cell cultures treated with and without drug candidates underwent bulk RNA-seq to identify drug responses. One of the candidate drugs N-acetylglucosamine (GlcNAc) significantly upregulated Batten disease risk gene CLN5 expression and several other lysosomal markers within CLN3 brain cells, and modulated several pathways implicated in lysosomal storage disorders. Importantly, GlcNAc significantly reduced lipofuscin burden in both CLN3 iPSC-derived neurons and astrocytes, supporting its investigation as an additional therapy for Batten disease.

neuroscience↗

bioRxiv2021

A systematic analysis of genetically regulated differences in gene expression and the role of co-expression networks across 16 psychiatric disorders and substance use phenotypes

Genome-wide association studies (GWASs) have identified thousands of risk loci for many psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (collectively termed "mental health phenotypes") using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation due to predicted genetically regulated expression between pairs of mental health phenotypes, and compared the results with the genetic correlations. We identified 1,645 genes with at least one significant trait association, comprising 2,176 significant associations across the 16 mental health phenotypes of which 572 (26%) are novel. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, showed a much higher transcriptomic correlation (r=0.84) than genetic correlation (r=0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from prefrontal cortex. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.

bioinformatics↗

bioRxiv2019

eMAGMA: An eQTL-informed method to identify risk genes using genome-wide association study summary statistics

Identifying genes underlying genetic associations of complex disease is challenging because most common risk variants reside in non-protein coding regions of the genome and likely alter the expression of target genes by disrupting tissue and cell-type specific regulatory elements. To address this challenge, we developed a methodological framework, eQTL-MAGMA (eMAGMA), that converts SNP-level summary statistics into gene-level association statistics by assigning non-coding SNPs to their putative genes based on tissue-specific eQTL information. We compared eMAGMA to three eQTL informed gene-based approaches--S-PrediXcan, FUSION, and SMR--using simulated phenotype data. Phenotypes were simulated based on eQTL reference data using GCTA for all genes with at least one eQTL at chromosome 1 (651 genes). We performed 10 simulations per gene. The eQTL-h2 (i.e., the proportion of variation explained by the eQTLs was set at 1%, 2%, and 5%. We found eMAGMA outperforms other gene-based approaches across a range of simulated parameters (e.g. the number of identified causal genes). When applied to genome-wide association summary statistics for major depression, eMAGMA identified substantially more putative candidate causal genes compared to other eQTL-based approaches. By integrating tissue-specific eQTL information, these results show eMAGMA will help to identify novel candidate causal genes from genome-wide association summary statistics and thereby improve the understanding of the biological basis of complex disorders.

genetics↗

bioRxiv2019

An Integrative Network-based Analysis Reveals Gene Networks, Biological Mechanisms, and Novel Drug Targets in Alzheimer's Disease

Alzheimers disease is a highly heritable and severe neuropsychiatric condition. Genome-wide association studies have identified multiple genetic risk factors underlying susceptibility to Alzheimers disease, however their functional impact remains poorly understood. To overcome this shortcoming, we integrated genome-wide association summary statistics (71,880 cases, 338,378 controls) with tissue-specific gene co-expression networks derived from GTEx to identify functional gene co-expression networks underlying the disease. We found genetic variants associated with Alzheimers disease are enriched in gene co-expression networks involved in immune response-related biological processes. The implicated gene co-expression networks are preserved across multiple brain and peripheral tissues, highlighting the potential utility of peripheral tissues in genetic studies of Alzheimers disease. We also performed a computational drug repositioning analysis by integrating gene expression changes within Alzheimers disease modules with drug-gene signatures from the Connectivity Map, and show disease implicated networks retrieve known Alzheimers disease drugs and novel drug repurposing candidates for follow-up functional studies. Our results improve the biological interpretation of recent genetic data for Alzheimers disease and provide a list of potential anti-dementia drug repositioning candidates of which the efficacy should be investigated in functional validation studies.

genetics↗

bioRxiv2018

Evidence of causal effect of major depression on alcohol dependence: Findings from the Psychiatric Genomics Consortium

BackgroundDespite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood.\n\nMethodsThis study was conducted using genome-wide data from the Psychiatric Genomics Consortium (MD: 135,458 cases and 344,901 controls; AD: 10,206 cases and 28,480 controls) and UK Biobank (AC-Frequency: from \"daily or almost daily\" to \"never\", 438,308 individuals; AC-Quantity: total units of alcohol per week, 307,098 individuals). Linkage disequilibrium score regression and Mendelian Randomization (MR) analyses were applied to investigate shared genetic mechanisms (horizontal pleiotropy) and causal relationships (mediated pleiotropy) among these traits.\n\nOutcomesPositive genetic correlation was observed between MD and AD (rgMD-AD=+0.47, P=6.6x10-10). AC-Quantity showed positive genetic correlation with both AD (rgAD-AC-Quantity=+0.75, P=1.8x10-14) and MD (rgMD-AC-Quantity=+0.14, P=2.9x10-7), while there was negative correlation of AC-Frequency with MD (rgMD-AC-Frequency=-0.17, P=1.5x10-10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e., causal relationship) with a causal role of MD on AD (beta=0.28, P=1.29x10-6) that does not appear to be biased by confounding such as horizontal pleiotropy. No evidence of reverse causation was observed as the AD genetic instrument did not show a causal effect on MD.\n\nInterpretationResults support a causal role for MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity not only addresses important public health concerns but also has the potential to facilitate prevention and intervention efforts.\n\nFundingNational Institute of Mental Health and National Institute on Drug Abuse.\n\nPutting data into contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed up to August 24, 2018, for research studies that investigated causality among alcohol-and depression related phenotypes using Mendelian randomization approaches. We used the search terms \"alcohol\" AND \"depression\" AND \"Mendelian Randomization\". No restrictions were applied to language, date, or article type. Ten articles were retrieved, but only two were focused on alcohol consumption and depression-related traits. The studies were based on genetic variants in alcohol dehydrogenase (ADH) genes only, did not find evidence for a causal effect of alcohol consumption on depression phenotypes, with one study finding a causal effect of alcohol consumption on alcoholism. Both studies noted that future studies are needed with increased sample sizes and clinically derived phenotypes. To our knowledge, no previous study has applied two-sample Mendelian randomization to investigate causal relationships between alcohol dependence and major depression.\n\nTwin studies show genetic factors influence susceptibility to MD, AD, and alcohol consumption. Differently from observational approaches where several studies have investigated the relationship between alcohol-and depression-related phenotypes, very limited use of molecular genetic data has been applied to investigate this issue. Additionally, the use of genetic information has been shown to be less biased by confounders and reverse causation than observation data. However, genetic approaches, like Mendelian randomization, require large sample sizes to be informative.\n\nAdded value of this studyIn this study, we used genome-wide data from the Psychiatric Genomic Consortium and UK Biobank, which include information regarding hundred thousands of individuals, to test the presence of shared genetic mechanisms and causal relationships among major depression, alcohol dependence, and alcohol consumption. The results support a causal influence of MD on AD, while alcohol consumption showed shared genetic mechanisms with respect to both major depression and alcohol dependence.\n\nImplications of all the available evidenceGiven the significant morbidity and mortality associated with MD, AD, and the comorbid condition, understanding mechanisms underlying these associations not only address important public health concerns but also has the potential to facilitate prevention and intervention efforts.

genetics↗

bioRxiv2018

Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder

The major depressive disorder (MDD) working group of the Psychiatric Genomics Consortium (PGC) has published a genome-wide association study (GWAS) for MDD in 130,664 cases, identifying 44 risk variants. We used these results to investigate potential drug targets and repurposing opportunities. We built easily interpretable bipartite drug-target networks integrating interactions between drugs and their targets, genome-wide association statistics and genetically predicted expression levels in different tissues, using our online tool Drug Targetor (drugtargetor.com). We also investigated drug-target relationships and drug effects on gene expression that could be impacting MDD. MAGMA was used to perform pathway analyses and S-PrediXcan to investigate the directionality of tissue-specific expression levels in patients vs. controls. Outside the major histocompatibility complex (MHC) region, 25 druggable genes were significantly associated with MDD after multiple testing correction, and 19 were suggestively significant. Several drug classes were significantly enriched, including monoamine reuptake inhibitors, sex hormones, antipsychotics and antihistamines, indicating an effect on MDD and potential repurposing opportunities. These findings require validation in model systems and clinical examination, but also show that GWAS may become a rich source of new therapeutic hypotheses for MDD and other psychiatric disorders that need new - and better - treatment options.

genomics↗

bioRxiv2017

Sex differences in the genetic architecture of obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD), a highly heritable complex phenotype, demonstrates sexual dimorphism in age of onset and clinical presentation, suggesting a possible sex difference in underlying genetic architecture. We present the first genome-wide characterization of the sex-specific genetic architecture of OCD, utilizing the largest set of OCD cases and controls available from the Psychiatric Genomics Consortium. We assessed evidence for several mechanisms that may contribute to sexual-dimorphism including a sexually dimorphic liability threshold, the presence of individual sex-specific risk variants on the autosomes and the X chromosome, genetic and phenotypic heterogeneity, and sex-specific pleiotropic effects. We observed a strong genetic correlation between male and female OCD and no evidence for a sexually dimorphic liability threshold model. While we did not detect any sex-specific genome-wide associations, we observed that the SNPs with sexually dimorphic effects showed an enrichment of regulatory variants influencing expression of genes in immune tissues. Furthermore, top sex-specific genome-wide associations were enriched for regulatory variants in different tissues, suggesting evidence for potential sex difference in the biology underlying risk for OCD. These findings suggest that future studies with larger sample sizes hold great promise for the identification of sex-specific risk factors for OCD, significantly advancing our understanding of the differences in the genetic basis of sexually dimorphic neuropsychiatric traits.

genomics↗