Variations and expression features of CYP2D6 contribute to schizophrenia risk

Ma, Liang; Chetty, Sundari

doi:10.1101/659102

Cited by 3 publications

(4 citation statements)

References 41 publications

(56 reference statements)

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“…Thereby, decreased CYP2D6 activity may confer susceptibility to the development of schizophrenia by increasing hippocampal hyperactivity and dopaminergic burden. It was also confirmed that CYP2D6 variations contributed to schizophrenia risk ( Ma et al, 2020 ).…”

Section: Alterations In Expressions and Functions Of Cytochrome P450s And Udp-glucuronosyltransferases Under Diseases And Their Impacts Omentioning

confidence: 72%

Alterations of Cytochrome P450s and UDP-Glucuronosyltransferases in Brain Under Diseases and Their Clinical Significances

Yun

Yang

et al. 2021

Front. Pharmacol.

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Cytochrome P450s (CYPs) and UDP-glucuronosyltransferases (UGTs) are both greatly important metabolic enzymes in various tissues, including brain. Although expressions of brain CYPs and UGTs and their contributions to drug disposition are much less than liver, both CYPs and UGTs also mediate metabolism of endogenous substances including dopamine and serotonin as well as some drugs such as morphine in brain, demonstrating their important roles in maintenance of brain homeostasis or pharmacological activity of drugs. Some diseases such as epilepsy, Parkinson’s disease and Alzheimer’s disease are often associated with the alterations of CYPs and UGTs in brain, which may be involved in processes of these diseases via disturbing metabolism of endogenous substances or resisting drugs. This article reviewed the alterations of CYPs and UGTs in brain, the effects on endogenous substances and drugs and their clinical significances. Understanding the roles of CYPs and UGTs in brain provides some new strategies for the treatment of central nervous system diseases.

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Section: Alterations In Expressions and Functions Of Cytochrome P450s And Udp-glucuronosyltransferases Under Diseases And Their Impacts Omentioning

confidence: 72%

Alterations of Cytochrome P450s and UDP-Glucuronosyltransferases in Brain Under Diseases and Their Clinical Significances

Yun

Yang

et al. 2021

Front. Pharmacol.

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“…Current available large-scale schizophrenia GWAS often have sample overlap, so we were unable to replicate the genes in an independent GWAS. We found that a few genes identified by MSG had been reported to influence schizophrenia risk via splicing (Supplementary Note Section 1C), including SNX19 [54], AS3MT [55], and CYP2D6 [54] (Fig 6C ).…”

Section: Application To Schizophreniamentioning

confidence: 92%

“…We obtained LDL-C genes (Supplementary Note Section 1B) from [43] that included genes from KEGG pathways and existing literature. We obtained a list of genes that influence schizophrenia via splicing (Supplementary Note Section 1C) from [54,[71][72][73].…”

Section: Compilation Of Well-known Trait-associated Gene Listsmentioning

confidence: 99%

Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery

Wei

Chen

et al. 2021

Preprint

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A common strategy for the functional interpretation of genome-wide association study (GWAS) findings has been the integrative analysis of GWAS and expression data. Using this strategy, many association methods (e.g., PrediXcan and FUSION) have been successful in identifying trait-associated genes via mediating effects on RNA expression. However, these approaches often ignore the effects of splicing, which carries as much disease risk as expression. Compared to expression data, one challenge to detect associations using splicing data is the large multiple testing burden due to multidimensional splicing events within genes. Here, we introduce a multidimensional splicing gene (MSG) approach, which consists of two stages: 1) we use sparse canonical correlation analysis (sCCA) to construct latent canonical vectors (CVs) by identifying sparse linear combinations of genetic variants and splicing events that are maximally correlated with each other; and 2) we test for the association between the genetically regulated splicing CVs and the trait of interest using GWAS summary statistics. Simulations show that MSG has proper type I error control and substantial power gains over existing multidimensional expression analysis methods (i.e., S-MultiXcan, UTMOST, and sCCA+ACAT) under diverse scenarios. When applied to the Genotype-Tissue Expression Project data and GWAS summary statistics of 14 complex human traits, MSG identified on average 83%, 115%, and 223% more significant genes than sCCA+ACAT, S-MultiXcan, and UTMOST, respectively. We highlight MSG’s applications to Alzheimer’s disease, low-density lipoprotein cholesterol, and schizophrenia, and found that the majority of MSG-identified genes would have been missed from expression-based analyses. Our results demonstrate that aggregating splicing data through MSG can improve power in identifying gene-trait associations and help better understand the genetic risk of complex traits.Author summaryWhile genome-wide association studies (GWAS) have successfully mapped thousands of loci associated with complex traits, it remains difficult to identify which genes they regulate and in which biological contexts. This interpretation challenge has motivated the development of computational methods to prioritize causal genes at GWAS loci. Most available methods have focused on linking risk variants with differential gene expression. However, genetic control of splicing and expression are comparable in their complex trait risk, and few studies have focused on identifying causal genes using splicing information. To study splicing mediated effects, one important statistical challenge is the large multiple testing burden generated from multidimensional splicing events. In this study, we develop a new approach, MSG, to test the mediating role of splicing variation on complex traits. We integrate multidimensional splicing data using sparse canonocial correlation analysis and then combine evidence for splicing-trait associations across features using a joint test. We show this approach has higher power to identify causal genes using splicing data than current state-of-art methods designed to model multidimensional expression data. We illustrate the benefits of our approach through extensive simulations and applications to real data sets of 14 complex traits.

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“…Antigen levels for DPYD were found to be elevated in individuals with schizophrenia compared with healthy controls, and it is known to be associated with schizophrenia via previous genome-wide association studies 38,39 . Moreover, there is evidence indicating that splicing variation of CYP2D6 may play a role in schizophrenia as well 40 . Lastly, the ATC group A04A associated with Alzheimer's disease targets a number of serotonergic receptors, which have shown promise as therapeutic targets in animal models by reducing levels of amyloid-beta 41 .…”

Section: Drug Group Enrichmentmentioning

confidence: 99%

Using genome-wide association results to identify drug repurposing candidates

Bell

Uffelmann

Walree

et al. 2022

Preprint

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Drug repurposing may provide a solution to the substantial challenges facing de novo drug development. Given that 66% of FDA-approved drugs in 2021 were supported by human genetic evidence, drug repurposing methods based on genome-wide association studies (GWAS), such as drug gene-set analysis, may prove an efficient way to identify new treatments. However, to our knowledge, drug gene-set analysis has not been tested in non-psychiatric phenotypes, and previous implementations may have contained statistical biases when testing groups of drugs. Here, 1201 drugs were tested for association with hypercholesterolemia, type 2 diabetes, coronary artery disease, asthma, schizophrenia, bipolar disorder, Alzheimer's disease, and Parkinson's disease. We show that drug gene-set analysis can identify clinically relevant drugs (e.g., simvastatin for hypercholesterolemia [p = 2.82E-06]; mitiglinide for type 2 diabetes [p = 2.66E-07]) and drug groups (e.g., C10A for coronary artery disease [p = 2.31E-05]; insulin secretagogues for type 2 diabetes [p = 1.09E-11]) for non-psychiatric phenotypes. Additionally, we demonstrate that when the overlap of genes between drug-gene sets is considered we find no groups containing approved drugs for the psychiatric phenotypes tested. However, several drug groups were identified for psychiatric phenotypes that may contain possible repurposing candidates, such as ATC codes J02A (p = 2.99E-09) and N07B (p = 0.0001) for schizophrenia. Our results demonstrate that clinically relevant drugs and groups of drugs can be identified using drug gene-set analysis for a number of phenotypes. These findings have implications for quickly identifying novel treatments based on the genetic mechanisms underlying diseases.

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Variations and expression features of CYP2D6 contribute to schizophrenia risk

Cited by 3 publications

References 41 publications

Alterations of Cytochrome P450s and UDP-Glucuronosyltransferases in Brain Under Diseases and Their Clinical Significances

Alterations of Cytochrome P450s and UDP-Glucuronosyltransferases in Brain Under Diseases and Their Clinical Significances

Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery

Using genome-wide association results to identify drug repurposing candidates

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