scholarly journals Autosomal sex-biased genetic regulation of the stress response

2020 ◽  
Author(s):  
Sarah R. Moore ◽  
Thorhildur Halldorsdottir ◽  
Jade Martins ◽  
Susanne Lucae ◽  
Bertram Müller-Myhsok ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Psychiatric Disorders ◽  
Association Studies ◽  
Genetic Regulation ◽  
Transcriptional Response ◽  
Genetic Profile ◽  
Genome Wide Association Studies ◽  
Stress Hormone ◽  
Males And Females

ABSTRACTSubstantial sex differences have been reported in the physiological response to stress at multiple levels, including the release of the stress hormone, cortisol. How these differences relate to differential risks for stress-related psychiatric disorders is still poorly understood. We have previously identified genomic variants in males regulating the initial transcriptional response to cortisol via glucocorticoid receptor (GR) activation, and these variants are associated with risk for major depressive disorder (MDD) and other psychiatric disorders. Here, we extend these investigations to a sample of males and females in order to examine sex-biased genetic regulation of the transcriptional response to the stress hormone.Gene expression levels in peripheral blood were obtained before and after GR-stimulation with the selective GR agonist dexamethasone to identify differential expression following GR-activation (GR-DE) in 93 women and 196 men. We first explored sex differences in the transcriptional GR-response followed by the identification of sex-biased expression quantitative trait loci (eQTLs) by associating gene expression and genotype data stratified by sex.While GR-response transcripts mostly overlapped between males and females, GR-response eQTLs showed strong sex-bias. A total of 804 significant GR-response cis-eQTL bins were found in the joint sample, 648 in females only, and 705 in males only. However, only 46 sex-biased GR-eQTL transcripts (etranscripts) overlapped between the sexes. The sets of associated sex-biased GR eQTL SNPs (eSNPs) were located in different functional genomic elements. Male and female sex-biased etranscripts were enriched within postmortem brain transcriptional profiles associated with MDD specifically in males and females in the cingulate cortex but not other brain regions. Female-biased GR-eSNPs were enriched among SNPs linked to MDD in genome wide association studies (GWAS). Finally, transcriptional sensitive genetic profile scores indexing sex-biased larger transcriptional changes to GR-stimulation were predictive of depression status and depressive symptoms in a sex-concordant manner in a child and adolescent cohort (n = 584).Taken together, while the GR-DE effects were similar between females and males, the genetic moderation of these effects was highly sex-biased and associated with depression-related molecular profiles and symptoms in a similarly sex-biased manner. These results suggest potential of GR-response eQTLs as sex-biased biomarkers of risk for stress-related psychiatric disorders.

scholarly journals Sex differences in the genetic regulation of the blood transcriptome response to glucocorticoid receptor activation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarah R. Moore ◽  
Thorhildur Halldorsdottir ◽  
Jade Martins ◽  
Susanne Lucae ◽  
Bertram Müller-Myhsok ◽  
...  
Keyword(s):  
Sex Differences ◽  
Glucocorticoid Receptor ◽  
Association Studies ◽  
Genetic Regulation ◽  
Receptor Activation ◽  
Postmortem Brain ◽  
Genetic Profile ◽  
Genome Wide Association Studies ◽  
Males And Females ◽  
Differential Transcription

AbstractSubstantial sex differences have been reported in the physiological response to stress at multiple levels, including the release of the stress hormone, cortisol. Here, we explore the genomic variants in 93 females and 196 males regulating the initial transcriptional response to cortisol via glucocorticoid receptor (GR) activation. Gene expression levels in peripheral blood were obtained before and after GR-stimulation with the selective GR agonist dexamethasone to identify differential expression following GR-activation. Sex stratified analyses revealed that while the transcripts responsive to GR-stimulation were mostly overlapping between males and females, the quantitative trait loci (eQTLs) regulation differential transcription to GR-stimulation was distinct. Sex-stratified eQTL SNPs (eSNPs) were located in different functional genomic elements and sex-stratified transcripts were enriched within postmortem brain transcriptional profiles associated with Major Depressive Disorder (MDD) specifically in males and females in the cingulate cortex. Female eSNPs were enriched among SNPs linked to MDD in genome-wide association studies. Finally, transcriptional sensitive genetic profile scores derived from sex-stratified eSNPS regulating differential transcription to GR-stimulation were predictive of depression status and depressive symptoms in a sex-concordant manner in a child and adolescent cohort (n = 584). These results suggest the potential of eQTLs regulating differential transcription to GR-stimulation as biomarkers of sex-specific biological risk for stress-related psychiatric disorders.

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

2021 ◽  
Author(s):  
Zachary F Gerring ◽  
Jackson G Thorp ◽  
Eric R Gamazon ◽  
Eske M Derks
Keyword(s):  
Gene Expression ◽  
Mental Health ◽  
Substance Use ◽  
Prefrontal Cortex ◽  
Psychiatric Disorders ◽  
Developmental Disorders ◽  
Association Studies ◽  
Genetic Correlations ◽  
Autism Spectrum ◽  
Genome Wide Association Studies

ABSTRACTGenome-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.

scholarly journals Novel susceptibility loci and genetic regulation mechanisms for type 2 diabetes

2018 ◽  
Author(s):  
Angli Xue ◽  
Yang Wu ◽  
Zhihong Zhu ◽  
Futao Zhang ◽  
Kathryn E Kemper ◽  
...  
Keyword(s):  
Gene Expression ◽  
Type 2 Diabetes ◽  
Rare Variants ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
European Ancestry ◽  
Genome Wide Association Studies

AbstractWe conducted a meta-analysis of genome-wide association studies (GWAS) with ∼16 million genotyped/imputed genetic variants in 62,892 type 2 diabetes (T2D) cases and 596,424 controls of European ancestry. We identified 139 common and 4 rare (minor allele frequency < 0.01) variants associated with T2D, 42 of which (39 common and 3 rare variants) were independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2,765) and other T2D-relevant tissues (n = up to 385) with the GWAS results identified 33 putative functional genes for T2D, three of which were targeted by approved drugs. A further integration of DNA methylation (n = 1,980) and epigenomic annotations data highlighted three putative T2D genes (CAMK1D, TP53INP1 and ATP5G1) with plausible regulatory mechanisms whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. We further found evidence that the T2D-associated loci have been under purifying selection.

scholarly journals PrediXcan: Trait Mapping Using Human Transcriptome Regulation

2015 ◽  
Author(s):  
Eric R Gamazon ◽  
Heather E Wheeler ◽  
Kaanan Shah ◽  
Sahar V Mozaffari ◽  
Keston Aquino-Michaels ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Prediction Models ◽  
Association Studies ◽  
Regulatory Function ◽  
Genetic Profile ◽  
Genome Wide Association Studies ◽  
Reverse Causality ◽  
Reference Transcriptome ◽  
Regulated Gene Expression

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates the “imputed” gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene- based approaches such as reduced multiple testing burden, more comprehensive annotation of gene function compared to that derived from single variants, and a principled approach to the design of follow-up experiments while also integrating knowledge of regulatory function. Since no actual expression data are used in the analysis of GWAS data - only in silico expression - reverse causality problems are largely avoided. PrediXcan harnesses reference transcriptome data for disease mapping studies. Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.

scholarly journals ERASE: Extended Randomization for assessment of annotation enrichment in ASE datasets

2019 ◽  
Author(s):  
Karishma D’Sa ◽  
Regina H. Reynolds ◽  
Sebastian Guelfi ◽  
David Zhang ◽  
Sonia Garcia Ruiz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Association Studies ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Gwas Data ◽  
Genome Wide Association Studies ◽  
Specific Expression ◽  
Individual Snps

AbstractGenome-wide association studies (GWAS) have identified thousands of genetic variants associated with various human phenotypes and many of these loci are thought to act at a molecular level by regulating gene expression. Detection of allele specific expression (ASE), namely preferential usage of an allele at a transcribed locus, is an increasingly important means of studying the genetic regulation of gene expression. However, there are currently a paucity of tools available to link ASE sites with GWAS risk loci. Existing integration methods first use ASE sites to infer cis-acting expression quantitative trait loci (eQTL) and then apply eQTL-based approaches. ERASE is a method that assesses the enrichment of risk loci amongst ASE sites directly. Furthermore, ERASE enables additional biological insights to be made through the addition of other SNP level annotations. ERASE is based on a randomization approach and controls for read depth, a significant confounder in ASE analyses. In this paper, we demonstrate that ERASE can efficiently detect the enrichment of eQTLs and risk loci within ASE data and that it remains sensitive even when used with underpowered GWAS datasets. Finally, using ERASE in combination with GWAS data for Parkinson’s disease and data on the splicing potential of individual SNPs, we provide evidence to suggest that risk loci for Parkinson’s disease are enriched amongst ASEs likely to affect splicing. Thus, we show that ERASE is an important new tool for the integration of ASE and GWAS data, capable of providing novel insights into the pathophysiology of complex diseases.

scholarly journals TCF19 Impacts a Network of Inflammatory and DNA Damage Response Genes in the Pancreatic β-Cell

Metabolites ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 513
Author(s):  
Grace H. Yang ◽  
Danielle A. Fontaine ◽  
Sukanya Lodh ◽  
Joseph T. Blumer ◽  
Avtar Roopra ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Damage ◽  
Dna Damage Response ◽  
Association Studies ◽  
Cell Cycle Gene ◽  
Genome Wide Association Studies ◽  
Β Cell ◽  
Nucleotide Incorporation ◽  
Damage Response ◽  
The Impact

Transcription factor 19 (TCF19) is a gene associated with type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in genome-wide association studies. Prior studies have demonstrated that Tcf19 knockdown impairs β-cell proliferation and increases apoptosis. However, little is known about its role in diabetes pathogenesis or the effects of TCF19 gain-of-function. The aim of this study was to examine the impact of TCF19 overexpression in INS-1 β-cells and human islets on proliferation and gene expression. With TCF19 overexpression, there was an increase in nucleotide incorporation without any change in cell cycle gene expression, alluding to an alternate process of nucleotide incorporation. Analysis of RNA-seq of TCF19 overexpressing cells revealed increased expression of several DNA damage response (DDR) genes, as well as a tightly linked set of genes involved in viral responses, immune system processes, and inflammation. This connectivity between DNA damage and inflammatory gene expression has not been well studied in the β-cell and suggests a novel role for TCF19 in regulating these pathways. Future studies determining how TCF19 may modulate these pathways can provide potential targets for improving β-cell survival.

scholarly journals Transcriptome-wide Mendelian randomization study prioritising novel tissue-dependent genes for glioma susceptibility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jamie W. Robinson ◽  
Richard M. Martin ◽  
Spiridon Tsavachidis ◽  
Amy E. Howell ◽  
Caroline L. Relton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Tissue Expression ◽  
Tissue Type ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Causal Pathways ◽  
Genome Wide ◽  
Glioma Risk ◽  
Brain Tissues

AbstractGenome-wide association studies (GWAS) have discovered 27 loci associated with glioma risk. Whether these loci are causally implicated in glioma risk, and how risk differs across tissues, has yet to be systematically explored. We integrated multi-tissue expression quantitative trait loci (eQTLs) and glioma GWAS data using a combined Mendelian randomisation (MR) and colocalisation approach. We investigated how genetically predicted gene expression affects risk across tissue type (brain, estimated effective n = 1194 and whole blood, n = 31,684) and glioma subtype (all glioma (7400 cases, 8257 controls) glioblastoma (GBM, 3112 cases) and non-GBM gliomas (2411 cases)). We also leveraged tissue-specific eQTLs collected from 13 brain tissues (n = 114 to 209). The MR and colocalisation results suggested that genetically predicted increased gene expression of 12 genes were associated with glioma, GBM and/or non-GBM risk, three of which are novel glioma susceptibility genes (RETREG2/FAM134A, FAM178B and MVB12B/FAM125B). The effect of gene expression appears to be relatively consistent across glioma subtype diagnoses. Examining how risk differed across 13 brain tissues highlighted five candidate tissues (cerebellum, cortex, and the putamen, nucleus accumbens and caudate basal ganglia) and four previously implicated genes (JAK1, STMN3, PICK1 and EGFR). These analyses identified robust causal evidence for 12 genes and glioma risk, three of which are novel. The correlation of MR estimates in brain and blood are consistently low which suggested that tissue specificity needs to be carefully considered for glioma. Our results have implicated genes yet to be associated with glioma susceptibility and provided insight into putatively causal pathways for glioma risk.

scholarly journals Prediction of response to drug therapy in psychiatric disorders

Open Biology ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 180031 ◽  
Author(s):  
Shani Stern ◽  
Sara Linker ◽  
Krishna C. Vadodaria ◽  
Maria C. Marchetto ◽  
Fred H. Gage
Keyword(s):  
Autism Spectrum Disorder ◽  
Bipolar Disorder ◽  
Major Depression ◽  
Personalized Medicine ◽  
Psychiatric Disorders ◽  
Association Studies ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Genome Wide Association Studies ◽  
Patient Reported

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

Sex differences in white adipose tissue expansion: emerging molecular mechanisms

2021 ◽  
Vol 135 (24) ◽  
pp. 2691-2708
Author(s):  
Simon T. Bond ◽  
Anna C. Calkin ◽  
Brian G. Drew
Keyword(s):  
Sex Differences ◽  
Sexual Dimorphism ◽  
Gene Networks ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Tissue Expansion ◽  
Economic Systems ◽  
Genomic Association ◽  
Males And Females

Abstract The escalating prevalence of individuals becoming overweight and obese is a rapidly rising global health problem, placing an enormous burden on health and economic systems worldwide. Whilst obesity has well described lifestyle drivers, there is also a significant and poorly understood component that is regulated by genetics. Furthermore, there is clear evidence for sexual dimorphism in obesity, where overall risk, degree, subtype and potential complications arising from obesity all differ between males and females. The molecular mechanisms that dictate these sex differences remain mostly uncharacterised. Many studies have demonstrated that this dimorphism is unable to be solely explained by changes in hormones and their nuclear receptors alone, and instead manifests from coordinated and highly regulated gene networks, both during development and throughout life. As we acquire more knowledge in this area from approaches such as large-scale genomic association studies, the more we appreciate the true complexity and heterogeneity of obesity. Nevertheless, over the past two decades, researchers have made enormous progress in this field, and some consistent and robust mechanisms continue to be established. In this review, we will discuss some of the proposed mechanisms underlying sexual dimorphism in obesity, and discuss some of the key regulators that influence this phenomenon.

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