scholarly journals Transcriptome-Wide Association Study Provides Insights Into the Genetic Component of Gene Expression in Anxiety

2021 ◽  
Vol 12 ◽  
Author(s):  
Xi Su ◽  
Wenqiang Li ◽  
Luxian Lv ◽  
Xiaoyan Li ◽  
Jinfeng Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Anxiety Disorders ◽  
Association Study ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Component ◽  
Genome Wide Association Studies ◽  
Risk Variants ◽  
Genome Wide ◽  
Regulated Expression

Anxiety disorders are common mental disorders that often result in disability. Recently, large-scale genome-wide association studies (GWASs) have identified several novel risk variants and loci for anxiety disorders (or anxiety traits). Nevertheless, how the reported risk variants confer risk of anxiety remains unknown. To identify genes whose cis-regulated expression levels are associated with risk of anxiety traits, we conducted a transcriptome-wide association study (TWAS) by integrating genome-wide associations from a large-scale GWAS (N = 175,163) (which evaluated anxiety traits based on Generalized Anxiety Disorder 2-item scale (GAD-2) score) and brain expression quantitative trait loci (eQTL) data (from the PsychENCODE and GTEx). We identified 19 and 17 transcriptome-wide significant (TWS) genes in the PsychENCODE and GTEx, respectively. Intriguingly, 10 genes showed significant associations with anxiety in both datasets, strongly suggesting that genetic risk variants may confer risk of anxiety traits by regulating the expression of these genes. Top TWS genes included RNF123, KANSL1-AS1, GLYCTK, CRHR1, DND1P1, MAPT and ARHGAP27. Of note, 25 TWS genes were not implicated in the original GWAS. Our TWAS identified 26 risk genes whose cis-regulated expression were significantly associated with anxiety, providing important insights into the genetic component of gene expression in anxiety disorders/traits and new clues for future drug development.

scholarly journals Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Ana Viñuela ◽  
Arushi Varshney ◽  
Martijn van de Bunt ◽  
Rashmi B. Prasad ◽  
Olof Asplund ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association ◽  
Regulatory Sequences ◽  
Genome Wide Association Studies ◽  
Risk Variants ◽  
Genome Wide ◽  
Gwas Signal ◽  
Using Data

Abstract Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.

Genetic contributions to anxiety disorders: where we are and where we are heading

2021 ◽  
pp. 1-16
Author(s):  
Helga Ask ◽  
Rosa Cheesman ◽  
Eshim S. Jami ◽  
Daniel F. Levey ◽  
Kirstin L. Purves ◽  
...  
Keyword(s):  
Anxiety Disorders ◽  
Psychiatric Disorders ◽  
Large Scale ◽  
Current Knowledge ◽  
Association Studies ◽  
Twin Studies ◽  
Future Research ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Anxiety disorders are among the most common psychiatric disorders worldwide. They often onset early in life, with symptoms and consequences that can persist for decades. This makes anxiety disorders some of the most debilitating and costly disorders of our time. Although much is known about the synaptic and circuit mechanisms of fear and anxiety, research on the underlying genetics has lagged behind that of other psychiatric disorders. However, alongside the formation of the Psychiatric Genomic Consortium Anxiety workgroup, progress is rapidly advancing, offering opportunities for future research. Here we review current knowledge about the genetics of anxiety across the lifespan from genetically informative designs (i.e. twin studies and molecular genetics). We include studies of specific anxiety disorders (e.g. panic disorder, generalised anxiety disorder) as well as those using dimensional measures of trait anxiety. We particularly address findings from large-scale genome-wide association studies and show how such discoveries may provide opportunities for translation into improved or new therapeutics for affected individuals. Finally, we describe how discoveries in anxiety genetics open the door to numerous new research possibilities, such as the investigation of specific gene–environment interactions and the disentangling of causal associations with related traits and disorders. We discuss how the field of anxiety genetics is expected to move forward. In addition to the obvious need for larger sample sizes in genome-wide studies, we highlight the need for studies among young people, focusing on specific underlying dimensional traits or components of anxiety.

scholarly journals Transcriptome-wide association supplements genome-wide association in Zea mays

2018 ◽  
Author(s):  
Karl A. G. Kremling ◽  
Christine H. Diepenbrock ◽  
Michael A. Gore ◽  
Edward S. Buckler ◽  
Nonoy B. Bandillo
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Large Scale ◽  
New Technologies ◽  
Seed Quality ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Organization ◽  
Genome Wide

AbstractModern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The potential of using endophenotypes for dissecting traits of interest remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299 genotype and 7 tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation for agronomic and seed quality (carotenoid, tocochromanol) traits is regulatory. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits, beating the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This improves not only the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.Author summaryWe examined the ability to associate variability in gene expression directly with terminal phenotypes of interest, as a supplement linking genotype to phenotype. We found that transcriptome-wide association studies (TWAS) are a useful accessory to genome-wide association studies (GWAS). In a combined test with GWAS results, TWAS improves the capacity to re-detect genes known to underlie quantitative trait loci for kernel and agronomic phenotypes. This improves not only the capacity to link genes to phenotypes, but also illustrates the widespread importance of regulation for phenotype.

scholarly journals Brain transcriptional regulatory architecture and schizophrenia etiology converge between East Asian and European ancestral populations

2021 ◽  
Author(s):  
Sihan Liu ◽  
Yu Chen ◽  
Feiran Wang ◽  
Yi Jiang ◽  
Fangyuan Duan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Association Studies ◽  
Allelic Frequency ◽  
Genome Wide Association Studies ◽  
Disease Etiology ◽  
Risk Genes ◽  
Regulatory Architecture ◽  
Genome Wide ◽  
Transcriptional Regulatory

AbstractUnderstanding the genetic architecture of gene expression and splicing in human brain is critical to unlocking the mechanisms of complex neuropsychiatric disorders like schizophrenia (SCZ). Large-scale brain transcriptomic studies are based primarily on populations of European (EUR) ancestry. The uniformity of mono-racial resources may limit important insights into the disease etiology. Here, we characterized brain transcriptional regulatory architecture of East Asians (EAS; n=151), identifying 3,278 expression quantitative trait loci (eQTL) and 4,726 spliceQTL (sQTL). Comparing these to PsychENCODE/BrainGVEX confirmed our hypothesis that the transcriptional regulatory architecture in EAS and EUR brains align. Furthermore, distinctive allelic frequency and linkage disequilibrium impede QTL translation and gene-expression prediction accuracy. Integration of eQTL/sQTL with genome-wide association studies reveals common and novel SCZ risk genes. Pathway-based analyses showing shared SCZ biology point to synaptic and GTPase dysfunction as a prospective pathogenesis. This study elucidates the transcriptional landscape of the EAS brain and emphasizes an essential convergence between EAS and EUR populations.

scholarly journals Untargeted metabolome- and transcriptome-wide association study identifies causal genes modulating metabolite concentrations in urine

2020 ◽  
Author(s):  
Reyhan Sönmez Flitman ◽  
Bita Khalili ◽  
Zoltan Kutalik ◽  
Rico Rueedi ◽  
Sven Bergmann
Keyword(s):  
Gene Expression ◽  
Association Study ◽  
Association Studies ◽  
Urine Concentration ◽  
Resonance Spectroscopy ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Rnaseq Data ◽  
Metabolite Concentrations ◽  
Causal Genes

SummaryIn this study we investigate the results of a metabolome- and transcriptome-wide association study to identify genes influencing the human metabolome. We used RNAseq data from lymphoblastoid cell lines (LCLs) derived from 555 Caucasian individuals to characterize their transcriptome. As for the metabolome we took an untargeted approach using binned features from 1H nuclear magnetic resonance spectroscopy (NMR) of urine samples from the same subjects allowing for data-driven discovery of associated compounds (rather than working with a limited set of quantified metabolites).Using pairwise linear regression we identified 21 study-wide significant associations between metabolome features and gene expression levels. We observed the most significant association between the gene ALMS1 and two adjacent metabolome features at 2.0325 and 2.0375 ppm. By using our previously developed metabomatching methodology, we found N-Acetylaspartate (NAA) as the potential underlying metabolite whose urine concentration is correlated with ALMS1 expression. Indeed, a number of metabolome- and genome-wide association studies (mGWAS) had already suggested the locus of this gene to be involved in regulation of N-acetylated compounds, yet were not able to identify unambiguously the exact metabolite, nor to disambiguate between ALMS1 and NAT8, another gene found in the same locus as the mediator gene. The second highest significant association was observed between HPS1 and two metabolome features at 2.8575 and 2.8725 ppm. Metabomatching of the association profile of HPS1 with all metabolite features pointed at trimethylamine (TMA) as the most likely underlying metabolite. mGWAS had previously implicated a locus containing HPS1 to be associated with TMA concentrations in urine but could not disambiguate this association signal from PYROXD2, a gene in the same locus. We used Mendelian randomization to show for both ALMS1 and HPS1 that their expression is causally linked to the respective metabolite concentrations.Our study provides evidence that the integration of metabolomics with gene expression data can support mQTL analysis, helping to identify the most likely gene involved in the modulation of the metabolite concentration.

scholarly journals Large-scale transcriptome-wide association study identifies new prostate cancer risk regions

2018 ◽  
Author(s):  
Nicholas Mancuso ◽  
Simon Gayther ◽  
Alexander Gusev ◽  
Wei Zheng ◽  
Kathryn L. Penney ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Association Study ◽  
Large Scale ◽  
Association Studies ◽  
Probabilistic Approach ◽  
Prostate Cancer Risk ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Causal Genes ◽  
Credible Set

AbstractAlthough genome-wide association studies (GWAS) for prostate cancer (PrCa) have identified more than 100 risk regions, most of the risk genes at these regions remain largely unknown. Here, we integrate the largest PrCa GWAS (N=142,392) with gene expression measured in 45 tissues (N=4,458), including normal and tumor prostate, to perform a multi-tissue transcriptomewide association study (TWAS) for PrCa. We identify 235 genes at 87 independent 1Mb regions associated with PrCa risk, 9 of which are regions with no genome-wide significant SNP within 2Mb. 24 genes are significant in TWAS only for alternative splicing models in prostate tumor thus supporting the hypothesis of splicing driving risk for continued oncogenesis. Finally, we use a Bayesian probabilistic approach to estimate credible sets of genes containing the causal gene at pre-defined level; this reduced the list of 235 associations to 120 genes in the 90% credible set. Overall, our findings highlight the power of integrating expression with PrCa GWAS to identify novel risk loci and prioritize putative causal genes at known risk loci.

scholarly journals A Transcriptome-Wide Association Study Identifies Novel Candidate Susceptibility Genes for Pancreatic Cancer

2020 ◽  
Vol 112 (10) ◽  
pp. 1003-1012 ◽  
Author(s):  
Jun Zhong ◽  
Ashley Jermusyk ◽  
Lang Wu ◽  
Jason W Hoskins ◽  
Irene Collins ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Cancer Risk ◽  
Association Study ◽  
Association Studies ◽  
Tissue Expression ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Pancreatic Cancer Risk ◽  
Genome Wide

Abstract Background Although 20 pancreatic cancer susceptibility loci have been identified through genome-wide association studies in individuals of European ancestry, much of its heritability remains unexplained and the genes responsible largely unknown. Methods To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74–421 samples). Results We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction. Conclusions By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.

scholarly journals The Parkinson’s Disease GWAS Locus Browser

2020 ◽  
Author(s):  
Francis P. Grenn ◽  
Jonggeol J. Kim ◽  
Mary B. Makarious ◽  
Hirotaka Iwaki ◽  
Anastasia Illarionova ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Large Scale ◽  
Association Studies ◽  
Age At Onset ◽  
Genome Wide Association Studies ◽  
Functional Studies ◽  
Risk Variants ◽  
Genome Wide

AbstractParkinson’s disease (PD) is a neurodegenerative disease with an often complex genetic component identifiable by genome-wide association studies (GWAS). The most recent large scale PD GWASes have identified more than 90 independent risk variants for PD risk and progression across 80 loci. One major challenge in current genomics is identifying the causal gene(s) and variant(s) from each GWAS locus. Here we present a GWAS locus browser application that combines data from multiple databases to aid in the prioritization of genes associated with PD GWAS loci. We included 92 independent genome-wide significant signals from multiple recent PD GWAS studies including the PD risk GWAS, age-at-onset GWAS and progression GWAS. We gathered data for all 2336 genes within 1Mb up and downstream of each variant to allow users to assess which gene(s) are most associated with the variant of interest based on a set of self-ranked criteria. Our aim is that the information contained in this browser (https://pdgenetics.shinyapps.io/GWASBrowser/) will assist the PD research community with the prioritization of genes for follow-up functional studies and as potential therapeutic targets.

scholarly journals Rare risk variants associate with epigenetic dysregulation in migraine

2021 ◽  
Author(s):  
Tanya Ramdal Techlo ◽  
Mona Ameri Chalmer ◽  
Peter Loof Møller ◽  
Lisette Johanna Antonia Kogelman ◽  
Isa Amalie Olofsson ◽  
...  
Keyword(s):  
Large Scale ◽  
Rare Variants ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Risk Variant ◽  
Polycomb Response Element ◽  
Risk Variants ◽  
Genome Wide ◽  
Independent Case

Migraine has a heritability of up to 65%. Genome-wide association studies (GWAS) on migraine have identified 123 risk loci, explaining only 10.6% of migraine heritability. Thus, there is a considerable genetic component not identified with GWAS. Further, the causality of the identified risk loci remains inconclusive. Rare variants contribute to the risk of migraine but GWAS are often underpowered to detect these. Whole genome sequencing is reliable for analyzing rare variants but is not frequently used in large-scale. We assessed if rare variants in the migraine risk loci associated with migraine. We used a large cohort of whole genome sequenced migraine patients (1,040 individuals from 155 families). The findings were replicated in an independent case-control cohort (2,027 migraine patients, 1,650 controls). We found rare variants (minor allele frequency<0.1%) associated with migraine in a Polycomb Response Element in the ASTN2 locus. The association was independent of the GWAS lead risk variant in the locus. The findings place rare variants as risk factors for migraine. We propose a biological mechanism by which epigenetic regulation by Polycomb Response Elements plays a crucial role in migraine etiology.

scholarly journals Genome-Wide Meta-Analysis Identifies Two Novel Risk Loci for Epilepsy

2021 ◽  
Vol 15 ◽  
Author(s):  
Meng Song ◽  
Jiewei Liu ◽  
Yongfeng Yang ◽  
Luxian Lv ◽  
Wenqiang Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
New Drugs ◽  
Normal Brain ◽  
Genome Wide Association Studies ◽  
Metabotropic Receptor ◽  
Risk Variants ◽  
Genome Wide ◽  
Normal Brain Function

Epilepsy (affects about 70 million people worldwide) is one of the most prevalent brain disorders and imposes a huge economic burden on society. Epilepsy has a strong genetic component. In this study, we perform the largest genome-wide meta-analysis of epilepsy (N = 8,00,869 subjects) by integrating four large-scale genome-wide association studies (GWASs) of epilepsy. We identified three genome-wide significant (GWS) (p &lt; 5 × 10–8) risk loci for epilepsy. The risk loci on 7q21.11 [lead single nucleotide polymorphism (SNP) rs11978015, p = 9.26 × 10–9] and 8p23.1 (lead SNP rs28634186, p = 4.39 × 10–8) are newly identified in the present study. Of note, rs11978015 resides in upstream of GRM3, which encodes glutamate metabotropic receptor 3. GRM3 has pivotal roles in neurotransmission and is involved in most aspects of normal brain function. In addition, we also identified three genes (TTC21B, RP11-375N15.2, and TNKS) whose cis-regulated expression level are associated with epilepsy, indicating that risk variants may confer epilepsy risk through regulating the expression of these genes. Our study not only provides new insights into genetic architecture of epilepsy but also prioritizes potential molecular targets (including GRM3 and TTC21B) for development of new drugs and therapeutics for epilepsy.

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