scholarly journals Meta-Analysis of Transcriptome-Wide Association Studies Across 13 Brain Tissues Identified Novel Clusters of Genes Associated with Nicotine Addiction

2021 ◽  
Author(s):  
Zhenyao Ye ◽  
Chen Mo ◽  
Hongjie Ke ◽  
Qi Yan ◽  
Chixiang Chen ◽  
...  
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Tissue Expression ◽  
Nicotine Addiction ◽  
Reference Panel ◽  
Strong Linkage Disequilibrium ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Eqtl Data ◽  
Brain Tissues

Genome-wide association studies (GWAS) have identified and reproduced thousands of diseases associated loci but many of them are not directly interpretable due to the strong linkage disequilibrium among variants. Transcriptome-wide association studies (TWAS) incorporated expression quantitative trait loci (eQTL) cohorts as reference panel to detect associations with the phenotype at the gene level and were gaining popularity in recent years. For nicotine addiction, several important susceptible genetic variants were identified by GWAS, but TWAS that detected genes associated with nicotine addiction and unveiled the underlying molecular mechanism were still lacking. In this study, we used eQTL data from the Genotype-Tissue Expression (GTEx) consortium as reference panel to conduct tissue specific TWAS on cigarettes per day (CPD) over 13 brain tissues in two large cohorts: UK Biobank (UKBB; N=142,202) and the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN; N=143,210), and then meta-analyzed the results across tissues while considering the heterogeneity across tissues. We identified three major clusters of genes with different meta-patterns across tissues consistent in both cohorts, including homogenous genes associated with CPD in all brain tissues, partially homogeneous genes associated with CPD in cortex, cerebellum and hippocampus tissues, and lastly the tissue-specific genes associated with CPD in only few specific brain tissues. Downstream enrichment analyses on each gene cluster identified unique biological pathways associated with CPD and provided important biological insights into the regulatory mechanism of nicotine dependence in the brain.

scholarly journals Meta-Analysis of Transcriptome-Wide Association Studies across 13 Brain Tissues Identified Novel Clusters of Genes Associated with Nicotine Addiction

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Zhenyao Ye ◽  
Chen Mo ◽  
Hongjie Ke ◽  
Qi Yan ◽  
Chixiang Chen ◽  
...  
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Tissue Expression ◽  
Nicotine Addiction ◽  
Reference Panel ◽  
Strong Linkage Disequilibrium ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Eqtl Data ◽  
Brain Tissues

Genome-wide association studies (GWAS) have identified and reproduced thousands of diseases associated loci, but many of them are not directly interpretable due to the strong linkage disequilibrium among variants. Transcriptome-wide association studies (TWAS) incorporated expression quantitative trait loci (eQTL) cohorts as a reference panel to detect associations with the phenotype at the gene level and have been gaining popularity in recent years. For nicotine addiction, several important susceptible genetic variants were identified by GWAS, but TWAS that detected genes associated with nicotine addiction and unveiled the underlying molecular mechanism were still lacking. In this study, we used eQTL data from the Genotype-Tissue Expression (GTEx) consortium as a reference panel to conduct tissue-specific TWAS on cigarettes per day (CPD) over thirteen brain tissues in two large cohorts: UK Biobank (UKBB; number of participants (N) = 142,202) and the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN; N = 143,210), then meta-analyzing the results across tissues while considering the heterogeneity across tissues. We identified three major clusters of genes with different meta-patterns across tissues consistent in both cohorts, including homogenous genes associated with CPD in all brain tissues; partially homogeneous genes associated with CPD in cortex, cerebellum, and hippocampus tissues; and, lastly, the tissue-specific genes associated with CPD in only a few specific brain tissues. Downstream enrichment analyses on each gene cluster identified unique biological pathways associated with CPD and provided important biological insights into the regulatory mechanism of nicotine dependence in the brain.

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 Genome-wide meta-analysis of insomnia in over 2.3 million individuals implicates involvement of specific biological pathways through gene-prioritization

2020 ◽  
Author(s):  
Kyoko Watanabe ◽  
Philip R. Jansen ◽  
Jeanne E. Savage ◽  
Priyanka Nandakumar ◽  
Xin Wang ◽  
...  
Keyword(s):  
Statistical Power ◽  
Association Studies ◽  
Meta Analysis ◽  
Tissue Expression ◽  
Gene Prioritization ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Gene Sets ◽  
Genome Wide ◽  
Novel Strategy

AbstractInsomnia is a heritable, highly prevalent sleep disorder, for which no sufficient treatment currently exists. Previous genome-wide association studies (GWASs) with up to 1.3 million subjects identified over 200 associated loci. This extreme polygenicity suggested many more loci to be discovered. The current study almost doubled the sample size to over 2.3 million individuals thereby increasing statistical power. We identified 554 risk loci (confirming 190 previously associated loci and detecting 364 novel), and capitalizing on this large number of loci, we propose a novel strategy to prioritize genes using external biological resources and information on functional interactions between genes across risk loci. Of all 3,898 genes naively implicated from the risk loci, we prioritize 289. For these, we find brain-tissue expression specificity and enrichment in specific gene-sets of synaptic signaling functions and neuronal differentiation. We show that the novel gene prioritization strategy yields specific hypotheses on causal mechanisms underlying insomnia, which would not fully have been detected using traditional approaches.

scholarly journals Trans-ethnic eQTL meta-analysis of human brain reveals regulatory architecture and candidate causal variants for brain-related traits

2021 ◽  
Author(s):  
Gabriel Hoffman ◽  
Biao Zeng ◽  
Jaroslav Bendl ◽  
Roman Kosoy ◽  
John Fullard ◽  
...  
Keyword(s):  
Sample Size ◽  
Fine Mapping ◽  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
Integrative Approach ◽  
Effective Sample Size ◽  
Strong Linkage Disequilibrium ◽  
Genome Wide Association Studies ◽  
Causal Variants

Abstract While large-scale genome-wide association studies (GWAS) have identified hundreds of loci associated with neuropsychiatric and neurodegenerative traits, identifying the variants, genes and molecular mechanisms underlying these traits remains challenging. Integrating GWAS results with expression quantitative trait loci (eQTLs) and identifying shared genetic architecture has been widely adopted to nominate genes and candidate causal variants. However, this integrative approach is often limited by the sample size, the statistical power of the eQTL dataset, and the strong linkage disequilibrium between variants. Here we developed the multivariate multiple QTL (mmQTL) approach and applied it to perform a large-scale trans-ethnic eQTL meta-analysis to increase power and fine-mapping resolution. Importantly, this method also increases power to identify conditional eQTL’s that are enriched for cell type specific regulatory effects. Analysis of 3,188 RNA-seq samples from 2,029 donors, including 444 non-European individuals, yields an effective sample size of 2,974, which is substantially larger than previous brain eQTL efforts. Joint statistical fine-mapping of eQTL and GWAS identified 301 variant-trait pairs for 23 brain-related traits driven by 189 unique candidate causal variants for 179 unique genes. This integrative analysis identifies novel disease genes and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer’s disease.

scholarly journals Trans-ethnic eQTL meta-analysis of human brain reveals regulatory architecture and candidate causal variants for brain-related traits

2021 ◽  
Author(s):  
Biao Zeng ◽  
Jaroslav Bendl ◽  
Roman Kosoy ◽  
John F. Fullard ◽  
Gabriel E. Hoffman ◽  
...  
Keyword(s):  
Sample Size ◽  
Fine Mapping ◽  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
Integrative Approach ◽  
Effective Sample Size ◽  
Strong Linkage Disequilibrium ◽  
Genome Wide Association Studies ◽  
Causal Variants

AbstractWhile large-scale genome-wide association studies (GWAS) have identified hundreds of loci associated with neuropsychiatric and neurodegenerative traits, identifying the variants, genes and molecular mechanisms underlying these traits remains challenging. Integrating GWAS results with expression quantitative trait loci (eQTLs) and identifying shared genetic architecture has been widely adopted to nominate genes and candidate causal variants. However, this integrative approach is often limited by the sample size, the statistical power of the eQTL dataset, and the strong linkage disequilibrium between variants. Here we developed the multivariate multiple QTL (mmQTL) approach and applied it to perform a large-scale trans-ethnic eQTL meta-analysis to increase power and fine-mapping resolution. Importantly, this method also increases power to identify conditional eQTL’s that are enriched for cell type specific regulatory effects. Analysis of 3,188 RNA-seq samples from 2,029 donors, including 444 non-European individuals, yields an effective sample size of 2,974, which is substantially larger than previous brain eQTL efforts. Joint statistical fine-mapping of eQTL and GWAS identified 301 variant-trait pairs for 23 brain-related traits driven by 189 unique candidate causal variants for 179 unique genes. This integrative analysis identifies novel disease genes and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer’s disease.

scholarly journals Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to identify eQTLs and increase the number of eGenes

2017 ◽  
Author(s):  
Dat Duong ◽  
Lisa Gai ◽  
Sagi Snir ◽  
Eun Yong Kang ◽  
Buhm Han ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Tissue Expression ◽  
Great Effort ◽  
Genome Wide Association Studies ◽  
Biological Insight ◽  
Positive Rate ◽  
Computationally Intensive

AbstractDuring the last decade, with the advent of inexpensive microarray and RNA-seq technologies, there have been many expression quantitative trait loci (eQTL) studies for identifying genetic variants called eQTLs that regulate gene expression. Discovering eQTLs has been increasingly important as they may elucidate the functional consequence of non-coding variants identified from genome-wide association studies. Recently, several eQTL studies such as the Genotype-Tissue Expression (GTEx) consortium have made a great effort to obtain gene expression from multiple tissues. One advantage of these multi-tissue eQTL datasets is that they may allow one to identify more eQTLs by combining information across multiple tissues. Although a few methods have been proposed for multi-tissue eQTL studies, they are often computationally intensive and may not achieve optimal power because they do not consider a biological insight that a genetic variant regulates gene expression similarly in related tissues. In this paper, we propose an efficient meta-analysis approach for identifying eQTLs from large multi-tissue eQTL datasets. We name our method RECOV because it uses a random effects (RE) meta-analysis with an explicit covariance (COV) term to model the correlation of effect that eQTLs have across tissues. Our approach is faster than the previous approaches and properly controls the false-positive rate. We apply our approach to the real multi-tissue eQTL dataset from GTEx that contains 44 tissues, and show that our approach detects more eQTLs and eGenes than previous approaches.

scholarly journals A Gene Co-expression Network-based Analysis of Multiple Brain Tissues Reveals Novel Genes and Molecular Pathways Underlying Major Depression

2019 ◽  
Author(s):  
Zachary F Gerring ◽  
Eric R Gamazon ◽  
Eske M Derks ◽  
Keyword(s):  
Major Depression ◽  
Whole Blood ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Risk Genes ◽  
Tissue Specific ◽  
Genome Wide ◽  
Gene Modules ◽  
Brain Tissues

AbstractMajor depression is a common and severe psychiatric disorder with a highly polygenic genetic architecture. Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with major depression, but the exact causal genes and biological mechanisms are largely unknown. Tissue-specific network approaches may identify molecular mechanisms underlying major depression and provide a biological substrate for integrative analyses. We provide a framework for the identification of individual risk genes and gene co-expression networks using genome-wide association summary statistics and gene expression information across multiple human brain tissues and whole blood. We developed a novel gene-based method called eMAGMA that leverages multi-tissue eQTL information to identify 99 biologically plausible risk genes associated with major depression, of which 58 are novel. Among these novel associations is Complement Factor 4A (C4A), recently implicated in schizophrenia through its role in synaptic pruning during postnatal development. Major depression risk genes were enriched in gene co-expression modules in multiple brain tissues and the implicated gene modules contained genes involved in synaptic signalling, neuronal development, and cell transport pathways. Modules enriched with major depression signals were strongly preserved across brain tissues, but were weakly preserved in whole blood, highlighting the importance of using disease-relevant tissues in genetic studies of psychiatric traits. We identified tissue-specific genes and gene co-expression networks associated with major depression. Our novel analytical framework can be used to gain fundamental insights into the functioning of the nervous system in major depression and other brain-related traits.Author summaryAlthough genome-wide association studies have identified genetic risk variants associated with major depression, our understanding of the mechanisms through which they influence disease susceptibility remain largely unknown. Genetic risk variants are highly enriched in non-coding regions of the genome and affect gene expression. Genes are known to interact and regulate the activity of one another and form highly organized (co-expression) networks. Here, we generate tissue-specific gene co-expression networks, each containing groups of functionally related genes or “modules”, to delineate interactions between genes and thereby facilitate the identification of gene processes in major depression. We developed and applied a novel research methodology (called “eMagma”) which integrates genetic and transcriptomic information in a tissue-specific analysis and tests for their enrichment in gene co-expression modules. Using this novel approach, we identified gene modules in multiple tissues that are both enriched with major depression genetic association signals and biologically meaningful pathways. We also show gene modules are strongly preserved across brain regions, but not in whole blood, suggesting blood may not be a useful tissue surrogate for the genetic dissection of major depression. Our novel analytical framework provides fundamental insights into the functional genetics major depression and can be applied to other neuropsychiatric disorders.

scholarly journals Targeted sequencing of Parkinson's disease loci genes highlights SYT11, FGF20 and other associations

2020 ◽  
Author(s):  
Uladzislau Rudakou ◽  
Eric Yu ◽  
Lynne M Krohn ◽  
Jennifer A Ruskey ◽  
Farnaz Asayesh ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rare Variants ◽  
Association Studies ◽  
Meta Analysis ◽  
Strong Linkage Disequilibrium ◽  
Genome Wide Association Studies ◽  
Common Variants ◽  
Common Genetic Variants

Genome-wide association studies (GWAS) have identified numerous loci associated with Parkinson's disease. The specific genes and variants that drive the associations within the vast majority of these loci are unknown. We aimed to perform a comprehensive analysis of selected genes to determine the potential role of rare and common genetic variants within these loci. We fully sequenced 32 genes from 25 loci previously associated with Parkinson's disease in 2,657 patients and 3,647 controls from three cohorts. Capture was done using molecular inversion probes targeting the exons, exon-intron boundaries and untranslated regions (UTRs) of the genes of interest, followed by sequencing. Quality control was performed to include only high-quality variants. We examined the role of rare variants (minor allele frequency < 0.01) using optimized sequence Kernel association tests (SKAT-O). The association of common variants was estimated using regression models adjusted for age, sex and ethnicity as required in each cohort, followed by a meta-analysis. After Bonferroni correction, we identified a burden of rare variants in SYT11, FGF20 and GCH1 associated with Parkinson's disease. Nominal associations were identified in 21 additional genes. Previous reports suggested that the SYT11 GWAS association is driven by variants in the nearby GBA gene. However, the association of SYT11 was mainly driven by a rare 3' UTR variant (rs945006601) and was independent of GBA variants (p=5.23E-05 after exclusion of all GBA variant carriers). The association of FGF20 was driven by a rare 5' UTR variant (rs1034608171) located in the promoter region. The previously reported association of GCH1 with Parkinson's Disease is driven by rare nonsynonymous variants, some of which are known to cause dopamine-responsive dystonia. We also identified two LRRK2 variants, p.Arg793Met and p.Gln1353Lys, in ten and eight controls, respectively, but not in patients. We identified common variants associated with Parkinson's disease in MAPT, TMEM175, BST1, SNCA and GPNMB which are all in strong linkage disequilibrium (LD) with known GWAS hits in their respective loci. A common coding PM20D1 variant, p.Ile149Val, was nominally associated with reduced risk of Parkinson's disease (OR 0.73, 95% CI 0.60-0.89, p=1.161E-03). This variant is not in LD with the top GWAS hits within this locus and may represent a novel association. These results further demonstrate the importance of fine mapping of GWAS loci, and suggest that SYT11, FGF20, and potentially PM20D1, BST1 and GPNMB should be considered for future studies as possible Parkinson's disease-related genes.

scholarly journals KCND3 is a novel susceptibility locus for early repolarization

2019 ◽  
Author(s):  
Alexander Teumer ◽  
Teresa Trenkwalder ◽  
Thorsten Kessler ◽  
Yalda Jamshidi ◽  
Marten E. van den Berg ◽  
...  
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
European Ancestry ◽  
Strong Linkage Disequilibrium ◽  
Genome Wide Association Studies ◽  
Pathophysiological Mechanism ◽  
Genetic Determinants ◽  
Early Repolarization ◽  
Genome Wide ◽  
A Genome

AbstractThe presence of an early repolarization pattern (ERP) on the surface electrocardiogram (ECG) is associated with risk of ventricular fibrillation and sudden cardiac death. Family studies have shown that ERP is a highly heritable trait but molecular genetic determinants are unknown. We assessed the ERP in 12-lead ECGs of 39,456 individuals and conducted a two-stage meta-analysis of genome-wide association studies (GWAS). In the discovery phase, we included 2,181 cases and 23,641 controls from eight European ancestry studies and identified 19 genome-wide significant (p<5E-8) variants in the KCND3 (potassium voltage gated channel subfamily D member 3) gene with a p-value of 4.6E-10. Replication of two loci in four additional studies including 1,124 cases and 12,510 controls confirmed the association at the KCND3 gene locus with a pooled odds ratio of 0.82, p=7.7E-12 (rs1545300 minor allele T). A subsequent GWAS meta-analysis combining all samples did not reveal additional loci. The lead SNP of the discovery stage (rs12090194) was in strong linkage disequilibrium with rs1545300 (r2=0.96, D’=1). Summary statistics based conditional analysis did not reveal any secondary signals. Co-localization analyses indicate causal effects of KCND3 gene expression levels on ERP in both the left ventricle of the heart and in tibial artery.In this study we identified for the first time a genome-wide significant association of a genetic variant with ERP. Our findings of a locus in the KCND3 gene not only provide insights into the genetic determinants but also into the pathophysiological mechanism of ERP, revealing a promising candidate for functional studies.

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