1,724,123 research outputs found
XingyiGuo/transTF-TWAS: TransTF-TWAS
No full text<p>Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data, while trans-located variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-located variants to enhance model building. Using data from the Genotype-Tissue Expression project and extensive disease GWAS datasets, we demonstrate that transTF-TWAS approach significantly contributes to the discovery of disease risk genes.</p>
XingyiGuo/TF-TWAS: TF-TWAS
No full textTF-TWAS is a tool integrating susceptible transcription factor occupancy with transcriptome-wide association studies (TWAS) to identify susceptibility genes in human cancers and non-cancer diseases
TWAS models for eQTLGen
No full textTWAS models based on eQTLGen summary statistics. Instructions on how these weights can be used for TWAS can be found here: TBC.
Please cite:
Study describing how the TWAS models were generated: TBC
Original study for eQTLGen in which the eQTL summary statistics were generated: https://doi.org/10.1038/s41588-021-00913-z
Contact Oliver Pain for further information ([email protected])
TWAS models for MetaBrain
No full textTWAS models based on MetaBrain summary statistics. Instructions on how these weights can be used for TWAS can be found here: TBC.
Please cite:
Study describing how the TWAS models were generated: TBC
Original study for MetaBrain in which the eQTL summary statistics were generated: https://doi.org/10.1101/2021.03.01.433439
Contact Oliver Pain for further information ([email protected])
FarmGTEx TWAS-server: An Interactive Web Server for Customized TWAS Analysis
Get PDFTranscriptome-wide association study (TWAS) is a powerful approach for investigating the molecular mechanisms linking genetic loci to complex phenotypes. However, the complexity of the TWAS analytical pipeline, including the construction of gene expression reference panels, gene expression prediction, and association analysis using data from genome-wide association studies (GWASs), poses challenges for genetic studies in many species. In this study, we provide the Farm Animal Genotype-Tissue Expression (FarmGTEx) TWAS-server, an interactive and user-friendly multispecies platform designed to streamline the translation of genetic findings across tissues and species. The server incorporates gene expression data from 49 human tissues (838 individuals), 34 pig tissues (5457 individuals), and 23 cattle tissues (4889 individuals), providing prediction models for 38,180 human genes, 21,037 pig genes, and 17,942 cattle genes. It supports genotype-based gene expression prediction, GWAS summary statistics imputation, customizable TWAS analysis, functional annotation, and result visualization. Additionally, we provide 479,203, 1208, and 657 tissue-gene-trait associations for 1129 human traits, 41 cattle traits, and 11 pig traits, respectively. Utilizing the TWAS-server, we validated the association of the ABCD4 gene with pig teat number. Furthermore, we identified that pig backfat thickness may share genetic similarities with human diastolic blood pressure, sarcoidosis (Löfgren syndrome), and body mass index. The FarmGTEx TWAS-server offers a comprehensive and accessible platform for researchers to perform TWAS analyses across tissues and species. It is freely available at https://twas.farmgtex.org, with regular updates planned as the FarmGTEx project expands to include more species.</p
Overview of available TWAS analysis methods.
No full textTranscriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a genetic relationship between gene expression and a phenotype, but this interpretation is not consistent with the null hypothesis that is evaluated in the traditional TWAS framework. In this study we provide a mathematical outline of this TWAS framework, and elucidate what interpretations are warranted given the null hypothesis it actually tests. We then use both simulations and real data analysis to assess the implications of misinterpreting TWAS results as indicative of a genetic relationship between gene expression and the phenotype. Our simulation results show considerably inflated type 1 error rates for TWAS when interpreted this way, with 41% of significant TWAS associations detected in the real data analysis found to have insufficient statistical evidence to infer such a relationship. This demonstrates that in current implementations, TWAS cannot reliably be used to investigate genetic relationships between gene expression and a phenotype, but that local genetic correlation analysis can serve as a potential alternative.</div
OTTERS: a powerful TWAS framework leveraging summary-level reference data
Get PDFMost existing TWAS tools require individual-level eQTL reference data and thus are not applicable to summary-level reference eQTL datasets. The development of TWAS methods that can harness summary-level reference data is valuable to enable TWAS in broader settings and enhance power due to increased reference sample size. Thus, we develop a TWAS framework called OTTERS (Omnibus Transcriptome Test using Expression Reference Summary data) that adapts multiple polygenic risk score (PRS) methods to estimate eQTL weights from summary-level eQTL reference data and conducts an omnibus TWAS. We show that OTTERS is a practical and powerful TWAS tool by both simulations and application studies
Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults.
No full textAs a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing. However, the development of TWAS has outpaced statistical evaluation of TWAS gene prioritization performance. Current TWAS methods vary in underlying biological assumptions about tissue specificity of transcriptional regulatory mechanisms. In a previous study from our group, this may have affected whether TWAS methods better identified associations in single tissues versus multiple tissues. We therefore designed simulation analyses to examine how the interplay between particular TWAS methods and tissue specificity of gene expression affects power and type I error rates for gene prioritization. We found that cross-tissue identification of expression quantitative trait loci (eQTLs) improved TWAS power. Single-tissue TWAS (i.e., PrediXcan) had robust power to identify genes expressed in single tissues, but, often found significant associations in the wrong tissues as well (therefore had high false positive rates). Cross-tissue TWAS (i.e., UTMOST) had overall equal or greater power and controlled type I error rates for genes expressed in multiple tissues. Based on these simulation results, we applied a tissue specificity-aware TWAS (TSA-TWAS) analytic framework to look for gene-based associations with pre-treatment laboratory values from AIDS Clinical Trial Group (ACTG) studies. We replicated several proof-of-concept transcriptionally regulated gene-trait associations, including UGT1A1 (encoding bilirubin uridine diphosphate glucuronosyltransferase enzyme) and total bilirubin levels (p = 3.59×10-12), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49×10-12). We also identified several novel genes associated with metabolic and virologic traits, as well as pleiotropic genes that linked plasma viral load, absolute basophil count, and/or triglyceride levels. By highlighting the advantages of different TWAS methods, our simulation study promotes a tissue specificity-aware TWAS analytic framework that revealed novel aspects of HIV-related traits
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