ArticleAmerican journal of human genetics2021
Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization.
Article in American journal of human genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Calming sterile inflammation in intervertebral disc degeneration: taurine disrupts mitochondria-cGAS-STING signaling via autophagy enhancement.Precision clinical medicine · 2026Article
- Decoding causal m6A: a bioinformatics roadmap for psychiatric disorders.Briefings in bioinformatics · 2026Review
- mFABIO: An integrative multi-tissue TWAS fine-mapping approach to prioritize potentially causal genes and tissues underlying binary traits.PLoS genetics · 2026Article
- Integrative analysis of GWAS, Bayesian fine-mapping, Mendelian randomization and colocalization reveals genetic determinants underlying milk-related traits in dairy cattle.Genetics, selection, evolution : GSE · 2026Article
- Efficient Mendelian randomization analysis with self-adaptive determination of sample structure and multiple pleiotropic effects.American journal of human genetics · 2025Article
- Uncovering causal gene-tissue pairs and variants through a multivariate TWAS controlling for infinitesimal effects.Nature communications · 2025Article
- Multi-Ancestry Transcriptome-Wide Association Studies of Cognitive Function, White Matter Hyperintensity, and Alzheimer's Disease.International journal of molecular sciences · 2025Article
- Multiome-wide Association Studies: Novel Approaches for Understanding Diseases.Genomics, proteomics & bioinformatics · 2024Review
- FABIO: TWAS fine-mapping to prioritize causal genes for binary traits.PLoS genetics · 2024Article
- Protein-centric omics integration analysis identifies candidate plasma proteins for multiple autoimmune diseases.Human genetics · 2024Article
- Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data.Genetic epidemiology · 2024Article
- An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs.Science China. Life sciences · 2024Review
- simmrd: An open-source tool to perform simulations in Mendelian randomization.Genetic epidemiology · 2024Article
- Brain tissue- and cell type-specific eQTL Mendelian randomization reveals efficacy of FADS1 and FADS2 on cognitive function.Translational psychiatry · 2024Article
- Conditional transcriptome-wide association study for fine-mapping candidate causal genes.Nature genetics · 2024Article
- Causal relationship between complementFrontiers in genetics · 2024Article
- Translation of genome-wide association study: from genomic signals to biological insights.Frontiers in genetics · 2024Review
- eQTL studies: from bulk tissues to single cells.Journal of genetics and genomics = Yi chuan xue bao · 2023Review
- twas_sim, a Python-based tool for simulation and power analysis of transcriptome-wide association analysis.Bioinformatics (Oxford, England) · 2023Article
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5 authors.
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Abstract
A transcriptome-wide association study (TWAS) integrates data from genome-wide association studies and gene expression mapping studies for investigating the gene regulatory mechanisms underlying diseases. Existing TWAS methods are primarily univariate in nature, focusing on analyzing one outcome trait at a time. However, many complex traits are correlated with each other and share a common genetic basis. Consequently, analyzing multiple traits jointly through multivariate analysis can potentially improve the power of TWASs. Here, we develop a method, moPMR-Egger (multiple outcome probabilistic Mendelian randomization with Egger assumption), for analyzing multiple outcome traits in TWAS applications. moPMR-Egger examines one gene at a time, relies on its cis-SNPs that are in potential linkage disequilibrium with each other to serve as instrumental variables, and tests its causal effects on multiple traits jointly. A key feature of moPMR-Egger is its ability to test and control for potential horizontal pleiotropic effects from instruments, thus maximizing power while minimizing false associations for TWASs. In simulations, moPMR-Egger provides calibrated type I error control for both causal effects testing and horizontal pleiotropic effects testing and is more powerful than existing univariate TWAS approaches in detecting causal associations. We apply moPMR-Egger to analyze 11 traits from 5 trait categories in the UK Biobank. In the analysis, moPMR-Egger identified 13.15% more gene associations than univariate approaches across trait categories and revealed distinct regulatory mechanisms underlying systolic and diastolic blood pressures.
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