ArticlePLoS genetics2021
MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies.
Article in PLoS genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genetically regulated expression in late-onset Alzheimer's disease implicates risk genes within known and novel loci.Translational psychiatry · 2021Pooled it
- Co-expression-based models improve eQTL predictions for transcriptome-wide association studies and highlight new schizophrenia-associated genes.Nature genetics · 2026Article
- Transcriptome-informed brain cartography of polygenic risk and association with brain structure in major psychiatric disorders.Molecular psychiatry · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Co-expression-wide association studies link genetically regulated interactions with complex traits.Nature communications · 2025Article
- The integration of genome-wide and transcriptome-wide association studies in neurodegenerative diseases: opportunities, challenges, and current methodological innovations.Briefings in bioinformatics · 2025Review
- Integrative genomics sheds light on the immunogenetics of tuberculosis in cattle.Communications biology · 2025Article
- From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care.Biomedicines · 2025Review
- Enhancing disease risk gene discovery by integrating transcription factor-linked trans-variants into transcriptome-wide association analyses.Nucleic acids research · 2025Article
- Multiome-wide Association Studies: Novel Approaches for Understanding Diseases.Genomics, proteomics & bioinformatics · 2024Review
- MOSES: a methylation-based gene association approach for unveiling environmentally regulated genes linked to a trait or disease.Clinical epigenetics · 2024Article
- Identifying genetic variants associated with chromatin looping and genome function.Nature communications · 2024Article
- SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.Nature communications · 2024Article
- Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer.HGG advances · 2024Article
- An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs.Science China. Life sciences · 2024Review
- Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes.Nature communications · 2024Article
- Identification of blood metabolites associated with risk of Alzheimer's disease by integrating genomics and metabolomics data.Molecular psychiatry · 2024Article
- Article
- Conditional transcriptome-wide association study for fine-mapping candidate causal genes.Nature genetics · 2024Article
- Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges.Current protocols · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or other molecular effects underlying the SNP-gene association. Here, we outline multi-omics strategies for transcriptome imputation from germline genetics to allow more powerful testing of gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final predictive model of gene expression, along with local SNPs. In the second extension, we assess distal-eQTLs (SNPs associated with genes not in a local window around it) for their mediation effect through mediating biomarkers local to these distal-eSNPs. Distal-eSNPs with large indirect mediation effects are then included in the transcriptomic prediction model with the local SNPs around the gene of interest. Using simulations and real data from ROS/MAP brain tissue and TCGA breast tumors, we show considerable gains of percent variance explained (1-2% additive increase) of gene expression and TWAS power to detect gene-trait associations. This integrative approach to transcriptome-wide imputation and association studies aids in identifying the complex interactions underlying genetic regulation within a tissue and important risk genes for various traits and disorders.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.