ArticlePLoS genetics2021
Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer's dementia.
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 43 papers.
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Who cites it
43 citing papers in PubMed, 81 citations in OpenAlex.
- A multiancestry polygenic risk score for Alzheimer's disease is associated with cognitive decline and neuropathological hallmarks in diverse populations.Nature genetics · 2026Article
- LncRNA Gas5, a Target of the Nonsense-Mediated Decay Pathway in the Brain, Regulates Neuroinflammation and Neurodegenerative Disease Pathways in a Tauopathy Mouse Model.International journal of molecular sciences · 2026Article
- IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.PLoS computational biology · 2026Article
- Multi-ancestry transcriptome-wide association study reveals shared and population-specific genetic effects in Alzheimer disease.American journal of human genetics · 2026Article
- Integrative transcriptome-wide association analyses reveal PRKCG-linked GABAergic dysfunction in Fragile X-associated tremor/ataxia syndrome.Nature communications · 2026Article
- ASTWAS: modeling alternative polyadenylation and SNP effects in kernel-driven TWAS reveal novel genetic associations for complex traits.Briefings in bioinformatics · 2026Article
- Multi-ancestry Transcriptome-Wide Association Study Reveals Shared and Population-Specific Genetic Effects in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2025Article
- A multi-ancestry polygenic risk score for Alzheimer disease is associated with cognitive decline, hippocampal atrophy and neuropathological hallmarks in diverse populations.medRxiv : the preprint server for health sciences · 2025Article
- CADET: Enhanced transcriptome-wide association analyses in admixed samples using eQTL summary data.American journal of human genetics · 2025Article
- The integration of genome-wide and transcriptome-wide association studies in neurodegenerative diseases: opportunities, challenges, and current methodological innovations.Briefings in bioinformatics · 2025Review
- Proteome-wide association studies using summary pQTL data of brain, CSF, and plasma identify 30 risk genes of Alzheimer's disease dementia.Alzheimer's research & therapy · 2025Article
- Generative prediction of causal gene sets responsible for complex traits.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Enhancing nonlinear transcriptome- and proteome-wide association studies via trait imputation with applications to Alzheimer's disease.PLoS genetics · 2025Article
- An Update on Neuroaging on Earth and in Spaceflight.International journal of molecular sciences · 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
- Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases.Computational and structural biotechnology journal · 2024Article
- Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data.Genetic epidemiology · 2024Article
- Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia.American journal of human genetics · 2024Article
- Leveraging Random Effects in Cistrome-Wide Association Studies for Decoding the Genetic Determinants of Prostate Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL's estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer's dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.
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