SynthesisGenetic epidemiology2022
A large-scale transcriptome-wide association study (TWAS) of 10 blood cell phenotypes reveals complexities of TWAS fine-mapping.
Synthesis in Genetic epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Improved multiancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk.Nature genetics · 2025Article
- Powerful mapping ofmedRxiv : the preprint server for health sciences · 2024Article
- Improved multi-ancestry fine-mapping identifiesmedRxiv : the preprint server for health sciences · 2024Article
- Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.Human molecular genetics · 2022Article
- Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants.Frontiers in cell and developmental biology · 2022Review
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Hematological measures are important intermediate clinical phenotypes for many acute and chronic diseases and are highly heritable. Although genome-wide association studies (GWAS) have identified thousands of loci containing trait-associated variants, the causal genes underlying these associations are often uncertain. To better understand the underlying genetic regulatory mechanisms, we performed a transcriptome-wide association study (TWAS) to systematically investigate the association between genetically predicted gene expression and hematological measures in 54,542 Europeans from the Genetic Epidemiology Research on Aging cohort. We found 239 significant gene-trait associations with hematological measures; we replicated 71 associations at p < 0.05 in a TWAS meta-analysis consisting of up to 35,900 Europeans from the Women's Health Initiative, Atherosclerosis Risk in Communities Study, and BioMe Biobank. Additionally, we attempted to refine this list of candidate genes by performing conditional analyses, adjusting for individual variants previously associated with hematological measures, and performed further fine-mapping of TWAS loci. To facilitate interpretation of our findings, we designed an R Shiny application to interactively visualize our TWAS results by integrating them with additional genetic data sources (GWAS, TWAS from multiple reference panels, conditional analyses, known GWAS variants, etc.). Our results and application highlight frequently overlooked TWAS challenges and illustrate the complexity of TWAS fine-mapping.
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Registered trials
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