ArticleNAR genomics and bioinformatics2020
IGREX for quantifying the impact of genetically regulated expression on phenotypes.
Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types.Frontiers in genetics · 2020Pooled it
- A gene-specific variance-control approach corrects polygenicity-driven inflation observed in transcriptome-wide association studies.American journal of human genetics · 2026Article
- A unified framework for identification of cell-type-specific spatially variable genes in spatial transcriptomic studies.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Expression interplay of genes coding for calcium-binding proteins and transcription factors during the osmotic phase provides insights on salt stress response mechanisms in bread wheat.Plant molecular biology · 2024Article
- XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias.Nature communications · 2023Article
- PALM: a powerful and adaptive latent model for prioritizing risk variants with functional annotations.Bioinformatics (Oxford, England) · 2023Article
- Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data.Journal of the American Statistical Association · 2023Article
- Leveraging the local genetic structure for trans-ancestry association mapping.American journal of human genetics · 2022Article
- webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study.Nucleic acids research · 2022Article
- Integration of Distinct Analysis Strategies Improves Tissue-Trait Association Identification.Frontiers in genetics · 2022Article
- A robust two-sample transcriptome-wide Mendelian randomization method integrating GWAS with multi-tissue eQTL summary statistics.Genetic epidemiology · 2021Article
- A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits.American journal of human genetics · 2021Article
- A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies.Nucleic acids research · 2020Article
- Transcriptome profiling at osmotic and ionic phases of salt stress response in bread wheat uncovers trait-specific candidate genes.BMC plant biology · 2020Article
- Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits.Human molecular genetics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
By leveraging existing GWAS and eQTL resources, transcriptome-wide association studies (TWAS) have achieved many successes in identifying trait-associations of genetically regulated expression (GREX) levels. TWAS analysis relies on the shared GREX variation across GWAS and the reference eQTL data, which depends on the cellular conditions of the eQTL data. Considering the increasing availability of eQTL data from different conditions and the often unknown trait-relevant cell/tissue-types, we propose a method and tool, IGREX, for precisely quantifying the proportion of phenotypic variation attributed to the GREX component. IGREX takes as input a reference eQTL panel and individual-level or summary-level GWAS data. Using eQTL data of 48 tissue types from the GTEx project as a reference panel, we evaluated the tissue-specific IGREX impact on a wide spectrum of phenotypes. We observed strong GREX effects on immune-related protein biomarkers. By incorporating trans-eQTLs and analyzing genetically regulated alternative splicing events, we evaluated new potential directions for TWAS analysis.
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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 OpenQuestion graph.