ArticleGenetic epidemiology2021
Multi-tissue transcriptome-wide association studies.
Article in Genetic epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- The integration of genome-wide and transcriptome-wide association studies in neurodegenerative diseases: opportunities, challenges, and current methodological innovations.Briefings in bioinformatics · 2025Review
- Tisslet tissues-based learning estimation for transcriptomics.BMC bioinformatics · 2025Article
- Serological markers of exocrine pancreatic function are differentially informative for distinguishing individuals progressing to type 1 diabetes.BMJ open diabetes research & care · 2025Article
- Improving polygenic prediction from summary data by learning patterns of effect sharing across multiple phenotypes.PLoS genetics · 2025Article
- Improving polygenic prediction from summary data by learning patterns of effect sharing across multiple phenotypes.bioRxiv : the preprint server for biology · 2024Article
- Multi-tissue transcriptome-wide association study identifies novel candidate susceptibility genes for cataract.Frontiers in ophthalmology · 2024Article
- Transcriptome-wide association studies: recent advances in methods, applications and available databases.Communications biology · 2023Review
- Transcriptome-wide association study identifies novel candidate susceptibility genes for migraine.HGG advances · 2023Article
- Using GWAS summary data to impute traits for genotyped individuals.HGG advances · 2023Article
- A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes.PLoS genetics · 2023Article
- Accounting for nonlinear effects of gene expression identifies additional associated genes in transcriptome-wide association studies.Human molecular genetics · 2022Article
- InTACT: An adaptive and powerful framework for joint-tissue transcriptome-wide association studies.Genetic epidemiology · 2021Article
- Imputed gene expression risk scores: a functionally informed component of polygenic risk.Human molecular genetics · 2021Article
- Multi-tissue transcriptome-wide association studies.Genetic epidemiology · 2021Article
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Authors and funding
2 authors.
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Abstract
A transcriptome-wide association study (TWAS) attempts to identify disease associated genes by imputing gene expression into a genome-wide association study (GWAS) using an expression quantitative trait loci (eQTL) data set and then testing for associations with a trait of interest. Regulatory processes may be shared across related tissues and one natural extension of TWAS is harnessing cross-tissue correlation in gene expression to improve prediction accuracy. Here, we studied multi-tissue extensions of lasso regression and random forests (RF), joint lasso and RF-MTL (multi-task learning RF), respectively. We found that, on our chosen eQTL data set, multi-tissue methods were generally more accurate than their single-tissue counterparts, with RF-MTL performing the best. Simulations showed that these benefits generally translated into more associated genes identified, although highlighted that joint lasso had a tendency to erroneously identify genes in one tissue if there existed an eQTL signal for that gene in another. Applying the four methods to a type 1 diabetes GWAS, we found that multi-tissue methods found more unique associated genes for most of the tissues considered. We conclude that multi-tissue methods are competitive and, for some cell types, superior to single-tissue approaches and hold much promise for TWAS studies.
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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.