ArticleAmerican journal of human genetics2016
Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx.
Article in American journal of human genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Alternative tandem transcription initiation links noncoding variants to human disease through translational control.Nature communications · 2026Article
- Estimating genotype-tissue specific gene expression using hybrid deep learning.Communications biology · 2026Article
- Pan-cancer analysis reveals DDIAS as a potential diagnostic biomarker, prognostic indicator, and tumor-immune correlate.Discover oncology · 2026Article
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.The annals of applied statistics · 2025Article
- Discovering disease genetic variation impacting gene expression in 103 brain tissues with the Brain Ontology Expression (BRONTE) graph neural network model.bioRxiv : the preprint server for biology · 2025Article
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.bioRxiv : the preprint server for biology · 2025Article
- Prioritizing Patient Selection in Clinical Trials: A Machine Learning Algorithm for Dynamic Prediction of In-Hospital Mortality for ICU Admitted Patients Using Repeated Measurement Data.Journal of clinical medicine · 2025Article
- Rapid and accurate multi-phenotype imputation for millions of individuals.Nature communications · 2025Article
- A multi-modal framework improves prediction of tissue-specific gene expression from a surrogate tissue.EBioMedicine · 2024Article
- Hypergraph factorization for multi-tissue gene expression imputation.Nature machine intelligence · 2023Article
- MTM: a multi-task learning framework to predict individualized tissue gene expression profiles.Bioinformatics (Oxford, England) · 2023Article
- Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning.Microbiology spectrum · 2023Article
- Biosecurity and antimicrobial use in broiler farms across nine European countries: toward identifying farm-specific options for reducing antimicrobial usage.Epidemiology and infection · 2022Article
- Tissue-Specific Variations in Transcription Factors Elucidate Complex Immune System Regulation.Genes · 2022Article
- Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study.Genes · 2021Article
- A COVARIANCE-ENHANCED APPROACH TO MULTI-TISSUE JOINT EQTL MAPPING WITH APPLICATION TO TRANSCRIPTOME-WIDE ASSOCIATION STUDIES.The annals of applied statistics · 2021Article
- Transcriptome prediction performance across machine learning models and diverse ancestries.HGG advances · 2021Article
- Predicting tissue-specific gene expression from whole blood transcriptome.Science advances · 2021Article
- Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders.Genome research · 2021Article
- A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies.Nucleic acids research · 2020Article
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9 authors.
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Abstract
Gene expression and its regulation can vary substantially across tissue types. In order to generate knowledge about gene expression in human tissues, the Genotype-Tissue Expression (GTEx) program has collected transcriptome data in a wide variety of tissue types from post-mortem donors. However, many tissue types are difficult to access and are not collected in every GTEx individual. Furthermore, in non-GTEx studies, the accessibility of certain tissue types greatly limits the feasibility and scale of studies of multi-tissue expression. In this work, we developed multi-tissue imputation methods to impute gene expression in uncollected or inaccessible tissues. Via simulation studies, we showed that the proposed methods outperform existing imputation methods in multi-tissue expression imputation and that incorporating imputed expression data can improve power to detect phenotype-expression correlations. By analyzing data from nine selected tissue types in the GTEx pilot project, we demonstrated that harnessing expression quantitative trait loci (eQTLs) and tissue-tissue expression-level correlations can aid imputation of transcriptome data from uncollected GTEx tissues. More importantly, we showed that by using GTEx data as a reference, one can impute expression levels in inaccessible tissues in non-GTEx expression studies.
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