ArticleNature genetics2020
A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis.
Article in Nature genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 206 papers, 6 of them syntheses that pooled it.
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Who cites it
206 citing papers in PubMed, 6 syntheses or guidelines pooled it, 296 citations in OpenAlex.
- Integrative genome-wide analysis unveils the genetic landscape of gallstone disease and highlights novel loci with therapeutic potential.BMJ open gastroenterology · 2025Pooled it
- Large-scale multi-omics analyses in Hispanic/Latino populations identify genes for cardiometabolic traits.Nature communications · 2025Pooled it
- Genome-wide meta-analysis identifies novel risk loci for uterine fibroids within and across multiple ancestry groups.Nature communications · 2025Pooled it
- Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation.Cell reports. Medicine · 2024Pooled it
- Transcriptomic risk scores for attention deficit/hyperactivity disorder.Molecular psychiatry · 2023Pooled it
- A large-scale transcriptome-wide association study (TWAS) of 10 blood cell phenotypes reveals complexities of TWAS fine-mapping.Genetic epidemiology · 2022Pooled it
- Microvascular insulin resistance with enhanced muscle glucose disposal in CD36 deficiency.Diabetologia · 2025Trial
- Transcriptome-wide association analysis of Alzheimer's disease: construction and clinical validation of transcriptomic risk scores.European archives of psychiatry and clinical neuroscience · 2026Article
- Paediatric proteomic signatures of cardiometabolic disease-associated traits predict adult disease outcomes.Nature metabolism · 2026Article
- Identification of immune cell type-specific susceptibility genes in multiple cancers using transcriptome-wide association studies.Journal of the National Cancer Institute · 2026Article
- Conditionally Significant eQTL and TWAS Analyses Identify MsRD26 as a Major Candidate Positive Regulator of Salt Tolerance in Medicago sativa L.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Genetic evidence links hypertension to accelerated brain aging.Biogerontology · 2026Article
- Hypothalamic-pituitary deficiency after radiation in childhood cancer survivors is associated with rare variants in TNS2.The Journal of clinical endocrinology and metabolism · 2026Article
- Multi-ancestry gene expression models amplify transcriptome-wide association study discovery and validation.Nature communications · 2026Article
- G2DR: a genotype-first framework for genetics-informed target prioritization and drug repurposing.Briefings in bioinformatics · 2026Article
- A Multi-Context Regulome-Wide Association Atlas for Genetic Studies of Aging Brain Disorders.medRxiv : the preprint server for health sciences · 2026Article
- Multi-ancestry transcriptome-wide association study reveals shared and population-specific genetic effects in Alzheimer disease.American journal of human genetics · 2026Article
- Shared genetic architecture between obesity and psychiatric disorders reveals comorbid etiology and therapeutic targets.Life medicine · 2026Article
- Increased Arrhythmic Risk in Obesity Is Transduced by Adipose Tissue-Derived Extracellular Vesicles.JACC. Basic to translational science · 2026Article
- Proteogenomic Analysis of Coronary Artery Calcification in Human Populations.Arteriosclerosis, thrombosis, and vascular biology · 2026Article
146 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
Here, we present a joint-tissue imputation (JTI) approach and a Mendelian randomization framework for causal inference, MR-JTI. JTI borrows information across transcriptomes of different tissues, leveraging shared genetic regulation, to improve prediction performance in a tissue-dependent manner. Notably, JTI includes the single-tissue imputation method PrediXcan as a special case and outperforms other single-tissue approaches (the Bayesian sparse linear mixed model and Dirichlet process regression). MR-JTI models variant-level heterogeneity (primarily due to horizontal pleiotropy, addressing a major challenge of transcriptome-wide association study interpretation) and performs causal inference with type I error control. We make explicit the connection between the genetic architecture of gene expression and of complex traits and the suitability of Mendelian randomization as a causal inference strategy for transcriptome-wide association studies. We provide a resource of imputation models generated from GTEx and PsychENCODE panels. Analysis of biobanks and meta-analysis data, and extensive simulations show substantially improved statistical power, replication and causal mapping rate for JTI relative to existing approaches.
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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.