ArticleNature genetics2023
Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.
Article in Nature genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes.Nature human behaviour · 2024Pooled it
- The landscape of the methodology in drug repurposing using human genomic data: a systematic review.Briefings in bioinformatics · 2024Pooled it
- Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus.Nature communications · 2023Pooled it
- Multi-ancestry transcriptome-wide association study reveals shared and population-specific genetic effects in Alzheimer disease.American journal of human genetics · 2026Article
- Ancestral diversity in complex disease genetics: from discovery to translation.Nature reviews. Genetics · 2026Review
- scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.Nature communications · 2026Article
- Review
- Integrating multi-omics and multi-context QTL data with GWAS reveals the genetic architecture of complex traits and improves the discovery of risk genes.medRxiv : the preprint server for health sciences · 2026Article
- Transcriptome-wide association studies at cell-state level using single-cell eQTL data.Cell genomics · 2026Article
- The China Brain Multi-omics Atlas Project (CBMAP).Molecular psychiatry · 2026Review
- Leveraging genomic and transcriptomic data of diverse ancestry to uncover mechanisms of psychiatric risk in the adult and developing brain.Nature communications · 2025Article
- Proteomic signatures of smoking and their associations with risk of incident diseases and mortality in diverse populations.Nature communications · 2025Article
- Integrating axis quantitative trait loci looks beyond cell types and offers insights into brain-related traits.Nature communications · 2025Article
- Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives.Journal of advanced research · 2025Review
- scTWAS: A powerful statistical framework for single-cell transcriptome-wide association studies.Research square · 2025Article
- Incorporating local ancestry information to predict genetically associated DNA methylation in admixed populations.Briefings in bioinformatics · 2025Observational
- An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment.Cell genomics · 2025Article
- Genetically regulated eRNA expression predicts chromatin contact frequency and reveals genetic mechanisms at GWAS loci.Nature communications · 2025Article
- Enhancing nonlinear transcriptome- and proteome-wide association studies via trait imputation with applications to Alzheimer's disease.PLoS genetics · 2025Article
- Integrative genomics sheds light on the immunogenetics of tuberculosis in cattle.Communications biology · 2025Article
Corrections and comments
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90 authors.
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Abstract
Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.
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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.