ArticleCell genomics2022
Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative.
Article in Cell genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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Who cites it
26 citing papers in PubMed.
- Multi-ancestry gene expression models amplify transcriptome-wide association study discovery and validation.Nature communications · 2026Article
- Multi-ancestry transcriptome-wide association study reveals shared and population-specific genetic effects in Alzheimer disease.American journal of human genetics · 2026Article
- Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies.American journal of human genetics · 2026Article
- Co-expression-wide association studies link genetically regulated interactions with complex traits.Nature communications · 2025Article
- Isoform-level analyses of 6 cancers uncover extensive genetic risk mechanisms undetected at the gene-level.British journal of cancer · 2025Article
- Improved multiancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk.Nature genetics · 2025Article
- Genetic regulation of gene expression across multiple tissues in chickens.Nature genetics · 2025Article
- Trans-ancestry Genome-Wide Analyses in UK Biobank Yield Novel Risk Loci for Major Depression.medRxiv : the preprint server for health sciences · 2025Article
- Allele frequency impacts the cross-ancestry portability of gene expression prediction in lymphoblastoid cell lines.American journal of human genetics · 2024Article
- Multiome-wide Association Studies: Novel Approaches for Understanding Diseases.Genomics, proteomics & bioinformatics · 2024Review
- A multi-ancestry cerebral cortex transcriptome-wide association study identifies genes associated with smoking behaviors.Molecular psychiatry · 2024Article
- Heterogeneity-aware integrative regression for ancestry-specific association studies.Biometrics · 2024Article
- Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale.PLoS biology · 2024Article
- Splicing-specific transcriptome-wide association uncovers genetic mechanisms for schizophrenia.American journal of human genetics · 2024Article
- Fine-mapping causal tissues and genes at disease-associated loci.medRxiv : the preprint server for health sciences · 2024Article
- Improved multi-ancestry fine-mapping identifiesmedRxiv : the preprint server for health sciences · 2024Article
- Open Science Practices in Psychiatric Genetics: A Primer.Biological psychiatry global open science · 2024Review
- A compendium of genetic regulatory effects across pig tissues.Nature genetics · 2024Article
- Isoform-level transcriptome-wide association uncovers genetic risk mechanisms for neuropsychiatric disorders in the human brain.Nature genetics · 2023Article
- Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations.HGG advances · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
The Global Biobank Meta-analysis Initiative (GBMI), through its diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ancestry study populations across biobanks, GBMI presents unique challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWASs) boost detection power for and provide biological context to genetic associations by integrating genetic variant-to-trait associations from genome-wide association studies (GWASs) with predictive models of gene expression. TWASs present unique challenges beyond GWASs, especially in a multi-biobank, meta-analytic setting. Here, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity, meta-analytic strategies, and open challenges at every step of the framework. We advise conducting ancestry-stratified TWASs using ancestry-specific expression models and meta-analyzing results using inverse-variance weighting, showing the least test statistic inflation. Our work provides a foundation for adding transcriptomic context to biobank-linked GWASs, allowing for ancestry-aware discovery to accelerate genomic medicine.
Identifiers
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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.