ArticleiScience2020
Population-Matched Transcriptome Prediction Increases TWAS Discovery and Replication Rate.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Brain transcriptome-wide association studies in diverse ancestral populations reveal genes implicated in an anxiety-related phenotype.G3 (Bethesda, Md.) · 2026Pooled it
- Large-scale multi-omics analyses in Hispanic/Latino populations identify genes for cardiometabolic traits.Nature communications · 2025Pooled it
- Cross-ancestry information transfer framework improves protein abundance prediction and protein-trait association identification.Briefings in bioinformatics · 2026Article
- Transcriptome-wide association study of alternative polyadenylation identifies susceptibility genes in non-small cell lung cancer.Oncogene · 2025Article
- A cross-tissue transcriptome-wide association study identified susceptibility genes for age-related macular degeneration.Scientific reports · 2025Article
- Transferability of Single- and Cross-Tissue Transcriptome Imputation Models Across Ancestry Groups.Genetic epidemiology · 2025Article
- Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges.Current protocols · 2024Article
- Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations.HGG advances · 2023Article
- Integrating eQTL and GWAS data characterises established and identifies novel migraine risk loci.Human genetics · 2023Article
- Gene expression in African Americans, Puerto Ricans and Mexican Americans reveals ancestry-specific patterns of genetic architecture.Nature genetics · 2023Article
- Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations.bioRxiv : the preprint server for biology · 2023Article
- Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative.Cell genomics · 2022Article
- Biological insights into systemic lupus erythematosus through an immune cell-specific transcriptome-wide association study.Annals of the rheumatic diseases · 2022Article
- Article
- Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits.American journal of human genetics · 2022Article
- Protein prediction for trait mapping in diverse populations.PloS one · 2022Article
- The accelerated aging phenotype: The role of race and social determinants of health on aging.Ageing research reviews · 2022Review
- Article
- Is Response to Genotoxic Stress Similar in Populations of African and European Ancestry? A Study of Dose-Response AfterFrontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Most genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) focus on European populations; however, these results cannot always be accurately applied to non-European populations due to genetic architecture differences. Using GWAS summary statistics in the Population Architecture using Genomics and Epidemiology study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we perform TWAS to determine gene-trait associations. We compared results using three transcriptome prediction models derived from Multi-Ethnic Study of Atherosclerosis populations: the African American and Hispanic/Latino (AFHI) model, the European (EUR) model, and the African American, Hispanic/Latino, and European (ALL) model. We identified 240 unique significant trait-associated genes. We found more significant, colocalized genes that replicate in larger cohorts when applying the AFHI model than the EUR or ALL model. Thus, TWAS with population-matched transcriptome models have more power for discovery and replication, demonstrating the need for more transcriptome studies in diverse populations.
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Registered trials
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