ArticleFrontiers in genetics2019
Accuracy of Gene Expression Prediction From Genotype Data With PrediXcan Varies Across and Within Continental Populations.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Large-scale multi-omics analyses in Hispanic/Latino populations identify genes for cardiometabolic traits.Nature communications · 2025Pooled it
- Pooled it
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- Fine-tuning sequence-to-expression models on personal genome and transcriptome data.Genome biology · 2026Article
- Assessing long-term pleiotropic effects of potential novel triglyceride-lowering medications using variants identified by Mendelian randomization.medRxiv : the preprint server for health sciences · 2025Article
- Transferability of Single- and Cross-Tissue Transcriptome Imputation Models Across Ancestry Groups.Genetic epidemiology · 2025Article
- Allele frequency impacts the cross-ancestry portability of gene expression prediction in lymphoblastoid cell lines.American journal of human genetics · 2024Article
- Cross-population enhancement of PrediXcan predictions with a gnomAD-based east Asian reference framework.Briefings in bioinformatics · 2024Article
- The Potential of Genomics and Electronic Health Records to Invigorate Drug Development.Biological psychiatry · 2024Article
- Associations between low serum levels of ANRIL and some common gene SNPs in Iranian patients with premature coronary artery disease.Scientific reports · 2024Article
- LA-GEM: imputation of gene expression with incorporation of Local Ancestry.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations.HGG advances · 2023Article
- Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations.bioRxiv : the preprint server for biology · 2023Article
- Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits.Genetic epidemiology · 2022Article
- Including diverse and admixed populations in genetic epidemiology research.Genetic epidemiology · 2022Article
- A translational genomics approach identifies IL10RB as the top candidate gene target for COVID-19 susceptibility.NPJ genomic medicine · 2022Article
- Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies.American journal of human genetics · 2022Article
- Article
- Development of a Machine Learning Model to Predict Non-Durable Response to Anti-TNF Therapy in Crohn's Disease Using Transcriptome Imputed from Genotypes.Journal of personalized medicine · 2022Article
- Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits.American journal of human genetics · 2022Article
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2 authors.
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Abstract
Using genetic data to predict gene expression has garnered significant attention in recent years. PrediXcan has become one of the most widely used gene-based methods for testing associations between predicted gene expression values and a phenotype, which has facilitated novel insights into the relationship between complex traits and the component of gene expression that can be attributed to genetic variation. The gene expression prediction models for PrediXcan were developed using supervised machine learning methods and training data from the Depression Genes and Networks (DGN) study and the Genotype-Tissue Expression (GTEx) project, where the majority of subjects are of European descent. Many genetic studies, however, include samples from multi-ethnic populations, and in this paper we evaluate the accuracy of PrediXcan for predicting gene expression in diverse populations. Using transcriptomic data from the GEUVADIS (Genetic European Variation in Disease) RNA sequencing project and whole genome sequencing data from the 1000 Genomes project, we evaluate and compare the predictive performance of PrediXcan in an African population (Yoruban) and four European ancestry populations for thousands of genes. We evaluate a range of models from the PrediXcan weight databases and use Pearson's correlation coefficient to assess gene expression prediction accuracy with PrediXcan. From our evaluation, we find that the predictive performance of PrediXcan varies substantially among populations from different continents (
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