ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2018
Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-wide analysis of dental caries and periodontitis combining clinical and self-reported data.Nature communications · 2019Pooled it
- Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Trial
- Brain neurotransmitter transporter/receptor genomics and efavirenz central nervous system adverse events.Pharmacogenetics and genomics · 2018Trial
- Overlapping genetic etiology of pediatric and adult germ cell tumors.Journal of the National Cancer Institute · 2026Article
- Statistical modelling of an outcome variable with integrated multi-omics.BMC bioinformatics · 2025Article
- Aberrant gene expression prediction across human tissues.Nature communications · 2025Article
- Transferability of Single- and Cross-Tissue Transcriptome Imputation Models Across Ancestry Groups.Genetic epidemiology · 2025Article
- MOSES: a methylation-based gene association approach for unveiling environmentally regulated genes linked to a trait or disease.Clinical epigenetics · 2024Article
- Tissue-specific atlas of trans-models for gene regulation elucidates complex regulation patterns.BMC genomics · 2024Article
- DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.Genome medicine · 2023Article
- Transcriptome-wide association studies: recent advances in methods, applications and available databases.Communications biology · 2023Review
- Genetically personalised organ-specific metabolic models in health and disease.Nature communications · 2022Article
- MicroRNA-7 regulates melanocortin circuits involved in mammalian energy homeostasis.Nature communications · 2022Article
- Associations of carotid intima media thickness with gene expression in whole blood and genetically predicted gene expression across 48 tissues.Human molecular genetics · 2022Article
- Disentangling genetic feature selection and aggregation in transcriptome-wide association studies.Genetics · 2022Article
- Power analysis of transcriptome-wide association study: Implications for practical protocol choice.PLoS genetics · 2021Article
- The transcriptome-wide association search for genes and genetic variants which associate with BMI and gestational weight gain in women with type 1 diabetes.Molecular medicine (Cambridge, Mass.) · 2021Article
- Alterations observed in the interferon α and β signaling pathway in MDD patients are marginally influenced by cis-acting alleles.Scientific reports · 2021Article
- From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries.Frontiers in genetics · 2021Review
- Genotype-Based Gene Expression in Colon Tissue-Prediction Accuracy and Relationship with the Prognosis of Colorectal Cancer Patients.International journal of molecular sciences · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Genome-wide association studies (GWAS) have been successful in facilitating the understanding of genetic architecture behind human diseases, but this approach faces many challenges. To identify disease-related loci with modest to weak effect size, GWAS requires very large sample sizes, which can be computational burdensome. In addition, the interpretation of discovered associations remains difficult. PrediXcan was developed to help address these issues. With built in SNP-expression models, PrediXcan is able to predict the expression of genes that are regulated by putative expression quantitative trait loci (eQTLs), and these predicted expression levels can then be used to perform gene-based association studies. This approach reduces the multiple testing burden from millions of variants down to several thousand genes. But most importantly, the identified associations can reveal the genes that are under regulation of eQTLs and consequently involved in disease pathogenesis. In this study, two of the most practical functions of PrediXcan were tested: 1) predicting gene expression, and 2) prioritizing GWAS results. We tested the prediction accuracy of PrediXcan by comparing the predicted and observed gene expression levels, and also looked into some potential influential factors and a filter criterion with the aim of improving PrediXcan performance. As for GWAS prioritization, predicted gene expression levels were used to obtain gene-trait associations, and background regions of significant associations were examined to decrease the likelihood of false positives. Our results showed that 1) PrediXcan predicted gene expression levels accurately for some but not all genes; 2) including more putative eQTLs into prediction did not improve the prediction accuracy; and 3) integrating predicted gene expression levels from the two PrediXcan whole blood models did not eliminate false positives. Still, PrediXcan was able to prioritize GWAS associations that were below the genome-wide significance threshold in GWAS, while retaining GWAS significant results. This study suggests several ways to consider PrediXcan's performance that will be of value to eQTL and complex human disease research.
Indexed as
Identifiers
29218904PMC5749400What OpenQuestion holds
Registered trials
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