ArticleEuropean journal of human genetics : EJHG2018
Comparison of methods for transcriptome imputation through application to two common complex diseases.
Article in European journal of human genetics : EJHG, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Leveraging genomic and transcriptomic data of diverse ancestry to uncover mechanisms of psychiatric risk in the adult and developing brain.Nature communications · 2025Article
- Utilising genomic association data for causal inference in anorexia nervosa.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Review
- Transferability of Single- and Cross-Tissue Transcriptome Imputation Models Across Ancestry Groups.Genetic epidemiology · 2025Article
- Integrative multi-omics analysis to gain new insights into COVID-19.Scientific reports · 2024Article
- Transcriptomic imputation of genetic risk variants uncovers novel whole-blood biomarkers of Parkinson's disease.NPJ Parkinson's disease · 2024Article
- Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Bioinformatics detection of modulators controlling splicing factor-dependent intron retention in the human brain.Human mutation · 2022Article
- Multiple-ancestry genome-wide association study identifies 27 loci associated with measures of hemolysis following blood storage.The Journal of clinical investigation · 2021Article
- Beyond association: successes and challenges in linking non-coding genetic variation to functional consequences that modulate Alzheimer's disease risk.Molecular neurodegeneration · 2021Review
- A catalog of GWAS fine-mapping efforts in autoimmune disease.American journal of human genetics · 2021Review
- An integrative systems-based analysis of substance use: eQTL-informed gene-based tests, gene networks, and biological mechanisms.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2021Article
- Investigation of genetically regulated gene expression and response to treatment in rheumatoid arthritis highlights an association betweenAnnals of the rheumatic diseases · 2020Article
- Fine-mapping and QTL tissue-sharing information improves the reliability of causal gene identification.Genetic epidemiology · 2020Article
- Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome-wide association studies.Genetic epidemiology · 2020Article
- Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits.Human molecular genetics · 2020Article
- Systems genetics applications in metabolism research.Nature metabolism · 2019Review
- Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits.Nature communications · 2019Article
- Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies.The Lancet. Respiratory medicine · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
Transcriptome imputation has become a popular method for integrating genotype data with publicly available expression data to investigate the potentially causal role of genes in complex traits. Here, we compare three approaches (PrediXcan, MetaXcan and FUSION) via application to genome-wide association study (GWAS) data for Crohn's disease and type 1 diabetes from the Wellcome Trust Case Control Consortium. We investigate: (i) how the results of each approach compare with each other and with those of standard GWAS analysis; and (ii) how variants in the models used by the prediction tools compare with variants previously reported as eQTLs. We find that all approaches produce highly correlated results when applied to the same GWAS data, although for a subset of genes, mostly in the major histocompatibility complex, the approaches strongly disagree. We also observe that most associations detected by these methods occur near known GWAS risk loci. PrediXcan and MetaXcan's models for predicting expression more consistently recapitulate known effects of genotype on expression, suggesting they are more robust than FUSION. Application of these transcriptome imputation approaches to summary statistics from meta-analyses in Crohn's disease and type 1 diabetes detects 53 significant expression-Crohn's disease associations and 154 significant expression-type 1 diabetes associations, providing insight into biology underlying these diseases. We conclude that while current implementations of transcriptome imputation typically detect fewer associations than GWAS, they nonetheless provide an interesting way of interpreting association signals to identify potentially causal genes, and that PrediXcan and MetaXcan generally produce more reliable results than FUSION.
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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.