ArticleThe pharmacogenomics journal2017
Transcriptomic variation of pharmacogenes in multiple human tissues and lymphoblastoid cell lines.
Article in The pharmacogenomics journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 33 citations in OpenAlex.
- Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data.British journal of clinical pharmacology · 2025Review
- Deciphering single-cell gene expression variability and its role in drug response.Human molecular genetics · 2024Article
- Principal Component Analysis of Alternative Splicing Profiles Revealed by Long-Read ONT Sequencing in Human Liver Tissue and Hepatocyte-Derived HepG2 and Huh7 Cell Lines.International journal of molecular sciences · 2023Article
- Article
- Molecular characterisation of ILRUN, a novel inhibitor of proinflammatory and antimicrobial cytokines.Heliyon · 2020Article
- Consistent RNA sequencing contamination in GTEx and other data sets.Nature communications · 2020Article
- Article
- Large-scale analysis of human gene expression variability associates highly variable drug targets with lower drug effectiveness and safety.Bioinformatics (Oxford, England) · 2019Article
- Genome sequencing analysis of blood cells identifies germline haplotypes strongly associated with drug resistance in osteosarcoma patients.BMC cancer · 2019Article
- psichomics: graphical application for alternative splicing quantification and analysis.Nucleic acids research · 2019Article
- Pharmacogenomics and big genomic data: from lab to clinic and back again.Human molecular genetics · 2018Review
- Post-transcriptional Regulation of UGT2B10 Hepatic Expression and Activity by Alternative Splicing.Drug metabolism and disposition: the biological fate of chemicals · 2018Article
- Quantitative profiling of the UGT transcriptome in human drug-metabolizing tissues.The pharmacogenomics journal · 2018Article
- Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data.Frontiers in pharmacology · 2018Review
- Alternative Splicing: Expanding Diversity in Major ABC and SLC Drug Transporters.The AAPS journal · 2017Review
- Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx.American journal of human genetics · 2016Article
- The CYP2C19 Intron 2 Branch Point SNP is the Ancestral Polymorphism Contributing to the Poor Metabolizer Phenotype in Livers with CYP2C19*35 and CYP2C19*2 Alleles.Drug metabolism and disposition: the biological fate of chemicals · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors at 10 institutions in 2 countries.
Funding
Abstract
Variation in the expression level and activity of genes involved in drug disposition and action ('pharmacogenes') can affect drug response and toxicity, especially when in tissues of pharmacological importance. Previous studies have relied primarily on microarrays to understand gene expression differences, or have focused on a single tissue or small number of samples. The goal of this study was to use RNA-sequencing (RNA-seq) to determine the expression levels and alternative splicing of 389 Pharmacogenomics Research Network pharmacogenes across four tissues (liver, kidney, heart and adipose) and lymphoblastoid cell lines, which are used widely in pharmacogenomics studies. Analysis of RNA-seq data from 139 different individuals across the 5 tissues (20-45 individuals per tissue type) revealed substantial variation in both expression levels and splicing across samples and tissue types. Comparison with GTEx data yielded a consistent picture. This in-depth exploration also revealed 183 splicing events in pharmacogenes that were previously not annotated. Overall, this study serves as a rich resource for the research community to inform biomarker and drug discovery and use.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.