ArticleBMC genomics2014
Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 10 citations in OpenAlex.
- IBI-DT: a novel approach combining individualized Bayesian inference and decision tree for identifying cancer drivers and their interactions.Briefings in bioinformatics · 2025Article
- Deciphering single-cell gene expression variability and its role in drug response.Human molecular genetics · 2024Article
- Article
- Genetic ancestry plays a central role in population pharmacogenomics.Communications biology · 2021Article
- Genetic Predisposition to Glioma Mediated by a MAPKAP1 Enhancer Variant.Cellular and molecular neurobiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGene-based analysis has become popular in genomic research because of its appealing biological and statistical properties compared with those of a single-locus analysis. However, only a few, if any, studies have discussed a mapping of expression quantitative trait loci (eQTL) in a gene-based framework. Neither study has discussed ancestry-informative eQTL nor investigated their roles in pharmacogenetics by integrating single nucleotide polymorphism (SNP)-based eQTL (s-eQTL) and gene-based eQTL (g-eQTL).
resultsIn this g-eQTL mapping study, the transcript expression levels of genes (transcript-level genes; T-genes) were correlated with the SNPs of genes (sequence-level genes; S-genes) by using a method of gene-based partial least squares (PLS). Ancestry-informative transcripts were identified using a rank-score-based multivariate association test, and ancestry-informative eQTL were identified using Fisher's exact test. Furthermore, key ancestry-predictive eQTL were selected in a flexible discriminant analysis. We analyzed SNPs and gene expression of 210 independent people of African-, Asian- and European-descent. We identified numerous cis- and trans-acting g-eQTL and s-eQTL for each population by using PLS. We observed ancestry information enriched in eQTL. Furthermore, we identified 2 ancestry-informative eQTL associated with adverse drug reactions and/or drug response. Rs1045642, located on MDR1, is an ancestry-informative eQTL (P = 2.13E-13, using Fisher's exact test) associated with adverse drug reactions to amitriptyline and nortriptyline and drug responses to morphine. Rs20455, located in KIF6, is an ancestry-informative eQTL (P = 2.76E-23, using Fisher's exact test) associated with the response to statin drugs (e.g., pravastatin and atorvastatin). The ancestry-informative eQTL of drug biotransformation genes were also observed; cross-population cis-acting expression regulators included SPG7, TAP2, SLC7A7, and CYP4F2. Finally, we also identified key ancestry-predictive eQTL and established classification models with promising training and testing accuracies in separating samples from close populations.
conclusionsIn summary, we developed a gene-based PLS procedure and a SAS macro for identifying g-eQTL and s-eQTL. We established data archives of eQTL for global populations. The program and data archives are accessible at http://www.stat.sinica.edu.tw/hsinchou/genetics/eQTL/HapMapII.htm. Finally, the results from our investigations regarding the interrelationship between eQTL, ancestry information, and pharmacodynamics provide rich resources for future eQTL studies and practical applications in population genetics and medical genetics.
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.