ArticleBioData mining2012
Multivariate methods and software for association mapping in dose-response genome-wide association studies.
Article in BioData mining, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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.
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Who cites it
15 citing papers in PubMed.
- Article
- Article
- Article
- Review
- Gene expression and linkage analysis implicate CBLB as a mediator of rituximab resistance.The pharmacogenomics journal · 2018Article
- Article
- SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes.BMC bioinformatics · 2017Article
- An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.BMC bioinformatics · 2016Article
- In vitro screening for population variability in toxicity of pesticide-containing mixtures.Environment international · 2015Article
- Population-based in vitro hazard and concentration-response assessment of chemicals: the 1000 genomes high-throughput screening study.Environmental health perspectives · 2015Article
- An investigation of gene-gene interactions in dose-response studies with Bayesian nonparametrics.BioData mining · 2015Article
- Statin-induced changes in gene expression in EBV-transformed and native B-cells.Human molecular genetics · 2014Article
- Beyond ICJournal of pharmacogenomics & pharmacoproteomics · 2014Article
- Genome-wide association and pharmacological profiling of 29 anticancer agents using lymphoblastoid cell lines.Pharmacogenomics · 2014Article
- Lymphoblastoid cell lines models of drug response: successes and lessons from this pharmacogenomic model.Current molecular medicine · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundThe large sample sizes, freedom of ethical restrictions and ease of repeated measurements make cytotoxicity assays of immortalized lymphoblastoid cell lines a powerful new in vitro method in pharmacogenomics research. However, previous studies may have over-simplified the complex differences in dose-response profiles between genotypes, resulting in a loss of power.
methodsThe current study investigates four previously studied methods, plus one new method based on a multivariate analysis of variance (MANOVA) design. A simulation study was performed using differences in cancer drug response between genotypes for biologically meaningful loci. These loci also showed significance in separate genome-wide association studies. This manuscript builds upon a previous study, where differences in dose-response curves between genotypes were constructed using the hill slope equation.
conclusionOverall, MANOVA was found to be the most powerful method for detecting real signals, and was also the most robust method for detection using alternatives generated with the previous simulation study. This method is also attractive because test statistics follow their expected distributions under the null hypothesis for both simulated and real data. The success of this method inspired the creation of the software program MAGWAS. MAGWAS is a computationally efficient, user-friendly, open source software tool that works on most platforms and performs GWASs for individuals having multivariate responses using standard file formats.
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.