ArticlePLoS genetics2012
Mixed effects modeling of proliferation rates in cell-based models: consequence for pharmacogenomics and cancer.
Article in PLoS genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Pooled it
- Physiological hypoxia improves growth and functional differentiation of human intestinal epithelial organoids.Frontiers in immunology · 2023Article
- A conserved expression signature predicts growth rate and reveals cell & lineage-specific differences.PLoS computational biology · 2021Article
- Gender and the Sex Hormone Estradiol Affect Multiple Sclerosis Risk Gene Expression in Epstein-Barr Virus-Infected B Cells.Frontiers in immunology · 2021Article
- The interaction of Multiple Sclerosis risk loci with Epstein-Barr virus phenotypes implicates the virus in pathogenesis.Scientific reports · 2020Article
- Evidence from genome wide association studies implicates reduced control of Epstein-Barr virus infection in multiple sclerosis susceptibility.Genome medicine · 2019Article
- Alternative drug sensitivity metrics improve preclinical cancer pharmacogenomics.Nature biotechnology · 2017Article
- Genetic variations associated with gemcitabine treatment outcome in pancreatic cancer.Pharmacogenetics and genomics · 2016Article
- MicroRNA biogenesis and cellular proliferation.Translational research : the journal of laboratory and clinical medicine · 2015Article
- Identification and validation of genetic variants that influence transcription factor and cell signaling protein levels.American journal of human genetics · 2014Article
- Poly-omic prediction of complex traits: OmicKriging.Genetic epidemiology · 2014Article
- The impact of microRNA expression on cellular proliferation.Human genetics · 2014Article
- Integrative analyses of genetic variation, epigenetic regulation, and the transcriptome to elucidate the biology of platinum sensitivity.BMC genomics · 2014Article
- Protein quantitative trait loci identify novel candidates modulating cellular response to chemotherapy.PLoS genetics · 2014Article
- Genetic variation is the major determinant of individual differences in leukocyte endothelial adhesion.PloS one · 2014Article
- Host genetic variants and gene expression patterns associated with Epstein-Barr virus copy number in lymphoblastoid cell lines.PloS one · 2014Article
- Genetic and epigenetic variants contributing to clofarabine cytotoxicity.Human molecular genetics · 2013Article
- Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments.Nature biotechnology · 2013Article
- Genome-wide variation of cytosine modifications between European and African populations and the implications for complex traits.Genetics · 2013Article
- A genome-wide systematic analysis reveals different and predictive proliferation expression signatures of cancerous vs. non-cancerous cells.PLoS genetics · 2013Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
The International HapMap project has made publicly available extensive genotypic data on a number of lymphoblastoid cell lines (LCLs). Building on this resource, many research groups have generated a large amount of phenotypic data on these cell lines to facilitate genetic studies of disease risk or drug response. However, one problem that may reduce the usefulness of these resources is the biological noise inherent to cellular phenotypes. We developed a novel method, termed Mixed Effects Model Averaging (MEM), which pools data from multiple sources and generates an intrinsic cellular growth rate phenotype. This intrinsic growth rate was estimated for each of over 500 HapMap cell lines. We then examined the association of this intrinsic growth rate with gene expression levels and found that almost 30% (2,967 out of 10,748) of the genes tested were significant with FDR less than 10%. We probed further to demonstrate evidence of a genetic effect on intrinsic growth rate by determining a significant enrichment in growth-associated genes among genes targeted by top growth-associated SNPs (as eQTLs). The estimated intrinsic growth rate as well as the strength of the association with genetic variants and gene expression traits are made publicly available through a cell-based pharmacogenomics database, PACdb. This resource should enable researchers to explore the mediating effects of proliferation rate on other phenotypes.
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.