ArticleHFSP journal2010
Systems genetics: the added value of gene expression.
Article in HFSP journal, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Predicting the phenotypic values of physiological traits using SNP genotype and gene expression data in mice.PloS one · 2014Article
- Assumptions and properties of limiting pathway models for analysis of epistasis in complex traits.PloS one · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding causal relationships between genotypes and phenotypes is a long-standing aim in genetics. In addition to high-throughput technologies that allow the measurement of many DNA variants it is possible to measure gene expression in specific tissues using array technology. "Systems genetics" is an emerging discipline that combines dense data on genotypes, gene expression, and outcome phenotypes to answer fundamental questions about causal pathways from genotype to phenotype. A recent paper by Chen et al. [Mol. Syst. Biol. 5, 310 (2009)] addressed the question of whether relative levels of mRNA expression help to elucidate causal paths from genotype to phenotype, using drug resistance in yeast as a model. The authors show that data on genetic markers and on gene expression, measured in a drug-free environment, can be combined to predict the growth of a yeast strain in the presence of a drug. They argue that their prediction can be used to identify causal pathways and for a subset of the genes used in prediction, the authors demonstrate that these genes cause an effect on drug sensitivity by deleting the gene or overexpressing it or swapping alleles between strains of yeast. This approach can also be applied to other species, including humans, and may become a tool in the study of personalized medicine.
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.