ReviewAdvances in genetics2008
Integrating global gene expression analysis and genetics.
Review in Advances in genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Systems genetics approaches for understanding complex traits with relevance for human disease.eLife · 2023Review
- Methylation quantitative trait loci are largely consistent across disease states in Crohn's disease.G3 (Bethesda, Md.) · 2022Article
- Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics.Frontiers in plant science · 2022Review
- A promising whole-blood biomarker to aid Leprosy control.EBioMedicine · 2021Article
- Identification of New Genes and Loci Associated With Bone Mineral Density Based on Mendelian Randomization.Frontiers in genetics · 2021Article
- Mouse genome-wide association and systems genetics identifies Lhfp as a regulator of bone mass.PLoS genetics · 2019Article
- Dissecting the Genetics of Osteoporosis using Systems Approaches.Trends in genetics : TIG · 2019Review
- Article
- Using GWAS to identify novel therapeutic targets for osteoporosis.Translational research : the journal of laboratory and clinical medicine · 2017Review
- Integrative analysis of GWASs, human protein interaction, and gene expression identified gene modules associated with BMDs.The Journal of clinical endocrinology and metabolism · 2014Article
- Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density.The Journal of clinical investigation · 2014Article
- Sex- and age-interacting eQTLs in human complex diseases.Human molecular genetics · 2014Article
- Transcriptome study of differential expression in schizophrenia.Human molecular genetics · 2013Article
- Systems genetics: a novel approach to dissect the genetic basis of osteoporosis.Current osteoporosis reports · 2012Review
- Gene expression in the Parkinson's disease brain.Brain research bulletin · 2012Review
- Decoding the informational properties of the RNA polymerase II Carboxy Terminal Domain.BMC research notes · 2012Article
- Systems genetic analysis of osteoblast-lineage cells.PLoS genetics · 2012Article
- Genetics and regulatory impact of alternative polyadenylation in human B-lymphoblastoid cells.PLoS genetics · 2012Article
- Genetic dissection of behavioral flexibility: reversal learning in mice.Biological psychiatry · 2011Article
- Proinflammatory fecal mRNA and childhood bacterial enteric infections.Gut microbes · 2010Article
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.
Funding
Abstract
The transcriptome is defined as the collection of all RNAs produced in a cell or tissue at a defined time in development and is one of many stages that make up a biological system. It is also one of the most important; providing the critical link in the flow of information between genes and disease. Therefore, identifying gene expression changes that are reacting to or causing disease promises to significantly enhance our understanding of common disorders. However, only recently has the technology, in the form of DNA microarrays, been in place to quantitate gene expression levels on a genome-wide scale. DNA microarrays are small chips that contain arrays of DNA sequences and are capable of simultaneously quantifying the expression of thousands of genes. When applied to samples representing diseased and normal states, microarray-based expression profiling can identify differentially expressed genes that may play a role in the disease or predict progression or severity. Additionally, the integration of genetics and gene expression promises to aid in uncovering common genetic variations that control a particular disease. In animal models, this approach has already been used to identify genes correlated with disease, prioritized candidates, model causal interactions between genes and traits, and generate gene coexpression networks; all of which have shed light on novel disease mechanisms. In this chapter, we provide an overview of DNA microarray technologies and discuss ways in which microarray expression data can be combined with more traditional experimental approaches to dissect the genetic basis of disease.
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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.