ReviewIn silico pharmacology2013
Convergent functional genomics in addiction research - a translational approach to study candidate genes and gene networks.
Review in In silico pharmacology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Identification of Endometriosis Pathophysiologic-Related Genes Based on Meta-Analysis and Bayesian Approach.International journal of molecular sciences · 2025Pooled it
- Invited Expert Opinion- Bioinformatic and Limitation Directives to Help Adopt Genetic Addiction Risk Screening and Identify Preaddictive Reward Dysregulation: Required Analytic Evidence to Induce Dopamine Homeostatsis.Medical research archives · 2023Article
- Using hierarchical similarity to examine the genetics of Behçet's disease.BMC research notes · 2021Article
- Intracranial self-stimulation and concomitant behaviors following systemic methamphetamine administration in Hnrnph1 mutant mice.Psychopharmacology · 2021Article
- Animal models of addiction.Dialogues in clinical neuroscience · 2017Review
- XRCC5 as a risk gene for alcohol dependence: evidence from a genome-wide gene-set-based analysis and follow-up studies in Drosophila and humans.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2015Article
- Addiction and reward-related genes show altered expression in the postpartum nucleus accumbens.Frontiers in behavioral neuroscience · 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
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Convergent functional genomics (CFG) is a translational methodology that integrates in a Bayesian fashion multiple lines of evidence from studies in human and animal models to get a better understanding of the genetics of a disease or pathological behavior. Here the integration of data sets that derive from forward genetics in animals and genetic association studies including genome wide association studies (GWAS) in humans is described for addictive behavior. The aim of forward genetics in animals and association studies in humans is to identify mutations (e.g. SNPs) that produce a certain phenotype; i.e. "from phenotype to genotype". Most powerful in terms of forward genetics is combined quantitative trait loci (QTL) analysis and gene expression profiling in recombinant inbreed rodent lines or genetically selected animals for a specific phenotype, e.g. high vs. low drug consumption. By Bayesian scoring genomic information from forward genetics in animals is then combined with human GWAS data on a similar addiction-relevant phenotype. This integrative approach generates a robust candidate gene list that has to be functionally validated by means of reverse genetics in animals; i.e. "from genotype to phenotype". It is proposed that studying addiction relevant phenotypes and endophenotypes by this CFG approach will allow a better determination of the genetics of addictive behavior.
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.