ArticleGenome research2003
Automated detection of informative combined effects in genetic association studies of complex traits.
Article in Genome research, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- A faster pedigree-based generalized multifactor dimensionality reduction method for detecting gene-gene interactions.Statistics and its interface · 2011Article
- Polymorphisms of the lamina maturation pathway and their association with the metabolic syndrome: the DESIR prospective study.Journal of molecular medicine (Berlin, Germany) · 2010Article
- Identification of gene-gene interactions in the presence of missing data using the multifactor dimensionality reduction method.Genetic epidemiology · 2009Article
- Use of pathway information in molecular epidemiology.Human genomics · 2009Review
- Lack of association between polymorphisms of the IL18R1 and IL18RAP genes and cardiovascular risk: the MORGAM Project.BMC medical genetics · 2009Article
- Clique-finding for heterogeneity and multidimensionality in biomarker epidemiology research: the CHAMBER algorithm.PloS one · 2009Article
- Article
- Polymorphisms in 33 inflammatory genes and risk of myocardial infarction--a system genetics approach.Journal of molecular medicine (Berlin, Germany) · 2007Article
- Association Rule Discovery Has the Ability to Model Complex Genetic Effects.IEEE Symposium on Computational Intelligence and Data Mining. IEEE Symposium on Computational Intelligence and Data Mining · 2007Article
- A survey of data mining methods for linkage disequilibrium mapping.Human genomics · 2006Review
- Large recursive partitioning analysis of complex disease pharmacogenetic studies. II. Statistical considerations.Pharmacogenomics · 2005Review
- Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach.Journal of the American Medical Informatics Association : JAMIAArticle
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
No grant is acknowledged in the PubMed record.
Abstract
There is a growing body of evidence suggesting that the relationships between gene variability and common disease are more complex than initially thought and require the exploration of the whole polymorphism of candidate genes as well as several genes belonging to biological pathways. When the number of polymorphisms is relatively large and the structure of the relationships among them complex, the use of data mining tools to extract the relevant information is a necessity. Here, we propose an automated method for the detection of informative combined effects (DICE) among several polymorphisms (and nongenetic covariates) within the framework of association studies. The algorithm combines the advantages of the regressive approaches with those of data exploration tools. Importantly, DICE considers the problem of interaction between polymorphisms as an effect of interest and not as a nuisance effect. We illustrate the method with three applications on the relationship between (1). the P-selectin gene and myocardial infarction, (2). the cholesteryl ester transfer protein gene and plasma high-density-lipoprotein cholesterol concentration, and (3). genes of the renin-angiotensin-aldosterone system and myocardial infarction. The applications demonstrated that the method was able to recover results already found using other approaches, but in addition detected biologically sensible effects not previously described.
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.