TY  - JOUR
A1  - Tahri-Daizadeh, Nadia
A1  - Tregouet, David-Alexandre
A1  - Nicaud, Viviane
A1  - Manuel, Nicolas
A1  - Cambien, François
A1  - Tiret, Laurence
T1  - Automated Detection of Informative Combined Effects in Genetic Association Studies of Complex Traits
Y1  - 2003/08/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1952 
EP  - 1960 
DO  - 10.1101/gr.1254203 
VL  - 13 
IS  - 8 
UR  - http://genome.cshlp.org/content/13/8/1952.abstract 
N2  - 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. 
ER  -