Survey of Metaheuristics and Statistical Methods for Multifactorial Diseases Analyses

Abstract

The identification of the interactions of polymorphisms with other genetic or environmental factors for the detection of multifactorial diseases has now become both a challenge and an objective for geneticists. Unlike monogenic Mendelian diseases, the classical methods have not become too efficient for the identification of these interactions, especially with the exponential increase in the number of genetic interactions as well as the number of combinations of genotypes. Several methods have been proposed for the detection of susceptibility variants such as metaheuristics and statistical methods. Using metaheuristics, we focus on the feature selection of variables, and more precisely on the determination of the genes that increase the susceptibility to the disease, especially as these methods are more suitable for the description of complex data. Statistical methods are divided into two submethods including linkage studies and association studies. Generally these two methods are used one after the other since they are complementary. The linkage study is used initially because its objective is the localization of the chromosomal regions containing the gene(s) involved in the disease. Then, in a second step, the association study is set up to specify precisely the location of the gene. In this paper, we will present a survey of metaheuristics and statistical methods integrated in the field of human genetics and specifically multifactorial diseases in order to help genetics to find interaction between genes and environemental factor involved in those diseases.

Keywords

• sequence analysis
• data mining
• metaheuristics
• statistical methods
• computational genomics
• multifactoral diseases