Genetic diseases are one of the most critical diseases facing human societies, their risk lies in the transmission of genetic characteristics from one generation to another, where the imbalance of these characteristics leads to an unhealthy offspring, which negatively affects the effort of this offspring and its services to society. Genetic disease is caused by a mutation in the Deoxyribonucleic Acid (DNA), these genetic mutations are generated by nonlinear interactions between two or more genes and / or environmental exposures. The aim of this paper is to discover both of gene-environment interactions and gene-gene interactions causing a genetic disease, the proposed methodology is based on both of the filter and wrapper feature selection methods, it uses the filter method using a Relief algorithm to detect the gene-environment interactions, wrapper method using genetic algorithm to discover gene-gene interactions, and classification decision tree algorithm to generate the conditional rules of gene-gene interactions. It has been evaluated using many different classifier models on four benchmark databases, and compared its performance with an Apriori algorithm for generating rules of gene-gene interactions, the proposed methodology achieved the highest performance and better classification accuracy on all databases containing patients affected by gene-environment interactions or gene-gene interactions or both of gene-environment and gene-gene interactions.

遗传病是人类社会面临的最严重的疾病之一，其风险在于遗传特征从一代传给另一世代，这些特征的失衡导致不健康的后代，对后代及其后代的努力产生负面影响服务社会。遗传疾病是由脱氧核糖核酸（DNA）中的突变引起的，这些遗传突变是由两个或多个基因之间的非线性相互作用和/或环境暴露引起的。本文的目的是发现导致遗传疾病的基因-环境相互作用和基因-基因相互作用，该方法基于过滤器和包装器特征选择方法，并使用Relief算法进行过滤。检测基因与环境的相互作用，包装器方法使用遗传算法发现基因-基因相互作用，而分类决策树算法生成基因-基因相互作用的条件规则。已在四个基准数据库上使用许多不同的分类器模型对它进行了评估，并将其性能与Apriori算法生成基因-基因相互作用的规则进行了比较，该方法在所有包含受基因影响的患者的数据库中均获得了最高的性能和更好的分类准确性-环境相互作用或基因-基因相互作用或基因-环境和基因-基因相互作用两者。