Statistical Modeling Analysis In Genetic Interaction Data

In biological systems,genetic interaction refers to the phenomenon that the phenotypes of two genes are mutated at the same time differ from their respective mutant phenotypic superposition effects.In recent years,with the development of high-throughput technology,high-throughput screening of genetic interactions has been achieved,resulting in a large number of genetic interactions.Pathway genes refer to a group of genes that they cooperate with each other on the same biological pathway,and they complete a life process together.The study of the pathway genes in genetic interaction data can know how genes interact with each other to influence a phenotype.It is an important way to understand the structure and function of biological pathways,the evolution of biological systems,and the study of complex diseases.However,due to gene pleiotropic and difficulty to deal with high-dimensional data and other issues,how to find the pathway genes in genetic interaction data is very challenging.In this paper,we study the correlation between genes using the improved partial correlation coefficient algorithm by calculating the conditional independence of gene,that is to say,removing the effects of known genes in the pathway.The known genes will be used as seed genes to excavate other genes.We will discuss the effect of the algorithm in simulated data and real data to prove the validity in the application of genetic interaction data.And explore the interaction of gene function in known pathway genes and new pathway genes.We get the interaction of genes,and they complete life activities together.