A systematic way to generate synthetic sick or lethal (SSL)
interactions, which has set a landmark in yeast genome research, 
(Tong et al., 2001, Tong et al. 2004) will be introduced first. Since 
then inferring transcriptional compensation interactions among SSL 
gene pairs has raised some attention (Kafri et al., 2005; Wong and
Roth, 2005; Collins et al., 2007; Shieh et al, 2008 and Chuang et
al., 2008). The importance of genetic networks (such as SSL and 
TC) lies in that similar networks may underlie complex genetics and 
inherited phenotypes in higher organisms, e.g. human complex
 disease.  

A few approaches to infer genetic interactions will be introduced.
For instance, time–lagged correlation (Schmitt et al., 2004), gene 
co-expression network (Dong and Horvath, 2007) and  nonlinear 
correlation (Chuang et al., 2008). Applications of inferred genetic 
networks to uncovering mechanisms of autosomal dominant polycystic 
kidneys disease (ADPKD) in mouse and obesity in human will be 
demonstrated.