Various genome-wide approaches to identifying the genetic components that underlie phenotypes in animals and humans have been developed during the last several decades. The relationship between gene and phenotype, however, cannot be represented by a simple one-to-one correspondence. Rather, many genes are typically related to a single phenotype and many phenotypes can be associated with a single gene, a major theme within the study of complex phenotypes. Therefore, to dissect the genetics of complex phenotypes, one must not only identify the genetic components involved but also the relationships between genes. To fulfill this new goal in modern genetics, the field of network science has recently tackled the complexity of phenotypes. There are various types of gene networks, which are defined by their differential representation of network edges (i.e., relationships). Different networks map physical, genetic, functional, and regulatory interactions between genes. Gene networks can be constructed using a wide variety of experimental and computational methods, which provide complimentary information about the genetic organization of phenotypes. The predictive power of a gene network is further augmented via integration with functional genomics or genetics data, including expression, loss-of-function, or chromosomal interval or nucleotide position data associated with a phenotype. Although the field of network-based genetics has made phenomenal progress during the last decade, many limitations, such as the completeness and dynamicity of gene networks, must still be overcome.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (2010-0017649, 2012M3A9B4028641, 2012M 3A9C7050151) and the Next-Generation BioGreen 21 Program (SSAC, PJ009029).
All Science Journal Classification (ASJC) codes
- Animal Science and Zoology
- Biochemistry, Genetics and Molecular Biology(all)