Skip to main content
ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #326571
Title: Revealing gene regulation and association through biological networks

Author
item LISERON-MONFILS, CHRISTOPHE - Cold Spring Harbor Laboratory
item Ware, Doreen

Submitted to: Current Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/2/2015
Publication Date: 11/10/2015
Publication URL: http://DOI: 10.1016/j.cpb.2015.11.001
Citation: Liseron-Monfils, C., Ware, D. 2015. Revealing gene regulation and association through biological networks. Current Biology. 3-4:30-39.

Interpretive Summary: The traditional methods to identify candidate leads for crop improvement are to use natural biological evolution and careful selection of plant varieties by farmers. Modern agriculture started to use molecular biology to improve marker development and reduce the time to selective breeding, such as quantitative genetic methods (QTL). With the advanced technologies in sequencing, now it’s easy to access the global expression data. This review article describes the development of co-expression and molecular networks. It has shown how network analysis can reinforce the discovery of candidate genes/loci more rapidly and with higher confidence. These improvements will serve to accelerate genetic engineering and molecular breeding as modern agriculture confronts the challenging times ahead, with the increase of abiotic stresses for crops as drought, heat, high salinity soil or waterlogging.

Technical Abstract: This review had first summarized traditional methods used by plant breeders for genetic improvement, such as QTL analysis and transcriptomic analysis. With accumulating data, we can draw a network that comprises all possible links between members of a community, including protein–protein interaction, protein-DNA interaction, and metabolic network. These networks can be interpreted by co-expression patterns. The review also summarizes several databases and repositories, and different methods. To determine the candidate genes, we can use quantitative genetics and transcriptomics, network topography, and forward and reverse “edgetics.” It's important to integrate prioritization strategies to fully integrate these different types of analyses.