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MaizeNet: a co‐functional network for network‐assisted systems genetics in Zea mays
Author(s) -
Lee Tak,
Lee Sungho,
Yang Sunmo,
Lee Insuk
Publication year - 2019
Publication title -
the plant journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.058
H-Index - 269
eISSN - 1365-313X
pISSN - 0960-7412
DOI - 10.1111/tpj.14341
Subject(s) - biology , quantitative trait locus , gene , computational biology , genetics , functional genomics , genome , gene regulatory network , candidate gene , genome wide association study , microbiology and biotechnology , genomics , gene expression , single nucleotide polymorphism , genotype
Summary Maize ( Zea mays ) has multiple uses in human food, animal fodder, starch and sweetener production and as a biofuel, and is accordingly the most extensively cultivated cereal worldwide. To enhance maize production, genetic factors underlying important agricultural traits, including stress tolerance and flowering, have been explored through forward and reverse genetics approaches. Co‐functional gene networks are systems biology resources useful in identifying trait‐associated genes in plants by prioritizing candidate genes. Here, we present MaizeNet ( http://www.inetbio.org/maizenet/ ), a genome‐scale co‐functional network of Z. mays genes, and a companion web server for network‐assisted systems genetics. We describe the validation of MaizeNet network quality and its ability to functionally predict molecular pathways and complex traits in maize. Furthermore, we demonstrate that MaizeNet‐based prioritization of candidate genes can facilitate the identification of cell wall biosynthesis genes and detect network communities associated with flowering‐time candidate genes derived from genome‐wide association studies. The demonstrated gene prioritization and subnetwork analysis can be conducted by simply submitting maize gene models based on the commonly used B73 RefGen_v3 and the latest B73 RefGen_v4 reference genomes on the MaizeNet web server. MaizeNet‐based network‐assisted systems genetics will substantially accelerate the discovery of trait‐associated genes for crop improvement.