A network-based method for the identification of putative genes related to infertility.
Infertility has become one of the major health problems worldwide, with its incidence having risen markedly in recent decades. There is an urgent need to investigate the pathological mechanisms behind infertility and to design effective treatments. However, this is made difficult by the fact that various biological factors have been identified to be related to infertility, including genetic factors.



A network-based method was established to identify new genes potentially related to infertility. A network constructed using human protein-protein interactions based on previously validated infertility-related genes enabled the identification of some novel candidate genes. These genes were then filtered by a permutation test and their functional and structural associations with infertility-related genes.



Our method identified 23 novel genes, which have strong functional and structural associations with previously validated infertility-related genes.



Substantial evidence indicates that the identified genes are strongly related to dysfunction of the four main biological processes of fertility: reproductive development and physiology, gametogenesis, meiosis and recombination, and hormone regulation.



The newly discovered genes may provide new directions for investigating infertility. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
College of Life Science, Shanghai University, Shanghai 200444, China. Electronic address: wsptfb@163.com.
ShaoPeng
S
Wang
College of Life Science, Shanghai University, Shanghai 200444, China. Electronic address: guohuahhn@126.com.
GuoHua
G
Huang
School of Computer Science and Technology, Tianjin University, Tianjin 300072, China; State Key Laboratory of System Bioengineering of the Ministry of Education, Tianjin University, Tianjin 300072, China. Electronic address: huqinghua@tju.edu.cn.
Qinghua
Q
Hu
School of Computer Science and Technology, Tianjin University, Tianjin 300072, China; State Key Laboratory of Medicinal Chemical Biology, NanKai University, Tianjin 300071, China. Electronic address: zouquan@tju.edu.cn.
Quan
Q
Zou
D006728:Hormones / Q000235:genetics; D006801:Humans; D007246:Infertility / Q000235:genetics* / Q000378:metabolism; D008540:Meiosis / Q000235:genetics; D060066:Protein Interaction Maps / Q000235:genetics*; D012098:Reproduction / Q000235:genetics*
D006728:Hormones
BLAST; Infertility; Protein–protein interaction; Shortest path