来自延世大学李仁锡教授，利用'机能遗传子网络'预测模型，开发的可以有效地发掘调解复杂疾患遗传子的新方法。
此次研究是通过与欧洲分子生物学研究所的Lehner博士，德克萨斯州立大学的Marcotte博士，加拿大多伦多大学的Fraser博士的国际共同研究所进行的研究，也是得到教科部及韩国研究财团的'优秀研究中心(S/ERC)育成事业'与'一半研究者支援事业'支援的随性研究。
李仁锡教授研究组利用'机能遗传子网络'的生物信息学基础预测模型，同时，通过C. elegans将复杂疾患的调节遗传子比原有的随意探测法或知识基础预测可以低廉有效地发掘的事实被立正。
如果利用李教授研究组的机能遗传子网络的话，以研究对象疾患的调节遗传子所传播的遗传子的邻近遗传子群，可以作为新的调节遗传子候补来预测。
李仁锡教授讲到："通过此次研究，今后将利用人间机能遗传子网络，可以有效的发掘复杂疾患调节遗传子群，不仅可以查明类似癌症•；糖尿的复杂疾患的发病心理机制，并且打开了治疗法开发的新的可能性"。
此次研究成果刊登在学术期刊Genome Research上。
doi:10.1101/gr.102749.109
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Predicting genetic modifier loci using functional gene networks
Insuk Lee1,2,7, Ben Lehner3,4,7, Tanya Vavouri3, Junha Shin1, Andrew G. Fraser5,7 and Edward M. Marcotte2,6,7
Most phenotypes are genetically complex, with contributions from mutations in many different genes. Mutations in more than one gene can combine synergistically to cause phenotypic change, and systematic studies in model organisms show that these genetic interactions are pervasive. However, in human association studies such nonadditive genetic interactions are very difficult to identify because of a lack of statistical power—simply put, the number of potential interactions is too vast. One approach to resolve this is to predict candidate modifier interactions between loci, and then to specifically test these for associations with the phenotype. Here, we describe a general method for predicting genetic interactions based on the use of integrated functional gene networks. We show that in both Saccharomyces cerevisiae and Caenorhabditis elegans a single high-coverage, high-quality functional network can successfully predict genetic modifiers for the majority of genes. For C. elegans we also describe the construction of a new, improved, and expanded functional network, WormNet 2. Using this network we demonstrate how it is possible to rapidly expand the number of modifier loci known for a gene, predicting and validating new genetic interactions for each of three signal transduction genes. We propose that this approach, termed network-guided modifier screening, provides a general strategy for predicting genetic interactions. This work thus suggests that a high-quality integrated human gene network will provide a powerful resource for modifier locus discovery in many different diseases.
