來自延世大學李仁錫教授,，利用'機能遺傳子網(wǎng)絡'預測模型,，開發(fā)的可以有效地發(fā)掘調解復雜疾患遺傳子的新方法。
此次研究是通過與歐洲分子生物學研究所的Lehner博士,，德克薩斯州立大學的Marcotte博士,，加拿大多倫多大學的Fraser博士的國際共同研究所進行的研究，也是得到教科部及韓國研究財團的'優(yōu)秀研究中心(S/ERC)育成事業(yè)'與'一半研究者支援事業(yè)'支援的隨性研究,。
李仁錫教授研究組利用'機能遺傳子網(wǎng)絡'的生物信息學基礎預測模型,，同時，通過C. elegans將復雜疾患的調節(jié)遺傳子比原有的隨意探測法或知識基礎預測可以低廉有效地發(fā)掘的事實被立正,。
如果利用李教授研究組的機能遺傳子網(wǎng)絡的話,，以研究對象疾患的調節(jié)遺傳子所傳播的遺傳子的鄰近遺傳子群，可以作為新的調節(jié)遺傳子候補來預測,。
李仁錫教授講到："通過此次研究,，今后將利用人間機能遺傳子網(wǎng)絡，可以有效的發(fā)掘復雜疾患調節(jié)遺傳子群,，不僅可以查明類似癌癥•,；糖尿的復雜疾患的發(fā)病心理機制，并且打開了治療法開發(fā)的新的可能性",。
此次研究成果刊登在學術期刊Genome Research上,。
doi:10.1101/gr.102749.109
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PMID:
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.
