Exploring candidate genes for human brain diseases from a brain-specific gene network.
It is believed that large numbers of genes are involved in common human brain diseases. Here, we propose a novel computational strategy for simultaneously identifying multiple candidate genes for genetic human brain diseases from a brain-specific gene network-level perspective. By integrating diverse genomic and proteomic datasets based on Bayesian statistical model, we built a large-scale human brain-specific gene network. Based on this network and minor prior knowledge of a specific brain disease, we can effectively identify multiple candidate genes for this disease. When four known Alzheimer's disease genes were used as the prior knowledge, among the top 46 high-scoring genes that we have found, 37 were previously reported to be associated with Alzheimer's disease. And the higher score a gene has, the more likely this gene is a disease-related one. The results suggest that the proposed method is effective, convenient, and applicable in the future genetic studies.
Liu
Bing
B
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, PR China.
Jiang
Tianzi
T
Ma
Songde
S
Zhao
Huizhi
H
Li
Jun
J
Jiang
Xingpeng
X
Zhang
Jing
J
D000544:Alzheimer Disease / Q000378:metabolism*; D001921:Brain / Q000378:metabolism*; D003198:Computer Simulation; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D052066:N-Ethylmaleimide-Sensitive Proteins / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction
D020543:Proteome; D052066:N-Ethylmaleimide-Sensitive Proteins