Document Type
Article
Publication Date
6-9-2014
Abstract
In order to elucidate the overall relationships between gene expressions and genetic perturbations, we propose a network inference method to infer gene regulatory network where single nucleotide polymorphism (SNP) is involved as a regulator of genes. In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings. In this paper, we propose a SGRN inference method without predefined eQTL information assuming a gene is regulated by a single SNP at most. To evaluate the performance, the proposed method was applied to random data generated from synthetic networks and parameters. There are three main contributions. First, the proposed method provides both the gene regulatory inference and the eQTL identification. Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances. Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data.
Recommended Citation
Kim, D.-C., Wang, J., Liu, C., & Gao, J. (2014). Inference of SNP-Gene Regulatory Networks by Integrating Gene Expressions and Genetic Perturbations. BioMed Research International, 2014, 629697. https://doi.org/10.1155/2014/629697
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
BioMed Research International
DOI
10.1155/2014/629697
Comments
© 2014 Dong-Chul Kim et al.