Work

Inference of SNP-Gene Regulatory Networks by Integrating Gene Expressions and Genetic Perturbations

Year: 2014

Type: article

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.

Source: BioMed Research International

DOI: 10.1155/2014/629697

Authors: Dong-Chul Kim, Jiao Wang, Chunyu Liu, Jean Gao

Institutions: The University of Texas at Arlington, University of Illinois Chicago

Language: English

Topic: Gene Regulatory Network Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,680