Limits...
Inference of SNP-gene regulatory networks by integrating gene expressions and genetic perturbations.

Kim DC, Wang J, Liu C, Gao J - Biomed Res Int (2014)

Bottom Line: 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.There are three main contributions.Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.

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.

Show MeSH

Related in: MedlinePlus

The inferred SGRN with 14 pairs of gene and SNP selected from [22–24].
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4127230&req=5

fig3: The inferred SGRN with 14 pairs of gene and SNP selected from [22–24].

Mentions: In this section, the proposed method is applied to real gene expression and genotype data for psychiatric disorder. In the application to real data, we explore the performance of GRN inferences and eQTL identifications through the inferred networks. As far as we know, the proposed method is the first solution to provide both GRN inference and eQTL identification. Thus, the performance comparison with other methods was not performed. The psychiatric disorder data consists of gene expression data of 25833 genes and 852963 SNPs for 131 samples, which were measured from human brain. Since we focus on the network inference but not gene selection, the network construction is performed with a predefined set of genes and SNPs that are selected by preliminary test of multiple sets of genes and eQTLs based on related GWAS for psychiatric disorders. The result of SGRN inference is displayed in Figure 3 where two yellow colored genes, EGFR and CACNA1C, are selected from [23, 24] and the rest of two pairs are from [22]. In applying IAL2 to the data, the weights of α and β are set to 0.5 instead of 1. Otherwise, Ne(fi) tends to be zero. The reason for this is that gene variables are more correlated with their eQTLs because generally eQTLs are independently selected to other genes. In Figure 3, SNP and gene are distinguished by node shape, and a red edge indicates a correct edge from eQTL to corresponding gene. A blue edge represents false positive eQTL mapping. For eQTL identification, one false positive edge appears and thirteen true positive edges are detected (TPR = 0.9286, FDR = 0.0714).


Inference of SNP-gene regulatory networks by integrating gene expressions and genetic perturbations.

Kim DC, Wang J, Liu C, Gao J - Biomed Res Int (2014)

The inferred SGRN with 14 pairs of gene and SNP selected from [22–24].
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4127230&req=5

fig3: The inferred SGRN with 14 pairs of gene and SNP selected from [22–24].
Mentions: In this section, the proposed method is applied to real gene expression and genotype data for psychiatric disorder. In the application to real data, we explore the performance of GRN inferences and eQTL identifications through the inferred networks. As far as we know, the proposed method is the first solution to provide both GRN inference and eQTL identification. Thus, the performance comparison with other methods was not performed. The psychiatric disorder data consists of gene expression data of 25833 genes and 852963 SNPs for 131 samples, which were measured from human brain. Since we focus on the network inference but not gene selection, the network construction is performed with a predefined set of genes and SNPs that are selected by preliminary test of multiple sets of genes and eQTLs based on related GWAS for psychiatric disorders. The result of SGRN inference is displayed in Figure 3 where two yellow colored genes, EGFR and CACNA1C, are selected from [23, 24] and the rest of two pairs are from [22]. In applying IAL2 to the data, the weights of α and β are set to 0.5 instead of 1. Otherwise, Ne(fi) tends to be zero. The reason for this is that gene variables are more correlated with their eQTLs because generally eQTLs are independently selected to other genes. In Figure 3, SNP and gene are distinguished by node shape, and a red edge indicates a correct edge from eQTL to corresponding gene. A blue edge represents false positive eQTL mapping. For eQTL identification, one false positive edge appears and thirteen true positive edges are detected (TPR = 0.9286, FDR = 0.0714).

Bottom Line: 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.There are three main contributions.Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.

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.

Show MeSH
Related in: MedlinePlus