Top SNP-induced gene network modules.

a<p>NCBI RefSeq ID of the genes that are cis- located with eQTL (sQTL) SNPs. ^b x indicates the association between the gene and a disease has been reported in literature as collected by DAVID <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0078868#pone.0078868-Huang2" target="_blank">[28]</a>.</p

The distribution profiles of eQTL SNPs and sQTL SNPs across different genomic regions.

<p>In plot <b>A</b>, the result was summarized according to the involved genes (RefSeq mRNAs). In plot <b>B</b>, the result was summarized according to the involved SNPs. In the bar charts, the quantities for the entire set of the eQTL (sQTL) SNPs are represented by black bars and the quantities for the tag-SNPs (gene-wide most significant SNPs) are represented by grey bars. U0-1K/D0-1K represents the 0-1 kilo-bases upper-/down- stream region of a RefSeq gene and U1-20K/D1-20K represents the 1−20 kilo-bases upper-/down- stream region of a RefSeq gene. Plots <b>C</b>-<b>D</b> are drawn for eQTLs and Plots <b>E</b>-<b>F</b> are drawn for sQTLs. In plots <b>C</b> and <b>E</b>, “proportion” represents the ratio of the number of eQTL (sQTL) SNPs in the corresponding region to the total number of eQTL (sQTL) SNPs. In plots <b>D</b> and <b>F</b>, “density index” is calculated by dividing the proportion of eQTL (sQTL) SNPs with the average length (in kilo-base) of the corresponding genomic region.</p

Summary of SNP induced gene networks.

<p>Summary of SNP induced gene networks.</p

Inferring Polymorphism-Induced Regulatory Gene Networks Active in Human Lymphocyte Cell Lines by Weighted Linear Mixed Model Analysis of Multiple RNA-Seq Datasets

<div><p>Single-nucleotide polymorphisms (SNPs) contribute to the between-individual expression variation of many genes. A regulatory (trait-associated) SNP is usually located near or within a (host) gene, possibly influencing the gene’s transcription or/and post-transcriptional modification. But its targets may also include genes that are physically farther away from it. A heuristic explanation of such multiple-target interferences is that the host gene transfers the SNP genotypic effects to the distant gene(s) by a transcriptional or signaling cascade. These connections between the host genes (regulators) and the distant genes (targets) make the genetic analysis of gene expression traits a promising approach for identifying unknown regulatory relationships. In this study, through a mixed model analysis of multi-source digital expression profiling for 140 human lymphocyte cell lines (LCLs) and the genotypes distributed by the international HapMap project, we identified 45 thousands of potential SNP-induced regulatory relationships among genes (the significance level for the underlying associations between expression traits and SNP genotypes was set at FDR < 0.01). We grouped the identified relationships into four classes (paradigms) according to the two different mechanisms by which the regulatory SNPs affect their cis- and trans- regulated genes, modifying mRNA level or altering transcript splicing patterns. We further organized the relationships in each class into a set of network modules with the cis- regulated genes as hubs. We found that the target genes in a network module were often characterized by significant functional similarity, and the distributions of the target genes in three out of the four networks roughly resemble a power-law, a typical pattern of gene networks obtained from mutation experiments. By two case studies, we also demonstrated that significant biological insights can be inferred from the identified network modules.</p></div

Case studies of polymorphism-induced gene regulation network and the biological implications.

<p>The sub-network (<i>cisSplicing_transExpression</i> paradigm) on the left shows that IQGAP1 gene, whose alternative splicing is associated with the genotypes of six SNPs, is a potential regulator of 115 trans- gene RefSeq genes at 113 loci, each of which has the expression level statistically affected by at least one SNP of the same set. These target genes are widely involved in cell cycle (see the left section of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0078868#pone-0078868-t006" target="_blank"><b>Table 6</b></a>). The sub-network (<i>cisExpression_transExpression</i> paradigm) on the right shows that PKHD1L1 gene, whose expression level is associated with the genotypes of ten SNPs, is a potential regulator of 61 trans RefSeq genes at 60 loci, each of which has the expression level statistically affected by at least one SNP of the same set. A few immunity related GO terms are over-represented by these target genes as summerized in the right section of</p

Overview of the analyzed RNA-seq Data.

<p>SR: The number of biological samples. CL: the number of HapMap cell lines. RD: the median of the numbers of mapped reads (in millions). RL: read length (<i>nt</i>). LAB: the institute or company that generated the data.</p

Functional enrichment analysis of cis- located eQTL genes.

<p>Functional enrichment analysis of cis- located eQTL genes.</p

The uneven distributions of the regulated target genes.

<p>In drawing the plots, cis- located eQTL (sQTL) genes (regulators) are ordered by the numbers of their trans- located target genes and each of them is assigned an integer index (1, 2, 3,...).</p

The schematic presentation of the study flow.

<p>The schematic presentation of the study flow.</p

Functional enrichment analysis of cis- located sQTL genes.

<p>Functional enrichment analysis of cis- located sQTL genes.</p