Polymorphic cis- and trans-regulation of human gene expression

Abstract

Expression levels of human genes vary extensively among individuals. This variation facilitates analyses of expression levels as quantitative phenotypes in genetic studies where the entire genome can be scanned for regulators without prior knowledge of the regulatory mechanisms, thus enabling the identification of unknown regulatory relationships. Here, we carried out such genetic analyses with a large sample size and identified cis- and trans-acting polymorphic regulators for about 1,000 human genes. We validated the cis-acting regulators by demonstrating differential allelic expression with sequencing of transcriptomes (RNA-Seq) and the trans-regulators by gene knockdown, metabolic assays, and chromosome conformation capture analysis. The majority of the regulators act in trans to the target (regulated) genes. Most of these trans-regulators were not known to play a role in gene expression regulation. The identification of these regulators enabled the characterization of polymorphic regulation of human gene expression at a resolution that was unattainable in the past.

Figure 1. Genome scans of six expression phenotypes.

The name of the target gene and its chromosomal location (in parenthesis) are shown. Evidence of linkage as indicated by p value (−log₁₀) is shown on the vertical axis and genomic locations are shown on the horizontal axis of each graph. The top two panels are examples of phenotypes with proximal linkage peaks, and the bottom four panels are phenotypes with distal linkage peaks.

Figure 2. Allelic expression from RNA-Seq confirms prediction by association analysis.

Graphical presentations of two genes that show differential allelic expression. The thick lines represent the higher expressing allelic forms of CHI3L2 and CRYZ (A). Regression of expression phenotypes (expression levels shown on vertical axis) of two genes on nearby SNPs (genotypes shown on horizontal axis) (B). Number of reads (vertical axis) from RNA-Seq for each allelic form (horizontal axis) of the genes; only data for individuals who are heterozygous at the coding SNPs are shown. For each individual, the number of reads for each allele of an SNP is connected by a line. For example, in the panel for rs8535 (CHI3L2), the individual represented by a red line had 268 reads of the A-bearing form of CHI3L2 and 49 reads of the C-bearing form of CHI3L2 (C).

Figure 3. INSR activation following insulin treatment in human primary fibroblasts.

Fibroblasts were serum starved for 18 h and then treated with 100 nM insulin for 5 min. Cell lysates were incubated with α-INSR or α-IGF1R antibodies. Input and immunoprecipitated products were analyzed by western blots using α-phosphotyrosine, α-INSR, or α-IGF1R antibodies.

Figure 4. Directed subnetworks.

Regulators are connected to their target genes based on results (p<0.05) from QTDT analyses. Directions of the arrows go from regulators to their target genes. The two examples correspond to genes connected to KIAA1468, a gene with no known function (a), and WDR7, a gene associated with age of onset of multiple sclerosis (b) .