Powerful Identification of Cis-regulatory SNPs in Human Primary Monocytes Using Allele-Specific Gene Expression

Abstract

A large number of genome-wide association studies have been performed during the past five years to identify associations between SNPs and human complex diseases and traits. The assignment of a functional role for the identified disease-associated SNP is not straight-forward. Genome-wide expression quantitative trait locus (eQTL) analysis is frequently used as the initial step to define a function while allele-specific gene expression (ASE) analysis has not yet gained a wide-spread use in disease mapping studies. We compared the power to identify cis-acting regulatory SNPs (cis-rSNPs) by genome-wide allele-specific gene expression (ASE) analysis with that of traditional expression quantitative trait locus (eQTL) mapping. Our study included 395 healthy blood donors for whom global gene expression profiles in circulating monocytes were determined by Illumina BeadArrays. ASE was assessed in a subset of these monocytes from 188 donors by quantitative genotyping of mRNA using a genome-wide panel of SNP markers. The performance of the two methods for detecting cis-rSNPs was evaluated by comparing associations between SNP genotypes and gene expression levels in sample sets of varying size. We found that up to 8-fold more samples are required for eQTL mapping to reach the same statistical power as that obtained by ASE analysis for the same rSNPs. The performance of ASE is insensitive to SNPs with low minor allele frequencies and detects a larger number of significantly associated rSNPs using the same sample size as eQTL mapping. An unequivocal conclusion from our comparison is that ASE analysis is more sensitive for detecting cis-rSNPs than standard eQTL mapping. Our study shows the potential of ASE mapping in tissue samples and primary cells which are difficult to obtain in large numbers.

Single Nucleotide Polymorphisms with Cis-Regulatory Effects on Long Non-Coding Transcripts in Human Primary Monocytes

“…To determine ASE, we genotyped RNA (cDNA) and genomic DNA from the monocytes using a genome-wide panel of 1.2 million SNP markers [4]. To be informative for the detection of ASE, a SNP has to be heterozygous in DNA and expressed at a detectable level in RNA.…”

Interrogation of allelic chromatin states in human cells by high-density ChIP-genotyping

“…Given the large increase in power of allelic-analyses previously demonstrated using AE-mapping vs. eQTL studies, 3 allelic-ChIP mapping presents a potentially cost-effective means of identifying common genetic variants associated to regulation of specific chromatin states. Combining this information with cis-rSNP data and GWAS data, may be a powerful means to improve our understanding of the specific mechanisms underlying common genetic polymorphisms associated to complex disease.…”

“…2 In a recent comparison of expression quantitative trait loci (eQTL) vs. AE-mapping studies using the same samples, AE showed equivalent power to detect cis-rSNPs with an up to 8-fold smaller sample size compared with the eQTL approach. 3 Despite these recent advances in cis-rSNP detection, fine-mapping of cis-rSNPs to single causal variants is complicated by the fact that cis-rSNPs commonly lie in haplotype blocks of high linkage disequilibrium (LD). Since regulatory elements, where cis-rSNPs are likely to lie, are tightly correlated with chromatin state (e.g., histone modifications), it has been suggested that allele-specific characterization of the chromatin landscape could provide one tool for precisely identifying causal AE-mapped cis-rSNPs, which locally affect allelic chromatin state, and thereby associate to allelic differences in expression of nearby transcripts.…”

Using Gene Expression to Improve the Power of Genome-Wide Association Analysis

“…To date, relatively few published studies have utilized gene expression data from the same patients studied by GWA study [38, 39, 40, 41, 42]. However, as appreciation grows for the power of eQTL analysis, efforts such as the Genotype-Tissue Expression project will come to fruition [43], and methods such as ours will facilitate the analysis of such datasets, we believe the collection of RNA for expression profiling from GWA study participants will be useful.…”