Allele-specific expression analysis methods for high-density SNP microarray data

Germline allelic expression of genes at 17q22 locus associates with risk of breast cancer

INTRODUCTION

Translation of genome-wide association study (GWAS) findings into preventive approaches is challenged by the identification of the causal risk variants and the understanding of the biological mechanisms by which they act. We present using allelic expression (AE) ratios to perform quantitative case-control analysis as a novel approach to identify risk associations, causal regulatory variants, and target genes.

METHODS

Using the breast cancer (BC) risk locus 17q22 to validate this approach, we measured AE ratios in normal breast tissue samples from controls and cases, as well as from unmatched blood samples. Then we used in-silico and in-vitro analysis to map and functionally characterised candidate causal variants.

RESULTS

We found a significant shift in the AE patterns of STXBP4 (rs2628315) and COX11 (rs17817901) in the normal breast tissue of cases and healthy controls. Preferential expression of the G-rs2628315 and A-rs17817901 alleles, more often observed in cases, was associated with an increased risk for BC. Analysis of blood samples from cases and controls found a similar association. Furthermore, we identified two putative cis-regulatory variants - rs17817901 and rs8066588 - that affect a miRNA and a transcription factor binding site, respectively.

CONCLUSION

We propose causal variants and target genes for the 17q22 BC risk locus and show that using AE ratios in case-control association studies is helpful in identifying risk and mapping causal variants.

Allelic expression imbalance of PIK3CA mutations is frequent in breast cancer and prognostically significant

PIK3CA mutations are the most common in breast cancer, particularly in the estrogen receptor-positive cohort, but the benefit of PI3K inhibitors has had limited success compared with approaches targeting other less common mutations. We found a frequent allelic expression imbalance between the missense mutant and wild-type PIK3CA alleles in breast tumors from the METABRIC (70.2%) and the TCGA (60.1%) projects. When considering the mechanisms controlling allelic expression, 27.7% and 11.8% of tumors showed imbalance due to regulatory variants in cis, in the two studies respectively. Furthermore, preferential expression of the mutant allele due to cis-regulatory variation is associated with poor prognosis in the METABRIC tumors (P = 0.031). Interestingly, ER-, PR-, and HER2+ tumors showed significant preferential expression of the mutated allele in both datasets. Our work provides compelling evidence to support the clinical utility of PIK3CA allelic expression in breast cancer in identifying patients of poorer prognosis, and those with low expression of the mutated allele, who will unlikely benefit from PI3K inhibitors. Furthermore, our work proposes a model of differential regulation of a critical cancer-promoting gene in breast cancer.

Allele-specific miRNA-binding analysis identifies candidate target genes for breast cancer risk

Most breast cancer (BC) risk-associated single-nucleotide polymorphisms (raSNPs) identified in genome-wide association studies (GWAS) are believed to cis-regulate the expression of genes. We hypothesise that cis-regulatory variants contributing to disease risk may be affecting microRNA (miRNA) genes and/or miRNA binding. To test this, we adapted two miRNA-binding prediction algorithms-TargetScan and miRanda-to perform allele-specific queries, and integrated differential allelic expression (DAE) and expression quantitative trait loci (eQTL) data, to query 150 genome-wide significant ( P ≤ 5 × 10 - 8 ) raSNPs, plus proxies. We found that no raSNP mapped to a miRNA gene, suggesting that altered miRNA targeting is an unlikely mechanism involved in BC risk. Also, 11.5% (6 out of 52) raSNPs located in 3'-untranslated regions of putative miRNA target genes were predicted to alter miRNA::mRNA (messenger RNA) pair binding stability in five candidate target genes. Of these, we propose RNF115, at locus 1q21.1, as a strong novel target gene associated with BC risk, and reinforce the role of miRNA-mediated cis-regulation at locus 19p13.11. We believe that integrating allele-specific querying in miRNA-binding prediction, and data supporting cis-regulation of expression, improves the identification of candidate target genes in BC risk, as well as in other common cancers and complex diseases.

Quantification of allelic differential expression using a simple Fluorescence primer PCR-RFLP-based method

Allelic differential expression (ADE) is common in diploid organisms, and is often the key reason for specific phenotype variations. Thus, ADE detection is important for identification of major genes and causal mutations. To date, sensitive and simple methods to detect ADE are still lacking. In this study, we have developed an accurate, simple, and sensitive method, named fluorescence primer PCR-RFLP quantitative method (fPCR-RFLP), for ADE analysis. This method involves two rounds of PCR amplification using a pair of primers, one of which is double-labeled with an overhang 6-FAM. The two alleles are then separated by RFLP and quantified by fluorescence density. fPCR-RFLP could precisely distinguish ADE cross a range of 1- to 32-fold differences. Using this method, we verified PLAG1 and KIT, two candidate genes related to growth rate and immune response traits of pigs, to be ADE both at different developmental stages and in different tissues. Our data demonstrates that fPCR-RFLP is an accurate and sensitive method for detecting ADE on both DNA and RNA level. Therefore, this powerful tool provides a way to analyze mutations that cause ADE.

Whole transcriptome RNA-Seq allelic expression in human brain

Background

Measuring allelic RNA expression ratios is a powerful approach for detecting cis-acting regulatory variants, RNA editing, loss of heterozygosity in cancer, copy number variation, and allele-specific epigenetic gene silencing. Whole transcriptome RNA sequencing (RNA-Seq) has emerged as a genome-wide tool for identifying allelic expression imbalance (AEI), but numerous factors bias allelic RNA ratio measurements. Here, we compare RNA-Seq allelic ratios measured in nine different human brain regions with a highly sensitive and accurate SNaPshot measure of allelic RNA ratios, identifying factors affecting reliable allelic ratio measurement. Accounting for these factors, we subsequently surveyed the variability of RNA editing across brain regions and across individuals.

Results

We find that RNA-Seq allelic ratios from standard alignment methods correlate poorly with SNaPshot, but applying alternative alignment strategies and correcting for observed biases significantly improves correlations. Deploying these methods on a transcriptome-wide basis in nine brain regions from a single individual, we identified genes with AEI across all regions (SLC1A3, NHP2L1) and many others with region-specific AEI. In dorsolateral prefrontal cortex (DLPFC) tissues from 14 individuals, we found evidence for frequent regulatory variants affecting RNA expression in tens to hundreds of genes, depending on stringency for assigning AEI. Further, we find that the extent and variability of RNA editing is similar across brain regions and across individuals.

Conclusions

These results identify critical factors affecting allelic ratios measured by RNA-Seq and provide a foundation for using this technology to screen allelic RNA expression on a transcriptome-wide basis. Using this technology as a screening tool reveals tens to hundreds of genes harboring frequent functional variants affecting RNA expression in the human brain. With respect to RNA editing, the similarities within and between individuals leads us to conclude that this post-transcriptional process is under heavy regulatory influence to maintain an optimal degree of editing for normal biological function.

Research progress in allele-specific expression and its regulatory mechanisms

Although the majority of genes are expressed equally from both alleles, some genes are differentially expressed. Organisms possess characteristics to preferentially express a particular allele under regulatory factors, which is termed allele-specific expression (ASE). It is one of the important genetic factors that lead to phenotypic variation and can be used to identify the variance of gene regulation factors. ASE indicates mechanisms such as DNA methylation, histone modifications, and non-coding RNAs function. Here, we review a broad survey of progress in ASE studies, and what this simple yet very effective approach can offer in functional genomics, and possible implications toward our better understanding of the underlying mechanisms of complex traits.