Genome-wide identification of allele-specific effects on gene expression for single and multiple individuals

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function. Here, we present regSNPs-ASB, a generalized linear model-based approach to identify regulatory SNPs that are located in transcription factor binding sites. The input for this model includes ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) raw read counts from heterozygous loci, where differential transposase-cleavage patterns between two alleles indicate preferential transcription factor binding to one of the alleles. Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression. By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also in eQTL regions. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In summary, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of genetic variants identified as eQTL for further studies to validate their causal regulatory function. Ultimately, identifying causal genetic variants will further our understanding of the underlying molecular mechanisms of disease and the eventual development of potential therapeutic targets.

An empirical Bayes test for allelic-imbalance detection in ChIP-seq

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) has enabled discovery of genomic regions enriched with biological signals such as transcription factor binding and histone modifications. Allelic-imbalance (ALI) detection is a complementary analysis of ChIP-seq data for associating biological signals with single nucleotide polymorphisms (SNPs). It has been successfully used in elucidating functional roles of non-coding SNPs. Commonly used statistical approaches for ALI detection are often based on binomial testing and mixture models, both of which rely on strong assumptions on the distribution of the unobserved allelic probability, and have significant practical shortcomings. We propose Non-Parametric Binomial (NPBin) test for ALI detection and for modeling Binomial data in general. NPBin models the density of the unobserved allelic probability non-parametrically, and estimates its empirical null distribution via curve fitting. We demonstrate the advantages of NPBin in terms of interpretability of the estimated density and the accuracy in ALI detection using simulations and analysis of several ChIP-seq data sets. We also illustrate the generality of our modeling framework beyond ALI detection by an application to a baseball batting average prediction problem. This article has supplementary material available at Biostatistics online. The code and the sample input data have been also deposited to github https://github.com/QiZhangStat/ALIdetection.

Allele-specific splicing effects on DKKL1 and ZNF419 transcripts in HeLa cells

Allele-specific splicing is the production of different RNA isoforms from different alleles of a gene. Altered splicing patterns such as exon skipping can have a dramatic effect on the final protein product yet have traditionally proven difficult to predict. We investigated the splicing effects of a set of nine single nucleotide polymorphisms (SNPs) which are predicted to have a direct impact on mRNA splicing, each in a different gene. Predictions were based on SNP location relative to splice junctions and intronic/exonic splicing elements, combined with an analysis of splice isoform expression data from public sources. Of the nine genes tested, six SNPs led to direct impacts on mRNA splicing as determined by the splicing reporter minigene assay and RT-PCR in human HeLa cells, of which four were allele-specific effects. These included previously unreported alternative splicing patterns in the genes ZNF419 and DKKL1. Notably, the SNP in ZNF419, a transcription factor, leads to the deletion of a DNA-binding domain from the protein and is associated with an expression QTL, while the SNP in DKKL1 leads to shortened transcripts predicted to produce a truncated protein. We conclude that the impact of SNP mutations on mRNA splicing, and its biological relevance, can be predicted by integrating SNP position with available data on relative isoform abundance in human cell lines.

A uniform survey of allele-specific binding and expression over 1000-Genomes-Project individuals

Large-scale sequencing in the 1000 Genomes Project has revealed multitudes of single nucleotide variants (SNVs). Here, we provide insights into the functional effect of these variants using allele-specific behaviour. This can be assessed for an individual by mapping ChIP-seq and RNA-seq reads to a personal genome, and then measuring 'allelic imbalances' between the numbers of reads mapped to the paternal and maternal chromosomes. We annotate variants associated with allele-specific binding and expression in 382 individuals by uniformly processing 1,263 functional genomics data sets, developing approaches to reduce the heterogeneity between data sets due to overdispersion and mapping bias. Since many allelic variants are rare, aggregation across multiple individuals is necessary to identify broadly applicable 'allelic elements'. We also found SNVs for which we can anticipate allelic imbalance from the disruption of a binding motif. Our results serve as an allele-specific annotation for the 1000 Genomes variant catalogue and are distributed as an online resource (alleledb.gersteinlab.org).

Protective effects of HLA-DPA1/DPB1 variants against Hepatitis B virus infection in an Indonesian population

Human leukocyte antigen (HLA) DPA1/DPB1 variants have been reported to influence Hepatitis B virus (HBV) infection. HLA-DPA1/DPB1 plays a pivotal role in antigen presentation to CD4(+) helper T cells and influences the outcome of HBV infection. To investigate the influence of HLA-DP variants on the outcome of HBV infection in an Indonesian population where it has the third-highest prevalence of HBV infection worldwide, we performed a case-control study of 686 participants, including patients with HBV-related advanced or nonadvanced liver disease, patients with spontaneously resolved HBV, and healthy controls. Single-nucleotide polymorphisms in HLA-DPA1 (rs3077) and HLA-DPB1 (rs3135021, rs9277535, and rs228388) were genotyped using real-time TaqMan® genotyping assays. Because rs2281388 deviated from Hardy-Weinberg equilibrium, it was excluded from subsequent analyses. The results of logistic regression analyses showed that the HLA-DPB1 rs9277535 variants were associated with a reduced risk of persistent HBV infection (odds ratio [OR] 0.70, 95% confidence interval [95% CI] 0.52-0.96, P=0.026, additive genetic model; OR 0.60, 95% CI 0.38-0.96, P=0.033, dominant genetic model). The HLA-DPA1 rs3077 variant was associated with a protective effect increasing the spontaneously resolved HBV infection (OR 0.64, 95% CI 0.41-0.98, P=0.039, dominant genetic model). By contrast, the HLA-DPB1 rs3135021 variant was not associated with the outcome of HBV infection, including susceptibility, spontaneously resolved, or disease progression. Combinations of haplotype markers were also associated with HBV susceptibility (CA for rs3077-rs9277535, OR 0.57, 95% CI 0.36-0.92, P=0.021; GA for rs3135021-rs9277535, OR 0.56, 95% CI 0.36-0.86, P=0.0087). In conclusion, these findings confirm that HLA-DPA1/DPB1 variants were associated with the outcomes of HBV infection in an Indonesian population.

Analyzing allele specific RNA expression using mixture models

Background

Measuring allele-specific RNA expression provides valuable insights into cis-acting genetic and epigenetic regulation of gene expression. Widespread adoption of high-throughput sequencing technologies for studying RNA expression (RNA-Seq) permits measurement of allelic RNA expression imbalance (AEI) at heterozygous single nucleotide polymorphisms (SNPs) across the entire transcriptome, and this approach has become especially popular with the emergence of large databases, such as GTEx. However, the existing binomial-type methods used to model allelic expression from RNA-seq assume a strong negative correlation between reference and variant allele reads, which may not be reasonable biologically.

Results

Here we propose a new strategy for AEI analysis using RNA-seq data. Under the null hypothesis of no AEI, a group of SNPs (possibly across multiple genes) is considered comparable if their respective total sums of the allelic reads are of similar magnitude. Within each group of “comparable” SNPs, we identify SNPs with AEI signal by fitting a mixture of folded Skellam distributions to the absolute values of read differences. By applying this methodology to RNA-Seq data from human autopsy brain tissues, we identified numerous instances of moderate to strong imbalanced allelic RNA expression at heterozygous SNPs. Findings with SLC1A3 mRNA exhibiting known expression differences are discussed as examples.

Conclusion

The folded Skellam mixture model searches for SNPs with significant difference between reference and variant allele reads (adjusted for different library sizes), using information from a group of “comparable” SNPs across multiple genes. This model is particularly suitable for performing AEI analysis on genes with few heterozygous SNPs available from RNA-seq, and it can fit over-dispersed read counts without specifying the direction of the correlation between reference and variant alleles.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1749-0) contains supplementary material, which is available to authorized users.

Differential Gene Expression in Age-Related Macular Degeneration

Gene expression is the first step in ascribing function between an associated gene and disease. Understanding how variation in a gene influences expression, particularly in tissues affected by the disease, may help elucidate what influences the phenotypic outcome of that disease. Previous studies of the genetics of age-related macular degeneration (AMD) have identified several risk factors, but have not yet bridged the gap between gene association and identifying a specific mechanism or function that is involved in the pathogenesis of AMD. Advances in genomic technologies, such as RNA sequencing (RNA-seq), single cell RNA-seq, bilsulfite sequencing, and/or whole genome methylation, will be powerful tools for identifying genes/pathways that are differentially expressed in those with AMD versus those without AMD. These technologies should advance the field of AMD research so that appropriate preventive and therapeutic targets can be developed.