Advances in the identification and analysis of allele-specific expression

Differential abundance of allelic transcripts in a diploid organism, commonly referred to as allele specific expression (ASE), is a biologically significant phenomenon and can be examined using single nucleotide polymorphisms (SNPs) from RNA-seq. Quantifying ASE aids in our ability to identify and understand cis-regulatory mechanisms that influence gene expression, and thereby assist in identifying causal mutations. This study examines ASE in breast muscle, abdominal fat, and liver of commercial broiler chickens using variants called from a large sub-set of the samples (n = 68). ASE analysis was performed using a custom software called VCF ASE Detection Tool (VADT), which detects ASE of biallelic SNPs using a binomial test. On average ~ 174,000 SNPs in each tissue passed our filtering criteria and were considered informative, of which ~ 24,000 (~ 14%) showed ASE. Of all ASE SNPs, only 3.7% exhibited ASE in all three tissues, with ~ 83% showing ASE specific to a single tissue. When ASE genes (genes containing ASE SNPs) were compared between tissues, the overlap among all three tissues increased to 20.1%. Our results indicate that ASE genes show tissue-specific enrichment patterns, but all three tissues showed enrichment for pathways involved in translation.

Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq), and introns are under lower purifying selection and thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. To further investigate ASE in the context of human disease, we applied our methods to a Parkinson’s disease cohort of 94 individuals and show that ASE analysis had more power than eQTL analysis to identify significant SNP/gene pairs in our direct comparison of the two methods. Overall, we provide an end-to-end experimental and computational approach for future studies.

BackgroundGenetic analysis of gene expression level is a promising approach for characterizing candidate genes that are involved in complex economic traits such as meat quality. In the present study, we conducted expression quantitative trait loci (eQTL) and allele-specific expression (ASE) analyses based on RNA-sequencing (RNAseq) data from the longissimus muscle of 189 Duroc × Luchuan crossed pigs in order to identify some candidate genes for meat quality traits.ResultsUsing a genome-wide association study based on a mixed linear model, we identified 7192 cis-eQTL corresponding to 2098 cis-genes (p ≤ 1.33e-3, FDR ≤ 0.05) and 6400 trans-eQTL corresponding to 863 trans-genes (p ≤ 1.13e-6, FDR ≤ 0.05). ASE analysis using RNAseq SNPs identified 9815 significant ASE-SNPs in 2253 unique genes. Integrative analysis between the cis-eQTL and ASE target genes identified 540 common genes, including 33 genes with expression levels that were correlated with at least one meat quality trait. Among these 540 common genes, 63 have been reported previously as candidate genes for meat quality traits, such as PHKG1 (q-value = 1.67e-6 for the leading SNP in the cis-eQTL analysis), NUDT7 (q-value = 5.67e-13), FADS2 (q-value = 8.44e-5), and DGAT2 (q-value = 1.24e-3).ConclusionsThe present study confirmed several previously published candidate genes and identified some novel candidate genes for meat quality traits via eQTL and ASE analyses, which will be useful to prioritize candidate genes in further studies.

Polymorphism in cis-regulatory sequences can lead to different levels of expression for the two alleles of a gene, providing a starting point for the evolution of gene expression. Little is known about the genome-wide abundance of genetic variation in gene regulation in natural populations but analysis of allele-specific expression (ASE) provides a means for investigating such variation. We performed RNA-seq of multiple tissues from population samples of two closely related flycatcher species and developed a Bayesian algorithm that maximizes data usage by borrowing information from the whole data set and combines several SNPs per transcript to detect ASE. Of 2,576 transcripts analyzed in collared flycatcher, ASE was detected in 185 (7.2%) and a similar frequency was seen in the pied flycatcher. Transcripts with statistically significant ASE commonly showed the major allele in >90% of the reads, reflecting that power was highest when expression was heavily biased toward one of the alleles. This would suggest that the observed frequencies of ASE likely are underestimates. The proportion of ASE transcripts varied among tissues, being lowest in testis and highest in muscle. Individuals often showed ASE of particular transcripts in more than one tissue (73.4%), consistent with a genetic basis for regulation of gene expression. The results suggest that genetic variation in regulatory sequences commonly affects gene expression in natural populations and that it provides a seedbed for phenotypic evolution via divergence in gene expression.

Background: Genome-wide association study (GWAS) have identified over 45 susceptibility loci for lung cancer; many studies including our own group, have focused on low-frequency and rare coding variants using fine mapping and exome sequencing. This strategy, however, has met with limited success as about 90% of GWAS hits are noncoding and act primarily through altering transcriptional regulation in an allele-specific manner. The RNA-Seq based allele-specific expression (ASE) analysis affords an innovative approach to study preferential expression of an allele in direct relationship to its genotype, providing information on cis-regulatory effects for the expression of putative genes. However currently, there are no lung cancer studies that have rigorously evaluated the ASE variation in lung tumor and adjacent tissues. Methods: Leveraging The Cancer Genome Atlas (TCGA) resource, we performed transcriptomic-wide ASE analysis using existing RNA-Seq datasets of paired tumor and adjacent tissues from 54 lung adenocarcinoma patients. We first quantified the RNA read counts of Referent and Alternate alleles of heterozygous variants, then evaluated the allelic imbalance on a per-sample basis using Beta-binomial test, and explored the differential ASE between tumor and adjacent tissues using paired Wilcoxon test. Functional regulatory consequences were generated from Ensembl Variant Effect Predictor. Results: We identified total 208 significant ASEs, including 35 tissue-specific (only in tumor or only in adjacent), 28 sharing, and 145 differential variants. Of the 208 candidates, 41 were from the human leukocyte antigen (HLA) locus (primary DQA2, DQB1, DRB1, H and J), 26 were from the immunoglobulin (IG) superfamily (primary IGH, IGL, IGK and F11R). About 80% candidates were noncoding (mostly in 5’ and 3’ untranslated regions) and with regulatory features (21 promoter, seven enhancer, seven open chromatin region, two induce nonsense-mediated mRNA decay, one CTCF-binding site, and one transcription factor binding site). Other top genes included MDM2, APOL1, and CTSB. Pathway analyses revealed 27 genes involved in immune response pathway, and 12 genes involved in HLA antigen processing and presentation pathway. Conclusion: This study is the first transcriptomics ASE analysis in lung adenocarcinoma. The key somatic cis-regulatory ASE variants identified from this study, especially immunogenic allelic variations from HLA and IG genes, could be used for identifying high-risk individuals for targeted lung cancer checkpoint blockade and related immunotherapies. Citation Format: Yanhong Liu, Spiridon Tsavachidis, Farrah Kheradmand, Margaret R. Spitz, Chris Amos. Transcriptome analysis links immune genes allelic expression imbalances to lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1584.

Somatic copy number variations (CNVs), including abnormal chromosome numbers and structural changes leading to gain or loss of genetic material, play a crucial role in initiation and progression of cancer. CNVs are believed to cause gene dosage imbalances and modify cis-regulatory elements, leading to allelic expression imbalances in genes that influence cell division and thereby contribute to cancer development. However, the impact of CNVs on allelic gene expression in cancer remains unclear. Allele-specific expression (ASE) analysis, a potent method for investigating genome-wide allelic imbalance profiles in tumors, assesses the relative expression of two alleles using high-throughput sequencing data. However, many existing methods for gene-level ASE detection rely on only RNA sequencing data, which present challenges in interpreting the genetic mechanisms underlying ASE in cancer. To address this issue, we developed a robust framework that integrates allele-specific copy number calls into ASE calling algorithms by leveraging paired genome and transcriptome data from the same sample. This integration enhances the interpretability of the genetic mechanisms driving ASE, thereby facilitating the identification of driver events triggered by CNVs in cancer. In this study, we utilized BASE to conduct a comprehensive analysis of ASE in high hyperdiploid acute lymphoblastic leukemia (HeH ALL), a prevalent childhood malignancy characterized by gains of chromosomes X, 4, 6, 10, 14, 17, 18, and 21. Our analysis unveiled the comprehensive ASE landscape in HeH ALL. Through a multi-perspective examination of HeH ASEs, we offer a systematic understanding of how CNVs impact ASE in HeH, providing valuable insights to guide ASE studies in cancer.

Allele-specific expression (ASE) analysis improves the understanding of transcription’s cis-regulation. Herein, we used imputed SNPs along with RNA-Seq data from the Longissiumus thoracis muscle of 190 Nelore steers to identify functional cis-regulatory variants from ASE analysis. Using a Binomial Test, we identified 38,177 SNPs in ASE regions (ASE SNPs; FDR ≤0.05). We then searched for aseQTLs (SNPs potentially regulating the ASE) by comparing their heterozygosity to the measured allelic ratio under a Wilcoxon Rank Sum test. We identified 21,543 aseQTLs potentially regulating a total of 430 ASE SNPs (FDR ≤0.05). Based on a linear model, ASE SNPs and aseQTLs were associated with transcript abundance. We identified 3,333 SNPs acting as cis-eQTLs (FDR≤0.05). Results were integrated with previous ASE, functional regions, and meat quality-related differentially expressed genes data. This study described novel SNPs potentially regulating the transcription of genes that may affect beef traits.

Transcriptomic diversity across human populations reflects differential regulatory mechanisms. Allelic-imbalanced gene expression is a genetic regulatory mechanism that contributes to human phenotypic variation. To systematically investigate genome-wide allele-specific expression (ASE), we analyzed RNA-Seq data from European and African populations provided by the Geuvadis project. We identified 11 sites in 8 genes showing ASE in both Europeans and Africans, and 9 sites in 9 genes showing population-specific ASE, including both novel and known ASE signals. Notably, the top signal of differentiated ASE between inter-continental populations was observed in DNAJC15, of which the derived allele of rs12015, a single nucleotide polymorphism (SNP), showed significantly higher expression than did the ancestral allele specifically in European individuals. We identified a unique haplotype of DNAJC15, where a few SNPs highly differentiated between European and African populations were strongly linked to sites with high ASE. Among these, SNP rs17553284 affected the binding of several transcription factors as well as the genotype-dependent expression of DNAJC15. Therefore, we speculated that rs17553284 could be a regulatory causal variant that mediates the ASE of rs12015. We found several variations in ASE between intercontinental populations. The highly differentiated ASE genes identified here may implicate in the phenotypic variations among populations that are both evolutionarily and medically important.

Marek’s disease (MD) is a commercially important neoplastic disease of chickens caused by Marek’s disease virus (MDV), a naturally occurring oncogenic alphaherpesvirus. Selecting for increased genetic resistance to MD is a control strategy that can augment vaccinal control measures. To identify high-confidence candidate MD resistance genes, we conducted a genome-wide screen for allele-specific expression (ASE) amongst F1 progeny of two inbred chicken lines that differ substantially in MD resistance. High throughput sequencing was initially used to profile transcriptomes from pools of uninfected and infected individuals at 4 days post-infection to identify any genes showing ASE in response to MDV infection. RNA sequencing identified 22,655 single nucleotide polymorphisms (SNPs) of which 5,360 in 3,773 genes exhibited significant allelic imbalance. Illumina GoldenGate assays were subsequently used to quantify regulatory variation controlled at the gene (cis) and elsewhere in the genome (trans) by examining differences in expression between F1 individuals and artificial F1 RNA pools over six time periods in 1,536 of the most significant SNPs identified by RNA sequencing. Allelic imbalance as a result of cis-regulatory changes was confirmed in 861 of the 1,233 GoldenGate assays successfully examined. Furthermore we have identified seven genes that display trans-regulation only in infected animals and ∼500 SNP that show a complex interaction between cis- and trans-regulatory changes. Our results indicate ASE analyses are a powerful approach to identify regulatory variation responsible for differences in transcript abundance in genes underlying complex traits. And the genes with SNPs exhibiting ASE provide a strong foundation to further investigate the causative polymorphisms and genetic mechanisms for MD resistance. Finally, the methods used here for identifying specific genes and SNPs have practical implications for applying marker-assisted selection to complex traits that are difficult to measure in agricultural species, when expression differences are expected to control a portion of the phenotypic variance.

Genome-Wide Analysis of Allele-Specific Expression Genes in Pediatric B-Cell Precursor Acute Lymphoblastic Leukemia

Background: CCAAT/enhancer-binding protein gamma (C/EBPG) transcription factor expression is correlated with that of ERCC5 and other key DNA repair genes in normal bronchial epithelial cells (NBEC) suggesting a regulatory role. In prior studies, ERCC5 transcript expression was increased in a human lung carcinoma cell line H23 following CEBPG overexpression and in NBEC from 81 subjects, A allele at putative ERCC5 cis-regulatory SNP (rSNP) rs751402 and T allele at rSNP rs2296147 were associated with higher expression of ERCC5 marker SNP rs1047768 T allele transcript. rs751402 is located in open chromatin region identified by FAIRE-seq in NBEC and variation at rs751402 is predicted to alter binding of C/EBP. These studies support the hypothesis that allelic differential affinity to C/EBPG at rs751402 contributes to hereditary inter-individual variation in regulation of ERCC5 either directly or through interaction with complexes bound at rs2296147. The purpose of this study was to further investigate the role of C/EBPG in ERCC5 cis-regulation in an independent cohort of subjects and lung cancer cell lines. Methods: We knocked-down C/EBPG transcript level by C/EBPG siRNA transfection in human non-small cell lung carcinoma cell line H1703. Total and allele-specific expression (ASE) at rs1047768 was measured through multiplex competitive PCR-based amplicon sequencing library preparation followed by Illumina HiSeq next generation sequencing (NGS). This NGS controls for inter-target variation in PCR amplification during library preparation by measuring each transcript native template relative to a known number of synthetic competitive template internal standard copies. The genotype at rs751402 and rs2296147 in NBEC from 78 subjects and 14 human lung carcinoma cell lines was determined by TaqMan SNP genotyping assays. Direct assessment of the syntenic relationship of alleles in gDNA from poly-heterozygous individuals was assessed by allele-specific PCR followed by sequencing. Results: CEBPG transcript expression was knocked-down by 93% in H1703 cells and this was associated with 4-fold reduction in the ERCC5 transcript level at rs1047768. ERCC5 displayed significant inter-individual variation in allele specific expression (ASE) in NBEC from 85 subjects. Thirty nine out of 92 subjects including 3 cell lines were heterozygous at rs751402 and rs2296147 rSNPs and rs1047768 marker SNP and will be assessed for haplotypes comprising those sites. Conclusions: Results are consistent with CEBPG regulation of ERCC5 in the cell line H1703. The results obtained will enable us to test the hypothesis that haplotypes comprising particular alleles syntenic between rs1047768 and rs751402 are associated with higher allele-specific ERCC5 transcript abundance. Cell lines heterozygous at three sites will be subjected to CEBPG up and/or down regulation to assess effect on allele-specific ERCC5 expression at rs1047768. Citation Format: Xiaolu Zhang, Jiyoun Yeo, Erin Crawford, James C. Willey. Genetic variation at a cis-acting C/EBPG binding site is associated with allele-specific ERCC5 transcript expression. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2084. doi:10.1158/1538-7445.AM2015-2084

ObjectiveWe tried to analyze allele-specific expression in the pig neocortex using bioinformatic analysis of high-throughput sequencing results from the parental genomes and offspring transcriptomes from reciprocal crosses between Korean Native and Landrace pigs.MethodsWe carried out sequencing of parental genomes and offspring transcriptomes using next generation sequencing. We subsequently carried out genome scale identification of single nucleotide polymorphisms (SNPs) in two different ways using either individual genome mapping or joint genome mapping of the same breed parents that were used for the reciprocal crosses. Using parent-specific SNPs, allele-specifically expressed genes were analyzed.ResultsBecause of the low genome coverage (~4×) of the sequencing results, most SNPs were non-informative for parental lineage determination of the expressed alleles in the offspring and were thus excluded from our analysis. Consequently, 436 SNPs covering 336 genes were applicable to measure the imbalanced expression of paternal alleles in the offspring. By calculating the read ratios of parental alleles in the offspring, we identified seven genes showing allele-biased expression (p<0.05) including three previously reported and four newly identified genes in this study.ConclusionThe newly identified allele-specifically expressing genes in the neocortex of pigs should contribute to improving our knowledge on genomic imprinting in pigs. To our knowledge, this is the first study of allelic imbalance using high throughput analysis of both parental genomes and offspring transcriptomes of the reciprocal cross in outbred animals. Our study also showed the effect of the number of informative animals on the genome level investigation of allele-specific expression using RNA-seq analysis in livestock species.

Combining allele-specific expression (ASE) analysis with single-cell RNA-seq can elucidate how genomic variation affects RNA expression at the single-cell level. We explore how experimental and computational choices impact the power of ASE-based methods and develop a suite of single-cell ASE computational tools. With single-nucleus RNA-Seq, we extract more ASE information from reads in intronic than exonic regions. We show how read length can increase power and that hybrid selection improves power to detect ASE in targeted genes. We apply our methods to a Parkinson's disease cohort and show that ASE analysis has more power than eQTL analysis.

BackgroundFor a diploid organism such as human, the two alleles of a particular gene can be expressed at different levels due to X chromosome inactivation, gene imprinting, different local promoter activity, or mRNA stability. Recently, imbalanced allelic expression was found to be common in human and can follow Mendelian inheritance. Here we present a method that employs real competitive PCR for allele-specific expression analysis.ResultsA transcribed mutation such as a single nucleotide polymorphism (SNP) is used as the marker for allele-specific expression analysis. A synthetic mutation created in the competitor is close to a natural mutation site in the cDNA sequence. PCR is used to amplify the two cDNA sequences from the two alleles and the competitor. A base extension reaction with a mixture of ddNTPs/dNTP is used to generate three oligonucleotides for the two cDNAs and the competitor. The three products are identified and their ratios are calculated based on their peak areas in the MALDI-TOF mass spectrum. Several examples are given to illustrate how allele-specific gene expression can be applied in different biological studies.ConclusionsThis technique can quantify the absolute expression level of each individual allele of a gene with high precision and throughput.

Background: Catalase (CAT) is a key antioxidant gene expressed at high levels in most human tissues, including normal bronchial epithelial cells (NBEC). NBEC CAT expression is more disperse (more high and low extreme values) among subjects with cancer compared to controls. CAT shares this property with 14 other antioxidant and DNA repair genes comprised by the Lung Cancer Risk Test (LCRT) reported from this laboratory. We hypothesize that inter-individual variation in CAT regulation in NBEC is in part due to inter-individual variation at one or more cis-regulatory single nucleotide polymorphisms (SNPs). If so, this should manifest as inter-individual variation in allele-specific CAT expression in NBEC. Through funding in part from RC2 CA148572 and HL108016 we collected NBEC samples from over 500 subjects at risk for lung cancer. In this pilot study, we assessed allele-specific and total expression of multiple genes in NBEC samples from 85 subjects and assessed the genotype at putative cis-regulatory CAT SNP rs12807961 in gDNA from 95 subjects. Methods: RNA extracted from normal bronchial airway brush specimens of 85 subjects (26 cancer cases and 59 non-cancer controls) was reverse transcribed. Using next generation sequencing (NGS), allele-specific expression (ASE) was measured as allelic imbalance in each cDNA at three marker SNPs in the CAT coding region (rs1049982, rs769217, and rs704724) and one putative regulatory SNP (rs12807961) that was 4364 bases upstream of transcription start site, using gDNA as control. Specifically, each cDNA and matched peripheral blood cell gDNA sample was subjected to targeted competitive template multiplex PCR amplicon library generation followed by NGS (Blomquist et al, PLOS one, 2013) on Illumina Hiseq platform. The genotype at putative cis-regulatory SNP rs12807961 was assessed in gDNA from 95 subjects including those assessed for ASE (a total of 31 cancer cases and 64 non-cancer controls) using a TaqMan® SNP Genotyping Assay. Results: Among heterozygotes, there was significant inter-individual variation in cDNA allelic imbalance at rs1049982 (p&amp;lt;0.001, n=40) and rs769217 (p&amp;lt;0.001, n=28) as measured by F-test using matched gDNA controls. In this cohort there was insufficient number of heterozygotes at rs704724 (n=2) to assess ASE. Among all 95 subjects assessed for rs12807961 genotype, nine were homozygous minor allele at the rs12807961 CAT SNP. Of these, 7/31 cancer cases and 2/64 non-cancer controls were homozygous minor allele. This difference was significant by two-tailed Fisher exact test (P&amp;lt;0.05) following Bonferroni adjustment for multiple testing. Conclusions: These data support the hypothesis that cis-regulatory DNA variants contribute to inter-individual variation in CAT regulation in NBEC and that this is associated with lung cancer risk. Citation Format: Jiyoun Yeo, Xaiolu Zhang, Erin L. Crawford, James C. Willey. Inter-individual variation in allele specific expression of catalase (CAT) in normal bronchial epithelial cells and association of putative cis-regulatory CAT SNP rs12807961 with lung cancer risk. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4156. doi:10.1158/1538-7445.AM2014-4156