Investigation of allele specific expression in various tissues of broiler chickens using the detection tool VADT

Differential Allele-Specific Expression Uncovers Breast Cancer Genes Dysregulated by Cis Noncoding Mutations.

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

A large-scale behavior of allelic dropout and imbalance caused by DNA methylation changes in an early-ripening bud sport of peach.

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<0.001, n=40) and rs769217 (p<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<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

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.

BackgroundAllele specific gene expression (ASE), with the paternal allele more expressed than the maternal allele or vice versa, appears to be a common phenomenon in humans and mice. In other species the extent of ASE is unknown, and even in humans and mice there are several outstanding questions. These include; to what extent is ASE tissue specific? how often does the direction of allele expression imbalance reverse between tissues? how often is only one of the two alleles expressed? is there a genome wide bias towards expression of the paternal or maternal allele; and finally do genes that are nearby on a chromosome share the same direction of ASE? Here we use gene expression data (RNASeq) from 18 tissues from a single cow to investigate each of these questions in turn, and then validate some of these findings in two tissues from 20 cows.ResultsBetween 40 and 100 million sequence reads were generated per tissue across three replicate samples for each of the eighteen tissues from the single cow (the discovery dataset). A bovine gene expression atlas was created (the first from RNASeq data), and differentially expressed genes in each tissue were identified. To analyse ASE, we had access to unambiguously phased genotypes for all heterozygous variants in the cow’s whole genome sequence, where these variants were homozygous in the whole genome sequence of her sire, and as a result we were able to map reads to parental genomes, to determine SNP and genes showing ASE in each tissue. In total 25,251 heterozygous SNP within 7985 genes were tested for ASE in at least one tissue. ASE was pervasive, 89 % of genes tested had significant ASE in at least one tissue. This large proportion of genes displaying ASE was confirmed in the two tissues in a validation dataset.For individual tissues the proportion of genes showing significant ASE varied from as low as 8–16 % of those tested in thymus to as high as 71–82 % of those tested in lung. There were a number of cases where the direction of allele expression imbalance reversed between tissues. For example the gene SPTY2D1 showed almost complete paternal allele expression in kidney and thymus, and almost complete maternal allele expression in the brain caudal lobe and brain cerebellum. Mono allelic expression (MAE) was common, with 1349 of 4856 genes (28 %) tested with more than one heterozygous SNP showing MAE. Across all tissues, 54.17 % of all genes with ASE favoured the paternal allele. Genes that are closely linked on the chromosome were more likely to show higher expression of the same allele (paternal or maternal) than expected by chance. We identified several long runs of neighbouring genes that showed either paternal or maternal ASE, one example was five adjacent genes (GIMAP8, GIMAP7 copy1, GIMAP4, GIMAP7 copy 2 and GIMAP5) that showed almost exclusive paternal expression in brain caudal lobe.ConclusionsInvestigating the extent of ASE across 18 bovine tissues in one cow and two tissues in 20 cows demonstrated 1) ASE is pervasive in cattle, 2) the ASE is often MAE but ranges from MAE to slight overexpression of the major allele, 3) the ASE is most often tissue specific and that more than half the time displays divergent allele specific expression patterns across tissues, 4) across all genes there is a slight bias towards expression of the paternal allele and 5) genes expressing the same parental allele are clustered together more than expected by chance, and there are several runs of large numbers of genes expressing the same parental allele.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2174-0) contains supplementary material, which is available to authorized users.

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.

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.

Genome-wide analysis of spatiotemporal allele-specific expression in F1 hybrids of meat- and egg-type chickens

Cytokinins play important roles in the growth and development of plants. Physiological and photosynthetic characteristics are common indicators to measure the growth and development in plants. However, few reports have described the molecular mechanisms of physiological and photosynthetic changes in response to cytokinin, particularly in woody plants. DNA methylation is an essential epigenetic modification that dynamically regulates gene expression in response to the external environment. In this study, we examined genome-wide DNA methylation variation and transcriptional variation in poplar (Populus tomentosa) after short-term treatment with the synthetic cytokinin 6-benzylaminopurine (6-BA). We identified 460 significantly differentially methylated regions (DMRs) in response to 6-BA treatment. Transcriptome analysis showed that 339 protein-coding genes, 262 long non-coding RNAs (lncRNAs), and 15,793 24-nt small interfering RNAs (siRNAs) were differentially expressed under 6-BA treatment. Among these, 79% were differentially expressed between alleles in P. tomentosa, and 102,819 allele-specific expression (ASE) loci in 19,200 genes were detected showing differences in ASE levels after 6-BA treatment. Combined DNA methylation and gene expression analysis demonstrated that DNA methylation plays an important role in regulating allele-specific gene expression. To further investigate the relationship between these 6-BA-responsive genes and phenotypic variation, we performed SNP analysis of 460 6-BA-responsive DMRs via re-sequencing using a natural population of P. tomentosa, and we identified 206 SNPs that were significantly associated with growth and wood properties. Association analysis indicated that 53% of loci with allele-specific expression had primarily dominant effects on poplar traits. Our comprehensive analyses of P. tomentosa DNA methylation and the regulation of allele-specific gene expression suggest that DNA methylation is an important regulator of imbalanced expression between allelic loci.

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.

We developed a digital RNA allelotyping method for quantitatively interrogating allele-specific gene expression. This method involves ultra-deep sequencing of padlock captured SNPs from the transcriptome. We characterized four cell lines established from two human subjects in the Personal Genome Project. Approximately 11–22% of the heterozygous mRNA-associated SNPs show allele-specific expression in each cell line; and 4.3–8.5% are tissue-specific, suggesting the presence of tissue-specific cis-regulation. When applied to two pairs of sibling human embryonic stem cell lines, the sibling lines were more similar in allele-specific expression than were the genetically unrelated lines. We found that the variation of allelic ratios in gene expression among different cell lines is primarily explained by genetic variations, much more so than by specific tissue types or culturing conditions. Comparison of expressed SNPs on the sense and anti-sense transcripts suggested that allelic ratios are primarily determined by cis-regulatory mechanisms on the sense transcripts.

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.

Recent large-scale genomic studies established the occurrence of multiple DNA sequence variants in genomes of healthy individuals that differ from the reference sequence. Among these variants mostly represented by germline single nucleotide polymorphisms disease-related alleles are detected including alleles which are associated with monogenic disorders, and putative deleterious genetic variants. Apart from functional significance of a particular variant and of a gene harboring it, the penetrance of these allelic variants depends on their expression level and can be determined by preferential expression of a particular allele, or allele-specific expression. It is estimated that 20–30 % of genes present in the human genome display allelic bias in a tissue-specific manner. Allele-specific expression is defined by a range of genetic and epigenetic mechanisms including cis-regulatory polymorphisms, allele-specific binding of transcription factors, allele-specific DNA methylation and regulation through non-coding RNA. Although the data on the issue are scarce, allele-specific expression has been reported to be implicated in several hereditary disorders including benign and malignant tumors of the large intestine. Recent studies that estimate allele-specific expression incidence in tumors and identify wide range of genes displaying allelic imbalance indicate that allele-specific expression might play a significant role in carcinogenesis. Eventually, estimation of transcriptional rate of allelic variants which cause dysfunction of oncogenes and tumor suppressors may prove to be essential for rational choice of antitumor therapeutic strategy. In this review, we outline the main concepts and mechanisms of allele-specific expression and the data on allelic imbalance in tumors.

Allele-specific expression (ASE) is a phenomenon in which one allele is preferentially expressed over the other. Genetic and epigenetic factors cause ASE by altering the final composition of a gene's product, leading to expression imbalances that can have functional consequences on phenotypes. Environmental signals also impact allele-specific expression, but how they contribute to this cross talk remains understudied. Here, we explored how genotype, parent-of-origin, tissue, sex, and dietary fat simultaneously influence ASE biases. Male and female mice from a F1 reciprocal cross of the LG/J and SM/J strains were fed a high or low fat diet. We harnessed strain-specific variants to distinguish between two ASE classes: parent-of-origin-dependent (unequal expression based on parental origin) and sequence-dependent (unequal expression based on nucleotide identity). We present a comprehensive map of ASE patterns in 2853 genes across three tissues and nine environmental contexts. We found that both ASE classes are highly dependent on tissue and environmental context. They vary across metabolically relevant tissues, between males and females, and in response to dietary fat. We also found 45 genes with inconsistent ASE biases that switched direction across tissues and/or environments. Finally, we integrated ASE and QTL data from published intercrosses of the LG/J and SM/J strains. Our ASE genes are often enriched in QTLs for metabolic and musculoskeletal traits, highlighting how this orthogonal approach can prioritize candidate genes. Together, our results provide novel insights into how genetic, epigenetic, and environmental mechanisms govern allele-specific expression, which is an essential step toward deciphering the genotype-to-phenotype map.