Analysis of Allele-Specific Gene Expression

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.

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

Imprinted inactivation of the paternal X chromosome in marsupials is the primordial mechanism of dosage compensation for X-linked genes between females and males in Therians. In Eutherian mammals, X chromosome inactivation (XCI) evolved into a random process in cells from the embryo proper, where either the maternal or paternal X can be inactivated. However, species like mouse and bovine maintained imprinted XCI exclusively in extraembryonic tissues. The existence of imprinted XCI in humans remains controversial, with studies based on the analyses of only one or two X-linked genes in different extraembryonic tissues. Here we readdress this issue in human term placenta by performing a robust analysis of allele-specific expression of 22 X-linked genes, including XIST, using 27 SNPs in transcribed regions. We show that XCI is random in human placenta, and that this organ is arranged in relatively large patches of cells with either maternal or paternal inactive X. In addition, this analysis indicated heterogeneous maintenance of gene silencing along the inactive X, which combined with the extensive mosaicism found in placenta, can explain the lack of agreement among previous studies. Our results illustrate the differences of XCI mechanism between humans and mice, and highlight the importance of addressing the issue of imprinted XCI in other species in order to understand the evolution of dosage compensation in placental mammals.

Human variation is largely caused by deoxyribonucleic acid polymorphism and difference in gene expression. Common disease/common variant hypotheses suggest that quantitative differences among different alleles may be the basis for complex diseases. Quantitative difference in gene expression between alleles may affect most complex diseases. We have developed a gene chip-based method to quantitatively examine allele-specific gene expression of 1063 transcribed single-nucleotide polymorphisms using Affymetrix HuSNP oligo arrays. Among the 602 genes that were heterozygous and expressed in kidney or liver tissues from seven individuals, 326 (54%) showed preferential expression of one allele in at least one individual. The genes that showed allele-specific expression are distributed throughout the genome. We showed that variation of gene expression between alleles is common and that this variation may contribute to human variation. Our studies demonstrate the feasibility to perform genome-wide analysis of allele-specific gene expression.

Analysis of allele-specific gene expression (ASE) is a powerful approach for studying gene regulation, particularly when sample sizes are small, such as for rare diseases, or when studying the effects of rare genetic variation. However, detection of ASE events relies on accurate alignment of RNA sequencing reads, where challenges still remain, particularly for reads containing genetic variants or those that align to many different genomic locations. We have developed the Personalised ASE Caller (PAC), a tool that combines multiple steps to improve the quantification of allelic reads, including personalized (i.e., diploid) read alignment with improved allocation of multimapping reads. Using simulated RNA sequencing data, we show that PAC outperforms standard alignment approaches for ASE detection, reducing the number of sites with incorrect biases (>10%) by ∼80% and increasing the number of sites that can be reliably quantified by ∼3%. Applying PAC to real RNA sequencing data from 670 whole-blood samples, we show that genetic regulatory signatures inferred from ASE data more closely match those from population-based methods that are less prone to alignment biases. Finally, we use PAC to characterize cell type–specific ASE events that would be missed by standard alignment approaches, and in doing so identify disease relevant genes that may modulate their effects through the regulation of gene expression. PAC can be applied to the vast quantity of existing RNA sequencing data sets to better understand a wide array of fundamental biological and disease processes.

F1 hybrids resulting from intercrosses of inbred strains have provided an invaluable tool for the study of imprinting. The hybrids can be used to analyze parent-of-origin differences in expression of any gene, provided sequence differences exist between the two parental alleles. Methods used to detect allele-specific expression include ribonuclease protection assays (1) and allele-specific RNA in situ hybridization (2), as well as a number of reverse transcriptase polymerase chain reaction (RT-PCR)-based assays (see, for example, refs. 3 and 4). We describe here two such assays that are quantitative and require only single base differences between the two alleles. Both assays rely on the amplification of the RNA of interest by RT-PCR using primer sets that flank the sequence polymorphism, a method shown previously to yield amplicons whose allelic ratio is proportional to the ratio in the starting material, regardless of the number of cycles of amplification (5).

Allelic differences in expression are important genetic factors contributing to quantitative trait variation in various organisms. However, the extent of genome-wide allele-specific expression by different modes of gene regulation has not been well characterized in plants. In this study we developed a new methodology for allele-specific expression analysis by applying Massively Parallel Signature Sequencing (MPSS), an open ended and sequencing based mRNA profiling technology. This methodology enabled a genome-wide evaluation of cis- and trans-effects on allelic expression in six meristem stages of the maize hybrid. Summarization of data from nearly 400 pairs of MPSS allelic signature tags showed that 60% of the genes in the hybrid meristems exhibited differential allelic expression. Because both alleles are subjected to the same trans-acting factors in the hybrid, the data suggest the abundance of cis-regulatory differences in the genome. Comparing the same allele expressed in the hybrid versus its inbred parents showed that 40% of the genes were differentially expressed, suggesting different trans-acting effects present in different genotypes. Such trans-acting effects may result in gene expression in the hybrid different from allelic additive expression. With this approach we quantified gene expression in the hybrid relative to its inbred parents at the allele-specific level. As compared to measuring total transcript levels, this study provides a new level of understanding of different modes of gene regulation in the hybrid and the molecular basis of heterosis.

Complex human traits are influenced by variation in regulatory DNA through mechanisms that are not fully understood. Since regulatory elements are conserved between humans and mice, a thorough annotation of cis regulatory variants in mice could aid in this process. Here we provide a detailed portrait of mouse gene expression across multiple tissues in a three-way diallel. Greater than 80% of mouse genes have cis regulatory variation. These effects influence complex traits and usually extend to the human ortholog. Further, we estimate that at least one in every thousand SNPs creates a cis regulatory effect. We also observe two types of parent-of-origin effects, including classical imprinting and a novel, global allelic imbalance in favor of the paternal allele. We conclude that, as with humans, pervasive regulatory variation influences complex genetic traits in mice and provide a new resource toward understanding the genetic control of transcription in mammals.

Loss of imprinting (LOI) of the insulin-like growth factor-II (IGF2) gene, an epigenetic alteration associated with expression of the normally silent maternal allele, was observed first in Wilms tumor. Although LOI has subsequently been detected in most adult tumors, the biologic role of LOI in cancer remains obscure. We analyzed the imprinting status of Wilms tumors with respect to pathologic subtype, stage, and patient's age at diagnosis and examined the expression of genes potentially affected by LOI. Of 60 Wilms tumors examined, 25 were informative for an ApaI polymorphism in the IGF2 gene, allowing analysis of allele-specific gene expression, and could be classified by pathologic subtype. Gene expression was measured quantitatively by real-time polymerase chain reaction, and pathologic analysis was blinded for genetic status. All statistical tests were two-sided. We observed LOI of IGF2 in nine (90%) of 10 Wilms tumors classified as having a pathologic subtype associated with a later stage of renal development and in only one (6.7%) of 15 Wilms tumors with a pathologic subtype associated with an earlier stage of renal development (P< .001). LOI was associated with a 2.2-fold increase (95% confidence interval [CI] = 1.6-fold to 3.1-fold) in IGF2 expression (P< .001). Children whose Wilms tumors displayed LOI of IGF2 were statistically significantly older at diagnosis (median = 65 months; interquartile range [IQR] = 47-83 months) than children whose tumors displayed normal imprinting (median = 24 months; IQR = 13-35 months; P< .001). These data demonstrate a clear relationship between LOI and altered expression of IGF2 in Wilms tumors and provide a molecular basis for understanding the divergent pathogenesis of this cancer. Analysis of LOI could provide a valuable molecular tool for the classification of Wilms tumor.

Complex human traits are influenced by variation in regulatory DNA through mechanisms that are not fully understood. Since regulatory elements are conserved between humans and mice, a thorough annotation of cis regulatory variants in mice could aid in this process. Here we provide a detailed portrait of mouse gene expression across multiple tissues in a three-way diallel. Greater than 80% of mouse genes have cis regulatory variation. These effects influence complex traits and usually extend to the human ortholog. Further, we estimate that at least one in every thousand SNPs creates a cis regulatory effect. We also observe two types of parent-of-origin effects, including classical imprinting and a novel, global allelic imbalance in favor of the paternal allele. We conclude that, as with humans, pervasive regulatory variation influences complex genetic traits in mice and provide a new resource toward understanding the genetic control of transcription in mammals.

Faculty Opinions recommendation of Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance.

Abstract. Knight JC (University of Oxford, Oxford, UK). Resolving the variable genome and epigenome in human disease (Review). J Intern Med 2012; 271: 379–391. The individual human genome and epigenome are being defined at unprecedented resolution by current advances in sequencing technologies with important implications for human disease. This review uses examples relevant to clinical practice to illustrate the functional consequences of genetic and epigenetic variation. The insights gained from genome-wide association studies are described together with current efforts to understand the role of rare variants in common disease, set in the context of recent successes in Mendelian traits through the application of whole exome sequencing. The application of functional genomics to interrogate the genome and epigenome, build up an integrated picture of the regulatory genomic landscape and inform disease association studies is discussed, together with the role of expression quantitative trait mapping and analysis of allele-specific gene expression.

The individual human genome and epigenome are being defined at unprecedented resolution by current advances in sequencing technologies with important implications for human disease. This review uses examples relevant to clinical practice to illustrate the functional consequences of genetic and epigenetic variation. The insights gained from genome-wide association studies are described together with current efforts to understand the role of rare variants in common disease, set in the context of recent successes in Mendelian traits through the application of whole exome sequencing. The application of functional genomics to interrogate the genome and epigenome, build up an integrated picture of the regulatory genomic landscape and inform disease association studies is discussed, together with the role of expression quantitative trait mapping and analysis of allele-specific gene expression.

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.

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.