MBASED: allele-specific expression detection in cancer tissues and cell lines

Multi-Omics Analysis Revealed the rSNPs Potentially Involved in T2DM Pathogenic Mechanism and Metformin Response

The goal of our study was to identify and assess the functionally significant SNPs with potentially important roles in the development of type 2 diabetes mellitus (T2DM) and/or their effect on individual response to antihyperglycemic medication with metformin. We applied a bioinformatics approach to identify the regulatory SNPs (rSNPs) associated with allele-asymmetric binding and expression events in our paired ChIP-seq and RNA-seq data for peripheral blood mononuclear cells (PBMCs) of nine healthy individuals. The rSNP outcomes were analyzed using public data from the GWAS (Genome-Wide Association Studies) and Genotype-Tissue Expression (GTEx). The differentially expressed genes (DEGs) between healthy and T2DM individuals (GSE221521), including metformin responders and non-responders (GSE153315), were searched for in GEO RNA-seq data. The DEGs harboring rSNPs were analyzed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 14,796 rSNPs in the promoters of 5132 genes of human PBMCs. We found 4280 rSNPs to associate with both phenotypic traits (GWAS) and expression quantitative trait loci (eQTLs) from GTEx. Between T2DM patients and controls, 3810 rSNPs were detected in the promoters of 1284 DEGs. Based on the protein-protein interaction (PPI) network, we identified 31 upregulated hub genes, including the genes involved in inflammation, obesity, and insulin resistance. The top-ranked 10 enriched KEGG pathways for these hubs included insulin, AMPK, and FoxO signaling pathways. Between metformin responders and non-responders, 367 rSNPs were found in the promoters of 131 DEGs. Genes encoding transcription factors and transcription regulators were the most widely represented group and many were shown to be involved in the T2DM pathogenesis. We have formed a list of human rSNPs that add functional interpretation to the T2DM-association signals identified in GWAS. The results suggest candidate causal regulatory variants for T2DM, with strong enrichment in the pathways related to glucose metabolism, inflammation, and the effects of metformin.

Bayesian Estimation of Allele-Specific Expression in the Presence of Phasing Uncertainty

Motivation

Allele-specific expression (ASE) analyses aim to detect imbalanced expression of maternal versus paternal copies of an autosomal gene. Such allelic imbalance can result from a variety of cis-acting causes, including disruptive mutations within one copy of a gene that impact the stability of transcripts, as well as regulatory variants outside the gene that impact transcription initiation. Current methods for ASE estimation suffer from a number of shortcomings, such as relying on only one variant within a gene, assuming perfect phasing information across multiple variants within a gene, or failing to account for alignment biases and possible genotyping errors.

Results

We developed BEASTIE, a Bayesian hierarchical model designed for precise ASE quantification at the gene level, based on given genotypes and RNA-Seq data. BEASTIE addresses the complexities of allelic mapping bias, genotyping error, and phasing errors by incorporating empirical phasing error rates derived from Genome-in-a-Bottle individual NA12878. BEASTIE surpasses existing methods in accuracy, especially in scenarios with high phasing errors. This improvement is critical for identifying rare genetic variants often obscured by such errors. Through rigorous validation on simulated data and application to real data from the 1000 Genomes Project, we establish the robustness of BEASTIE. These findings underscore the value of BEASTIE in revealing patterns of ASE across gene sets and pathways.

Availability and Implementation

The software is freely available from https://github.com/x811zou/BEASTIE . BEASTIE is available as Python source code and as a Docker image.

Supplementary information

Additional information is available online.

Computational methods for allele-specific expression in single cells

Allele-specific expression (ASE) is a powerful signal that can be used to investigate multiple molecular mechanisms, such as cis-regulatory effects and imprinting. Single-cell RNA-sequencing (scRNA-seq) enables ASE characterization at the resolution of individual cells. In this review, we highlight the computational methods for processing and analyzing single-cell ASE data. We first describe a bioinformatics pipeline to obtain ASE counts from raw reads synthesized from previous literature. We then discuss statistical methods for detecting allelic imbalance and its variability across conditions using scRNA-seq data. In addition, we describe other methods that use single-cell ASE to address specific biological questions. Finally, we discuss future directions and emphasize the need for an integrated, optimized bioinformatics pipeline, and further development of statistical methods for different technologies.

Non-additive genetic components contribute significantly to population-wide gene expression variation

Gene expression variation, an essential step between genotype and phenotype, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence gene expression variation remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originating from genetically divergent Saccharomyces cerevisiae isolates. Our analysis across 5,087 transcript abundance traits showed that non-additive components account for 36% of the gene expression variance on average. By comparing allele-specific read counts in parent-hybrid trios, we found that trans-regulatory changes underlie the majority of gene expression variation in the population. Remarkably, most cis-regulatory variations are also exaggerated or attenuated by additional trans effects. Overall, we showed that the transcriptome is globally buffered at the genetic level mainly due to trans-regulatory variation in the population.

Enhanced bovine genome annotation through integration of transcriptomics and epi-transcriptomics datasets facilitates genomic biology

BACKGROUND

The accurate identification of the functional elements in the bovine genome is a fundamental requirement for high-quality analysis of data informing both genome biology and genomic selection. Functional annotation of the bovine genome was performed to identify a more complete catalog of transcript isoforms across bovine tissues.

RESULTS

A total of 160,820 unique transcripts (50% protein coding) representing 34,882 unique genes (60% protein coding) were identified across tissues. Among them, 118,563 transcripts (73% of the total) were structurally validated by independent datasets (PacBio isoform sequencing data, Oxford Nanopore Technologies sequencing data, de novo assembled transcripts from RNA sequencing data) and comparison with Ensembl and NCBI gene sets. In addition, all transcripts were supported by extensive data from different technologies such as whole transcriptome termini site sequencing, RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression, chromatin immunoprecipitation sequencing, and assay for transposase-accessible chromatin using sequencing. A large proportion of identified transcripts (69%) were unannotated, of which 86% were produced by annotated genes and 14% by unannotated genes. A median of two 5' untranslated regions were expressed per gene. Around 50% of protein-coding genes in each tissue were bifunctional and transcribed both coding and noncoding isoforms. Furthermore, we identified 3,744 genes that functioned as noncoding genes in fetal tissues but as protein-coding genes in adult tissues. Our new bovine genome annotation extended more than 11,000 annotated gene borders compared to Ensembl or NCBI annotations. The resulting bovine transcriptome was integrated with publicly available quantitative trait loci data to study tissue-tissue interconnection involved in different traits and construct the first bovine trait similarity network.

CONCLUSIONS

These validated results show significant improvement over current bovine genome annotations.

Mechanisms of transcriptional regulation in Anopheles gambiae revealed by allele specific expression

Malaria control relies on insecticides targeting the mosquito vector, but this is increasingly compromised by insecticide resistance, which can be achieved by elevated expression of detoxifying enzymes that metabolize the insecticide. In diploid organisms, gene expression is regulated both in cis, by regulatory sequences on the same chromosome, and by trans acting factors, affecting both alleles equally. Differing levels of transcription can be caused by mutations in cis-regulatory modules (CRM), but few of these have been identified in mosquitoes. We crossed bendiocarb resistant and susceptible Anopheles gambiae strains to identify cis-regulated genes that might be responsible for the resistant phenotype using RNAseq, and cis-regulatory module sequences controlling gene expression in insecticide resistance relevant tissues were predicted using machine learning. We found 115 genes showing allele specific expression in hybrids of insecticide susceptible and resistant strains, suggesting cis regulation is an important mechanism of gene expression regulation in Anopheles gambiae. The genes showing allele specific expression included a higher proportion of Anopheles specific genes on average younger than genes those with balanced allelic expression.

Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects

Differential allele-specific expression (ASE) is a powerful tool to study context-specific cis-regulation of gene expression. Such effects can reflect the interaction between genetic or epigenetic factors and a measured context or condition. Single-cell RNA sequencing (scRNA-seq) allows the measurement of ASE at individual-cell resolution, but there is a lack of statistical methods to analyze such data. We present Differential Allelic Expression using Single-Cell data (DAESC), a powerful method for differential ASE analysis using scRNA-seq from multiple individuals, with statistical behavior confirmed through simulation. DAESC accounts for non-independence between cells from the same individual and incorporates implicit haplotype phasing. Application to data from 105 induced pluripotent stem cell (iPSC) lines identifies 657 genes dynamically regulated during endoderm differentiation, with enrichment for changes in chromatin state. Application to a type-2 diabetes dataset identifies several differentially regulated genes between patients and controls in pancreatic endocrine cells. DAESC is a powerful method for single-cell ASE analysis and can uncover novel insights on gene regulation.

Testing for Allele-specific Expression from Human Brain Samples

Many single nucleotide polymorphisms (SNPs) identified by genome-wide association studies exert their effects on disease risk as expression quantitative trait loci (eQTL) via allele-specific expression (ASE). While databases for probing eQTLs in tissues from normal individuals exist, one may wish to ascertain eQTLs or ASE in specific tissues or disease-states not characterized in these databases. Here, we present a protocol to assess ASE of two possible target genes (GPNMB and KLHL7) of a known genome-wide association study (GWAS) Parkinson's disease (PD) risk locus in postmortem human brain tissue from PD and neurologically normal individuals. This was done using a sequence of RNA isolation, cDNA library generation, enrichment for transcripts of interest using customizable cDNA capture probes, paired-end RNA sequencing, and subsequent analysis. This method provides increased sensitivity relative to traditional bulk RNAseq-based and a blueprint that can be extended to the study of other genes, tissues, and disease states. Key features • Analysis of GPNMB allele-specific expression (ASE) in brain lysates from cognitively normal controls (NC) and Parkinson's disease (PD) individuals. • Builds on the ASE protocol of Mayba et al. (2014) and extends application from cells to human tissue. • Increased sensitivity by enrichment for desired transcript via RNA CaptureSeq (Mercer et al., 2014). • Optimized for human brain lysates from cingulate gyrus, caudate nucleus, and cerebellum.

MET exon 14 skipping is overexpressed in an allele-specific manner in lung adenocarcinoma primary samples

MET exon 14 skipping ( METΔ14 ) is a well-characterized oncogene in the Ras-MAPK pathway driving lung adenocarcinoma (LUAD). Previous studies on METΔ14 revealed this aberrantly spliced oncogene is expressed in LUAD primary samples and is associated with heterozygous somatic mutations and deletions near exon 14 splice sites. Upon further examination of DNA and RNA sequencing data from primary samples, we highlight that METΔ14 is overexpressed in an allele-specific manner. These data suggest that dose-dependence of METΔ14 may be critical to oncogenesis.

Non-additive genetic components contribute significantly to population-wide gene expression variation

Gene expression variation, an essential step between genomic variation and phenotypic landscape, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence the heritability of expression traits remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originated from genetically divergent yeast isolates. We estimated the broad- and narrow-sense heritability across 5,087 transcript abundance traits and showed that non-additive components account for 36% of the phenotypic variance on average. By comparing allelic expression ratios in the hybrid and the corresponding parental pair, we identified regulatory changes in 25% of all cases, with a majority acting in trans. We further showed that trans-regulation could underlie coordinated expression variation across highly connected genes, resulting in significantly higher non-additive variance and most likely in some of the missing heritability of gene expression traits.

Exploration of genotype-by-environment interactions affecting gene expression responses in porcine immune cells

As one of the keys to healthy performance, robustness of farm animals is gaining importance, and with this comes increasing interest in genetic dissection of genotype-by-environment interactions (G×E). Changes in gene expression are among the most sensitive responses conveying adaptation to environmental stimuli. Environmentally responsive regulatory variation thus likely plays a central role in G×E. In the present study, we set out to detect action of environmentally responsive cis-regulatory variation by the analysis of condition-dependent allele specific expression (cd-ASE) in porcine immune cells. For this, we harnessed mRNA-sequencing data of peripheral blood mononuclear cells (PBMCs) stimulated in vitro with lipopolysaccharide, dexamethasone, or their combination. These treatments mimic common challenges such as bacterial infection or stress, and induce vast transcriptome changes. About two thirds of the examined loci showed significant ASE in at least one treatment, and out of those about ten percent exhibited cd-ASE. Most of the ASE variants were not yet reported in the PigGTEx Atlas. Genes showing cd-ASE were enriched in cytokine signaling in immune system and include several key candidates for animal health. In contrast, genes showing no ASE featured cell-cycle related functions. We confirmed LPS-dependent ASE for one of the top candidates, SOD2, which ranks among the major response genes in LPS-stimulated monocytes. The results of the present study demonstrate the potential of in vitro cell models coupled with cd-ASE analysis for the investigation of G×E in farm animals. The identified loci may benefit efforts to unravel the genetic basis of robustness and improvement of health and welfare in pigs.

GPNMB confers risk for Parkinson's disease through interaction with α-synuclein

Many risk loci for Parkinson's disease (PD) have been identified by genome-wide association studies (GWASs), but target genes and mechanisms remain largely unknown. We linked the GWAS-derived chromosome 7 locus (sentinel single-nucleotide polymorphism rs199347) to GPNMB through colocalization analyses of expression quantitative trait locus and PD risk signals, confirmed by allele-specific expression studies in the human brain. In cells, glycoprotein nonmetastatic melanoma protein B (GPNMB) coimmunoprecipitated and colocalized with α-synuclein (aSyn). In induced pluripotent stem cell-derived neurons, loss of GPNMB resulted in loss of ability to internalize aSyn fibrils and develop aSyn pathology. In 731 PD and 59 control biosamples, GPNMB was elevated in PD plasma, associating with disease severity. Thus, GPNMB represents a PD risk gene with potential for biomarker development and therapeutic targeting.

Quantification of mutant–allele expression at isoform level in cancer from RNA-seq data

Even though the role of DNA mutations in cancer is well recognized, current quantification of the RNA expression, performed either at gene or isoform level, typically ignores the mutation status. Standard methods for estimating allele-specific expression (ASE) consider gene-level expression, but the functional impact of a mutation is best assessed at isoform level. Hence our goal is to quantify the mutant–allele expression at isoform level. We have developed and implemented a method, named MAX, for quantifying mutant–allele expression given a list of mutations. For a gene of interest, a mutant reference is constructed by incorporating all possible mutant versions of the wild-type isoforms in the transcriptome annotation. The mutant reference is then used for the RNA-seq reads mapping, which in principle works similarly for any quantification tool. We apply an alternating EM algorithm to the read-count data from the mapping step. In a simulation study, MAX performs well against standard isoform-quantification methods. Also, MAX achieves higher accuracy than conventional gene-based ASE methods such as ASEP. An analysis of a real dataset of acute myeloid leukemia reveals a subgroup of NPM1-mutated patients responding well to a kinase inhibitor. Our findings indicate that quantification of mutant–allele expression at isoform level is feasible and has potential added values for assessing the functional impact of DNA mutations in cancers.

Allele-specific expression reveals genes with recurrent cis-regulatory alterations in high-risk neuroblastoma

BACKGROUND

Neuroblastoma is a pediatric malignancy with a high frequency of metastatic disease at initial diagnosis. Neuroblastoma tumors have few recurrent protein-coding mutations but contain extensive somatic copy number alterations (SCNAs) suggesting that mutations that alter gene dosage are important drivers of tumorigenesis. Here, we analyze allele-specific expression in 96 high-risk neuroblastoma tumors to discover genes impacted by cis-acting mutations that alter dosage.

RESULTS

We identify 1043 genes with recurrent, neuroblastoma-specific allele-specific expression. While most of these genes lie within common SCNA regions, many of them exhibit allele-specific expression in copy neutral samples and these samples are enriched for mutations that are predicted to cause nonsense-mediated decay. Thus, both SCNA and non-SCNA mutations frequently alter gene expression in neuroblastoma. We focus on genes with neuroblastoma-specific allele-specific expression in the absence of SCNAs and find 26 such genes that have reduced expression in stage 4 disease. At least two of these genes have evidence for tumor suppressor activity including the transcription factor TFAP2B and the protein tyrosine phosphatase PTPRH.

CONCLUSIONS

In summary, our allele-specific expression analysis discovers genes that are recurrently dysregulated by both large SCNAs and other cis-acting mutations in high-risk neuroblastoma.

cis-Regulatory changes in locomotor genes are associated with the evolution of burrowing behavior

How evolution modifies complex, innate behaviors is largely unknown. Divergence in many morphological traits, and some behaviors, is linked to cis-regulatory changes in gene expression. Given this, we compare brain gene expression of two interfertile sister species of Peromyscus mice that show large and heritable differences in burrowing behavior. Species-level differential expression and allele-specific expression in F₁ hybrids indicate a preponderance of cis-regulatory divergence, including many genes whose cis-regulation is affected by burrowing behavior. Genes related to locomotor coordination show the strongest signals of lineage-specific selection on burrowing-induced cis-regulatory changes. Furthermore, genetic markers closest to these candidate genes associate with variation in burrow shape in a genetic cross, suggesting an enrichment for loci affecting burrowing behavior near these candidate locomotor genes. Our results provide insight into how cis-regulated gene expression can depend on behavioral context and how this dynamic regulatory divergence between species may contribute to behavioral evolution.

TranSNPs: A class of functional SNPs affecting mRNA translation potential revealed by fraction-based allelic imbalance

Few studies have explored the association between SNPs and alterations in mRNA translation potential. We developed an approach to identify SNPs that can mark allele-specific protein expression levels and could represent sources of inter-individual variation in disease risk. Using MCF7 cells under different treatments, we performed polysomal profiling followed by RNA sequencing of total or polysome-associated mRNA fractions and designed a computational approach to identify SNPs showing a significant change in the allelic balance between total and polysomal mRNA fractions. We identified 147 SNPs, 39 of which located in UTRs. Allele-specific differences at the translation level were confirmed in transfected MCF7 cells by reporter assays. Exploiting breast cancer data from TCGA we identified UTR SNPs demonstrating distinct prognosis features and altering binding sites of RNA-binding proteins. Our approach produced a catalog of tranSNPs, a class of functional SNPs associated with allele-specific translation and potentially endowed with prognostic value for disease risk.

Leveraging Allele-Specific Expression for Therapeutic Response Gene Discovery in Glioblastoma

Glioblastoma is the most prevalent primary malignant brain tumor in adults and is characterized by poor prognosis and universal tumor recurrence. Effective glioblastoma treatments are lacking, in part due to somatic mutations and epigenetic reprogramming that alter gene expression and confer drug resistance. To investigate recurrently dysregulated genes in glioblastoma we interrogated allele-specific expression (ASE), the difference in expression between two alleles of a gene, in glioblastoma stem cells (GSC) derived from 43 patients. A total of 118 genes were found with recurrent ASE preferentially in GSCs compared to normal tissues. These genes were enriched for apoptotic regulators, including schlafen family member 11 (SLFN11). Loss of SLFN11 gene expression was associated with aberrant promoter methylation and conferred resistance to chemotherapy and PARP inhibition. Conversely, low SLFN11 expression rendered GSCs susceptible to the oncolytic flavivirus Zika. This discovery effort based upon ASE revealed novel points of vulnerability in GSCs, suggesting a potential alternative treatment strategy for chemotherapy resistant glioblastoma.

Power calculator for detecting allelic imbalance using hierarchical Bayesian model

OBJECTIVE

Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Methods for testing AI exist, but methods are needed to estimate type I error and power for detecting AI and difference of AI between conditions. As the costs of the technology plummet, what is more important: reads or replicates?

RESULTS

We find that a minimum of 2400, 480, and 240 allele specific reads divided equally among 12, 5, and 3 replicates is needed to detect a 10, 20, and 30%, respectively, deviation from allelic balance in a condition with power > 80%. A minimum of 960 and 240 allele specific reads divided equally among 8 replicates is needed to detect a 20 or 30% difference in AI between conditions with comparable power. Higher numbers of replicates increase power more than adding coverage without affecting type I error. We provide a Python package that enables simulation of AI scenarios and enables individuals to estimate type I error and power in detecting AI and differences in AI between conditions.

TIGER: The gene expression regulatory variation landscape of human pancreatic islets

Genome-wide association studies (GWASs) identified hundreds of signals associated with type 2 diabetes (T2D). To gain insight into their underlying molecular mechanisms, we have created the translational human pancreatic islet genotype tissue-expression resource (TIGER), aggregating >500 human islet genomic datasets from five cohorts in the Horizon 2020 consortium T2DSystems. We impute genotypes using four reference panels and meta-analyze cohorts to improve the coverage of expression quantitative trait loci (eQTL) and develop a method to combine allele-specific expression across samples (cASE). We identify >1 million islet eQTLs, 53 of which colocalize with T2D signals. Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identify eight cASE colocalizations, among which we found a T2D-associated SLC30A8 variant. We make all data available through the TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translates into therapeutic insight and precision medicine for T2D.

Uncovering Divergence in Gene Expression Regulation in the Adaptation of Yeast to Nitrogen Scarcity

Saccharomyces cerevisiae rewires its transcriptional output to survive stressful environments, such as nitrogen scarcity under fermentative conditions. Although divergence in nitrogen metabolism among natural yeast populations has been reported, the impact of regulatory genetic variants modulating gene expression and nitrogen consumption remains to be investigated. Here, we employed an F1 hybrid from two contrasting S. cerevisiae strains, providing a controlled genetic environment to map cis factors involved in the divergence of gene expression regulation in response to nitrogen scarcity. We used a dual approach to obtain genome-wide allele-specific profiles of chromatin accessibility, transcription factor binding, and gene expression through ATAC-seq (assay for transposase accessible chromatin) and RNA-seq (transcriptome sequencing). We observed large variability in allele-specific expression and accessibility between the two genetic backgrounds, with a third of these differences specific to a deficient nitrogen environment. Furthermore, we discovered events of allelic bias in gene expression correlating with allelic bias in transcription factor binding solely under nitrogen scarcity, where the majority of these transcription factors orchestrates the nitrogen catabolite repression regulatory pathway and demonstrates a cis × environment-specific response. Our approach allowed us to find cis variants modulating gene expression, chromatin accessibility, and allelic differences in transcription factor binding in response to low nitrogen culture conditions. IMPORTANCE Historically, coding variants were prioritized when searching for causal mechanisms driving adaptation of natural populations to stressful environments. However, the recent focus on noncoding variants demonstrated their ubiquitous role in adaptation. Here, we performed genome-wide regulatory variation profiles between two divergent yeast strains when facing nitrogen nutritional stress. The open chromatin availability of several regulatory regions changes in response to nitrogen scarcity. Importantly, we describe regulatory events that deviate between strains. Our results demonstrate a widespread variation in gene expression regulation between naturally occurring populations in response to stressful environments.

Complex evolution of novel red floral color in Petunia

Red flower color has arisen multiple times and is generally associated with hummingbird pollination. The majority of evolutionary transitions to red color proceeded from purple lineages and tend to be genetically simple, almost always involving a few loss-of-function mutations of major phenotypic effect. Here we report on the complex evolution of a novel red floral color in the hummingbird-pollinated Petunia exserta (Solanaceae) from a colorless ancestor. The presence of a red color is remarkable because the genus cannot synthesize red anthocyanins and P. exserta retains a nonfunctional copy of the key MYB transcription factor AN2. We show that moderate upregulation and a shift in tissue specificity of an AN2 paralog, DEEP PURPLE, restores anthocyanin biosynthesis in P. exserta. An essential shift in anthocyanin hydroxylation occurred through rebalancing the expression of three hydroxylating genes. Furthermore, the downregulation of an acyltransferase promotes reddish hues in typically purple pigments by preventing acyl group decoration of anthocyanins. This study presents a rare case of a genetically complex evolutionary transition toward the gain of a novel red color.

Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.

Perspectives on Allele-Specific Expression

Diploidy has profound implications for population genetics and susceptibility to genetic diseases. Although two copies are present for most genes in the human genome, they are not necessarily both active or active at the same level in a given individual. Genomic imprinting, resulting in exclusive or biased expression in favor of the allele of paternal or maternal origin, is now believed to affect hundreds of human genes. A far greater number of genes display unequal expression of gene copies due to cis-acting genetic variants that perturb gene expression. The availability of data generated by RNA sequencing applied to large numbers of individuals and tissue types has generated unprecedented opportunities to assess the contribution of genetic variation to allelic imbalance in gene expression. Here we review the insights gained through the analysis of these data about the extent of the genetic contribution to allelic expression imbalance, the tools and statistical models for gene expression imbalance, and what the results obtained reveal about the contribution of genetic variants that alter gene expression to complex human diseases and phenotypes.

Enhancer release and retargeting activates disease-susceptibility genes

The functional engagement between an enhancer and its target promoter ensures precise gene transcription1. Understanding the basis of promoter choice by enhancers has important implications for health and disease. Here we report that functional loss of a preferred promoter can release its partner enhancer to loop to and activate an alternative promoter (or alternative promoters) in the neighbourhood. We refer to this target-switching process as 'enhancer release and retargeting'. Genetic deletion, motif perturbation or mutation, and dCas9-mediated CTCF tethering reveal that promoter choice by an enhancer can be determined by the binding of CTCF at promoters, in a cohesin-dependent manner-consistent with a model of 'enhancer scanning' inside the contact domain. Promoter-associated CTCF shows a lower affinity than that at chromatin domain boundaries and often lacks a preferred motif orientation or a partnering CTCF at the cognate enhancer, suggesting properties distinct from boundary CTCF. Analyses of cancer mutations, data from the GTEx project and risk loci from genome-wide association studies, together with a focused CRISPR interference screen, reveal that enhancer release and retargeting represents an overlooked mechanism that underlies the activation of disease-susceptibility genes, as exemplified by a risk locus for Parkinson's disease (NUCKS1-RAB7L1) and three loci associated with cancer (CLPTM1L-TERT, ZCCHC7-PAX5 and PVT1-MYC).

Replicate sequencing libraries are important for quantification of allelic imbalance

A sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing (RNA-seq) data. A near-universal practice in such studies is to prepare and sequence only one library per RNA sample. We present theoretical and experimental evidence that data from a single RNA-seq library is insufficient for reliable quantification of the contribution of technical noise to the observed AI signal; consequently, reliance on one-replicate experimental design can lead to unaccounted-for variation in error rates in allele-specific analysis. We develop a computational approach, Qllelic, that accurately accounts for technical noise by making use of replicate RNA-seq libraries. Testing on new and existing datasets shows that application of Qllelic greatly decreases false positive rate in allele-specific analysis while conserving appropriate signal, and thus greatly improves reproducibility of AI estimates. We explore sources of technical overdispersion in observed AI signal and conclude by discussing design of RNA-seq studies addressing two biologically important questions: quantification of transcriptome-wide AI in one sample, and differential analysis of allele-specific expression between samples.

A survey of cancer genome signatures identifies genes connected to distinct chromosomal instability phenotypes

Certain breast and ovarian cancers are characterised by high levels of chromosomal instability. We established a suite of eleven SNP array-based signatures of various forms of chromosomal instability. To understand what biological mechanisms might underpin these signatures, we generated and assembled genetic-feature data including allele-specific expression, fusion genes, gene expression, methylation, somatic coding mutations and protein expression. For each signature, we extracted a compendium of significantly associated genetic features using machine learning. We established an association between subchromosomal allelic imbalance-based measures and DNA repair genes. Numerical chromosomal instability and chromothripsis were found to have distinct genetic associations but shared a relationship to mitotic processes, while whole-genome doubling was characterised by TP53 mutation, and high AURKA and GINS1 expression. Furthermore, we identified two genetically distinct forms of tandem duplicator phenotypes. These findings identify potentially novel genomic targets for validation and drug development for specific subsets of breast and ovarian cancer.

Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning

Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15%-20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

Cis-regulatory mutations with driver hallmarks in major cancers

Despite the recent availability of complete genome sequences of tumors from thousands of patients, isolating disease-causing (driver) non-coding mutations from the plethora of somatic variants remains challenging, and only a handful of validated examples exist. By integrating whole-genome sequencing, genetic data, and allele-specific gene expression from TCGA, we identified 320 somatic non-coding mutations that affect gene expression in cis (FDR<0.25). These mutations cluster into 47 cis-regulatory elements that modulate expression of their subject genes through diverse molecular mechanisms. We further show that these mutations have hallmark features of non-coding drivers; namely, that they preferentially disrupt transcription factor binding motifs, are associated with a selective advantage, increased oncogene expression and decreased tumor suppressor expression.

•

Enrichment of functional non-coding somatic mutations predicts drivers

•

Elevated variant allele frequencies are consistent with roles in tumorigenesis

•

Putative non-coding drivers disrupt transcription factor binding motifs

•

Predicted drivers associate with increased oncogene and decreased TSG expression

Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data

Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provides a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases.

Detection of allelic expression imbalance (AEI), a phenomenon where the two alleles of a gene differ in their expression magnitude, is a key step towards the understanding of phenotypic variations among individuals. Existing methods detect AEI using bulk RNA sequencing (RNA-seq) data and ignore AEI variations among different cell types. Although single-cell RNA sequencing (scRNA-seq) has enabled the characterization of cell-to-cell heterogeneity in gene expression, the high costs have limited its application in AEI analysis. To overcome this limitation, we developed BSCET to characterize cell-type-specific AEI using the widely available bulk RNA-seq data by integrating cell-type composition information inferred from scRNA-seq samples. Since the degree of AEI may vary with disease phenotypes, we further extended BSCET to detect genes whose cell-type-specific AEIs are associated with clinical factors. Through extensive benchmark evaluations and analyses of two pancreatic islet bulk RNA-seq datasets, we demonstrated BSCET’s ability to refine bulk-level AEI to cell-type resolution, and to identify genes whose cell-type-specific AEIs are associated with the progression of type 2 diabetes. With the vast amount of easily accessible bulk RNA-seq data, we believe BSCET will be a valuable tool for elucidating cell type contributions in human diseases.

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

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.

Few Fixed Variants between Trophic Specialist Pupfish Species Reveal Candidate Cis-Regulatory Alleles Underlying Rapid Craniofacial Divergence

Investigating closely related species that rapidly evolved divergent feeding morphology is a powerful approach to identify genetic variation underlying variation in complex traits. This can also lead to the discovery of novel candidate genes influencing natural and clinical variation in human craniofacial phenotypes. We combined whole-genome resequencing of 258 individuals with 50 transcriptomes to identify candidate cis-acting genetic variation underlying rapidly evolving craniofacial phenotypes within an adaptive radiation of Cyprinodon pupfishes. This radiation consists of a dietary generalist species and two derived trophic niche specialists-a molluscivore and a scale-eating species. Despite extensive morphological divergence, these species only diverged 10 kya and produce fertile hybrids in the laboratory. Out of 9.3 million genome-wide SNPs and 80,012 structural variants, we found very few alleles fixed between species-only 157 SNPs and 87 deletions. Comparing gene expression across 38 purebred F1 offspring sampled at three early developmental stages, we identified 17 fixed variants within 10 kb of 12 genes that were highly differentially expressed between species. By measuring allele-specific expression in F1 hybrids from multiple crosses, we found that the majority of expression divergence between species was explained by trans-regulatory mechanisms. We also found strong evidence for two cis-regulatory alleles affecting expression divergence of two genes with putative effects on skeletal development (dync2li1 and pycr3). These results suggest that SNPs and structural variants contribute to the evolution of novel traits and highlight the utility of the San Salvador Island pupfish system as an evolutionary model for craniofacial development.

Allele-specific expression: applications in cancer and technical considerations

Allele-specific gene expression can influence disease traits. Non-coding germline genetic variants that alter regulatory elements can cause allele-specific gene expression and contribute to cancer susceptibility. In tumors, both somatic copy number alterations and somatic single nucleotide variants have been shown to lead to allele-specific expression of genes, many of which are considered drivers of tumor growth. Here, we review recent studies revealing the pervasive presence of this phenomenon in cancer susceptibility and progression. Furthermore, we underscore the importance of careful experimental design and computational analysis for accurate allelic expression quantification and avoidance of false positives. Finally, we discuss additional methodological challenges encountered in cancer studies and in the burgeoning field of single-cell transcriptomics.

Identification of transcription factors controlling floral morphology in wild Petunia species with contrasting pollination syndromes

Adaptation to different pollinators is an important driver of speciation in the angiosperms. Genetic approaches such as QTL mapping have been successfully used to identify the underlying speciation genes. However, these methods are often limited by widespread suppression of recombination due to divergence between species. While the mutations that caused the interspecific differences in floral color and scent have been elucidated in a variety of plant genera, the genes that are responsible for morphological differences remain mostly unknown. Differences in floral organ length determine the pollination efficiency of hawkmoths and hummingbirds, and therefore the genes that control these differences are potential speciation genes. Identifying such genes is challenging, especially in non-model species and when studying complex traits for which little prior genetic and biochemical knowledge is available. Here we combine transcriptomics with detailed growth analysis to identify candidate transcription factors underlying interspecific variation in the styles of Petunia flowers. Starting from a set of 2284 genes, stepwise filtering for expression in styles, differential expression between species, correlation with growth-related traits, allele-specific expression in interspecific hybrids, and/or high-impact polymorphisms resulted in a set of 43 candidate speciation genes. Validation by virus-induced gene silencing identified two MYB transcription factors, EOBI and EOBII, that were previously shown to regulate floral scent emission, a trait associated with pollination by hawkmoths.

BYASE: a Python library for estimating gene and isoform level allele-specific expression

Summary

Allele-specific expression (ASE) is involved in many important biological mechanisms. We present a python package BYASE and its graphical user interface (GUI) tool BYASE-GUI for the identification of ASE from single-end and paired-end RNA-seq data based on Bayesian inference, which can simultaneously report differences in gene-level and isoform-level expression. BYASE uses both phased SNPs and non-phased SNPs, and supports polyploid organisms.

Availability and implementation

The source codes of BYASE and BYASE-GUI are freely available at https://github.com/ncjllld/byase and https://github.com/ncjllld/byase_gui.

Supplementary information

Supplementary data are available at Bioinformatics online.

Discovery of regulatory noncoding variants in individual cancer genomes by using cis-X

We developed cis-X, a computational method for discovering regulatory noncoding variants in cancer by integrating whole-genome and transcriptome sequencing data from a single cancer sample. cis-X first finds aberrantly cis-activated genes that exhibit allele-specific expression accompanied by an elevated outlier expression. It then searches for causal noncoding variants that may introduce aberrant transcription factor binding motifs or enhancer hijacking by structural variations. Analysis of 13 T-lineage acute lymphoblastic leukemias identified a recurrent intronic variant predicted to cis-activate the TAL1 oncogene, a finding validated in vivo by chromatin immunoprecipitation sequencing of a patient-derived xenograft. Candidate oncogenes include the prolactin receptor PRLR activated by a focal deletion that removes a CTCF-insulated neighborhood boundary. cis-X may be applied to pediatric and adult solid tumors that are aneuploid and heterogeneous. In contrast to existing approaches, which require large sample cohorts, cis-X enables the discovery of regulatory noncoding variants in individual cancer genomes.

Ecological divergence in sympatry causes gene misexpression in hybrids

Ecological speciation occurs when reproductive isolation evolves as a byproduct of adaptive divergence between populations. Selection favouring gene regulatory divergence between species could result in transgressive levels of gene expression in F1 hybrids that may lower hybrid fitness. We combined 58 resequenced genomes with 124 transcriptomes to identify patterns of hybrid gene misexpression that may be driven by adaptive regulatory divergence within a young radiation of Cyprinodon pupfishes, which consists of a dietary generalist and two trophic specialists-a molluscivore and a scale-eater. We found more differential gene expression between closely related sympatric specialists than between allopatric generalist populations separated by 1,000 km. Intriguingly, 9.6% of genes that were differentially expressed between sympatric species were also misexpressed in F1 hybrids. A subset of these genes were in highly differentiated genomic regions and enriched for functions important for trophic specialization, including head, muscle and brain development. These regions also included genes that showed evidence of hard selective sweeps and were significantly associated with oral jaw length-the most rapidly diversifying skeletal trait in this radiation. Our results indicate that divergent ecological selection in sympatry can contribute to hybrid gene misexpression which may act as a reproductive barrier between nascent species.

ASEP: Gene-based detection of allele-specific expression across individuals in a population by RNA sequencing

Allele-specific expression (ASE) analysis, which quantifies the relative expression of two alleles in a diploid individual, is a powerful tool for identifying cis-regulated gene expression variations that underlie phenotypic differences among individuals. Existing methods for gene-level ASE detection analyze one individual at a time, therefore failing to account for shared information across individuals. Failure to accommodate such shared information not only reduces power, but also makes it difficult to interpret results across individuals. However, when only RNA sequencing (RNA-seq) data are available, ASE detection across individuals is challenging because the data often include individuals that are either heterozygous or homozygous for the unobserved cis-regulatory SNP, leading to sample heterogeneity as only those heterozygous individuals are informative for ASE, whereas those homozygous individuals have balanced expression. To simultaneously model multi-individual information and account for such heterogeneity, we developed ASEP, a mixture model with subject-specific random effect to account for multi-SNP correlations within the same gene. ASEP only requires RNA-seq data, and is able to detect gene-level ASE under one condition and differential ASE between two conditions (e.g., pre- versus post-treatment). Extensive simulations demonstrated the convincing performance of ASEP under a wide range of scenarios. We applied ASEP to a human kidney RNA-seq dataset, identified ASE genes and validated our results with two published eQTL studies. We further applied ASEP to a human macrophage RNA-seq dataset, identified genes showing evidence of differential ASE between M0 and M1 macrophages, and confirmed our findings by results from cardiometabolic trait-relevant genome-wide association studies. To the best of our knowledge, ASEP is the first method for gene-level ASE detection at the population level that only requires the use of RNA-seq data. With the growing adoption of RNA-seq, we believe ASEP will be well-suited for various ASE studies for human diseases.

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

Identifying cancer-relevant mutations in noncoding regions is challenging due to the large numbers of such mutations, their low levels of recurrence, and difficulties in interpreting their functional impact. To uncover genes that are dysregulated due to somatic mutations in cis, we build upon the concept of differential allele-specific expression (ASE) and introduce methods to identify genes within an individual's cancer whose ASE differs from what is found in matched normal tissue. When applied to breast cancer tumor samples, our methods detect the known allele-specific effects of copy number variation and nonsense-mediated decay. Further, genes that are found to recurrently exhibit differential ASE across samples are cancer relevant. Genes with cis mutations are enriched for differential ASE, and we find 147 potentially functional noncoding mutations cis to genes that exhibit significant differential ASE. We conclude that differential ASE is a promising means for discovering gene dysregulation due to cis noncoding mutations.

Tumor suppressor genes and allele-specific expression: mechanisms and significance

Recent findings indicate that allele-specific expression (ASE) at specific cancer driver gene loci may be of importance in onset/progression of the disease. Of particular interest are loss-of-function (LOF) of tumor suppressor gene (TSGs) alleles. While LOF tumor suppressor mutations are typically considered to be recessive, if these mutant alleles can be significantly differentially expressed relative to wild-type alleles in heterozygotes, the clinical consequences could be significant. LOF TSG alleles are shown to be segregating at high frequencies in world-wide populations of normal/healthy individuals. Matched sets of normal and tumor tissues isolated from 233 cancer patients representing four diverse tumor types demonstrate functionally important changes in patterns of ASE in individuals heterozygous for LOF TSG alleles associated with cancer onset/progression. While a variety of molecular mechanisms were identified as potentially contributing to changes in ASE patterns in cancer, changes in DNA copy number and allele-specific alternative splicing possibly mediated by antisense RNA emerged as predominant factors. In conclusion, LOF TSGs are segregating in human populations at significant frequencies indicating that many otherwise healthy individuals are at elevated risk of developing cancer. Changes in ASE between normal and cancer tissues indicates that LOF TSG alleles may contribute to cancer onset/progression even when heterozygous with wild-type functional alleles.

Genetic and genomic analyses underpin the feasibility of concomitant genetic improvement of milk yield and mastitis resistance in dairy sheep

Milk yield is the most important dairy sheep trait and constitutes the key genetic improvement goal via selective breeding. Mastitis is one of the most prevalent diseases, significantly impacting on animal welfare, milk yield and quality, while incurring substantial costs. Our objectives were to determine the feasibility of a concomitant genetic improvement programme for enhanced milk production and resistance to mastitis. Individual records for milk yield, and four mastitis-related traits (milk somatic cell count, California Mastitis Test score, total viable bacterial count in milk and clinical mastitis presence) were collected monthly throughout lactation for 609 ewes of the Chios breed. All ewes were genotyped with a mastitis specific custom-made 960 single nucleotide polymorphism (SNP) array. We performed targeted genomic association studies, (co)variance component estimation and pathway enrichment analysis, and characterised gene expression levels and the extent of allelic expression imbalance. Presence of heritable variation for milk yield was confirmed. There was no significant genetic correlation between milk yield and mastitis traits. Environmental factors appeared to favour both milk production and udder health. There were no overlapping of SNPs associated with mastitis resistance and milk yield in Chios sheep. Furthermore, four distinct Quantitative Trait Loci (QTLs) affecting milk yield were detected on chromosomes 2, 12, 16 and 19, in locations other than those previously identified to affect mastitis resistance. Five genes (DNAJA1, GHR, LYPLA1, NUP35 and OXCT1) located within the QTL regions were highly expressed in both the mammary gland and milk transcriptome, suggesting involvement in milk synthesis and production. Furthermore, the expression of two of these genes (NUP35 and OXCT1) was enriched in immune tissues implying a potentially pleiotropic effect or likely role in milk production during udder infection, which needs to be further elucidated in future studies. In conclusion, the absence of genetic antagonism between milk yield and mastitis resistance suggests that simultaneous genetic improvement of both traits be achievable.

Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease

The MHC region is highly associated with autoimmune and infectious diseases. Here we conduct an in-depth interrogation of associations between genetic variation, gene expression and disease. We create a comprehensive map of regulatory variation in the MHC region using WGS from 419 individuals to call eight-digit HLA types and RNA-seq data from matched iPSCs. Building on this regulatory map, we explored GWAS signals for 4083 traits, detecting colocalization for 180 disease loci with eQTLs. We show that eQTL analyses taking HLA type haplotypes into account have substantially greater power compared with only using single variants. We examined the association between the 8.1 ancestral haplotype and delayed colonization in Cystic Fibrosis, postulating that downregulation of RNF5 expression is the likely causal mechanism. Our study provides insights into the genetic architecture of the MHC region and pinpoints disease associations that are due to differential expression of HLA genes and non-HLA genes.

Modeling allele-specific expression at the gene and SNP levels simultaneously by a Bayesian logistic mixed regression model

BACKGROUND

High-throughput sequencing experiments, which can determine allele origins, have been used to assess genome-wide allele-specific expression. Despite the amount of data generated from high-throughput experiments, statistical methods are often too simplistic to understand the complexity of gene expression. Specifically, existing methods do not test allele-specific expression (ASE) of a gene as a whole and variation in ASE within a gene across exons separately and simultaneously.

RESULTS

We propose a generalized linear mixed model to close these gaps, incorporating variations due to genes, single nucleotide polymorphisms (SNPs), and biological replicates. To improve reliability of statistical inferences, we assign priors on each effect in the model so that information is shared across genes in the entire genome. We utilize Bayesian model selection to test the hypothesis of ASE for each gene and variations across SNPs within a gene. We apply our method to four tissue types in a bovine study to de novo detect ASE genes in the bovine genome, and uncover intriguing predictions of regulatory ASEs across gene exons and across tissue types. We compared our method to competing approaches through simulation studies that mimicked the real datasets. The R package, BLMRM, that implements our proposed algorithm, is publicly available for download at https://github.com/JingXieMIZZOU/BLMRM .

CONCLUSIONS

We will show that the proposed method exhibits improved control of the false discovery rate and improved power over existing methods when SNP variation and biological variation are present. Besides, our method also maintains low computational requirements that allows for whole genome analysis.

Allele-specific NKX2-5 binding underlies multiple genetic associations with human electrocardiographic traits

The cardiac transcription factor (TF) gene NKX2-5 has been associated with electrocardiographic (EKG) traits through genome-wide association studies (GWASs), but the extent to which differential binding of NKX2-5 at common regulatory variants contributes to these traits has not yet been studied. We analyzed transcriptomic and epigenomic data from induced pluripotent stem cell-derived cardiomyocytes from seven related individuals, and identified ~2,000 single-nucleotide variants associated with allele-specific effects (ASE-SNVs) on NKX2-5 binding. NKX2-5 ASE-SNVs were enriched for altered TF motifs, for heart-specific expression quantitative trait loci and for EKG GWAS signals. Using fine-mapping combined with epigenomic data from induced pluripotent stem cell-derived cardiomyocytes, we prioritized candidate causal variants for EKG traits, many of which were NKX2-5 ASE-SNVs. Experimentally characterizing two NKX2-5 ASE-SNVs (rs3807989 and rs590041) showed that they modulate the expression of target genes via differential protein binding in cardiac cells, indicating that they are functional variants underlying EKG GWAS signals. Our results show that differential NKX2-5 binding at numerous regulatory variants across the genome contributes to EKG phenotypes.

Elimination of Reference Mapping Bias Reveals Robust Immune Related Allele-Specific Expression in Crossbred Sheep

Pervasive allelic variation at both gene and single nucleotide level (SNV) between individuals is commonly associated with complex traits in humans and animals. Allele-specific expression (ASE) analysis, using RNA-Seq, can provide a detailed annotation of allelic imbalance and infer the existence of cis-acting transcriptional regulation. However, variant detection in RNA-Seq data is compromised by biased mapping of reads to the reference DNA sequence. In this manuscript, we describe an unbiased standardized computational pipeline for allele-specific expression analysis using RNA-Seq data, which we have adapted and developed using tools available under open license. The analysis pipeline we present is designed to minimize reference bias while providing accurate profiling of allele-specific expression across tissues and cell types. Using this methodology, we were able to profile pervasive allelic imbalance across tissues and cell types, at both the gene and SNV level, in Texel×Scottish Blackface sheep, using the sheep gene expression atlas data set. ASE profiles were pervasive in each sheep and across all tissue types investigated. However, ASE profiles shared across tissues were limited, and instead, they tended to be highly tissue-specific. These tissue-specific ASE profiles may underlie the expression of economically important traits and could be utilized as weighted SNVs, for example, to improve the accuracy of genomic selection in breeding programs for sheep. An additional benefit of the pipeline is that it does not require parental genotypes and can therefore be applied to other RNA-Seq data sets for livestock, including those available on the Functional Annotation of Animal Genomes (FAANG) data portal. This study is the first global characterization of moderate to extreme ASE in tissues and cell types from sheep. We have applied a robust methodology for ASE profiling to provide both a novel analysis of the multi-dimensional sheep gene expression atlas data set and a foundation for identifying the regulatory and expressed elements of the genome that are driving complex traits in livestock.

Hybrid gene misregulation in multiple developing tissues within a recent adaptive radiation of Cyprinodon pupfishes

Genetic incompatibilities constitute the final stages of reproductive isolation and speciation, but little is known about incompatibilities that occur within recent adaptive radiations among closely related diverging populations. Crossing divergent species to form hybrids can break up coadapted variation, resulting in genetic incompatibilities within developmental networks shaping divergent adaptive traits. We crossed two closely related sympatric Cyprinodon pupfish species–a dietary generalist and a specialized molluscivore–and measured expression levels in their F1 hybrids to identify regulatory variation underlying the novel craniofacial morphology found in this recent microendemic adaptive radiation. We extracted mRNA from eight day old whole-larvae tissue and from craniofacial tissues dissected from 17–20 day old larvae to compare gene expression between a total of seven F1 hybrids and 24 individuals from parental species populations. We found 3.9% of genes differentially expressed between generalists and molluscivores in whole-larvae tissues and 0.6% of genes differentially expressed in craniofacial tissue. We found that 2.1% of genes were misregulated in whole-larvae hybrids whereas 19.1% of genes were misregulated in hybrid craniofacial tissues, after correcting for sequencing biases. We also measured allele specific expression across 15,429 heterozygous sites to identify putative compensatory regulatory mechanisms underlying differential expression between generalists and molluscivores. Together, our results highlight the importance of considering misregulation as an early indicator of genetic incompatibilities in the context of rapidly diverging adaptive radiations and suggests that compensatory regulatory divergence drives hybrid gene misregulation in developing tissues that give rise to novel craniofacial traits.

Allelic Switching of DLX5, GRB10, and SVOPL during Colorectal Cancer Tumorigenesis

Allele-specific expression (ASE) is found in approximately 20-30% of human genes. During tumorigenesis, ASE changes due to somatic alterations that change the regulatory landscape. In colorectal cancer (CRC), many chromosomes show frequent gains or losses while homozygosity of chromosome 7 is rare. We hypothesized that genes essential to survival show allele-specific expression (ASE) on both alleles of chromosome 7. Using a panel of 21 recently established low-passage CRC cell lines, we performed ASE analysis by hybridizing DNA and cDNA to Infinium HumanExome-12 v1 BeadChips containing cSNPs in 392 chromosome 7 genes. The results of this initial analysis were extended and validated in a set of 89 paired normal mucosa and CRC samples. We found that 14% of genes showed ASE in one or more cell lines and identified allelic switching of the potential cell survival genes DLX5, GRB10, and SVOPL on chromosome 7, whereby the most abundantly expressed allele in the normal tissue is the lowest expressed allele in the tumor and vice versa. We established that this allelic switch does not result from loss of imprinting. The allelic switching of SVOPL may be a result of transcriptional downregulation, while the exact mechanisms resulting in the allelic switching of DLX5 and GRB10 remain to be elucidated. In conclusion, our results show that profound changes take place in allelic transcriptional regulation during the tumorigenesis of CRC.

Subtle changes in chromatin loop contact propensity are associated with differential gene regulation and expression

While genetic variation at chromatin loops is relevant for human disease, the relationships between contact propensity (the probability that loci at loops physically interact), genetics, and gene regulation are unclear. We quantitatively interrogate these relationships by comparing Hi-C and molecular phenotype data across cell types and haplotypes. While chromatin loops consistently form across different cell types, they have subtle quantitative differences in contact frequency that are associated with larger changes in gene expression and H3K27ac. For the vast majority of loci with quantitative differences in contact frequency across haplotypes, the changes in magnitude are smaller than those across cell types; however, the proportional relationships between contact propensity, gene expression, and H3K27ac are consistent. These findings suggest that subtle changes in contact propensity have a biologically meaningful role in gene regulation and could be a mechanism by which regulatory genetic variants in loop anchors mediate effects on expression.

Allele-Specific Expression Analysis in Cancer Using Next-Generation Sequencing Data

Allele-specific expression arises when transcriptional activity at the different alleles of a gene differs considerably. Although extensive research has been carried out to detect and characterize this phenomenon, the landscape of allele-specific expression in cancer is still poorly understood. In this chapter, we describe a fast and reliable analysis pipeline to study allele-specific expression in cancer using next-generation sequencing data. The pipeline provides a gene-level analysis approach that exploits paired germline DNA and tumor RNA sequencing data and benefits from parallel computation resources when available.

Pseudogenization and Resurrection of a Speciation Gene

A persistent question in evolutionary biology is how complex phenotypes evolve and whether phenotypic transitions are reversible. Multiple losses of floral pigmentation have been documented in the angiosperms, but color re-gain has not yet been described, supporting that re-gain is unlikely. Pollinator-mediated selection in Petunia has resulted in several color shifts comprised of both losses and gains of color. The R2R3-MYB transcription factor AN2 has been identified as a major locus responsible for shifts in pollinator preference. Whereas the loss of visible color has previously been attributed to repeated pseudogenization of AN2, here, we describe the mechanism of an independent re-gain of floral color via AN2 evolution. In P. secreta, purple color is restored through the improbable resurrection of AN2 gene function from a non-functional AN2-ancestor by a single reading-frame-restoring mutation. Thus, floral color evolution in Petunia is mechanistically dependent on AN2 functionality, highlighting its role as a hotspot in color transitions and a speciation gene for the genus.

Behavior-dependent cis regulation reveals genes and pathways associated with bower building in cichlid fishes

Many behaviors are associated with heritable genetic variation [Kendler and Greenspan (2006) Am J Psychiatry 163:1683-1694]. Genetic mapping has revealed genomic regions or, in a few cases, specific genes explaining part of this variation [Bendesky and Bargmann (2011) Nat Rev Gen 12:809-820]. However, the genetic basis of behavioral evolution remains unclear. Here we investigate the evolution of an innate extended phenotype, bower building, among cichlid fishes of Lake Malawi. Males build bowers of two types, pits or castles, to attract females for mating. We performed comparative genome-wide analyses of 20 bower-building species and found that these phenotypes have evolved multiple times with thousands of genetic variants strongly associated with this behavior, suggesting a polygenic architecture. Remarkably, F₁ hybrids of a pit-digging and a castle-building species perform sequential construction of first a pit and then a castle bower. Analysis of brain gene expression in these hybrids showed that genes near behavior-associated variants display behavior-dependent allele-specific expression with preferential expression of the pit-digging species allele during pit digging and of the castle-building species allele during castle building. These genes are highly enriched for functions related to neurodevelopment and neural plasticity. Our results suggest that natural behaviors are associated with complex genetic architectures that alter behavior via cis-regulatory differences whose effects on gene expression are specific to the behavior itself.