Assessing allele-specific expression across multiple tissues from RNA-seq read data

Molecular quantitative trait loci in reproductive tissues impact male fertility in cattle

Breeding bulls are well suited to investigate inherited variation in male fertility because they are genotyped and their reproductive success is monitored through semen analyses and thousands of artificial inseminations. However, functional data from relevant tissues are lacking in cattle, which prevents fine-mapping fertility-associated genomic regions. Here, we characterize gene expression and splicing variation in testis, epididymis, and vas deferens transcriptomes of 118 mature bulls and conduct association tests between 414,667 molecular phenotypes and 21,501,032 genome-wide variants to identify 41,156 regulatory loci. We show broad consensus in tissue-specific and tissue-enriched gene expression between the three bovine tissues and their human and murine counterparts. Expression- and splicing-mediating variants are more than three times as frequent in testis than epididymis and vas deferens, highlighting the transcriptional complexity of testis. Finally, we identify genes (WDR19, SPATA16, KCTD19, ZDHHC1) and molecular phenotypes that are associated with quantitative variation in male fertility through transcriptome-wide association and colocalization analyses.

Transcriptomics and chromatin accessibility in multiple African population samples

Mapping the functional human genome and impact of genetic variants is often limited to European-descendent population samples. To aid in overcoming this limitation, we measured gene expression using RNA sequencing in lymphoblastoid cell lines (LCLs) from 599 individuals from six African populations to identify novel transcripts including those not represented in the hg38 reference genome. We used whole genomes from the 1000 Genomes Project and 164 Maasai individuals to identify 8,881 expression and 6,949 splicing quantitative trait loci (eQTLs/sQTLs), and 2,611 structural variants associated with gene expression (SV-eQTLs). We further profiled chromatin accessibility using ATAC-Seq in a subset of 100 representative individuals, to identity chromatin accessibility quantitative trait loci (caQTLs) and allele-specific chromatin accessibility, and provide predictions for the functional effect of 78.9 million variants on chromatin accessibility. Using this map of eQTLs and caQTLs we fine-mapped GWAS signals for a range of complex diseases. Combined, this work expands global functional genomic data to identify novel transcripts, functional elements and variants, understand population genetic history of molecular quantitative trait loci, and further resolve the genetic basis of multiple human traits and disease.

Hybrid Epigenomes Reveal Extensive Local Genetic Changes to Chromatin Accessibility Contribute to Divergence in Embryonic Gene Expression Between Species

Chromatin accessibility plays an important role in shaping gene expression, yet little is known about the genetic and molecular mechanisms that influence the evolution of chromatin configuration. Both local (cis) and distant (trans) genetic influences can in principle influence chromatin accessibility and are based on distinct molecular mechanisms. We, therefore, sought to characterize the role that each of these plays in altering chromatin accessibility in 2 closely related sea urchin species. Using hybrids of Heliocidaris erythrogramma and Heliocidaris tuberculata, and adapting a statistical framework previously developed for the analysis of cis and trans influences on the transcriptome, we examined how these mechanisms shape the regulatory landscape at 3 important developmental stages, and compared our results to similar analyses of the transcriptome. We found extensive cis- and trans-based influences on evolutionary changes in chromatin, with cis effects generally larger in effect. Evolutionary changes in accessibility and gene expression are correlated, especially when expression has a local genetic basis. Maternal influences appear to have more of an effect on chromatin accessibility than on gene expression, persisting well past the maternal-to-zygotic transition. Chromatin accessibility near gene regulatory network genes appears to be distinctly regulated, with trans factors appearing to play an outsized role in the configuration of chromatin near these genes. Together, our results represent the first attempt to quantify cis and trans influences on evolutionary divergence in chromatin configuration in an outbred natural study system and suggest that chromatin regulation is more genetically complex than was previously appreciated.

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale

MOTIVATION

Analysis of allele-specific expression is strongly affected by the technical noise present in RNA-seq experiments. Previously, we showed that technical replicates can be used for precise estimates of this noise, and we provided a tool for correction of technical noise in allele-specific expression analysis. This approach is very accurate but costly due to the need for two or more replicates of each library. Here, we develop a spike-in approach which is highly accurate at only a small fraction of the cost.

RESULTS

We show that a distinct RNA added as a spike-in before library preparation reflects technical noise of the whole library and can be used in large batches of samples. We experimentally demonstrate the effectiveness of this approach using combinations of RNA from species distinguishable by alignment, namely, mouse, human, and Caenorhabditis elegans. Our new approach, controlFreq, enables highly accurate and computationally efficient analysis of allele-specific expression in (and between) arbitrarily large studies at an overall cost increase of ∼5%.

AVAILABILITY AND IMPLEMENTATION

Analysis pipeline for this approach is available at GitHub as R package controlFreq (github.com/gimelbrantlab/controlFreq).

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Evolution of Spatial and Temporal cis-Regulatory Divergence in Sticklebacks

Cis-regulatory changes are thought to play a major role in adaptation. Threespine sticklebacks have repeatedly colonized freshwater habitats in the Northern Hemisphere, where they have evolved a suite of phenotypes that distinguish them from marine populations, including changes in physiology, behavior, and morphology. To understand the role of gene regulatory evolution in adaptive divergence, here we investigate cis-regulatory changes in gene expression between marine and freshwater ecotypes through allele-specific expression (ASE) in F1 hybrids. Surveying seven ecologically relevant tissues, including three sampled across two developmental stages, we identified cis-regulatory divergence affecting a third of genes, nearly half of which were tissue-specific. Next, we compared allele-specific expression in dental tissues at two timepoints to characterize cis-regulatory changes during development between marine and freshwater fish. Applying a genome-wide test for selection on cis-regulatory changes, we find evidence for lineage-specific selection on several processes between ecotypes, including the Wnt signaling pathway in dental tissues. Finally, we show that genes with ASE, particularly those that are tissue-specific, are strongly enriched in genomic regions of repeated marine-freshwater divergence, supporting an important role for these cis-regulatory differences in parallel adaptive evolution of sticklebacks to freshwater habitats. Altogether, our results provide insight into the cis-regulatory landscape of divergence between stickleback ecotypes across tissues and during development, and support a fundamental role for tissue-specific cis-regulatory changes in rapid adaptation to new environments.

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale

Motivation

Analysis of allele-specific expression is strongly affected by the technical noise present in RNA-seq experiments. Previously, we showed that technical replicates can be used for precise estimates of this noise, and we provided a tool for correction of technical noise in allele-specific expression analysis. This approach is very accurate but costly due to the need for two or more replicates of each library. Here, we develop a spike-in approach that is highly accurate at only a small fraction of the cost.

Results

We show that a distinct RNA added as a spike-in before library preparation reflects technical noise of the whole library and can be used in large batches of samples. We experimentally demonstrate the effectiveness of this approach using combinations of RNA from species distinguishable by alignment, namely, mouse, human, and C.elegans . Our new approach, controlFreq , enables highly accurate and computationally efficient analysis of allele-specific expression in (and between) arbitrarily large studies at an overall cost increase of ~ 5%.

Availability

Analysis pipeline for this approach is available at GitHub as R package controlFreq ( github.com/gimelbrantlab/controlFreq ).

Contact

agimelbrant@altius.org.

Hybrid epigenomes reveal extensive local genetic changes to chromatin accessibility contribute to divergence in embryonic gene expression between species

Chromatin accessibility plays an important role in shaping gene expression patterns across development and evolution; however, little is known about the genetic and molecular mechanisms that influence chromatin configuration itself. Because cis and trans influences can both theoretically influence the accessibility of the epigenome, we sought to better characterize the role that both of these mechanisms play in altering chromatin accessibility in two closely related sea urchin species. Using hybrids of the two species, and adapting a statistical framework previously developed for the analysis of cis and trans influences on the transcriptome, we examined how these mechanisms shape the regulatory landscape at three important developmental stages, and compared our results to similar patterns in the transcriptome. We found extensive cis- and trans-based influences on evolutionary changes in chromatin, with cis effects slightly more numerous and larger in effect. Genetic mechanisms influencing gene expression and chromatin configuration are correlated, but differ in several important ways. Maternal influences also appear to have more of an effect on chromatin accessibility than on gene expression, persisting well past the maternal-to-zygotic transition. Furthermore, chromatin accessibility near GRN genes appears to be regulated differently than the rest of the epigenome, and indicates that trans factors may play an outsized role in the configuration of chromatin near these genes. Together, our results represent the first attempt to quantify cis and trans influences on evolutionary divergence in chromatin configuration in an outbred natural study system, and suggest that the regulation of chromatin is more genetically complex than was previously appreciated.

Recombination affects allele-specific expression of deleterious variants in human populations

How the genetic composition of a population changes through stochastic processes, such as genetic drift, in combination with deterministic processes, such as selection, is critical to understanding how phenotypes vary in space and time. Here, we show how evolutionary forces affecting selection, including recombination and effective population size, drive genomic patterns of allele-specific expression (ASE). Integrating tissue-specific genotypic and transcriptomic data from 1500 individuals from two different cohorts, we demonstrate that ASE is less often observed in regions of low recombination, and loci in high or normal recombination regions are more efficient at using ASE to underexpress harmful mutations. By tracking genetic ancestry, we discriminate between ASE variability due to past demographic effects, including subsequent bottlenecks, versus local environment. We observe that ASE is not randomly distributed along the genome and that population parameters influencing the efficacy of natural selection alter ASE levels genome wide.

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.

BrewerIX enables allelic expression analysis of imprinted and X-linked genes from bulk and single-cell transcriptomes

Genomic imprinting and X chromosome inactivation (XCI) are two prototypical epigenetic mechanisms whereby a set of genes is expressed mono-allelically in order to fine-tune their expression levels. Defects in genomic imprinting have been observed in several neurodevelopmental disorders, in a wide range of tumours and in induced pluripotent stem cells (iPSCs). Single Nucleotide Variants (SNVs) are readily detectable by RNA-sequencing allowing the determination of whether imprinted or X-linked genes are aberrantly expressed from both alleles, although standardised analysis methods are still missing. We have developed a tool, named BrewerIX, that provides comprehensive information about the allelic expression of a large, manually-curated set of imprinted and X-linked genes. BrewerIX does not require programming skills, runs on a standard personal computer, and can analyze both bulk and single-cell transcriptomes of human and mouse cells directly from raw sequencing data. BrewerIX confirmed previous observations regarding the bi-allelic expression of some imprinted genes in naive pluripotent cells and extended them to preimplantation embryos. BrewerIX also identified misregulated imprinted genes in breast cancer cells and in human organoids and identified genes escaping XCI in human somatic cells. We believe BrewerIX will be useful for the study of genomic imprinting and XCI during development and reprogramming, and for detecting aberrations in cancer, iPSCs and organoids. Due to its ease of use to non-computational biologists, its implementation could become standard practice during sample assessment, thus raising the robustness and reproducibility of future studies.

BrewerIX is an easy-to-use computational tool that can assess bi-allelic expression of imprinted and X-linked genes from RNA-seq data.

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.

Genome-wide association analyses highlight etiological differences underlying newly defined subtypes of diabetes

Type 2 diabetes has been reproducibly clustered into five subtypes with different disease progression and risk of complications; however, etiological differences are unknown. We used genome-wide association and genetic risk score (GRS) analysis to compare the underlying genetic drivers. Individuals from the Swedish ANDIS (All New Diabetics In Scania) study were compared to individuals without diabetes; the Finnish DIREVA (Diabetes register in Vasa) and Botnia studies were used for replication. We show that subtypes differ with regard to family history of diabetes and association with GRS for diabetes-related traits. The severe insulin-resistant subtype was uniquely associated with GRS for fasting insulin but not with variants in the TCF7L2 locus or GRS reflecting insulin secretion. Further, an SNP (rs10824307) near LRMDA was uniquely associated with mild obesity-related diabetes. Therefore, we conclude that the subtypes have partially distinct genetic backgrounds indicating etiological differences.

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.

Biased allelic expression in tissues of F1 hybrids between tropical and temperate lotus (Nelumbo nuicfera)

The genome-wide allele-specific expression in F1 hybrids from the cross of tropical and temperate lotus unveils how cis-regulatory divergences affect genes in key pathways related to ecotypic divergence. Genetic variation, particularly cis-regulatory variation, plays a crucial role in phenotypic variation and adaptive evolution in plants. Temperate and tropical lotus, the two ecotypes of Nelumbo nucifera, show distinction in the degree of rhizome enlargement, which is associated with winter dormancy. To understand the roles of genome-wide cis-regulatory divergences on adaptive evolution of temperate and tropical lotus (Nelumbo nucifera), here we performed allele-specific expression (ASE) analyses on the tissues including flowers, leaves and rhizome from F1 hybrids of tropical and temperate lotus. For all investigated tissues in F1s, about 36% of genes showed ASE and about 3% of genes showed strong consistent ASE. Most of ASEs were biased towards the tropical parent in all surveyed samples, indicating that the tropical genome might be dominant over the temperate genome in gene expression of tissues from their F1 hybrids. We found that promoter sequences with similar allelic expression are more conserved than genes with significant or conditional ASE, suggesting the cis-regulatory sequence divergence underlie the allelic expression bias. We further uncovered biased genes being related to phenotypic differentiation between two lotus ecotypes, especially metabolic and phytohormone-related pathways in the rhizome. Overall, our study provides a global landscape of cis-regulatory variations between two lotus ecotypes and highlights their roles in rhizome growth variation for the climatic adaptation.

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.

aScan: A Novel Method for the Study of Allele Specific Expression in Single Individuals

In diploid organisms, two copies of each allele are normally inherited from parents. Paternal and maternal alleles can be regulated and expressed unequally, which is referred to as allele-specific expression (ASE). In this work, we present aScan, a novel method for the identification of ASE from the analysis of matched individual genomic and RNA sequencing data. By performing extensive analyses of both real and simulated data, we demonstrate that aScan can correctly identify ASE with high accuracy and sensitivity in different experimental settings. Additionally, by applying our method to a small cohort of individuals that are not included in publicly available databases of human genetic variation, we outline the value of possible applications of ASE analysis in single individuals for deriving a more accurate annotation of "private" low-frequency genetic variants associated with regulatory effects on transcription. All in all, we believe that aScan will represent a beneficial addition to the set of bioinformatics tools for the analysis of ASE. Finally, while our method was initially conceived for the analysis of RNA-seq data, it can in principle be applied to any quantitative NGS assay for which matched genotypic and expression data are available. AVAILABILITY: aScan is currently available in the form of an open source standalone software package at: https://github.com/Federico77z/aScan/. aScan version 1.0.3, available at https://github.com/Federico77z/aScan/releases/tag/1.0.3, has been used for all the analyses included in this manuscript. A Docker image of the tool has also been made available at https://github.com/pmandreoli/aScanDocker.

Epigenetics of Muscle- and Brain-Specific Expression of KLHL Family Genes

KLHL and the related KBTBD genes encode components of the Cullin-E3 ubiquitin ligase complex and typically target tissue-specific proteins for degradation, thereby affecting differentiation, homeostasis, metabolism, cell signaling, and the oxidative stress response. Despite their importance in cell function and disease (especially, KLHL40, KLHL41, KBTBD13, KEAP1, and ENC1), previous studies of epigenetic factors that affect transcription were predominantly limited to promoter DNA methylation. Using diverse tissue and cell culture whole-genome profiles, we examined 17 KLHL or KBTBD genes preferentially expressed in skeletal muscle or brain to identify tissue-specific enhancer and promoter chromatin, open chromatin (DNaseI hypersensitivity), and DNA hypomethylation. Sixteen of the 17 genes displayed muscle- or brain-specific enhancer chromatin in their gene bodies, and most exhibited specific intergenic enhancer chromatin as well. Seven genes were embedded in super-enhancers (particularly strong, tissue-specific clusters of enhancers). The enhancer chromatin regions typically displayed foci of DNA hypomethylation at peaks of open chromatin. In addition, we found evidence for an intragenic enhancer in one gene upregulating expression of its neighboring gene, specifically for KLHL40/HHATL and KLHL38/FBXO32 gene pairs. Many KLHL/KBTBD genes had tissue-specific promoter chromatin at their 5' ends, but surprisingly, two (KBTBD11 and KLHL31) had constitutively unmethylated promoter chromatin in their 3' exons that overlaps a retrotransposed KLHL gene. Our findings demonstrate the importance of expanding epigenetic analyses beyond the 5' ends of genes in studies of normal and abnormal gene regulation.

OneStopRNAseq: A Web Application for Comprehensive and Efficient Analyses of RNA-Seq Data

Over the past decade, a large amount of RNA sequencing (RNA-seq) data were deposited in public repositories, and more are being produced at an unprecedented rate. However, there are few open source tools with point-and-click interfaces that are versatile and offer streamlined comprehensive analysis of RNA-seq datasets. To maximize the capitalization of these vast public resources and facilitate the analysis of RNA-seq data by biologists, we developed a web application called OneStopRNAseq for the one-stop analysis of RNA-seq data. OneStopRNAseq has user-friendly interfaces and offers workflows for common types of RNA-seq data analyses, such as comprehensive data-quality control, differential analysis of gene expression, exon usage, alternative splicing, transposable element expression, allele-specific gene expression quantification, and gene set enrichment analysis. Users only need to select the desired analyses and genome build, and provide a Gene Expression Omnibus (GEO) accession number or Dropbox links to sequence files, alignment files, gene-expression-count tables, or rank files with the corresponding metadata. Our pipeline facilitates the comprehensive and efficient analysis of private and public RNA-seq data.

Enhancer occlusion transcripts regulate the activity of human enhancer domains via transcriptional interference: a computational perspective

Analysis of ENCODE long RNA-Seq and ChIP-seq (Chromatin Immunoprecipitation Sequencing) datasets for HepG2 and HeLa cell lines uncovered 1647 and 1958 transcripts that interfere with transcription factor binding to human enhancer domains. TFBSs (Transcription Factor Binding Sites) intersected by these 'Enhancer Occlusion Transcripts' (EOTrs) displayed significantly lower relative transcription factor (TF) binding affinities compared to TFBSs for the same TF devoid of EOTrs. Expression of most EOTrs was regulated in a cell line specific manner; analysis for the same TFBSs across cell lines, i.e. in the absence or presence of EOTrs, yielded consistently higher relative TF/DNA-binding affinities for TFBSs devoid of EOTrs. Lower activities of EOTr-associated enhancer domains coincided with reduced occupancy levels for histone tail modifications H3K27ac and H3K9ac. Similarly, the analysis of EOTrs with allele-specific expression identified lower activities for alleles associated with EOTrs. ChIA-PET (Chromatin Interaction Analysis by Paired-End Tag Sequencing) and 5C (Carbon Copy Chromosome Conformation Capture) uncovered that enhancer domains associated with EOTrs preferentially interacted with poised gene promoters. Analysis of EOTr regions with GRO-seq (Global run-on) data established the correlation of RNA polymerase pausing and occlusion of TF-binding. Our results implied that EOTr expression regulates human enhancer domains via transcriptional interference.

Synonymous GATA2 mutations result in selective loss of mutated RNA and are common in patients with GATA2 deficiency

Deficiency of the transcription factor GATA2 is a highly penetrant genetic disorder predisposing to myelodysplastic syndromes (MDS) and immunodeficiency. It has been recognized as the most common cause underlying primary MDS in children. Triggered by the discovery of a recurrent synonymous GATA2 variant, we systematically investigated 911 patients with phenotype of pediatric MDS or cellular deficiencies for the presence of synonymous alterations in GATA2. In total, we identified nine individuals with five heterozygous synonymous mutations: c.351C>G, p.T117T (N = 4); c.649C>T, p.L217L; c.981G>A, p.G327G; c.1023C>T, p.A341A; and c.1416G>A, p.P472P (N = 2). They accounted for 8.2% (9/110) of cases with GATA2 deficiency in our cohort and resulted in selective loss of mutant RNA. While for the hotspot mutation (c.351C>G) a splicing error leading to RNA and protein reduction was identified, severe, likely late stage RNA loss without splicing disruption was found for other mutations. Finally, the synonymous mutations did not alter protein function or stability. In summary, synonymous GATA2 substitutions are a new common cause of GATA2 deficiency. These findings have broad implications for genetic counseling and pathogenic variant discovery in Mendelian disorders.

Genetic basis for divergence in developmental gene expression in two closely related sea urchins

The genetic basis for divergence in developmental gene expression among species is poorly understood, despite growing evidence that such changes underlie many interesting traits. Here we quantify transcription in hybrids of Heliocidaris tuberculata and Heliocidaris erythrogramma, two closely related sea urchins with highly divergent developmental gene expression and life histories. We find that most expression differences between species result from genetic influences that affect one stage of development, indicating limited pleiotropic consequences for most mutations that contribute to divergence in gene expression. Activation of zygotic transcription is broadly delayed in H. erythrogramma, the species with the derived life history, despite its overall faster premetamorphic development. Altered expression of several terminal differentiation genes associated with the derived larval morphology of H. erythrogramma is based largely on differences in the expression or function of their upstream regulators, providing insights into the genetic basis for the evolution of key life history traits.

Wild Dictyostelium discoideum social amoebae show plastic responses to the presence of nonrelatives during multicellular development

When multiple strains of microbes form social groups, such as the multicellular fruiting bodies of Dictyostelium discoideum, conflict can arise regarding cell fate. Both fixed and plastic differences among strains can contribute to cell fate, and plastic responses may be particularly important if social environments frequently change. We used RNA-sequencing and photographic time series analysis to detect possible conflict-induced plastic differences between wild D. discoideum aggregates formed by single strains compared with mixed pairs of strains (chimeras). We found one hundred and two differentially expressed genes that were enriched for biological processes including cytoskeleton organization and cyclic AMP response (up-regulated in chimeras), and DNA replication and cell cycle (down-regulated in chimeras). In addition, our data indicate that in reference to a time series of multicellular development in the laboratory strain AX4, chimeras may be slightly behind clonal aggregates in their development. Finally, phenotypic analysis supported slower splitting of aggregates and a nonsignificant trend for larger group sizes in chimeras. The transcriptomic comparison and phenotypic analyses support discoordination among aggregate group members due to social conflict. These results are consistent with previously observed factors that affect cell fate decision in D. discoideum and provide evidence for plasticity in cAMP signaling and phenotypic coordination during development in response to social conflict in D. discoideum and similar microbial social groups.

Leveraging allelic imbalance to refine fine-mapping for eQTL studies

Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. Previous studies have found enrichment of expression quantitative trait loci (eQTLs) in disease risk loci, indicating that identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allelic imbalance (AIM) that measures imbalance in gene expression on two chromosomes of a diploid organism. In this work, we develop a novel statistical method that leverages both AIM and total expression data to detect causal variants that regulate gene expression. We illustrate through simulations and application to 10 tissues of the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. Across all tissues and genes, our method achieves a median reduction rate of 11% in the number of putative causal variants. We use chromatin state data from the Roadmap Epigenomics Consortium to show that the putative causal variants identified by our method are enriched for active regions of the genome, providing orthogonal support that our method identifies causal variants with increased specificity.

In recent years, many studies have identified genetic variants that are associated with the expression of genes (eQTLs). While thousands of eQTLs have been identified, not all associated variants cause changes in gene expression. This is in part due to the complex patterns of genetic correlation in the human genome. If a region of the genome contains many genetic variants that are highly correlated with each other, non-causal genetic variants close to a causal variant are also correlated with gene expression. Statistical fine-mapping is the process of identifying true causal variants from a set of candidate variants. In regions with high genetic correlation, previous fine-mapping methods may not be able to differentiate causal variants from nearby variants. We propose a method that utilizes a complementary source of information called allelic imbalance (AIM). We show that by combining eQTL and AIM data, we can identify the true causal variants more efficiently and substantially decrease the number of putative causal variants for downstream analysis.

Evolution of cis- and trans-regulatory divergence in the chicken genome between two contrasting breeds analyzed using three tissue types at one-day-old

Background

Gene expression variation is a key underlying factor influencing phenotypic variation, and can occur via cis- or trans-regulation. To understand the role of cis- and trans-regulatory variation on population divergence in chicken, we developed reciprocal crosses of two chicken breeds, White Leghorn and Cornish Game, which exhibit major differences in body size and reproductive traits, and used them to determine the degree of cis versus trans variation in the brain, liver, and muscle tissue of male and female 1-day-old specimens.

Results

We provided an overview of how transcriptomes are regulated in hybrid progenies of two contrasting breeds based on allele specific expression analysis. Compared with cis-regulatory divergence, trans-acting genes were more extensive in the chicken genome. In addition, considerable compensatory cis- and trans-regulatory changes exist in the chicken genome. Most importantly, stronger purifying selection was observed on genes regulated by trans-variations than in genes regulated by the cis elements.

Conclusions

We present a pipeline to explore allele-specific expression in hybrid progenies of inbred lines without a specific reference genome. Our research is the first study to describe the regulatory divergence between two contrasting breeds. The results suggest that artificial selection associated with domestication in chicken could have acted more on trans-regulatory divergence than on cis-regulatory divergence.

Pervasive Inter-Individual Variation in Allele-Specific Expression in Monozygotic Twins

Despite being developed from one zygote, heterokaryotypic monozygotic (MZ) co-twins exhibit discordant karyotypes. Epigenomic studies in biological samples from heterokaryotypic MZ co-twins are of the most significant value for assessing the effects on gene- and allele-specific expression of an extranumerary chromosomal copy or structural chromosomal disparities in otherwise nearly identical germline genetic contributions. Here, we use RNA-Seq data from existing repositories to establish within-pair correlations for the breadth and magnitude of allele-specific expression (ASE) in heterokaryotypic MZ co-twins discordant for trisomy 21 and maternal 21q inheritance, as well as homokaryotypic co-twins. We show that there is a genome-wide disparity at ASE sites between the heterokaryotypic MZ co-twins. Although most of the disparity corresponds to changes in the magnitude of biallelic imbalance, ASE sites switching from either strictly monoallelic to biallelic imbalance or the reverse occur in few genes that are known or predicted to be imprinted, subject to X-chromosome inactivation or A-to-I(G) RNA edited. We also uncovered comparable ASE differences between homokaryotypic MZ twins. The extent of ASE discordance in MZ twins (2.7%) was about 10-fold lower than the expected between pairs of unrelated, non-twin males or females. The results indicate that the observed within-pair dissimilarities in breadth and magnitude of ASE sites in the heterokaryotypic MZ co-twins could not solely be attributable to the aneuploidy and the missing allelic heritability at 21q.

ISoLDE: a data-driven statistical method for the inference of allelic imbalance in datasets with reciprocal crosses

MOTIVATION

Allelic imbalance (AI), i.e. the unequal expression of the alleles of the same gene in a single cell, affects a subset of genes in diploid organisms. One prominent example of AI is parental genomic imprinting, which results in parent-of-origin-dependent, mono-allelic expression of a limited number of genes in metatherian and eutherian mammals and in angiosperms. Currently available methods for identifying AI rely on data modeling and come with the associated limitations.

RESULTS

We have designed ISoLDE (Integrative Statistics of alleLe Dependent Expression), a novel nonparametric statistical method that takes into account both AI and the characteristics of RNA-seq data to infer allelic expression bias when at least two biological replicates are available for reciprocal crosses. ISoLDE learns the distribution of a specific test statistic from the data and calls genes 'allelically imbalanced', 'bi-allelically expressed' or 'undetermined'. Depending on the number of replicates, predefined thresholds or permutations are used to make calls. We benchmarked ISoLDE against published methods, and showed that ISoLDE compared favorably with respect to sensitivity, specificity and robustness to the number of replicates. Using ISoLDE on different RNA-seq datasets generated from hybrid mouse tissues, we did not discover novel imprinted genes (IGs), confirming the most conservative estimations of IG number.

AVAILABILITY AND IMPLEMENTATION

ISoLDE has been implemented as a Bioconductor package available at http://bioconductor.org/packages/ISoLDE/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Understanding human DNA variants affecting pre-mRNA splicing in the NGS era

Pre-mRNA splicing, an essential step in eukaryotic gene expression, relies on recognition of short sequences on the primary transcript intron ends and takes place along transcription by RNA polymerase II. Exonic and intronic auxiliary elements may modify the strength of exon definition and intron recognition. Splicing DNA variants (SV) have been associated with human genetic diseases at canonical intron sites, as well as exonic substitutions putatively classified as nonsense, missense or synonymous variants. Their effects on mRNA may be modulated by cryptic splice sites associated to the SV allele, comprehending exon skipping or shortening, and partial or complete intron retention. As splicing mRNA outputs result from combinatorial effects of both intrinsic and extrinsic factors, in vitro functional assays supported by computational analyses are recommended to assist SV pathogenicity assessment for human Mendelian inheritance diseases. The increasing use of next-generating sequencing (NGS) targeting full genomic gene sequence has raised awareness of the relevance of deep intronic SV in genetic diseases and inclusion of pseudo-exons into mRNA. Finally, we take advantage of recent advances in sequencing and computational technologies to analyze alternative splicing in cancer. We explore the Catalog of Somatic Mutations in Cancer (COSMIC) to describe the proportion of splice-site mutations in cis and trans regulatory elements. Genomic data from large cohorts of different cancer types are increasingly available, in addition to repositories of normal and somatic genetic variations. These are likely to bring new insights to understanding the genetic control of alternative splicing by mapping splicing quantitative trait loci in tumors.

Single nucleotide variant counts computed from RNA sequencing and cellular traffic into human kidney allografts

Advances in bioinformatics allow identification of single nucleotide polymorphisms (variants) from RNA sequence data. In an allograft biopsy, 2 genomes contribute to the RNA pool, 1 from the donor organ and the other from the infiltrating recipient's cells. We hypothesize that imbalances in genetic variants of RNA sequence data of kidney allograft biopsies provide an objective measure of cellular infiltration of the allograft. We performed mRNA sequencing of 40 kidney allograft biopsies, selected to represent a comprehensive range of diagnostic categories. We analyzed the sequencing reads of these biopsies and of 462 lymphoblastoid cell lines from the 1000 Genomes Project, for RNA variants. The ratio of heterozygous to nonreference genome homozygous variants (Het/Hom ratio) on all autosomes was determined for each sample, and the estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) score was computed as a complementary estimate of the degree of cellular infiltration into biopsies. The Het/Hom ratios (P = .02) and the ESTIMATE scores (P < .001) were associated with the biopsy diagnosis. Both measures correlated significantly (r = .67, P < .0001), even though the Het/Hom ratio is based on mRNA sequence variation, while the ESTIMATE score uses mRNA expression. Het/Hom ratio and the ESTIMATE score may offer unbiased and quantitative parameters for characterizing cellular traffic into human kidney allografts.

Developmental plasticity: Bridging research in evolution and human health

Early life experiences can have profound and persistent effects on traits expressed throughout the life course, with consequences for later life behavior, disease risk, and mortality rates. The shaping of later life traits by early life environments, known as 'developmental plasticity', has been well-documented in humans and non-human animals, and has consequently captured the attention of both evolutionary biologists and researchers studying human health. Importantly, the parallel significance of developmental plasticity across multiple fields presents a timely opportunity to build a comprehensive understanding of this phenomenon. We aim to facilitate this goal by highlighting key outstanding questions shared by both evolutionary and health researchers, and by identifying theory and empirical work from both research traditions that is designed to address these questions. Specifically, we focus on: (i) evolutionary explanations for developmental plasticity, (ii) the genetics of developmental plasticity and (iii) the molecular mechanisms that mediate developmental plasticity. In each section, we emphasize the conceptual gains in human health and evolutionary biology that would follow from filling current knowledge gaps using interdisciplinary approaches. We encourage researchers interested in developmental plasticity to evaluate their own work in light of research from diverse fields, with the ultimate goal of establishing a cross-disciplinary understanding of developmental plasticity.

Ancestral Variations of the PCDHG Gene Cluster Predispose to Dyslexia in a Multiplex Family

Dyslexia is a heritable neurodevelopmental disorder characterized by difficulties in reading and writing. In this study, we describe the identification of a set of 17 polymorphisms located across 1.9 Mb region on chromosome 5q31.3, encompassing genes of the PCDHG cluster, TAF7, PCDH1 and ARHGAP26, dominantly inherited with dyslexia in a multi-incident family. Strikingly, the non-risk form of seven variations of the PCDHG cluster, are preponderant in the human lineage, while risk alleles are ancestral and conserved across Neanderthals to non-human primates. Four of these seven ancestral variations (c.460A > C [p.Ile154Leu], c.541G > A [p.Ala181Thr], c.2036G > C [p.Arg679Pro] and c.2059A > G [p.Lys687Glu]) result in amino acid alterations. p.Ile154Leu and p.Ala181Thr are present at EC2: EC3 interacting interface of γA3-PCDH and γA4-PCDH respectively might affect trans-homophilic interaction and hence neuronal connectivity. p.Arg679Pro and p.Lys687Glu are present within the linker region connecting trans-membrane to extracellular domain. Sequence analysis indicated the importance of p.Ile154, p.Arg679 and p.Lys687 in maintaining class specificity. Thus the observed association of PCDHG genes encoding neural adhesion proteins reinforces the hypothesis of aberrant neuronal connectivity in the pathophysiology of dyslexia. Additionally, the striking conservation of the identified variants indicates a role of PCDHG in the evolution of highly specialized cognitive skills critical to reading.

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A set of seventeen common variations on chr5q31.3 co-segregate with dyslexia

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Ancestral risk forms are conserved throughoutNeanderthals to primates while non-risks are preponderant in modern humans

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p.Ile154Leu and p.Ala181Thr, present in interacting interface of EC2: EC3

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Species specific isoform identity of p.Ile154Leu, p.Arg679Pro and p.Lys687Glu

Worldwide epidemiological data suggests that one in every ten children is affected with dyslexia which is an alarming number and possesses a serious burden on mental health. We identified single nucleotide variations on protocadherin gamma (PCDHG) gene cluster co-segregate with dyslexia in a multiincident family. The described variants present on the interacting domain of protocadherin gamma reiterates the underlying dysregulated functional connectivity in dyslexia pathophysiology. This finding may help toward understanding the basic molecular mechanisms of dyslexia, and may help in identifying points of therapeutic intervention.

Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease

Genetic variants have been associated with myriad molecular phenotypes that provide new insight into the range of mechanisms underlying genetic traits and diseases. Identifying any particular genetic variant's cascade of effects, from molecule to individual, requires assaying multiple layers of molecular complexity. We introduce the Enhancing GTEx (eGTEx) project that extends the GTEx project to combine gene expression with additional intermediate molecular measurements on the same tissues to provide a resource for studying how genetic differences cascade through molecular phenotypes to impact human health.

Landscape of X chromosome inactivation across human tissues

X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of 'escape' from inactivation varying between genes and individuals. The extent to which XCI is shared between cells and tissues remains poorly characterized, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression and phenotypic traits. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.

IDP-ASE: haplotyping and quantifying allele-specific expression at the gene and gene isoform level by hybrid sequencing

Allele-specific expression (ASE) is a fundamental problem in studying gene regulation and diploid transcriptome profiles, with two key challenges: (i) haplotyping and (ii) estimation of ASE at the gene isoform level. Existing ASE analysis methods are limited by a dependence on haplotyping from laborious experiments or extra genome/family trio data. In addition, there is a lack of methods for gene isoform level ASE analysis. We developed a tool, IDP-ASE, for full ASE analysis. By innovative integration of Third Generation Sequencing (TGS) long reads with Second Generation Sequencing (SGS) short reads, the accuracy of haplotyping and ASE quantification at the gene and gene isoform level was greatly improved as demonstrated by the gold standard data GM12878 data and semi-simulation data. In addition to methodology development, applications of IDP-ASE to human embryonic stem cells and breast cancer cells indicate that the imbalance of ASE and non-uniformity of gene isoform ASE is widespread, including tumorigenesis relevant genes and pluripotency markers. These results show that gene isoform expression and allele-specific expression cooperate to provide high diversity and complexity of gene regulation and expression, highlighting the importance of studying ASE at the gene isoform level. Our study provides a robust bioinformatics solution to understand ASE using RNA sequencing data only.

Role of DNA methylation in expression control of the IKZF3-GSDMA region in human epithelial cells

Chromosomal region 17q12-q21 is associated with asthma and harbors regulatory polymorphisms that influence expression levels of all five protein-coding genes in the region: IKAROS family zinc finger 3 (Aiolos) (IKZF3), zona pellucida binding protein 2 (ZPBP2), ORMDL sphingolipid biosynthesis regulator 3 (ORMDL3), and gasdermins A and B (GSDMA, GSDMB). Furthermore, DNA methylation in this region has been implicated as a potential modifier of the genetic risk of asthma development. To further characterize the effect of DNA methylation, we examined the impact of treatment with DNA methyltransferase inhibitor 5-aza-2’-deoxycytidine (5-aza-dC) that causes DNA demethylation, on expression and promoter methylation of the five 17q12-q21 genes in the human airway epithelium cell line NuLi-1, embryonic kidney epithelium cell line 293T and human adenocarcinoma cell line MCF-7. 5-aza-dC treatment led to upregulation of expression of GSDMA in all three cell lines. ZPBP2 was upregulated in NuLi-1, but remained repressed in 293T and MCF-7 cells, whereas ORMDL3 was upregulated in 293T and MCF-7 cells, but not NuLi-1. Upregulation of ZPBP2 and GSDMA was accompanied by a decrease in promoter methylation. Moreover, 5-aza-dC treatment modified allelic expression of ZPBP2 and ORMDL3 suggesting that different alleles may respond differently to treatment. We also identified a polymorphic CTCF-binding site in intron 1 of ORMDL3 carrying a CG SNP rs4065275 and determined its methylation level. The site’s methylation was unaffected by 5-aza-dC treatment in NuLi-1 cells. We conclude that modest changes (8–13%) in promoter methylation levels of ZPBP2 and GSDMA may cause substantial changes in RNA levels and that allelic expression of ZPBP2 and ORMDL3 is mediated by DNA methylation.

Impacts of Neanderthal-Introgressed Sequences on the Landscape of Human Gene Expression

Regulatory variation influencing gene expression is a key contributor to phenotypic diversity, both within and between species. Unfortunately, RNA degrades too rapidly to be recovered from fossil remains, limiting functional genomic insights about our extinct hominin relatives. Many Neanderthal sequences survive in modern humans due to ancient hybridization, providing an opportunity to assess their contributions to transcriptional variation and to test hypotheses about regulatory evolution. We developed a flexible Bayesian statistical approach to quantify allele-specific expression (ASE) in complex RNA-seq datasets. We identified widespread expression differences between Neanderthal and modern human alleles, indicating pervasive cis-regulatory impacts of introgression. Brain regions and testes exhibited significant downregulation of Neanderthal alleles relative to other tissues, consistent with natural selection influencing the tissue-specific regulatory landscape. Our study demonstrates that Neanderthal-inherited sequences are not silent remnants of ancient interbreeding but have measurable impacts on gene expression that contribute to variation in modern human phenotypes.

Genetics and Genomics of Coronary Artery Disease

Coronary artery disease (or coronary heart disease), is the leading cause of mortality in many of the developing as well as the developed countries of the world. Cholesterol-enriched plaques in the heart's blood vessels combined with inflammation lead to the lesion expansion, narrowing of blood vessels, reduced blood flow, and may subsequently cause lesion rupture and a heart attack. Even though several environmental risk factors have been established, such as high LDL-cholesterol, diabetes, and high blood pressure, the underlying genetic composition may substantially modify the disease risk; hence, genome composition and gene-environment interactions may be critical for disease progression. Ongoing scientific efforts have seen substantial advancements related to the fields of genetics and genomics, with the major breakthroughs yet to come. As genomics is the most rapidly advancing field in the life sciences, it is important to present a comprehensive overview of current efforts. Here, we present a summary of various genetic and genomics assays and approaches applied to coronary artery disease research.

A haplotype-based normalization technique for the analysis and detection of allele specific expression

Background

Allele specific expression (ASE) has become an important phenotype, being utilized for the detection of cis-regulatory variation, nonsense mediated decay and imprinting in the personal genome, and has been used to both identify disease loci and consider the penetrance of damaging alleles. The detection of ASE using high throughput technologies relies on aligning short-read sequencing data, a process that has inherent biases, and there is still a need to develop fast and accurate methods to detect ASE given the unprecedented growth of sequencing information in big data projects.

Results

Here, we present a new approach to normalize RNA sequencing data in order to call ASE events with high precision in a short time-frame. Using simulated datasets we find that our approach dramatically improves reference allele quantification at heterozygous sites versus default mapping methods and also performs well compared to existing techniques for ASE detection, such as filtering methods and mapping to parental genomes, without the need for complex and time consuming manipulation. Finally, by sequencing the exomes and transcriptomes of 96 well-phenotyped individuals of the CARTaGENE cohort, we characterise the levels of ASE across individuals and find a significant association between the proportion of sites undergoing ASE within the genome and smoking.

Conclusions

The correct treatment and analysis of RNA sequencing data is vital to control for mapping biases and detect genuine ASE signals. By normalising RNA sequencing information after mapping, we show that this approach can be used to identify biologically relevant signals in personal genomes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1238-8) contains supplementary material, which is available to authorized users.

The rules and impact of nonsense-mediated mRNA decay in human cancers

Premature termination codons (PTCs) cause a large proportion of inherited human genetic diseases. PTC-containing transcripts can be degraded by an mRNA surveillance pathway termed nonsense-mediated mRNA decay (NMD). However, the efficiency of NMD varies; it is inefficient when a PTC is located downstream of the last exon junction complex (EJC). We used matched exome and transcriptome data from 9,769 human tumors to systematically elucidate the rules of NMD targeting in human cells. An integrated model incorporating multiple rules beyond the canonical EJC model explains approximately three-quarters of the non-random variance in NMD efficiency across thousands of PTCs. We also show that dosage compensation may mask the effects of NMD. Applying the NMD model identifies signatures of both positive and negative selection on NMD-triggering mutations in human tumors and provides a classification of tumor suppressor genes.

A statistical method for detecting differentially expressed SNVs based on next-generation RNA-seq data

In this article, we propose a new statistical method-MutRSeq-for detecting differentially expressed single nucleotide variants (SNVs) based on RNA-seq data. Specifically, we focus on nonsynonymous mutations and employ a hierarchical likelihood approach to jointly model observed mutation events as well as read count measurements from RNA-seq experiments. We then introduce a likelihood ratio-based test statistic, which detects changes not only in overall expression levels, but also in allele-specific expression patterns. In addition, this method can jointly test multiple mutations in one gene/pathway. The simulation studies suggest that the proposed method achieves better power than a few competitors under a range of different settings. In the end, we apply this method to a breast cancer data set and identify genes with nonsynonymous mutations differentially expressed between the triple negative breast cancer tumors and other subtypes of breast cancer tumors.

GeneiASE: Detection of condition-dependent and static allele-specific expression from RNA-seq data without haplotype information

Allele-specific expression (ASE) is the imbalance in transcription between maternal and paternal alleles at a locus and can be probed in single individuals using massively parallel DNA sequencing technology. Assessing ASE within a single sample provides a static picture of the ASE, but the magnitude of ASE for a given transcript may vary between different biological conditions in an individual. Such condition-dependent ASE could indicate a genetic variation with a functional role in the phenotypic difference. We investigated ASE through RNA-sequencing of primary white blood cells from eight human individuals before and after the controlled induction of an inflammatory response, and detected condition-dependent and static ASE at 211 and 13021 variants, respectively. We developed a method, GeneiASE, to detect genes exhibiting static or condition-dependent ASE in single individuals. GeneiASE performed consistently over a range of read depths and ASE effect sizes, and did not require phasing of variants to estimate haplotypes. We observed condition-dependent ASE related to the inflammatory response in 19 genes, and static ASE in 1389 genes. Allele-specific expression was confirmed by validation of variants through real-time quantitative RT-PCR, with RNA-seq and RT-PCR ASE effect-size correlations r = 0.67 and r = 0.94 for static and condition-dependent ASE, respectively.

A null mutation in ANGPTL8 does not associate with either plasma glucose or type 2 diabetes in humans

BACKGROUND

Experiments in mice initially suggested a role for the protein angiopoietin-like 8 (ANGPTL8) in glucose homeostasis. However, subsequent experiments in model systems have challenged this proposed role. We sought to better understand the importance of ANGPTL8 in human glucose homeostasis by examining the association of a null mutation in ANGPTL8 with fasting glucose levels and risk for type 2 diabetes.

METHODS

A naturally-occurring null mutation in human ANGPTL8 (rs145464906; c.361C > T; p.Q121X) is carried by ~1 in 1000 individuals of European ancestry and is associated with higher levels of plasma high-density lipoprotein cholesterol, suggesting that this mutation has functional significance. We examined the association of p.Q121X with fasting glucose levels and risk for type 2 diabetes in up to 95,558 individuals (14,824 type 2 diabetics and 80,734 controls).

RESULTS

We found no significant association of p.Q121X with either fasting glucose or type 2 diabetes (p-value = 0.90 and 0.65, respectively). Given our sample sizes, we had >98 % power to detect at least a 0.23 mmol/L effect on plasma glucose and >95 % power to detect a 70 % increase in risk for type 2 diabetes.

CONCLUSION

Disruption of ANGPTL8 function in humans does not seem to have a large effect on measures of glucose tolerance.

Tools and best practices for data processing in allelic expression analysis

Allelic expression analysis has become important for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. We analyze the properties of allelic expression read count data and technical sources of error, such as low-quality or double-counted RNA-seq reads, genotyping errors, allelic mapping bias, and technical covariates due to sample preparation and sequencing, and variation in total read depth. We provide guidelines for correcting such errors, show that our quality control measures improve the detection of relevant allelic expression, and introduce tools for the high-throughput production of allelic expression data from RNA-sequencing data.