Analysis of allele-specific expression in mouse liver by RNA-Seq: a comparison with Cis-eQTL identified using genetic linkage

Identification and effective regulation of scarb1 gene involved in pigmentation change in autotetraploid Carassius auratus

The autotetraploid Carassius auratus (4nRR, 4 n=200, RRRR) is derived from whole-genome duplication of Carassius auratus red var. (RCC, 2 n=100, RR). In the current study, we demonstrated that chromatophores and pigment changes directly caused the coloration and variation of 4nRR skin (red in RCC, brownish-yellow in 4nRR). To further explore the molecular mechanisms underlying coloration formation and variation in 4nRR, we performed transcriptome profiling and molecular functional verification in RCC and 4nRR. Results revealed that scarb1, associated with carotenoid metabolism, underwent significant down-regulation in 4nRR. Efficient editing of this candidate pigment gene provided clear evidence of its significant role in RCC coloration. Subsequently, we identified four divergent scarb1 homeologs in 4nRR: two original scarb1 homeologs from RCC and two duplicated ones. Notably, three of these homeologs possessed two highly conserved alleles, exhibiting biased and allele-specific expression in the skin. Remarkably, after precise editing of both the original and duplicated scarb1 homeologs and/or alleles, 4nRR individuals, whether singly or multiply mutated, displayed a transition from brownish-yellow skin to a cyan-gray phenotype. Concurrently, the proportional areas of the cyan-gray regions displayed a gene-dose correlation. These findings illustrate the subfunctionalization of duplicated scarb1, with all scarb1 genes synergistically and equally contributing to the pigmentation of 4nRR. This is the first report concerning the functional differentiation of duplicated homeologs in an autopolyploid fish, substantially enriching our understanding of coloration formation and change within this group of organisms.

Transcriptome Comparison Reveals the Difference in Liver Fat Metabolism between Different Sheep Breeds

Hu sheep and Tibetan sheep are two commonly raised local sheep breeds in China, and they have different morphological characteristics, such as tail type and adaptability to extreme environments. A fat tail in sheep is the main adipose depot in sheep, whereas the liver is an important organ for fat metabolism, with the uptake, esterification, oxidation, and secretion of fatty acids (FAs). Meanwhile, adaptations to high-altitude and arid environments also affect liver metabolism. Therefore, in this study, RNA-sequencing (RNA-seq) technology was used to characterize the difference in liver fat metabolism between Hu sheep and Tibetan sheep. We identified 1179 differentially expressed genes (DEGs) (Q-value < 0.05) between the two sheep breeds, including 25 fat-metabolism-related genes. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, 16 pathways were significantly enriched (Q-value < 0.05), such as the proteasome, glutamatergic synapse, and oxidative phosphorylation pathways. In particular, one of these pathways was enriched to be associated with fat metabolism, namely the thermogenesis pathway, to which fat-metabolism-related genes such as ACSL1, ACSL4, ACSL5, CPT1A, CPT1C, SLC25A20, and FGF21 were enriched. Then, the expression levels of ACSL1, CPT1A, and FGF21 were verified in mRNA and protein levels via qRT-PCR and Western blot analysis between the two sheep breeds. The results showed that the mRNA and protein expression levels of these three genes were higher in the livers of Tibetan sheep than those of Hu sheep. The above genes are mainly related to FAs oxidation, involved in regulating the oxidation of liver FAs. So, this study suggested that Tibetan sheep liver has a greater FAs oxidation level than Hu sheep liver. In addition, the significant enrichment of fat-metabolism-related genes in the thermogenesis pathway appears to be related to plateau-adaptive thermogenesis in Tibetan sheep, which may indicate that liver- and fat-metabolism-related genes have an impact on adaptive thermogenesis.

Development of a bioinformatics platform for analysis of quantitative transcriptomics and proteomics data: the OMnalysis

BACKGROUND

In the past decade, RNA sequencing and mass spectrometry based quantitative approaches are being used commonly to identify the differentially expressed biomarkers in different biological conditions. Data generated from these approaches come in different sizes (e.g., count matrix, normalized list of differentially expressed biomarkers, etc.) and shapes (e.g., sequences, spectral data, etc.). The list of differentially expressed biomarkers is used for functional interpretation and retrieve biological meaning, however, it requires moderate computational skills. Thus, researchers with no programming expertise find difficulty in data interpretation. Several bioinformatics tools are available to analyze such data; however, they are less flexible for performing the multiple steps of visualization and functional interpretation.

IMPLEMENTATION

We developed an easy-to-use Shiny based web application (named as OMnalysis) that provides users with a single platform to analyze and visualize the differentially expressed data. The OMnalysis accepts the data in tabular form from edgeR, DESeq2, MaxQuant Perseus, R packages, and other similar software, which typically contains the list of differentially expressed genes or proteins, log of the fold change, log of the count per million, the P value, q-value, etc. The key features of the OMnalysis are multiple image type visualization and their dimension customization options, seven multiple hypothesis testing correction methods to get more significant gene ontology, network topology-based pathway analysis, and multiple databases support (KEGG, Reactome, PANTHER, biocarta, NCI-Nature Pathway Interaction Database PharmGKB and STRINGdb) for extensive pathway enrichment analysis. OMnalysis also fetches the literature information from PubMed to provide supportive evidence to the biomarkers identified in the analysis. In a nutshell, we present the OMnalysis as a well-organized user interface, supported by peer-reviewed R packages with updated databases for quick interpretation of the differential transcriptomics and proteomics data to biological meaning.

AVAILABILITY

The OMnalysis codes are entirely written in R language and freely available at https://github.com/Punit201016/OMnalysis. OMnalysis can also be accessed from - http://lbmi.uvlf.sk/omnalysis.html. OMnalysis is hosted on a Shiny server at https://omnalysis.shinyapps.io/OMnalysis/. The minimum system requirements are: 4 gigabytes of RAM, i3 processor (or equivalent). It is compatible with any operating system (windows, Linux or Mac). The OMnalysis is heavily tested on Chrome web browsers; thus, Chrome is the preferred browser. OMnalysis works on Firefox and Safari.

RNA-Seq Data for Reliable SNP Detection and Genotype Calling: Interest for Coding Variant Characterization and Cis-Regulation Analysis by Allele-Specific Expression in Livestock Species

In addition to their common usages to study gene expression, RNA-seq data accumulated over the last 10 years are a yet-unexploited resource of SNPs in numerous individuals from different populations. SNP detection by RNA-seq is particularly interesting for livestock species since whole genome sequencing is expensive and exome sequencing tools are unavailable. These SNPs detected in expressed regions can be used to characterize variants affecting protein functions, and to study cis-regulated genes by analyzing allele-specific expression (ASE) in the tissue of interest. However, gene expression can be highly variable, and filters for SNP detection using the popular GATK toolkit are not yet standardized, making SNP detection and genotype calling by RNA-seq a challenging endeavor. We compared SNP calling results using GATK suggested filters, on two chicken populations for which both RNA-seq and DNA-seq data were available for the same samples of the same tissue. We showed, in expressed regions, a RNA-seq precision of 91% (SNPs detected by RNA-seq and shared by DNA-seq) and we characterized the remaining 9% of SNPs. We then studied the genotype (GT) obtained by RNA-seq and the impact of two factors (GT call-rate and read number per GT) on the concordance of GT with DNA-seq; we proposed thresholds for them leading to a 95% concordance. Applying these thresholds to 767 multi-tissue RNA-seq of 382 birds of 11 chicken populations, we found 9.5 M SNPs in total, of which ∼550,000 SNPs per tissue and population with a reliable GT (call rate ≥ 50%) and among them, ∼340,000 with a MAF ≥ 10%. We showed that such RNA-seq data from one tissue can be used to (i) detect SNPs with a strong predicted impact on proteins, despite their scarcity in each population (16,307 SIFT deleterious missenses and 590 stop-gained), (ii) study, on a large scale, cis-regulations of gene expression, with ∼81% of protein-coding and 68% of long non-coding genes (TPM ≥ 1) that can be analyzed for ASE, and with ∼29% of them that were cis-regulated, and (iii) analyze population genetic using such SNPs located in expressed regions. This work shows that RNA-seq data can be used with good confidence to detect SNPs and associated GT within various populations and used them for different analyses as GTEx studies.

Genome-wide analysis of expression QTL (eQTL) and allele-specific expression (ASE) in pig muscle identifies candidate genes for meat quality traits

BACKGROUND

Genetic analysis of gene expression level is a promising approach for characterizing candidate genes that are involved in complex economic traits such as meat quality. In the present study, we conducted expression quantitative trait loci (eQTL) and allele-specific expression (ASE) analyses based on RNA-sequencing (RNAseq) data from the longissimus muscle of 189 Duroc × Luchuan crossed pigs in order to identify some candidate genes for meat quality traits.

RESULTS

Using a genome-wide association study based on a mixed linear model, we identified 7192 cis-eQTL corresponding to 2098 cis-genes (p ≤ 1.33e-3, FDR ≤ 0.05) and 6400 trans-eQTL corresponding to 863 trans-genes (p ≤ 1.13e-6, FDR ≤ 0.05). ASE analysis using RNAseq SNPs identified 9815 significant ASE-SNPs in 2253 unique genes. Integrative analysis between the cis-eQTL and ASE target genes identified 540 common genes, including 33 genes with expression levels that were correlated with at least one meat quality trait. Among these 540 common genes, 63 have been reported previously as candidate genes for meat quality traits, such as PHKG1 (q-value = 1.67e-6 for the leading SNP in the cis-eQTL analysis), NUDT7 (q-value = 5.67e-13), FADS2 (q-value = 8.44e-5), and DGAT2 (q-value = 1.24e-3).

CONCLUSIONS

The present study confirmed several previously published candidate genes and identified some novel candidate genes for meat quality traits via eQTL and ASE analyses, which will be useful to prioritize candidate genes in further studies.

Allele-specific expression is widespread in Bos indicus muscle and affects meat quality candidate genes

Differences between the expression of the two alleles of a gene are known as allele-specific expression (ASE), a common event in the transcriptome of mammals. Despite ASE being a source of phenotypic variation, its occurrence and effects on genetic prediction of economically relevant traits are still unexplored in bovines. Furthermore, as ASE events are likely driven by cis-regulatory mutations, scanning them throughout the bovine genome represents a significant step to elucidate the mechanisms underlying gene expression regulation. To address this question in a Bos indicus population, we built the ASE profile of the skeletal muscle tissue of 190 Nelore steers, using RNA sequencing data and SNPs genotypes from the Illumina BovineHD BeadChip (770 K bp). After quality control, 820 SNPs showed at least one sample with ASE. These SNPs were widespread among all autosomal chromosomes, being 32.01% found in 3'UTR and 31.41% in coding regions. We observed a considerable variation of ASE profile among individuals, which highlighted the need for biological replicates in ASE studies. Functional analysis revealed that ASE genes play critical biological functions in the development and maintenance of muscle tissue. Additionally, some of these genes were previously reported as associated with beef production and quality traits in livestock, thus indicating a possible source of bias on genomic predictions for these traits.

The contribution of cis- and trans-acting variants to gene regulation in wild and domesticated barley under cold stress and control conditions

Barley, like other crops, has experienced a series of genetic changes that have impacted its architecture and growth habit to suit the needs of humans, termed the domestication syndrome. Domestication also resulted in a concomitant bottleneck that reduced sequence diversity in genes and regulatory regions. Little is known about regulatory changes resulting from domestication in barley. We used RNA sequencing to examine allele-specific expression in hybrids between wild and domesticated barley. Our results show that most genes have conserved regulation. In contrast to studies of allele-specific expression in interspecific hybrids, we find almost a complete absence of trans effects. We also find that cis regulation is largely stable in response to short-term cold stress. Our study has practical implications for crop improvement using wild relatives. Genes regulated in cis are more likely to be expressed in a new genetic background at the same level as in their native background.

Transcriptomic comparison of liver tissue between Anqing six-end-white pigs and Yorkshire pigs based on RNA sequencing

Chinese indigenous pig and Western commercial pig breeds show different patterns of lipid metabolism, fat deposition, and fatty acid composition; for these reasons, they have become vitally important models of energy metabolism and obesity in humans. To compare the mechanisms underlying lipid metabolism between Yorkshire pigs (lean type) and Anqing six-end-white pigs (obese type), the liver transcriptomes of six castrated boars with a body weight of approximately 100 kg (three Yorkshire and three Anqing) were analyzed by RNA-seq. The total number of reads produced for each liver sample ranged from 47.05 to 62.6 million. Among 362 differentially expressed genes, 142 were up-regulated and 220 were down-regulated in Anqing six-end-white pigs. Based on these data, 79 GO terms were significantly enriched. The top 10 (the 10 with lowest corrected P-value) significantly enriched GO terms were identified, including lipid metabolic process and carboxylic acid metabolic process. Pathway analysis revealed three significantly enriched KEGG pathways including PPAR signaling pathway, steroid hormone biosynthesis, and retinol metabolism. Based on protein–protein interaction networks, multiple genes responsible for lipid metabolism were identified, such as PCK1, PPARA, and CYP7A1, and these were considered promising candidate genes that could affect porcine liver lipid metabolism and fat deposition. Our results provide abundant transcriptomic information that will be useful for animal breeding and biomedical research.

Characterization of genetic-origin-dependent monoallelic expression in mouse embryonic stem cells

Monoallelic gene expression occurs in various mammalian cells and can be regulated genetically, epigenetically and/or stochastically. We identified 145 monoallelically expressed genes (MoEGs), including seven known imprinted genes, in mouse embryonic stem cells (ESCs) derived from reciprocal F1 hybrid blastocysts and cultured in 2i/LIF. As all MoEGs except for the imprinted genes were expressed in a genetic-origin-dependent manner, we focused on this class of MoEGs for mechanistic studies. We showed that a majority of the genetic-origin-dependent MoEGs identified in 2i/LIF ESCs remain monoallelically expressed in serum/LIF ESCs, but become more relaxed or even biallelically expressed upon differentiation. These MoEGs and their regulatory regions were highly enriched for single nucleotide polymorphisms. In addition, some MoEGs were associated with retrotransposon insertions/deletions, consistent with the fact that certain retrotransposons act as regulatory elements in pluripotent stem cells. Interestingly, most MoEGs showed allelic differences in enrichment of histone H3K27me and H3K4me marks, linking allelic epigenetic differences and monoallelic expression. In contrast, there was little or no allelic difference in CpG methylation or H3K9me. Taken together, our study highlights the impact of genetic variation including single nucleotide polymorphisms and retrotransposon insertions/deletions on monoallelic epigenetic marks and expression in ESCs.

Characterization of eQTLs associated with androstenone by RNA sequencing in porcine testis

Characterization of genetic variants affecting genome-wide gene expression levels (expression quantitative trait loci or eQTLs) in pig testes may improve our understanding of genetic architecture of boar taint (an animal welfare trait) and helps in genome-assisted or genomic selection programs. The aims of this study were to identify eQTLs associated with androstenone, to find candidate eQTLs for low androstenone, and to validate the top eQTL by reverse transcriptase quantitative PCR (RT-qPCR). Gene expression profiles were obtained by RNA sequencing in testis from Danish cross-bred pigs and genotype data by 80K single nucleotide polymorphism panel. A total of 262 eQTLs [false discovery rate (FDR) < 0.05] were identified by using two software packages: Matrix eQTL and Krux eQTL. Of these, 149 cis-acting eQTLs were significantly associated with androstenone concentrations and gene expression (FDR < 0.05). The eQTLs were associated with several genes of boar taint relevance including CYP1A2, CYB5D1, and SPHK2. One eQTL gene, AMPH, was differentially expressed (FDR < 0.05) and affected by chicory. Five candidate eQTLs associated with low androstenone concentrations were discovered, including the top eQTL associated with CYP1A2. RT-qPCR confirmed target gene expression to be significantly (P < 0.05) different based on eQTL genotypes. Furthermore, eQTLs were enriched as QTLs for 15 boar taint related traits from the PigQTLdb. This is the first study to report eQTLs in testes of commercial crossbred pigs used in pork production and to reveal genetic architecture of boar taint. Potential applications include development of a DNA test and in advanced genomic selection models for boar taint.

Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis

Expression quantitative trait loci (eQTL) analyses detect genetic variants (SNPs) associated with RNA expression levels of genes. The conventional eQTL analysis is to perform individual tests for each gene-SNP pair using simple linear regression and to perform the test on each tissue separately ignoring the extensive information known about RNA expression in other tissue(s). Although Bayesian models have been recently developed to improve eQTL prediction on multiple tissues, they are often based on uninformative priors or treat all tissues equally. In this study, we develop a novel tissue augmented Bayesian model for eQTL analysis (TA-eQTL), which takes prior eQTL information from a different tissue into account to better predict eQTL for another tissue. We demonstrate that our modified Bayesian model has comparable performance to several existing methods in terms of sensitivity and specificity using allele-specific expression (ASE) as the gold standard. Furthermore, the tissue augmented Bayesian model improves the power and accuracy for local-eQTL prediction especially when the sample size is small. In summary, TA-eQTL's performance is comparable to existing methods but has additional flexibility to evaluate data from different platforms, can focus prediction on one tissue using only summary statistics from the secondary tissue(s), and provides a closed form solution for estimation.

Survey of allele specific expression in bovine muscle

Allelic imbalance is a common phenomenon in mammals that plays an important role in gene regulation. An Allele Specific Expression (ASE) approach can be used to detect variants with a cis-regulatory effect on gene expression. In cattle, this type of study has only been done once in Holstein. In our study we performed a genome-wide analysis of ASE in 19 Limousine muscle samples. We identified 5,658 ASE SNPs (Single Nucleotide Polymorphisms showing allele specific expression) in 13% of genes with detectable expression in the Longissimus thoraci muscle. Interestingly we found allelic imbalance in AOX1, PALLD and CAST genes. We also found 2,107 ASE SNPs located within genomic regions associated with meat or carcass traits. In order to identify causative cis-regulatory variants explaining ASE we searched for SNPs altering binding sites of transcription factors or microRNAs. We identified one SNP in the 3’UTR region of PRNP that could be a causal regulatory variant modifying binding sites of several miRNAs. We showed that ASE is frequent within our muscle samples. Our data could be used to elucidate the molecular mechanisms underlying gene expression imbalance.

Streamlined computational pipeline for genetic background characterization of genetically engineered mice based on next generation sequencing data

BACKGROUND

Genetically engineered mice (GEM) are essential tools for understanding gene function and disease modeling. Historically, gene targeting was first done in embryonic stem cells (ESCs) derived from the 129 family of inbred strains, leading to a mixed background or congenic mice when crossed with C57BL/6 mice. Depending on the number of backcrosses and breeding strategies, genomic segments from 129-derived ESCs can be introgressed into the C57BL/6 genome, establishing a unique genetic makeup that needs characterization in order to obtain valid conclusions from experiments using GEM lines. Currently, SNP genotyping is used to detect the extent of 129-derived ESC genome introgression into C57BL/6 recipients; however, it fails to detect novel/rare variants.

RESULTS

Here, we present a computational pipeline implemented in the Galaxy platform and in BASH/R script to determine genetic introgression of GEM using next generation sequencing data (NGS), such as whole genome sequencing (WGS), whole exome sequencing (WES) and RNA-Seq. The pipeline includes strategies to uncover variants linked to a targeted locus, genome-wide variant visualization, and the identification of potential modifier genes. Although these methods apply to congenic mice, they can also be used to describe variants fixed by genetic drift. As a proof of principle, we analyzed publicly available RNA-Seq data from five congenic knockout (KO) lines and our own RNA-Seq data from the Sall2 KO line. Additionally, we performed target validation using several genetics approaches.

CONCLUSIONS

We revealed the impact of the 129-derived ESC genome introgression on gene expression, predicted potential modifier genes, and identified potential phenotypic interference in KO lines. Our results demonstrate that our new approach is an effective method to determine genetic introgression of GEM.

Comparing allele specific expression and local expression quantitative trait loci and the influence of gene expression on complex trait variation in cattle

BACKGROUND

The mutations changing the expression level of a gene, or expression quantitative trait loci (eQTL), can be identified by testing the association between genetic variants and gene expression in multiple individuals (eQTL mapping), or by comparing the expression of the alleles in a heterozygous individual (allele specific expression or ASE analysis). The aims of the study were to find and compare ASE and local eQTL in 4 bovine RNA-sequencing (RNA-Seq) datasets, validate them in an independent ASE study and investigate if they are associated with complex trait variation.

RESULTS

We present a novel method for distinguishing between ASE driven by polymorphisms in cis and parent of origin effects. We found that single nucleotide polymorphisms (SNPs) driving ASE are also often local eQTL and therefore presumably cis eQTL. These SNPs often, but not always, affect gene expression in multiple tissues and, when they do, the allele increasing expression is usually the same. However, there were systematic differences between ASE and local eQTL and between tissues and breeds. We also found that SNPs significantly associated with gene expression (p < 0.001) were likely to influence some complex traits (p < 0.001), which means that some mutations influence variation in complex traits by changing the expression level of genes.

CONCLUSION

We conclude that ASE detects phenomenon that overlap with local eQTL, but there are also systematic differences between the SNPs discovered by the two methods. Some mutations influencing complex traits are actually eQTL and can be discovered using RNA-Seq including eQTL in the genes CAST, CAPN1, LCORL and LEPROTL1.

Lead Modulates trans- and cis-Expression Quantitative Trait Loci (eQTLs) in Drosophila melanogaster Heads

Lead exposure has long been one of the most important topics in global public health because it is a potent developmental neurotoxin. Here, an eQTL analysis, which is the genome-wide association analysis of genetic variants with gene expression, was performed. In this analysis, the male heads of 79 Drosophila melanogaster inbred lines from Drosophila Synthetic Population Resource (DSPR) were treated with or without developmental exposure, from hatching to adults, to 250 μM lead acetate [Pb(C₂H₃O₂)₂]. The goal was to identify genomic intervals that influence the gene-expression response to lead. After detecting 1798 cis-eQTLs and performing an initial trans-eQTL analysis, we focused our analysis on lead-sensitive "trans-eQTL hotspots," defined as genomic regions that are associated with a cluster of genes in a lead-dependent manner. We noticed that the genes associated with one of the 14 detected trans-eQTL hotspots, Chr 2L: 6,250,000 could be roughly divided into two groups based on their differential expression profile patterns and different categories of function. This trans-eQTL hotspot validates one identified in a previous study using different recombinant inbred lines. The expression of all the associated genes in the trans-eQTL hotspot was visualized with hierarchical clustering analysis. Besides the overall expression profile patterns, the heatmap displayed the segregation of differential parental genetic contributions. This suggested that trans-regulatory regions with different genetic contributions from the parental lines have significantly different expression changes after lead exposure. We believe this study confirms our earlier study, and provides important insights to unravel the genetic variation in lead susceptibility in Drosophila model.

Deciphering the genetic regulation of peripheral blood transcriptome in pigs through expression genome-wide association study and allele-specific expression analysis

BACKGROUND

Efforts to improve sustainability in livestock production systems have focused on two objectives: investigating the genetic control of immune function as it pertains to robustness and disease resistance, and finding predictive markers for use in breeding programs. In this context, the peripheral blood transcriptome represents an important source of biological information about an individual's health and immunological status, and has been proposed for use as an intermediate phenotype to measure immune capacity. The objective of this work was to study the genetic architecture of variation in gene expression in the blood of healthy young pigs using two approaches: an expression genome-wide association study (eGWAS) and allele-specific expression (ASE) analysis.

RESULTS

The blood transcriptomes of 60-day-old Large White pigs were analyzed by expression microarrays for eGWAS (242 animals) and by RNA-Seq for ASE analysis (38 animals). Using eGWAS, the expression levels of 1901 genes were found to be associated with expression quantitative trait loci (eQTLs). We recovered 2839 local and 1752 distant associations (Single Nucleotide Polymorphism or SNP located less or more than 1 Mb from expression probe, respectively). ASE analyses confirmed the extensive cis-regulation of gene transcription in blood, and revealed allelic imbalance in 2286 SNPs, which affected 763 genes. eQTLs and ASE-genes were widely distributed on all chromosomes. By analyzing mutually overlapping eGWAS results, we were able to describe putative regulatory networks, which were further refined using ASE data. At the functional level, genes with genetically controlled expression that were detected by eGWAS and/or ASE analyses were significantly enriched in biological processes related to RNA processing and immune function. Indeed, numerous distant and local regulatory relationships were detected within the major histocompatibility complex region on chromosome 7, revealing ASE for most class I and II genes.

CONCLUSIONS

This study represents, to the best of our knowledge, the first genome-wide map of the genetic control of gene expression in porcine peripheral blood. These results represent an interesting resource for the identification of genetic markers and blood biomarkers associated with variations in immunity traits in pigs, as well as any other complex traits for which blood is an appropriate surrogate tissue.

Interplay of cis and trans mechanisms driving transcription factor binding and gene expression evolution

Noncoding regulatory variants play a central role in the genetics of human diseases and in evolution. Here we measure allele-specific transcription factor binding occupancy of three liver-specific transcription factors between crosses of two inbred mouse strains to elucidate the regulatory mechanisms underlying transcription factor binding variations in mammals. Our results highlight the pre-eminence of cis-acting variants on transcription factor occupancy divergence. Transcription factor binding differences linked to cis-acting variants generally exhibit additive inheritance, while those linked to trans-acting variants are most often dominantly inherited. Cis-acting variants lead to local coordination of transcription factor occupancies that decay with distance; distal coordination is also observed and may be modulated by long-range chromatin contacts. Our results reveal the regulatory mechanisms that interplay to drive transcription factor occupancy, chromatin state, and gene expression in complex mammalian cell states.

Bayesian Inference of Allele-Specific Gene Expression Indicates Abundant Cis-Regulatory Variation in Natural Flycatcher Populations

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

RNA-Seq Analyses Identify Frequent Allele Specific Expression and No Evidence of Genomic Imprinting in Specific Embryonic Tissues of Chicken

Epigenetic and genetic cis-regulatory elements in diploid organisms may cause allele specific expression (ASE) – unequal expression of the two chromosomal gene copies. Genomic imprinting is an intriguing type of ASE in which some genes are expressed monoallelically from either the paternal allele or maternal allele as a result of epigenetic modifications. Imprinted genes have been identified in several animal species and are frequently associated with embryonic development and growth. Whether genomic imprinting exists in chickens remains debatable, as previous studies have reported conflicting evidence. Albeit no genomic imprinting has been reported in the chicken embryo as a whole, we interrogated the existence or absence of genomic imprinting in the 12-day-old chicken embryonic brain and liver by examining ASE in F1 reciprocal crosses of two highly inbred chicken lines (Fayoumi and Leghorn). We identified 5197 and 4638 ASE SNPs, corresponding to 18.3% and 17.3% of the genes with a detectable expression in the embryonic brain and liver, respectively. There was no evidence detected of genomic imprinting in 12-day-old embryonic brain and liver. While ruling out the possibility of imprinted Z-chromosome inactivation, our results indicated that Z-linked gene expression is partially compensated between sexes in chickens.

Mapping the mouse Allelome reveals tissue-specific regulation of allelic expression

To determine the dynamics of allelic-specific expression during mouse development, we analyzed RNA-seq data from 23 F1 tissues from different developmental stages, including 19 female tissues allowing X chromosome inactivation (XCI) escapers to also be detected. We demonstrate that allelic expression arising from genetic or epigenetic differences is highly tissue-specific. We find that tissue-specific strain-biased gene expression may be regulated by tissue-specific enhancers or by post-transcriptional differences in stability between the alleles. We also find that escape from X-inactivation is tissue-specific, with leg muscle showing an unexpectedly high rate of XCI escapers. By surveying a range of tissues during development, and performing extensive validation, we are able to provide a high confidence list of mouse imprinted genes including 18 novel genes. This shows that cluster size varies dynamically during development and can be substantially larger than previously thought, with the Igf2r cluster extending over 10 Mb in placenta.

DOI:http://dx.doi.org/10.7554/eLife.25125.001

The abundance of cis-acting loci leading to differential allele expression in F1 mice and their relationship to loci harboring genes affecting complex traits

Background

Genome-wide surveys have detected cis-acting quantitative trait loci altering levels of RNA transcripts (RNA-eQTLs) by associating SNV alleles to transcript levels. However, the sensitivity and specificity of detection of cis- expression quantitative trait loci (eQTLs) by genetic approaches, reliant as it is on measurements of transcript levels in recombinant inbred strains or offspring from arranged crosses, is unknown, as is their relationship to QTL’s for complex phenotypes.

Results

We used transcriptome-wide differential allele expression (DAE) to detect cis-eQTLs in forebrain and kidney from reciprocal crosses between three mouse inbred strains, 129S1/SvlmJ, DBA/2J, and CAST/EiJ and C57BL/6 J. Two of these crosses were previously characterized for cis-eQTLs and QTLs for various complex phenotypes by genetic analysis of recombinant inbred (RI) strains. 5.4 %, 1.9 % and 1.5 % of genes assayed in forebrain of B6/129SF1, B6/DBAF1, and B6/CASTF1 mice, respectively, showed differential allelic expression, indicative of cis-acting alleles at these genes. Moreover, the majority of DAE QTLs were observed to be tissue-specific with only a small fraction showing cis-effects in both tissues. Comparing DAE QTLs in F1 mice to cis-eQTLs previously mapped in RI strains we observed that many of the cis-eQTLs were not confirmed by DAE. Additionally several novel DAE-QTLs not identified as cis-eQTLs were identified suggesting that there are differences in sensitivity and specificity for QTL detection between the two methodologies. Strain specific DAE QTLs in B6/DBAF1 mice were located in excess at candidate genes for alcohol use disorders, seizures, and angiogenesis previously implicated by genetic linkage in C57BL/6J × DBA/2JF2 mice or BXD RI strains.

Conclusions

Via a survey for differential allele expression in F1 mice, a substantial proportion of genes were found to have alleles altering expression in cis-acting fashion. Comparing forebrain and kidney, many or most of these alleles were tissue-specific in action. The identification of strain specific DAE QTLs, can assist in assessment of candidate genes located within the large intervals associated with trait QTLs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2922-9) contains supplementary material, which is available to authorized users.

Dissection of expression-quantitative trait locus and allele specificity using a haploid/diploid plant system - insights into compensatory evolution of transcriptional regulation within populations

Regulation of gene expression plays a central role in translating genotypic variation into phenotypic variation. Dissection of the genetic basis of expression variation is key to understanding how expression regulation evolves. Such analyses remain challenging in contexts where organisms are outbreeding, highly heterozygous and long-lived such as in the case of conifer trees. We developed an RNA sequencing (RNA-seq)-based approach for both expression-quantitative trait locus (eQTL) mapping and the detection of cis-acting (allele-specific) vs trans-acting (non-allele-specific) eQTLs. This method can be potentially applied to many conifers. We used haploid and diploid meiotic seed tissues of a single self-fertilized white spruce (Picea glauca) individual to dissect eQTLs according to linkage and allele specificity. The genetic architecture of local eQTLs linked to the expressed genes was particularly complex, consisting of cis-acting, trans-acting and, surprisingly, compensatory cis-trans effects. These compensatory effects influence expression in opposite directions and are neutral when combined in homozygotes. Nearly half of local eQTLs were under compensation, indicating that close linkage between compensatory cis-trans factors is common in spruce. Compensated genes were overrepresented in developmental and cell organization functions. Our haploid-diploid eQTL analysis in spruce revealed that compensatory cis-trans eQTLs segregate within populations and evolve in close genetic linkage.

From Loci to Biology: Functional Genomics of Genome-Wide Association for Coronary Disease

Genome-wide association studies have provided a rich collection of ≈ 58 coronary artery disease (CAD) loci that suggest the existence of previously unsuspected new biology relevant to atherosclerosis. However, these studies only identify genomic loci associated with CAD, and many questions remain even after a genomic locus is definitively implicated, including the nature of the causal variant(s) and the causal gene(s), as well as the directionality of effect. There are several tools that can be used for investigation of the functional genomics of these loci, and progress has been made on a limited number of novel CAD loci. New biology regarding atherosclerosis and CAD will be learned through the functional genomics of these loci, and the hope is that at least some of these new pathways relevant to CAD pathogenesis will yield new therapeutic targets for the prevention and treatment of CAD.

The Hybrid Mouse Diversity Panel: a resource for systems genetics analyses of metabolic and cardiovascular traits

The Hybrid Mouse Diversity Panel (HMDP) is a collection of approximately 100 well-characterized inbred strains of mice that can be used to analyze the genetic and environmental factors underlying complex traits. While not nearly as powerful for mapping genetic loci contributing to the traits as human genome-wide association studies, it has some important advantages. First, environmental factors can be controlled. Second, relevant tissues are accessible for global molecular phenotyping. Finally, because inbred strains are renewable, results from separate studies can be integrated. Thus far, the HMDP has been studied for traits relevant to obesity, diabetes, atherosclerosis, osteoporosis, heart failure, immune regulation, fatty liver disease, and host-gut microbiota interactions. High-throughput technologies have been used to examine the genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes of the mice under various environmental conditions. All of the published data are available and can be readily used to formulate hypotheses about genes, pathways and interactions.

Allelome.PRO, a pipeline to define allele-specific genomic features from high-throughput sequencing data

Detecting allelic biases from high-throughput sequencing data requires an approach that maximises sensitivity while minimizing false positives. Here, we present Allelome.PRO, an automated user-friendly bioinformatics pipeline, which uses high-throughput sequencing data from reciprocal crosses of two genetically distinct mouse strains to detect allele-specific expression and chromatin modifications. Allelome.PRO extends approaches used in previous studies that exclusively analyzed imprinted expression to give a complete picture of the ‘allelome’ by automatically categorising the allelic expression of all genes in a given cell type into imprinted, strain-biased, biallelic or non-informative. Allelome.PRO offers increased sensitivity to analyze lowly expressed transcripts, together with a robust false discovery rate empirically calculated from variation in the sequencing data. We used RNA-seq data from mouse embryonic fibroblasts from F1 reciprocal crosses to determine a biologically relevant allelic ratio cutoff, and define for the first time an entire allelome. Furthermore, we show that Allelome.PRO detects differential enrichment of H3K4me3 over promoters from ChIP-seq data validating the RNA-seq results. This approach can be easily extended to analyze histone marks of active enhancers, or transcription factor binding sites and therefore provides a powerful tool to identify candidate cis regulatory elements genome wide.

Allelic Imbalance Is a Prevalent and Tissue-Specific Feature of the Mouse Transcriptome

In mammals, several classes of monoallelic genes have been identified, including those subject to X-chromosome inactivation (XCI), genomic imprinting, and random monoallelic expression (RMAE). However, the extent to which these epigenetic phenomena are influenced by underlying genetic variation is unknown. Here we perform a systematic classification of allelic imbalance in mouse hybrids derived from reciprocal crosses of divergent strains. We observe that deviation from balanced biallelic expression is common, occurring in ∼20% of the mouse transcriptome in a given tissue. Allelic imbalance attributed to genotypic variation is by far the most prevalent class and typically is tissue-specific. However, some genotype-based imbalance is maintained across tissues and is associated with greater genetic variation, especially in 5' and 3' termini of transcripts. We further identify novel random monoallelic and imprinted genes and find that genotype can modify penetrance of parental origin even in the setting of large imprinted regions. Examination of nascent transcripts in single cells from inbred parental strains reveals that genes showing genotype-based imbalance in hybrids can also exhibit monoallelic expression in isogenic backgrounds. This surprising observation may suggest a competition between alleles and/or reflect the combined impact of cis- and trans-acting variation on expression of a given gene. Our findings provide novel insights into gene regulation and may be relevant to human genetic variation and disease.

Next-generation gene discovery for variants of large impact on lipid traits

PURPOSE OF REVIEW

Detection of high-impact variants on lipid traits is complicated by complex genetic architecture. Although genome-wide association studies (GWAS) successfully identified many novel genes associated with lipid traits, it was less successful in identifying variants with a large impact on the phenotype. This is not unexpected, as the more common variants detectable by GWAS typically have small effects. The availability of large familial datasets and sequence data has changed the paradigm for successful genomic discovery of the novel genes and pathogenic variants underlying lipid disorders.

RECENT FINDINGS

Novel loci with large effects have been successfully mapped in families, and next-generation sequencing allowed for the identification of the underlying lipid-associated variants of large effect size. The success of this strategy relies on the simplification of the underlying genetic variation by focusing on large single families segregating extreme lipid phenotypes.

SUMMARY

Rare, high-impact variants are expected to have large effects and be more relevant for medical and pharmaceutical applications. Family data have many advantages over population-based data because they allow for the efficient detection of high-impact variants with an exponentially smaller sample size and increased power for follow-up studies.

Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels

Background

RNA-sequencing (RNA-seq) is a powerful technique for the identification of genetic variants that affect gene-expression levels, either through expression quantitative trait locus (eQTL) mapping or through allele-specific expression (ASE) analysis. Given increasing numbers of RNA-seq samples in the public domain, we here studied to what extent eQTLs and ASE effects can be identified when using public RNA-seq data while deriving the genotypes from the RNA-sequencing reads themselves.

Methods

We downloaded the raw reads for all available human RNA-seq datasets. Using these reads we performed gene expression quantification. All samples were jointly normalized and subjected to a strict quality control. We also derived genotypes using the RNA-seq reads and used imputation to infer non-coding variants. This allowed us to perform eQTL mapping and ASE analyses jointly on all samples that passed quality control. Our results were validated using samples for which DNA-seq genotypes were available.

Results

4,978 public human RNA-seq runs, representing many different tissues and cell-types, passed quality control. Even though these data originated from many different laboratories, samples reflecting the same cell type clustered together, suggesting that technical biases due to different sequencing protocols are limited. In a joint analysis on the 1,262 samples with high quality genotypes, we identified cis-eQTLs effects for 8,034 unique genes (at a false discovery rate ≤0.05). eQTL mapping on individual tissues revealed that a limited number of samples already suffice to identify tissue-specific eQTLs for known disease-associated genetic variants. Additionally, we observed strong ASE effects for 34 rare pathogenic variants, corroborating previously observed effects on the corresponding protein levels.

Conclusions

By deriving and imputing genotypes from RNA-seq data, it is possible to identify both eQTLs and ASE effects. Given the exponential growth of the number of publicly available RNA-seq samples, we expect this approach will become especially relevant for studying the effects of tissue-specific and rare pathogenic genetic variants to aid clinical interpretation of exome and genome sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0152-4) contains supplementary material, which is available to authorized users.

The genetic architecture of the genome-wide transcriptional response to ER stress in the mouse

Endoplasmic reticulum (ER) stress occurs when misfolded proteins accumulate in the ER. The cellular response to ER stress involves complex transcriptional and translational changes, important to the survival of the cell. ER stress is a primary cause and a modifier of many human diseases. A first step to understanding how the ER stress response impacts human disease is to determine how the transcriptional response to ER stress varies among individuals. The genetic diversity of the eight mouse Collaborative Cross (CC) founder strains allowed us to determine how genetic variation impacts the ER stress transcriptional response. We used tunicamycin, a drug commonly used to induce ER stress, to elicit an ER stress response in mouse embryonic fibroblasts (MEFs) derived from the CC founder strains and measured their transcriptional responses. We identified hundreds of genes that differed in response to ER stress across these genetically diverse strains. Strikingly, inflammatory response genes differed most between strains; major canonical ER stress response genes showed relatively invariant responses across strains. To uncover the genetic architecture underlying these strain differences in ER stress response, we measured the transcriptional response to ER stress in MEFs derived from a subset of F1 crosses between the CC founder strains. We found a unique layer of regulatory variation that is only detectable under ER stress conditions. Over 80% of the regulatory variation under ER stress derives from cis-regulatory differences. This is the first study to characterize the genetic variation in ER stress transcriptional response in the laboratory mouse. Our findings indicate that the ER stress transcriptional response is highly variable among strains and arises from genetic variation in individual downstream response genes, rather than major signaling transcription factors. These results have important implications for understanding how genetic variation impacts the ER stress response, an important component of many human diseases.

Combined QTL and selective sweep mappings with coding SNP annotation and cis-eQTL analysis revealed PARK2 and JAG2 as new candidate genes for adiposity regulation

Very few causal genes have been identified by quantitative trait loci (QTL) mapping because of the large size of QTL, and most of them were identified thanks to functional links already known with the targeted phenotype. Here, we propose to combine selection signature detection, coding SNP annotation, and cis-expression QTL analyses to identify potential causal genes underlying QTL identified in divergent line designs. As a model, we chose experimental chicken lines divergently selected for only one trait, the abdominal fat weight, in which several QTL were previously mapped. Using new haplotype-based statistics exploiting the very high SNP density generated through whole-genome resequencing, we found 129 significant selective sweeps. Most of the QTL colocalized with at least one sweep, which markedly narrowed candidate region size. Some of those sweeps contained only one gene, therefore making them strong positional causal candidates with no presupposed function. We then focused on two of these QTL/sweeps. The absence of nonsynonymous SNPs in their coding regions strongly suggests the existence of causal mutations acting in cis on their expression, confirmed by cis-eQTL identification using either allele-specific expression or genetic mapping analyses. Additional expression analyses of those two genes in the chicken and mice contrasted for adiposity reinforces their link with this phenotype. This study shows for the first time the interest of combining selective sweeps mapping, coding SNP annotation and cis-eQTL analyses for identifying causative genes for a complex trait, in the context of divergent lines selected for this specific trait. Moreover, it highlights two genes, JAG2 and PARK2, as new potential negative and positive key regulators of adiposity in chicken and mice.

Extensive cis-regulatory variation robust to environmental perturbation in Arabidopsis

cis- and trans-acting factors affect gene expression and responses to environmental conditions. However, for most plant systems, we lack a comprehensive map of these factors and their interaction with environmental variation. Here, we examined allele-specific expression (ASE) in an F1 hybrid to study how alleles from two Arabidopsis thaliana accessions affect gene expression. To investigate the effect of the environment, we used drought stress and developed a variance component model to estimate the combined genetic contributions of cis- and trans-regulatory polymorphisms, environmental factors, and their interactions. We quantified ASE for 11,003 genes, identifying 3318 genes with consistent ASE in control and stress conditions, demonstrating that cis-acting genetic effects are essentially robust to changes in the environment. Moreover, we found 1618 genes with genotype x environment (GxE) interactions, mostly cis x E interactions with magnitude changes in ASE. We found fewer trans x E interactions, but these effects were relatively less robust across conditions, showing more changes in the direction of the effect between environments; this confirms that trans-regulation plays an important role in the response to environmental conditions. Our data provide a detailed map of cis- and trans-regulation and GxE interactions in A. thaliana, laying the ground for mechanistic investigations and studies in other plants and environments.

Quality control method for RNA-seq using single nucleotide polymorphism allele frequency

RNA sequencing (RNA-seq) provides information not only about the level of expression of individual genes but also about genomic sequences of host cells. When we use transcriptome data with whole-genome single nucleotide polymorphism (SNP) variant information, the allele frequency can show the genetic composition of the cell population and/or chromosomal aberrations. Here, I show how SNPs in mRNAs can be used to evaluate RNA-seq experiments by focusing on RNA-seq data based on a recently retracted paper on stimulus-triggered acquisition of pluripotency (STAP) cells. The analysis indicated that different types of cells and chromosomal abnormalities might have been erroneously included in the dataset. This re-evaluation showed that observing allele frequencies could help in assessing the quality of samples during a study and with retrospective evaluation of experimental quality.

Allele-specific expression and eQTL analysis in mouse adipose tissue

Background

The simplest definition of cis-eQTLs versus trans, refers to genetic variants that affect expression in an allele specific manner, with implications on underlying mechanism. Yet, due to technical limitations of expression microarrays, the vast majority of eQTL studies performed in the last decade used a genomic distance based definition as a surrogate for cis, therefore exploring local rather than cis-eQTLs.

Results

In this study we use RNAseq to explore allele specific expression (ASE) in adipose tissue of male and female F1 mice, produced from reciprocal crosses of C57BL/6J and DBA/2J strains. Comparison of the identified cis-eQTLs, to local-eQTLs, that were obtained from adipose tissue expression in two previous population based studies in our laboratory, yields poor overlap between the two mapping approaches, while both local-eQTL studies show highly concordant results. Specifically, local-eQTL studies show ~60% overlap between themselves, while only 15-20% of local-eQTLs are identified as cis by ASE, and less than 50% of ASE genes are recovered in local-eQTL studies. Utilizing recently published ENCODE data, we also find that ASE genes show significant bias for SNPs prevalence in DNase I hypersensitive sites that is ASE direction specific.

Conclusions

We suggest a new approach to analysis of allele specific expression that is more sensitive and accurate than the commonly used fisher or chi-square statistics. Our analysis indicates that technical differences between the cis and local-eQTL approaches, such as differences in genomic background or sex specificity, account for relatively small fraction of the discrepancy. Therefore, we suggest that the differences between two eQTL mapping approaches may facilitate sorting of SNP-eQTL interactions into true cis and trans, and that a considerable portion of local-eQTL may actually represent trans interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-471) contains supplementary material, which is available to authorized users.

Transcriptome-wide investigation of genomic imprinting in chicken

Genomic imprinting is an epigenetic mechanism by which alleles of some specific genes are expressed in a parent-of-origin manner. It has been observed in mammals and marsupials, but not in birds. Until now, only a few genes orthologous to mammalian imprinted ones have been analyzed in chicken and did not demonstrate any evidence of imprinting in this species. However, several published observations such as imprinted-like QTL in poultry or reciprocal effects keep the question open. Our main objective was thus to screen the entire chicken genome for parental-allele-specific differential expression on whole embryonic transcriptomes, using high-throughput sequencing. To identify the parental origin of each observed haplotype, two chicken experimental populations were used, as inbred and as genetically distant as possible. Two families were produced from two reciprocal crosses. Transcripts from 20 embryos were sequenced using NGS technology, producing ∼200 Gb of sequences. This allowed the detection of 79 potentially imprinted SNPs, through an analysis method that we validated by detecting imprinting from mouse data already published. However, out of 23 candidates tested by pyrosequencing, none could be confirmed. These results come together, without a priori, with previous statements and phylogenetic considerations assessing the absence of genomic imprinting in chicken.

Systems genetics approaches to understand complex traits

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease.