A Comprehensive Allele Specific Expression Resource for the Equine Transcriptome

A Comprehensive Allele Specific Expression Resource for the Equine Transcriptome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Comprehensive Allele Specific Expression Resource for the Equine Transcriptome Harrison Heath, Sichong Peng, Tomasz Szmatola, Stephanie Ryan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4182812/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jan, 2025 Read the published version in BMC Genomics → Version 1 posted 4 You are reading this latest preprint version Abstract Background: Allele-specific expression (ASE) analysis provides a nuanced view of cis-regulatory mechanisms affecting gene expression. Results: An equine ASE analysis was performed, using integrated Iso-seq and short-read RNA sequencing data from four healthy Thoroughbreds (2 mares and 2 stallions) across 9 tissues from the Functional Annotation of Animal Genomes (FAANG) project. Allele expression was quantified by haplotypes from long-read data, with 42,900 allele expression events compared. Within these events, 635 (1.48%) demonstrated ASE, with liver tissue containing the highest proportion. Genetic variants within ASE events were in histone modified regions 64.2% of the time. Validation of allele-specific variants, using a set of 66 equine liver samples from multiple breeds, confirmed that 97% of variants demonstrated ASE. Conclusions: This valuable publicly accessible resource is poised to facilitate investigations into regulatory variation in equine tissues. Our results highlight the tissue-specific nature of allelic imbalance in the equine genome. epigenetics FAANG haplotype horse RNA-sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Background In diploid mammalian cells, autosomal genes are usually equally expressed. 1 In some cases however, a gene can exhibit expression biased for one allele over the other. 2 This allele-specific expression (ASE) often results from cis-acting genetic variants on the same chromosome that are in proximity to or within the affected gene. Genetic variants within a gene can affect gene expression, mRNA stability, or mRNA function in different ways, leading to one allele being expressed more than the other. Various cis-acting have the potential to cause ASE. Although not changing the amino acid, synonymous variants could affect mRNA stability, splicing, or translational efficiency, potentially causing ASE. 3 Missense variants can affect the function of the RNA, possibly leading to a difference in expression between alleles. 3 Variants in the 3' untranslated region (UTR) can influence mRNA stability, localization, and translation, all of which can contribute to ASE. Variants identified in the 5' prime UTR could influence ASE by affecting the initiation of translation and the stability of mRNA thus altering the amount of protein produced from each allele. 4 Lastly, variants in splice regions can impact allele expression by altering splicing efficiency, exon skipping, creation or loss of splice sites, or affecting regulatory protein binding, all potentially influencing mRNA function and expression. 4 ASE may also arise from trans effects, or genetic influences on the other chromosome of an affected gene or elsewhere in the genome. 5 , 6 Epigenetic factors, such as DNA methylation or chromatin structure, also have the potential to significantly impact gene expression between alleles. 1 , 6 Interestingly, ASE predominantly manifests as tissue-specific phenomena, with loci displaying distinct expression patterns across different tissues. 2 , 5 Therefore, ASE analysis can provide a way to inspect gene regulation patterns and their broader impact on biological pathways in specific tissues. Advances in next-generation sequencing technologies, particularly RNA-sequencing (RNA-Seq), have revolutionized our ability to analyze gene expression and genetic variation. Furthermore, long-read RNA-Seq allows for the straightforward identification of variants that are inherited together as haplotypes from full-length transcripts. 7 , 8 The loci of these haplotypes can then be overlaid with short-read RNA-Seq reads. This integration provides the read counts of nucleotides existing at each locus, which can then be used to quantify the expression level of each allele for each gene that contains heterozygous loci. Finally, expression levels of each allele can be compared against one another to identify an allelic imbalance. The study of ASE in horses is enabled by the availability of a high-quality reference genome 9 and long- and short-read sequencing technologies, contributing to a more comprehensive transcriptome annotation. 10 , 11 While prior studies on ASE in horses were focused on paternal/maternal imprinting in early development, this research distinguishes itself by examining ASE in adult mares and stallions. 12 – 14 In this study, we performed an ASE analysis of the equine transcriptome using a combinatorial Iso-seq and short-read RNA-Seq approach. Our primary goal of this research is to contribute to the Functional Annotation of the Animal Genome (FAANG) project, a large-scale collaborative effort aimed to identify all functional elements for animals. 15 To accomplish this, we provide a comprehensive resource of allelic expression detected from linked heterozygous loci. We have designed this publicly accessible database to enable future research into important regulatory variants that may impact equine health and disease at the molecular level. By extending the examination of ASE to the equine species using this method, we can further understand the uses of emerging next-generation sequencing technologies and the mechanisms underlying gene regulation in the equine genome. Methods Generation of Data Data from nine tissues from prior FAANG analyses were selected for ASE analyses: lamina, liver, left lung, left ventricle of the heart, longissimus muscle, skin, parietal cortex, testes, and ovary from 4 healthy Thoroughbreds (2 mares and 2 stallions). 16 , 17 This selection aimed to encompass a broad spectrum of biological functions and, consequently, varied gene expression. The RNA isolation for Iso-seq was performed separately from the same tissues as the RNA utilized for mRNA-Seq, using an identical protocol. 11 For Iso-seq, we selected the highest quality RNA per sex for each tissue (except for parietal cortex and sex-specific tissues). Since parietal cortex was the pilot tissue, long-read RNA sequencing was performed on all four horses. All selected RNA samples had integrity (RIN) values greater than or equal to 7. Selected tissues were processed for Iso-seq in a single batch. The cDNA libraries were developed and sequenced at the UC Berkeley QB3 Genomics core facility. Two randomly selected libraries were combined and then sequenced together on a single SMRT cell of the PacBio Sequel II system, as previously described. 10 , 11 ChIP-seq data for the histone modifications evaluated in this study were sourced and analyzed from prior publications that examined the same eight tissues. 11 , 18 Identifying Haplotypes The overall workflow for identifying ASE events is outlined in Supplementary Fig. 1 . Key to our approach was the use of Iso-seq data to extrapolate haplotypes for each horse individually, utilizing the isophase Cupcake software v29.0. 19 The software's function was to identify variations between otherwise identical full-length reads, thereby pinpointing specific positions of heterozygous SNPs. Integration of Short-Read RNA Library To prepare short-read RNA-Seq data for analysis, sequences were trimmed to remove adapters low-quality read, and PCR duplicates using trim-galore v0.6.10 20 and Cutadapt v4.7. 21 Read qualities were inspected using fastQC v0.11.7 22 and multiQC v1.16 23 and filtered for reads > = 50bp and quality > = 30. From the identified heterozygous SNP loci, the varying nucleotides expressed at these positions were generated using SAMtools mpileup from SAMtools 1.18. 24 We ensured that both the short-read and long-read RNA-Seq analyses were based on the same tissues and samples. Quantify Expression for Heterozygous Loci After overlaying reads found at the heterozygous loci, we quantified the expressed nucleotides at these positions. This was accomplished using a custom Python tool. 28 This tool tallies the occurrences of each nucleotide variant at a given locus from the output of the SAMtools program. For example, if a particular position has both 'A' and 'G' variants, the tool calculates the frequencies of 'A' and 'G' in the reads. This process is repeated across all identified heterozygous sites in the genome. Quantify Allele Expression Per Haplotype The expression values from these heterozygous positions were then aggregated to quantify expression values representing each allele. Expression values of each nucleotide at each heterozygous loci were summed according to their respective haplotype sequence. When a certain haplotype sequence is expressed more frequently compared to its counterpart, it is evidence that this particular allele is being expressed at a higher level. The distribution of read counts across all samples is provided in Supplementary Fig. 2 . Identify Significant Allele Specific Expression After deriving allele expression, we then pinpointed ASE using filtering and statistical techniques outlined below. First, we excluded cases where neither of the allele expression values being compared met or exceeded a threshold of 10. We then assessed ASE by examining the differential expression between haplotypes. To do so, we applied a log transformation to calculate allele expression fold change (aeFC) between both allele expression values. The equation for aeFC is shown below: $$allele exp. fold change=log2(allele 1 exp. value) - log2(allele 2 exp. value)$$ The distribution of aeFC across all samples is provided in Supplementary Fig. 3 . For further support of aeFC results, we used another statistical test to examine disparities between calculated expression values. Operating under the null hypothesis that allele expression values for any gene should be roughly equivalent between alleles, we calculated p-values using a binomial test. Considering the count-based nature of our data, we employed the Benjamini-Hochberg procedure to manage the expected false discovery rate, yielding adjusted p-values. For the final stage of significant ASE identification, we established stringent criteria to distinguish instances of significant allele expression imbalance: the aeFC between two alleles had to be at least an absolute value of 2, the adjusted p-value needed to be ≤ .05, and at least one of the calculated allele expression values be ≥ 5. Incorporating Histone Modification Data ChIP-seq data was integrated by delineating a region extending from the initial to the last heterozygous position in our haplotype transcripts. Leveraging this defined region, we ascertained any overlaps with key chromatin peaks, specifically H3K27me3, H3K27ac, H3K4me1, and H3K4me3, on a tissue and sample basis. Identify Genes with ASE We then identified the genes shown to exhibit ASE by employing Ensembl’s Variant Effect Predictor (VEP) v10 25 on the delineated heterozygous loci. VEP may provide multiple annotations for a single variant, therefore, variants predicted as intronic or intergenic were filtered out to only annotate variants strictly from RNA-Seq. If a variant's annotation was predicted solely as intergenic or intronic, the data was not included in the downstream analyses. Validation of ASE Events Our validation set consisted of short-read RNA-seq data from 66 liver samples, 41 males and 26 females, with no evidence of liver disease. These samples were comprised of 12 breeds including 27 Quarter Horses, 17 Warmbloods, 8 Thoroughbreds, 3 Andalusians, 3 Arabians, 2 Lusitanos, 1 Percheron, 1 Shire, 1 Ponys of the Americas, 1 Friesian, 1 Mustang, and 1 Gypsy Vanner. The average age of this cohort was 3.49 years and ranged from 1 month to 8 years of age. RNA-seq was performed with Illumina polyA-selection with a read length of 2 x 150bp. Adapter trimming, poly-A trimming, N trimming, quality trimming, length filtering, and removal of PCR duplicates were conducted using HTStream, version 1.3.3. 26 Reads were aligned to EquCab.3.0 using STAR, version 2.7.10b. 27 Expression counts at identified ASE loci in liver were generated using SAMtools mpileup from SAMtools 1.18. 24 Loci that were not heterozygous were filtered out. We used the same significance criteria as our original cohort - aeFC ≥ 2 and adjusted p-value ≤ .05 Allele Specific Differentially Expressed Gene Enrichment Analysis In our investigation of allele-specific expression (ASE) and its impact on biological pathways, we performed gene enrichment analysis (GEA) using the KOBAS KEGG Orthology-Based Annotation System. 28 Our method involved a deliberate combination of allele specifically expressed genes (ASDEGs) from various samples on a tissue-specific basis. By inputting these lists of genes into KOBAS for each tissue type, we could identify pathways with a significant proportion of ASDEGs. P-values were supplied by KOBAS for each impacted pathway, and significant pathways were identified as having a p-value ≤ 0.05. Data frame management and statistical analyses were performed using scipy 29 , numpy 30 , and pandas. 31 Data visualization was achieved using matplotlib 32 and seaborn. 33 Results Allele Specific Expression in the Horse Genome Recent data from the equine functional annotation of animal genomes (FAANG) initiative was leveraged for this analysis. 10 Haplotypes for each horse/tissue sample were identified from Iso-seq data. Across all samples, we identified 87,174 heterozygous loci. Using these loci to differentiate alleles, and subsequently quantifying the nucleotide reads at these positions using associated short-read RNA data from the same horse/tissue sample, 42,900 allele expression events were compared. After filtering and performing statistical analyses described in Methods , we compiled this data into an allele expression resource . Using this resource, we identified 635 (1.48%) of allele expression events as having demonstrated ASE. ASE was found to occur in each tissue analyzed, with the liver containing the highest proportion of ASE occurrences ( Table 1 ). Of the genes showing evidence of ASE, referred to here as allele specific differentially expressed genes (ASDEGs), 80 exhibited ASE in more than one tissue or sample ( Figure 1 ). Table 1. Distribution of Analyzed Genes Across Tissues An overview of the alleles examined across multiple tissue types. The "Alleles Compared" column enumerates the number of allele comparisons within each specific tissue type (2 alleles for each comparison). The "Significant Allele Imbalance" column identifies the subset of these alleles that exhibited notable expression differences from the expected equilibrium in our study. Investigating Heterozygous Loci A total of 774 heterozygous loci were identified within ASE events. At these loci, variant effects were predicted, and approximately 43% were located within 3' untranslated regions ( Table 2 ). A total of 497 (64.2%) of the identified variants in ASE events fell within histone modified regions. ASE events were most commonly found in association with H3K27ac peaks (n=377, 55.3%), followed by H3K4me3 peaks (n=293, 43.0%), H3K4me1 peaks (n=268, 39.4%), and H3K27me3 peaks (n=170, 24.9%). From the 497 ASE events identified as having SNPs associated with histone modification regions, 369 (74.2%) showed overlap of multiple histone marks. The three most common overlapping histone modification regions with identified variants were H3K4me3 and H3K27ac, H3K27ac and H3K4me1, and H3K27ac with H3K4me1 and H3K4me3 ( Figure 2 ). Table 2. Variant Types Detected in ASE Events Variant types identified in allele-specific expression events within the study's sample set. Variant predictions were made using VEP. 25 Differentially Expressed Gene Enrichment Analysis We identified 168 KEGG pathways containing a significant proportion of ASDEGs, including metabolic pathways, endocytosis, and the Ras signaling pathway. In our study, the liver contained the greatest number of pathways significantly impacted by ASE ( Figure 3 ). Validation of Allele Specific Variants To validate our putatively identified ASE loci in liver tissue, we examined the loci in a larger dataset of liver tissue, consisting of 66 samples from horses of various breeds. All 155 heterozygous loci identified in liver tissue from our original FAANG horses were tested in the validation set, resulting in 8,849 heterozygous loci expression comparisons. Specifically, 7,436 (84%) of the comparisons made across all n=66 samples in our validation set were confirmed to show ASE, with 7019 (94.4%) of these comparisons in the same direction (i.e. allele A demonstrates higher expression and allele B demonstrates lower expression: Figure 4 ). Of the 155 ASE loci we tested, 96.7% showed ASE in at least one of the samples used in our validation set, with 85 (54.8%) showing ASE in at least 90% of the n=66 samples tested ( Supplementary Figure 4 ). There was no specific effect of breed (data not shown). Discussion ASE Analysis The foundation of our ASE analysis was the identification and assignment of heterozygous loci. The incorporation of full-length transcripts from Iso-seq from the equine FAANG initiative enabled these assignments. Short-read RNA-Seq’s segmented view of genetic sequences presents challenges in congruent sequence construction, 1 while the unfragmented view of the transcriptome obtained from Iso-seq facilitates a more robust identification of haplotypes. 7 , 8 This methodology surpasses evaluations based solely on expression values of individual heterozygous variants, ensuring a more thorough and accurate assessment. 6 – 8 To reduce the likelihood of including artifacts or minor variations that do not represent true differential expression, we excluded ASE events with variants that were only identified as within intergenic or intronic regions. Given the nature of RNA-Seq data, this filtering ensures that the analysis prioritizes variants that are most pertinent to the corresponding genes. This supplementary data could possibly be used to extend currently annotated transcribed regions, since this data was generated strictly from high-quality RNA-Seq. Additionally, we excluded cases where neither of the allele expression values being compared met or exceeded a threshold of 10 reads. Lastly, sequencing data from sex chromosomes was also filtered out because of this study’s focus on autosomal genes. These filtered out events are still available for analysis in supplementary materials. Tissue Regulation via ASE One of the main goals of this study was to identify tissue specific ASE. We discovered genes demonstrating ASE in one tissue, while exhibiting equal expression across alleles in other tissues. This suggests that the regulatory mechanisms contributing to these ASDEGs are unique to the particular tissues where they were found. 1 , 2 Such specificity could be due to tissue-specific promoters, enhancers, or other regulatory elements that influence gene expression differently in each tissue type. 1 , 3 The apolipoprotein E gene ( APOE ), was among these genes and demonstrated ASE in the two liver tissues examined (one from each sex), while having equal allele expression in testis, parietal cortex, and lung tissues. Notably, all ASE events involving APOE favored the allele with the same missense variant at locus chr10:15714449. Another gene in this study, SLC6A17 , is a part of the SLC6 family of transporters. This gene exhibited ASE in 2 out of 4 parietal cortex tissues in this study. Interestingly, both cases of SLC6A17 ASE were in parietal cortex tissue of mares, while stallions had a bi-alleleic expression for SLC6A17 in parietal cortex tissues. Both ASE events involving SLC6A17 favored the allele with the same 3 prime UTR variant at locus chr5:54600372. This example alludes to the use of our resource to compare ASE across sexes, helping to identify putatively sex-specific tissue regulation. Lastly, transmembrane glycoprotein gene ENPP5 demonstrated evidence of ASE in 2 out of 2 samples of liver tissue used in this study, while having equal expression across alleles in parietal cortex and lung tissues. Both instances of ASE involving ENPP5 involved favoring alleles with 3 prime UTR variants at chr20:45654742. When evaluating other ASE patterns, we discovered ASE events that were common among multiple tissues, suggesting more broadly used regulation mechanisms. Allele specific differentially expressed genes (ASDEGs), demonstrating ASE in more than 4 out of 9 tissues from this study, included tubulin gene UBA1 (9 tissues), L ribosomal protein RPL3 (8 tissues), serine/threonine protein kinase gene SGK1 (6 tissues), WT1 -associating gene WTAP (5 tissues), Coat Complex Subunit Alpha gene COPA (5 tissues), and lysine-rich coiled-coil protein KRCC1 (5 tissues). Histone Modifications ChIP-seq data from the same FAANG horse/tissue samples was integrated to provide a broader evaluation of allele-specific expression in the context of annotated histone modifications. 21 , 35 This leads to an improved perspective on the epigenetic acting influences in allele-specific gene expression, illuminating the complex interplay between genetic variations and epigenetic regulation. Variants within H3K27ac peaks, typically present at active transcription start sites (TSS), highlight the potential relationship between coding changes within TSS and allelic expression. 35 Additionally, intersection of ASE events with H3K4me1 peaks, often associated with enhancer regions, suggests that variants within these regions could lead to enhanced expression relative to the other allele. 35 Variants were also identified within H3K4me3 peaks, which are commonly identified near promoters of actively transcribed genes. This suggests that a particular promoter sequence may be favored for transcription. 35 Similarly the repressive mark, H3K27me3, further emphasizes the possible interplay between allele imbalance and the epigenetic landscape. 35 The co-localization of ASE with active marks such as H3K27ac and H3K4me1, often found at transcription start sites and enhancer regions, respectively, suggests that these epigenetically active domains may predispose certain alleles for increased expression by facilitating a more accessible chromatin state. 35 Simultaneously, the intersection with H3K4me3, associated with promoters of actively transcribed genes, and H3K27me3, a mark of transcriptional repression, indicates a nuanced regulatory landscape where alleles may be differentially expressed due to the combinatorial effects of epigenetic modifications. 35 These overlapping epigenetic regions may serve as hotspots for ASE, where the orchestration of gene activation and silencing is fine-tuned by histone marks and coding variants. However, it is important to note that not all impactful epigenetic markers can be found in transcribed regions. Interestingly, a large portion (~ 30%) of heterozygous loci within ASE events were not found to be within this histone modified regions. This could mean that the nucleotide variation across alleles may be directly responsible for the significant expression disparity. Validation To further validate our findings, we employed short-read RNA sequencing data from 66 additional equine liver samples across various breeds of horses. Liver tissue was selected for validation because it showed the highest frequency of ASE events in our original cohort (Table 1 ). This robust validation approach enabled us to confirm the reliability and reproducibility of our ASE observations across breeds and ages of horses, reinforcing the utility of our integrated Iso-seq and short-read RNA-Seq methodologies for uncovering the complexities of gene expression regulation and the prevalence of ASE in the equine genome. Limitations & Future Direction This research presents the first ASE analysis of its kind for the horse, however there are limitations. First, the FAANG sample size was relatively small, and we did not analyze all tissues, which may affect the generalizability of our findings. Additionally, we did not have parental sequencing to inspect the origin of heterozygous loci. It is also important to note that this combinatorial RNA-Seq method simply will not detect all instances of ASE. 36 Our approach of using heterozygous gene expression markers reduces the total number of ASE events we could identify in the study since some locations in the genome may be homozygous yet still exhibit ASE due to epigenetic factors alone, such as parental imprinting. 37 Studies utilizing both RNA-Seq and whole genome sequencing have estimated the percent of genes exhibiting ASE from 1–20%, depending on the strength of statistical filtering. 1 , 36 , 37 In this study, we used high filtering standards to declare a significant allele imbalance and identified ASE in 1.48% of alleles compared. While we may not be able to detect all ASE events, using this method provides a way to inspect linked variants and their putatively high impact on gene expression. Future investigations will extend this approach to a larger set of samples and tissues. Furthermore, the identified heterozygous loci used in this analysis could be overlaid with short-read RNA-Seq from horses in different developmental stages. This may demonstrate that specific allele sequences are pertinent to development and become otherwise unneeded as the horse reaches maturity. This integration will lay the foundation for a deeper understanding of the intricate relationship between imbalances in allele expression and their role within the equine genome. Conclusion This study introduces the first multi-tissue analysis of ASE specific to the equine genome, spanning 4 individual Thoroughbred horses and 9 diverse tissues, with validation of liver ASE across multiple horse breeds. Here, we provide an allele expression resource for the equine community to advance future gene regulation investigations. This resource was designed to easily allow researchers to investigate genes of interest and set their own filtering criteria to detect allelic imbalance. Additionally, this main database was divided into tissue specific tracts, thereby allowing for tissue-specific allele expression analysis. With this resource, we inspected the heterozygous loci across alleles, potentially responsible for significant regulation of gene expression, identified pathways containing a significant proportion of these regulatory events, and pinpointed ASE patterns on a tissue-wide scale. As a result, we have demonstrated the potential that this method provides to enrich our understanding of the intricacies of equine genetic regulation by identifying notable loci variations that putatively have a significant impact on gene expression. Abbreviations aeFC - allelic expression fold change ASE - allele specific expression ASDEG - allele specific differentially expressed gene Iso-Seq - Isoform Sequencing RNA-Seq – RNA-sequencing SNP - single nucleotide polymorphism GEA - gene enrichment analysis Declarations Ethics approval and consent to participate All protocols were approved by the University of California Davis Institutional Animal Care and Use Committee (Protocol #19037). Consent for publication Not applicable. Availability of data and materials The master and tissue separated allele expression data frames generated and analyzed in this study are available as Supplementary Files 3-10 . The short-read RNA sequencing analyzed in this study is available in the ENA and SRA repositories under the accession number PRJEB26787 (female tissues - https://www.ebi.ac.uk/ena/browser/view/PRJEB26787) and PRJEB53382 (male tissues - https://www.ebi.ac.uk/ena/browser/view/PRJEB53382). The Iso-seq data analyzed in this study is available in the ENA and SRA repositories under the accession number PRJEB53020. (https://www.ebi.ac.uk/ena/browser/view/PRJEB53020) The short-read RNA sequencing from our validation set used in this study is available in the SRA repository under the accession number SUB14222280. Histone ChIP-seq analyzed in this study is available via Kingsley et al. (https://doi.org/10.3390/genes11010003) and Barber et al (thesis; https://digitalcommons.unl.edu/animalscidiss/233/). Competing interests The authors declare that they have no competing interests, Funding This project was supported by the Grayson-Jockey Club Research Foundation (C.J.F., J.L.P, R.R.B.), USDA National Institute of Food and Agriculture Animal Breeding and Functional Annotation of Genomes (A1201) Grant 2019-67015-29340 (J.L.P, C.J.F., R.B.) as well as NRSP-8 Species Coordinator Funds from the USDA National Institute of Food and Agriculture (C.J.F., J.L.P, R.R.B.), and the UC Davis Center for Equine Health with funds provided by the State of California pari-mutuel fund and contributions by private donors (C.J.F., J.L.P, R.R.B.). Additional support for S.R. was provided by the Foundation for the Horse Young Investigators Research Grant and the Ann T. Bowling Fellowship from the Veterinary Genetics Laboratory at UC Davis. C.J.F. was provided by NIH NCATS L40 TR001136. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors’ Contributions H.H., S.P, C.J.F. made substantial contributions to the conception and design of the work. H.H., S.P., C.J.F., T.S., S.R, R.B., J.P. contributed to the acquisition, analysis, and interpretation of data. H.H., S.P., C.J.F. drafted the manuscript. All authors reviewed the draft, provided edits and and agreed both to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. Acknowledgements We would like to acknowledge the UC Berkeley QB3 Genomics core and Dr. Blythe Durbin-Johnson for their support with this study. Codes/Scripts Computer codes/scripts used in this analysis are publicly available on github at : https://github.com/hdheath/ASE_equine_transcriptome/blob/main/README.md References Cleary S, Seoighe C. Perspectives on Allele-Specific Expression. Annual Rev Biomedical Data Sci. 2021;4(1):101–22. https://doi.org/10.1146/annurev-biodatasci-021621-122219 . Castel SE, Aguet F, Mohammadi P, Aguet F, Anand S, Ardlie KG, Gabriel S, Getz GA, Graubert A, Hadley K, Handsaker RE, Huang KH, Kashin S, Li X, MacArthur DG, Meier SR, Nedzel JL, Nguyen DT, Segrè AV, GTEx Consortium. A vast resource of allelic expression data spanning human tissues. Genome Biol. 2020;21(1):234. https://doi.org/10.1186/s13059-020-02122-z . Steri M, Idda ML, Whalen MB, Orrù V. Genetic variants in mRNA untranslated regions. WIREs RNA. 2018;9(4):e1474. https://doi.org/10.1002/wrna.1474 . Li S, Mason CE. The Pivotal Regulatory Landscape of RNA Modifications. 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Generation of a Biobank From Two Adult Thoroughbred Stallions for the Functional Annotation of Animal Genomes Initiative. Front Genet. 2021;12. https://www.frontiersin.org/articles/10.3389/fgene.2021.650305 . Burns EN, Bordbari MH, Mienaltowski MJ, Affolter VK, Barro MV, Gianino F, Gianino G, Giulotto E, Kalbfleisch TS, Katzman SA, Lassaline M, Leeb T, Mack M, Müller EJ, MacLeod JN, Ming-Whitfield B, Alanis CR, Raudsepp T, Scott E, Finno CJ. Generation of an equine biobank to be used for Functional Annotation of Animal Genomes project. Anim Genet. 2018;49(6):564–70. https://doi.org/10.1111/age.12717 . Kingsley NB, Kern C, Creppe C, Hales EN, Zhou H, Kalbfleisch TS, MacLeod JN, Petersen JL, Finno CJ, Bellone RR. Functionally Annotating Regulatory Elements in the Equine Genome Using Histone Mark ChIP-Seq. Genes. 2019;11(1):3. https://doi.org/10.3390/genes11010003 . IsoPhase: Haplotyping using Iso Seq data. (n.d.), GitHub. https://github.com/Magdoll/cDNA_Cupcake/wiki/IsoPhase:-Haplotyping-using-Iso-Seq-data Accessed 23 September 2023. Krueger F. (2023). Trim Galore. https://github.com/FelixKrueger/TrimGalore (Original work published 2016) Accessed 7 July 2023. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17(1):10–2. https://doi.org/10.14806/ej.17.1.200 . Andrews S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. Accessed 8 July 2023. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8. https://doi.org/10.1093/bioinformatics/btw354 . Twelve years of SAMtools and BCFtools, Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, Shane A, McCarthy RM, Davies H, Li. February, GigaScience, Volume 10, Issue 2, 2021, giab008, https://doi.org/10.1093/gigascience/giab008 . McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, Flicek P, Cunningham F. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17:122. https://doi.org/10.1186/s13059-016-0974-4 . HTStream. (2020) [Source code]. https://github.com/s4hts/HTStream . Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. 10.1093/bioinformatics/bts635 . Epub 2012 Oct 25. PMID: 23104886; PMCID: PMC3530905. Bu D, Luo H, Huo P, Wang Z, Zhang S, He Z, Wu Y, Zhao L, Liu J, Guo J, Fang S, Cao W, Yi L, Zhao Y, Kong L. KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis. Nucleic Acids Res. 2021;49(W1):W317–25. https://doi.org/10.1093/nar/gkab447 . Pauli Virtanen R, Gommers TE, Oliphant M, Haberland T, Reddy D, Cournapeau E, Burovski P, Peterson W, Weckesser J, van der Bright, Stéfan J, Brett J, Wilson K, Jarrod Millman N, Mayorov ARJ, Nelson E, Jones R, Kern E, Larson CJ, Carey İlhan, Polat Y, Feng EW, Moore, Quintero EA, Harris CR, Archibald AM. Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen,, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. (2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat Methods, 17(3), 261–72. Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, Kern R, Picus M, Hoyer S, van Kerkwijk MH, Brett M, Haldane A, del Río JF, Wiebe M, Peterson P, Oliphant TE. Array programming with NumPy. Nature. 2020;585(7825):357–62. https://doi.org/10.1038/s41586-020-2649-2 . McKinney W. & others. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference (Vol. 445, pp. 51–56). Matplotlib. A 2D Graphics Environment. (n.d.) https://ieeexplore.ieee.org/document/4160265/ Accessed 23 September 2023. Waskom ML. seaborn: statistical data visualization. J Open Source Softw. 2021;6(60):3021. https://doi.org/10.21105/joss.03021 . Castel SE, Levy-Moonshine A, Mohammadi P, Banks E, Lappalainen T. Genome Biol. 2015;16(1):195. https://doi.org/10.1186/s13059-015-0762-6 . Tools and best practices for data processing in allelic expression analysis. Li X, Wang X, He K, Ma Y, Su N, He H, Stolc V, Tongprasit W, Jin W, Jiang J, Terzaghi W, Li S, Deng XW. High-Resolution Mapping of Epigenetic Modifications of the Rice Genome Uncovers Interplay between DNA Methylation, Histone Methylation, and Gene Expression. Plant Cell. 2008;20(2):259–76. https://doi.org/10.1105/tpc.107.056879 . Ghazanfar S, Vuocolo T, Morrison JL, Nicholas LM, McMillen IC, Yang JYH, Buckley MJ, Tellam RL. Gene expression allelic imbalance in ovine brown adipose tissue impacts energy homeostasis. PLoS ONE. 2017;12(6):e0180378. https://doi.org/10.1371/journal.pone.0180378 . Hoguin A, Rastogi A, Bowler C, Tirichine L. Genome-wide analysis of allele-specific expression of genes in the model diatom Phaeodactylum tricornutum. Sci Rep. 2021;11(1):2954. https://doi.org/10.1038/s41598-021-82529-1 . Additional Declarations No competing interests reported. Supplementary Files SupplementalFilesHeath2024.zip SupplementaryFilesFigureTableLegends.docx Cite Share Download PDF Status: Published Journal Publication published 30 Jan, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 02 Apr, 2024 Submission checks completed at journal 01 Apr, 2024 Editor assigned by journal 01 Apr, 2024 First submitted to journal 28 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4182812","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286691513,"identity":"6d624fd3-d32f-43da-9ad2-1f41be7f29c9","order_by":0,"name":"Harrison Heath","email":"","orcid":"","institution":"University of California, Davis","correspondingAuthor":false,"prefix":"","firstName":"Harrison","middleName":"","lastName":"Heath","suffix":""},{"id":286691516,"identity":"a29f5408-76ad-41b9-8ecf-36fc5bd4a5e1","order_by":1,"name":"Sichong Peng","email":"","orcid":"","institution":"University of California, Davis","correspondingAuthor":false,"prefix":"","firstName":"Sichong","middleName":"","lastName":"Peng","suffix":""},{"id":286691517,"identity":"db29e942-c523-43b2-86c5-73ddd3cc46ee","order_by":2,"name":"Tomasz Szmatola","email":"","orcid":"","institution":"University of Agriculture in Krakow","correspondingAuthor":false,"prefix":"","firstName":"Tomasz","middleName":"","lastName":"Szmatola","suffix":""},{"id":286691519,"identity":"9122569b-2254-4e4a-9395-f234f6231347","order_by":3,"name":"Stephanie Ryan","email":"","orcid":"","institution":"University of California, Davis","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"","lastName":"Ryan","suffix":""},{"id":286691521,"identity":"4493fb92-9a4b-4ce0-9bee-815afe42b3fc","order_by":4,"name":"Rebecca Bellone","email":"","orcid":"","institution":"University of California, Davis","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"","lastName":"Bellone","suffix":""},{"id":286691523,"identity":"fba64b3b-498b-4bc9-a66e-29ccb1f1ef35","order_by":5,"name":"Theodore Kalbfleisch","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Theodore","middleName":"","lastName":"Kalbfleisch","suffix":""},{"id":286691524,"identity":"ee34a1b0-2a9f-45c7-84d0-0462385e92ba","order_by":6,"name":"Jessica Petersen","email":"","orcid":"","institution":"University of Nebraska–Lincoln","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Petersen","suffix":""},{"id":286691525,"identity":"aca1035b-a27f-4fbd-a74b-c68c8e38f9db","order_by":7,"name":"Carrie Finno","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYFCCgw8OAEk5IDYAIwaGBEJaDhuAtBiTooUZrCyxAaqesBb+xsOMhwt32KRvOH5442eegjoGfvYcA7xaJA4cZjg880xa7oYzacXSPAaHGSR73uDXYsBw/sBh3rbDuRtu8BhIzgD6y+AGAVsMGIC2ALWkG9zgMf45w6COwZ5YLQlALWYSHwyYGQwkiPELb1uaIdA/ZRYfDA7zSJx5VoBXC/+Mw8yfedts5PmOH958I+FPnRx/e/IGvFqA1qDyefArB1vTQFjNKBgFo2AUjHAAAIGrSTt7FrTDAAAAAElFTkSuQmCC","orcid":"","institution":"University of California, Davis","correspondingAuthor":true,"prefix":"","firstName":"Carrie","middleName":"","lastName":"Finno","suffix":""}],"badges":[],"createdAt":"2024-03-28 14:07:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4182812/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4182812/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-11240-6","type":"published","date":"2025-01-30T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54111984,"identity":"e8bb38df-9e14-40dd-92d2-d72daa396cf2","added_by":"auto","created_at":"2024-04-04 18:35:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":192038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eASDEGs comparisons across tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe scatter plots display the absolute log2-fold change in allele expression for identified ASDEGs across various tissues. Each gene featured has at least 2 identified ASE events across all horses and tissues. ASDEGs are graphed in alphabetical order. Each dot represents the expression fold change for a gene in a specific tissue, plotted against the gene symbol on the x-axis and the absolute log2-fold change on the y-axis. The dotted line indicates the significance threshold of a 2-fold change. The color coding corresponds to different tissues, as indicated in the legend.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4182812/v1/cd06af11652d1fe11f9889af.png"},{"id":54111985,"identity":"8bb90fad-e53a-457f-9f9b-1ec4e2023d63","added_by":"auto","created_at":"2024-04-04 18:35:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistone modified regions overlapping variants of ASE events\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpset plot representing the distribution and overlap of histone modifications across heterozygous loci associated with allele-specific differentially expressed genes (ASDEGs). Each circle corresponds to a specific histone modification as indicated by the legend (H3K27ac, H3K4me1, H3K27me3, H3K4me3). Circles, and their respective frequency bars, denote the count of SNP regions that exhibit the corresponding histone modifications.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4182812/v1/427a3a1e27827388fc650bfe.png"},{"id":54111988,"identity":"54c0f2fa-2261-44e0-b0f9-5869631874d5","added_by":"auto","created_at":"2024-04-04 18:35:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eASDEG enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBar chart depicting the number of ASDEGs (Allele Specific Differentially Expressed Genes) present within significantly enriched pathways identified in each tissue type from KOBAS enrichment analysis. Tissues analyzed include Liver, Ovary, Testis, Lamina, Heart, Parietal Cortex, and Adipose.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4182812/v1/467d05e16a7cd24004ae9ae5.png"},{"id":54111987,"identity":"fc718588-2d7f-49b1-b750-6a2a49dd1f94","added_by":"auto","created_at":"2024-04-04 18:35:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":243621,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of ASE in heterozygous loci in liver tissue from an independent dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDistribution of absolute log-fold changes at heterozygous loci identified within allele-specific expression (ASE) events in liver tissue, and their overlay with a validation set of short-read RNA-seq data. Two categories are presented: non-significant events (blue) and significant events (orange) that exhibit ASE. The dashed red line indicates the significance threshold, with the loci to the right deemed to show significant ASE.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4182812/v1/f87cba2b27819f5f21b9cc54.png"},{"id":75351245,"identity":"08c84751-e565-41e7-9487-5dc2a68d81e4","added_by":"auto","created_at":"2025-02-03 16:08:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1907445,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4182812/v1/9cd17348-4f50-493c-be81-a244fb18cefd.pdf"},{"id":54111989,"identity":"3ed35aaf-9689-4a63-b43d-d16e4ff5a75c","added_by":"auto","created_at":"2024-04-04 18:35:43","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8188229,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFilesHeath2024.zip","url":"https://assets-eu.researchsquare.com/files/rs-4182812/v1/fc15644d0dffa645880ad3c7.zip"},{"id":54111986,"identity":"5fcee06a-82f2-4009-ae8e-a7aef32c6a90","added_by":"auto","created_at":"2024-04-04 18:35:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12269,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFilesFigureTableLegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-4182812/v1/2bdf988859b2463bceb5e437.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Comprehensive Allele Specific Expression Resource for the Equine Transcriptome","fulltext":[{"header":"Background","content":"\u003cp\u003eIn diploid mammalian cells, autosomal genes are usually equally expressed.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e In some cases however, a gene can exhibit expression biased for one allele over the other.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e This allele-specific expression (ASE) often results from cis-acting genetic variants on the same chromosome that are in proximity to or within the affected gene. Genetic variants within a gene can affect gene expression, mRNA stability, or mRNA function in different ways, leading to one allele being expressed more than the other. Various cis-acting have the potential to cause ASE. Although not changing the amino acid, synonymous variants could affect mRNA stability, splicing, or translational efficiency, potentially causing ASE.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Missense variants can affect the function of the RNA, possibly leading to a difference in expression between alleles.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Variants in the 3' untranslated region (UTR) can influence mRNA stability, localization, and translation, all of which can contribute to ASE. Variants identified in the 5' prime UTR could influence ASE by affecting the initiation of translation and the stability of mRNA thus altering the amount of protein produced from each allele.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Lastly, variants in splice regions can impact allele expression by altering splicing efficiency, exon skipping, creation or loss of splice sites, or affecting regulatory protein binding, all potentially influencing mRNA function and expression.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eASE may also arise from trans effects, or genetic influences on the other chromosome of an affected gene or elsewhere in the genome.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Epigenetic factors, such as DNA methylation or chromatin structure, also have the potential to significantly impact gene expression between alleles.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Interestingly, ASE predominantly manifests as tissue-specific phenomena, with loci displaying distinct expression patterns across different tissues. \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Therefore, ASE analysis can provide a way to inspect gene regulation patterns and their broader impact on biological pathways in specific tissues.\u003c/p\u003e \u003cp\u003eAdvances in next-generation sequencing technologies, particularly RNA-sequencing (RNA-Seq), have revolutionized our ability to analyze gene expression and genetic variation. Furthermore, long-read RNA-Seq allows for the straightforward identification of variants that are inherited together as haplotypes from full-length transcripts.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e The loci of these haplotypes can then be overlaid with short-read RNA-Seq reads. This integration provides the read counts of nucleotides existing at each locus, which can then be used to quantify the expression level of each allele for each gene that contains heterozygous loci. Finally, expression levels of each allele can be compared against one another to identify an allelic imbalance.\u003c/p\u003e \u003cp\u003eThe study of ASE in horses is enabled by the availability of a high-quality reference genome\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and long- and short-read sequencing technologies, contributing to a more comprehensive transcriptome annotation.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e While prior studies on ASE in horses were focused on paternal/maternal imprinting in early development, this research distinguishes itself by examining ASE in adult mares and stallions.\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e In this study, we performed an ASE analysis of the equine transcriptome using a combinatorial Iso-seq and short-read RNA-Seq approach. Our primary goal of this research is to contribute to the Functional Annotation of the Animal Genome (FAANG) project, a large-scale collaborative effort aimed to identify all functional elements for animals.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e To accomplish this, we provide a comprehensive resource of allelic expression detected from linked heterozygous loci. We have designed this publicly accessible database to enable future research into important regulatory variants that may impact equine health and disease at the molecular level. By extending the examination of ASE to the equine species using this method, we can further understand the uses of emerging next-generation sequencing technologies and the mechanisms underlying gene regulation in the equine genome.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGeneration of Data\u003c/h2\u003e \u003cp\u003eData from nine tissues from prior FAANG analyses were selected for ASE analyses: lamina, liver, left lung, left ventricle of the heart, longissimus muscle, skin, parietal cortex, testes, and ovary from 4 healthy Thoroughbreds (2 mares and 2 stallions).\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e This selection aimed to encompass a broad spectrum of biological functions and, consequently, varied gene expression.\u003c/p\u003e \u003cp\u003eThe RNA isolation for Iso-seq was performed separately from the same tissues as the RNA utilized for mRNA-Seq, using an identical protocol.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e For Iso-seq, we selected the highest quality RNA per sex for each tissue (except for parietal cortex and sex-specific tissues). Since parietal cortex was the pilot tissue, long-read RNA sequencing was performed on all four horses. All selected RNA samples had integrity (RIN) values greater than or equal to 7. Selected tissues were processed for Iso-seq in a single batch. The cDNA libraries were developed and sequenced at the UC Berkeley QB3 Genomics core facility. Two randomly selected libraries were combined and then sequenced together on a single SMRT cell of the PacBio Sequel II system, as previously described.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eChIP-seq data for the histone modifications evaluated in this study were sourced and analyzed from prior publications that examined the same eight tissues.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying Haplotypes\u003c/h2\u003e \u003cp\u003eThe overall workflow for identifying ASE events is outlined in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e. Key to our approach was the use of Iso-seq data to extrapolate haplotypes for each horse individually, utilizing the isophase Cupcake software v29.0.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e The software's function was to identify variations between otherwise identical full-length reads, thereby pinpointing specific positions of heterozygous SNPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIntegration of Short-Read RNA Library\u003c/h2\u003e \u003cp\u003eTo prepare short-read RNA-Seq data for analysis, sequences were trimmed to remove adapters low-quality read, and PCR duplicates using trim-galore v0.6.10\u003csup\u003e20\u003c/sup\u003e and Cutadapt v4.7.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Read qualities were inspected using fastQC v0.11.7\u003csup\u003e22\u003c/sup\u003e and multiQC v1.16\u003csup\u003e23\u003c/sup\u003e and filtered for reads\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;50bp and quality\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30. From the identified heterozygous SNP loci, the varying nucleotides expressed at these positions were generated using SAMtools mpileup from SAMtools 1.18.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e We ensured that both the short-read and long-read RNA-Seq analyses were based on the same tissues and samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQuantify Expression for Heterozygous Loci\u003c/h2\u003e \u003cp\u003eAfter overlaying reads found at the heterozygous loci, we quantified the expressed nucleotides at these positions. This was accomplished using a custom Python tool.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e This tool tallies the occurrences of each nucleotide variant at a given locus from the output of the SAMtools program. For example, if a particular position has both 'A' and 'G' variants, the tool calculates the frequencies of 'A' and 'G' in the reads. This process is repeated across all identified heterozygous sites in the genome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eQuantify Allele Expression Per Haplotype\u003c/h2\u003e \u003cp\u003eThe expression values from these heterozygous positions were then aggregated to quantify expression values representing each allele. Expression values of each nucleotide at each heterozygous loci were summed according to their respective haplotype sequence. When a certain haplotype sequence is expressed more frequently compared to its counterpart, it is evidence that this particular allele is being expressed at a higher level.\u003c/p\u003e \u003cp\u003eThe distribution of read counts across all samples is provided in \u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentify Significant Allele Specific Expression\u003c/h2\u003e \u003cp\u003eAfter deriving allele expression, we then pinpointed ASE using filtering and statistical techniques outlined below. First, we excluded cases where neither of the allele expression values being compared met or exceeded a threshold of 10. We then assessed ASE by examining the differential expression between haplotypes. To do so, we applied a log transformation to calculate allele expression fold change (aeFC) between both allele expression values. The equation for aeFC is shown below:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$allele exp. fold change=log2(allele 1 exp. value) - log2(allele 2 exp. value)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe distribution of aeFC across all samples is provided in \u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eFor further support of aeFC results, we used another statistical test to examine disparities between calculated expression values. Operating under the null hypothesis that allele expression values for any gene should be roughly equivalent between alleles, we calculated p-values using a binomial test. Considering the count-based nature of our data, we employed the Benjamini-Hochberg procedure to manage the expected false discovery rate, yielding adjusted p-values.\u003c/p\u003e \u003cp\u003eFor the final stage of significant ASE identification, we established stringent criteria to distinguish instances of significant allele expression imbalance: the aeFC between two alleles had to be at least an absolute value of 2, the adjusted p-value needed to be \u0026le;\u0026thinsp;.05, and at least one of the calculated allele expression values be \u0026ge;\u0026thinsp;5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIncorporating Histone Modification Data\u003c/h2\u003e \u003cp\u003eChIP-seq data was integrated by delineating a region extending from the initial to the last heterozygous position in our haplotype transcripts. Leveraging this defined region, we ascertained any overlaps with key chromatin peaks, specifically H3K27me3, H3K27ac, H3K4me1, and H3K4me3, on a tissue and sample basis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIdentify Genes with ASE\u003c/h2\u003e \u003cp\u003eWe then identified the genes shown to exhibit ASE by employing Ensembl\u0026rsquo;s Variant Effect Predictor (VEP) v10\u003csup\u003e25\u003c/sup\u003e on the delineated heterozygous loci. VEP may provide multiple annotations for a single variant, therefore, variants predicted as intronic or intergenic were filtered out to only annotate variants strictly from RNA-Seq.\u0026nbsp;If a variant's annotation was predicted solely as intergenic or intronic, the data was not included in the downstream analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eValidation of ASE Events\u003c/h2\u003e \u003cp\u003eOur validation set consisted of short-read RNA-seq data from 66 liver samples, 41 males and 26 females, with no evidence of liver disease. These samples were comprised of 12 breeds including 27 Quarter Horses, 17 Warmbloods, 8 Thoroughbreds, 3 Andalusians, 3 Arabians, 2 Lusitanos, 1 Percheron, 1 Shire, 1 Ponys of the Americas, 1 Friesian, 1 Mustang, and 1 Gypsy Vanner. The average age of this cohort was 3.49 years and ranged from 1 month to 8 years of age. RNA-seq was performed with Illumina polyA-selection with a read length of 2 x 150bp. Adapter trimming, poly-A trimming, N trimming, quality trimming, length filtering, and removal of PCR duplicates were conducted using HTStream, version 1.3.3.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Reads were aligned to EquCab.3.0 using STAR, version 2.7.10b.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Expression counts at identified ASE loci in liver were generated using SAMtools mpileup from SAMtools 1.18.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Loci that were not heterozygous were filtered out. We used the same significance criteria as our original cohort - aeFC\u0026thinsp;\u0026ge;\u0026thinsp;2 and adjusted p-value\u0026thinsp;\u0026le;\u0026thinsp;.05\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAllele Specific Differentially Expressed Gene Enrichment Analysis\u003c/h2\u003e \u003cp\u003eIn our investigation of allele-specific expression (ASE) and its impact on biological pathways, we performed gene enrichment analysis (GEA) using the KOBAS KEGG Orthology-Based Annotation System.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Our method involved a deliberate combination of allele specifically expressed genes (ASDEGs) from various samples on a tissue-specific basis. By inputting these lists of genes into KOBAS for each tissue type, we could identify pathways with a significant proportion of ASDEGs. P-values were supplied by KOBAS for each impacted pathway, and significant pathways were identified as having a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eData frame management and statistical analyses were performed using scipy\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, numpy\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, and pandas.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Data visualization was achieved using matplotlib\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and seaborn.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eAllele Specific Expression in the Horse Genome\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRecent data from the equine functional annotation of animal genomes (FAANG) initiative was leveraged for this analysis.\u003csup\u003e10\u003c/sup\u003e Haplotypes for each horse/tissue sample were identified from Iso-seq data. Across all samples, we identified 87,174 heterozygous loci. Using these loci to differentiate alleles, and subsequently quantifying the nucleotide reads at these positions using associated short-read RNA data from the same horse/tissue sample, 42,900 allele expression events were compared. After filtering and performing statistical analyses described in \u003cem\u003eMethods\u003c/em\u003e, we compiled this data into an allele expression resource\u003cem\u003e.\u0026nbsp;\u003c/em\u003eUsing this resource, we identified 635 (1.48%) of allele expression events as having demonstrated ASE. ASE was found to occur in each tissue analyzed, with the liver containing the highest proportion of ASE occurrences (\u003cstrong\u003eTable 1\u003c/strong\u003e). Of the genes showing evidence of ASE, referred to here as allele specific differentially expressed genes (ASDEGs), 80 exhibited ASE in more than one tissue or sample (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Distribution of Analyzed Genes Across Tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"603\" 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\" alt=\"image\" height=\"387\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAn overview of the alleles examined across multiple tissue types. The \u0026quot;Alleles Compared\u0026quot; column enumerates the number of allele comparisons within each specific tissue type (2 alleles for each comparison). The \u0026quot;Significant Allele Imbalance\u0026quot; column identifies the subset of these alleles that exhibited notable expression differences from the expected equilibrium in our study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInvestigating Heterozygous Loci\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 774 heterozygous loci were identified within ASE events. \u0026nbsp;At these loci, variant effects were predicted, and approximately 43% were located within 3\u0026apos; untranslated regions (\u003cstrong\u003eTable 2\u003c/strong\u003e). A total of 497 (64.2%) of the identified variants in ASE events fell within histone modified regions. ASE events were most commonly found in association with H3K27ac peaks (n=377, 55.3%), followed by H3K4me3 peaks (n=293, 43.0%), H3K4me1 peaks (n=268, 39.4%), and H3K27me3 peaks (n=170, 24.9%). From the 497 ASE events identified as having SNPs associated with histone modification regions, 369 (74.2%) showed overlap of multiple histone marks. The three most common overlapping histone modification regions with identified variants were H3K4me3 and H3K27ac, H3K27ac and H3K4me1, and H3K27ac with H3K4me1 and H3K4me3 (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 2. Variant Types Detected in ASE Events\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"603\" 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\" alt=\"image\" height=\"460\"\u003e\u003c/p\u003e\n\u003cp\u003eVariant types identified in allele-specific expression events within the study\u0026apos;s sample set. Variant predictions were made using VEP. \u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDifferentially Expressed Gene Enrichment Analysis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe identified 168 KEGG pathways containing a significant proportion of ASDEGs, including metabolic pathways, endocytosis, and the Ras signaling pathway. In our study, the liver contained the greatest number of pathways significantly impacted by ASE (\u003cstrong\u003eFigure 3\u003c/strong\u003e).\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003eValidation of Allele Specific Variants\u0026nbsp;\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eTo validate our putatively identified ASE loci in liver tissue, we examined the loci in a larger dataset of liver tissue, consisting of 66 samples from horses of various breeds. All 155 heterozygous loci identified in liver tissue from our original FAANG horses were tested in the validation set, resulting in 8,849 heterozygous loci expression comparisons. Specifically, 7,436 (84%) of the comparisons made across all n=66 samples in our validation set were confirmed to show ASE, with 7019 (94.4%) of these comparisons in the same direction (i.e. allele A demonstrates higher expression and allele B demonstrates lower expression: \u003cstrong\u003eFigure 4\u003c/strong\u003e). Of the 155 ASE loci we tested, 96.7% showed ASE in at least one of the samples used in our validation set, with 85 (54.8%) showing ASE in at least 90% of the n=66 samples tested (\u003cstrong\u003eSupplementary Figure 4\u003c/strong\u003e). There was no specific effect of breed (data not shown).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eASE Analysis\u003c/h2\u003e \u003cp\u003eThe foundation of our ASE analysis was the identification and assignment of heterozygous loci. The incorporation of full-length transcripts from Iso-seq from the equine FAANG initiative enabled these assignments. Short-read RNA-Seq\u0026rsquo;s segmented view of genetic sequences presents challenges in congruent sequence construction,\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e while the unfragmented view of the transcriptome obtained from Iso-seq facilitates a more robust identification of haplotypes.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e This methodology surpasses evaluations based solely on expression values of individual heterozygous variants, ensuring a more thorough and accurate assessment.\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTo reduce the likelihood of including artifacts or minor variations that do not represent true differential expression, we excluded ASE events with variants that were only identified as within intergenic or intronic regions. Given the nature of RNA-Seq data, this filtering ensures that the analysis prioritizes variants that are most pertinent to the corresponding genes. This supplementary data could possibly be used to extend currently annotated transcribed regions, since this data was generated strictly from high-quality RNA-Seq.\u0026nbsp;Additionally, we excluded cases where neither of the allele expression values being compared met or exceeded a threshold of 10 reads. Lastly, sequencing data from sex chromosomes was also filtered out because of this study\u0026rsquo;s focus on autosomal genes. These filtered out events are still available for analysis in supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTissue Regulation via ASE\u003c/h2\u003e \u003cp\u003eOne of the main goals of this study was to identify tissue specific ASE. We discovered genes demonstrating ASE in one tissue, while exhibiting equal expression across alleles in other tissues. This suggests that the regulatory mechanisms contributing to these ASDEGs are unique to the particular tissues where they were found.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Such specificity could be due to tissue-specific promoters, enhancers, or other regulatory elements that influence gene expression differently in each tissue type.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The apolipoprotein E gene (\u003cem\u003eAPOE\u003c/em\u003e), was among these genes and demonstrated ASE in the two liver tissues examined (one from each sex), while having equal allele expression in testis, parietal cortex, and lung tissues. Notably, all ASE events involving \u003cem\u003eAPOE\u003c/em\u003e favored the allele with the same missense variant at locus chr10:15714449. Another gene in this study, \u003cem\u003eSLC6A17\u003c/em\u003e, is a part of the SLC6 family of transporters. This gene exhibited ASE in 2 out of 4 parietal cortex tissues in this study. Interestingly, both cases of \u003cem\u003eSLC6A17\u003c/em\u003e ASE were in parietal cortex tissue of mares, while stallions had a bi-alleleic expression for \u003cem\u003eSLC6A17\u003c/em\u003e in parietal cortex tissues. Both ASE events involving \u003cem\u003eSLC6A17\u003c/em\u003e favored the allele with the same 3 prime UTR variant at locus chr5:54600372. This example alludes to the use of our resource to compare ASE across sexes, helping to identify putatively sex-specific tissue regulation. Lastly, transmembrane glycoprotein gene \u003cem\u003eENPP5\u003c/em\u003e demonstrated evidence of ASE in 2 out of 2 samples of liver tissue used in this study, while having equal expression across alleles in parietal cortex and lung tissues. Both instances of ASE involving \u003cem\u003eENPP5\u003c/em\u003e involved favoring alleles with 3 prime UTR variants at chr20:45654742.\u003c/p\u003e \u003cp\u003eWhen evaluating other ASE patterns, we discovered ASE events that were common among multiple tissues, suggesting more broadly used regulation mechanisms. Allele specific differentially expressed genes (ASDEGs), demonstrating ASE in more than 4 out of 9 tissues from this study, included tubulin gene \u003cem\u003eUBA1\u003c/em\u003e (9 tissues), L ribosomal protein \u003cem\u003eRPL3\u003c/em\u003e (8 tissues), serine/threonine protein kinase gene \u003cem\u003eSGK1\u003c/em\u003e (6 tissues), \u003cem\u003eWT1\u003c/em\u003e-associating gene \u003cem\u003eWTAP\u003c/em\u003e (5 tissues), Coat Complex Subunit Alpha gene \u003cem\u003eCOPA\u003c/em\u003e (5 tissues), and lysine-rich coiled-coil protein \u003cem\u003eKRCC1\u003c/em\u003e (5 tissues).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eHistone Modifications\u003c/h2\u003e \u003cp\u003eChIP-seq data from the same FAANG horse/tissue samples was integrated to provide a broader evaluation of allele-specific expression in the context of annotated histone modifications.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e This leads to an improved perspective on the epigenetic acting influences in allele-specific gene expression, illuminating the complex interplay between genetic variations and epigenetic regulation. Variants within H3K27ac peaks, typically present at active transcription start sites (TSS), highlight the potential relationship between coding changes within TSS and allelic expression.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Additionally, intersection of ASE events with H3K4me1 peaks, often associated with enhancer regions, suggests that variants within these regions could lead to enhanced expression relative to the other allele.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Variants were also identified within H3K4me3 peaks, which are commonly identified near promoters of actively transcribed genes. This suggests that a particular promoter sequence may be favored for transcription.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Similarly the repressive mark, H3K27me3, further emphasizes the possible interplay between allele imbalance and the epigenetic landscape.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e The co-localization of ASE with active marks such as H3K27ac and H3K4me1, often found at transcription start sites and enhancer regions, respectively, suggests that these epigenetically active domains may predispose certain alleles for increased expression by facilitating a more accessible chromatin state.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Simultaneously, the intersection with H3K4me3, associated with promoters of actively transcribed genes, and H3K27me3, a mark of transcriptional repression, indicates a nuanced regulatory landscape where alleles may be differentially expressed due to the combinatorial effects of epigenetic modifications.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e These overlapping epigenetic regions may serve as hotspots for ASE, where the orchestration of gene activation and silencing is fine-tuned by histone marks and coding variants. However, it is important to note that not all impactful epigenetic markers can be found in transcribed regions. Interestingly, a large portion (~\u0026thinsp;30%) of heterozygous loci within ASE events were not found to be within this histone modified regions. This could mean that the nucleotide variation across alleles may be directly responsible for the significant expression disparity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eValidation\u003c/h2\u003e \u003cp\u003eTo further validate our findings, we employed short-read RNA sequencing data from 66 additional equine liver samples across various breeds of horses. Liver tissue was selected for validation because it showed the highest frequency of ASE events in our original cohort (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This robust validation approach enabled us to confirm the reliability and reproducibility of our ASE observations across breeds and ages of horses, reinforcing the utility of our integrated Iso-seq and short-read RNA-Seq methodologies for uncovering the complexities of gene expression regulation and the prevalence of ASE in the equine genome.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eLimitations \u0026amp; Future Direction\u003c/h2\u003e \u003cp\u003eThis research presents the first ASE analysis of its kind for the horse, however there are limitations. First, the FAANG sample size was relatively small, and we did not analyze all tissues, which may affect the generalizability of our findings. Additionally, we did not have parental sequencing to inspect the origin of heterozygous loci. It is also important to note that this combinatorial RNA-Seq method simply will not detect all instances of ASE.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Our approach of using heterozygous gene expression markers reduces the total number of ASE events we could identify in the study since some locations in the genome may be homozygous yet still exhibit ASE due to epigenetic factors alone, such as parental imprinting.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Studies utilizing both RNA-Seq and whole genome sequencing have estimated the percent of genes exhibiting ASE from 1\u0026ndash;20%, depending on the strength of statistical filtering.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e In this study, we used high filtering standards to declare a significant allele imbalance and identified ASE in 1.48% of alleles compared. While we may not be able to detect all ASE events, using this method provides a way to inspect linked variants and their putatively high impact on gene expression. Future investigations will extend this approach to a larger set of samples and tissues. Furthermore, the identified heterozygous loci used in this analysis could be overlaid with short-read RNA-Seq from horses in different developmental stages. This may demonstrate that specific allele sequences are pertinent to development and become otherwise unneeded as the horse reaches maturity. This integration will lay the foundation for a deeper understanding of the intricate relationship between imbalances in allele expression and their role within the equine genome.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study introduces the first multi-tissue analysis of ASE specific to the equine genome, spanning 4 individual Thoroughbred horses and 9 diverse tissues, with validation of liver ASE across multiple horse breeds. Here, we provide an allele expression resource for the equine community to advance future gene regulation investigations. This resource was designed to easily allow researchers to investigate genes of interest and set their own filtering criteria to detect allelic imbalance. Additionally, this main database was divided into tissue specific tracts, thereby allowing for tissue-specific allele expression analysis. With this resource, we inspected the heterozygous loci across alleles, potentially responsible for significant regulation of gene expression, identified pathways containing a significant proportion of these regulatory events, and pinpointed ASE patterns on a tissue-wide scale. As a result, we have demonstrated the potential that this method provides to enrich our understanding of the intricacies of equine genetic regulation by identifying notable loci variations that putatively have a significant impact on gene expression.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eaeFC - allelic expression fold change\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eASE - allele specific expression\u003c/p\u003e\n\u003cp\u003eASDEG - allele specific differentially expressed gene\u003c/p\u003e\n\u003cp\u003eIso-Seq - Isoform Sequencing\u003c/p\u003e\n\u003cp\u003eRNA-Seq \u0026ndash; RNA-sequencing\u003c/p\u003e\n\u003cp\u003eSNP - single nucleotide polymorphism\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGEA - gene enrichment analysis\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll protocols were approved by the University of California Davis Institutional Animal Care and Use Committee (Protocol #19037).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe master and tissue separated allele expression data frames generated and analyzed in this study are available as \u003cstrong\u003eSupplementary Files 3-10\u003c/strong\u003e. \u003c/p\u003e\n\n\u003cp\u003eThe short-read RNA sequencing analyzed in this study is available in the ENA and SRA repositories under the accession number PRJEB26787 (female tissues - https://www.ebi.ac.uk/ena/browser/view/PRJEB26787) and PRJEB53382 (male tissues - https://www.ebi.ac.uk/ena/browser/view/PRJEB53382).\u003c/p\u003e\n\n\u003cp\u003eThe Iso-seq data analyzed in this study is available in the ENA and SRA repositories under the accession number PRJEB53020. (https://www.ebi.ac.uk/ena/browser/view/PRJEB53020) \u003c/p\u003e\n\n\u003cp\u003eThe short-read RNA sequencing from our validation set used in this study is available in the SRA repository under the accession number SUB14222280.\u003c/p\u003e\n\n\u003cp\u003eHistone ChIP-seq analyzed in this study is available via Kingsley et al. (https://doi.org/10.3390/genes11010003) and Barber et al (thesis; https://digitalcommons.unl.edu/animalscidiss/233/).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests,\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by the Grayson-Jockey Club Research Foundation (C.J.F., J.L.P, R.R.B.), USDA National Institute of Food and Agriculture Animal Breeding and Functional Annotation of Genomes (A1201) Grant 2019-67015-29340 (J.L.P, C.J.F., R.B.) as well as NRSP-8 Species Coordinator Funds from the USDA National Institute of Food and Agriculture (C.J.F., J.L.P, R.R.B.), and the UC Davis Center for Equine Health with funds provided by the State of California pari-mutuel fund and contributions by private donors (C.J.F., J.L.P, R.R.B.). Additional support for S.R. was provided by the Foundation for the Horse Young Investigators Research Grant and the Ann T. Bowling Fellowship from the Veterinary Genetics Laboratory at UC Davis. C.J.F. was provided by NIH NCATS L40 TR001136. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.H., S.P, C.J.F. made substantial contributions to the conception and design of the work. H.H., S.P., C.J.F., T.S., S.R, R.B., J.P. contributed to the acquisition, analysis, and interpretation of data. H.H., S.P., C.J.F. drafted the manuscript. All authors reviewed the draft, provided edits and and agreed both to be personally accountable for the author\u0026apos;s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the UC Berkeley QB3 Genomics core and Dr. Blythe Durbin-Johnson for their support with this study. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCodes/Scripts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComputer codes/scripts used in this analysis are publicly available on github at : https://github.com/hdheath/ASE_equine_transcriptome/blob/main/README.md\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCleary S, Seoighe C. 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Genome-wide analysis of allele-specific expression of genes in the model diatom Phaeodactylum tricornutum. Sci Rep. 2021;11(1):2954. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-82529-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-82529-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"epigenetics, FAANG, haplotype, horse, RNA-sequencing","lastPublishedDoi":"10.21203/rs.3.rs-4182812/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4182812/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAllele-specific expression (ASE) analysis provides a nuanced view of cis-regulatory mechanisms affecting gene expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAn equine ASE analysis was performed, using integrated Iso-seq and short-read RNA sequencing data from four healthy Thoroughbreds (2 mares and 2 stallions) across 9 tissues from the Functional Annotation of Animal Genomes (FAANG) project. Allele expression was quantified by haplotypes from long-read data, with 42,900 allele expression events compared. Within these events, 635 (1.48%) demonstrated ASE, with liver tissue containing the highest proportion. Genetic variants within ASE events were in histone modified regions 64.2% of the time. Validation of allele-specific variants, using a set of 66 equine liver samples from multiple breeds, confirmed that 97% of variants demonstrated ASE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis valuable publicly accessible resource is poised to facilitate investigations into regulatory variation in equine tissues. Our results highlight the tissue-specific nature of allelic imbalance in the equine genome.\u003c/p\u003e","manuscriptTitle":"A Comprehensive Allele Specific Expression Resource for the Equine Transcriptome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-04 18:35:38","doi":"10.21203/rs.3.rs-4182812/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-02T14:33:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-01T19:21:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-01T19:21:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-03-28T14:06:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21a31fa4-9e97-452a-ac03-c98549066f0a","owner":[],"postedDate":"April 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:01:48+00:00","versionOfRecord":{"articleIdentity":"rs-4182812","link":"https://doi.org/10.1186/s12864-025-11240-6","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-01-30 15:57:29","publishedOnDateReadable":"January 30th, 2025"},"versionCreatedAt":"2024-04-04 18:35:38","video":"","vorDoi":"10.1186/s12864-025-11240-6","vorDoiUrl":"https://doi.org/10.1186/s12864-025-11240-6","workflowStages":[]},"version":"v1","identity":"rs-4182812","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4182812","identity":"rs-4182812","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}