Abstract: Severe equine asthma, also known as recurrent airway obstruction (RAO), is a debilitating, performance limiting, obstructive respiratory condition in horses that is phenotypically similar to human asthma. Past genome wide association studies (GWAS) have not discovered coding variants associated with RAO, leading to the hypothesis that causative variant(s) underlying the signals are likely non-coding, regulatory variant(s). Regions of the genome containing variants that influence the number of expressed RNA molecules are expression quantitative trait loci (eQTLs). Variation associated with RAO that also regulates a gene's expression in a disease relevant tissue could help identify candidate genes that influence RAO if that gene's expression is also associated with RAO disease status. Results: We searched for eQTLs by analyzing peripheral blood mononuclear cells (PBMCs) from two half-sib families and one unrelated cohort of 82 European Warmblood horses that were previously treated in vitro with: no stimulation (MCK), lipopolysaccharides (LPS), recombinant cyathostomin antigen (RCA), and hay-dust extract (HDE). We identified high confidence eQTLs that did not violate linear modeling assumptions and were not significant due to single outlier individuals. We identified a mean of 4347 high confidence eQTLs in four treatments of PBMCs, and discovered two trans regulatory hotspots regulating genes involved in related biological pathways. We corroborated previous RAO associated single nucleotide polymorphisms (SNPs), and increased the resolution of past GWAS by analyzing 1,056,195 SNPs in 361 individuals. We identified four RAO-associated SNPs that only regulate gene expression of dexamethasone-induced protein (DEXI), however we found no significant association between DEXI gene expression and presence of RAO. Conclusions: Thousands of genetic variants regulate gene expression in PBMCs of European Warmblood horses in cis and trans. Most high confidence eSNPs are significantly enriched near the transcription start sites of their target genes. Two trans regulatory hotspots on chromosome 11 and 13 regulate many genes involved in transmembrane cell signaling and neurological development respectively when PBMCs are treated with HDE. None of the top fifteen RAO associated SNPs strongly influence disease status through gene expression regulation.
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.
The research article is an attempt to better understand severe equine asthma, a respiratory condition in horses that significantly affects their performance. By analyzing the genetic variants which regulate gene expression in the blood cells of European Warmblood horses, the researchers discovered thousands of these regulatory variants and increased the resolution of previous genomic studies.
Expression Quantitative Trait Loci (eQTLs)
In the study, the team focused on eQTLs, genomic regions containing variations that influence the number of expressed RNA molecules.
These eQTLs were believed to provide insights into the underlying genetic variants associated with severe equine asthma, which was hypothesized to involve non-coding, regulatory variants.
Peripheral Blood Mononuclear Cells (PBMCs)
The research team analyzed PBMCs (a type of immune cell) from 82 European Warmblood horses, belonging to two familial groups and one unrelated group.
The PMBCs were treated in various ways: no stimulation (MCK), with lipopolysaccharides (LPS), recombinant cyathostomin antigen (RCA), and hay-dust extract (HDE), and assessed for any resultant eQTLs.
Findings
As a result of this approach, an average of 4347 high-confidence eQTLs was identified from the different treatment methods.
Trans regulatory hotspots (regions that regulate numerous genes) were found, notably involved in cell signaling and neurological development when PBMCs were treated with HDE.
The study supported previous findings regarding RAO-associated single nucleotide polymorphisms (SNPs), which are variation in a single nucleotide that occurs at a specific position in the genome.
This investigation analyzed a vast number of SNPs (1,056,195) in 361 individuals, significantly expanding the scale and resolution of previous genome-wide association studies.
Association with Asthma
The study identified four SNPs associated with RAO that regulate the gene expression of dexamethasone-induced protein (DEXI).
However, no significant association between DEXI gene expression and the presence of RAO was found.
Consequently, none of the top fifteen RAO-associated SNPs had a strong influence on disease status via gene expression regulation.
Conclusions
Overall, the study highlights the complexity of the genetic underpinnings of RAO in horses.
It shows how vast numbers of genetic variants can regulate gene expressions in PBMCs.
However, a direct linkage between these gene expression regulations and the disease status of severe equine asthma was not established in this study, indicating the need for further research in this area.
Cite This Article
APA
Mason VC, Schaefer RJ, McCue ME, Leeb T, Gerber V.
(2018).
eQTL discovery and their association with severe equine asthma in European Warmblood horses.
BMC Genomics, 19(1), 581.
https://doi.org/10.1186/s12864-018-4938-9
Department of Clinical Veterinary Medicine, Swiss Institute of Equine Medicine, Vetsuisse Faculty, University of Bern, and Agroscope, Länggassstrasse 124, 3012, Bern, Switzerland. victor.mason@vetsuisse.unibe.ch.
Schaefer, Robert J
Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Ave, Saint Paul, MN, 55108, USA.
McCue, Molly E
Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Ave, Saint Paul, MN, 55108, USA.
Leeb, Tosso
Department of Clinical Research and Veterinary Public Health, Institute of Genetics, Vetsuisse Faculty, University of Bern, Bremgartenstrasse 109A, 3012, Bern, Switzerland.
Gerber, Vinzenz
Department of Clinical Veterinary Medicine, Swiss Institute of Equine Medicine, Vetsuisse Faculty, University of Bern, and Agroscope, Länggassstrasse 124, 3012, Bern, Switzerland.
MeSH Terms
Animals
Asthma / chemically induced
Asthma / genetics
Asthma / veterinary
Dust
Gene Expression Profiling / veterinary
Gene Expression Regulation
Gene Regulatory Networks / drug effects
Genetic Predisposition to Disease
Genome-Wide Association Study / veterinary
Horse Diseases / chemically induced
Horse Diseases / genetics
Horses
Leukocytes, Mononuclear / drug effects
Lipopolysaccharides / adverse effects
Polymorphism, Single Nucleotide
Quantitative Trait Loci
Grant Funding
31003A-162548/1 / Schweizerischer Nationalfonds zur Fu00f6rderung der Wissenschaftlichen Forschung
Conflict of Interest Statement
All animal experiments were performed according to the local regulations and with the consent of the horse owners. This study was approved by the Animal Experimentation Committee of the Canton of Bern, Switzerland (BE33/07, BE58/10 and BE10/13). Not applicable. The authors declare that they have no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Thibodeau SN, French AJ, McDonnell SK, Cheville J, Middha S, Tillmans L, Riska S, Baheti S, Larson MC, Fogarty Z, Zhang Y, Larson N, Nair A, O'Brien D, Wang L, Schaid DJ. Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set.. Nat Commun 2015 Nov 27;6:8653.
Peters JE, Lyons PA, Lee JC, Richard AC, Fortune MD, Newcombe PJ, Richardson S, Smith KG. Insight into Genotype-Phenotype Associations through eQTL Mapping in Multiple Cell Types in Health and Immune-Mediated Disease.. PLoS Genet 2016 Mar;12(3):e1005908.
Li X, Hastie AT, Hawkins GA, Moore WC, Ampleford EJ, Milosevic J, Li H, Busse WW, Erzurum SC, Kaminski N, Wenzel SE, Meyers DA, Bleecker ER. eQTL of bronchial epithelial cells and bronchial alveolar lavage deciphers GWAS-identified asthma genes.. Allergy 2015 Oct;70(10):1309-18.
Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, Montgomery GW, Goddard ME, Wray NR, Visscher PM, Yang J. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.. Nat Genet 2016 May;48(5):481-7.
Yu CH, Pal LR, Moult J. Consensus Genome-Wide Expression Quantitative Trait Loci and Their Relationship with Human Complex Trait Disease.. OMICS 2016 Jul;20(7):400-14.
Huang YT, Liang L, Moffatt MF, Cookson WO, Lin X. iGWAS: Integrative Genome-Wide Association Studies of Genetic and Genomic Data for Disease Susceptibility Using Mediation Analysis.. Genet Epidemiol 2015 Jul;39(5):347-56.
Yao C, Joehanes R, Johnson AD, Huan T, Liu C, Freedman JE, Munson PJ, Hill DE, Vidal M, Levy D. Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits.. Am J Hum Genet 2017 Apr 6;100(4):571-580.
Koh W, Sheng CT, Tan B, Lee QY, Kuznetsov V, Kiang LS, Tanavde V. Analysis of deep sequencing microRNA expression profile from human embryonic stem cells derived mesenchymal stem cells reveals possible role of let-7 microRNA family in downstream targeting of hepatic nuclear factor 4 alpha.. BMC Genomics 2010 Feb 10;11 Suppl 1(Suppl 1):S6.
Farrell D, Shaughnessy RG, Britton L, MacHugh DE, Markey B, Gordon SV. The Identification of Circulating MiRNA in Bovine Serum and Their Potential as Novel Biomarkers of Early Mycobacterium avium subsp paratuberculosis Infection.. PLoS One 2015;10(7):e0134310.
Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering.. Am J Hum Genet 2007 Nov;81(5):1084-97.
Liu G, Wang Y, Wong L. FastTagger: an efficient algorithm for genome-wide tag SNP selection using multi-marker linkage disequilibrium.. BMC Bioinformatics 2010 Jan 29;11:66.
Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, Thomas PD. PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements.. Nucleic Acids Res 2017 Jan 4;45(D1):D183-D189.
Urbut SM, Wang G, Stephens M. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. bioRxiv 2016.
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R. The variant call format and VCFtools.. Bioinformatics 2011 Aug 1;27(15):2156-8.
Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, Morgan MT, Carey VJ. Software for computing and annotating genomic ranges.. PLoS Comput Biol 2013;9(8):e1003118.
Stegle O, Parts L, Durbin R, Winn J. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.. PLoS Comput Biol 2010 May 6;6(5):e1000770.
Stegle O, Parts L, Piipari M, Winn J, Durbin R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses.. Nat Protoc 2012 Feb 16;7(3):500-7.
Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments.. Bioinformatics 2012 Mar 15;28(6):882-3.
Benjamin Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 1995;57:289–300.
Cook RD. Detection of influential observation in linear regression. Technometrics 1977;19:15–18.
Crawley MJ. Statistical Modelling. The R book 2012; pp. 388–448.
Peña EA, Slate EH. Global Validation of Linear Model Assumptions.. J Am Stat Assoc 2006 Mar 1;101(473):341.
Marrella MA, Biase FH. Robust identification of regulatory variants (eQTLs) using a differential expression framework developed for RNA-sequencing. J Anim Sci Biotechnol 2023 May 5;14(1):62.
Fitzgerald T, Brettell I, Leger A, Wolf N, Kusminski N, Monahan J, Barton C, Herder C, Aadepu N, Gierten J, Becker C, Hammouda OT, Hasel E, Lischik C, Lust K, Sokolova N, Suzuki R, Tsingos E, Tavhelidse T, Thumberger T, Watson P, Welz B, Khouja N, Naruse K, Birney E, Wittbrodt J, Loosli F. The Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel. Genome Biol 2022 Feb 21;23(1):59.
Yuan K, Zeng T, Chen L. Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype-Phenotype Association Study. Front Cell Dev Biol 2021;9:720321.
Couetil L, Cardwell JM, Leguillette R, Mazan M, Richard E, Bienzle D, Bullone M, Gerber V, Ivester K, Lavoie JP, Martin J, Moran G, Niedźwiedź A, Pusterla N, Swiderski C. Equine Asthma: Current Understanding and Future Directions. Front Vet Sci 2020;7:450.