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International journal of molecular sciences2024; 26(1); 26; doi: 10.3390/ijms26010026

Genomic-Inbreeding Landscape and Selection Signatures in the Polo Argentino Horse Breed.

Abstract: Analyzing genetic variability and inbreeding trends is essential for effective breed management in animal populations. To this, the characterization of runs of homozygosity (ROH) provides a good genomic approach to study the phenomena. The Polo Argentino (PA) breed, globally recognized as the best adapted to playing polo, is known for its strong influence of Thoroughbreds, intense selective breeding, and extensive use of reproductive biotechnologies. This study investigates the PA's genomic variability, by characterizing the ROH landscape and identifying ROH islands (ROHi) as potential genomic footprints for the breed. PA horses ( = 506) were genotyped using EquineGGP™ array v5 (70 k). We calculated the inbreeding coefficient based on ROH (F-ancestral and recent) using a chromosomal approach. Finally, we identified genomic regions with increased ROH frequency (ROHi) and their associated genes. An average of 79.5 ROH per horse was detected, with a mean length of 4.6 Mb. The average F was 0.151, but most of them (54%) corresponded to ancestral inbreeding (ROH < 5.5 Mb). However, 4 ROHi were identified in ECA 1, 3, 7 and 17, containing 67 genes, some of which were related to behavior, neurodevelopment, and metabolic functions. This genomic analysis determined, for the first time, the length and location of homozygosity segments in the PA breed and identified ROHi associated with potential genomic regions and genes for positive selection in the breed.
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Publication Date: 2024-12-24 PubMed ID: 39795883PubMed Central: PMC11720259DOI: 10.3390/ijms26010026Google Scholar: Lookup
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  • Journal Article

Summary

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.

This research focuses on understanding the genetic diversity and levels of inbreeding in the Polo Argentino horse breed. Through genomic analysis, the study identifies areas of homozygosity, regions with increased homozygosity, and their relation to certain genes.

Objective of the Study

  • The study aims to analyze the genetic variability and inbreeding trends in the Polo Argentino (PA) horse breed using a genomic approach.
  • It seeks to identify runs of homozygosity (ROH) – regions where two identical segments of a chromosome are inherited from both parents, and their associated genes in the breed.
  • The research also aims to find “ROH islands” (ROHi), which are genomic regions with increased ROH occurrence that could have influenced the breed’s characteristics.

Methodology

  • A total of 506 Polo Argentino horses were genotyped using EquineGGP™ array v5 (70 k), a tool that helps identify genetic variants in equine populations.
  • They calculated the inbreeding coefficient (F) based on the ROH using a chromosomal approach. Two types were considered: F-ancestral (ROH < 5.5Mb) and recent.
  • They then identified regions with high ROH frequency (ROHi) and the associated genes within these regions.

Results and Findings

  • An average of 79.5 ROH per horse was detected, with a mean length of 4.6Mb.
  • The average inbreeding coefficient (F) was 0.151. Most of the inbreeding detected (54%) was ancestral inbreeding (ROH < 5.5Mb).
  • Four ROHi were identified on chromosomes 1, 3, 7, and 17, containing 67 genes. Some of these genes were associated with behavior, neurodevelopment, and metabolic functions.

Conclusion

  • This study, for the first time, defines and locates homozygosity segments in the Polo Argentino breed.
  • It also recognizes areas of increased homozygosity (ROHi) linked with potentially beneficial genomic regions and genes – a key insight for selecting traits in the breed’s management.

Cite This Article

APA
Azcona F, Molina A, Demyda-Peyrás S. (2024). Genomic-Inbreeding Landscape and Selection Signatures in the Polo Argentino Horse Breed. Int J Mol Sci, 26(1), 26. https://doi.org/10.3390/ijms26010026

Publication

International journal of molecular sciences
ISSN: 1422-0067
NlmUniqueID: 101092791
Country: Switzerland
Language: English
Volume: 26
Issue: 1
PII: 26

Researcher Affiliations

Azcona, Florencia
  • Cátedra de Medicina Equina, Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, Calle 60 y 118 s/n, La Plata 1900, Argentina.
  • Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CCT La Plata, La Plata 1900, Argentina.
Molina, Antonio
  • Departamento de Genética, Universidad de Córdoba, CN IV KM 396 Edificio Gregor Mendel, 14007 Córdoba, Spain.
Demyda-Peyrás, Sebastián
  • Departamento de Genética, Universidad de Córdoba, CN IV KM 396 Edificio Gregor Mendel, 14007 Córdoba, Spain.

MeSH Terms

  • Animals
  • Horses / genetics
  • Inbreeding
  • Homozygote
  • Selection, Genetic
  • Genomics / methods
  • Genome
  • Polymorphism, Single Nucleotide
  • Genetic Variation
  • Genotype
  • Selective Breeding / genetics
  • Breeding
  • Male
  • Female

Grant Funding

  • PID2018-0003 / Agencia Nacional de Promociu00f3n de la Investigaciu00f3n, el Desarrollo Tecnolu00f3gico y la Innovaciu00f3n
  • RYC2021-031781-I / MINECO

Conflict of Interest Statement

The authors declare no conflicts of interest.

References

This article includes 75 references
  1. Machmoum M, Boujenane I, Azelhak R, Badaoui B, Petit D, Piro M. Genetic Diversity and Population Structure of Arabian Horse Populations Using Microsatellite Markers. J. Equine Vet. Sci. 2020;93:103200.
    doi: 10.1016/j.jevs.2020.103200pubmed: 32972687google scholar: lookup
  2. Cole JB. Perspective: Can we actually do anything about inbreeding?. J. Dairy Sci. 2024;107:643–648.
    doi: 10.3168/jds.2023-23958pubmed: 37777000google scholar: lookup
  3. Wellmann R, Hartwig S, Bennewitz J. Optimum contribution selection for conserved populations with historic migration. Genet. Sel. Evol. 2012;44:34.
    doi: 10.1186/1297-9686-44-34pmc: PMC3807754pubmed: 23153196google scholar: lookup
  4. Rothschild MF. Genomics and genetics: A daily double for the horse industry. Equine Vet. J. 2017;49:260–262.
    doi: 10.1111/evj.12668pubmed: 28224670google scholar: lookup
  5. Kardos M, Luikart G, Allendorf FW. Measuring individual inbreeding in the age of genomics: Marker-based measures are better than pedigrees. Heredity 2015;115:63–72.
    doi: 10.1038/hdy.2015.17pmc: PMC4815495pubmed: 26059970google scholar: lookup
  6. Leroy G. Inbreeding depression in livestock species: Review and meta-analysis. Anim. Genet. 2014;45:618–628.
    doi: 10.1111/age.12178pubmed: 24975026google scholar: lookup
  7. Charlesworth D, Willis JH. The genetics of inbreeding depression. Nat. Rev. Genet. 2009;10:783–796.
    doi: 10.1038/nrg2664pubmed: 19834483google scholar: lookup
  8. McQuillan R, Leutenegger AL, Abdel-Rahman R, Franklin CS, Pericic M, Barac-Lauc L, Smolej-Narancic N, Janicijevic B, Polasek O, Tenesa A. Runs of Homozygosity in European Populations. Am. J. Hum. Genet. 2008;83:359–372.
    doi: 10.1016/j.ajhg.2008.08.007pmc: PMC2556426pubmed: 18760389google scholar: lookup
  9. Hill EW, McGivney BA, MacHugh DE. Inbreeding depression and durability in the North American Thoroughbred horse. Anim. Genet. 2023;54:408–411.
    doi: 10.1111/age.13309pubmed: 36843349google scholar: lookup
  10. McGivney BA, Han H, Corduff LR, Katz LM, Tozaki T, MacHugh DE, Hill EW. Genomic inbreeding trends, influential sire lines and selection in the global Thoroughbred horse population. Sci. Rep. 2020;10:466.
    doi: 10.1038/s41598-019-57389-5pmc: PMC6965197pubmed: 31949252google scholar: lookup
  11. Fisher RA. A fuller theory of junctions in inbreeding. Heredity 1954;8:187–197.
    doi: 10.1038/hdy.1954.17google scholar: lookup
  12. Doekes HP, Veerkamp RF, Bijma P, de Jong G, Hiemstra SJ, Windig JJ. Inbreeding depression due to recent and ancient inbreeding in Dutch Holstein-Friesian dairy cattle. Genet. Sel. Evol. 2019;51:54.
    doi: 10.1186/s12711-019-0497-zpmc: PMC6764141pubmed: 31558150google scholar: lookup
  13. Saravanan KA, Panigrahi M, Kumar H, Bhushan B, Dutt T, Mishra BP. Selection signatures in livestock genome: A review of concepts, approaches and applications. Livest. Sci. 2020;241:104257.
    doi: 10.1016/j.livsci.2020.104257google scholar: lookup
  14. Grilz-Seger G, Druml T, Neuditschko M, Mesarič M, Cotman M, Brem G. Analysis of ROH patterns in the Noriker horse breed reveals signatures of selection for coat color and body size. Anim. Genet. 2019;50:334–346.
    doi: 10.1111/age.12797pmc: PMC6617995pubmed: 31199540google scholar: lookup
  15. Laseca N, Molina A, Ramón M, Valera M, Azcona F, Encina A, Demyda-Peyrás S. Fine-Scale Analysis of Runs of Homozygosity Islands Affecting Fertility in Mares. Front. Vet. Sci. 2022;9:754028.
    doi: 10.3389/fvets.2022.754028pmc: PMC8891756pubmed: 35252415google scholar: lookup
  16. Bower MA, McGivney BA, Campana MG, Gu J, Andersson LS, Barrett E, Davis CR, Mikko S, Stock F, Voronkova V. The genetic origin and history of speed in the Thoroughbred racehorse. Nat. Commun. 2012;3:643.
    doi: 10.1038/ncomms1644pubmed: 22273681google scholar: lookup
  17. Azcona F, Valera M, Molina A, Trigo P, Peral-Garcia P, Sole M, Demyda-Peyras S. Impact of reproductive biotechnologies on genetic variability of Argentine Polo horses. Livest. Sci. 2020;231:103848.
    doi: 10.1016/j.livsci.2019.103848google scholar: lookup
  18. Azcona F, Molina Alcalá A, Peral Garcia P, Demyda-Peyrás S. Genomic data reveals a serious underestimation of pedigree inbreeding levels in Polo Argentino horses. Proceedings of the 2021 International Society for Animal Genetics Meeting; Virtual. 26–30 July 2021.
  19. Poyato-Bonilla J, Laseca N, Demyda Peyrás S, Molina Alcalá A, Valera M. 500 years of breeding in the Carthusian Strain of Pura Raza Español horse: An evolutional analysis using genealogical and genomic data. J. Anim. Breed. Genet. 2021;139:84–99.
    doi: 10.1111/jbg.12641pubmed: 34363624google scholar: lookup
  20. Druml T, Neuditschko M, Grilz-Seger G, Horna M, Ricard A, Mesaric M, Cotman M, Pausch H, Brem G. Population Networks Associated with Runs of Homozygosity Reveal New Insights into the Breeding History of the Haflinger Horse. J. Hered. 2018;109:384–392.
    doi: 10.1093/jhered/esx114pubmed: 29294044google scholar: lookup
  21. Petersen JL, Mickelson JR, Cothran EG, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P. Genetic Diversity in the Modern Horse Illustrated from Genome-Wide SNP Data. PLoS ONE 2013;8:e54997.
    doi: 10.1371/journal.pone.0054997pmc: PMC3559798pubmed: 23383025google scholar: lookup
  22. Martinez MM, Costa M, Corva PM. Analysis of Genetic Variability in the Argentine Polo Horse With a Panel of Microsatellite Markers. J. Equine Vet. Sci. 2021;96:103320.
    doi: 10.1016/j.jevs.2020.103320pubmed: 33349401google scholar: lookup
  23. Cunningham EP, Dooley JJ, Splan RK, Bradley DG. Microsatellite diversity, pedigree relatedness and the contributions of founder lineages to thoroughbred horses. Anim. Genet. 2001;32:360–364.
    doi: 10.1046/j.1365-2052.2001.00785.xpubmed: 11736806google scholar: lookup
  24. Todd E, Ho S, Thomson P, Ang R, Velie B, Hamilton N. Founder-specific inbreeding depression affects racing performance in Thoroughbred horses. Sci. Rep. 2018;8:6167.
    doi: 10.1038/s41598-018-24663-xpmc: PMC5906619pubmed: 29670190google scholar: lookup
  25. Hill EW, Stoffel MA, McGivney BA, MacHugh DE, Pemberton JM. Inbreeding depression and the probability of racing in the Thoroughbred horse. Proc. Biol. Sci. 2022;289:20220487.
    doi: 10.1098/rspb.2022.0487pmc: PMC9240673pubmed: 35765835google scholar: lookup
  26. Meyermans R, Gorssen W, Buys N, Janssens S. How to study runs of homozygosity using PLINK? A guide for analyzing medium density SNP data in livestock and pet species. BMC Genom. 2020;21:94.
    doi: 10.1186/s12864-020-6463-xpmc: PMC6990544pubmed: 31996125google scholar: lookup
  27. Schurink A, Shrestha M, Eriksson S, Bosse M, Bovenhuis H, Back W, Johansson AM, Ducro BJ. The Genomic Makeup of Nine Horse Populations Sampled in the Netherlands. Genes 2019;10:480.
    doi: 10.3390/genes10060480pmc: PMC6627704pubmed: 31242710google scholar: lookup
  28. Sumreddee P, Hay EH, Toghiani S, Roberts A, Aggrey SE, Rekaya R. Grid search approach to discriminate between old and recent inbreeding using phenotypic, pedigree and genomic information. BMC Genom. 2021;22:538.
    doi: 10.1186/s12864-021-07872-zpmc: PMC8278650pubmed: 34256689google scholar: lookup
  29. Baumung R, Farkas J, Boichard D, Mészáros G, Sölkner J, Curik I. grain: A computer program to calculate ancestral and partial inbreeding coefficients using a gene dropping approach. J. Anim. Breed. Genet. 2015;132:100–108.
    doi: 10.1111/jbg.12145pubmed: 25823836google scholar: lookup
  30. Hedrick PW, Garcia-Dorado A. Understanding Inbreeding Depression, Purging, and Genetic Rescue. Trends Ecol. Evol. 2016;31:940–952.
    doi: 10.1016/j.tree.2016.09.005pubmed: 27743611google scholar: lookup
  31. Fawcett JA, Sato F, Sakamoto T, Iwasaki WM, Tozaki T, Innan H. Genome-wide SNP analysis of Japanese Thoroughbred racehorses. PLoS ONE 2019;14:e0218407.
    doi: 10.1371/journal.pone.0218407pmc: PMC6655603pubmed: 31339891google scholar: lookup
  32. Petersen JL, Mickelson JR, Rendahl AK, Valberg SJ, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS. Genome-Wide Analysis Reveals Selection for Important Traits in Domestic Horse Breeds. PLoS Genet. 2013;9:e1003211.
    doi: 10.1371/journal.pgen.1003211pmc: PMC3547851pubmed: 23349635google scholar: lookup
  33. Yokomori T, Ohnuma A, Tozaki T, Segawa T, Itou T. Identification of Personality-Related Candidate Genes in Thoroughbred Racehorses Using a Bioinformatics-Based Approach Involving Functionally Annotated Human Genes. Animals 2023;13:769.
    doi: 10.3390/ani13040769pmc: PMC9951868pubmed: 36830556google scholar: lookup
  34. Azcona F, Karlau A, Trigo P, Molina A, Demyda-Peyrás S. Genomic tools for early selection among Thoroughbreds and Polo Argentino horses for practicing polo. J. Equine Vet. Sci. 2024;138:105098.
    doi: 10.1016/j.jevs.2024.105098pubmed: 38763367google scholar: lookup
  35. Wickens C, Brooks SA. Genetics of Equine Behavioral Traits. Vet. Clin. N. Am. Equine Pract. 2020;36:411–424.
    doi: 10.1016/j.cveq.2020.03.014pubmed: 32534854google scholar: lookup
  36. Álvarez RP, Demyda Peyrás S, Prado Silva RH, Arroyo P, Trigo PI. Análisis de componentes principales en las etapas clasificatorias de una prueba de doma. Proceedings of the XXXIII Conferencias Internacionales de Veterinaria Equina, FCV UNR; San Antonio de Areco, Argentina. 7–8 November 2022.
  37. Galizzi Vecchiotti G, Galanti R. Evidence of heredity of cribbing, weaving and stall-walking in thoroughbred horses. Livest. Prod. Sci. 1986;14:91–95.
    doi: 10.1016/0301-6226(86)90098-9google scholar: lookup
  38. Yokomori T, Tozaki T, Ohnuma A, Ishimaru M, Sato F, Hori Y, Segawa T, Itou T. Non-Synonymous Substitutions in Cadherin 13, Solute Carrier Family 6 Member 4, and Monoamine Oxidase A Genes are Associated with Personality Traits in Thoroughbred Horses. Behav. Genet. 2024;54:333–341.
    doi: 10.1007/s10519-024-10186-xpubmed: 38856811google scholar: lookup
  39. McGivney BA, Hernandez B, Katz LM, MacHugh DE, McGovern SP, Parnell AC, Wiencko HL, Hill EW. A genomic prediction model for racecourse starts in the Thoroughbred horse. Anim. Genet. 2019;50:347–357.
    doi: 10.1111/age.12798pubmed: 31257665google scholar: lookup
  40. Pan Y, Wang KS, Aragam N. NTM and NR3C2 polymorphisms influencing intelligence: Family-based association studies. Prog. Neuro-Psychopharmacol. Biol. Psychiatry. 2011;35:154–160.
    doi: 10.1016/j.pnpbp.2010.10.016pubmed: 21036197google scholar: lookup
  41. Gurgul A, Jasielczuk I, Semik-Gurgul E, Pawlina-Tyszko K, Stefaniuk-Szmukier M, Szmatola T, Polak G, Tomczyk-Wrona I, Bugno-Poniewierska M. A genome-wide scan for diversifying selection signatures in selected horse breeds. PLoS ONE 2019;14:e0210751.
    doi: 10.1371/journal.pone.0210751pmc: PMC6353161pubmed: 30699152google scholar: lookup
  42. Velie BD, Fegraeus KJ, Solé M, Rosengren MK, Røed KH, Ihler C-F, Strand E, Lindgren G. A genome-wide association study for harness racing success in the Norwegian-Swedish coldblooded trotter reveals genes for learning and energy metabolism. BMC Genet. 2018;19:80.
    doi: 10.1186/s12863-018-0670-3pmc: PMC6114527pubmed: 30157760google scholar: lookup
  43. Felício D, du Mérac TR, Amorim A, Martins S. Functional implications of paralog genes in polyglutamine spinocerebellar ataxias. Hum. Genet. 2023;142:1651–1676.
    doi: 10.1007/s00439-023-02607-4pmc: PMC10676324pubmed: 37845370google scholar: lookup
  44. Sturgill ER, Aoki K, Lopez PH, Colacurcio D, Vajn K, Lorenzini I, Majić S, Yang WH, Heffer M, Tiemeyer M. Biosynthesis of the major brain gangliosides GD1a and GT1b. Glycobiology 2012;22:1289–1301.
    doi: 10.1093/glycob/cws103pmc: PMC3425327pubmed: 22735313google scholar: lookup
  45. Cao X, Lenk GM, Mikusevic V, Mindell JA, Meisler MH. The chloride antiporter CLCN7 is a modifier of lysosome dysfunction in FIG4 and VAC14 mutants. PLoS Genet. 2023;19:e1010800.
    doi: 10.1371/journal.pgen.1010800pmc: PMC10328317pubmed: 37363915google scholar: lookup
  46. Corona-Rivera JR, Zenteno JC, Ordoñez-Labastida V, Cruz-Cruz JP, Cortés-Pastrana RC, Peña-Padilla C, Bobadilla-Morales L, Corona-Rivera A, Martínez-Herrera A. MTSS2-related neurodevelopmental disorder: Further delineation of the phenotype. Eur. J. Med. Genet. 2023;66:104826.
    doi: 10.1016/j.ejmg.2023.104826pubmed: 37657631google scholar: lookup
  47. Araujo AC, Carneiro PLS, Alvarenga AB, Oliveira HR, Miller SP, Retallick K, Brito LF. Haplotype-Based Single-Step GWAS for Yearling Temperament in American Angus Cattle. Genes 2021;13:17.
    doi: 10.3390/genes13010017pmc: PMC8775055pubmed: 35052358google scholar: lookup
  48. Wakatsuki S, Araki T. Novel insights into the mechanism of reactive oxygen species-mediated neurodegeneration. Neural Regen. Res. 2023;18:746–749.
    doi: 10.4103/1673-5374.354509pmc: PMC9700119pubmed: 36204830google scholar: lookup
  49. Babaev O, Cruces-Solis H, Piletti Chatain C, Hammer M, Wenger S, Ali H, Karalis N, de Hoz L, Schlüter OM, Yanagawa Y. IgSF9b regulates anxiety behaviors through effects on centromedial amygdala inhibitory synapses. Nat. Commun. 2018;9:5400.
    doi: 10.1038/s41467-018-07762-1pmc: PMC6302093pubmed: 30573727google scholar: lookup
  50. Coleman JRI, Bryois J, Gaspar HA, Jansen PR, Savage JE, Skene N, Plomin R, Muñoz-Manchado AB, Linnarsson S, Crawford G. Biological annotation of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals. Mol. Psychiatry 2019;24:182–197.
    doi: 10.1038/s41380-018-0040-6pmc: PMC6330082pubmed: 29520040google scholar: lookup
  51. Zeng L, Ming C, Li Y, Su LY, Su YH, Otecko NO, Liu HQ, Wang MS, Yao YG, Li HP. Rapid Evolution of Genes Involved in Learning and Energy Metabolism for Domestication of the Laboratory Rat. Mol. Biol. Evol. 2017;34:3148–3153.
    doi: 10.1093/molbev/msx238pubmed: 28961982google scholar: lookup
  52. Aomine Y, Sakurai K, Macpherson T, Ozawa T, Miyamoto Y, Yoneda Y, Oka M, Hikida T. Importin α3 (KPNA3) Deficiency Augments Effortful Reward-Seeking Behavior in Mice. Front. Neurosci. 2022;16:905991.
    doi: 10.3389/fnins.2022.905991pmc: PMC9279672pubmed: 35844217google scholar: lookup
  53. Deutschman E, Ward JR, Kumar A, Ray G, Welch N, Lemieux ME, Dasarathy S, Longworth MS. Condensin II protein dysfunction impacts mitochondrial respiration and mitochondrial oxidative stress responses. J. Cell Sci. 2019;132:jcs233783.
    doi: 10.1242/jcs.233783pmc: PMC6899004pubmed: 31653782google scholar: lookup
  54. Muhammad Aslam MK, Sharma VK, Pandey S, Kumaresan A, Srinivasan A, Datta TK, Mohanty TK, Yadav S. Identification of biomarker candidates for fertility in spermatozoa of crossbred bulls through comparative proteomics. Theriogenology 2018;119:43–51.
    doi: 10.1016/j.theriogenology.2018.06.021pubmed: 29982135google scholar: lookup
  55. Mi Y, Shi Z, Li J. Spata19 is critical for sperm mitochondrial function and male fertility. Mol. Reprod. Dev. 2015;82:907–913.
    doi: 10.1002/mrd.22536pubmed: 26265198google scholar: lookup
  56. Obholz KL, Akopyan A, Waymire KG, MacGregor GR. FNDC3A is required for adhesion between spermatids and Sertoli cells. Dev. Biol. 2006;298:498–513.
    doi: 10.1016/j.ydbio.2006.06.054pmc: PMC3049804pubmed: 16904100google scholar: lookup
  57. Bi Y, He Y, Huang JY, Xu L, Tang N, He TC, Feng T. Induced maturation of hepatic progenitor cells in vitro. Braz. J. Med. Biol. Res. 2013;46:559–566.
    doi: 10.1590/1414-431X20132455pmc: PMC3859339pubmed: 23903683google scholar: lookup
  58. Müller D, Kuiper H, Böneker C, Mömke S, Drögemüller C, Chowdhary BP, Distl O. Assignment of BGLAP, BMP2, CHST4, SLC1A3, SLC4A1, SLC9A5 and SLC20A1 to equine chromosomes by FISH and confirmation by RH mapping. Anim. Genet. 2005;36:457–461.
    doi: 10.1111/j.1365-2052.2005.01347.xpubmed: 16167999google scholar: lookup
  59. Kim K, Kang JK, Jung YH, Lee SB, Rametta R, Dongiovanni P, Valenti L, Pajvani UB. Adipocyte PHLPP2 inhibition prevents obesity-induced fatty liver. Nat. Commun. 2021;12:1822.
    doi: 10.1038/s41467-021-22106-2pmc: PMC7988046pubmed: 33758172google scholar: lookup
  60. Yan J, Lawson JE, Reed LJ. Role of the regulatory subunit of bovine pyruvate dehydrogenase phosphatase. Proc. Natl. Acad. Sci. USA. 1996;93:4953–4956.
    doi: 10.1073/pnas.93.10.4953pmc: PMC39386pubmed: 8643510google scholar: lookup
  61. Votion DM, Gnaiger E, Lemieux H, Mouithys-Mickalad A, Serteyn D. Physical fitness and mitochondrial respiratory capacity in horse skeletal muscle. PLoS ONE 2012;7:e34890.
    doi: 10.1371/annotation/e24ced68-9c2d-4303-9553-0661decb9a51pmc: PMC3329552pubmed: 22529950google scholar: lookup
  62. Littiere TO, Castro GHF, Rodriguez MdPR, Bonafé CM, Magalhães AFB, Faleiros RR, Vieira JIG, Santos CG, Verardo LL. Identification and Functional Annotation of Genes Related to Horses’ Performance: From GWAS to Post-GWAS. Animals 2020;10:1173.
    doi: 10.3390/ani10071173pmc: PMC7401650pubmed: 32664293google scholar: lookup
  63. Arya SB, Kumar G, Kaur H, Kaur A, Tuli A. ARL11 regulates lipopolysaccharide-stimulated macrophage activation by promoting mitogen-activated protein kinase (MAPK) signaling. J. Biol. Chem. 2018;293:9892–9909.
    doi: 10.1074/jbc.RA117.000727pmc: PMC6016484pubmed: 29618517google scholar: lookup
  64. Dos SJT, Dos SCR, Alcântara-Neves NM, Barreto ML, Figueiredo CA. Variants in the CYSLTR2 are associated with asthma, atopy markers and helminths infections in the Brazilian population. Prostaglandins Leukot. Essent. Fatty Acids. 2019;145:15–22.
    doi: 10.1016/j.plefa.2019.05.003pubmed: 31126515google scholar: lookup
  65. Ben Hamouda S, Miglino MA, de Sá Schiavo Matias G, Beauchamp G, Lavoie JP. Asthmatic Bronchial Matrices Determine the Gene Expression and Behavior of Smooth Muscle Cells in a 3D Culture Model. Front. Allergy 2021;2:762026.
    doi: 10.3389/falgy.2021.762026pmc: PMC8974673pubmed: 35387054google scholar: lookup
  66. Devienne MF, Guezennec CY. Energy expenditure of horse riding. Eur. J. Appl. Physiol. 2000;82:499–503.
    doi: 10.1007/s004210000207pubmed: 10985607google scholar: lookup
  67. Weibel ER, Taylor CR, Hoppeler H, Karas RH. Adaptive variation in the mammalian respiratory system in relation to energetic demand: I. Introduction to problem and strategy. Respir. Physiol. 1987;69:1–6.
    doi: 10.1016/0034-5687(87)90097-1pubmed: 3616184google scholar: lookup
  68. Ahern BJ, Sole A, de Klerk K, Hogg LR, Vallance SA, Bertin FR, Franklin SH. Evaluation of postsale endoscopy as a predictor of future racing performance in an Australian thoroughbred yearling population. Aust. Vet. J. 2022;100:254–260.
    doi: 10.1111/avj.13155pmc: PMC9305470pubmed: 35191021google scholar: lookup
  69. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, De Bakker PIW, Daly MJ. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007;81:559–575.
    doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
  70. Biscarini F, Cozzi P, Gaspa G, Marras G. detectRUNS: Detect Runs of Homozygosity and Runs of Heterozygosity in Diploid Genomes. 2019. [(accessed on 6 November 2024)].
  71. Lencz T, Lambert C, DeRosse P, Burdick KE, Morgan TV, Kane JM, Kucherlapati R, Malhotra AK. Runs of homozygosity reveal highly penetrant recessive loci in schizophrenia. Proc. Natl. Acad. Sci. USA. 2007;104:19942–19947.
    doi: 10.1073/pnas.0710021104pmc: PMC2148402pubmed: 18077426google scholar: lookup
  72. Purfield DC, Berry DP, McParland S, Bradley DG. Runs of homozygosity and population history in cattle. BMC Genet. 2012;13:70.
    doi: 10.1186/1471-2156-13-70pmc: PMC3502433pubmed: 22888858google scholar: lookup
  73. Goszczynski D, Molina A, Terán E, Morales-Durand H, Ross P, Cheng H, Giovambattista G, Demyda-Peyrás S. Runs of homozygosity in a selected cattle population with extremely inbred bulls: Descriptive and functional analyses revealed highly variable patterns. PLoS ONE 2018;13:e0200069.
    doi: 10.1371/journal.pone.0200069pmc: PMC6037354pubmed: 29985951google scholar: lookup
  74. Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 2015;12:115–121.
    doi: 10.1038/nmeth.3252pmc: PMC4509590pubmed: 25633503google scholar: lookup
  75. Lawrence M. HelloRanges: Introduce *Ranges to Bedtools Users. R Package Version 1.28.0 2023. [(accessed on 6 November 2024)].

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