FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Genes2023; 14(7); doi: 10.3390/genes14071511

The Genetic Diversity of Stallions of Different Breeds in Russia.

Abstract: The specifics of breeding and selection significantly affect genetic diversity and variability within a breed. We present the data obtained from the genetic analysis of 21 thoroughbred and warmblood horse breeds. The most detailed information is described from the following breeds: Arabian, Trakehner, French Trotter, Standardbred, and Soviet Heavy Horse. The analysis of 509,617 SNP variants in 87 stallions from 21 populations made it possible to estimate the genetic diversity at the genome-wide level and distinguish the studied horse breeds from each other. In this study, we searched for heterozygous and homozygous ROH regions, evaluated inbreeding using FROH analysis, and generated a population structure using Admixture 1.3 software. Our findings indicate that the Arabian breed is an ancestor of many horse breeds. The study of the full-genome architectonics of breeds is of great practical importance for preserving the genetic characteristics of breeds and managing breeding. Studies were carried out to determine homozygous regions in individual breeds and search for candidate genes in these regions. Fifty-six candidate genes for the influence of selection pressure were identified. Our research reveals genetic diversity consistent with breeding directions and the breeds' history of origin.
Get Full Text
Publication Date: 2023-07-24 PubMed ID: 37510415PubMed Central: PMC10378902DOI: 10.3390/genes14071511Google Scholar: Lookup
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.
LinkedInCopy
  • Journal Article
  • Research Support
  • Non-U.S. Gov't

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 study explores the genetic diversity and specific characteristics of various horse breeds in Russia, examining over half a million Single Nucleotide Polymorphism (SNP) variants in 87 stallions across 21 breeds. The research establishes relationships between different equine breeds, highlighting the Arabian horse as a common ancestry. In addition, the study identified genes potentially influenced by selection pressures and provides valuable insights into breed origins and breeding strategies.

Study Design and Data Collection

  • The study centers around genetic exploration of 21 thoroughbred and warmblood horse breeds, primarily focusing on Arabian, Trakehner, French Trotter, Standardbred, and Soviet Heavy Horse breeds.
  • Researchers analyzed 509,617 SNP variants from 87 stallions across the different breeds. This massive data set allowed for an understanding of the genome-wide level of horse breeds and their distinctive variances.
  • The research effort aimed to locate homogenous and heterogenous Runs of Homozygosity (ROH) regions and evaluated inbreeding using the method called FROH analysis. This method helps identify regions with a consecutive string of similar genes in a line of horses and is commonly used to examine genetic diversity in a population.

Genetic Analysis and Software Used

  • Genetic analysis was carried out through the Admixture 1.3 software, a software tool that employs a statistical method to extract meaningful information about population structure and ancestry.
  • Using this software, the team generated a population structure that provided insights into the genetic interconnections among different horse breeds.

Key Findings and Future Perspectives

  • The findings indicate that the Arabian breed is a common ancestor of many horse breeds, underscoring its importance in the genus.
  • This study also went beyond identifying genetic traits and looked for candidate genes influenced by selection pressure. Here, selection pressure refers to the different environmental factors that can cause specific traits to become more prevalent in a population over time. This research uncovered 56 genes that were likely influenced by selection pressure.
  • The researchers highlight that understanding the full-genome architecture of breeds has significant practical implications. It aids in preserving the unique genetic characteristics of various breeds and provides insights for managing breeding processes effectively.
  • The research also has implications for the ongoing efforts to preserve equine biodiversity, and it offers areas of further exploration for genetic research in the context of breed preservation and development.

Cite This Article

APA
Dementieva N, Nikitkina E, Shcherbakov Y, Nikolaeva O, Mitrofanova O, Ryabova A, Atroshchenko M, Makhmutova O, Zaitsev A. (2023). The Genetic Diversity of Stallions of Different Breeds in Russia. Genes (Basel), 14(7). https://doi.org/10.3390/genes14071511

Publication

Genes
ISSN: 2073-4425
NlmUniqueID: 101551097
Country: Switzerland
Language: English
Volume: 14
Issue: 7

Researcher Affiliations

Dementieva, Natalia
  • Russian Research Institute of Farm Animal Genetics and Breeding-Branch of the L.K. Ernst Federal Research Center for Animal Husbandry, 55A, Moskovskoye Sh., Tyarlevo, Pushkin, St. Petersburg 196625, Russia.
Nikitkina, Elena
  • Russian Research Institute of Farm Animal Genetics and Breeding-Branch of the L.K. Ernst Federal Research Center for Animal Husbandry, 55A, Moskovskoye Sh., Tyarlevo, Pushkin, St. Petersburg 196625, Russia.
Shcherbakov, Yuri
  • Russian Research Institute of Farm Animal Genetics and Breeding-Branch of the L.K. Ernst Federal Research Center for Animal Husbandry, 55A, Moskovskoye Sh., Tyarlevo, Pushkin, St. Petersburg 196625, Russia.
Nikolaeva, Olga
  • Russian Research Institute of Farm Animal Genetics and Breeding-Branch of the L.K. Ernst Federal Research Center for Animal Husbandry, 55A, Moskovskoye Sh., Tyarlevo, Pushkin, St. Petersburg 196625, Russia.
Mitrofanova, Olga
  • Russian Research Institute of Farm Animal Genetics and Breeding-Branch of the L.K. Ernst Federal Research Center for Animal Husbandry, 55A, Moskovskoye Sh., Tyarlevo, Pushkin, St. Petersburg 196625, Russia.
Ryabova, Anna
  • Russian Research Institute of Farm Animal Genetics and Breeding-Branch of the L.K. Ernst Federal Research Center for Animal Husbandry, 55A, Moskovskoye Sh., Tyarlevo, Pushkin, St. Petersburg 196625, Russia.
Atroshchenko, Mikhail
  • All-Russian Research Institute of Horse Breeding (ARRIH), Ryazan Region, Divovo, Rybnovskij District 391105, Russia.
Makhmutova, Oksana
  • All-Russian Research Institute of Horse Breeding (ARRIH), Ryazan Region, Divovo, Rybnovskij District 391105, Russia.
Zaitsev, Alexander
  • All-Russian Research Institute of Horse Breeding (ARRIH), Ryazan Region, Divovo, Rybnovskij District 391105, Russia.

MeSH Terms

  • Horses / genetics
  • Animals
  • Male
  • Polymorphism, Single Nucleotide / genetics
  • Homozygote
  • Inbreeding
  • Genome
  • Russia

Conflict of Interest Statement

The authors declare no conflict of interest.

References

This article includes 74 references
  1. Librado P, Fages A, Gaunitz C, Leonardi M, Wagner S, Khan N, Hanghøj K, Alquraishi SA, Alfarhan AH, Al-Rasheid KA. The Evolutionary Origin and Genetic Makeup of Domestic Horses. Genetics 2016;204:423–434.
    doi: 10.1534/genetics.116.194860pmc: PMC5068837pubmed: 27729493google scholar: lookup
  2. Noskova MV, Arkhilaeva MS. Economic Problems and Prospects for the Development of Horse Breeding in Russia. Bull. Altai State Agrar. Univ. 2009;12:98–103.
  3. Yun J, Oyungerel B, Kong HS. Genetic Diversity and Population Structure of Mongolian Regional Horses with 14 Microsatellite Markers. Anim. Biosci. 2022;35:1121–1128.
    doi: 10.5713/ab.21.0497pmc: PMC9262727pubmed: 35240022google scholar: lookup
  4. Luttman AM, Komine M, Thaiwong T, Carpenter T, Ewart SL, Kiupel M, Langohr IM, Venta PJ. Development of a 17-Plex of Penta- and Tetra-Nucleotide Microsatellites for DNA Profiling and Paternity Testing in Horses. Front. Vet. Sci. 2022;9:861623.
    doi: 10.3389/fvets.2022.861623pmc: PMC9021955pubmed: 35464354google scholar: lookup
  5. 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:e54997V.
    doi: 10.1371/journal.pone.0054997pmc: PMC3559798pubmed: 23383025google scholar: lookup
  6. Rosengren MK, Sigurðardóttir H, Eriksson S, Naboulsi R, Jouni A, Novoa-Bravo M, Albertsdóttir E, Kristjánsson Þ, Rhodin M, Viklund Å. A QTL for Conformation of Back and Croup Influences Lateral Gait Quality in Icelandic Horses. BMC Genom. 2021;22:267.
    doi: 10.1186/s12864-021-07454-zpmc: PMC8048352pubmed: 33853519google scholar: lookup
  7. Nikitkina EV, Dementieva NV, Shcherbakov YS, Atroshchenko MM, Kudinov AA, Samoylov OI, Pozovnikova MV, Dysin AP, Krutikova AA, Musidray AA. Genome-Wide Association Study for Frozen-Thawed Sperm Motility in Stallions across Various Horse Breeds. Anim. Biosci. 2022;35:1827–1838.
    doi: 10.5713/ab.21.0504pmc: PMC9659452pubmed: 35240017google scholar: lookup
  8. Drabbe A, Janssens S, Blott S, Ducro BJ, Fontanel M, Francois L, Schurink A, Stinckens A, Lindgren G, Van Mol B. Genome-Wide Association Analyses of Osteochondrosis in Belgian Warmbloods Reveal Candidate Genes Associated With Chondrocyte Development. J. Equine Vet. Sci. 2022;111:103870.
    doi: 10.1016/j.jevs.2022.103870pubmed: 35074400google scholar: lookup
  9. Laseca N, Demyda-Peyrás S, Valera M, Ramón M, Escribano B, Perdomo-González DI, Molina A. A Genome-Wide Association Study of Mare Fertility in the Pura Raza Español Horse. Animal 2022;16:100476.
    doi: 10.1016/j.animal.2022.100476pubmed: 35247706google scholar: lookup
  10. Affolter VK, Dalley B, Kass PH, Brown EA, Sonder C, Bannasch DL. Chronic Progressive Lymphoedema in Friesian Horses: Suggestive Phenotype of Affected Horses and Genome-Wide Association Study. Vet. Derm. 2020;31:234-e51.
    doi: 10.1111/vde.12831pubmed: 31908060google scholar: lookup
  11. Solé M, Ablondi M, Binzer-Panchal A, Velie BD, Hollfelder N, Buys N, Ducro BJ, François L, Janssens S, Schurink A. Inter- and Intra-Breed Genome-Wide Copy Number Diversity in a Large Cohort of European Equine Breeds. BMC Genom. 2019;20:759.
    doi: 10.1186/s12864-019-6141-zpmc: PMC6805398pubmed: 31640551google scholar: lookup
  12. Grilz-Seger G, Druml T, Neuditschko M, Dobretsberger M, Horna M, Brem G. High-Resolution Population Structure and Runs of Homozygosity Reveal the Genetic Architecture of Complex Traits in the Lipizzan Horse. BMC Genom. 2019;20:174.
    doi: 10.1186/s12864-019-5564-xpmc: PMC6402180pubmed: 30836959google scholar: lookup
  13. Bizarria dos Santos W, Pimenta Schettini G, Fonseca MG, Pereira GL, Loyola Chardulo LA, Rodrigues Machado Neto O, Baldassini WA, Nunes de Oliveira H, Abdallah Curi R. Fine-Scale Estimation of Inbreeding Rates, Runs of Homozygosity and Genome-Wide Heterozygosity Levels in the Mangalarga Marchador Horse Breed. J. Anim. Breed. Genet. 2021;138:161–173.
    doi: 10.1111/jbg.12508pubmed: 32949478google scholar: lookup
  14. Santos WB, Schettini GP, Maiorano AM, Bussiman FO, Balieiro JCC, Ferraz GC, Pereira GL, Baldassini WA, Neto ORM, Oliveira HN. Genome-Wide Scans for Signatures of Selection in Mangalarga Marchador Horses Using High-Throughput SNP Genotyping. BMC Genom. 2021;22:737.
    doi: 10.1186/s12864-021-08053-8pmc: PMC8515666pubmed: 34645387google scholar: lookup
  15. Chang CC, Chow CC, Tellier L, Vattikuti S, Purcell SM, Lee JJ. Second-Generation PLINK: Rising to the Challenge of Larger and Richer Datasets. Gigascience 2015;4:7.
    doi: 10.1186/s13742-015-0047-8pmc: PMC4342193pubmed: 25722852google scholar: lookup
  16. Alexander DH, Novembre J, Lange K. Fast Model-Based Estimation of Ancestryin Unrelated Individuals. Genome Res. 2009;19:1655–1664.
    doi: 10.1101/gr.094052.109pmc: PMC2752134pubmed: 19648217google scholar: lookup
  17. Francis RM. Pophelper: An R Package and Web App to Analyse and Visualize Population Structure. Mol. Ecol. Resour. 2017;17:27–32.
    doi: 10.1111/1755-0998.12509pubmed: 26850166google scholar: lookup
  18. Anderson CA, Pettersson FH, Clarke GM, Cardon LR, Morris AP, Zondervan KT. Data Quality Control in Genetic Case-Control Association Studies. Nat. Protoc. 2010;5:1564–1573.
    doi: 10.1038/nprot.2010.116pmc: PMC3025522pubmed: 21085122google scholar: lookup
  19. Biscarini F, Cozzi P, Gaspa G, Marras G. detectRUNS: An R package to detect runs of homozygosity and heterozygosity in diploid genomes. R Package Version 0.9.6 2019.
  20. Hamann H, Distl O. Genetic Variability in Hanoverian Warmblood Horses Using Pedigree Analysis. J. Anim. Sci. 2008;86:1503–1513.
    doi: 10.2527/jas.2007-0382pubmed: 18310493google scholar: lookup
  21. Mucha S, Windig JJ. Effects of Incomplete Pedigree on Genetic Management of the Dutch Landrace Goat. J. Anim. Breed. Genet. 2009;126:250–256.
    doi: 10.1111/j.1439-0388.2008.00757.xpubmed: 19646154google scholar: lookup
  22. Abdelmanova AS, Dotsev AV, Romanov MN, Stanishevskaya OI, Gladyr EA, Rodionov AN, Vetokh AN, Volkova NA, Fedorova ES, Gusev IV. Unveiling Comparative Genomic Trajectories of Selection and Key Candidate Genes in Egg-Type Russian White and Meat-Type white Cornish Chickens. Biology 2021;10:876.
    doi: 10.3390/biology10090876pmc: PMC8469556pubmed: 34571753google scholar: lookup
  23. Salek Ardestani S, Zandi MB, Vahedi SM, Janssens S. Population Structure And Genomic Footprints Of Selection In Five Major Iranian Horse Breeds. Anim Genet. 2022;53:627–639.
    doi: 10.1111/age.13243pubmed: 35919961google scholar: lookup
  24. Cosgrove EJ, Sadeghi R, Schlamp F, Holl HM, Moradi-Shahrbabak M, Miraei-Ashtiani SR, Abdalla S, Shykind B, Troedsson M, Stefaniuk-Szmukier M. Genome Diversity and the Origin of the Arabian Horse. Sci. Rep. 2020;10:9702.
    doi: 10.1038/s41598-020-66232-1pmc: PMC7298027pubmed: 32546689google scholar: lookup
  25. Peripolli E, Munari DP, Silva MVGB, Lima ALF, Irgang R, Baldi F. Runs of Homozygosity: Current Knowledge and Applications in Livestock. Anim. Genet. 2017;48:255–271.
    doi: 10.1111/age.12526pubmed: 27910110google scholar: lookup
  26. Cendron F, Perini F, Mastrangelo S, Tolone M, Criscione A, Bordonaro S, Iaffaldano N, Castellini C, Marzoni M, Buccioni A. Genome-Wide Snp Analysis Reveals the Population Structure and the Conservation Status of 23 Italian Chicken Breeds. Animals 2020;10:1441.
    doi: 10.3390/ani10081441pmc: PMC7460279pubmed: 32824706google scholar: lookup
  27. Esdaile E, Avila F, Bellone RR. Analysis of Genetic Diversity in the American Standardbred Horse Utilizing Short Tandem Repeats and Single Nucleotide Polymorphisms. J. Hered. 2022;113:238–247.
    doi: 10.1093/jhered/esab070pmc: PMC9270868pubmed: 34893836google scholar: lookup
  28. Knorr F. A History of the Arabian Horse and Its Influence on Modern Breeds. J. Hered. 1912;3:174–180.
    doi: 10.1093/oxfordjournals.jhered.a105899google scholar: lookup
  29. Nolte W, Thaller G, Kuehn C. Selection Signatures in Four German Warmblood Horse Breeds: Tracing Breeding History in the Modern Sport Horse. PLoS ONE 2019;14:e0215913.
    doi: 10.1371/journal.pone.0215913pmc: PMC6483353pubmed: 31022261google scholar: lookup
  30. Ma S, Dubin AE, Zhang Y, Mousavi SAR, Wang Y, Coombs AM, Loud M, Andolfo I, Patapoutian A. A Role of PIEZO1 in Iron Metabolism in Mice and Humans. Cell 2021;184:969–982.e13.
    doi: 10.1016/j.cell.2021.01.024pmc: PMC7927959pubmed: 33571427google scholar: lookup
  31. Wang L, You X, Lotinun S, Zhang L, Wu N, Zou W. Mechanical Sensing Protein PIEZO1 Regulates Bone Homeostasis via Osteoblast-Osteoclast Crosstalk. Nat. Commun. 2020;11:282.
    doi: 10.1038/s41467-019-14146-6pmc: PMC6962448pubmed: 31941964google scholar: lookup
  32. Zhang W, Li J, Guo Y, Zhang L, Xu L, Gao X, Zhu B, Gao H, Ni H, Chen Y. Multi-Strategy Genome-Wide Association Studies Identify the DCAF16-NCAPG Region as a Susceptibility Locus for Average Daily Gain in Cattle. Sci. Rep. 2016;6:38073.
    doi: 10.1038/srep38073pmc: PMC5125095pubmed: 27892541google scholar: lookup
  33. Szmatoła T, Gurgul A, Jasielczuk I, Oclon E, Ropka-Molik K, Stefaniuk-Szmukier M, Polak G, Tomczyk-Wrona I, Bugno-Poniewierska M. Assessment and Distribution of Runs of Homozygosity in Horse Breeds Representing Different Utility Types. Animals 2022;12:3293.
    doi: 10.3390/ani12233293pmc: PMC9736150pubmed: 36496815google scholar: lookup
  34. Legrand R, Tiret L, Abitbol M. Two Recessive Mutations in FGF5 Are Associated with the Long-Hair Phenotype in Donkeys. Genet. Sel. Evol. 2014;46:65.
    doi: 10.1186/s12711-014-0065-5pmc: PMC4175617pubmed: 25927731google scholar: lookup
  35. Haythorn A, Young M, Stanton J, Zhang J, Mueller POE, Halper J. Differential Gene Expression in Skin RNA of Horses Affected with Degenerative Suspensory Ligament Desmitis. J. Orthop. Surg. Res. 2020;15:460.
    doi: 10.1186/s13018-020-01994-ypmc: PMC7541307pubmed: 33028365google scholar: lookup
  36. Bao Q, Ma X, Jia C, Wu X, Wu Y, Meng G, Bao P, Chu M, Guo X, Liang C. Resequencing and Signatures of Selective Scans Point to Candidate Genetic Variants for Hair Length Traits in Long-Haired and Normal-Haired Tianzhu White Yak. Front. Genet. 2022;13:798076.
    doi: 10.3389/fgene.2022.798076pmc: PMC8962741pubmed: 35360871google scholar: lookup
  37. Waldmann I, Spillner C, Kehlenbach RH. The Nucleoporin-like Protein NLP1 (HCG1) Promotes CRM1-Dependent Nuclear Protein Export. J. Cell Sci. 2012;125:144–154.
    doi: 10.1242/jcs.090316pubmed: 22250199google scholar: lookup
  38. Cho YK, Son Y, Saha A, Kim D, Choi C, Kim M, Park J-H, Im H, Han J, Kim K. STK3/STK4 Signalling in Adipocytes Regulates Mitophagy and Energy Expenditure. Nat. Metab. 2021;3:428–441.
    doi: 10.1038/s42255-021-00362-2pubmed: 33758424google scholar: lookup
  39. Ostrowski K, Rohde T, Asp S, Schjerling P, Klarlund Pedersen B. Chemokines Are Elevated in Plasma after Strenuous Exercise in Humans. Eur. J. Appl. Physiol. 2001;84:244–245.
    doi: 10.1007/s004210170012pubmed: 11320643google scholar: lookup
  40. Capomaccio S, Cappelli K, Spinsanti G, Mencarelli M, Muscettola M, Felicetti M, Supplizi A, Bonifazi M. Athletic Humans and Horses: Comparative Analysis of Interleukin-6 (IL-6) and IL-6 Receptor (IL-6R) Expression in Peripheral Blood Mononuclear Cells in Trained and Untrained Subjects at Rest. BMC Physiol. 2011;11:3.
    doi: 10.1186/1472-6793-11-3pmc: PMC3036646pubmed: 21255427google scholar: lookup
  41. Chakraborty S, Kahali B. Exome-Wide Analysis Reveals Role of LRP1 and Additional Novel Loci in Cognition. Hum. Genet. Genom. Adv. 2023;4:100208.
    doi: 10.1016/j.xhgg.2023.100208pmc: PMC10248556pubmed: 37305557google scholar: lookup
  42. Meech R, Gonzalez KN, Barro M, Gromova A, Zhuang L, Hulin J-A, Makarenkova HP. Barx2 Is Expressed in Satellite Cells and Is Required for Normal Muscle Growth and Regeneration. Stem Cells 2012;30:253–265.
    doi: 10.1002/stem.777pmc: PMC4547831pubmed: 22076929google scholar: lookup
  43. Tanioku T, Nishibata M, Tokinaga Y, Konno K, Watanabe M, Hemmi H, Fukuda-Ohta Y, Kaisho T, Furue H, Kawamata T. Tmem45b Is Essential for Inflammation- and Tissue Injury–Induced Mechanical Pain Hypersensitivity. Proc. Natl. Acad. Sci. USA 2022;119:e2121989119.
    doi: 10.1073/pnas.2121989119pmc: PMC9659417pubmed: 36322717google scholar: lookup
  44. Chen C, Zhu B, Tang X, Chen B, Liu M, Gao N, Li S, Gu J. Genome-Wide Assessment of Runs of Homozygosity by Whole-Genome Sequencing in Diverse Horse Breeds Worldwide. Genes 2023;14:1211.
    doi: 10.3390/genes14061211pmc: PMC10298080pubmed: 37372391google scholar: lookup
  45. 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
  46. Grilz-Seger G, Neuditschko M, Ricard A, Velie B, Lindgren G, Mesarič M, Cotman M, Horna M, Dobretsberger M, Brem G. Genome-Wide Homozygosity Patterns and Evidence for Selection in a Set of European and Near Eastern Horse Breeds. Genes 2019;10:491.
    doi: 10.3390/genes10070491pmc: PMC6679042pubmed: 31261764google scholar: lookup
  47. 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
  48. Witkowski M, Duliban M, Rak A, Profaska-Szymik M, Gurgul A, Arent ZJ, Galuszka A, Kotula-Balak M. Next-Generation Sequencing Analysis Discloses Genes Implicated in Equine Endometrosis That May Lead to Tumorigenesis. Theriogenology 2022;189:158–166.
    doi: 10.1016/j.theriogenology.2022.06.015pubmed: 35760027google scholar: lookup
  49. Alpoim-Moreira J, Fernandes C, Rebordão MR, Amaral A, Pinto-Bravo P, Bliebernicht M, Skarzynski DJ, Ferreira-Dias G. Collagens and DNA Methyltransferases in Mare Endometrosis. Reprod. Domest. Anim. 2019;54:46–52.
    doi: 10.1111/rda.13515pubmed: 31512314google scholar: lookup
  50. Deutschman E, Ward JR, Kumar A, Ray G, Welch N, Lemieux ME, Dasarathy S, Longworth MS. Condensin II Protein Dysfunction Impacts Mitochondrial Respiration and Stress Response. J. Cell Sci. 2019;132:jcs233783.
    doi: 10.1242/jcs.233783pmc: PMC6899004pubmed: 31653782google scholar: lookup
  51. Momen M, Brounts SH, Binversie EE, Sample SJ, Rosa GJM, Davis BW, Muir P. Selection Signature Analyses and Genome-Wide Association Reveal Genomic Hotspot Regions That Reflect Differences between Breeds of Horse with Contrasting Risk of Degenerative Suspensory Ligament Desmitis. G3 Genes Genomes Genet. 2022;12:jkac179.
    doi: 10.1093/g3journal/jkac179pmc: PMC9526059pubmed: 35866615google scholar: lookup
  52. Li H, Xu S, Gao X, Ren H. Structure of the Bovine ACAD8 Gene and the Association of Its Polymorphism with the Production Traits. J. Genet. Genom. 2007;34:315–320.
    doi: 10.1016/S1673-8527(07)60033-2pubmed: 17498629google scholar: lookup
  53. Fan Y, Shen S, Yang J, Yao D, Li M, Mao C, Wang Y, Hao X, Ma D, Li J. Repeat Expansion Is Associated with Movement Disorders. Ann. Neurol. 2022;91:704–715.
    doi: 10.1002/ana.26325pubmed: 35152460google scholar: lookup
  54. Wang Q, Li D, Cao G, Shi Q, Zhu J, Zhang M, Cheng H, Wen Q, Xu H, Zhu L. IL-27 Signalling Promotes Adipocyte Thermogenesis and Energy Expenditure. Nature 2021;600:314–318.
    doi: 10.1038/s41586-021-04127-5pubmed: 34819664google scholar: lookup
  55. Calvez J, Ávila C, Timofeeva E. Sex-specific Effects of Relaxin-3 on Food Intake and Body Weight Gain. Br. J. Pharmacol. 2017;174:1049–1060.
    doi: 10.1111/bph.13530pmc: PMC5406289pubmed: 27245781google scholar: lookup
  56. Bundgaard L, Åhrman E, Malmström J, auf dem Keller U, Walters M, Jacobsen S. Effective Protein Extraction Combined with Data Independent Acquisition Analysis Reveals a Comprehensive and Quantifiable Insight into the Proteomes of Articular Cartilage and Subchondral Bone. Osteoarthr. Cartil. 2022;30:137–146.
    doi: 10.1016/j.joca.2021.09.006pubmed: 34547431google scholar: lookup
  57. Xu L, Humphries F, Delagic N, Wang B, Holland AS, Edgar KS, Hombrebueno JR, Stolz DB, Oleszycka E, Rodgers AM. ECSIT Is a Critical Limiting Factor for Cardiac Function. JCI Insight 2021;6:e142801.
    doi: 10.1172/jci.insight.142801pmc: PMC8262467pubmed: 34032637google scholar: lookup
  58. Teng Z, Zhu Y, Lin D, Hao Q, Yue Q, Yu X, Sun S, Jiang L, Lu S. Deciphering the Chromatin Spatial Organization Landscapes during BMMSC Differentiation. J. Genet. Genom. 2023;50:264–275.
    doi: 10.1016/j.jgg.2023.01.009pubmed: 36720443google scholar: lookup
  59. Bisom TC, White LA, Lanchy J-M, Lodmell JS. RIOK3 and Its Alternatively Spliced Isoform Have Disparate Roles in the Innate Immune Response to Rift Valley Fever Virus (MP12) Infection. Viruses 2022;14:2064.
    doi: 10.3390/v14092064pmc: PMC9502082pubmed: 36146870google scholar: lookup
  60. 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
  61. Huang H, Li C, Ma W, Yin S, Zhao H, Deng S, Shu X, Wu D, Li J, Huang R. Sorting Nexin 11 Knockout Mice Exhibit Enhanced Thermosensing Behaviour. Genes Brain Behav. 2020;19:e12625.
    doi: 10.1111/gbb.12625pubmed: 31730264google scholar: lookup
  62. Ren S, Bian Y, Hou Y, Wang Z, Zuo Z, Liu Z, Teng Y, Fu J, Wang H, Xu Y. The Roles of NFE2L1 in Adipocytes: Structural and Mechanistic Insight from Cell and Mouse Models. Redox. Biol. 2021;44:102015.
    doi: 10.1016/j.redox.2021.102015pmc: PMC8170497pubmed: 34058615google scholar: lookup
  63. Kuroda Y, Iwata-Otsubo A, Dias K-R, Temple SEL, Nagao K, De Hayr L, Zhu Y, Isobe S-Y, Nishibuchi G, Fiordaliso SK. Dominant-Negative Variants in CBX1 Cause a Neurodevelopmental Disorder. Genet. Med. 2023;25:100861.
    doi: 10.1016/j.gim.2023.100861pubmed: 37087635google scholar: lookup
  64. Hunyady Á, Hajna Z, Gubányi T, Scheich B, Kemény Á, Gaszner B, Borbély É, Helyes Z. Hemokinin-1 Is an Important Mediator of Pain in Mouse Models of Neuropathic and Inflammatory Mechanisms. Brain Res. Bull. 2019;147:165–173.
    doi: 10.1016/j.brainresbull.2019.01.015pubmed: 30664920google scholar: lookup
  65. Sun K, Jing X, Guo J, Yao X, Guo F. Mitophagy in Degenerative Joint Diseases. Autophagy 2021;17:2082–2092.
    doi: 10.1080/15548627.2020.1822097pmc: PMC8496714pubmed: 32967533google scholar: lookup
  66. Han H, McGivney BA, Allen L, Bai D, Corduff LR, Davaakhuu G, Davaasambuu J, Dorjgotov D, Hall TJ, Hemmings AJ. Common Protein-Coding Variants Influence the Racing Phenotype in Galloping Racehorse Breeds. Commun. Biol. 2022;5:1320.
    doi: 10.1038/s42003-022-04206-xpmc: PMC9748125pubmed: 36513809google scholar: lookup
  67. Wu C, Tan S, Liu L, Cheng S, Li P, Li W, Liu H, Zhang F, Wang S, Ning Y. Transcriptome-Wide Association Study Identifies Susceptibility Genes for Rheumatoid Arthritis. Arthritis Res. Ther. 2021;23:38.
    doi: 10.1186/s13075-021-02419-9pmc: PMC7821659pubmed: 33482886google scholar: lookup
  68. Catomeris AJ, Ballios BG, Sangermano R, Wagner NE, Comander JI, Pierce EA, Place EM, Bujakowska KM, Huckfeldt RM. Novel RCBTB1 Variants Causing Later-Onset Non-Syndromic Retinal Dystrophy with Macular Chorioretinal Atrophy. Ophthalmic Genet. 2022;43:332–339.
    doi: 10.1080/13816810.2021.2023196pmc: PMC9177531pubmed: 35057699google scholar: lookup
  69. Aomine Y, Sakurai K, Macpherson T, Ozawa T, Miyamoto Y, Yoneda Y, Oka M, Hikida T. Importin A3 (KPNA3) Deficiency Augments Effortful Reward-Seeking Behavior in Mice. Front. Neurosci. 2022;16:905991.
    doi: 10.3389/fnins.2022.905991pmc: PMC9279672pubmed: 35844217google scholar: lookup
  70. Brunner SM, Schrödl F, Preishuber-Pflügl J, Runge C, Koller A, Lenzhofer M, Reitsamer HA, Trost A. Distribution of the Cysteinyl Leukotriene System Components in the Human, Rat and Mouse Eye. Exp. Eye Res. 2023;232:109517.
    doi: 10.1016/j.exer.2023.109517pubmed: 37211287google scholar: lookup
  71. Kane M, Mele V, Liberatore RA, Bieniasz PD. Inhibition of Spumavirus Gene Expression by PHF11. PLoS Pathog. 2020;16:e1008644.
    doi: 10.1371/journal.ppat.1008644pmc: PMC7390438pubmed: 32678836google scholar: lookup
  72. Rostampour N, Appelt CM, Abid A, Boughner JC. Expression of New Genes in Vertebrate Tooth Development and P63 Signaling. Dev. Dyn. 2019;248:744–755.
    doi: 10.1002/dvdy.26pubmed: 30875130google scholar: lookup
  73. Cisneros-Larios B, Elias CF. Sex Differences in the Coexpression of Prokineticin Receptor 2 and Gonadal Steroids Receptors in Mice. Front. Neuroanat. 2023;16:1057727.
    doi: 10.3389/fnana.2022.1057727pmc: PMC9853983pubmed: 36686573google scholar: lookup
  74. Martinez-Mayer J, Perez-Millan MI. Phenotypic and Genotypic Landscape of PROKR2 in Neuroendocrine Disorders. Front. Endocrinol. 2023;14:1132787.
    doi: 10.3389/fendo.2023.1132787pmc: PMC9945519pubmed: 36843573google scholar: lookup

Citations

This article has been cited 4 times.
  1. Jafari H, Abebe BK, Cong L, Ahmed Z, Zhaofei W, Sun M, Muhatai G, Chuzhao L, Dang R. Review: Genomic insights into the adaptive traits and stress resistance in modern horses. Stress Biol 2026 Jan 12;6(1):5.
    doi: 10.1007/s44154-025-00274-1pubmed: 41521281google scholar: lookup
  2. Sievers J, Distl O. Genomic Patterns of Homozygosity and Genetic Diversity in the Rhenish German Draught Horse. Genes (Basel) 2025 Mar 11;16(3).
    doi: 10.3390/genes16030327pubmed: 40149478google scholar: lookup
  3. Argun Karsli B, Demir E, Bilginer U, Dogru H, Karsli T, Kaya S. Genome-wide discovery of selection signatures in four Anatolian sheep breeds revealed by ddRADseq. Sci Rep 2024 Sep 3;14(1):20518.
    doi: 10.1038/s41598-024-71617-7pubmed: 39227733google scholar: lookup
  4. Atroshchenko M, Dementieva N, Shcherbakov Y, Nikolaeva O, Azovtseva A, Ryabova A, Nikitkina E, Makhmutova O, Datsyshin A, Zakharov V, Zaitsev A. The Genetic Diversity of Horse Native Breeds in Russia. Genes (Basel) 2023 Nov 28;14(12).
    doi: 10.3390/genes14122148pubmed: 38136970google scholar: lookup
Subscribe to our newsletter
Join 99,658 horse owners like you who receive our email updates on horse health and nutrition.