FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
iScience2026; 29(7); 116320; doi: 10.1016/j.isci.2026.116320

Machine learning-based gait classification and genome-wide association identify a QTL for gait type in Colombian paso horses.

Abstract: Coordinated mammalian locomotion relies on spinal circuits where regulates strides and alternative gaits. However, does not explain differences between the Colombian paso horse breed's specialized gaits, Colombian trocha and Colombian trot. We used inertial sensors and machine learning to develop accurate phenotyping ( = 225 horses), before performing genome-wide association analysis ( = 85 horses, 670K array). We identified a 2.43 Mb quantitative trait locus (QTL) on ECA16, where haplotypes featuring lead variants (rs1147402472, = 1.95 × 10; rs1136628503, = 8.52 × 10) explained 48.6% of gait variance ( < 0.001). The QTL contained 11 genes linked to neurodevelopment and muscle regulation, including and . Interaction analyses suggested a functional link between the latter genes. rs1147402472-C was common across diverse gaited horse breeds, but the Colombian trot-specific haplotype (rs1147402472-C/rs1136628503-T) was absent elsewhere. These findings demonstrate that fine-scale genetic differentiation at ECA16 underlies neural adaptations distinguishing complex locomotor traits and highlight the power of AI-assisted phenotyping in genomics.
© 2026 The Authors.
Get Full Text
Publication Date: 2026-06-11 PubMed ID: 42325571PubMed Central: PMC13276308DOI: 10.1016/j.isci.2026.116320Google 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

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.

Overview

  • This study developed a machine learning approach to classify different walking styles (gaits) in Colombian paso horses and identified a specific genetic region linked to gait differences using genome-wide association analysis.

Background and Objective

  • Mammalian locomotion is controlled by neural circuits in the spinal cord that regulate stride length and the choice of gait (walking pattern).
  • However, these known mechanisms do not fully explain the unique gaits seen in the Colombian paso horse breed, specifically the Colombian trocha and Colombian trot gaits.
  • The aim was to characterize these complex gaits accurately and discover genetic factors influencing them.

Methods and Experimental Design

  • Phenotyping:
    • 225 Colombian paso horses were equipped with inertial sensors to collect detailed movement data.
    • Machine learning algorithms were applied to this sensor data to accurately classify horses by their gait type.
  • Genotyping:
    • 85 of these horses were genotyped using a high-density 670,000-marker genetic array.
    • Genome-wide association analysis (GWAS) was conducted to find genetic regions associated with gait differences.

Key Findings

  • A quantitative trait locus (QTL) spanning 2.43 megabases on horse chromosome 16 (ECA16) was found to be strongly associated with gait type.
  • Two lead genetic variants within this region—rs1147402472 and rs1136628503—showed highly significant associations (p-values of approximately 1.95 × 10⁻¹² and 8.52 × 10⁻¹² respectively).
  • This QTL explained 48.6% of the variation in gait type among these horses, indicating a major genetic influence.
  • The identified QTL contained 11 genes involved in neural development and muscle function, including specific genes known to influence these processes (though the abstract did not name them explicitly).
  • Statistical interaction analysis suggested functional connections between these genes, likely impacting locomotor neural circuits and muscle regulation.

Additional Observations and Implications

  • The rs1147402472-C variant was also found in various other horse breeds that perform gaits, indicating a common variant linked to gait traits across breeds.
  • However, the specific haplotype combination rs1147402472-C/rs1136628503-T that characterizes the Colombian trot was unique and not observed in other breeds, highlighting fine-scale genetic differentiation.
  • This suggests that evolutionary or selective breeding processes have led to distinct genetic adaptations in Colombian paso horses that result in their specialized gaits.
  • The use of advanced machine learning for precise phenotype classification combined with genome-wide genetics is demonstrated to be effective for dissecting complex locomotor traits.

Conclusions

  • The study identifies a major genetic locus on chromosome 16 that contributes to complex gait differences in Colombian paso horses.
  • These genetic differences likely affect neural and muscular systems controlling coordinated movement, underlying breed-specific locomotor specializations.
  • Integration of AI-assisted phenotyping with genomic analyses can uncover genetic bases of complex animal behaviors and traits.
  • The findings provide new insight into how fine-scale genetic variation shapes specialized locomotion patterns in horses.

Cite This Article

APA
Novoa-Bravo M, Meadows JRS, Serra-Bragança F, van de Vall B, Kullander K, Rhodin M, Lindgren G. (2026). Machine learning-based gait classification and genome-wide association identify a QTL for gait type in Colombian paso horses. iScience, 29(7), 116320. https://doi.org/10.1016/j.isci.2026.116320

Publication

iScience
ISSN: 2589-0042
NlmUniqueID: 101724038
Country: United States
Language: English
Volume: 29
Issue: 7
Pages: 116320
PII: 116320

Researcher Affiliations

Novoa-Bravo, Miguel
  • Genética Animal de Colombia SAS, Bogotá, Colombia.
Meadows, Jennifer R S
  • Department of Medical Biochemistry and Microbiology, Uppsala University, 75123 Uppsala, Sweden.
  • SciLifeLab, Uppsala University, 75123 Uppsala, Sweden.
Serra-Bragança, Filipe
  • Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht 3584CM, the Netherlands.
van de Vall, Britt
  • Department of Animal Biosciences, Swedish University of Agricultural Sciences, P.O. Box 7023, 75007 Uppsala, Sweden.
Kullander, Klas
  • Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Rhodin, Marie
  • Department of Animal Biosciences, Swedish University of Agricultural Sciences, P.O. Box 7023, 75007 Uppsala, Sweden.
Lindgren, Gabriella
  • Department of Animal Biosciences, Swedish University of Agricultural Sciences, P.O. Box 7023, 75007 Uppsala, Sweden.

Conflict of Interest Statement

The authors declare competing interests concerning the commercial applications of the current study. G.L. is a co-inventor of a patent application concerning commercial testing of DMRT3 mutation. The stated patent does not restrict research applications of the method. G.L. is also a shareholder and CEO of Capilet Genetics, Inc., and M.N.-B. is a shareholder of Genética Animal de Colombia SAS.

References

This article includes 86 references
  1. Jankovic J. Gait disorders. Neurol. Clin. 2015;33:249–268.
    doi: 10.1016/j.ncl.2014.09.007pubmed: 25432732google scholar: lookup
  2. Adams H.H.H., Verlinden V.J.A., Callisaya M.L., Van Duijn C.M., Hofman A., Thomson R., Uitterlinden A.G., Vernooij M.W., Van Der Geest J.N., Srikanth V.. Heritability and Genome-Wide Association Analyses of Human Gait Suggest Contribution of Common Variants. Journals of Gerontology - Series A Biological Sciences and Medical Sciences 2016;71:740–746.
    doi: 10.1093/gerona/glv081pmc: PMC4888380pubmed: 26219847google scholar: lookup
  3. Hutchinson J.R.. The evolutionary biomechanics of locomotor function in giant land animals. J. Exp. Biol. 2021;224.
    doi: 10.1242/jeb.217463pmc: PMC8214834pubmed: 34100541google scholar: lookup
  4. Basu C., Wilson A.M., Hutchinson J.R.. The locomotor kinematics and ground reaction forces of walking giraffes. J. Exp. Biol. 2018;222.
    doi: 10.1242/jeb.159277pubmed: 30510118google scholar: lookup
  5. Barrey E.. Reviewe: Genetics and genomics in equine exercise physiology: an overview of the new applications of molecular biology as positive and negative markers of performance and health. Equine Vet. J. 2010;42:561–568.
    doi: 10.1111/J.2042-3306.2010.00299.Xpubmed: 21059061google scholar: lookup
  6. Rustin M., Janssens S., Buys N., Gengler N.. Multi-trait animal model estimation of genetic parameters for linear type and gait traits in the Belgian warmblood horse. J. Animal Breeding Genetics 2009;126:378–386.
    doi: 10.1111/J.1439-0388.2008.00798.Xpubmed: 19765164google scholar: lookup
  7. Bailey E., Brooks S.A. In: Horse Genetics. Second ed. Hulbert S., editor. CABI; 2013.
    doi: 10.1079/9781780643298.0027google scholar: lookup
  8. Wutke S., Andersson L., Benecke N., Sandoval-castellanos E., Gonzalez J., Hallsson J.H., Lõugas L., Magnell O., Morales-Muniz A., Pálsdóttir A.H.. The origin of ambling horses. Curr. Biol. 2016;26:R697–R699.
    doi: 10.1016/j.cub.2016.07.001pubmed: 27505236google scholar: lookup
  9. Sigurðardóttir H., Boije H., Albertsdóttir E., Kristjansson T., Rhodin M., Lindgren G., Eriksson S.. The genetics of gaits in Icelandic horses goes beyond DMRT3, with RELN and STAU2 identified as two new candidate genes. Genet. Sel. Evol. 2023;55:89.
    doi: 10.1186/s12711-023-00863-6pmc: PMC10712087pubmed: 38082412google scholar: lookup
  10. Andersson L.S., Larhammar M., Memic F., Wootz H., Schwochow D., Rubin C.-J., Patra K., Arnason T., Wellbring L., Hjälm G.. Mutations in DMRT3 affect locomotion in horses and spinal circuit function in mice. Nature 2012;488:642–646.
    doi: 10.1038/nature11399pmc: PMC3523687pubmed: 22932389google scholar: lookup
  11. Promerová M., Andersson L.S., Juras R., Penedo M.C.T., Reissmann M., Tozaki T., Bellone R., Dunner S., Hořín P., Imsland F.. Worldwide frequency distribution of the “Gait keeper” mutation in the DMRT3 gene. Anim. Genet. 2014;45:274–282.
    doi: 10.1111/age.12120pubmed: 24444049google scholar: lookup
  12. Staiger E.A., Almén M.S., Promerová M., Brooks S., Cothran E.G., Imsland F., Jäderkvist Fegraeus K., Lindgren G., Mehrabani Yeganeh H., Mikko S.. The evolutionary history of the DMRT3 ‘ Gait keeper ’ haplotype. Anim. Genet. 2017;48:551–559.
    doi: 10.1111/age.12580pubmed: 28741731google scholar: lookup
  13. Vincelette A.. The Characteristics, Distribution, Function, and Origin of Alternative Lateral Horse Gaits. Animals 2023;13:2557.
    doi: 10.3390/ani13162557pmc: PMC10451235pubmed: 37627349google scholar: lookup
  14. Kalinkova L.V., Zaitsev A.M., Ivanov R.V.. POPULATION FOR GENES MC1R, ASIP, DMRT3, AND MSTN. S.-kh. Biol. 2022;57:272–282.
    doi: 10.15389/agrobiology.2022.2.272eng.rugoogle scholar: lookup
  15. Srikanth K., Kim N.Y., Park W., Kim J.M., Kim K.D., Lee K.T., Son J.H., Chai H.H., Choi J.W., Jang G.W.. Author Correction: Comprehensive genome and transcriptome analyses reveal genetic relationship, selection signature, and transcriptome landscape of small-sized Korean native Jeju horse. Scientific Reports 2020;10.
    doi: 10.1038/s41598-020-74979-wpmc: PMC7577969pubmed: 33087804google scholar: lookup
  16. Patterson L, Staiger E.A, Brooks S.A. DMRT3 is associated with gait type in Mangalarga Marchador horses, but does not control gait ability.. Anim. Genet. 2015;46:213–215.
    doi: 10.1111/age.12273pubmed: 25690906google scholar: lookup
  17. Novoa-Bravo M, Jäderkvist Fegraeus K, Rhodin M, Strand E, García L.F, Lindgren G. Selection on the Colombian paso horse’s gaits has produced kinematic differences partly explained by the DMRT3 gene.. PLoS One 2018;13.
    pmc: PMC6097835pubmed: 30118522
  18. Borghuis B.G, Tadin D, Lankheet M.J.M, Lappin J.S, van de Grind W.A. Temporal Limits of Visual Motion Processing: Psychophysics and Neurophysiology.. Vision 2019;3.
    doi: 10.3390/VISION3010005pmc: PMC6802765pubmed: 31735806google scholar: lookup
  19. Zhou W, Nielsen J.B, Fritsche L.G, Dey R, Gabrielsen M.E, Wolford B.N, LeFaive J, VandeHaar P, Gagliano S.A, Gifford A. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies.. Nat. Genet. 2018;50:1335–1341.
    doi: 10.1038/s41588-018-0184-ypmc: PMC6119127pubmed: 30104761google scholar: lookup
  20. Nicodemus M, Friend M, Beranger J. PSXV-13 Relationship between Velocity and Temporal Variables in the Akhal-Teke Glide.. J. Anim. Sci. 2022;100:293–294.
    doi: 10.1093/JAS/SKAC247.533google scholar: lookup
  21. Waltereit R, Leimer U, Halbach O.v.B.u, Panke J, Hölter S.M, Garrett L, Wittig K, Schneider M, Schmitt C, Calzada-Wack J. Srgap3–/– mice present a neurodevelopmental disorder with schizophrenia-related intermediate phenotypes.. FASEB J. 2012;26:4418–4428.
    doi: 10.1096/FJ.11-202317pubmed: 22820399google scholar: lookup
  22. Chen Z.Y, Hayasaka S, Takagishi Y, Murata Y, Oda S.I. A Novel Mutant Mouse, joggle, with Inherited Ataxia.. Exp. Anim. 2006;55:411–414.
    doi: 10.1538/EXPANIM.55.411pubmed: 16880690google scholar: lookup
  23. Kozel P.J, Friedman R.A, Erway L.C, Yamoah E.N, Liu L.H, Riddle T, Duffy J.J, Doetschman T, Miller M.L, Cardell E.L, Shull G.E. Balance and hearing deficits in mice with a null mutation in the gene encoding plasma membrane Ca2+-ATPase isoform 2.. J. Biol. Chem. 1998;273:18693–18696.
    doi: 10.1074/jbc.273.30.18693pubmed: 9668038google scholar: lookup
  24. Deliu E, Arecco N, Morandell J, Dotter C.P, Contreras X, Girardot C, Käsper E.L, Kozlova A, Kishi K, Chiaradia I. Haploinsufficiency of the intellectual disability gene SETD5 disturbs developmental gene expression and cognition.. Nat. Neurosci. 2018;21:1717–1727.
    doi: 10.1038/s41593-018-0266-2pubmed: 30455454google scholar: lookup
  25. Moore S.M, Seidman J.S, Ellegood J, Gao R, Savchenko A, Troutman T.D, Abe Y, Stender J, Lee D, Wang S. Setd5 haploinsufficiency alters neuronal network connectivity and leads to autistic-like behaviors in mice.. Transl. Psychiatry 2019;9:24.
    doi: 10.1038/s41398-018-0344-ypmc: PMC6336863pubmed: 30655503google scholar: lookup
  26. Wu M, Tian H.L, Liu X, Lai J.H.C, Du S, Xia J. Impairment of Inhibitory Synapse Formation and Motor Behavior in Mice Lacking the NL2 Binding Partner LHFPL4/GARLH4.. Cell Rep. 2018;23:1691–1705.
    doi: 10.1016/J.CELREP.2018.04.015pubmed: 29742426google scholar: lookup
  27. Mostafavi S, Ray D, Warde-Farley D, Grouios C, Morris Q. GeneMANIA: A real-time multiple association network integration algorithm for predicting gene function.. Genome Biol. 2008;9.
    doi: 10.1186/gb-2008-9-s1-s4pmc: PMC2447538pubmed: 18613948google scholar: lookup
  28. Serra Bragança F.M, Broomé S, Rhodin M, Björnsdóttir S, Gunnarsson V, Voskamp J.P, Persson-Sjodin E, Back W, Lindgren G, Novoa-Bravo M. Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning.. Sci. Rep. 2020;10:1–9.
    doi: 10.1038/s41598-020-73215-9pmc: PMC7576586pubmed: 33082367google scholar: lookup
  29. Coleman S.J, Zeng Z, Hestand M.S, Liu J, Macleod J.N. Analysis of Unannotated Equine Transcripts Identified by mRNA Sequencing.. PLoS One 2013;8:e70125.
    doi: 10.1371/journal.pone.0070125pmc: PMC3726457pubmed: 23922931google scholar: lookup
  30. Watson C.J, Tempel B.L. A new Atp2b2 deafwaddler allele, dfwi5, interacts strongly with Cdh23 and other auditory modifiers.. Hear Res 2013;304:41–48.
    doi: 10.1016/J.HEARES.2013.06.003pmc: PMC3769430pubmed: 23792079google scholar: lookup
  31. Tsai Y.S, Pendse A, Moy S.S, Mohri I, Perez A, Crawley J.N, Suzuki K, Maeda N. A de novo deafwaddler mutation of Pmca2 arising in ES cells and hitchhiking with a targeted modification of the Pparg gene.. Mamm Genome 2006;17:716–722.
    doi: 10.1007/s00335-005-0191-zpubmed: 16845470google scholar: lookup
  32. Noben-Trauth K, Zheng Q.Y, Johnson K.R, Nishina P.M. mdfw:A Deafness Susceptibility Locus That Interacts with Deaf Waddler (dfw). Genomics 1997;44:266–272.
    doi: 10.1006/GENO.1997.4869pubmed: 9325047google scholar: lookup
  33. Bortolozzi M, Brini M, Parkinson N, Crispino G, Scimemi P, De Siati R.D, Di Leva F, Parker A, Ortolano S, Arslan E. The novel PMCA2 pump mutation Tommy impairs cytosolic calcium clearance in hair cells and links to deafness in mice.. J Biol Chem 2010;285:37693–37703.
    doi: 10.1074/JBC.M110.170092pmc: PMC2988374pubmed: 20826782google scholar: lookup
  34. Street V.A, McKee-Johnson J.W, Fonseca R.C, Tempel B.L, Noben-Trauth K. Mutations in a plasma membrane Ca2+-ATPase gene cause deafness in deafwaddler mice.. Nat Genet 1998;19:390–394.
    doi: 10.1038/1284pubmed: 9697703google scholar: lookup
  35. Takahashi K, Kitamura K. A Point Mutation in a Plasma Membrane Ca2+-ATPase Gene Causes Deafness in Wriggle Mouse Sagami.. Biochem Biophys Res Commun 1999;261:773–778.
    doi: 10.1006/BBRC.1999.1102pubmed: 10441500google scholar: lookup
  36. Xu L, Wang Z, Xiong X, Gu X, Gao X, Gao X. Identification of a Novel Point Mutation of Mouse Atp2b2 Induced by N-Ethyl-N-Nitrosourea Mutagenesis.. Exp Anim 2011;60:71–78.
    doi: 10.1538/EXPANIM.60.71pubmed: 21325754google scholar: lookup
  37. Spiden S.L, Bortolozzi M, Di Leva F, De Angelis M.H, Fuchs H, Lim D, Ortolano S, Ingham N.J, Brini M, Carafoli E. The Novel Mouse Mutation Oblivion Inactivates the PMCA2 Pump and Causes Progressive Hearing Loss.. PLoS Genet 2008;4.
    doi: 10.1371/JOURNAL.PGEN.1000238pmc: PMC2568954pubmed: 18974863google scholar: lookup
  38. Sun X.y, Chen Z.y, Hayashi Y, Kanou Y, Takagishi Y, Oda S.i, Murata Y. Insertion of an intracisternal A particle retrotransposon element in plasma membrane calcium ATPase 2 gene attenuates its expression and produces an ataxic phenotype in joggle mutant mice.. Gene 2008;411:94–102.
    doi: 10.1016/J.GENE.2008.01.013pubmed: 18280673google scholar: lookup
  39. Aleman M. Vestibular Disease.. Vet Clin N Am Equine Pract 2022;38:397–407.
    doi: 10.1016/j.cveq.2022.05.008pubmed: 35811200google scholar: lookup
  40. Lahunta A de, Glass E. Vestibular System: Special Proprioception. pp. 319–347.
    doi: 10.1016/B978-0-7216-6706-5.00012-3google scholar: lookup
  41. Carafoli E. The plasma membrane calcium pump in the hearing process: Physiology and pathology.. Sci China Life Sci 2011;54:686–690.
    doi: 10.1007/s11427-011-4200-zpubmed: 21786191google scholar: lookup
  42. Bonthuis P.J, Huang W.C, Stacher Hörndli C.N, Ferris E, Cheng T, Gregg C. Noncanonical Genomic Imprinting Effects in Offspring.. Cell Rep 2015;12:979–991.
    doi: 10.1016/J.CELREP.2015.07.017pubmed: 26235621google scholar: lookup
  43. Gregg C, Zhang J, Weissbourd B, Luo S, Schroth G.P, Haig D, Dulac C. High-resolution analysis of parent-of-origin allelic expression in the mouse brain.. Science 2010;329:643–648.
    doi: 10.1126/SCIENCE.1190830pmc: PMC3005244pubmed: 20616232google scholar: lookup
  44. Duda P, Wójcicka O, Wiśniewski J.R, Rakus D. Global quantitative TPA-based proteomics of mouse brain structures reveals significant alterations in expression of proteins involved in neuronal plasticity during aging. Aging 2018;10:1682–1697.
    doi: 10.18632/AGING.101501pmc: PMC6075443pubmed: 30026405google scholar: lookup
  45. Guan Y, Myers C.L, Lu R, Lemischka I.R, Bult C.J, Troyanskaya O.G. A genomewide functional network for the laboratory mouse. PLoS Comput. Biol. 2008;4.
    doi: 10.1371/JOURNAL.PCBI.1000165pmc: PMC2527685pubmed: 18818725google scholar: lookup
  46. Ueno T, Kameyama K, Hirata M, Ogawa M, Hatsuse H, Takagaki Y, Ohmura M, Osawa N, Kudo Y. A mouse with a point mutation in plasma membrane Ca2+-ATPase isoform 2 gene showed the reduced Ca2+ influx in cerebellar neurons. Neurosci. Res. 2002;42:287–297.
    doi: 10.1016/S0168-0102(02)00008-1pubmed: 11985881google scholar: lookup
  47. Kanjhan R, Noakes P.G, Bellingham M.C. Emerging Roles of Filopodia and Dendritic Spines in Motoneuron Plasticity during Development and Disease. Neural Plast. 2016;2016:1–31.
    doi: 10.1155/2016/3423267pmc: PMC4710938pubmed: 26843990google scholar: lookup
  48. Sun J, Yomogida K, Sakao S, Yamamoto H, Yoshida K, Watanabe K, Morita T, Araki K, Yamamura K.i, Tateishi S. Rad18 is required for long-term maintenance of spermatogenesis in mouse testes. Mech. Dev. 2009;126:173–183.
    doi: 10.1016/J.MOD.2008.11.004pubmed: 19068231google scholar: lookup
  49. Baldarelli R.M, Smith C.L, Ringwald M, Richardson J.E, Bult C.J, Anagnostopoulos A, Begley D.A, Bello S.M, Christie K, Finger J.H. Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse. Genetics 2024;227.
    doi: 10.1093/GENETICS/IYAE031pmc: PMC11075557pubmed: 38531069google scholar: lookup
  50. Shen J, Yu W.M, Brotto M, Scherman J.A, Guo C, Stoddard C, Nosek T.M, Valdivia H.H, Qu C.K. Deficiency of MIP/MTMR14 phosphatase induces a muscle disorder by disrupting Ca2+ homeostasis. Nat. Cell Biol. 2009;11:769–776.
    doi: 10.1038/ncb1884pmc: PMC2693472pubmed: 19465920google scholar: lookup
  51. Yan K, Rousseau J, Littlejohn R.O, Kiss C, Lehman A, Rosenfeld J.A, Stumpel C.T.R, Stegmann A.P.A, Robak L, Scaglia F. Mutations in the Chromatin Regulator Gene BRPF1 Cause Syndromic Intellectual Disability and Deficient Histone Acetylation. Am. J. Hum. Genet. 2017;100:91–104.
    doi: 10.1016/J.AJHG.2016.11.011pmc: PMC5223032pubmed: 27939640google scholar: lookup
  52. Mattioli F, Schaefer E, Magee A, Mark P, Mancini G.M, Dieterich K, Von Allmen G, Alders M, Coutton C, van Slegtenhorst M. Mutations in Histone Acetylase Modifier BRPF1 Cause an Autosomal-Dominant Form of Intellectual Disability with Associated Ptosis. Am. J. Hum. Genet. 2017;100:105–116.
    doi: 10.1016/j.ajhg.2016.11.010pmc: PMC5223023pubmed: 27939639google scholar: lookup
  53. Sheng Z, Oka S, Tsuchimoto D, Abolhassani N, Nomaru H, Sakumi K, Yamada H, Nakabeppu Y. 8-Oxoguanine causes neurodegeneration during MUTYH-mediated DNA base excision repair. J. Clin. Investig. 2012;122:4344–4361.
    doi: 10.1172/JCI65053pmc: PMC3533558pubmed: 23143307google scholar: lookup
  54. Zhang J, Li S.J, Miao W, Zhang X, Zheng J.J, Wang C, Yu X. Oxytocin Regulates Synaptic Transmission in the Sensory Cortices in a Developmentally Dynamic Manner. Front. Cell. Neurosci. 2021;15.
    doi: 10.3389/FNCEL.2021.673439pmc: PMC8221398pubmed: 34177467google scholar: lookup
  55. Mohibi S, Gurumurthy C.B, Nag A, Wang J, Mirza S, Mian Y, Quinn M, Katafiasz B, Eudy J, Pandey S. Mammalian alteration/deficiency in activation 3 (Ada3) is essential for embryonic development and cell cycle progression. J. Biol. Chem. 2012;287:29442–29456.
    doi: 10.1074/JBC.M112.378901pmc: PMC3436190pubmed: 22736770google scholar: lookup
  56. Timmers C, Taniguchi T, Hejna J, Reifsteck C, Lucas L, Bruun D, Thayer M, Cox B, Olson S, D’Andrea A.D. Positional cloning of a novel Fanconi anemia gene, FANCD2. Mol. Cell. 2001;7:241–248.
    doi: 10.1016/S1097-2765(01)00172-1pubmed: 11239453google scholar: lookup
  57. Bosch Grau M, Masson C, Gadadhar S, Rocha C, Tort O, Marques Sousa P, Vacher S, Bieche I, Janke C. Alterations in the balance of tubulin glycylation and glutamylation in photoreceptors leads to retinal degeneration. J. Cell Sci. 2017;130:938–949.
    doi: 10.1242/JCS.199091/pubmed: 28104815google scholar: lookup
  58. Cao X, An J, Cao Y, Lv J, Wang J, Ding Y, Lin X, Zhou X. EMC3 Is Essential for Retinal Organization and Neurogenesis During Mouse Retinal Development. Investig. Ophthalmol. Vis. Sci. 2021;62:31.
    doi: 10.1167/IOVS.62.2.31pmc: PMC7900856pubmed: 33605987google scholar: lookup
  59. Adams D.J, Barlas B, McIntyre R.E, Salguero I, van der Weyden L, Barros A, Vicente J.R, Karimpour N, Haider A, Ranzani M. Genetic determinants of micronucleus formation in vivo. Nature 2024;627:130–136.
    doi: 10.1038/s41586-023-07009-0pmc: PMC10917660pubmed: 38355793google scholar: lookup
  60. Xu M.J, Cai Y, Wang H, Altamirano J, Chang B, Bertola A, Odena G, Lu J, Tanaka N, Matsusue K. Fat-Specific Protein 27/CIDEC Promotes Development of Alcoholic Steatohepatitis in Mice and Humans. Gastroenterology 2015;149:1030–1041.e6.
    doi: 10.1053/J.GASTRO.2015.06.009/pmc: PMC4584194pubmed: 26099526google scholar: lookup
  61. Tang X, Snowball J.M, Xu Y, Na C.L, Weaver T.E, Clair G, Kyle J.E, Zink E.M, Ansong C, Wei W. EMC3 coordinates surfactant protein and lipid homeostasis required for respiration. J. Clin. Investig. 2017;127:4314–4325.
    doi: 10.1172/JCI94152pmc: PMC5707157pubmed: 29083321google scholar: lookup
  62. Ackert-Bicknell C.L, Salisbury J.L, Horowitz M, DeMambro V.E, Horton L.G, Shultz K.L, Lecka-Czernik B, Rosen C.J. A Chromosomal Inversion within a Quantitative Trait Locus Has a Major Effect on Adipogenesis and Osteoblastogenesis. Ann. N. Y. Acad. Sci. 2007;1116:291–305.
    doi: 10.1196/ANNALS.1402.010pubmed: 17584978google scholar: lookup
  63. Prins K, Huisman M, McLuskey A, Mies R, Karels B, Delhanty P.J.D, Visser J.A. Ghrelin deficiency sex-dependently affects food intake, locomotor activity, and adipose and hepatic gene expression in a binge-eating mouse model. Am. J. Physiol. Endocrinol. Metab. 2022;322:E494–E507.
    doi: 10.1152/AJPENDO.00432.2021pubmed: 35403437google scholar: lookup
  64. Groza T, Gomez F.L, Mashhadi H.H, Muñoz-Fuentes V, Gunes O, Wilson R, Cacheiro P, Frost A, Keskivali-Bond P, Vardal B. The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease. Nucleic Acids Res. 2023;51:D1038–D1045.
    doi: 10.1093/NAR/GKAC972pmc: PMC9825559pubmed: 36305825google scholar: lookup
  65. Briggs D.I, Lockie S.H, Wu Q, Lemus M.B, Stark R, Andrews Z.B. Calorie-restricted weight loss reverses high-fat diet-induced ghrelin resistance, which contributes to rebound weight gain in a ghrelin-dependent manner. Endocrinology 2013;154:709–717.
    doi: 10.1210/en.2012-1421pubmed: 23307790google scholar: lookup
  66. De Smet B, Depoortere I, Moechars D, Swennen Q, Moreaux B, Cryns K, Tack J, Buyse J, Coulie B, Peeters T.L. Energy homeostasis and gastric emptying in ghrelin knockout mice. J. Pharmacol. Exp. Ther. 2006;316:431–439.
    doi: 10.1124/JPET.105.091504/ASSET/118B340F-5D1D-439D-8936-D066C6F6314D/MAIN.ASSETS/GR10.JPGpubmed: 16203827google scholar: lookup
  67. Ma X, Lin L, Qin G, Lu X, Fiorotto M, Dixit V.D, Sun Y. Ablations of Ghrelin and Ghrelin Receptor Exhibit Differential Metabolic Phenotypes and Thermogenic Capacity during Aging. PLoS One 2011;6.
    doi: 10.1371/JOURNAL.PONE.0016391pmc: PMC3027652pubmed: 21298106google scholar: lookup
  68. Boztug K, Järvinen P.M, Salzer E, Racek T, Mönch S, Garncarz W, Gertz E.M, Schäffer A.A, Antonopoulos A, Haslam S.M. JAGN1 deficiency causes aberrant myeloid cell homeostasis and congenital neutropenia. Nat. Genet. 2014;46:1021–1027.
    doi: 10.1038/NG.3069pmc: PMC4829076pubmed: 25129144google scholar: lookup
  69. Ramirez-Carrozzi V, Sambandam A, Luis E, Lin Z, Jeet S, Lesch J, Hackney J, Kim J, Zhou M, Lai J. IL-17C regulates the innate immune function of epithelial cells in an autocrine manner. Nat. Immunol. 2011;12:1159–1166.
    doi: 10.1038/ni.2156pubmed: 21993848google scholar: lookup
  70. Hu Y, Ota N, Peng I, Refino C.J, Danilenko D.M, Caplazi P, Ouyang W. IL-17RC Is Required for IL-17A– and IL-17F–Dependent Signaling and the Pathogenesis of Experimental Autoimmune Encephalomyelitis. J. Immunol. 2010;184:4307–4316.
    doi: 10.4049/JIMMUNOL.0903614pubmed: 20231694google scholar: lookup
  71. Zhou H, Bulek K, Li X, Herjan T, Yu M, Qian W, Wang H, Zhou G, Chen X, Yang H. IRAK2 directs stimulus-dependent nuclear export of inflammatory mRNAs. eLife 2017;6.
    doi: 10.7554/ELIFE.29630pmc: PMC5675595pubmed: 28990926google scholar: lookup
  72. Kawagoe T., Sato S., Matsushita K., Kato H., Matsui K., Kumagai Y., Saitoh T., Kawai T., Takeuchi O., Akira S. Sequential control of Toll-like receptor–dependent responses by IRAK1 and IRAK2. Nat. Immunol. 2008;9:684–691. doi: 10.1038/ni.1606.
    doi: 10.1038/ni.1606pubmed: 18438411google scholar: lookup
  73. Redig J.K., Fouad G.T., Babcock D., Reshey B., Feingold E., Reeves R.H., Maslen C.L. Allelic Interaction between CRELD1 and VEGFA in the Pathogenesis of Cardiac Atrioventricular Septal Defects. AIMS Genet. 2014;1:1–19. doi: 10.3934/genet.2014.1.1.
    doi: 10.3934/genet.2014.1.1pmc: PMC4200510pubmed: 25328912google scholar: lookup
  74. Biggs L.M., Hammock E.A.D. Oxytocin via oxytocin receptor excites neurons in the endopiriform nucleus of juvenile mice. Sci. Rep. 2022;12:11401–11413. doi: 10.1038/s41598-022-15390-5.
    doi: 10.1038/s41598-022-15390-5pmc: PMC9259672pubmed: 35794163google scholar: lookup
  75. Wahis J., Baudon A., Althammer F., Kerspern D., Goyon S., Hagiwara D., Lefevre A., Barteczko L., Boury-Jamot B., Bellanger B., et al. Astrocytes mediate the effect of oxytocin in the central amygdala on neuronal activity and affective states in rodents. Nat. Neurosci. 2021;24:529–541. doi: 10.1038/s41593-021-00800-0.
    doi: 10.1038/s41593-021-00800-0pubmed: 33589833google scholar: lookup
  76. Bosch S., Serra Bragança F., Marin-Perianu M., Marin-Perianu R., van der Zwaag B.J., Voskamp J., Back W., Van Weeren R., Havinga P. Equimoves: A wireless networked inertial measurement system for objective examination of horse gait. Sensors (Switzerland) 2018;18:1–35. doi: 10.3390/s18030850.
    doi: 10.3390/s18030850pmc: PMC5877382pubmed: 29534022google scholar: lookup
  77. Guisande C., Heine J., García-Roselló E., González-Dacosta J., Perez-Schofield B.J.G., González-Vilas L., Vaamonde A., Lobo J.M. FactorsR: An RWizard Application for Identifying the Most Likely Causal Factors in Controlling Species Richness. Diversity. 2015;7:385–396. doi: 10.3390/D7040385.
    doi: 10.3390/D7040385google scholar: lookup
  78. Karssen L.C., van Duijn C.M., Aulchenko Y.S. The GenABEL Project for statistical genomics. F1000Res. 2016;5:914. doi: 10.12688/f1000research.8733.1.
    doi: 10.12688/f1000research.8733.1pmc: PMC4916982pubmed: 27347381google scholar: lookup
  79. Yang J., Lee S.H., Goddard M.E., Visscher P.M. GCTA: A Tool for Genome-wide Complex Trait Analysis. Am. J. Hum. Genet. 2011;88:76–82. doi: 10.1016/J.AJHG.2010.11.011.
    doi: 10.1016/J.AJHG.2010.11.011pmc: PMC3014363pubmed: 21167468google scholar: lookup
  80. Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M.A.R., Bender D., Maller J., Sklar P., de Bakker P.I.W., Daly M.J., et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007;81:559–575. doi: 10.1086/519795.
    doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
  81. Mi H., Muruganujan A., Huang X., Ebert D., Mills C., Guo X., Thomas P.D. Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0) Nat. Protoc. 2019;14:703–721. doi: 10.1038/S41596-019-0128-8.
    doi: 10.1038/S41596-019-0128-8pmc: PMC6519457pubmed: 30804569google scholar: lookup
  82. Novoa-Bravo M., Bernal-Pinilla E., García L.F. Microevolution operating in domestic animals: evidence from the Colombian Paso horses. Mamm. Biol. 2021;101:181–192. doi: 10.1007/s42991-021-00103-8.
    doi: 10.1007/s42991-021-00103-8google scholar: lookup
  83. Serra-Bragança F.M., Vernooij J.C.M., René Van Weeren P., Back W. Validation of Distal Limb Mounted Imu Sensors for Stride Detection and Locomotor Quantification in Warmblood Horses at Walk and Trot. Equine Vet. J. 2016;48:17. doi: 10.1111/evj.26_12595.
    doi: 10.1111/evj.26_12595pmc: PMC5484301pubmed: 27862238google scholar: lookup
  84. Jäderkvist K., Andersson L.S., Johansson A.M., Árnason T., Mikko S., Eriksson S., Andersson L., Lindgren G. The DMRT3 ‘gait keeper’ mutation affects performance of Nordic and Standardbred trotters. J. Anim. Sci. 2014;92:4279–4286. doi: 10.2527/jas2014-7803.
    doi: 10.2527/jas2014-7803pubmed: 25085403google scholar: lookup
  85. Kang H.M., Zaitlen N.A., Wade C.M., Kirby A., Heckerman D., Daly M.J., Eskin E. Efficient control of population structure in model organism association mapping. Genetics. 2008;178:1709–1723. doi: 10.1534/GENETICS.107.080101.
    doi: 10.1534/GENETICS.107.080101pmc: PMC2278096pubmed: 18385116google scholar: lookup
  86. Mi H., Ebert D., Muruganujan A., Mills C., Albou L.P., Mushayamaha T., Thomas P.D. PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API. Nucleic Acids Res. 2021;49:D394–D403. doi: 10.1093/NAR/GKAA1106.
    doi: 10.1093/NAR/GKAA1106pmc: PMC7778891pubmed: 33290554google scholar: lookup

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 106,972 horse owners like you who receive our email updates on horse health and nutrition.
OmegaPrime+ Extra-Strength Omega-3 Oil - Mad Barn
NEW PRODUCT
OmegaPrime+
Extra-Strength Omega-3 Oil
Shop Now
×