Analyze Diet

Single-Step Genome-Wide Association Study of Factors for Evaluated and Linearly Scored Traits in Swedish Warmblood Horses.

Abstract: Swedish Warmblood horses (SWB) are bred for show jumping and/or dressage with young horse test scores as indicator traits. This study aimed to investigate possible candidate genes and regions of importance for evaluated and linearly scored young horse test traits. A single-step genome-wide association study (ssGWAS) was done using the BLUPF90 suite of programs for factors scores from factor analysis of traits assessed at young horse tests together with height at withers. The ssGWAS included 20,814 SWB with factors scores for four factors for evaluated traits. A total of 6436 of these horses also had factor scores for 13 factors for linearly scored traits. Genotypes from a 670K SNP array were available for 380 of the horses in this study. All genotyped horses had factor scores for evaluated traits, and 379 also had factors scores for linearly scored traits. Significant SNPs associated with three factors related to size were located on ECA3 within or nearby a well-known region, including the genes ligand dependent nuclear receptor corepressor like (LCORL), non-SMC condensin I complex subunit G (NCAPG), DDB1 and CUL4 Associated Factor 16 (DCAF16), and the Family with Sequence Similarity 184 Member B (FAM184B). Significant SNPs were also detected for two factors for evaluated traits representing conformation and jumping, and four factors for linearly scored traits related to body length, neck conformation, walk and trot (hindleg position and activity), respectively. Among nearby genes, calcium/calmodulin-dependent protein kinase type 1D (CAMK1D) for the factor for linearly scored traits related to neck conformation and GLI Family Zinc Finger 2 (GLI2) for the factor for evaluated jumping traits, were most promising. For these, top associated SNPs were detected within the genes, and the known gene functions seems to be related to the phenotypes. In conclusion, ssGWAS is beneficial to detect plausible candidate genes/regions for desired traits in warmblood horses.
Publication Date: 2025-01-04 PubMed ID: 39754479PubMed Central: PMC12340361DOI: 10.1111/jbg.12923Google 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.
  • 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 study investigates potential genes and regions significant for certain traits in Swedish Warmblood Horses, using a single-step genome-wide association study. It reveals genes potentially linked to horse size, conformation, jumping ability, and gaits such as trotting and walking.

Objective and Methodology

  • The research aims to identify potential genes and regions within the genome that may influence young horse test traits in Swedish Warmblood horses, which are bred specifically for dressage and show jumping.
  • To achieve this aim, a single-step genome-wide association study (ssGWAS) was employed.
  • This method allows the researchers to simultaneously analyze the data of both genotyped and non-genotyped horses, enhancing the robustness of the results.
  • Factor scores from factor analysis of traits were evaluated at young horse tests, generating information on four different factors for ‘evaluated’ traits.
  • Genotype data from a 670K SNP array was available for a select group of horses within the study. These horses also had factor scores for ‘evaluated’ traits, providing a correlation worth investigating.

Results: Significant Associations

  • Several SNPs (single nucleotide polymorphisms) within specific genes were found to be significantly associated with different evaluated traits.
  • Three factors were related to the size of the horse, all of which were located within a region of equine chromosome 3 (ECA3) that includes several well-studied genes.
  • The genes in this region are ligand dependent nuclear receptor corepressor like (LCORL), non-SMC condensin I complex subunit G (NCAPG), DDB1 and CUL4 Associated Factor 16 (DCAF16), and the Family with Sequence Similarity 184 Member B (FAM184B).
  • Various significant SNPs for two factors related to traits of conformation and jumping, and four factors related to linearly scored traits of body length, neck conformation, walk, and trot (hindleg position and activity) were identified.
  • The calcium/calmodulin-dependent protein kinase type 1D (CAMK1D) gene and the GLI Family Zinc Finger 2 (GLI2) gene were the most promising nearby genes. These genes are linked to neck conformation and evaluated jumping traits, respectively.

Conclusion

  • The study concludes that the ssGWAS methodology can successfully reveal potential candidate genes and regions that might be relevant for the selective breeding of specific traits in Swedish Warmblood horses.
  • This can have benefits not just for Swedish Warmblood horses, but broadly for warmblood horses, enhancing opportunities for selective breeding based on desirable characteristics.
  • The results also open up potential for further research into gene behavior in equine species and additional studies into the role of the identified genes in shaping these traits.

Cite This Article

APA
Nazari-Ghadikolaei A, Fikse WF, Viklund ÅG, Mikko S, Eriksson S. (2025). Single-Step Genome-Wide Association Study of Factors for Evaluated and Linearly Scored Traits in Swedish Warmblood Horses. J Anim Breed Genet, 142(5), 499-512. https://doi.org/10.1111/jbg.12923

Publication

ISSN: 1439-0388
NlmUniqueID: 100955807
Country: Germany
Language: English
Volume: 142
Issue: 5
Pages: 499-512

Researcher Affiliations

Nazari-Ghadikolaei, Anahit
  • Department of Animal Biosciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Fikse, W Freddy
  • Växa, Uppsala, Sweden.
Viklund, Åsa Gelinder
  • Department of Animal Biosciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Mikko, Sofia
  • Department of Animal Biosciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Eriksson, Susanne
  • Department of Animal Biosciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.

MeSH Terms

  • Animals
  • Horses / genetics
  • Genome-Wide Association Study
  • Polymorphism, Single Nucleotide
  • Phenotype
  • Genotype
  • Sweden
  • Male
  • Female

Grant Funding

  • H1147215 / The Swedish-Norwegian Foundation for Equine Research

Conflict of Interest Statement

The Swedish Warmblood Association has provided the phenotype and pedigree data for this study, and Åsa Gelinder Viklund has regular commitments to Swedish Warmblood Association, regarding the routine genetic evaluation. We declare that there are no other conflicts of interest.

References

This article includes 87 references
  1. Abdi H. Factor Rotations in Factor Analyses. Encyclopedia for Research Methods for the Social Sciences 2003:792–795.
  2. Ablondi M, Eriksson S, Tetu S, Sabbioni A, Viklund Å, Mikko S. Genomic Divergence in Swedish Warmblood Horses Selected for Equestrian Disciplines. Genes 2019;10(12):976.
    doi: 10.3390/genes10120976pmc: PMC6947233pubmed: 31783652google scholar: lookup
  3. Ablondi M, Viklund Å, Lindgren G, Eriksson S, Mikko S. Signatures of Selection in the Genome of Swedish Warmblood Horses Selected for Sprt Performance. BMC Genomics 2019;20:1–12.
    doi: 10.1186/s12864-019-6079-1pmc: PMC6751828pubmed: 31533613google scholar: lookup
  4. Aguilar I, Legarra A, Cardoso F, Masuda Y, Lourenco D, Misztal I. Frequentist p‐Values for Large‐Scale‐Single Step Genome‐Wide Association, With an Application to Birth Weight in American Angus Cattle. Genetics Selection Evolution 2019;51(1):1–28.
    doi: 10.1186/s12711-019-0469-3pmc: PMC6584984pubmed: 31221101google scholar: lookup
  5. Aguilar I, Misztal I, Johnson D, Legarra A, Tsuruta S, Lawlor T. Hot Topic: A Unified Approach to Utilize Phenotypic, Full Pedigree, and Genomic Information for Genetic Evaluation of Holstein Final Score. Journal of Dairy Science 2010;93(2):743–752.
    doi: 10.3168/jds.2009-2730pubmed: 20105546google scholar: lookup
  6. Aguilar I, Misztal I, Tsuruta S, Legarra A, Wang H. PREGSF90–POSTGSF90: Computational Tools for the Implementation of Single‐Step Genomic Selection and Genome‐Wide Association With Ungenotyped Individuals in BLUPF90 Programs. 2014;In 10. World Congress on Genetics Applied to Livestock Production (WCGALP).
  7. Alexandre P A, Naval‐Sanchez M, Porto‐Neto L R, Ferraz J B S, Reverter A, Fukumasu H. Systems Biology Reveals NR2F6 and TGFB1 as Key Regulators of Feed Efficiency in Beef Cattle. Frontiers in Genetics 2019;10:230.
    doi: 10.3389/fgene.2019.00230pmc: PMC6439317pubmed: 30967894google scholar: lookup
  8. Andreasen C H, Mogensen M S, Borch‐Johnsen K. Studies of CTNNBL1 and FDFT1 Variants and Measures of Obesity: Analyses of Quantitative Traits and Case‐Control Studies in 18,014 Danes. BMC Medical Genetics 2009;10:1–9.
    doi: 10.1186/1471-2350-10-17pmc: PMC2669074pubmed: 19245693google scholar: lookup
  9. Aryal A C S, Miyai K, Izu Y. Nck Influences Preosteoblastic/Osteoblastic Migration and Bone Mass. Proceedings of the National Academy of Sciences 2015;112(50):15432–15437.
    doi: 10.1073/pnas.1518253112pmc: PMC4687572pubmed: 26621720google scholar: lookup
  10. Aulchenko Y S, Ripke S, Isaacs A, Van Duijn C M. GenABEL: An R Library for Genome‐Wide Association Analysis. Bioinformatics 2007;23(10):1294–1296.
    doi: 10.1093/bioinformatics/btm108pubmed: 17384015google scholar: lookup
  11. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B: Methodological 1995;57(1):289–300.
  12. Bonow S, Eriksson S, Thorén Hellsten E, Gelinder Viklund Å. Consequences of Specialized Breeding in the Swedish Warmblood Horse Population. Journal of Animal Breeding and Genetics 2023;140(1):79–91.
    doi: 10.1111/jbg.12731pmc: PMC10084081pubmed: 35830346google scholar: lookup
  13. Brard S, Ricard A. Genome‐Wide Association Study for Jumping Performances in French Sport Horses. Animal Genetics 2015;46(1):78–81.
    doi: 10.1111/age.12245pubmed: 25515185google scholar: lookup
  14. Carter R A, Treiber K, Geor R, Douglass L, Harris P A. Prediction of Incipient Pasture‐Associated Laminitis From Hyperinsulinaemia, Hyperleptinaemia and Generalised and Localised Obesity in a Cohort of Ponies. Equine Veterinary Journal 2009;41(2):171–178.
    doi: 10.2746/042516408X342975pubmed: 19418747google scholar: lookup
  15. Chen W, Alexandre P A, Ribeiro G. Identification of Predictor Genes for Feed Efficiency in Beef Cattle by Applying Machine Learning Methods to Multi‐Tissue Transcriptome Data. Frontiers in Genetics 2021;12:619857.
    doi: 10.3389/fgene.2021.619857pmc: PMC7921797pubmed: 33664767google scholar: lookup
  16. Cheong H S, Yoon D‐H, Kim L H. Growth Hormone‐Releasing Hormone (GHRH) Polymorphisms Associated With Carcass Traits of Meat in Korean Cattle. BMC Genetics 2006;7(1):1–6.
    doi: 10.1186/1471-2156-7-35pmc: PMC1524984pubmed: 16749938google scholar: lookup
  17. Cowerd R B, Asmar M M, Alderman J M. Adiponectin Lowers Glucose Production by Increasing SOGA. American Journal of Pathology 2010;177(4):1936–1945.
    doi: 10.2353/ajpath.2010.100363pmc: PMC2947288pubmed: 20813965google scholar: lookup
  18. de Oliveira Bussiman F, dos Santos B A, Silva B D C A. Genome‐Wide Association Study: Understanding the Genetic Basis of the Gait Type in Brazilian Mangalarga Marchador Horses, A Preliminary Study. Livestock Science 2020;231:103867.
  19. Duensing J, Stock K F, Krieter J. Implementation and Prospects of Linear Profiling in the Warmblood Horse. Journal of Equine Veterinary Science 2014;34(3):360–368.
  20. Ellis K, Zhou Y, Beshansky J. Genetic Modifiers of Response to Glucose–Insulin–Potassium (GIK) Infusion in Acute Coronary Syndromes and Associations With Clinical Outcomes in the IMMEDIATE Trial. Pharmacogenomics Journal 2015;15(6):488–495.
    doi: 10.1038/tpj.2015.10pmc: PMC4573824pubmed: 25778467google scholar: lookup
  21. Frischknecht M, Signer‐Hasler H, Leeb T, Rieder S, Neuditschko M. Genome‐Wide Association Studies Based on Sequence‐Derived Genotypes Reveal New QTL Associated With Conformation and Performance Traits in the Franches–Montagnes Horse Breed. Animal Genetics 2016;47(2):227–229.
    doi: 10.1111/age.12406pubmed: 26767322google scholar: lookup
  22. Fromont C, Atzori A, Kaur D. Discovery of Highly Selective Inhibitors of Calmodulin‐Dependent Kinases That Restore Insulin Sensitivity in the Diet‐Induced Obesity In Vivo Mouse Model. Journal of Medicinal Chemistry 2020;63(13):6784–6801.
  23. Geor R J. Metabolic Predispositions to Laminitis in Horses and Ponies: Obesity, Insulin Resistance and Metabolic Syndromes. Journal of Equine Veterinary Science 2008;28(12):753–759.
  24. Glickman M E, Rao S R, Schultz M R. False Discovery Rate Control Is a Recommended Alternative to Bonferroni‐Type Adjustments in Health Studies. Journal of Clinical Epidemiology 2014;67(8):850–857.
  25. Gmel A, Brem G, Neuditschko M. New Genomic Insights Into the Conformation of Lipizzan Horses. Scientific Reports 2023;13(1):8990.
    doi: 10.1038/s41598-023-36272-4pmc: PMC10238546pubmed: 37268682google scholar: lookup
  26. Gmel A I, Druml T, von Niederhäusern R, Leeb T, Neuditschko M. Genome‐Wide Association Studies Based on Equine Joint Angle Measurements Reveal New QTL Affecting the Conformation of Horses. Genes 2019;10(5):370.
    doi: 10.3390/genes10050370pmc: PMC6562990pubmed: 31091839google scholar: lookup
  27. Gonzalez M, Villa R, Villa C. Inspection of Real and Imputed Genotypes Reveled 76 SNPs Associated to Rear Udder Height in Holstein Cattle. Journal of Advanced Veterinary and Animal Research 2020;7(2):234–241.
    doi: 10.5455/javar.2020.g415pmc: PMC7320818pubmed: 32607355google scholar: lookup
  28. Gualdrón Duarte J, Cantet R, Bates R, Ernst C, Raney N, Steibel J. Rapid Screening for Phenotype‐Genotype Associations by Linear Transformations of Genomic Evaluations. BMC Bioinformatics 2014;15(1):246.
    pmc: PMC4112210pubmed: 25038782
  29. Heyne G W, Everson J L, Ansen‐Wilson L J. Gli2 Gene‐Environment Interactions Contribute to the Etiological Complexity of Holoprosencephaly: Evidence From a Mouse Model. Disease Models & Mechanisms 2016;9(11):1307–1315.
    doi: 10.1242/dmm.026328pmc: PMC5117230pubmed: 27585885google scholar: lookup
  30. Kalbfleisch T S, Rice E S, DePriest M S. Improved Reference Genome for the Domestic Horse Increases Assembly Contiguity and Composition. Communications Biology 2018;1(1):1–8.
    doi: 10.1038/s42003-018-0199-zpmc: PMC6240028pubmed: 30456315google scholar: lookup
  31. Legarra A, Christensen O F, Aguilar I, Misztal I. Single Step, a General Approach for Genomic Selection. Livestock Science 2014;166:54–65.
  32. Legarra A, Ricard A, Varona L. GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factors, With an Example in Detection of a Major Gene for Horse Gait. G3: Genes, Genomes, Genetics 2018;8(7):2301–2308.
    doi: 10.1534/g3.118.200336pmc: PMC6027892pubmed: 29748199google scholar: lookup
  33. Lindholm‐Perry A K, Artegoitia V M, Miles J R, Foote A P. Expression of Cytokine Genes and Receptors in White Blood Cells Associated With Divergent Body Weight Gain in Beef Steers. Agri Gene 2017;6:37–39.
  34. Lowe C, Yoneda T, Boyce B F, Chen H, Mundy G R, Soriano P. Osteopetrosis in Src‐Deficient Mice Is due to an Autonomous Defect of Osteoclasts. Proceedings of the National Academy of Sciences 1993;90(10):4485–4489.
    doi: 10.1073/pnas.90.10.4485pmc: PMC46536pubmed: 7685105google scholar: lookup
  35. Lu X, Abdalla I M, Nazar M. Genome‐Wide Association Study on Reproduction‐Related Body‐Shape Traits of Chinese Holstein Cows. Animals 2021;11(7):1927.
    doi: 10.3390/ani11071927pmc: PMC8300307pubmed: 34203505google scholar: lookup
  36. Mancini G, Gargani M, Chillemi G. Signatures of Selection in Five Italian Cattle Breeds Detected by a 54K SNP Panel. Molecular Biology Reports 2014;41:957–965.
    doi: 10.1007/s11033-013-2940-5pmc: PMC3929051pubmed: 24442315google scholar: lookup
  37. Martin F J, Amode M R, Aneja A. Ensembl 2023. Nucleic Acids Research 2023;51(D1):D933–D941.
    doi: 10.1093/nar/gkac958pmc: PMC9825606pubmed: 36318249google scholar: lookup
  38. Mastranestasis I, Kominakis A, Hager‐Theodorides A, Ekateriniadou L, Ligda C, Theodorou K. Associations Between Genetic Polymorphisms and Phenotypic Traits in the Lesvos Dairy Sheep. Small Ruminant Research 2016;144:205–210.
  39. McCoy A M, Beeson S K, Splan R K. Identification and Validation of Risk Loci for Osteochondrosis in Standardbreds. BMC Genomics 2016;17:1–11.
    doi: 10.1186/s12864-016-2385-zpmc: PMC4709891pubmed: 26753841google scholar: lookup
  40. Messler S, Kropp S, Episkopou V. The TGF‐β Signaling Modulators TRAP1/TGFBRAP1 and VPS39/Vam6/TLP Are Essential for Early Embryonic Development. Immunobiology 2011;216(3):343–350.
    doi: 10.1016/j.imbio.2010.07.006pubmed: 20961651google scholar: lookup
  41. Metzger J, Schrimpf R, Philipp U, Distl O. Expression Levels of LCORL Are Associated With Body Size in Horses. PLoS One 2013;8(2):e56497.
  42. Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee D. BLUPF90 and Related Programs (BGF90). 2002;In Proceedings of the 7th World Congress on Genetics Applied to Livestock Production.
  43. Naccache F, Metzger J, Distl O. Genetic Risk Factors for Osteochondrosis in Various Horse Breeds. Equine Veterinary Journal 2018;50(5):556–563.
    doi: 10.1111/evj.12824pubmed: 29498750google scholar: lookup
  44. Nazari‐Ghadikolaei A, Fikse F, Gelinder Viklund Å, Eriksson S. Factor Analysis of Evaluated and Linearly Scored Traits in Swedish Warmblood Horses. Journal of Animal Breeding and Genetics 2023;140:366–375.
    doi: 10.1111/jbg.12764pubmed: 36852464google scholar: lookup
  45. Noble W S. How Does Multiple Testing Correction Work?. Nature Biotechnology 2009;27(12):1135–1137.
    doi: 10.1038/nbt1209-1135pmc: PMC2907892pubmed: 20010596google scholar: lookup
  46. Norris J M, Rich S S. Genetics of Glucose Homeostasis: Implications for Insulin Resistance and Metabolic Syndrome. Arteriosclerosis, Thrombosis, and Vascular Biology 2012;32(9):2091–2096.
    doi: 10.1161/ATVBAHA.112.255463pmc: PMC3988457pubmed: 22895670google scholar: lookup
  47. Palmer N, Langefeld C, Ziegler J. Candidate Loci for Insulin Sensitivity and Disposition Index From a Genome‐Wide Association Analysis of Hispanic Participants in the Insulin Resistance Atherosclerosis (IRAS) Family Study. Diabetologia 2010;53:281–289.
    doi: 10.1007/s00125-009-1586-2pmc: PMC2809812pubmed: 19902172google scholar: lookup
  48. Perdry H, Dandine‐Roulland C. gaston: Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models. 2022.
  49. Pereira G, Chardulo L, Silva J I, Faria R, Curi R. Genomic Regions Associated With Performance in Racing Line of Quarter Horses. Livestock Science 2018;211:42–51.
  50. Pryce J E, Hayes B J, Bolormaa S, Goddard M E. Polymorphic Regions Affecting Human Height Also Control Stature in Cattle. Genetics 2011;187(3):981–984.
    pmc: PMC3048786pubmed: 21212230
  51. Purcell S, Neale B, Todd‐Brown K. PLINK: A Tool Set for Whole‐Genome Association and Population‐Based Linkage Analyses. American Journal of Human Genetics 2007;81(3):559–575.
    doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
  52. nnQTLdb, Hn. 2023. https://www.animalgenome.org/cgi‐bin/QTLdb/EC/index (Accessed May 2023).
  53. R Core Team. R: A Language and Environment for Statistical Computing. 2019.
  54. R Core Team. R: A Language and Environment for Statistical Computing. 2022.
  55. Rappaport N, Nativ N, Stelzer G. MalaCards: An Integrated Compendium for Diseases and Their Annotation. Database 2013:bat018.
    doi: 10.1093/database/bat018pmc: PMC3625956pubmed: 23584832google scholar: lookup
  56. Rausch J C, Lavine J E, Chalasani N. Genetic Variants Associated With Obesity and Insulin Resistance in Hispanic Boys With Nonalcoholic Fatty Liver Disease. Journal of Pediatric Gastroenterology and Nutrition 2018;66(5):789–796.
  57. Reich P, Möller S, Stock K F. Genomic Analyses of Withers Height and Linear Conformation Traits in German Warmblood Horses Using Imputed Sequence‐Level Genotypes. Genetics Selection Evolution 2024;56(1):45.
    doi: 10.1186/s12711-024-00914-6pmc: PMC11177368pubmed: 38872118google scholar: lookup
  58. Revelle W. How to: Use the Psych Package for Factor Analysis and Data Reduction. 2022.
  59. Ricard A, Duluard A. Genomic Analysis of Gaits and Racing Performance of the French Trotter. Journal of Animal Breeding and Genetics 2021;138(2):204–222.
    doi: 10.1111/jbg.12526pmc: PMC7898598pubmed: 33249655google scholar: lookup
  60. Ricard A, Dumont Saint Priest B, Chassier M, Sabbagh M, Danvy S. Genetic Consistency Between Gait Analysis by Accelerometry and Evaluation Scores at Breeding Shows for the Selection of Jumping Competition Horses. PLoS One 2020;15(12):e0244064.
  61. Safran M, Rosen N, Twik M. The Genecards Suite, Practical Guide to Life Science Databases. 2021:27–56.
  62. SAS Institute Inc. SAS/STAT® 14.1 User's Guide. 2015.
  63. Sayers E W, Bolton E E, Brister J R. Database Resources of the National Center for Biotechnology Information. Nucleic Acids Research 2022;50(D1):D20–D26.
    doi: 10.1093/nar/gkab1112pmc: PMC8728269pubmed: 34850941google scholar: lookup
  64. Schröder W, Klostermann A, Stock K, Distl O. A Genome‐Wide Association Study for Quantitative Trait Loci of Show‐Jumping in Hanoverian Warmblood Horses. Animal Genetics 2012;43(4):392–400.
  65. Sevane N, Dunner S, Boado A, Cañon J. Polymorphisms in Ten Candidate Genes Are Associated With Conformational and Locomotive Traits in Spanish Purebred Horses. Journal of Applied Genetics 2017;58:355–361.
    doi: 10.1007/s13353-016-0385-ypubmed: 27917442google scholar: lookup
  66. Signer‐Hasler H, Flury C, Haase B. A Genome‐Wide Association Study Reveals Loci Influencing Height and Other Conformation Traits in Horses. PLoS One 2012a;7(5):e37282.
  67. Signer‐Hasler H, Flury C, Haase B. A Genome‐Wide Association Study Reveals Loci Influencing Height and Other Conformation Traits in Horses. PLoS One 2012b;7(5):e37282.
  68. Sigurðardóttir H, Albertsdóttir E, Eriksson S. Analysis of New Temperament Traits to Better Understand the Trait Spirit Assessed in Breeding Field Tests for Icelandic Horses. Acta Agriculturae Scandinavica Section A Animal Science 2017;67(1–2):46–57.
  69. Slavi N, Balasubramanian R, Lee M A. CyclinD2‐Mediated Regulation of Neurogenic Output From the Retinal Ciliary Margin Is Perturbed in Albinism. Neuron 2023;111(1):49–64.e45.
  70. Staiger E, Al Abri M, Pflug K M. Skeletal Variation in Tennessee Walking Horses Maps to the LCORL/NCAPG Gene Region. Physiological Genomics 2016;48(5):325–335.
  71. Staiger E, Albright J, Brooks S. Genome‐Wide Association Mapping of Heritable Temperament Variation in the Tennessee Walking Horse. Genes, Brain and Behavior 2016;15(5):514–526.
    doi: 10.1111/gbb.12290pubmed: 26991152google scholar: lookup
  72. Steiger J H. Structural Model Evaluation and Modification: An Interval Estimation Approach. Multivariate Behavioral Research 1990;25(2):173–180.
    doi: 10.1207/s15327906mbr2502_4pubmed: 26794479google scholar: lookup
  73. Stock K F, Jönsson L, Ricard A, Mark T. Genomic Applications in Horse Breeding. Animal Frontiers 2016;6(1):45–52.
    doi: 10.2527/af.2016-0007google scholar: lookup
  74. . Avelsplan för SWB 2021–2026. 2021.
  75. Tetens J, Widmann P, Kühn C, Thaller G. A Genome‐Wide Association Study Indicates LCORL/NCAPG as a Candidate Locus for Withers Height in German Warmblood Horses. Animal Genetics 2013;44(4):467–471.
    doi: 10.1111/age.12031pubmed: 23418885google scholar: lookup
  76. Thurstone L L. The Vectors of Mind: Multiple‐Factor Analysis for the Isolation of Primary Traits. .
  77. Tucker L R, Lewis C. A Reliability Coefficient for Maximum Likelihood Factor Analysis. Psychometrika 1973;38(1):1–10.
    doi: 10.1007/BF02291170google scholar: lookup
  78. Tucker R, Hall Y, Hughes T, Parker R. Osteochondral Fragmentation of the Cervical Articular Process Joints; Prevalence in Horses Undergoing CT for Investigation of Cervical Dysfunction. Equine Veterinary Journal 2022;54(1):106–113.
    doi: 10.1111/evj.13410pubmed: 33368552google scholar: lookup
  79. Van Buuren S, Groothuis‐Oudshoorn K. Mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software 2011;45:1–67.
    doi: 10.18637/jss.v045.i03google scholar: lookup
  80. Viklund Å, Eriksson S. Genetic Analyses of Linear Profiling Data on 3‐Year‐Old Swedish Warmblood Horses. Journal of Animal Breeding and Genetics 2018;135(1):62–72.
    doi: 10.1111/jbg.12311pubmed: 29345075google scholar: lookup
  81. Viklund Å, Näsholm A, Strandberg E, Philipsson J. Genetic Trends for Performance of Swedish Warmblood Horses. Livestock Science 2011;141(2–3):113–122.
  82. Vosgerau S, Krattenmacher N, Falker‐Gieske C. Genetic and Genomic Characterization Followed by Single‐Step Genomic Evaluation of Withers Height in German Warmblood Horses. Journal of Applied Genetics 2022;63:369–378.
    doi: 10.1007/s13353-021-00681-wpmc: PMC8979901pubmed: 35028913google scholar: lookup
  83. Wang H, Misztal I, Aguilar I, Legarra A, Muir W. Genome‐Wide Association Mapping Including Phenotypes From Relatives Without Genotypes. Genetics Research 2012;94(2):73–83.
    doi: 10.1017/S0016672312000274pubmed: 22624567google scholar: lookup
  84. Wobbe M, Alkhoder H, Stock K F. Single‐Step Genomic Evaluation in German Riding Horses. 2021;In 72th Anuual Meeting of the EAAP, Davos, Switzerland, 172.
  85. Xia J, Fan H, Chang T. Searching for New Loci and Candidate Genes for Economically Important Traits Through Gene‐Based Association Analysis of Simmental Cattle. Scientific Reports 2017;7(1):1–9.
    doi: 10.1038/srep42048pmc: PMC5294460pubmed: 28169328google scholar: lookup
  86. Yin L. CMplot: Circle Manhattan Plot. R Package Version 4.2.0. 2022.
  87. Zhang W, Li J, Guo Y. Multi‐Strategy Genome‐Wide Association Studies Identify the DCAF16‐NCAPG Region as a Susceptibility Locus for Average Daily Gain in Cattle. Scientific Reports 2016;6(1):38073.
    doi: 10.1038/srep38073pmc: PMC5125095pubmed: 27892541google scholar: lookup