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Home / Equine Research Bank / BMC Vet Res / Fine-scale assessment of ROH patterns and genomic inbreeding...
BMC veterinary research2025; 21(1); 688; doi: 10.1186/s12917-025-05143-7

Fine-scale assessment of ROH patterns and genomic inbreeding in diverse horse breeds using two SNP array densities.

Abstract: Inbreeding is caused by mating between related individuals and is associated with reduced fitness and performance. Generally, in the horse population, inbreeding is caused by geographically restricted areas and intensive natural or artificial selection. For this reason, assessing accurate inbreeding is essential for developing and implementing effective breeding strategies aimed at preserving genetic diversity and reducing the harmful consequences of inbreeding. One of the most accurate approaches for assessing genomic inbreeding and autozygosity is through the analysis of runs of homozygosity (ROH), which are long stretches of homozygous genotypes inherited from common ancestors and provide valuable insights into population diversity, demographic history, and selection. Methods: In this study, we analyzed the distribution of ROH, estimated genomic inbreeding coefficients, and mapped ROH islands across 14 diverse horse breeds. We used 670 K and MNEc2M SNP array datasets, comprising 279,040 SNPs from 424 horses and 1,083,942 SNPs from 438 horses, respectively. A total of 35,396 and 17,382 ROHs were detected in all breeds for the 670 K and MNEc2M SNP array datasets, respectively. Results: The majority of the detected ROHs were < 16 Mb (only 2.23% and 1.38% were greater than 16 Mb in for the 670 K and MNEc2M SNP array datasets, respectively) and the average total number of ROHs per individual were 81.36 ± 33.38 and 38.71 ± 22.73 for the 670 K and MNEc2M SNP array datasets, respectively. The mean ROH length per individual was 248.57 Mb for the 670 K SNP array and 89.56 Mb for the MNEc2M SNP array. Genomic inbreeding based on ROH (F) was relatively higher than homozygosity-based inbreeding (F) and ranged from 0.0364 ± 0.0049 to 0.2357 ± 0.0584 and from 0.004 ± 0.0044 to 0.1102 ± 0.0779 for 670 K and MNEc2M SNP array datasets, respectively. Conclusions: We identified genomic regions with high ROH coverage so called ROH islands that may reflect recent selection events. Several chromosomes, including ECA7 and ECA11, contained large ROH islands that include genes associated with important traits such as pigmentation, fertility, and performance. These ROH islands represent genomic footprints of past selection events and serve as candidate regions for future functional and association studies.
© 2025. The Author(s).
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Publication Date: 2025-11-25 PubMed ID: 41291721PubMed Central: PMC12648929DOI: 10.1186/s12917-025-05143-7Google Scholar: Lookup
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Summary

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Overview

  • This study examined patterns of runs of homozygosity (ROH) and genomic inbreeding in 14 different horse breeds using two different SNP array densities to better understand inbreeding levels and identify genomic regions influenced by selection.

Introduction to Inbreeding and ROH

  • Inbreeding occurs when related individuals mate, leading to reduced genetic diversity and potential fitness decline.
  • In horses, inbreeding often happens due to geographic restrictions and selective breeding practices.
  • Runs of Homozygosity (ROH) are long stretches of the genome where the DNA is homozygous, indicating inheritance from common ancestors.
  • ROH analysis is a precise method to assess genomic inbreeding, revealing insights into diversity, demographic history, and regions under selection.

Study Objectives and Datasets

  • The study focused on analyzing ROH distributions, estimating genomic inbreeding coefficients, and identifying ROH islands (genomic regions with high ROH coverage) across 14 horse breeds.
  • Two SNP array datasets were used:
    • 670 K SNP array with 279,040 SNPs from 424 horses.
    • MNEc2M SNP array with 1,083,942 SNPs from 438 horses.

Findings on ROH Detection and Distribution

  • Using the 670 K SNP array:
    • 35,396 ROHs were detected across all breeds.
    • The average number of ROHs per individual was approximately 81.
    • The majority (around 98%) of ROHs were less than 16 Mb in length.
    • Mean total ROH length per individual was about 248.57 Mb.
  • Using the MNEc2M SNP array:
    • 17,382 ROHs were detected across all breeds.
    • The average number of ROHs per individual was approximately 39.
    • About 98.6% of ROHs were shorter than 16 Mb.
    • Mean total ROH length per individual was about 89.56 Mb.

Genomic Inbreeding Estimates

  • Genomic inbreeding coefficients based on ROH (F_ROH) were higher compared to those based on simple homozygosity measures.
  • Ranges for F_ROH were:
    • 0.0364 ± 0.0049 to 0.2357 ± 0.0584 using the 670 K dataset.
    • 0.004 ± 0.0044 to 0.1102 ± 0.0779 using the MNEc2M dataset.
  • These differences reflect the array densities and resolution in detecting ROHs.

Identification of ROH Islands and Selection Signatures

  • ROH islands are genomic regions with a high frequency of ROH presence across individuals, often indicating recent selection pressures.
  • Several chromosomes, particularly equine chromosomes 7 (ECA7) and 11 (ECA11), exhibited large ROH islands.
  • Genes located in these ROH islands are linked to key traits such as:
    • Pigmentation (coat color and pattern).
    • Fertility traits.
    • Performance characteristics important for horse breeds.
  • These regions serve as candidates for further functional studies and genetic association analyses.

Conclusions and Implications

  • The research demonstrates the effectiveness of ROH analysis in revealing fine-scale patterns of inbreeding and selection across diverse horse breeds.
  • Findings suggest that moderate-density and high-density SNP arrays both can identify meaningful inbreeding signals but differ in resolution.
  • Identifying ROH islands provides valuable insight into the genomic footprints of past breeding and selection events.
  • Results support the development of breeding strategies focused on maintaining genetic diversity and managing inbreeding to improve horse health and performance.

Cite This Article

APA
Moghbeli Damane M, Ayatollahi Mehrgardi A, Esmailizadeh A, Momen M. (2025). Fine-scale assessment of ROH patterns and genomic inbreeding in diverse horse breeds using two SNP array densities. BMC Vet Res, 21(1), 688. https://doi.org/10.1186/s12917-025-05143-7

Publication

BMC veterinary research
ISSN: 1746-6148
NlmUniqueID: 101249759
Country: England
Language: English
Volume: 21
Issue: 1
Pages: 688
PII: 688

Researcher Affiliations

Moghbeli Damane, Moslem
  • Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran. m.moghbeli@agr.uk.ac.ir.
Ayatollahi Mehrgardi, Ahmad
  • Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran. mehrgardi@uk.ac.ir.
Esmailizadeh, Ali
  • Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.
Momen, Mehdi
  • Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin- Madison, Madison, WI, 53706, USA.

MeSH Terms

  • Animals
  • Horses / genetics
  • Polymorphism, Single Nucleotide
  • Inbreeding
  • Homozygote
  • Genotype
  • Genomics
  • Genome

Conflict of Interest Statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

References

This article includes 53 references
  1. Templeton AR, Read B. Inbreeding: one word, several meanings, much confusion.. In: Loeschcke V, Tomiuk J, Jain SK, editors. Conservation genetics. Basel: Birkhäuser; 1994. pp. 91–106.
    pubmed: 8032141
  2. Knief U, Hemmrich-Stanisak G, Wittig M. Quantifying realized inbreeding in wild and captive animal populations.. Heredity 2015;114:397–403.
    doi: 10.1038/hdy.2014.116pmc: PMC4359978pubmed: 25585923google scholar: lookup
  3. Charlesworth D, Willis J. The genetics of inbreeding depression.. Nat Rev Genet 2009;10:783–96.
    doi: 10.1038/nrg2664pubmed: 19834483google scholar: lookup
  4. Fernandez A, Toro MA, Lopez-Fanjul C. The effect of inbreeding on the redistribution of genetic variance of fecundity and viability in .. Heredity (Edinb) 1995;75:376–81.
    doi: 10.1038/hdy.1995.149google scholar: lookup
  5. Alvarez G, Ceballos FC, Quinteiro C. The role of inbreeding in the extinction of a European Royal dynasty.. PLoS ONE 2009;4:e5174.
    doi: 10.1371/journal.pone.0005174pmc: PMC2664480pubmed: 19367331google scholar: lookup
  6. Carothers AD, Rudan I, Kolcic I, Polasek O, Hayward C, Wright AF. Estimating human inbreeding coefficients: comparison of genealogical and marker heterozygosity approaches.. Ann Hum Genet 2006;70:666–76.
    doi: 10.1111/j.1469-1809.2006.00263.xpubmed: 16907711google scholar: lookup
  7. Suwanlee S, Baumung R, Sölkner J, Curik I. Evaluation of ancestral inbreeding coefficients: Ballou’s formula versus gene dropping.. Conserv Genet 2007;8:489–95.
    doi: 10.1007/s10592-006-9187-9google scholar: lookup
  8. Curik I, Sölkner J, Stipic N. Effects of models with finite loci, selection, dominance, epistasis and linkage on inbreeding coefficients based on pedigree and genotypic information.. J Anim Breed Genet 2002;119:101–15.
    doi: 10.1046/j.1439-0388.2002.00329.xgoogle scholar: lookup
  9. Broman KW, Weber JL. Long homozygous chromosomal segments in reference families from the centre d’etude du polymorphisme humain.. Am J Hum Genet 1999;65:1493–500.
    doi: 10.1086/302661pmc: PMC1288359pubmed: 10577902google scholar: lookup
  10. Curik I, Ferenčaković M, Sölkner J. Inbreeding and runs of homozygosity: a possible solution to an old problem.. Livest Sci 2014;166:26–34.
    doi: 10.1016/j.livsci.2014.05.034google scholar: lookup
  11. Ceballos FC, Joshi PK, Clark DW, Ramsay M, Wilson JF. Runs of homozygosity: windows into population history and trait architecture.. Nat Rev Genet 2018;19:220–34.
    doi: 10.1038/nrg.2017.109pubmed: 29335644google scholar: lookup
  12. Gibson J, Morton NE, Collins A. Extended tracts of homozygosity in outbred human populations.. Hum Mol Genet 2006;15(5):789–95.
    doi: 10.1093/hmg/ddi493pubmed: 16436455google scholar: lookup
  13. McQuillan R, Leutenegger AL, Abdel-Rahman R, Franklin CS, Pericic M, Barac-Lauc L. Runs of homozygosity in European populations.. Am J Hum Genet 2008;83(3):359–72.
    doi: 10.1016/j.ajhg.2008.08.007pmc: PMC2556426pubmed: 18760389google scholar: lookup
  14. Peripolli E, Munari DP, Silva M, Lima ALF, Irgang R, Baldi F. Runs of homozygosity: current knowledge and applications in livestock.. Anim Genet 2017;48:255–71.
    doi: 10.1111/age.12526pubmed: 27910110google scholar: lookup
  15. Keller MC, Visscher PM, Goddard ME. Quantification of inbreeding due to distant ancestors and its detection using dense SNP data.. Genetics 2011;189:237–49.
    doi: 10.1534/genetics.111.130922pmc: PMC3176119pubmed: 21705750google scholar: lookup
  16. Asti V, Summer A, Ablondi M, Cappelli K, Marras G, Castagnino AM. Selection signatures and inbreeding: exploring genetic diversity in five native horse breeds.. BMC Vet Res 2025;21:346.
    doi: 10.1186/s12917-025-04794-wpmc: PMC12082900pubmed: 40380299google scholar: lookup
  17. Shi L, Wang L, Liu J, Deng T, Yan H, Zhang L. Estimation of inbreeding and identification of regions under heavy selection based on runs of homozygosity in a large white pig population.. J Anim Sci Biotechnol 2020;11:46.
    doi: 10.1186/s40104-020-00447-0pmc: PMC7187514pubmed: 32355558google scholar: lookup
  18. Xu Z, Sun H, Zhang Z, Zhao Q, Olasege BS, Li Q. Assessment of autozygosity derived from runs of homozygosity in Jinhua pigs disclosed by sequencing data.. Front Genet 2019;10:274.
    doi: 10.3389/fgene.2019.00274pmc: PMC6448551pubmed: 30984245google scholar: lookup
  19. Zhang Z, Zhang Q, Xiao Q, Sun H, Gao H, Yang Y. Distribution of runs of homozygosity in Chinese and Western pig breeds evaluated by reduced-representation sequencing data.. Anim Genet 2018;49:579–91.
    doi: 10.1111/age.12730pubmed: 30324759google scholar: lookup
  20. Nosrati M. Run of homozygosity a procedure to detecting inbreeding in farm animals.. Iran J Appl Anim Sci 2017;7(4):533–41.
  21. 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
  22. Hamzić E. Levels of inbreeding derived from runs of homozygosity: a comparison of Austrian and Norwegian cattle breeds.. 2011.
  23. Ferenčaković M, Sölkner J, Curik I. Estimating autozygosity from high-throughput information: effects of SNP density and genotyping errors.. Genet Sel Evol 2013;45:42.
    doi: 10.1186/1297-9686-45-42pmc: PMC4176748pubmed: 24168655google scholar: lookup
  24. Khanshour A, Conant E, Juras R, Cothran EG. Microsatellite analysis of genetic diversity and population structure of Arabian horse populations.. J Hered 2013;104:386–98.
    doi: 10.1093/jhered/est003pubmed: 23450090google scholar: lookup
  25. Metzger J, Karwath M, Tonda R, Beltran S, Águeda L, Gut M. Runs of homozygosity reveal signatures of positive selection for reproduction traits in breed and non-breed horses.. BMC Genomics 2015;16:764.
    doi: 10.1186/s12864-015-1977-3pmc: PMC4600213pubmed: 26452642google scholar: lookup
  26. Chen C, Zhu B, Tang X. Genome-wide assessment of runs of homozygosity by whole-genome sequencing in diverse horse breeds worldwide.. Genes 2023;14(6):1211.
    doi: 10.3390/genes14061211pmc: PMC10298080pubmed: 37372391google scholar: lookup
  27. Dos Santos WB, Schettini GP, Fonseca MG, Pereira GL, Chardulo LAL, Neto RMO. 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(2):161–73.
    doi: 10.1111/jbg.12508pubmed: 32949478google scholar: lookup
  28. Schaefer RJ, Schubert M, Bailey E, Bannasch DL, Barrey E, Bar-Gal GK. Developing a 670k genotyping array to tag ~ 2 m SNPs across 24 horse breeds.. BMC Genomics 2017;18:565.
    doi: 10.1186/s12864-017-3943-8pmc: PMC5530493pubmed: 28750625google scholar: lookup
  29. Sadeghi R, Moradi-Shahrbabak M, Miraei Ashtiani SR, Schlamp F, Cosgrove EJ, Antczak DF. Genetic diversity of Persian Arabian horses and their relationship to other native Iranian horse breeds.. J Hered 2019;110(2):173–82.
    doi: 10.5061/dryad.54vb7f2pubmed: 30590570google scholar: lookup
  30. Momen M, Brauer K, Patterson MM, Sample SJ, Binversie EE, Davis BW. Genetic architecture and polygenic risk score prediction of degenerative suspensory ligament desmitis (DSLD) in the Peruvian horse.. Front Genet 2023;14:1201628.
    doi: 10.5061/dryad.cnp5hqc9ppmc: PMC10460910pubmed: 37645058google scholar: lookup
  31. Chang CC, Chow CC, Tellier LC, 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
  32. Browning BL, Zhou Y, Browning SR. A one-penny imputed genome from next generation reference panels.. Am J Hum Genet 2018;103(3):338–48.
    doi: 10.1016/j.ajhg.2018.07.015pmc: PMC6128308pubmed: 30100085google scholar: lookup
  33. Barbato M, Orozco-terWengel P, Tapio M, Bruford MW. SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data.. Front Genet 2015;6:109.
    doi: 10.3389/fgene.2015.00109pmc: PMC4367434pubmed: 25852748google scholar: lookup
  34. Ferencaković M, Hamzić E, Gredler B, Solberg TR, Klemetsdal G, Curik I. Estimates of autozygosity derived from runs of homozygosity: empirical evidence from selected cattle populations.. J Anim Breed Genet 2013;130:286–93.
    doi: 10.1111/jbg.12012pubmed: 23855630google scholar: lookup
  35. 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 Genomics 2020;21(1):94.
    doi: 10.1186/s12864-020-6463-xpmc: PMC6990544pubmed: 31996125google scholar: lookup
  36. Howrigan DP, Simonson MA, Keller MC. Detecting autozygosity through runs of homozygosity: a comparison of three autozygosity detection algorithms.. BMC Genomics 2011;12:460.
    doi: 10.1186/1471-2164-12-460pmc: PMC3188534pubmed: 21943305google scholar: lookup
  37. Colpitts J, McLoughlin PD, Poissant J. Runs of homozygosity in Sable Island feral horses reveal the genomic consequences of inbreeding and divergence from domestic breeds.. BMC Genomics 2022;23(1):501.
    doi: 10.1186/s12864-022-08729-9pmc: PMC9275264pubmed: 35820826google scholar: lookup
  38. Nosrati M, Asadollahpour Nanaei H, Javanmard A, Esmailizadeh A. The pattern of runs of homozygosity and genomic inbreeding in world-wide sheep populations.. Genomics 2021;113(3):1407–15.
    doi: 10.1016/j.ygeno.2021.03.005pubmed: 33705888google scholar: lookup
  39. Machová K, Marina H, Arranz JJ, Pelayo R, Rychtářová J, Milerski M. Genetic diversity of two native sheep breeds by genome-wide analysis of single nucleotide polymorphisms.. Animal 2023;17(1):100690.
    doi: 10.1016/j.animal.2022.100690pubmed: 36566708google scholar: lookup
  40. Mastrangelo S, Ciani E, Sardina MT, Sottile G, Pilla F, Portolano B. Runs of homozygosity reveal genome-wide autozygosity in Italian sheep breeds.. Anim Genet 2018;49:71–81.
    doi: 10.1111/age.12634pubmed: 29333609google scholar: lookup
  41. Purfield DC, McParland S, Wall E, Berry DP. The distribution of runs of homozygosity and selection signatures in six commercial meat sheep breeds.. PLoS ONE 2017;12:e0176780.
    doi: 10.1371/journal.pone.0176780pmc: PMC5413029pubmed: 28463982google scholar: lookup
  42. Pemberton TJ, Absher D, Feldman MW, Myers RM, Rosenberg NA, Li JZ. Genomic patterns of homozygosity in worldwide human populations.. Am J Hum Genet 2012;91:275–92.
    doi: 10.1016/j.ajhg.2012.06.014pmc: PMC3415543pubmed: 22883143google scholar: lookup
  43. Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update).. Nucleic Acids Res 2022;50(W1):W216–W221.
    pmc: PMC9252805pubmed: 35325185doi: 10.1093/nar/gkac194google scholar: lookup
  44. Grilz-Seger G, Druml T, Neuditschko M, Mesaric 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;50:334–46.
    doi: 10.1111/age.12797pmc: PMC6617995pubmed: 31199540google scholar: lookup
  45. Szmatoła T, Gurgul A, Jasielczuk I. Assessment and distribution of runs of homozygosity in horse breeds representing different utility types.. Animals 2022;12(23):3293.
    doi: 10.3390/ani12233293pmc: PMC9736150pubmed: 36496815google scholar: lookup
  46. Długosz B, Duda K, Chrapek B, Holmes P, Bauer EA. Assessment of the behavior and suitability of primitive horse breeds for hippotherapy using the ‘Hippotest’ empirical test scale.. Appl Anim Behav Sci 2025;286:106622.
    doi: 10.1016/j.applanim.2025.106622google scholar: lookup
  47. Mousavi SF, Razmkabir M, Rostamzadeh J, Seyedabadi HR, Naboulsi R, Petersen JL. Genetic diversity and signatures of selection in four Indigenous horse breeds of Iran.. Heredity (Edinb) 2023;131(2):96–108.
    doi: 10.1038/s41437-023-00624-7pmc: PMC10382556pubmed: 37308718google scholar: lookup
  48. Bazvand B, Rashidi A, Zandi MB, Moradi MH, Rostamzadeh J. Genome-wide analysis of population structure, effective population size and inbreeding in Iranian and exotic horses.. PLoS ONE 2024;19(3):e0299109.
    doi: 10.1371/journal.pone.0299109pmc: PMC10914290pubmed: 38442089google scholar: lookup
  49. Druml T, Neuditschko M, Grilz-Seger G, Horna M, Ricard A, Mesaric M. Population networks associated with runs of homozygosity reveal new insights into the breeding history of the Haflinger horse.. J Hered 2018;109:384–92.
    doi: 10.1093/jhered/esx114pubmed: 29294044google scholar: lookup
  50. Xu L, Zhao G, Yang L, Zhu BO, Chen Y, Zhang L. Genomic patterns of homozygosity in Chinese local cattle.. Sci Rep 2019;9:16977.
    doi: 10.1038/s41598-019-53274-3pmc: PMC6861314pubmed: 31740716google scholar: lookup
  51. Zhang Q, Guldbrandtsen B, Bosse M, Lund MS, Sahana G. Runs of homozygosity and distribution of functional variants in the cattle genome.. BMC Genomics 2015;16:542.
    doi: 10.1186/s12864-015-1715-xpmc: PMC4508970pubmed: 26198692google scholar: lookup
  52. Santos WB, Pereira CB, Maiorano AM, Arce CDS, Baldassini WA, Pereira GL. Genomic inbreeding estimation, runs of homozygosity, and heterozygosity-enriched regions uncover signals of selection in the quarter horse racing line.. J Anim Breed Genet 2023;140(6):583–95.
    doi: 10.1111/jbg.12812pubmed: 37282810google scholar: lookup
  53. Laseca N, Molina A, Ramón M, Valera M, Azcona F, Encina A. 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

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