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Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie2020; 138(2); 161-173; doi: 10.1111/jbg.12508

Fine-scale estimation of inbreeding rates, runs of homozygosity and genome-wide heterozygosity levels in the Mangalarga Marchador horse breed.

Abstract: With the availability of high-density SNP panels and the establishment of approaches for characterizing homozygosity and heterozygosity sites, it is possible to access fine-scale information regarding genomes, providing more than just comparisons of different inbreeding coefficients. This is the first study that seeks to access such information for the Mangalarga Marchador (MM) horse breed on a genomic scale. To this end, we aimed to assess inbreeding levels using different coefficients, as well as to characterize homozygous and heterozygous runs in the population. Using Axiom ® Equine Genotyping Array-670k SNP (Thermo Fisher), 192 horses were genotyped. Our results showed different estimates: inbreeding from genomic coefficients (F ) = 0.16; pedigree-based (F ) = 0.008; and a method based on excess homozygosity (F ) = 0.010. The correlations between the inbreeding coefficients were low to moderate, and some comparisons showed negative correlations, being practically null. In total, 85,295 runs of homozygosity (ROH) and 10,016 runs of heterozygosity (ROHet) were characterized for the 31 horse autosomal chromosomes. The class with the highest percentage of ROH was 0-2 Mbps, with 92.78% of the observations. In the ROHet results, only the 0-2 class presented observations, with chromosome 11 highlighted in a region with high genetic variability. Three regions from the ROHet analyses showed genes with known functions: tripartite motif-containing 37 (TRIM37), protein phosphatase, Mg /Mn dependent 1E (PPM1E) and carbonic anhydrase 10 (CA10). Therefore, our findings suggest moderate inbreeding, possibly attributed to breed formation, annulling possible recent inbreeding. Furthermore, regions with high variability in the MM genome were identified (ROHet), associated with the recent selection and important events in the development and performance of MM horses over generations.
© 2020 Wiley-VCH GmbH.
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Publication Date: 2020-09-19 PubMed ID: 32949478DOI: 10.1111/jbg.12508Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research used new genomic technologies to examine inbreeding and genetic variation in the Mangalarga Marchador horse breed. The study aimed to measure inbreeding levels and identify genome-wide patterns of homozygosity and heterozygosity, providing more detailed information than traditional methods.

Study Methodology

  • The study genotyped 192 Mangalarga Marchador horses using high-density SNP panels, specifically the Axiom® Equine Genotyping Array-670k SNP developed by Thermo Fisher.
  • The purpose was to estimate inbreeding levels using different coefficients, and to characterize homozygous and heterozygous runs in the horse population.

Findings on Inbreeding

  • The analysis of the genomic data provided different inbreeding estimates. Genomic coefficient-based inbreeding was estimated at 0.16, while pedigree-based inbreeding was estimated to be much lower, at 0.008.
  • An alternate method of estimating inbreeding based on an excess of homozygosity yielded an inbreeding estimate of 0.010.
  • The correlations between the different inbreeding coefficients were rated low to moderate, with some comparisons yielding virtually no correlation.

Analysis of Homozygosity and Heterozygosity

  • A total of 85,295 runs of homozygosity (ROH) and 10,016 runs of heterozygosity (ROHet) were characterized across the 31 horse autosomal chromosomes.
  • The category with the majority of homozygosity runs was identified within 0-2 Mbps, making up about 92.78% of the observations. However, in the heterozygosity analysis, only observations in the 0-2 Mbps category were represented.

Highlighted Genomic Variability

  • Among the regions with higher genetic variability, chromosome 11 stood out. This suggests that recent selection pressure and important changes in the development and performance of the Mangalarga Marchador breed may have contributed to this variability.
  • In the regions showing higher heterozygosity (ROHet), three genes were discovered with known functions. These are the tripartite motif-containing 37 (TRIM37), protein phosphatase, Mg/Mn dependent 1E (PPM1E), and carbonic anhydrase 10 (CA10) genes.
  • From their findings, researchers inferred moderate inbreeding which might be due to breed formation, but they found virtually no recent inbreeding.

These findings offer significant insights into the genetic health and diversity of the Mangalarga Marchador horse breed and demonstrate the value of high-density SNP panels for in-depth genomic research.

Cite This Article

APA
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. (2020). Fine-scale estimation of inbreeding rates, runs of homozygosity and genome-wide heterozygosity levels in the Mangalarga Marchador horse breed. J Anim Breed Genet, 138(2), 161-173. https://doi.org/10.1111/jbg.12508

Publication

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
ISSN: 1439-0388
NlmUniqueID: 100955807
Country: Germany
Language: English
Volume: 138
Issue: 2
Pages: 161-173

Researcher Affiliations

Bizarria Dos Santos, Wellington
  • School of Agricultural and Veterinary Sciences (FCAV), São Paulo State University (Unesp), Jaboticabal, Brazil.
Pimenta Schettini, Gustavo
  • School of Agricultural and Veterinary Sciences (FCAV), São Paulo State University (Unesp), Jaboticabal, Brazil.
Fonseca, Mayara Gonçalves
  • Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.
Pereira, Guilherme Luis
  • School of Veterinary Medicine and Animal Science (FMVZ), São Paulo State University (Unesp), Botucatu, Brazil.
Loyola Chardulo, Luis Artur
  • School of Veterinary Medicine and Animal Science (FMVZ), São Paulo State University (Unesp), Botucatu, Brazil.
Rodrigues Machado Neto, Otávio
  • School of Veterinary Medicine and Animal Science (FMVZ), São Paulo State University (Unesp), Botucatu, Brazil.
Baldassini, Welder Angelo
  • School of Veterinary Medicine and Animal Science (FMVZ), São Paulo State University (Unesp), Botucatu, Brazil.
Nunes de Oliveira, Henrique
  • School of Agricultural and Veterinary Sciences (FCAV), São Paulo State University (Unesp), Jaboticabal, Brazil.
Abdallah Curi, Rogério
  • School of Veterinary Medicine and Animal Science (FMVZ), São Paulo State University (Unesp), Botucatu, Brazil.

MeSH Terms

  • Animals
  • Genome
  • Genotype
  • Homozygote
  • Horses
  • Inbreeding
  • Polymorphism, Single Nucleotide

Grant Funding

  • 001 / Coordenau00e7u00e3o de Aperfeiu00e7oamento de Pessoal de Nu00edvel Superior
  • 2016/19081-9 / Fundau00e7u00e3o de Amparo u00e0 Pesquisa do Estado de Su00e3o Paulo

References

This article includes 64 references
  1. Ablondi M, Viklund Å, Lindgren G, Eriksson S, Mikko S. Signatures of selection in the genome of Swedish warmblood horses selected for sport performance. BMC Genomics 20, 717 (2019).
    doi: 10.1186/s12864-019-6079-1google scholar: lookup
  2. Aguilar I, Misztal I. Technical note: Recursive algorithm for inbreeding coefficients assuming nonzero inbreeding of unknown parents. Journal of Dairy Science 91, 1669-1672 (2008).
    doi: 10.3168/jds.2007-0575google scholar: lookup
  3. Al Abri M A, König von Borstel U, Strecker V, Brooks S A. Application of genomic estimation methods of inbreeding and population structure in an Arabian horse herd. Journal of Heredity 108, 361-368 (2017).
    doi: 10.1093/jhered/esx025google scholar: lookup
  4. Avila F, Mickelson J R, Schaefer R J, McCue M E. Genome-wide signatures of selection reveal genes associated with performance in american quarter horse subpopulations. Frontiers in Genetics 9, 249 (2018).
    doi: 10.3389/fgene.2018.00249google scholar: lookup
  5. Beeson S K, Schaefer R J, Mason V C, McCue M E. Robust remapping of equine SNP array coordinates to Eq쪳.0. Animal Genetics 50, 114-115 (2019).
    doi: 10.1111/age.12745google scholar: lookup
  6. Biscarini F, Cozzi P, Gaspa G, Marras G. detectRUNS: Detect runs of homozygosity and runs of heterozygosity in diploid genomes. R package version 0.9.6 (2019).
  7. Bull J J. Lethal gene drive selects inbreeding. Evolution, Medicine, and Public Health 1, 1-16 (2017).
    doi: 10.1093/emph/eow030google scholar: lookup
  8. Bussiman F O, Santos B A, Silva B C A, Perez B C, Pereira G L, Balieiro J C C. Allelic and genotypic frequencies of the DMRT3 gene in the Brazilian horse breed Mangalarga Marchador and their association with types of gait. Genetics and Molecular Research 18, gmr18217 (2019).
    doi: 10.4238/gmr18217google scholar: lookup
  9. Cassell B G, Adamec V, Pearson R E. Effect of incomplete pedigrees on estimates of inbreeding and inbreeding depression for days to first service and summit milk yield in Holsteins and Jerseys. Journal of Dairy Science 86, 2967-2976 (2003).
    doi: 10.3168/jds.s0022-0302(03)73894-6google scholar: lookup
  10. Ceballos F C, Joshi P K, Clark D W, Ramsay M, Wilson J F. Runs of homozygosity: Windows into population history and trait architecture. Nature Reviews Genetics 19, 220-234 (2018).
    doi: 10.1038/nrg.2017.109google scholar: lookup
  11. Clayton D G. Sex chromosomes and genetic association studies. Genome Medicine 1, 110 (2009).
    doi: 10.1186/gm110google scholar: lookup
  12. Dahlborn K, Jansson A, Nyman S, Morgan K, Holm L, Ridderstråle Y. Sweat production and localisation of carbonic anhydrase in the equine sweat gland during exercise at two ambient temperatures. Equine Veterinary Journal 30, 398-403 (1999).
    doi: 10.1111/j.2042-3306.1999.tb05255.xgoogle scholar: lookup
  13. Druml T, Neuditschko M, Grilz-Seger G, Horna M, Ricard A, Mesarič M, Brem G. Population networks associated with runs of homozygosity reveal new insights into the breeding history of the Haflinger horse. Journal of Heredity 109, 384-392 (2018).
    doi: 10.1093/jhered/esx114google scholar: lookup
  14. Fawcett J A, Sato F, Sakamoto T, Iwasaki W M, Tozaki T, Innan H. Genome-wide SNP analysis of Japanese thoroughbred racehorses. PLoS One 14, e021840 (2019).
    doi: 10.1371/journal.pone.0218407google scholar: lookup
  15. Ferenčaković M, Banadinović M, Mercvajler M, Khayat-Zadeh N, Mészáros G, Cubric-Curik V, Sölkner J. Mapping of heterozygosity rich regions in Austrian pinzgauer cattle. Acta Agriculturae Slovenica 5, 42 (2016).
  16. Ferenčaković M, Hamzic E, Gredler B, Curik I, Sölkner J. Runs of homozygosity reveal genome-wide autozygosity in the Austrian Fleckvieh cattle. Agriculturae Conspectus Scientificus 76, 325-328 (2011).
  17. Ferenčaković M, Hamzic E, Gredler B, Solberg T R, Klemetsdal G, Curik I, Sölkner J. Estimates of autozygosity derived from runs of homozygosity: Empirical evidence from selected cattle populations. Journal of Animal Breeding and Genetics 130, 286-293 (2013).
    doi: 10.1111/jbg.12012google scholar: lookup
  18. Ferenčaković M, Sölkner J, Curik I. Estimating autozygosity from high-throughput information: Effects of SNP density and genotyping errors. Genetics Selection Evolution 45, 42 (2013).
    doi: 10.1186/1297-9686-45-42google scholar: lookup
  19. Ferrer E S, García-Navas V, Sanz J J, Ortego J. The strength of the association between heterozygosity and probability of interannual local recruitment increases with environmental harshness in blue tits. Ecology and Evolution 6, 8857-8869 (2016).
    doi: 10.1002/ece3.2591google scholar: lookup
  20. Fijarczyk A, Babik W. Detecting balancing selection in genomes: Limits and prospects. Molecular Ecology 14, 3529-3545 (2015).
    doi: 10.1111/mec.13226google scholar: lookup
  21. Fonseca M G, Ferraz G C, Lage J, Pereira G L, Curi R A. A genome-wide association study reveals differences in the genetic mechanism of control of the two gait patterns of the Brazilian Mangalarga Marchador breed. Journal of Equine Veterinary Science 53, 64-67 (2017).
    doi: 10.1016/j.jevs.2016.01.015google scholar: lookup
  22. Forutan M, Ansari M S, Baes C, Melzer N, Schenkel F S, Sargolzaei M. Inbreeding and runs of homozygosity before and after genomic selection in North American Holstein cattle. BMC Genomics 98, 2-12 (2018).
    doi: 10.1186/s12864-018-4453-zgoogle scholar: lookup
  23. Gomez-Raya L, Rodríguez C, Barragán C, Silió L. Genomic inbreeding coefficients based on the distribution of the length of runs of homozygosity in a closed line of Iberian pigs. Genetics Selection Evolution 47, 81 (2015).
    doi: 10.1186/s12711-015-0153-1google scholar: lookup
  24. 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 Genomics 20, 174 (2019).
    doi: 10.1186/s12864-019-5564-xgoogle scholar: lookup
  25. Grilz-Seger G, Mesaric M, Cotman M, Neuditschko M, Druml T, Brem G. Runs of homozygosity and population history of three horse breeds with small population size. Journal of Equine Veterinary Science 71, 27-34 (2018).
    doi: 10.1016/j.jevs.2018.09.004google scholar: lookup
  26. Gurgul A, Szmatoła T, Topolski P, Jasielczuk I, Żukowski K, Bugno-Poniewierska M. The use of runs of homozygosity for estimation of recent inbreeding in Holstein cattle. Journal of Applied Genetics 57, 527-530 (2016).
    doi: 10.1007/s13353-016-0337-6google scholar: lookup
  27. Hedrick P W. What is the evidence for heterozygote advantage selection?. Trends in Ecology and Evolution 27, P698-P704 (2012).
    doi: 10.1016/j.tree.2012.08.012google scholar: lookup
  28. Hedrick P W, Kalinowski S T. Inbreeding depression and conservation biology. Annual Review of Ecology, Evolution, and Systematics 31, 139-162 (2000).
    doi: 10.1146/annurev.ecolsys.31.1.139google scholar: lookup
  29. Howard J T, Pryce J E, Baes C, Maltecca C. Inbreeding in the genomics era: Inbreeding, inbreeding depression, and management of genomic variability. Journal of Dairy Science 100, 6009-6024 (2017).
    doi: 10.3168/jds.2017-12787google scholar: lookup
  30. Jessen A L. Localization and truncation in brain tissue and effects on neuronal morphology in primary neuronal culture. Dissertation, Doctor rerum naturalium (2010).
  31. Kaeuffer R, Coltman D W, Chapuis J L, Pontier D, Réale D. Unexpected heterozygosity in an island mouflon population founded by a single pair of individuals. Proceedings of the Royal Society B: Biological Sciences 274, 527-533 (2007).
    doi: 10.1098/rspb.2006.3743google scholar: lookup
  32. Kardos M, Luikart G, Allendorf F W. Measuring individual inbreeding in the age of genomics: Marker-based measures are better than pedigrees. Heredity (Edinb) 115, 63-72 (2015).
    doi: 10.1038/hdy.2015.17google scholar: lookup
  33. Kasarda R, Moravčíková N, Kadlečík O, Trakovická A, Halo M, Candrák J. Level of inbreeding in Norik of Muran horse: Pedigree vs. genomic data. Acta Universitatis Agriculturae Et Silviculturae Mendelianae Brunensis 67(6), 1457-1463 (2019).
    doi: 10.11118/actaun201967061457google scholar: lookup
  34. Keller M C, Visscher P M, Goddard M E. Quantification of inbreeding due to distant ancestors and its detection using dense single nucleotide polymorphism data. Genetics 189, 237-249 (2011).
    doi: 10.1534/genetics.111.130922google scholar: lookup
  35. Kim E, Sonstegard T S, Tassell C P V, Wiggans G, Rothschild M F. The relationship between runs of homozygosity and inbreeding in Jersey Cattle under Selection. PLoS One 10, e0129967 (2015).
    doi: 10.1371/journal.pone.0129967google scholar: lookup
  36. Koh C, Tan E, Manser E, Lim L. The p21-activated kinase PAK is negatively regulated by POPX1 and POPX2, a pair of serine/threonine phosphatases of the PP2C family. Current Biology 12, 317-321 (2002).
    doi: 10.1016/s0960-9822(02)00652-8google scholar: lookup
  37. Larsson C. Protein kinase C and the regulation of the actin cytoskeleton. Cellular Signalling 18, 276-284 (2006).
    doi: 10.1016/j.cellsig.2005.07.010google scholar: lookup
  38. Lencz T, Lambert C, DeRosse P, Burdick K E, Morgan T V, Kane J M, Malhotra A K. Runs of homozygosity reveal highly penetrant recessive loci in schizophrenia. Proceedings of the National Academy of Sciences of the United States of America 104, 19942-19947 (2007).
    doi: 10.1073/pnas.0710021104google scholar: lookup
  39. Li M, Strandén I, Tiirikka T, Sevón-Aimonen M, Kantanen J A. Comparison of approaches to estimate the inbreeding coefficient and pairwise relatedness using genomic and pedigree data in a sheep population. PLoS One 6, e26256 (2011).
    doi: 10.1371/journal.pone.0026256google scholar: lookup
  40. Marras G, Gaspa G, Sorbolini S, Dimauro C, Ajmone-Marsan P, Valentini A, Macciotta N P P. Analysis of runs of homozygosity and their relationship with inbreeding in five cattle breeds farmed in Italy. Animal Genetics 46, 110-121 (2015).
    doi: 10.1111/age.12259google scholar: lookup
  41. Marras G, Wood B, Makanjuola B, Malchiodi F, Peeters K, Biscarini F. Characterization of runs of homozygosity and heterozygosity-rich regions in a commercial turkey (Meleagris gallopavo) population. Conference: 11th World congress on genetics applied to livestock production (pp. 1-5) (2018).
  42. Metzger J, Karwath M, Tonda R, Beltran S, Águeda L, Gut M, Distl O. Runs of homozygosity reveal signatures of positive selection for reproduction traits in breed and non-breed horses. BMC Genomics 16, 764 (2015).
    doi: 10.1186/s12864-015-1977-3google scholar: lookup
  43. . CA10 carbonic anhydrase 10 [Homo sapiens (human)] [webpage]. National Center for Biotechnology Information - NCBI (2019).
  44. Nowak C, Jost D, Vogt C, Oetken M, Schwenk K, Oehlmann J. Consequences of inbreeding and reduced genetic variation on tolerance to cadmium stress in the midge Chironomus riparius. Aquatic Toxicology 84, 278-284 (2007).
    doi: 10.1016/j.aquatox.2007.04.015google scholar: lookup
  45. Patterson L, Staiger E A, Brooks S A. DMRT3 is associated with gait type in Mangalarga Marchador horses, but does not control gait ability. Animal Genetics 46, 213-215 (2015).
    doi: 10.1111/age.12273google scholar: lookup
  46. Pekkala N, Knott K E, Kotiaho J S, Nissinen K, Puurtinen M. The effect of inbreeding rate on fitness, inbreeding depression and heterosis over a range of inbreeding coefficients. Evolutionary Applications 7, 1107-1119 (2014).
    doi: 10.1111/eva.12145google scholar: lookup
  47. Pemberton T J, Absher D, Feldman M W, Myers R M, Rosenberg N A, Li J Z. Genomic patterns of homozygosity in worldwide human populations. The American Journal of Human Genetics 91, 275-292 (2012).
    doi: 10.1016/j.ajhg.2012.06.014google scholar: lookup
  48. Peripolli E, Stafuzza N B, Munari D P, Lima A L F, Irgang R, Machado M A, da Silva M V G B. Assessment of runs of homozygosity islands and estimates of genomic inbreeding in Gyr (Bos indicus) dairy cattle. BMC Genomics 19, 34 (2018).
    doi: 10.1186/s12864-017-4365-3google scholar: lookup
  49. Pryce J E, Haile-Mariam M, Goddard M E, Hayes B J. Identification of genomic regions associated with inbreeding depression in Holstein and Jersey dairy cattle. Genetics Selection Evolution 46, 71 (2014).
    doi: 10.1186/s12711-014-0071-7google scholar: lookup
  50. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M A R, Bender D, Sham P C. PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics 81, 559-575 (2007).
    doi: 10.1086/519795google scholar: lookup
  51. Purfield D C, McParland S, Wall E, Berry D P. The distribution of runs of homozygosity and selection signatures in six commercial meat sheep breeds. PLoS One 12, e0176780 (2017).
    doi: 10.1371/journal.pone.0176780google scholar: lookup
  52. Renaud G, Hanghøj K, Korneliussen T S, Willerslev E, Orlando L. Joint estimates of heterozygosity and runs of homozygosity for modern and ancient samples. Genetics 212, 587-614 (2019).
    doi: 10.1534/genetics.119.302057google scholar: lookup
  53. Robert A, Toupance B, Tremblay M, Heyer E. Impact of inbreeding on fertility in a pre-industrial population. European Journal of Human Genetics 17, 673-681 (2009).
    doi: 10.1038/ejhg.2008.237google scholar: lookup
  54. Santos B A, Pereira G L, Bussiman F O, Victória R, Curi R A. Genomic analysis of the population structure in horses of the Brazilian T Mangalarga Marchador breed. Livestock Science 229, 49-55 (2019).
    doi: 10.1016/j.livsci.2019.09.010google scholar: lookup
  55. Santos W B. Genome-wide scan for selection signature and estimates of levels of autozygosity in Mangalarga Marchador horses. Dissertation (2020).
  56. Sayres M A W. Genetic diversity on the sex chromosomes. Genome Biology and Evolution 10, 1064-1078 (2018).
    doi: 10.1093/gbe/evy039google scholar: lookup
  57. Smedley D, Haider S, Ballester B, Holland R, London D, Thorisson G, Kasprzyk A. BioMart - Biological queries made easy. BMC Genomics 10(1), 22 (2009).
    doi: 10.1186/1471-2164-10-22google scholar: lookup
  58. Trevor J, Pembertona N A. Rosenbergb Population-genetic influences on genomic estimates of the inbreeding coefficient: A global perspective. Human Heredity 77, 37-48 (2015).
    doi: 10.1159/000362878google scholar: lookup
  59. VanRaden P M. Accounting for inbreeding and crossbreeding in genetic evaluation of large populations. Journal of Dairy Science 75, 3136-3144 (1992).
    doi: 10.3168/jds.s0022-0302(92)78077-1google scholar: lookup
  60. Velie B D, Solé M, Fegraeus K J, Rosengren M K, Røed K H, Ihler C-F, Lindgren G. Genomic measures of inbreeding in the Norwegian-Swedish Coldblooded Trotter and their associations with known QTL for reproduction and health traits. Genetics Selection Evolution 51, 22 (2019).
    doi: 10.1186/s12711-019-0465-7google scholar: lookup
  61. Voss M, Paterson J, Kelsall I R, Martín-Granados C, Hastie C J, Peggie M W, Cohen P T W. Ppm1E is an in cellulo AMP-activated protein kinase phosphatase. Cellular Signalling 23, 114-124 (2011).
    doi: 10.1016/j.cellsig.2010.08.010google scholar: lookup
  62. Williams J L, Hall S J, Del Corvo M, Ballingall K T, Colli L, Ajmone Marsan P, Biscarini F. Inbreeding and purging at the genomic Level: The Chillingham cattle reveal extensive, non-random SNP heterozygosity. Animal Genetics 47, 19-27 (2016).
    doi: 10.1111/age.12376google scholar: lookup
  63. Xu L, Zhao G, Yang L, Zhu B O, Chen Y, Zhang L, Li J. Genomic patterns of homozygosity in Chinese local cattle. Scientific Reports 9, 16977 (2019).
    doi: 10.1038/s41598-019-53274-3google scholar: lookup
  64. Yengo L, Zhu Z, Wray N R, Weir B S, Yang J, Robinson M R, Visscher P M. Detection and quantification of inbreeding depression for complex traits from SNP data. Proceedings of the National Academy of Sciences of the United States of America 114, 8602-8607 (2017).
    doi: 10.1073/pnas.1621096114google scholar: lookup

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  1. Dementieva N, Nikitkina E, Shcherbakov Y, Nikolaeva O, Mitrofanova O, Ryabova A, Atroshchenko M, Makhmutova O, Zaitsev A. The Genetic Diversity of Stallions of Different Breeds in Russia. Genes (Basel) 2023 Jul 24;14(7).
    doi: 10.3390/genes14071511pubmed: 37510415google scholar: lookup
  2. Tian S, Tang W, Zhong Z, Wang Z, Xie X, Liu H, Chen F, Liu J, Han Y, Qin Y, Tan Z, Xiao Q. Identification of Runs of Homozygosity Islands and Functional Variants in Wenchang Chicken. Animals (Basel) 2023 May 15;13(10).
    doi: 10.3390/ani13101645pubmed: 37238076google scholar: lookup
  3. Cardinali I, Giontella A, Tommasi A, Silvestrelli M, Lancioni H. Unlocking Horse Y Chromosome Diversity. Genes (Basel) 2022 Dec 2;13(12).
    doi: 10.3390/genes13122272pubmed: 36553539google scholar: lookup
  4. Perdomo-González DI, Laseca N, Demyda-Peyrás S, Valera M, Cervantes I, Molina A. Fine-tuning genomic and pedigree inbreeding rates in equine population with a deep and reliable stud book: the case of the Pura Raza Española horse. J Anim Sci Biotechnol 2022 Nov 7;13(1):127.
    doi: 10.1186/s40104-022-00781-5pubmed: 36336696google scholar: lookup
  5. Chen Z, Zhang Z, Wang Z, Zhang Z, Wang Q, Pan Y. Heterozygosity and homozygosity regions affect reproductive success and the loss of reproduction: A case study with litter traits in pigs. Comput Struct Biotechnol J 2022;20:4060-4071.
    doi: 10.1016/j.csbj.2022.07.039pubmed: 35983229google scholar: lookup
  6. Mulim HA, Brito LF, Batista Pinto LF, Moletta JL, Da Silva LR, Pedrosa VB. Genetic and Genomic Characterization of a New Beef Cattle Composite Breed (Purunã) Developed for Production in Pasture-Based Systems. Front Genet 2022;13:858970.
    doi: 10.3389/fgene.2022.858970pubmed: 35923708google scholar: lookup
  7. Li G, Tang J, Huang J, Jiang Y, Fan Y, Wang X, Ren J. Genome-Wide Estimates of Runs of Homozygosity, Heterozygosity, and Genetic Load in Two Chinese Indigenous Goat Breeds. Front Genet 2022;13:774196.
    doi: 10.3389/fgene.2022.774196pubmed: 35559012google scholar: lookup
  8. Mulim HA, Brito LF, Pinto LFB, Ferraz JBS, Grigoletto L, Silva MR, Pedrosa VB. Characterization of runs of homozygosity, heterozygosity-enriched regions, and population structure in cattle populations selected for different breeding goals. BMC Genomics 2022 Mar 16;23(1):209.
    doi: 10.1186/s12864-022-08384-0pubmed: 35291953google scholar: lookup
  9. Laseca N, Molina A, Ramón M, Valera M, Azcona F, Encina A, Demyda-Peyrás S. Fine-Scale Analysis of Runs of Homozygosity Islands Affecting Fertility in Mares. Front Vet Sci 2022;9:754028.
    doi: 10.3389/fvets.2022.754028pubmed: 35252415google scholar: lookup
  10. Ruan D, Yang J, Zhuang Z, Ding R, Huang J, Quan J, Gu T, Hong L, Zheng E, Li Z, Cai G, Wang X, Wu Z. Assessment of Heterozygosity and Genome-Wide Analysis of Heterozygosity Regions in Two Duroc Pig Populations. Front Genet 2021;12:812456.
    doi: 10.3389/fgene.2021.812456pubmed: 35154256google scholar: lookup
  11. Tsartsianidou V, Sánchez-Molano E, Kapsona VV, Basdagianni Z, Chatziplis D, Arsenos G, Triantafyllidis A, Banos G. A comprehensive genome-wide scan detects genomic regions related to local adaptation and climate resilience in Mediterranean domestic sheep. Genet Sel Evol 2021 Dec 2;53(1):90.
    doi: 10.1186/s12711-021-00682-7pubmed: 34856922google scholar: lookup
  12. Santos WB, Schettini GP, Maiorano AM, Bussiman FO, Balieiro JCC, Ferraz GC, Pereira GL, Baldassini WA, Neto ORM, Oliveira HN, Curi RA. Genome-wide scans for signatures of selection in Mangalarga Marchador horses using high-throughput SNP genotyping. BMC Genomics 2021 Oct 14;22(1):737.
    doi: 10.1186/s12864-021-08053-8pubmed: 34645387google scholar: lookup
  13. Selli A, Ventura RV, Fonseca PAS, Buzanskas ME, Andrietta LT, Balieiro JCC, Brito LF. Detection and Visualization of Heterozygosity-Rich Regions and Runs of Homozygosity in Worldwide Sheep Populations. Animals (Basel) 2021 Sep 15;11(9).
    doi: 10.3390/ani11092696pubmed: 34573664google scholar: lookup
  14. Biscarini F, Mastrangelo S, Catillo G, Senczuk G, Ciampolini R. Insights into Genetic Diversity, Runs of Homozygosity and Heterozygosity-Rich Regions in Maremmana Semi-Feral Cattle Using Pedigree and Genomic Data. Animals (Basel) 2020 Dec 3;10(12).
    doi: 10.3390/ani10122285pubmed: 33287320google scholar: lookup
  15. Chessari G, Reich P, Criscione A, Falker-Gieske C, Mastrangelo S, Tumino S, Bordonaro S, Marletta D, Tetens J. Comparison between SNP array and imputed data to estimate population structure and ROH hotspots in horse breeds. BMC Genomics 2025 Nov 29;26(1):1086.
    doi: 10.1186/s12864-025-12256-8pubmed: 41318401google scholar: lookup
  16. Nisa FU, Usman M, Ali A, Ali MB, Kaul H, Asif M, Mrode R, Mukhtar Z. Estimation of genome-wide patterns of homozygosity, heterozygosity and inbreeding in crossbred dairy cattle population in Pakistan. Trop Anim Health Prod 2025 Sep 26;57(8):396.
    doi: 10.1007/s11250-025-04654-7pubmed: 41003855google scholar: lookup
  17. Smaragdov M. Unravelling Heterozygosity-Rich Regions in the Holstein Genome. Animals (Basel) 2025 Aug 7;15(15).
    doi: 10.3390/ani15152320pubmed: 40805107google scholar: lookup
  18. Ablondi M, Eriksson S, Mikko S. Haplotype structure and heterozygosity around the fragile foal syndrome variant in Swedish Warmblod horses. Anim Genet 2025 Jun;56(3):e70022.
    doi: 10.1111/age.70022pubmed: 40524478google scholar: lookup
  19. Pegolo S, Bisutti V, Mota LFM, Cecchinato A, Amalfitano N, Dettori ML, Pazzola M, Vacca GM, Bittante G. Genome-wide landscape of genetic diversity, runs of homozygosity, and runs of heterozygosity in five Alpine and Mediterranean goat breeds. J Anim Sci Biotechnol 2025 Mar 3;16(1):33.
    doi: 10.1186/s40104-025-01155-3pubmed: 40025542google scholar: lookup
  20. Tsartsianidou V, Otapasidis A, Papakostas S, Karaiskou N, Vouraki S, Triantafyllidis A. Genome-Wide Patterns of Homozygosity and Heterozygosity and Candidate Genes in Greek Insular and Mainland Native Goats. Genes (Basel) 2024 Dec 27;16(1).
    doi: 10.3390/genes16010027pubmed: 39858574google scholar: lookup
  21. Sigurðardóttir H, Ablondi M, Kristjansson T, Lindgren G, Eriksson S. Genetic diversity and signatures of selection in Icelandic horses and Exmoor ponies. BMC Genomics 2024 Aug 8;25(1):772.
    doi: 10.1186/s12864-024-10682-8pubmed: 39118059google scholar: lookup
  22. Mulim HA, Pedrosa VB, Pinto LFB, Tiezzi F, Maltecca C, Schenkel FS, Brito LF. Detection and evaluation of parameters influencing the identification of heterozygous-enriched regions in Holstein cattle based on SNP chip or whole-genome sequence data. BMC Genomics 2024 Jul 26;25(1):726.
    doi: 10.1186/s12864-024-10642-2pubmed: 39060982google scholar: lookup
  23. Gmel AI, Mikko S, Ricard A, Velie BD, Gerber V, Hamilton NA, Neuditschko M. Using high-density SNP data to unravel the origin of the Franches-Montagnes horse breed. Genet Sel Evol 2024 Jul 10;56(1):53.
    doi: 10.1186/s12711-024-00922-6pubmed: 38987703google scholar: lookup
  24. Falchi L, Cesarani A, Criscione A, Hidalgo J, Garcia A, Mastrangelo S, Macciotta NPP. Effect of genotyping density on the detection of runs of homozygosity and heterozygosity in cattle. J Anim Sci 2024 Jan 3;102.
    doi: 10.1093/jas/skae147pubmed: 38798158google scholar: lookup
  25. Romanov MN, Shakhin AV, Abdelmanova AS, Volkova NA, Efimov DN, Fisinin VI, Korshunova LG, Anshakov DV, Dotsev AV, Griffin DK, Zinovieva NA. Dissecting Selective Signatures and Candidate Genes in Grandparent Lines Subject to High Selection Pressure for Broiler Production and in a Local Russian Chicken Breed of Ushanka. Genes (Basel) 2024 Apr 22;15(4).
    doi: 10.3390/genes15040524pubmed: 38674458google scholar: lookup
  26. Huang C, Zhao Q, Chen Q, Su Y, Ma Y, Ye S, Zhao Q. Runs of Homozygosity Detection and Selection Signature Analysis for Local Goat Breeds in Yunnan, China. Genes (Basel) 2024 Feb 28;15(3).
    doi: 10.3390/genes15030313pubmed: 38540373google scholar: lookup
  27. Chessari G, Criscione A, Marletta D, Crepaldi P, Portolano B, Manunza A, Cesarani A, Biscarini F, Mastrangelo S. Characterization of heterozygosity-rich regions in Italian and worldwide goat breeds. Sci Rep 2024 Jan 2;14(1):3.
    doi: 10.1038/s41598-023-49125-xpubmed: 38168531google scholar: lookup
  28. Arias KD, Gutiérrez JP, Fernández I, Álvarez I, Goyache F. Approaching autozygosity in a small pedigree of Gochu Asturcelta pigs. Genet Sel Evol 2023 Oct 25;55(1):74.
    doi: 10.1186/s12711-023-00846-7pubmed: 37880572google scholar: lookup
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