Analysis of the genetic diversity of Ayrshire cattle in Russia. Message 2. Genome analysis based on data on the distribution of ROH patterns in Ayrshire cows

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Abstract

BACKGROUND: The analysis of ROH distribution is an important focus of genetic resource conservation programs of cattle. Characterization of ROH-islands allows to identify genetic factors affecting productivity traits of dairy cattle.

AIM: was to analyze intra-breed genetic diversity and population structure of Ayrshire cattle, based on data on distribution of homozygosity patterns, as well as to identify loci associated with selection intensity and utility traits.

MATERIALS AND METHODS: ROH distribution data were obtained using whole genome genotyping on Illumina BovineSNP50 (50K) DNA chips (Illumina Inc., USA). The object of the study was the DNA of Ayrshire cows (600 cows), which belonged to farms with different levels of selection and breeding work.

RESULT: The results of our studies showed a generally similar level of inbredness of the analyzed Ayrshire cattle herds. The homogeneity of the population is confirmed by a large number of animals (72.83%) with FROH values between 0.10 and 0.20. Cluster analysis revealed consolidated groups of individuals, due to their ancestral origins. The discovered ROH-patterns included 268 genes, 32 of which were involved in regulation of the synthesis of protein and fat milk components. The results obtained may be used in breeding programs for Ayrshire cattle in Russia.

CONCLUSIONS: The Russian population of Ayrshire cattle is distinguished by unique qualities in protein and fat milk composition and genome architecture, while maintaining genetic diversity and insignificant traces of Ayrshire cattle gene pool.

About the authors

Anna E. Ryabova

Russian Research Institute of Farm Animal Genetics and Breeding — Branch of the L.K. Ernst Federal Research Center for Animal Husbandry

Author for correspondence.
Email: aniuta.riabova2016@yandex.ru
ORCID iD: 0000-0003-2362-2892
SPIN-code: 4336-0310
Scopus Author ID: 57941963400

junior research associate

Russian Federation, Saint Petersburg

Marina V. Pozovnikova

Russian Research Institute of Farm Animal Genetics and Breeding — Branch of the L.K. Ernst Federal Research Center for Animal Husbandry

Email: pozovnikova@gmail.com
ORCID iD: 0000-0002-8658-2026
SPIN-code: 5441-6996
Scopus Author ID: 57200383317

Cand. Sci. (Biol.), senior research associate

Russian Federation, Saint Petersburg

Natalia V. Dementieva

Russian Research Institute of Farm Animal Genetics and Breeding — Branch of the L.K. Ernst Federal Research Center for Animal Husbandry

Email: dementevan@mail.ru
ORCID iD: 0000-0003-0210-9344
SPIN-code: 8768-8906
Scopus Author ID: 57221619264

Cand. Sci. (Biol.), leading research associate

Russian Federation, Saint Petersburg

Yury S. Shcherbakov

Russian Research Institute of Farm Animal Genetics and Breeding — Branch of the L.K. Ernst Federal Research Center for Animal Husbandry

Email: yura.10.08.94.94@mail.ru
ORCID iD: 0000-0003-2949-0747
SPIN-code: 3547-1009
Scopus Author ID: 57189759592

junior research associate

Russian Federation, Saint Petersburg

Olga V. Tulinova

Russian Research Institute of Farm Animal Genetics and Breeding — Branch of the L.K. Ernst Federal Research Center for Animal Husbandry

Email: tulinova_59@mail.ru
SPIN-code: 3973-6337
Scopus Author ID: 57200384693

Cand. Sci. (Agricultural), leading research associate

Russian Federation, Saint Petersburg

Elena A. Romanova

Russian Research Institute of Farm Animal Genetics and Breeding — Branch of the L.K. Ernst Federal Research Center for Animal Husbandry

Email: splicing86@gmail.com
ORCID iD: 0000-0002-4225-5533
SPIN-code: 1444-3678

junior research associate

Russian Federation, Saint Petersburg

Anastasia I. Azovtseva

Russian Research Institute of Farm Animal Genetics and Breeding — Branch of the L.K. Ernst Federal Research Center for Animal Husbandry

Email: ase4ica15@mail.ru
ORCID iD: 0000-0002-2963-378X
SPIN-code: 5784-2786

junior research associate

Russian Federation, Saint Petersburg

References

  1. Howard JT, Pryce JE, Baes C, Maltecca C. Invited review: Inbreeding in the genomics era: Inbreeding, inbreeding depression, and management of genomic variability. J Dairy Sci. 2017;100(8):6009–6024. doi: 10.3168/jds.2017-12787
  2. Nedashkovsky IS, Sermyagin AA, Bogdanova TV, et al. Evaluation of inbreeding effect for milk production and fertility traits black-and-white cattle improved by Holstein breed. Journal of Dairy and Beef Cattle Farming. 2018;(7):17–22. (In Russ.) doi: 10.25632/MMS.2018.7.21450
  3. Gutiеrrez-Reinoso MA, Aponte PM, Cabezas J, et al. Genomic evaluation of primiparous high-producing dairy cows: inbreeding effects on genotypic and phenotypic production-reproductive traits. Animals (Basel). 2020;10(9):1704. doi: 10.3390/ani10091704
  4. Granado-Tajada I, Rodriguez-Ramilo ST, Legarra A, Ugarte E. Inbreeding, effective population size, and coancestry in the Latxa dairy sheep breed. J Dairy Sci. 2020;103(6):5215–5226. doi: 10.3168/jds.2019-17743
  5. Curik I, Ferencakovic M, Sоlkner J. Inbreeding and runs of homozygosity: A possible solution to an old problem. Livest Sci. 2014;166(1):26–34. doi: 10.1016/j.livsci.2014.05.034
  6. Peripolli E, Munari DP, Silva MVGB, et al. Runs of homozygosity: current knowledge and applications in livestock. Anim Genet. 2017;43(3):255–271. doi: 10.1111/age.12526
  7. Martikainen K, Koivula M, Uimari P. Identification of runs of homozygosity affecting female fertility and milk production traits in Finnish Ayrshire cattle. Sci Rep. 2020;10(1):3804. doi: 10.1038/s41598-020-60830-9
  8. Szmatoła T, Gurgul A, Ropka-Molik K, et al. Characteristics of runs of homozygosity in selected cattle breeds maintained in Poland. Livest Sci. 2016;188:72–80. doi: 10.1016/J.LIVSCI.2016.04.006
  9. Tulinova OV, Vasil’eva NV, Anistenok SV, et al. Geneticheskie resursy otechestvennykh regional’nykh populyatsii airshirskogo skota (spravochnoe posobie). Saint Petersburg, Pushkin: Argus, 2021. 238 p. (In Russ.)
  10. Yu GI, Song DK, Shin DH. Associations of IL1RAP and IL1RL1 gene polymorphisms with obesity and inflammation mediators. Inflamm Res. 2020;69(2):191–202. doi: 10.1007/s00011-019-01307-y
  11. Peters SO, Kızılkaya K, Ibeagha-Awemu EM, et al. Comparative accuracies of genetic values predicted for economically important milk traits, genome-wide association, and linkage disequilibrium patterns of Canadian Holstein cows. J Dairy Sci. 2021;104(2): 1900–1916. doi: 10.3168/jds.2020-18489
  12. Yang X, Aoki Y, Li X, et al. Structure of human holocarboxylase synthetase gene and mutation spectrum of holocarboxylase synthetase deficiency. Hum Genet. 2001;109(5):526–534. doi: 10.1007/s004390100603
  13. Li K-Y, Tang J-P, Jiang Y-L, et al. Holocarboxylase synthetase deficiency induced by HLCS gene mutations: a rare disease study. Zhongguo Dang Dai Er Ke Za Zhi. 2023;25(4):401–407. doi: 10.7499/j.issn.1008-8830.2211062
  14. Edea Z, Jung KS, Shin SS, et al. Signatures of positive selection underlying beef production traits in Korean cattle breeds. J Anim Sci Technol. 2020;62(3):293–305. doi: 10.5187/jast.2020.62.3.293
  15. Liang C, Wang G, Abbas Raza SH, et al. FAM13A promotes proliferation of bovine preadipocytes by targeting Hypoxia-Inducible factor-1 signaling pathway. Adipocyte. 2021;10(1):546–557. doi: 10.1080/21623945.2021.1986327
  16. Pedrosa VB, Schenkel FS, Chen S-Y, et al. Genomewide association analyses of lactation persistency and milk production traits in holstein cattle based on imputed whole-genome sequence data. Genes (Basel). 2021;12(11):1830. doi: 10.3390/genes12111830
  17. Do DN, Bissonnette N, Lacasse P, et al. Genome-wide association analysis and pathways enrichment for lactation persistency in Canadian Holstein cattle. J Dairy Sci. 2017;100(3):1955–1970. doi: 10.3168/jds.2016-11910
  18. Cohen-Ziri M, Seroussi E, Larkin DM, et al. Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Res. 2005;15(7):936–944. doi: 10.1101/gr.3806705
  19. Kołodziejski PA, Pruszynska-Oszmałek E, Wojciechowicz T, et al. The role of peptide hormones discovered in the 21st century in the regulation of adipose tissue functions. Genes (Basel). 2021;17(2):756. doi: 10.3390/genes12050756
  20. Shan S, Xu F, Bleyer M, et al. Association of α/β-hydrolase D16B with bovine conception rate and sperm plasma membrane lipid composition. Int J Mol Sci. 2020;21(2):627. doi: 10.3390/ijms21020627
  21. Shan S, Xu F, Hirschfeld M, et al. α/β-hydrolase D16B truncation results in premature sperm capacitation in cattle. Int J Mol Sci. 2022;23(14):7777. doi: 10.3390/ijms23147777
  22. Xin J, Chai Z, Zhang C, et al. Methylome and transcriptome profiles in three yak tissues revealed that DNA methylation and the transcription factor ZGPAT co-regulate milk production. BMC Genomics. 2020;21(1):731. doi: 10.1186/s12864-020-07151-3
  23. Machado PC, Brito LF, Martins R, et al. Genome-wide association analysis reveals novel loci related with visual score traits in nellore cattle raised in pasture-based systems. Animals (Basel). 2022;12(24):3526. doi: 10.3390/ani12243526
  24. Sun H-Z, Zhu Z, Zhou M, et al. Gene co-expression and alternative splicing analysis of key metabolic tissues to unravel the regulatory signatures of fatty acid composition in cattle. RNA Biology. 2021;18(6):854–862. doi: 10.1080/15476286.2020.1824060
  25. Buaban S, Lengnudum K, Boonkum W, Phakdeedindan P. Genome-wide association study on milk production and somatic cell score for Thai dairy cattle using weighted single-step approach with random regression test-day model. J Dairy Sci. 2022;105(1):468–494. DOI: 10.3168 / jds.2020-19826
  26. Malheiros JM, Enriquez-Valencia CE, Braga CP, et al. Application of proteomic to investigate the different degrees of meat tenderness in Nellore breed. J Proteomics. 2021;248:104331. doi: 10.1016/j.jprot.2021.104331
  27. Girardi E, Superti-Furga G. Caught in the genetic network: a novel regulator of lipid metabolism. Nat Metab. 2020;2(6):483–484. doi: 10.1038/s42255-020-0218-5
  28. Du C, Deng T, Zhou Y, et al. Systematic analyses for candidate genes of milk production traits in water buffalo (Bubalus bubalis). Anim Genet. 2019;50(3):207–216. doi: 10.1111/age.12739
  29. Feng X, Pan C, Liu S, et al. Identification of core genes affecting IMF deposition in bovine. Anim Biotechnol. 2023;34(7):2887–2899. doi: 10.1080/10495398.2022.2124167
  30. Liu X, Gong J, Wang L, et al. Genome-wide profiling of the microrna transcriptome regulatory network to identify putative candidate genes associated with backfat deposition in pigs. Animals. 2019;6(6):313. doi: 10.3390/ani9060313
  31. Abou-Rjeileh U, dos Santos Neto JM, Chirivi M, et al. Oleic acid abomasal infusion limits lipolysis and improves insulin sensitivity in adipose tissue from periparturient dairy cows. J Dairy Sci. 2023;106(6):4306–4323. doi: 10.3168/jds.2022-22402
  32. Liu Y, Mu Y, Wang W, et al. Analysis of genomic copy number variations through whole-genome scan in Chinese Qaidam cattle. Front Vet Sci. 2023;10:1148070. doi: 10.3389/fvets.2023
  33. Dong Y, Jin L, Liu X, et al. IMPACT and OSBPL1A are two isoform-specific imprinted genes in bovines. Theriogenology. 2022;184: 100–109. doi: 10.1016/j.theriogenology
  34. Toro Ospina AM, da Silva Faria RA, Vercesi Filho AE, et al. Genome-wide identification of runs of homozygosity islands in the Gyr breed (Bos indicus). Reprod Domes Anim. 2020;55(3):333–342. DOI: 10.1111 / rda.13639
  35. Zhang Y, Yu M, Dong J, et al. Nucleophosmin3 carried by small extracellular vesicles contribute to white adipose tissue brownin. J Nanobiotechnol. 2022;20(1):165. doi: 10.1186/s12951-022-01381-1
  36. Jayawardana JMDR, Lopez-Villalobos N, McNaughton LR, Hickson RE. Genomic regions associated with milk composition and fertility traits in spring-calved dairy cows in New Zealand. Genes. 2023;14(4):860. doi: 10.3390/genes14040860
  37. Senczu G, Guerra L, Mastrangelo S, et al. Fifteen shades of grey: combined analysis of genome-wide SNP data in steppe and mediterranean grey cattle sheds new light on the molecular basis of coat color. Genes. 2020;11(8):932. doi: 10.3390/genes11080932
  38. Visser C, Lashmar SF, Reding J, et al. Pedigree and genome-based patterns of homozygosity in the South African Ayrshire, Holstein, and Jersey breeds. Front Genet. 2023;14:1136078. doi: 10.3389/fgene.2023.1136078
  39. Pozovnikova MV, Tulinova OV, Sermyagin AA, et al. Analysis of the genetic diversity of Ayrshire cattle in Russia (part 1). Ecological genetics. 2022;20(1):5–12. (In Russ.) doi: 10.17816/ecogen88943
  40. Smaragdov MG, Kudinov AA. Assessing the power of principal components and wright’s fixation index analyzes applied to reveal the genome-wide genetic differences between herds of Holstein cows. BMC Genet. 2020;21(1):47. doi: 10.1186/s12863-020-00848-0
  41. Sarviaho K, Uimari P, Martikainen K. Estimating inbreeding rate and effective population size in the Finnish Ayrshire population in the era of genomic selection. J Anim Breed Genet. 2023;140(3):343–353. doi: 10.1111/jbg.12762
  42. Martikainen K, Sironen A, Uimari P. Estimation of intrachromosomal inbreeding depression on female fertility using runs of homozygosity in Finnish Ayrshire cattle. J Dairy Sci. 2018;101(12): 11097–11107. doi: 10.3168/jds.2018-14805
  43. Zhang Q, Guldbrandtsen B, Bosse M, et al. Runs of homozygosity and distribution of functional variants in the cattle genome. BMC Genomics. 2015;16(2):542. doi: 10.1186/s12864-015-1715-x
  44. Purfield DC, Evans RD, Berry DP. Breed-and trait-specific associations define the genetic architecture of calving performance traits in cattle. J Anim Sci. 2020;98(5):skaa151. doi: 10.1093/jas/skaa151
  45. Lozada-Soto E, Tiezzi F, Jicang J, et al. Genomic characterization of autozygosity and recent inbreeding trends in all major breeds of US dairy cattle. J Dairy Sci. 2022;105(11):8956–8971. doi: 10.3168/jds.2022-22116
  46. Forutan M, Ansari Mahyari S, Baes C, et al. Inbreeding and runs of homozygosity before and after genomic selection in North American Holstein cattle. BMC Genomics. 2018;19(1):98. doi: 10.1186/s12864-018-4453-z

Supplementary files

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2. Fig. 1. Inbreeding Index (FROH) for analyzed samples of Ayrshire cows

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3. Fig. 2. Principal Component Analysis (PCA) based on genome-wide SNP genotypes of studied Ayrshire cows (a, b) and their fathers (c, d)

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