Библиография

1. 1. Nakahama N. Museum specimens: An overlooked and valuable material for conservation genetics // Ecol. Res. 2021. № 36. P. 13–23. https://doi.org/10.1111/1440-1703.12181
2. 2. Nakahama N., Isagi Y. Recent transitions in genetic diversity and structure in the endangered semi-natural grassland butterfly, Melitaea protonella, in Japan // Insect Conserv. and Diversity. 2018. № 11. P. 330–340. https://doi.org/10.1111/icad.12280
3. 3. Marsoner T., Vigl L.V., Manck F. et al. Indigenous livestock breeds as indicators for cultural ecosystem services: A spatial analysis within the Alpine Space // Ecol. Indicators. 2018. № 94 (2). P. 55–63. https://doi.org/10.1016/j.ecolind.2017.06.046
4. 4. Zinovieva N.A., Sheiko I.P., Dotsev A.V. et al. Genome-wide SNP analysis clearly distinguished the Belarusian Red cattle from other European cattle breeds // Animal Genet. 2021. № 52 (5). P. 720–724. https://doi.org/10.1111/age.13102
5. 5. Zinovieva N.A., Sermyagin A.A., Dotsev A.V. et al. Animal genetic resources: Developing the research of allele pool of Russian cattle breeds – minireview // Sel’skokhozyaistvennaya Biologiya [Agricultural Biology]. 2019. № 54 (4). P. 631–641. https://doi.org/10.15389/agrobiology.2019.4.631eng
6. 6. Багиров В.А., Зиновьева Н.А. Современное состояние и мировой опыт сохранения генетических ресурсов сельскохозяйственных животных // Успехи наук о животных. 2024. № 1. C. 5–24.
7. 7. Шичкин Г.И., Бутусов Д.В. Ежегодник по племенной работе в молочном скотоводстве в хозяйствах Российской Федерации. 2022 год. М.: ВНИИ-плем, 2023. 255 с.
8. 8. Лискун Е.Ф. Русские отродья крупного рогатого скота. М.: Новый агроном, 1928. 211 с.
9. 9. Лискун Е.Ф. Отечественные породы крупного рогатого скота. М.: Гос. изд-во с.-х. лит., 1949. 176 с.
10. 10. Диамидов А.М., Жиркович Е.Ф. Разведение и породы крупного рогатого скота. М., 1934. 408 с.
11. 11. Лискун Е.Ф. Чем хорош русский северный скот. Петроград: Изд-во Наркомзема, 1919. 23 с.
12. 12. Сабанеев Л.П Очерки Зауралья и степное хозяйство на башкирских землях. М.: типография В. Гогье, 1873. 162 с.
13. 13. Felius M. Cattle Breeds – an Encyclopedia. Doetinchem: Senefelder Mis- set., 1995.
14. 14. Абдельманова А.С., Харзикова В.Р., Форнара М.С. и др. Оценка генетических взаимосвязей пород крупного рогатого скота черно-пестрого корня с предковыми популяциями на основе полно-геномного SNP-генотипирования современных и музейных образцов // С.-хоз. биология. 2024. Т. 59. № 4. C. 605–619. https://doi.org/10.15389/agrobiology.2024.4.605rus
15. 15. Zinovieva N.A., Dotsev A.V., Sermyagin A.A. et al. Study of genetic diversity and population structure of five Russian cattle breeds using whole-genome SNP analysis // Sel’skokhozyaistvennaya Biologiya [Agricultural Biology]. 2016. № 51 (2). P. 788–800. https://doi.org/10.15389/agrobiology.2016.6.788eng
16. 16. Sermyagin A.A., Dotsev A.V., Gladyr E.A. et al. Whole-genome SNP analysis elucidates the genetic structure of Russian cattle and its relationship with Eurasian taurine breeds // Genetics, Selection, Evolution. 2018. № 50. P. 37. https://doi.org/10.1186/s12711-018-0408-8
17. 17. Yurchenko A., Yudin N., Aitnazarov R. et al. Genome-wide genotyping uncovers genetic profiles and history of the Russian cattle breeds // Heredity. 2018. № 120(2). P. 125–137. https://doi.org/10.1038/s41437-017-0024-3
18. 18. Abdelmanova A.S., Kharzinova V.R., Volkova V.V. et al. Genetic diversity of historical and modern populations of Russian cattle breeds revealed by microsatellite analysis // Genes. 2020. № 11. P. 940. https://doi.org/10.3390/genes11080940
19. 19. Zinovieva N.A., Dotsev A.V., Sermyagin A.A. et al. Selection signatures in two oldest Russian native cattle breeds revealed using high-density single nucleotide polymorphism analysis // PLoS One. 2020. № 15 (11). https://doi.org/10.1371/journal.pone.0242200
20. 20. Wandeler P., Hoeck P.E., Keller L.F. Back to the future: Museum specimens in population genetics // Trends in Ecology & Evolution. 2007. № 22 (12). P. 634–642. https://doi.org/10.1016/j.tree.2007.08.017
21. 21. McHugo G.P., Dover M.J., MacHugh D.E. Unlocking the origins and biology of domestic animals using ancient DNA and paleogenomics // BMC Biol. 2019. V. 17. № 1. P. 98. https://doi.org/10.1186/s12915-019-0724-7
22. 22. Kijas J.W., Lenstra J.A., Hayes B. et al. Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection // PLoS Biol. 2012. № 10 (2). https://doi.org/10.1371/journal.pbio.1001258
23. 23. Qanbari S., Gianola D., Hayes B. et al. Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle // BMC Genomics. 2011. № 12. P. 318. https://doi.org/10.1186/1471-2164-12-318
24. 24. Yurchenko A.A., Daetwyler H.D., Yudin N. et al. Scans for signatures of selection in Russian cattle breed genomes reveal new candidate genes for environmental adaptation and acclimation // Sci. Rep. 2018. № 8. https://doi.org/10.1038/s41598-018-31304-w
25. 25. Ruvinskiy D., Igoshin A., Yurchenko A. et al. Resequencing the Yaroslavl cattle genomes reveals signatures of selection and a rare haplotype on BTA28 likely to be related to breed phenotypes // Animal Genet. 2022. № 53 (5). P. 680–684. https://doi.org/10.1111/age.13230
26. 26. Трухачев В.И., Боронецкая О.И., Остапчук А.М. и др. Краннологическая коллекция Музея животноводства им. Е.Ф. Лискуна как объект изучения морфологических, генетических и зоотехнических особенностей пород крупного рогатого скота // Аграрная наука. 2023. № 368 (3). C. 22–31. https://doi.org/10.32634/0869-8155-2023-368-3-22-31
27. 27. Purcell S., Neale B., Todd-Brown K. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses // Am. J. Hum. Genet. 2007. V. 81. № 3. P. 559–575. https://doi.org/10.1086/519795
28. 28. Zhao F., McParland S., Kearney F. et al. Detection of selection signatures in dairy and beef cattle using high-density genomic information // Genet. Sel. Evol. 2015. V. 47. P. 49. https://doi.org/10.1186/s12711-015-0127-3
29. 29. Sabeti P.C., Varilly P., Fry B. et al. Genome-wide detection and characterization of positive selection in human populations // Nature. 2007. № 449 (7164). P. 913–918. https://doi.org/10.1038/nature06250
30. 30. Gautier M., Klassmann A., Vitalis R. REHH 2.0: A reimplementation of the R package REHH to detect positive selection from haplotype structure // Mol. Ecol. Res. 2017. № 17. P. 78–90. https://doi.org/10.1111/1755-0998.12634
31. 31. McLaren W., Gil L., Hunt S.E. et al. The ensemble variant effect predictor // Genome Biol. 2016. V. 17. № 1. P. 122. https://doi.org/10.1186/s13059-016-0974-4
32. 32. Smedley D., Haider S., Durinck S. The BioMart community portal: an innovative alternative to large, centralized data repositories // Nucl. Acids Res. 2015. V. 43. P. 589–598. https://doi.org/10.1093/nar/gkv350
33. 33. Zerbino D.R., Achuthan P., Akanni W. et al. Ensembl 2018 // Nucl. Acids Res. 2018. V. 46. Is. D1. P. D754–D761. https://doi.org/10.1093/nar/gkx1098
34. 34. Perez G., Barber G.P., Benet-Pages A. et al. The UCSC genome browser database: 2025 update // Nucl. Acids Res. 2025. № 6: 53. P. D1243–D1249. https://doi.org/10.1093/nar/gkae974
35. 35. Huang D., Sherman B., Lempicki R. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources // Nat. Protoc. 2009. V. 4. P. 44–57. https://doi.org/10.1038/nprot.2008.211
36. 36. Huang D.W., Sherman B.T., Lempicki R.A. Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists // Nucl. Acids Res. 2009. V. 37. № 1. P. 1–13. https://doi.org/10.1093/nar/gkn923.
37. 37. Hu Z.-L., Park C.A., Reecy J.M. Bringing the Animal QTLdb and CorrDB into the future: Meeting new challenges and providing updated services // Nucl. Acids Res. 2022. V. 50. Is. D1. P. D956–D961. https://doi.org/10.1093/nar/gkab1116
38. 38. NCBI Resource Coordinators: Database resources of the National Center for Biotechnology Information // Nucl. Acids Res. 2016. V. 44. Is. D1. P. D7–D19. https://doi.org/10.1093/nar/gkv1290
39. 39. Safran M., Rosen N., Twik M. et al. The GeneCards suite // Practical Guide to Life Science Databases. Singapore: Springer, 2022. P. 27–56. https://doi.org/10.1007/978-981-16-5812-9_2
40. 40. Ruch P., Teodoro D. UniProt: The universal protein knowledgebase in 2021 // Nucl. Acids Res. 2021. V. 49. Is. D1. P. D480–D489. https://doi.org/10.1093/nar/gkaa1100.
41. 41. Lim D., Kim N., Lee S. et al. Characterization of genes for beef marbling based on applying gene coexpression network // Int. J. Genomics. 2014. P. 1–10. https://doi.org/10.1155/2014/708562
42. 42. Atashi H., Chen Y., Wilmot H. et al. Single-step genome-wide association for selected milk fatty acids in Dual-Purpose Belgian Blue cows // J. Dairy Sci. 2023. № 106 (9). P. 6299–6315. https://doi.org/10.3168/jds.2022-22432
43. 43. Gonzalez Guzman J.L., Lázaro S.F., do Nascimento A.V. et al. Genome-wide association study applied to type traits related to milk yield in water buffaloes (Bubalus bubalis) // J. Dairy Sci. 2020. № 103 (2). P. 1642–1650. https://doi.org/10.3168/jds.2019-16499
44. 44. Rodrigues G.R.D., Brito L.F., Mota L.F.M. et al. Genome-wide association studies and functional annotation of pre-weaning calf mortality and reproductive traits in Nellore cattle from experimental selection lines // BMC Genomics. 2024. № 18. V. 25: 1196. https://doi.org/10.1186/s12864-024-1113-4
45. 45. Ren H., He X., Lu Y. et al. Unveiling the common loci for six body measurement traits in Chinese Wenshan cattle // Front. Genet. 2023. V. 14. https://doi.org/10.3389/fgene.2023.1318679
46. 46. Lau L.Y., Nguyen L.T., Reverter A. et al. Gene regulation could be attributed to TCF3 and other key transcription factors in the muscle of pubertal heifers // Vet. Med. Sci. 2020. V. 6. Is. 4. P. 695–710. https://doi.org/10.1002/vms3.278
47. 47. Youssef O.A., Safran S.A., Nakamura T. et al. Potential role for snORNAs in PKR activation during metabolic stress // Proc. Natl Acad. Sci. 2015. V. 112 (16). P. 5023–5028. https://doi.org/10.1073/pnas.1424044112
48. 48. Ozaki H., Loenneke J.P., Buckner S.L., Abe T. Muscle growth across a variety of exercise modalities and intensities: Contributions of mechanical and metabolic stimuli // Med. Hypotheses. 2016. № 88. P. 22–26. https://doi.org/10.1016/j.mehy.2015.12.026
49. 49. Terakado A.P.N., Costa R.B., de Camargo G.M.F. et al. Genome-wide association study for growth traits in Nelore cattle // Animal. 2018. V. 12. Is. 7. P. 1358–1362. https://doi.org/10.1017/S1751731117003068
50. 50. Khan M.Z., Chen W., Naz S. et al. Determinant genetic markers of semen quality in livestock // Front. Endocrinol. (Lausanne). 2024. V. 15. https://doi.org/10.3389/fendo.2024.1456305
51. 51. Karuthadurai T., Das D.N., Kumaresan A. et al. Sperm transcripts associated with odorant binding and olfactory transduction pathways are altered in breeding bulls producing poor-quality semen // Front. Vet. Sci. 2022. V. 9. https://doi.org/10.3389/fvets.2022.799386
52. 52. Rafter P., Gormley I.C., Purfield D. et al. Genome-wide association analyses of carcass traits using copy number variants and raw intensity values of single nucleotide polymorphisms in cattle // BMC Genomics. 2021. V. 22 (1). P. 757. https://doi.org/10.1186/s12864-021-08075-2.
53. 53. Taye M., Lee W., Jeon S. et al. Exploring evidence of positive selection signatures in cattle breeds selected for different traits // Mamm. Genome. 2017. V. 28. P. 528–541. https://doi.org/10.1007/s00335-017-9715-6
54. 54. Marina H., Pelayo R., Suárez-Vega A. et al. Genome-wide association studies (GWAS) and post-GWAS analyses for technological traits in Assaf and Churra dairy breeds // J. Dairy Sci. 2021. V. 104 (11). P. 11850–11866. https://doi.org/10.3168/jds.2021-20510
55. 55. Jaton C., Schenkel F.S., Sargolzaei M. et al. Genome-wide association study and in silico functional analysis of the number of embryos produced by Holstein donors // J. Dairy Sci. 2018. V. 101 (8). P. 7248–7257. https://doi.org/10.3168/jds.2017-13848
56. 56. Ai H., Fang X., Yang B. et al. Adaptation and possible ancient interspecies introgression in pigs identified by whole-genome sequencing // Nat. Genet. 2015. V. 47. P. 217–225. https://doi.org/10.1038/ng.3199
57. 57. Jung E.J., Park H.B., Lee J.B. et al. Genome-wide association analysis identifies quantitative trait loci for growth in a Landrace purebred population. // Animal Genet. 2014. V. 45 (3). P. 442–444. https://doi.org/10.1111/age.12117