vestiagИзвестия Национальной академии наук Беларуси. Серия аграрных наукProceedings of the National Academy of Sciences of Belarus. Agrarian Series1817-72041817-7239The Republican Unitary Enterprise Publishing House "Belaruskaya Navuka"10.29235/1817-7204-2026-64-3-232-245vestiag-877Research ArticleЖЫВЁЛАГАДОЎЛЯ І ВЕТЭРЫНАРНАЯ МЕДЫЦЫНАANIMAL HUSBANDRY AND VETERINARY MEDICINEМетаанализ SNP-маркеров, ассоциированных с признаками молочной продуктивности крупного рогатого скота голштинской породы, на основе данных Animal QTLdb и GenBankMeta-analysis of SNP markers associated with milk production traits in Holstein cattle based on Animal QTLdb and GenBank datahttps://orcid.org/0000-0002-9384-2746МамедовМ. И.MammadovM. I.

Мамедов Махиль Иса – доктор философии технических наук, доцент кафедры информационных технологий

пр. Ататюрка, 450, Гянджа

Mahil Isa Mammadov – Ph. D. (Technical Sciences), Associate Professor at the Department of Information Technologies

450, Ata turk Av., Ganja

mahil.mammadov@adau.edu.azhttps://orcid.org/0000-0003-4049-9603ТурабовУ. Т.TurabovU. T.

Турабов Урфан Туран – доктор философии аграрных наук, доцент кафедры общей и частной зоотехнии

пр. Ататюрка, 450, Гянджа

Urfan Turan Turabov – Ph. D. (Agricultural Sciences), Associate Professor at the Department of General and Special Animal Husbandry

450, Ata turk Av., Ganja

urfan0766@mail.ruhttps://orcid.org/0009-0008-1923-4313ГанджаевИ. Ф.GanjaevI. F.

Ганджаев Илгар Фархад – доктор философии вете- ринарных наук, доцент кафедры анатомии и внутренних болезней

пр. Ататюрка, 450, Гянджа

Ilgar Farhad Ganjaev – Ph. D. (Veterinary Sciences), Associate Professor at the Department of Anatomy and Internal Diseases

450, Ata turk Av., Ganja

igenceyev@bk.ruАзербайджанский государственный аграрный университетАзербайджанAzerbaijan State Agricultural UniversityAzerbaijan202604082026643232245Copyright © Мамедов М.И., Турабов У.Т., Ганджаев И.Ф., 20262026Мамедов М.И., Турабов У.Т., Ганджаев И.Ф.Mammadov M.I., Turabov U.T., Ganjaev I.F.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestiagr.belnauka.by/jour/article/view/877

Целью исследования стала идентификация SNP-маркеров, ассоциированных с признаками молочной продуктивности у коров голштинской породы, на основе систематической интеграции данных международных баз Animal QTLdb (раздел CattleQTLdb) и GenBank, а также разработка оптимальной панели для их коммерческого применения. В рамках исследования было систематически отобрано 47 независимых научных работ, опубликованных в 2015–2024 гг. Совокупная выборка охватила данные по 23 456 животным, что обеспечивает высокую степень достоверности и репрезентативности полученных результатов. Идентифицированы 892 уникальных SNP-маркера, для 723 из которых были рассчитаны объединенные оценки эффектов с применением модели случайных эффектов. Проведены анализ гетерогенности, функциональная аннотация и приоритизация значимых вариантов. Полученные результаты показали, что 47 SNP достигли геномного уровня значимости (p < 10 – 6 ). Наиболее сильная ассоциация была установлена для rs109421300 в гене DGAT1, который повышает жирность молока в среднем на 0,42 % (95 % доверительный интервал (ДИ): 0,38–0,46; p = 1,2 × 10 –8 ), однако снижает удой на 153 кг. Для генов ABCG2, GHR, STAT5A, LEP и SCD1 выявлены консистентные эффекты в различных популяциях. Межпопуляционная вариабельность составила 15–25 %. Функциональная аннотация продемонстрировала, что 67 % значимых SNP связаны с метаболизмом липидов и лактацией. Анализ онтологии генов (Gene Ontology) выявил значительное обогащение категории биосинтеза липидов, содержащей 34 гена (FDR = 2,3 × 10 –12 ). На основе алгоритма приоритизации предложена оптимальная панель из 30 SNP для коммерческого генотипирования. Панель совместима с платформой Illumina BovineSNP50, а стоимость тестирования одного животного составляет 25–30 долл. США на одно животное. Моделирование показало, что применение предложенной панели может повысить эффективность геномной селекции на 14,7 % (95 % ДИ: 12,1–17,3 %). Полученные результаты формируют фундаментальную основу для совершенствования геномной селекции молочного скота.

This study aimed to identify SNP markers associated with milk yield, fat content, and protein percentage in Holstein cattle through a comprehensive meta-analysis of Animal QTLdb (CattleQTLdb section) and GenBank. A systematic review of 47 independent studies published between 2015 and 2024, covering 23,456 animals, was conducted. In total, 892 unique SNP markers were identified, and pooled effect estimates were calculated for 723 of them using a random-effects model. Heterogeneity analysis, functional annotation, and prioritization of significant variants were performed. The analysis revealed 47 SNPs reaching genome-wide significance (p < 10–6 ), with the strongest association observed for rs109421300 in the DGAT1 gene, which increased milk fat content by an average of 0.42 % (95 % CI: 0.38–0.46; p = 1.2 × 10 –8 ) but reduced yield by 153 kg. Consistent effects were also found for ABCG2, GHR, STAT5A, LEP, and SCD1 genes, with between-population variability ranging from 15 to 25 percent. Functional annotation showed that 67 % of significant SNPs were linked to lipid metabolism and lactation, and enrichment analysis revealed significant enrichment of the lipid biosynthesis category, containing 34 genes (FDR = 2.3 × 10 –12 ). Based on prioritization algorithms, a panel of 30 SNPs was proposed for commercial genotyping, compatible with Illumina BovineSNP50 technology, with a testing cost of $25–30 per animal. Modeling demonstrated that the application of this panel could improve the efficiency of genomic selection by 14.7 % (95 % CI: 12.1–17.3 %). These findings provide a solid foundation for enhancing genomic breeding programs in dairy cattle.

голштинская породаSNP-маркерымолочная продуктивностьметаанализDGAT1геномная селекцияQTLгены-кандидатыCattleQTLdbGenBankHolstein cattleSNP markersmilk production traitsmeta-analysisDGAT1genomic selectionQTLcandidate genesCattleQTLdbGenBankАвторы выражают искреннюю признательность руководству Азербайджанского государственного аграрного университета, руководству кафедры информационных технологий, кафедры общей и частной зоотехнии, кафедры анатомии и внутренних болезней, а также руководству научного отдела за поддержку в проведении данного исследования.The authors sincerely thank the administration of the Azerbaijan State Agricultural University and the heads of the Department of Information Technologies, the Department of General and Special Animal Husbandry, the Department of Anatomy and Internal Diseases, and the Scientific Department for their support in carrying out this research.ReferencesAlexandratos, N. World agriculture towards 2030/2050: the 2012 revision / N. Alexandratos, J. Bruinsma. – Rome: FAO, 2012. – 154 p. – (ESA Working Paper; no. 12-03).Alexandratos N., Bruinsma J. World agriculture towards 2030/2050: the 2012 revision. ESA Working Paper, no. 12-03. Rome, FAO, 2012. 154 p.Goddard, M. E. Mapping genes for complex traits in domestic animals and their use in breeding programmes / M. E. Goddard, B. J. Hayes // Nature Reviews Genetics. – 2009. – Vol. 10, № 6. – P. 381–391. https://doi.org/10.1038/nrg2575Goddard M. E., Hayes B. J. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics, 2009, vol. 10, no. 6, pp. 381–391. https://doi.org/10.1038/nrg2575Pryce, J. E. Designing dairy cattle breeding schemes under genomic selection: a review of international research / J. E. Pryce, H. D. Daetwyler // Animal Production Science. – 2012. – Vol. 52, № 3. – P. 107–114. https://doi.org/10.1071/AN11098Pryce J. E., Daetwyler H. D. Designing dairy cattle breeding schemes under genomic selection: a review of international research. Animal Production Science, 2012, vol. 52, no. 3, pp. 107–114. https://doi.org/10.1071/AN11098De novo assembly of the cattle reference genome with single-molecule sequencing / B. D. Rosen, D. M. Bickhart, R. D. Schnabel [et al.] // GigaScience. – 2020. – Vol. 9, № 3. – Art. giaa021. https://doi.org/10.1093/gigascience/giaa021Rosen B. D., Bickhart D. M., Schnabel R. D., Koren S., Elsik C. G., Tseng E. [et al.]. De novo assembly of the cattle reference genome with single-molecule sequencing. GigaScience, 2020, vol. 9, no. 3, art. giaa021. https://doi.org/10.1093/gigascience/giaa021Development and characterization of a high density SNP genotyping assay for cattle / L. K. Matukumalli, C. T. Lawley, R. D. Schnabel [et al.] // PLoS One. – 2009. – Vol. 4, № 4. – Art. e5350. https://doi.org/10.1371/journal.pone.0005350Matukumalli L. K., Lawley C. T., Schnabel R. D., Taylor J. F., Allan M. F., Heaton M. P. [et al.]. Development and characterization of a high density SNP genotyping assay for cattle. PLoS One, 2009, vol. 4, no. 4, art. e5350. https://doi.org/10.1371/journal.pone.0005350A review on SNP and other types of molecular markers and their use in animal genetics / A. Vignal, D. Milan, M. SanCristobal, A. Eggen // Genetics Selection Evolution. – 2002. – Vol. 34, № 3. – P. 275–305. https://doi.org/10.1186/12979686-34-3-275Vignal A., Milan D., SanCristobal M., Eggen A. A review on SNP and other types of molecular markers and their use in animal genetics. Genetics Selection Evolution, 2002, vol. 34, no. 3, pp. 275–305. https://doi.org/10.1186/1297-9686-34-3-275Goddard, M. E. Using the genomic relationship matrix to predict the accuracy of genomic selection / M. E. Goddard, B. J. Hayes, T. H. E. Meuwissen // Journal of Animal Breeding and Genetics. – 2011. – Vol. 128, № 6. – P. 409–421. https://doi.org/10.1111/j.1439-0388.2011.00964.xGoddard M. E., Hayes B. J., Meuwissen T. H. E. Using the genomic relationship matrix to predict the accuracy of genomic selection. Journal of Animal Breeding and Genetics, 2011, vol. 128, no. 6, pp. 409–421. https://doi.org/10.1111/j.1439-0388.2011.00964.x10 years of GWAS discovery: biology, function, and translation / P. M. Visscher, N. R. Wray, Q. Zhang [et al.] // American Journal of Human Genetics. – 2017. – Vol. 101, № 1. – P. 5–22. https://doi.org/10.1016/j.ajhg.2017.06.005Visscher P. M., Wray N. R., Zhang Q., Sklar P., McCarthy M. I., Brown M. A., Yang J. 10 years of GWAS discovery: biology, function, and translation. American Journal of Human Genetics, 2017, vol. 101, no. 1, pp. 5–22. https://doi.org/10.1016/j.ajhg.2017.06.005Genome wide association studies for milk production traits in Chinese Holstein population / L. Jiang, J. Liu, D. Sun [et al.] // PLoS One. – 2010. – Vol. 5, № 10. – Art. e13661. https://doi.org/10.1371/journal.pone.0013661Jiang L., Liu J., Sun D., Ma P., Ding X., Yu Y., Zhang Q. Genome wide association studies for milk production traits in Chinese Holstein population. PLoS One, 2010, vol. 5, no. 10, art. e13661. https://doi.org/10.1371/journal.pone.0013661A multi-trait meta-analysis with imputed sequence variants reveals twelve QTL for mammary gland morphology in Fleckvieh cattle / H. Pausch, R. Emmerling, H. Schwarzenbacher, R. Fries // Genetics Selection Evolution. – 2016. – Vol. 48. – Art. 14. https://doi.org/10.1186/s12711-016-0190-4Pausch H., Emmerling R., Schwarzenbacher H., Fries R. A multi-trait meta-analysis with imputed sequence variants reveals twelve QTL for mammary gland morphology in Fleckvieh cattle. Genetics Selection Evolution, 2016, vol. 48, art. 14. https://doi.org/10.1186/s12711-016-0190-4De Roos, A. P. W. Reliability of genomic predictions across multiple populations / A. P. W. De Roos, B. J. Hayes, M. E. Goddard // Genetics. – 2009. – Vol. 183, № 4. – P. 1545–1553. https://doi.org/10.1534/genetics.109.104935De Roos A. P. W., Hayes B. J., Goddard M. E. Reliability of genomic predictions across multiple populations. Genetics, 2009, vol. 183, no. 4, pp. 1545–1553. https://doi.org/10.1534/genetics.109.104935Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition / B. Grisart, W. Coppieters, F. Farnir [et al.] // Genome Research. – 2002. – Vol. 12, № 2. – P. 222–231. https://doi.org/10.1101/gr.224202Grisart B., Coppieters W., Farnir F., Karim L., Ford C., Berzi P. [et al.]. Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Research, 2002, vol. 12, no. 2, pp. 222–231. https://doi.org/10.1101/gr.224202Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and composition / B. Grisart, F. Farnir, L. Karimet [et al.] // PNAS USA. – 2004. – Vol. 101, № 8. – P. 2398–2403. https://doi.org/10.1073/pnas.0308518100Grisart B., Farnir F., Karim L., Cambisano N., Kim J. J., Kvasz A., Mni M., Simon P., Frère J. M., Coppieters W., Georges M. Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and composition. PNAS USA, 2004, vol. 101, no. 8, pp. 2398–2403. https://doi.org/10.1073/pnas.0308518100Effect of polymorphisms in the FASN, OLR1, PPARGC1A, PRL and STAT5A genes on bovine milk-fat composition / A. Schennink, H. Bovenhuis, K. M. Léon-Kloosterziel [et al.] // Animal Genetics. – 2009. – Vol. 40, № 6. – P. 909–916. https://doi.org/10.1111/j.1365-2052.2009.01940.xSchennink A., Bovenhuis H., Léon-Kloosterziel K. M., Van Arendonk J. A. M., Visker M. H. P. W. Effect of polymorphisms in the FASN, OLR1, PPARGC1A, PRL and STAT5A genes on bovine milk-fat composition. Animal Genetics, 2009, vol. 40, no. 6, pp. 909–916. https://doi.org/10.1111/j.1365-2052.2009.01940.xBovenhuis, H. Selection for milk fat and milk protein composition / H. Bovenhuis, M. H. P. W. Visker, A. Lundén // Advances in Animal Biosciences. – 2013. – Vol. 4, № 3. – P. 612–617. https://doi.org/10.1017/S2040470013000344Bovenhuis H., Visker M. H. P. W., Lundén A. Selection for milk fat and milk protein composition. Advances in Animal Biosciences, 2013, vol. 4, no. 3, pp. 612–617. https://doi.org/10.1017/S2040470013000344Identification 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 / M. Cohen-Zinder, E. Seroussi, D. M. Larkin [et al.] // Genome Research. – 2005. – Vol. 15, № 7. – P. 936–944. https://doi.org/10.1101/gr.3806705Cohen-Zinder M., Seroussi E., Larkin D. M., Loor J. J., Van der Wind A. E., Lee J.-H. [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 Research, 2005, vol. 15, no. 7, pp. 936–944. https://doi.org/10.1101/gr.3806705Mapping of a milk production quantitative trait locus to a 420-kb region on bovine chromosome 6 / H. G. Olsen, S. Lien, M. Gautier [et al.] // Genetics. – 2005. – Vol. 169, № 1. – P. 275–283. https://doi.org/10.1534/genetics.104.031559Olsen H. G., Lien S., Gautier M., Nilsen H., Roseth A., Berg P. R., Sundsaasen K. K., Svendsen M., Meuwissen T. H. E. Mapping of a milk production quantitative trait locus to a 420-kb region on bovine chromosome 6. Genetics, 2005, vol. 169, no. 1, pp. 275–283. https://doi.org/10.1534/genetics.104.031559Short communication: a polymorphism in ABCG2 in Bos indicus and Bos taurus cattle breeds / M. Ron, M. Cohen-Zinder, C. Peter [et al.] // Journal of Dairy Science. – 2006. – Vol. 89, № 1. – P. 4921–4923. https://doi.org/10.3168/jds.S0022-0302(06)72542-5Ron M., Cohen-Zinder M., Peter C., Weller J. I., Erhardt G. Short communication: a polymorphism in ABCG2 in Bos indicus and Bos taurus cattle breeds. Journal of Dairy Science, 2006, vol. 89, no. 12, pp. 4921–4923. https://doi.org/10.3168/jds.S0022-0302(06)72542-5Molecular dissection of a quantitative trait locus: a phenylalanine-to-tyrosine substitution in the transmembrane domain of the bovine growth hormone receptor is associated with a major effect on milk yield and composition / S. Blott, J. J. Kim, S. Moisio [et al.] // Genetics. – 2003. – Vol. 163, № 1. – P. 253–266. https://doi.org/10.1093/genetics/163.1.253Blott S., Kim J. J., Moisio S., Schmidt-Küntzel A., Cornet A., Berzi P. [et al.]. Molecular dissection of a quantitative trait locus: a phenylalanine-to-tyrosine substitution in the transmembrane domain of the bovine growth hormone receptor is associated with a major effect on milk yield and composition. Genetics, 2003, vol. 163, no. 1, pp. 253–266. https://doi.org/10.1093/genetics/163.1.253The role of the bovine growth hormone receptor and prolactin receptor genes in milk, fat and protein production in Finnish Ayrshire dairy cattle / S. Viitala, J. Szyda, S. Blott [et al.] // Genetics. – 2006. – Vol. 173, № 4. – P. 2151–2164. https://doi.org/10.1534/genetics.105.046730Viitala S., Szyda J., Blott S., Schulman N., Lidauer M., Mäki-Tanila A., Georges M., Vilkki J. The role of the bovine growth hormone receptor and prolactin receptor genes in milk, fat and protein production in Finnish Ayrshire dairy cattle. Genetics, 2006, vol. 173, no. 4, pp. 2151–2164. https://doi.org/10.1534/genetics.105.046730GWAS-based identification of new loci for milk yield, fat, and protein in Holstein cattle / L. Liu, J. Zhou, C. J. Chen [et al.] // Animals. – 2020. – Vol. 10, № 11. – Art. 2048. https://doi.org/10.3390/ani10112048Liu L., Zhou J., Chen C. J., Zhang J., Wen W., Tian J., Zhang Z., Gu Y. GWAS-based identification of new loci for milk yield, fat, and protein in Holstein cattle. Animals, 2020, vol. 10, no. 11, art. 2048. https://doi.org/10.3390/ani10112048DGAT1 underlies large genetic variation in milk-fat composition of dairy cows / A. Schennink, W. M. Stoop, M. H. P. W. Visker [et al.] // Animal Genetics. – 2007. – Vol. 38, № 5. – P. 467–473. https://doi.org/10.1111/j.1365-2052.2007.01635.xSchennink A., Stoop W. M., Visker M. H. P. W., Heck J. M., Bovenhuis H., Van der Poel J. J., Van Valenberg H. J., Van Arendonk J. A. DGAT1 underlies large genetic variation in milk-fat composition of dairy cows. Animal Genetics, 2007, vol. 38, no. 5, pp. 467–473. https://doi.org/10.1111/j.1365-2052.2007.01635.xA large-scale genome-wide association study in US Holstein cattle / J. Jiang, L. Ma, D. Prakapenka [et al.] // Frontiers in Genetics. – 2019. – Vol. 10. – Art. 412. https://doi.org/10.3389/fgene.2019.00412Jiang J., Ma L., Prakapenka D., VanRaden P. M., Cole J. B., Da Y. A large-scale genome-wide association study in US Holstein cattle. Frontiers in Genetics, 2019, vol. 10, art. 412. https://doi.org/10.3389/fgene.2019.00412Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals / A. C. Bouwman, H. D. Daetwyler, A. J. Chamberlain [et al.] // Nature Genetics. – 2018. – Vol. 50, № 3. – P. 362–367. https://doi.org/10.1038/s41588-018-0056-5Bouwman A. C., Daetwyler H. D., Chamberlain A. J., Ponce C. H., Sargolzaei M., Schenkel F. S. [et al.]. Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nature Genetics, 2018, vol. 50, no. 3, pp. 362–367. https://doi.org/10.1038/s41588-018-0056-5Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle / M. P. Sanchez, A. Govignon-Gion, P. Croiseau [et al.] // Genetics Selection Evolution. – 2017. – Vol. 49, № 1. – Art. 68. https://doi.org/10.1186/s12711-017-0344-zSanchez M. P., Govignon-Gion A., Croiseau P., Fritz S., Hozé C., Miranda G. [et al.]. Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle. Genetics Selection Evolution, 2017, vol. 49, no. 1, art. 68. https://doi.org/10.1186/s12711-017-0344-zPrioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle / Z. Cai, B. Guldbrandtsen, M. S. Lund, G. Sahana // BMC Genomics. – 2018. – Vol. 19, № 1. – Art. 656. https://doi.org/10.1186/s12864-018-5050-xCai Z., Guldbrandtsen B., Lund M. S., Sahana G. Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle. BMC Genomics, 2018, vol. 19, no. 1, art. 656. https://doi.org/10.1186/s12864-018-5050-xGenetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits / B. J. Hayes, J. Pryce, A. J. Chamberlain [et al.] // PLoS Genetics. – 2010. – Vol. 6, № 9. – Art. e1001139. https://doi.org/10.1371/journal.pgen.1001139Hayes B. J., Pryce J., Chamberlain A. J., Bowman P., Goddard M. E. Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits. PLoS Genetics, 2010, vol. 6, no. 9, art. e1001139. https://doi.org/10.1371/journal.pgen.1001139Genomic selection in dairy cattle: the USDA experience / G. R. Wiggans, J. B. Cole, S. M. Hubbard, T. S. Sonstegard // Annual Review of Animal Biosciences. – 2017. – Vol. 5. – P. 309–327. https://doi.org/10.1146/annurev-animal-021815-111422Wiggans G. R., Cole J. B., Hubbard S. M., Sonstegard T. S. Genomic selection in dairy cattle: the USDA experience. Annual Review of Animal Biosciences, 2017, vol. 5, pp. 309–327. https://doi.org/10.1146/annurev-animal-021815-111422Invited review: Genomic selection in dairy cattle: progress and challenges / B. J. Hayes, P. J. Bowman, A. J. Chamberlain, M. E. Goddard // Journal of Dairy Science. – 2009. – Vol. 92, № 2. – P. 433–443. https://doi.org/10.3168/jds.2008-1646Hayes B. J., Bowman P. J., Chamberlain A. J., Goddard M. E. Invited review: Genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science, 2009, vol. 92, iss. 2, pp. 433–443. https://doi.org/10.3168/jds.2008-1646Design of a bovine low-density SNP array optimized for imputation / D. Boichard, H. Chung, R. Dassonneville [et al.] // PLoS One. – 2012. – Vol. 7, № 3. – Art. e34130. https://doi.org/10.1371/journal.pone.0034130Boichard D., Chung H., Dassonneville R., David X., Eggen A., Fritz S. [et al.]. Design of a bovine low-density SNP array optimized for imputation. PLoS One, 2012, vol. 7, no. 3, art. e34130. https://doi.org/10.1371/journal.pone.0034130Stories and challenges of genome wide association studies in livestock – a review / A. Sharma, J. S. Lee, C. G. Dang [et al.] // Asian-Australasian Journal of Animal Sciences. – 2015. – Vol. 28, № 10. – P. 1371–1379. https://doi.org/10.5713/ajas.14.0715Sharma A., Lee J. S., Dang C. G., Sudrajad P., Kim H. C., Yeon S. H., Kang H. S., Lee S.-H. Stories and challenges of genome wide association studies in livestock – a review. Asian-Australasian Journal of Animal Sciences, 2015, vol. 28, no. 10, pp. 1371–1379. https://doi.org/10.5713/ajas.14.0715Review: How to improve genomic predictions in small dairy cattle populations / M. S. Lund, I. Van den Berg, P. Ma [et al.] // Animal. – 2016. – Vol. 10, № 11. – P. 1042–1049. https://doi.org/10.1017/S1751731115003031Lund M. S., Van den Berg I., Ma P., Brøndum R. F., Su G. Review: How to improve genomic predictions in small dairy cattle populations. Animal, 2016, vol. 10, no. 11, pp. 1042–1049. https://doi.org/10.1017/S1751731115003031Hu, Z. L. Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB / Z. L. Hu, C. A. Park, J. M. Reecy // Nucleic Acids Research. – 2019. – Vol. 47, iss. D1. – P. D701–D710. https://doi.org/10.1093/nar/gky1084Hu Z. L., Park C. A., Reecy J. M. Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB. Nucleic Acids Research, 2019, vol. 47, iss. D1, pp. D701–D710. https://doi.org/10.1093/nar/gky1084Database resources of the National Center for Biotechnology Information / E. W. Sayers, J. Beck, E. E. Bolton [et al.] // Nucleic Acids Research. – 2021. – Vol. 49, iss. D1. – P. D10–D17. https://doi.org/10.1093/nar/gkaa892Sayers E. W., Beck J., Bolton E. E., Bourexis D., Brister J. R., Canese K. [et al.]. Database resources of the National Center for Biotechnology Information. Nucleic Acids Research, 2021, vol. 49, iss. D1, pp. D10–D17. https://doi.org/10.1093/nar/gkaa892Evangelou, E. Meta-analysis methods for genome-wide association studies and beyond / E. Evangelou, J. P. A. Ioannidis // Nature Reviews Genetics. – 2013. – Vol. 14, № 6. – P. 379–389. https://doi.org/10.1038/nrg3472Evangelou E., Ioannidis J. P. A. Meta-analysis methods for genome-wide association studies and beyond. Nature Reviews Genetics, 2013, vol. 14, no. 6, pp. 379–389. https://doi.org/10.1038/nrg3472JASPAR 2020: update of the open-access database of transcription factor binding profiles / O. Fornes, J. A. Castro- Mondragon, A. Khan [et al.] // Nucleic Acids Research. – 2020. – Vol. 48, iss. D1. – P. D87–D92. https://doi.org/10.1093/nar/gkz1001Fornes O., Castro-Mondragon J. A., Khan A., Van der Lee R., Zhang X., Richmond P. A. [et al.]. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Research, 2020, vol. 48, iss. D1, pp. D87–D92. https://doi.org/10.1093/nar/gkz1001Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle / H. D. Daetwyler, A. Capitan, H. Pausch [et al.] // Nature Genetics. – 2014. – Vol. 46, № 8. – P. 858–865. https://doi.org/10.1038/ng.3034Daetwyler H. D., Capitan A., Pausch H., Stothard P., Van Binsbergen R., Brøndum R. F. [et al.]. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nature Genetics, 2014, vol. 46, no. 8, pp. 858–865. https://doi.org/10.1038/ng.3034Viechtbauer, W. Conducting meta-analyses in R with the metafor package / W. Viechtbauer // Journal of Statistical Software. – 2010. – Vol. 36, № 3. – P. 1–48. https://doi.org/10.18637/jss.v036.i03Viechtbauer W. Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 2010, vol. 36, no. 3, pp. 1–48. https://doi.org/10.18637/jss.v036.i03DerSimonian, R. Meta-analysis in clinical trials / R. DerSimonian, N. Laird // Controlled Clinical Trials. – 1986. – Vol. 7, № 3. – P. 177–188. https://doi.org/10.1016/0197-2456(86)90046-2DerSimonian R., Laird N. Meta-analysis in clinical trials. Controlled Clinical Trials, 1986, vol. 7, no. 3, pp. 177–188. https://doi.org/10.1016/0197-2456(86)90046-2

The authors declare that there are no conflicts of interest present.