Ассоциация трех однонуклеотидных полиморфизмов в гене LPIN1 с показателями молочной продуктивности у коров ярославской породы

Липин-1 является членом эволюционно-консервативного семейства белков и экспрессируется преимущественно в жировой ткани и скелетных мышцах. С одной стороны, липин-1 – это фермент, который дефосфорилирует фосфатидную кислоту до диацилглицерина и таким образом участвует в метаболических путях биосинтеза запасных липидов в клетке, фосфолипидов мембраны и внутриклеточных сигнальных молекул. С другой стороны, липин-1 способен транспортироваться из цитоплазмы в ядро и служит коактиватором транскрипции генов липидного метаболизма. С использованием анализа ассоциаций однонуклеотидных полиморфизмов (ОНП) было показано, что ген липина-1 (LPIN1) является перспективным геном-кандидатом признаков молочной продуктивности у коров голштинской и бурой швицкой пород. Однако неясно, насколько его эффект зависит от породы. Ярославская молочная порода крупного рогатого скота была выведена в XVIII–XIX вв. в России путем разведения «в себе» северного великорусского скота, который был низкорослым и малопродуктивным, но хорошо адаптированным к местным климатическим условиям и скудной кормовой базе. С помощью полногеномного генотипирования и секвенирования было показано, что ярославская порода обладает уникальной генетикой по сравнению с российскими и зарубежными породами крупного рогатого скота. Целью работы была оценка частоты аллелей и генотипов трех ОНП в гене LPIN1 и исследование ассоциации этих ОНП с показателями молочной продуктивности у коров ярославской породы. Образцы крови от 142 коров ярославской породы были получены из двух хозяйств Ярославской области. Генотипирование ОНП выполняли методом анализа полиморфизма длин рестрикционных фрагментов после проведения полимеразной цепной реакции. Ассоциации ОНП с удоем, выходом молочного жира и белка, а также с процентным содержанием жира и белка в молоке за 305 дней лактации были исследованы с первой по четвертую лактацию. Для статистического анализа использовали смешанную линейную модель с учетом родства между индивидами. При исследовании коров ярославской породы выявили три ОНП: rs110871255, rs207681322 и rs109039955 с частотой редкого аллеля 0.042–0.261. ОНП rs110871255 был ассоциирован с выходом жира за третью и четвертую лактации, rs207681322 был ассоциирован с удоем за вторую, третью и четвертую лактации, а также с выходом белка за третью лактацию. Таким образом, мы выявили достоверные ассоциации ОНП rs207681322 и rs110871255 в гене LPIN1 с рядом показателей молочной продуктивности в ходе нескольких лактаций у коров ярославской породы.

1. Abdelmanova A.S., Kharzinova V.R., Volkova V.V., Mishina A.I., Dotsev A.V., Sermyagin A.A., Boronetskaya O.I., Petrikeeva L.V., Chinarov R.Y., Brem G., Zinovieva N.A. Genetic diversity of historical and modern populations of Russian cattle breeds revealed by microsatellite analysis. Genes (Basel). 2020;11(8):940. DOI 10.3390/genes11080940

2. Ahmad S.M., Bhat B., Bhat S.A., Yaseen M., Mir S., Raza M., Iquebal M.A., Shah R.A., Ganai N.A. SNPs in mammary gland epithelial cells unraveling potential difference in milk production between Jersey and Kashmiri cattle using RNA sequencing. Front. Genet. 2021;12:666015. DOI 10.3389/fgene.2021.666015

3. Barroso E., Rodríguez-Calvo R., Serrano-Marco L., Astudillo A.M., Balsinde J., Palomer X., Vázquez-Carrera M. The PPARβ/δ activator GW501516 prevents the down-regulation of AMPK caused by a high-fat diet in liver and amplifies the PGC-1α-Lipin 1-PPARα pathway leading to increased fatty acid oxidation. Endocrinology. 2011;152(5):1848-1859. DOI 10.1210/en.2010-1468

4. Bekele R., Taye M., Abebe G., Meseret S. Genomic regions and candidate genes associated with milk production traits in Holstein and its crossbred cattle: a review. Int. J. Genomics. 2023;2023:8497453. DOI 10.1155/2023/8497453

5. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 1995;57(1):289-300. DOI 10.1111/j.2517-6161.1995.tb02031

6. Bionaz M., Loor J.J. ACSL1, AGPAT6, FABP3, LPIN1, and SLC27A6 are the most abundant isoforms in bovine mammary tissue and their expression is affected by stage of lactation. J. Nutr. 2008;138(6): 1019-1024. DOI 10.1093/jn/138.6.1019

7. Brahe L.K., Ängquist L., Larsen L.H., Vimaleswaran K.S., Hager J., Viguerie N., Loos R.J., Handjieva-Darlenska T., Jebb S.A., Hlavaty P., Larsen T.M., Martinez J.A., Papadaki A., Pfeiffer A.F., van Baak M.A., Sørensen T.I., Holst C., Langin D., Astrup A., Saris W.H. Influence of SNPs in nutrient-sensitive candidate genes and gene-diet interactions on blood lipids: the DiOGenes study. Br. J. Nutr. 2013;110(5):790-796. DOI 10.1017/S0007114512006058

8. Buaban S., Puangdee S., Duangjinda M., Boonkum W. Estimation of genetic parameters and trends for production traits of dairy cattle in Thailand using a multiple-trait multiple-lactation test day model. Asian-Australas. J. Anim. Sci. 2020;33(9):1387-1399. DOI 10.5713/ajas.19.0141

9. Cecchinato A., Ribeca C., Chessa S., Cipolat-Gotet C., Maretto F., Casellas J., Bittante G. Candidate gene association analysis for milk yield, composition, urea nitrogen and somatic cell scores in Brown Swiss cows. Animal. 2014;8(7):1062-1070. DOI 10.1017/S1751731114001098

10. Chen S.Y., Gloria L.S., Pedrosa V.B., Doucette J., Boerman J.P., Brito L.F. Unravelling the genomic background of resilience based on variability in milk yield and milk production levels in North American Holstein cattle through GWAS and Mendelian randomization analyses. J. Dairy Sci. 2023;107:1035-1053. DOI 10.3168/jds.2023-23650

11. Chen Y., Rui B.B., Tang L.Y., Hu C.M. Lipin family proteins – key regulators in lipid metabolism. Ann. Nutr. Metab. 2015;66(1):10-18. DOI 10.1159/000368661

12. Csaki L.S., Dwyer J.R., Fong L.G., Tontonoz P., Young S.G., Reue K. Lipins, lipinopathies, and the modulation of cellular lipid storage and signaling. Prog. Lipid Res. 2013;52(3):305-316. DOI 10.1016/j.plipres.2013.04.001

13. Dimov G., Albuquerque L.G., Keown J.F., Van Vleck L.D., Norman H.D. Variance of interaction effects of sire and herd for yield traits of Holsteins in California, New York, and Pennsylvania with an animal model. J. Dairy Sci. 1995;78(4):939-946. DOI 10.3168/jds.S0022-0302(95)76709-1

14. Dmitriev N.G. Breed Cattle by Countries of the World. Leningrad: Kolos Publ., 1978 (in Russian)

15. Dmitriev N.G., Ernst L.K. (Eds.) Animal Genetics Resources of the USSR. Rome: Food and Agriculture Organization of the United Nations, 1989

16. Du X., Zhou H., Liu X., Li Y., Hickford J.G.H. Sequence variation in the bovine lipin-1 gene (LPIN1) and its association with milk fat and protein contents in New Zealand Holstein-Friesian × Jersey (HF × J)-cross dairy cows. Animals (Basel). 2021;11(11):3223. DOI 10.3390/ani11113223

17. Dunin I.M., Dankvert A.G. (Eds.) Breeds and Types of Farm Animals in the Russian Federation. Moscow: All-Russia Research Institute of Animal Breeding, 2013 (in Russian)

18. Fang L., Cai W., Liu S., Canela-Xandri O., Gao Y., Jiang J., Rawlik K., Li B., Schroeder S.G., Rosen B.D., Li C.J., Sonstegard T.S., Alexander L.J., Van Tassell C.P., Van Raden P.M., Cole J.B., Yu Y., Zhang S., Tenesa A., Ma L., Liu G.E. Comprehensive analyses of 723 transcriptomes enhance genetic and biological interpretations for complex traits in cattle. Genome Res. 2020;30(5):790-801. DOI 10.1101/gr.250704.119

19. Gutierrez-Reinoso M.A., Aponte P.M., Garcia-Herreros M. Genomic analysis, progress and future perspectives in dairy cattle selection: a review. Animals (Basel). 2021;11(3):599. DOI 10.3390/ani11030599

20. Haldane J.B.S. An exact test for randomness of mating. J. Genet. 1954; 52:631-635. DOI 10.1007/BF02985085

21. Han B., Yuan Y., Liang R., Li Y., Liu L., Sun D. Genetic effects of LPIN1 polymorphisms on milk production traits in dairy cattle. Genes (Basel). 2019;10(4):265. DOI 10.3390/genes10040265

22. Han L.Q., Li H.J., Wang Y.Y., Zhu H.S., Wang L.F., Guo Y.J., Lu W.F., Wang Y.L., Yang G.Y. mRNAabundance and expression of SLC27A, ACC, SCD, FADS, LPIN, INSIG, and PPARGC1 gene isoforms in mouse mammary glands during the lactation cycle. Genet. Mol. Res. 2010;9(2):1250-1257. DOI 10.4238/vol9-2gmr814

23. He X.P., Xu X.W., Zhao S.H., Fan B., Yu M., Zhu M.J., Li C.C., Peng Z.Z., Liu B. Investigation of Lpin1 as a candidate gene for fat deposition in pigs. Mol. Biol. Rep. 2009;36(5):1175-1180. DOI 10.1007/s11033-008-9294-4

24. Hedrick P.W. Genetics of Populations. Jones & Bartlett Learning, 2005 Hillers J.K., Williams G.F. “Shook” factors and lactation milk yield. Extension Bull. (Wash. State Univ.). 1981;EB0779

25. Huang S., Huang S., Wang X., Zhang Q., Liu J., Leng Y. Downregulation of lipin-1 induces insulin resistance by increasing intracellular ceramide accumulation in C2C12 myotubes. Int. J. Biol. Sci. 2017;13(1):1-12. DOI 10.7150/ijbs.17149

26. Ilatsia E.D., Muasya T.K., Muhuyi W.B., Kahi A.K. Genetic and phenotypic parameters for test day milk yield of Sahiwal cattle in the semi-arid tropics. Animal. 2007;1(2):185-192. DOI 10.1017/S175173110739263X

27. Iso-Touru T., Tapio M., Vilkki J., Kiseleva T., Ammosov I., Ivanova Z., Popov R., Ozerov M., Kantanen J. Genetic diversity and genomic signatures of selection among cattle breeds from Siberia, eastern and northern Europe. Anim. Genet. 2016;47(6):647-657. DOI 10.1111/age.12473

28. Kadegowda A.K., Bionaz M., Piperova L.S., Erdman R.A., Loor J.J. Peroxisome proliferator-activated receptor-gamma activation and long-chain fatty acids alter lipogenic gene networks in bovine mammary epithelial cells to various extents. J. Dairy Sci. 2009;92(9): 4276-4289. DOI 10.3168/jds.2008-1932

29. Khatkar M.S., Thomson P.C., Tammen I., Raadsma H.W. Quantitative trait loci mapping in dairy cattle: review and meta-analysis. Genet. Sel. Evol. 2004;36(2):163-190. DOI 10.1186/1297-9686-36-2-163

30. Kim H.E., Bae E., Jeong D.Y., Kim M.J., Jin W.J., Park S.W., Han G.S., Carman G.M., Koh E., Kim K.S. Lipin1 regulates PPARγ transcriptional activity. Biochem. J. 2013;453(1):49-60. DOI 10.1042/BJ20121598

31. Lee Y.M., Dang C.G., Alam M.Z., Kim Y.S., Cho K.H., Park K.D., Kim J.J. The effectiveness of genomic selection for milk production traits of Holstein dairy cattle. Asian-Australas. J. Anim. Sci. 2020; 33(3):382-389. DOI 10.5713/ajas.19.0546

32. Li Q., Liang R., Li Y., Gao Y., Li Q., Sun D., Li J. Identification of candidate genes for milk production traits by RNA sequencing on bovine liver at different lactation stages. BMC Genet. 2020;21(1): 72. DOI 10.1186/s12863-020-00882-y

33. Lopdell T.J. Using QTL to identify genes and pathways underlying the regulation and production of milk components in cattle. Animals (Basel). 2023;13(5):911. DOI 10.3390/ani13050911

34. Lu G., Moriyama E.N. Vector NTI, a balanced all-in-one sequence analysis suite. Brief. Bioinform. 2004;5(4):378-388. DOI 10.1093/bib/5.4.378

35. Lv Y., Guan W., Qiao H., Wang C., Chen F., Zhang Y., Liao Z. Veterinary Medicine and Omics (Veterinomics): metabolic transition of milk triacylglycerol synthesis in sows from late pregnancy to lactation. OMICS. 2015;19(10):602-616. DOI 10.1089/omi.2015.0102

36. Madilindi M.A., Banga C.B., Bhebhe E., Sanarana Y.P., Nxumalo K.S., Taela M.G., Magagula B.S., Mapholi N.O. Genetic diversity and relationships among three Southern African Nguni cattle populations. Trop. Anim. Health Prod. 2020;52(2):753-762. DOI 10.1007/s11250-019-02066-y

37. Melka M.G., Schenkel F.S. Analysis of genetic diversity in Brown Swiss, Jersey and Holstein populations using genome-wide single nucleotide polymorphism markers. BMC Res. Notes. 2012;5:161. DOI 10.1186/1756-0500-5-161

38. Mohammad M.A., Haymond M.W. Regulation of lipid synthesis genes and milk fat production in human mammary epithelial cells during secretory activation. Am. J. Physiol. Endocrinol. Metab. 2013; 305(6):E700-E716. DOI 10.1152/ajpendo.00052.2013

39. Mul J.D., Nadra K., Jagalur N.B., Nijman I.J., Toonen P.W., Médard J.J., Grès S., de Bruin A., Han G.S., Brouwers J.F., Carman G.M., Saulnier-Blache J.S., Meijer D., Chrast R., Cuppen E. A hypomorphic mutation in Lpin1 induces progressively improving neuropathy and lipodystrophy in the rat. J. Biol. Chem. 2011;286(30):26781-26793. DOI 10.1074/jbc.M110.197947

40. Nayak S.S., Panigrahi M., Rajawat D., Ghildiyal K., Sharma A., Parida S., Bhushan B., Mishra B.P., Dutt T. Comprehensive selection signature analyses in dairy cattle exploiting purebred and crossbred genomic data. Mamm. Genome. 2023;34(4):615-631. DOI 10.1007/s00335-023-10021-4

41. Ocampo R.J., Martínez J.F., Martínez R. Assessment of genetic diversity and population structure of Colombian Creole cattle using microsatellites. Trop. Anim. Health Prod. 2021;53(1):122. DOI 10.1007/s11250-021-02563-z

42. Pegolo S., Cecchinato A., Mele M., Conte G., Schiavon S., Bittante G. Effects of candidate gene polymorphisms on the detailed fatty acids profile determined by gas chromatography in bovine milk. J. Dairy Sci. 2016;99(6):4558-4573. DOI 10.3168/jds.2015-10420

43. Persichilli C., Senczuk G., Mastrangelo S., Marusi M., van Kaam J.T., Finocchiaro R., Di Civita M., Cassandro M., Pilla F. Exploring genome-wide differentiation and signatures of selection in Italian and North American Holstein populations. J. Dairy Sci. 2023; 106(8):5537-5553. DOI 10.3168/jds.2022-22159

44. Péterfy M., Phan J., Xu P., Reue K. Lipodystrophy in the fld mouse results from mutation of a new gene encoding a nuclear protein, lipin. Nat. Genet. 2001;27(1):121-124. DOI 10.1038/83685

45. Peterson T.R., Sengupta S.S., Harris T.E., Carmack A.E., Kang S.A., Balderas E., Guertin D.A., Madden K.L., Carpenter A.E., Finck B.N., Sabatini D.M. mTOR complex 1 regulates lipin 1 localization to control the SREBP pathway. Cell. 2011;146(3):408-420. DOI 10.1016/j.cell.2011.06.034

46. Phan J., Reue K. Lipin, a lipodystrophy and obesity gene. Cell Metab. 2005;1(1):73-83. DOI 10.1016/j.cmet.2004.12.002

47. Porto-Neto L.R., Kijas J.W., Reverter A. The extent of linkage disequilibrium in beef cattle breeds using high-density SNP genotypes. Genet. Sel. Evol. 2014;46(1):22. DOI 10.1186/1297-9686-46-22

48. Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M.A., Bender D., Maller J., Sklar P., de Bakker P.I., Daly M.J., Sham P.C. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007;81(3):559-575. DOI 10.1086/519795

49. Rajawat D., Panigrahi M., Kumar H., Nayak S.S., Parida S., Bhushan B., Gaur G.K., Dutt T., Mishra B.P. Identification of important genomic footprints using eight different selection signature statistics in domestic cattle breeds. Gene. 2022;816:146165. DOI 10.1016/j.gene.2021.146165

50. Reue K., Zhang P. The lipin protein family: dual roles in lipid biosynthesis and gene expression. FEBS Lett. 2008;582(1):90-96. DOI 10.1016/j.febslet.2007.11.014

51. Robertson A., Hill W.G. Deviations from Hardy–Weinberg proportions: sampling variances and use in estimation of inbreeding coefficients. Genetics. 1984;107(4):703-718. DOI 10.1093/genetics/107.4.703

52. Ruvinskiy D., Igoshin A., Yurchenko A., Ilina A.V., Larkin D.M. Resequencing the Yaroslavl cattle genomes reveals signatures of selection and a rare haplotype on BTA28 likely to be related to breed phenotypes. Anim. Genet. 2022;53(5):680-684. DOI 10.1111/age.13230

53. Sambrook J., Russell D.W. The Condensed Protocols from Molecular Cloning: a Laboratory Manual. New York: Cold Spring Harbor Laboratory Press, 2006

54. Saydakova S.S., Morozova K.N., Kiseleva E.V. Lipin family proteins: structure, functions, and related diseases. Cell Tissue Biol. 2021; 15(4):317-325. DOI 10.1134/S1990519X21040076

55. Sermyagin A.A., Dotsev A.V., Gladyr E.A., Traspov A.A., Deniskova T.E., Kostyunina O.V., Reyer H., Wimmers K., Barbato M., Paronyan I.A., Plemyashov K.V., Sölkner J., Popov R.G., Brem G., Zinovieva N.A. Whole-genome SNP analysis elucidates the genetic structure of Russian cattle and its relationship with Eurasian taurine breeds. Genet. Sel. Evol. 2018;50(1):37. DOI 10.1186/s12711-018-0408-8

56. Shichkin G.I., Tyapugin E.E., Dunin I.M., Gerasimova E.V., Kozlova N.A., Myshkina M.S., Semenova N.V., Dunin M.I., Tyapugin S.E. The state of dairy cattle breeding in the Russian Federation. In: Yearbook on Breeding Work in Dairy Cattle Breeding in Farms of the Russian Federation. Moscow: All-Russia Research Institute of Animal Breeding, 2023;3-20 (in Russian)

57. Silpa M.V., König S., Sejian V., Malik P.K., Nair M.R.R., Fonseca V.F.C., Maia A.S.C., Bhatta R. Climate-resilient dairy cattle production: applications of genomic tools and statistical models. Front. Vet. Sci. 2021;8:625189. DOI 10.3389/fvets.2021.625189

58. Singh A., Malla W.A., Kumar A., Jain A., Thakur M.S., Khare V., Tiwari S.P. Review: genetic background of milk fatty acid synthesis in bovines. Trop. Anim. Health Prod. 2023;55(5):328. DOI 10.1007/s11250-023-03754-6

59. Siniossoglou S. Phospholipid metabolism and nuclear function: roles of the lipin family of phosphatidic acid phosphatases. Biochim. Biophys. Acta. 2013;1831(3):575-581. DOI 10.1016/j.bbalip.2012.09.014

60. Sinnwell J.P., Therneau T.M., Schaid D.J. The kinship2 R package for pedigree data. Hum. Hered. 2014;78(2):91-93. DOI 10.1159/000363105

61. Stolpovsky Yu.A., Gosteva E.R., Solodneva E.V. Genetic and Selection Aspects of the History of the Development of Cattle Breeding on the Territory of Russia. Moscow: Akvarel’ Publ., 2022 (in Russian)

62. Storey J.D., Bass A.J., Dabney A., Robinson D. qvalue: Q-value estimation for false discovery rate control. R Package version 2.34.0. 2023. DOI 10.18129/B9.bioc.qvalue. (https://bioconductor.org/packages/qvalue)

63. Suviolahti E., Reue K., Cantor R.M., Phan J., Gentile M., Naukkarinen J., Soro-Paavonen A., Oksanen L., Kaprio J., Rissanen A., Salomaa V., Kontula K., Taskinen M.R., Pajukanta P., Peltonen L. Cross-species analyses implicate Lipin 1 involvement in human glucose metabolism. Hum. Mol. Genet. 2006;15(3):377-386. DOI 10.1093/hmg/ddi448

64. Teng J., Wang D., Zhao C., Zhang X., Chen Z., Liu J., Sun D., Tang H., Wang W., Li J., Mei C., Yang Z., Ning C., Zhang Q. Longitudinal genome-wide association studies of milk production traits in Holstein cattle using whole-genome sequence data imputed from medium-density chip data. J. Dairy Sci. 2023;106(4):2535-2550. DOI 10.3168/jds.2022-22277

65. Thering B.J., Graugnard D.E., Piantoni P., Loor J.J. Adipose tissue lipogenic gene networks due to lipid feeding and milk fat depression in lactating cows. J. Dairy Sci. 2009;92(9):4290-4300. DOI 10.3168/jds.2008-2000

66. Weller J.I., Ron M. Invited review: quantitative trait nucleotide determination in the era of genomic selection. J. Dairy Sci. 2011;94(3): 1082-1090. DOI 10.3168/jds.2010-3793

67. Weller J.I., Ezra E., Ron M. Invited review: a perspective on the future of genomic selection in dairy cattle. J. Dairy Sci. 2017;100(11): 8633-8644. DOI 10.3168/jds.2017-12879

68. Wiggans G.R., Van Vleck L.D. Extending partial lactation milk and fat records with a function of last-sample production. J. Dairy Sci. 1979;62(2):316-325. DOI 10.3168/jds.S0022-0302(79)83242-7

69. Williams M., Sleator R.D., Murphy C.P., McCarthy J., Berry D.P. Exploiting genetic variability in the trajectory of lactation yield and somatic cell score with each progressing parity. J. Dairy Sci. 2022;105(4):3341-3354. DOI 10.3168/jds.2021-21306

70. Yamazaki T., Hagiya K., Takeda H., Osawa T., Yamaguchi S., Nagamine Y. Effects of stage of pregnancy on variance components, daily milk yields and 305-day milk yield in Holstein cows, as estimated by using a test-day model. Animal. 2016;10(8):1263-1270. DOI 10.1017/S1751731116000185

71. Ye J., Coulouris G., Zaretskaya I., Cutcutache I., Rozen S., Madden T.L. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics. 2012;13:134. DOI 10.1186/1471-2105-13-134

72. Yurchenko A.A., Daetwyler H.D., Yudin N., Schnabel R.D., Vander Jagt C.J., Soloshenko V., Lhasaranov B., Popov R., Taylor J.F., Larkin D.M. Scans for signatures of selection in Russian cattle breed genomes reveal new candidate genes for environmental adaptation and acclimation. Sci. Rep. 2018a;8(1):12984. DOI 10.1038/s41598-018-31304-w

73. Yurchenko A., Yudin N., Aitnazarov R., Plyusnina A., Brukhin V., Soloshenko V., Lhasaranov B., Popov R., Paronyan I.A., Plemyashov K.V., Larkin D.M. Genome-wide genotyping uncovers genetic profiles and history of the Russian cattle breeds. Heredity (Edinb). 2018b;120(2):125-137. DOI 10.1038/s41437-017-0024-3

74. Zhang R., Jiang F., Hu C., Yu W., Wang J., Wang C., Ma X., Tang S., Bao Y., Xiang K., Jia W. Genetic variants of LPIN1 indicate an association with Type 2 diabetes mellitus in a Chinese population. Diabet. Med. 2013;30(1):118-122. DOI 10.1111/j.1464-5491.2012.03758.x

75. Zinovieva N.A., Dotsev A.V., Sermyagin A.A., Deniskova T.E., Abdelmanova A.S., Kharzinova V.R., Sölkner J., Reyer H., Wimmers K., Brem G. Selection signatures in two oldest Russian native cattle breeds revealed using high-density single nucleotide polymorphism analysis. PLoS One. 2020;15(11):e0242200. DOI 10.1371/journal.pone.0242200

76. Ziyatdinov A., Vázquez-Santiago M., Brunel H., Martinez-Perez A., Aschard H., Soria J.M. lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals. BMC Bioinformatics. 2018;19(1):68. DOI 10.1186/s12859-018-2057-x