Генетическая предрасположенность к кетозу у крупного рогатого скота: современное состояние
- Авторы: Соколова О.В.1, Бытов М.В.1, Белоусов А.И.1, Безбородова Н.А.1, Зубарева В.Д.1, Мартынов Н.А.1, Зайцева О.С.1, Шкуратова И.А.1
-
Учреждения:
- Уральский федеральный аграрный научно-исследовательский центр Уральского отделения Российской академии наук
- Выпуск: Том 59, № 3 (2023)
- Страницы: 294-307
- Раздел: ОБЗОРНЫЕ И ТЕОРЕТИЧЕСКИЕ СТАТЬИ
- URL: https://journals.rcsi.science/0016-6758/article/view/134566
- DOI: https://doi.org/10.31857/S0016675823030116
- EDN: https://elibrary.ru/IQMSXQ
- ID: 134566
Цитировать
Аннотация
Высокая молочная продуктивность коров связана с интенсивным функционированием всех органов и систем, что предрасполагает к развитию различных форм нарушений обменных процессов. Формирование энергетического дисбаланса у высокопродуктивных коров в лактирующий период способствует развитию комплексных метаболических нарушений, отрицательно влияющих на продуктивное здоровье и репродуктивный потенциал. Интерес к разведению крупного рогатого скота, более устойчивого к кетозу, является глобальным, а поиск мутаций, аллельных вариантов генов и изучение молекулярно-генетических процессов, формирующих тот или иной фенотип, являются ключевыми этапами в понимании этиологии, степени предрасположенности к заболеванию и разработке успешных селекционных программ. В настоящем обзоре представлены результаты исследований, направленных на поиск генетических маркеров развития кетоза крупного рогатого скота на основе молекулярно-генетических методов. В обзоре представлены локализация SNPs по данным метаанализа GWAS, протеин–протеин взаимодействия ассоциированных с ними генов-кандидатов при помощи STRING, а также аннотация SNPs по ключевым биологическим процессам с их участием. Приведен профиль экспрессии генов-кандидатов для ассоциированных с кетозом тканей на основе известных данных по человеку с применением GTEx.
Об авторах
О. В. Соколова
Уральский федеральный аграрный научно-исследовательский центрУральского отделения Российской академии наук
Автор, ответственный за переписку.
Email: nauka_sokolova@mail.ru
Россия, 620142, Екатеринбург
М. В. Бытов
Уральский федеральный аграрный научно-исследовательский центрУральского отделения Российской академии наук
Email: nauka_sokolova@mail.ru
Россия, 620142, Екатеринбург
А. И. Белоусов
Уральский федеральный аграрный научно-исследовательский центрУральского отделения Российской академии наук
Email: nauka_sokolova@mail.ru
Россия, 620142, Екатеринбург
Н. А. Безбородова
Уральский федеральный аграрный научно-исследовательский центрУральского отделения Российской академии наук
Email: nauka_sokolova@mail.ru
Россия, 620142, Екатеринбург
В. Д. Зубарева
Уральский федеральный аграрный научно-исследовательский центрУральского отделения Российской академии наук
Email: nauka_sokolova@mail.ru
Россия, 620142, Екатеринбург
Н. А. Мартынов
Уральский федеральный аграрный научно-исследовательский центрУральского отделения Российской академии наук
Email: nauka_sokolova@mail.ru
Россия, 620142, Екатеринбург
О. С. Зайцева
Уральский федеральный аграрный научно-исследовательский центрУральского отделения Российской академии наук
Email: nauka_sokolova@mail.ru
Россия, 620142, Екатеринбург
И. А. Шкуратова
Уральский федеральный аграрный научно-исследовательский центрУральского отделения Российской академии наук
Email: nauka_sokolova@mail.ru
Россия, 620142, Екатеринбург
Список литературы
- Berry D.P., Bermingham M.L., Good M.,More S.J. Genetics of animal health and disease in cattle // Irish Veterinary J. 2011. V. 64. № 5. P. 1–10. https://doi.org/10.1186/2046-0481-64-5
- Zinovieva N.A. Haplotypes affecting fertility in holstein cattle // Sel’skokhozyaistvennaya Biologiya. 2016. V. 51. P. 423–435. https://doi.org/10.15389/agrobiology.2016.4.423eng
- Brito L.F., Bedere N., Douhard F. et al. Review: Genetic selection of high-yielding dairy cattle toward sustainable farming systems in a rapidly changing world // Animal. 2021. V. 15. P. 1–14. https://doi.org/10.1016/j.animal.2021.100292
- Белоусов А.И., Красноперов А.С., Опарина О.Ю., Суздальцева М.А. Метаболические признаки алиментарного кетоза у высокопродуктивных коров // Труды ВИЭВ. 2018. Т. 80. № 1. С. 88–100. https://doi.org/10.30917/ATT-PRINT-2018-1
- Белоусов А.И., Соколова О.В., Беспамятных Е.Н. Применение биохимического скрининга при оценке продуктивного здоровья высокопродуктивных коров в Свердловской области // Вопр. нормативно-правового регулирования в ветеринарии. 2018. Т. 4. С. 278–280. https://doi.org/10.17238/issn2072-6023.2018.4.278
- Михайлова И.И., Евглевская Е.П., Михайлова О.И. и др. Патобиохимические изменения в метаболическом статусе высокопродуктивных коров // Ветеринарная патология. 2016. Т. 1. № 55. С. 75–80.
- Kessel S., Stroehl M., Meyer H.H.D. et al. Individual variability in physiological adaptation to metabolic stress during early lactation in dairy cows kept under equal conditions // J. Animal Sci. 2008. V. 86. № 11. P. 2903–2912. https://doi.org/10.2527/jas.2008-1016
- van Dorland H.A., Richter S., Morel I. et al. Variation in hepatic regulation of metabolism during the dry period and in early lactation in dairy cows // J. Dairy Sci. 2009. V. 92. № 5. P. 1924–1940. https://doi.org/10.3168/jds.2008-1454
- Ковалюк Н.В., Якушева Л.И., Кузьминова Е.В. и др. Связь полиморфизмов гена лептина с предрасположенностью крупного рогатого скота к кетозу // Генетика и разведение животных. 2020. Т. 3. С. 20–26. https://doi.org/10.31043/2410-2733-2020-3-20-26
- Kroezen V., Schenkel F.S., Miglior F. et al. Candidate gene association analyses for ketosis resistance in Holsteins // J. Dairy Sci. 2018. V. 101. № 6. P. 5240–5249. https://doi.org/10.3168/jds.2017-13374
- Huang H., Cao J., Hanif Q. et al. Genome-wide association study identifies energy metabolism genes for resistance to ketosis in Chinese Holstein cattle // Anim. Genet. 2019. V. 50. № 4. P. 376–380. https://doi.org/10.1111/age.12802
- Nayeri S., Schenkel F., Fleming A. et al. Genome-wide association analysis for β-hydroxybutyrate concentration in Milk in Holstein dairy cattle // BMC Genetics. 2019. V. 20. № 58. P. 1–17. https://doi.org/10.1186/s12863-019-0761-9
- Yan Z., Huang H., Freebern E. et al. Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle // BMC Genomics. 2020. V. 21. № 489. P. 1–12. https://doi.org/10.1186/s12864-020-06909-z
- Wu Z.L., Chen S.Y., Qin C. et al. Clinical ketosis-associated alteration of gene expression in Holstein cows // Genes (Basel). 2020. V. 11. № 219. P. 1–11. https://doi.org/10.3390/genes11020219
- Mohsin M.A., Yu H., He R. et al. Differentiation of subclinical ketosis and liver function test indices in adipose tissues associated with hyperketonemia in postpartum dairy cattle // Front. Vet. Sci. 2021. V. 8. P. 1–14. https://doi.org/10.3389/fvets.2021.796494
- Loor J.J., Everts R.E., Bionaz M. et al. Nutrition-induced ketosis alters metabolic and signaling gene networks in liver of periparturient dairy cows // Physiol. Genomics. 2007. V. 32. № 1. P. 105–116. https://doi.org/10.1152/physiolgenomics.00188.2007
- Visscher P.M., Wray N.R., Zhang Q. et al. 10 Years of GWAS discovery: biology, function, and translation // Am. J. Hum. Genet. 2017. V. 101. № 1. P. 5–22. https://doi.org/10.1016/j.ajhg.2017.06.005
- Giacomini K.M., Yee S.W., Mushiroda T. et al. Genome-wide association studies of drug response and toxicity: An opportunity for genome medicine // Nat. Rev. Drug Discov. 2017. V. 16. № 1. P. 1–3. https://doi.org/10.1038/nrd.2016.234
- Shu L., Blencowe M., Yang X. Translating GWAS findings to novel therapeutic targets for coronary artery disease // Front. Cardiovasc. Med. 2018. V. 5. P. 1–9. https://doi.org/10.3389/fcvm.2018.00056
- Hillreiner M., Flinspach C., Pfaffl M.W., Kliem H. Effect of the ketone body beta-hydroxybutyrate on the innate defense capability of primary bovine mammary epithelial cells // PLoS One. 2016. V. 11. № 6. P. 1–18. https://doi.org/10.1371/journal.pone.0157774
- Esposito G., Irons P.C., Webb E.C., Chapwanya A. Interactions between negative energy balance, metabolic diseases, uterine health and immune response in transition dairy cows // Animal Reproduction Sci. 2014. V. 144. № 3. P. 60–71. https://doi.org/10.1016/j.anireprosci.2013.11.007
- Zhang S., Liu G., Xu C. et al. Perilipin 1 mediates lipid metabolism homeostasis and inhibits inflammatory cytokine synthesis in bovine adipocytes // Front. Immunol. 2018. V. 9. P. 1–14. https://doi.org/10.3389/fimmu.2018.00467
- Ha N.T., Gross J.J., van Dorland A. et al. Gene-based mapping and pathway analysis of metabolic traits in dairy cows // PLoS One. 2015. V. 10. № 3. P. 1–15. https://doi.org/10.1371/journal.pone.0122325
- Soares R.A.N., Vargas G., Muniz M.M.M. et al. Differential gene expression in dairy cows under negative energy balance and ketosis: A systematic review and meta-analysis // J. Dairy Sci. 2021. V. 104. № 1. P. 602–615. https://doi.org/10.3168/jds.2020-18883
- McLaren W., Gil L., Hunt S.E. et al. The Ensembl Variant Effect Predictor // Genome Biology. 2016. V. 17. № 1. P. 1–14. https://doi.org/10.1186/s13059-016-0974-4
- Huang D., Ovcharenko I. Identifying causal regulatory SNPs in ChIP-seq enhancers // Nucl. Acids Res. 2015. V. 43. № 1. P. 225–236. https://doi.org/10.1093/nar/gku1318
- Calvo S.E., Pagliarini D.J., Mootha V.K. Upstream open reading frames cause widespread reduction of protein expression and are polymorphic among humans // Proc. Natl Acad. Sci. USA. 2009. V. 106. № 18. P. 7507–7512. https://doi.org/10.1073/pnas.0810916106
- Chen J., Tian W. Explaining the disease phenotype of intergenic SNP through predicted long range regulation // Nucl. Acids Res. 2016. V. 44. № 18. P. 8641–8654. https://doi.org/10.1093/nar/gkw519
- Schmidt S.F., Larsen B.D., Loft A. et al. Acute TNF-induced repression of cell identity genes is mediated by NFκB-directed redistribution of cofactors from super-enhancers // Genome Research. 2015. V. 25. № 9. P. 1281–1294. https://doi.org/10.1101/gr.188300.114
- Vlahopoulos S.A. Aberrant control of NF-κB in cancer permits transcriptional and phenotypic plasticity, to curtail dependence on host tissue: molecular mode // Cancer Biology & Medicine. 2017. V. 14. № 3. P. 254–270. https://doi.org/10.20892/j.issn.2095-3941.2017.0029
- Birney E., Stamatoyannopoulos J.A., Dutta A. et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project // Nature. 2007. V. 447. P. 799–816. https://doi.org/10.1038/nature05874
- Mucaki E.J., Shirley B.C., Rogan P.K. Expression changes confirm genomic variants predicted to result in allele-specific, alternative mRNA splicing // Front. Genet. 2020. V. 11. P. 1–16. https://doi.org/10.3389/fgene.2020.00109
- Kalsotra A., Cooper T.A. Functional consequences of developmentally regulated alternative splicing // Nat. Rev. Genet. 2011. V. 12. № 10. P. 715–729. https://doi.org/10.1038/nrg3052
- Chasman D., Adams R.M. Predicting the functional consequences of non-synonymous single nucleotide polymorphisms: Structure-based assessment of amino acid variation // J. Mol. Biol. 2001. V. 307. № 2. P. 683–706. https://doi.org/10.1006/jmbi.2001.4510
- Dakal T.C., Kala D., Dhiman G. et al. Predicting the functional consequences of non-synonymous single nucleotide polymorphisms in IL8 gene // Scientific Reports. 2017. V. 7. № 1. P. 1–18. https://doi.org/10.1038/s41598-017-06575-4
- Sharma J., Keeling K.M., Rowe S.M. Pharmacological approaches for targeting cystic fibrosis nonsense mutations // Eur. J. Med. Chem. 2020. V. 200. P. 1–11. https://doi.org/10.1016/j.ejmech.2020.112436
- Petersen G.M., Parmigiani G., Thomas D. Missense mutations in Disease Genes: A Bayesian Approach to Evaluate Causality // Am. J. Human Genet. 1998. V. 62. № 6. P. 1516–1524. https://doi.org/10.1086/301871
- van der Velden A.W., Thomas A.A. The role of the 5' untranslated region of an mRNA in translation regulation during development // Int. J. Biochem. Cell Biol. 1999. V. 31. № 1. P. 87–106. https://doi.org/10.1016/s1357-2725(98)00134-4
- Jansen Ralf-P. mRNA localization: message on the move // Nature Reviews Mol. Cell Biol. 2001. V. 2. № 4. P. 247–256. https://doi.org/10.1038/35067016
- Bashirullah A., Cooperstock R.L., Lipshitz H.D. Spatial and temporal control of RNA stability // Proc. Natl Acad. Sci. USA. 2001. V. 98. № 13. P. 7025–7028. https://doi.org/10.1073/pnas.111145698
- Jiang H., Lucy M.C. Variants of the 5'-untranslated region of the bovine growth hormone receptor mRNA: Isolation, expression and effects on translational efficiency // Gene. 2001. V. 265. № 1. P. 45–53. https://doi.org/10.1016/s0378-1119(01)00356-0
- Hu L., Ma Y., Liu L. et al. Detection of functional polymorphisms in the hsp70 gene and association with cold stress response in Inner-Mongolia Sanhe cattle // Cell Stress and Chaperones. 2019. V. 24. № 2. P. 409–418. https://doi.org/10.1007/s12192-019-00973-5
- Dhamija S., Menon M.B. Non-coding transcript variants of protein-coding genes – what are they good for? // RNA Biology. 2018. V. 15. № 8. P. 1025–1031. https://doi.org/10.1080/15476286.2018.1511675
- Nelson C.D., Reinhardt T.A., Thacker T.C. et al. Modulation of the bovine innate immune response by production of 1α,25-dihydroxyvitamin D3 in bovine monocytes // J. Dairy Sci. 2010. V. 93. № 3. P. 1041–1049. https://doi.org/10.3168/jds.2009-2663
- Yu-fei S., Liu J., Wang X. et al. Essential role of the first intron in the transcription of hsp90β gene // FEBS Letters. 1997. V. 413. № 1. P. 92–98. https://doi.org/10.1016/S0014-5793(97)00883-1
- Jo B., Choi S.S. Introns: The functional benefits of introns in genomes // Genomics Inform. 2015. V. 13. № 4. P. 112–118. https://doi.org/10.5808/GI.2015.13.4.112
- Huang D., Chowdhury S., Wang H. et al. Multiomic analysis identifies CPT1A as a potential therapeutic target in platinum-refractory, high-grade serous ovarian cancer // Cell Reports Med. 2021. V. 2. № 12. P. 1–32. https://doi.org/10.1016/j.xcrm.2021.100471
- Ren Q., Guo M., Yang F. et al. Association of CPT1A gene polymorphism with the risk of gestational diabetes mellitus: A case-control study // J. Assist. Reprod. Genet. 2021. V. 38. № 7. P. 1861–1869. https://doi.org/10.1007/s10815-021-02143-y
- Mayr C. What are 3' UTRs doing? // Cold Spring Harb. Perspect. Biol. 2019. V. 11. № 10. P. 1–17. https://doi.org/10.1101/cshperspect.a034728
- Chekulaeva M., Landthaler M. Eyes on translation // Mol. Cell. 2016. V. 63. № 6. P. 918–925. https://doi.org/10.1016/j.molcel.2016.08.031
- Mayr C. Regulation by 3'-untranslated regions // Annu. Rev. Genet. 2017. V. 51. P. 171–194. https://doi.org/10.1146/annurev-genet-120116-024704
- Szklarczyk D., Gable A.L., Nastou K.C. et al. The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets // Nucl. Acids Res. 2021. V. 49. № D1. P. 605–612. https://doi.org/10.1093/nar/gkaa1074
- Freed A.S., Schwarz A.C., Brei B.K. et al. CHRNB1-associated congenital myasthenia syndrome: Expanding the clinical spectrum // Am. J. Med. Genetics. Part A. 2021. V. 185. № 3. P. 827–835. https://doi.org/10.1002/ajmg.a.62011
- Andre E., Beckerandre M. Expression of an N-terminally truncated form of human focal adhesion kinase in brain // Biochem. Biophys. Res. Communications. 1993. V. 190. № 1. P. 140–147. https://doi.org/10.1006/bbrc.1993.1022
- Li M., Zhong Di, Li G. Regulatory role of local tissue signal Del-1 in cancer and inflammation: a review // Cellular & Mol. Biol. Letters. 2021. V. 26. № 1. P. 1–12. https://doi.org/10.1186/s11658-021-00274-9
- Hsiao C.T., Cheng H.W., Huang C.M. et al. Fibronectin in cell adhesion and migration via N-glycosylation // Oncotarget. 2017. V. 8. № 41. P. 70653–70668. https://doi.org/10.18632/oncotarget.19969
- Dutta S., Mana-Capelli S., Paramasivam M. et al. TRIP6 inhibits Hippo signaling in response to tension at adherens junctions // EMBO Reports. 2018. V. 19. № 2. P. 337–350. https://doi.org/10.15252/embr.201744777
- Warfel J.D., Vandanmagsar B., Dubuisson O.S. et al. Examination of carnitine palmitoyltransferase 1 abundance in white adipose tissue: Implications in obesity research // Am. J. Physiol. Regul. Integr. Comp. Physiol. 2017. V. 312. № 5. P. 816–820. https://doi.org/10.1152/ajpregu.00520.2016
- Price N.T., Jackson V.N., Müller J. et al. Alternative exon usage in the single CPT1 gene of Drosophila generates functional diversity in the kinetic properties of the enzyme: Differential expression of alternatively spliced variants in Drosophila tissues // J. Biol. Chemistry. 2010. V. 285. № 11. P. 7857–7865. https://doi.org/10.1074/jbc.M109.072892
- Riancho J.A., Vázquez L., García-Pérez M.A. et al. Association of ACACB polymorphisms with obesity and diabetes // Mol. Genet. Metabolism. 2011. V. 104. № 4. P. 670–676. https://doi.org/10.1016/j.ymgme.2011.08.013
- Hellwege J.N., Stallings S., Torstenson E.S. et al. Heritability and genome-wide association study of benign prostatic hyperplasia (BPH) in the eMERGE network // Sci. Rep. 2019. V. 9. № 1. P. 1–10. https://doi.org/10.1038/s41598-019-42427-z
- Lao-On U., Cliff T.S., Dalton S., Jitrapakdee S. Pyruvate carboxylase supports basal ATP-linked respiration in human pluripotent stem cell-derived brown adipocytes // Biochem. Biophys. Res. Communications. 2021. V. 569. P. 139–146. https://doi.org/10.1016/j.bbrc.2021.06.096
- Rossi S.M., Konstantinidou G. Targeting long chain acyl-СоА synthetases for cancer therapy // Int. J. Mol. Sci. 2019. V. 20. № 15. https://doi.org/10.3390/ijms20153624
- Lee K., Kerner J., Hoppel C.L. Mitochondrial carnitine palmitoyltransferase 1a (CPT1a) is part of an outer membrane fatty acid transfer complex // J. Biol. Chem. 2011. V. 286. № 29. P. 25655–25662. https://doi.org/10.1074/jbc.M111.228692
- Nickkho-Amiry M., McVey R., Holland C. Peroxisome proliferator–activated receptors modulate proliferation and angiogenesis in human endometrial carcinoma // Mol. Cancer Res. 2012. V. 10. № 3. P. 441–453. https://doi.org/10.1158/1541-7786.MCR-11-0233
- Mostaghel E.A., Cho E., Zhang A. et al. Association of tissue abiraterone levels and SLCO genotype with intraprostatic steroids and pathologic response in men with high-risk localized prostate cancer // Clin. Cancer Res. 2017. V. 23. № 16. P. 4592–4601. https://doi.org/10.1158/1078-0432.CCR-16-2245
- Lu X., Chan T., Cheng Z. et al. The 5'-AMP-activated protein kinase regulates the function and expression of human organic anion transporting polypeptide 1A2 // Mol. Pharmacology. 2018. V. 94. № 6. P. 1–9. https://doi.org/10.1124/mol.118.113423
- Nayeri S., Stothard P. Tissues, metabolic pathways and genes of key importance in lactating dairy cattle // Springer Sci. Rev. 2016. V. 4. № 2. P. 49–77. https://doi.org/10.1007/s40362-016-0040-3
- Boyle E.I., Weng S., Gollub J. et al. GO::TermFinder – open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes // Bioinformatics. 2004. V. 20. № 18. P. 3710–3715. https://doi.org/10.1093/bioinformatics/bth456
- Supek F., Bošnjak M., Škunca N., Šmuc T. REVIGO summarizes and visualizes long lists of Gene Ontology terms // PLoS One. 2011. V. 6. № 7. P. 1–9. https://doi.org/10.1371/journal.pone.0021800
- Zhang G., Hailemariam D., Dervishi E. et al. Dairy cows affected by ketosis show alterations in innate immunity and lipid and carbohydrate metabolism during the dry off period and postpartum // Res. Veterinary Sci. 2016. V. 107. P. 246–256. https://doi.org/10.1016/j.rvsc.2016.06.012
- Gulinski P. Ketone bodies – causes and effects of their increased presence in cows’ body fluids: A review // Vet. World. 2021. V. 14. № 6. P. 1492–1503. https://doi.org/10.14202/vetworld.2021.1492-1503
- Wang Y., Gao Y., Xia C. et al. Pathway analysis of plasma different metabolites for dairy cow ketosis // Italian J. Animal Sci. 2016. V. 15. № 3. P. 545–551. https://doi.org/10.1080/1828051X.2016.1180643
- Zhang G., Ametaj B.N. Ketosis an old story under a new approach // Dairy. 2020. V. 1. № 1. P. 42–60. https://doi.org/10.3390/dairy1010005
- Dufour D.R., Lott J.A., Nolte F.S. et al. Diagnosis and monitoring of hepatic injury. II. Recommendations for use of laboratory tests in screening, diagnosis, and monitoring // Clin. Chem. 2000. V. 46. № 12. P. 2050–2068.https://doi.org/10.1093/clinchem/46.12.2050
- Tsukamoto K., Teramoto T. Carbohydrate and lipid metabolism in liver cirrhosis // Nihon Rinsho. 1994. V. 52. № 1. P. 150–158.
- Natesan V., Kim S.J. Lipid metabolism, disorders and therapeutic drugs – review // Biomol. Ther. (Seoul). 2021. V. 29. № 6. P. 596–604. https://doi.org/10.4062/biomolther.2021.122
- McCabe M., Waters S., Morris D. et al. RNA-seq analysis of differential gene expression in liver from lactating dairy cows divergent in negative energy balance // BMC Genomics. 2012. V. 13. P. 1–11. https://doi.org/10.1186/1471-2164-13-193
- Carithers L.J., Moore H.M. The Genotype-Tissue Expression (GTEx) project // Biopreserv. Biobank. 2015. V. 13. № 5. P. 307–308. https://doi.org/10.1038/ng.2653
- Goldinger A., Henders A.K., McRae A.F. et al. Genetic and nongenetic variation revealed for the principal components of human gene expression // Genetics. 2013. V. 195. № 3. P. 1117–1128. https://doi.org/10.1534/genetics.113.153221
- Genin E., Feingold J., Clerget-Darpoux F. Identifying modifier genes of monogenic disease: strategies and difficulties // Hum. Genet. 2008. V. 124. № 4. P. 357–368. https://doi.org/10.1007/s00439-008-0560-2
- Wright G.E.B., Caron N.S., Ng B. et al. Gene expression profiles complement the analysis of genomic modifiers of the clinical onset of Huntington disease // Hum. Mol. Genet. 2020. V. 29. № 16. P. 2788–2802. https://doi.org/10.1093/hmg/ddaa184
- Shahzad K., Lopreiato V., Liang Y. et al. Hepatic metabolomics and transcriptomics to study susceptibility to ketosis in response to prepartal nutritional management // J. Anim. Sci. Biotechnol. 2019. V. 10. P. 96. https://doi.org/10.1186/s40104-019-0404-z
- Pralle R.S., Li W., Murphy B.N. et al. Novel facets of the liver transcriptome are associated with the susceptibility and resistance to lipid-related metabolic disorders in periparturient Holstein cows // Animals (Basel). 2021. V. 11. № 9. P. 1–22. https://doi.org/10.3390/ani11092558
- Mezzetti M., Cattaneo L., Passamonti M.M. et al. The transition period updated: A review of the new insights into the adaptation of dairy cows to the new lactation // Dairy. 2021. V. 2. № 4. P. 617–636. https://doi.org/10.3390/dairy2040048
- Cuiyu Z., Chang Z., Jiang Z. et al. The relationship between insulin resistance and type II ketosis in dairy cows // Acta Scientiae Veterinariae. 2019. V. 47. № 1. P. 1–8. https://doi.org/10.22456/1679-9216.93425
- Klein S.L., Scheper C., Brügemann K. et al. Phenotypic relationships, genetic parameters, genome-wide associations, and identification of potential candidate genes for ketosis and fat-to-protein ratio in German Holstein cows // J. Dairy Sci. 2019. V. 102. № 7. P. 6276–6287. https://doi.org/10.3168/jds.2019-16237
- Heringstad B., Chang Y.M., Gianola D., Klemetsdal G. Genetic analysis of clinical mastitis, milk fever, ketosis, and retained placenta in three lactations of Norwegian red cows // J. Dairy Sci. 2005. V. 88. № 9. P. 3273–3281. https://doi.org/10.3168/jds.S0022-0302(05)73010-1
- Koeck A., Jamrozik J., Schenkel F.S. et al. Genetic analysis of milk β-hydroxybutyrate and its association with fat-to-protein ratio, body condition score, clinical ketosis, and displaced abomasum in early first lactation of Canadian Holsteins // J. Dairy Sci. 2014. V. 97. № 11. P. 7286–7292. https://doi.org/10.3168/jds.2014-8405
- Blanco-Gómez A., Castillo-Lluva S., Del Mar S.F.M. et al. Missing heritability of complex diseases: Enlightenment by genetic variants from intermediate phenotypes // BioEssays. 2016. V. 38. № 7. P. 664–673. https://doi.org/10.1002/bies.201600084
- Belay T.K., Svendsen M., Kowalski Z.M., Ådnøy T. Genetic parameters of blood β-hydroxybutyrate predicted from milk infrared spectra and clinical ketosis, and their associations with milk production traits in Norwegian Red cows // J. Dairy Sci. 2017. V. 100. № 8. P. 6298–6311. https://doi.org/10.3168/jds.2016-12458
- Oreland L., Lagravinese G., Toffoletto S. et al. Personality as an intermediate phenotype for genetic dissection of alcohol use disorder // J. Neural. Transm. (Vienna). 2018. V. 125. № 1. P. 107–130. https://doi.org/10.1007/s00702-016-1672-9
- Zuk O., Hechter E., Sunyaev S.R., Lander E.S. The mystery of missing heritability: Genetic interactions create phantom heritability // Proc. Natl Acad. Sci. USA. 2012. V. 109. № 4. P. 1193–1198. https://doi.org/10.1073/pnas.1119675109
- Marian A.J. Elements of missing heritability // Curr. Opin. Cardiol. 2012. V. 27. № 3. P. 197–201. https://doi.org/10.1097/HCO.0b013e328352707d
- van Calker D., Serchov T. The “missing heritability”–problem in psychiatry: Is the interaction of genetics, epigenetics and transposable elements a potential solution? // Neurosci. Biobehav. Rev. 2021. V. 126. P. 23–42. https://doi.org/10.1016/j.neubiorev.2021.03.019
- Manolio T.A., Collins F.S., Cox N.J. et al. Finding the missing heritability of complex diseases // Nature. 2009. V. 461. № 7265. P. 747–753. https://doi.org/10.1038/nature08494
- Genin E. Missing heritability of complex diseases: case solved? // Hum. Genet. 2020. V. 139. № 1. P. 103–113. https://doi.org/10.1007/s00439-019-02034-4
- Wagner G.P., Zhang J. The pleiotropic structure of the genotype-phenotype map: the evolvability of complex organisms // Nat. Rev. Genet. 2011. V. 12. № 3. P. 204–213. https://doi.org/10.1038/nrg2949
- Li Y., Huang J., Amos C.I. Genetic association analysis of complex diseases incorporating intermediate phenotype information // PLoS One. 2012. V. 7. № 10. P. 1–9. https://doi.org/10.1371/journal.pone.0046612
- Hackinger S., Zeggini E. Statistical methods to detect pleiotropy in human complex traits // Open Biol. 2017. V. 7. № 11. P. 1–13. https://doi.org/10.1098/rsob.170125
- Bone W.P., Siewert K.M., Jha A. et al. Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits // Alzheimers Res. Ther. 2021. V. 13. P. 1–14. https://doi.org/10.1186/s13195-021-00773-z
- Якушева Л.И., Абрамов А.А., Ковалюк Н.В., Сацук В.Ф. Связь полиморфизмов R25C и A80V гена лептина быков-производителей с оценкой их дочерей на предрасположенность к возникновению кетоза // Сб. науч. трудов Краснодарского науч. центра по зоотехнии и ветеринарии. 2019. Т. 8. № 3. С. 24–27. https://doi.org/10.34617/y47d-6h82
- Komisarek J. Impact of LEP and LEPR gene polymorphisms on functional traits in Polish Holstein-Friesian cattle // Anim. Sci. Pap. Rep. 2010. V. 28.
- Mahmoudi A., Zargaran A., Amini H.R. et al. A SNP in the 3'-untranslated region of AMPKgamma1 may associate with serum ketone body and milk production of Holstein dairy cows // Gene. 2015. V. 574. № 1. P. 48–52. https://doi.org/10.1016/j.gene.2015.07.077
- Yang L., Bai J., Ju Z. et al. Effect of functional single nucleotide polymorphism g.-572 A>G of apolipoprotein A1 gene on resistance to ketosis in Chinese Holstein cows // Res. Vet. Sci. 2021. V. 135. P. 310–316. https://doi.org/10.1016/j.rvsc.2020.10.006
- Tetens J., Heuer C., Heyer I. et al. Polymorphisms within the APOBR gene are highly associated with milk levels of prognostic ketosis biomarkers in dairy cows // Physiol. Genomics. 2015. V. 47. № 4. P. 129–137. https://doi.org/10.1152/physiolgenomics.00126.2014
Дополнительные файлы
![](/img/style/loading.gif)