biologyscienceТимирязевский биологический журналTimiryazev Biological Journal2949-4710ФГБОУ ВО РГАУ-МСХА имени К.А. Тимирязева (ФГБОУ ВО РГАУ-МСХА имени К.А. Тимирязева)10.26897/2949-4710-2023-2-37-48biologyscience-67Research ArticleГЕНЕТИКА, БИОТЕХНОЛОГИЯGENETICS, BIOTECHNOLOGYМаркер-ассоциированная и геномная селекция мясного скотаMarker-assisted and Genomic Selection of Beef Cattlehttps://orcid.org/0000-0002-9501-8080СелионоваМ. И.SelionovaM. I.

Марина Ивановна Селионова, доктор биол. наук, профессор РАН, заведующий кафедрой разведения, генетики и биотехнологии животных

127550, г. Москва, ул. Тимирязевская, 52

Marina I. Selionova, DSc (BIO), Professor of the Russian Academy of Sciences, Head of the Department of Animal Breeding, Genetics and Biotechnology

49, Timiryazevskaya Str., Moscow, 127434

selionova@rgau-msha.ruhttps://orcid.org/0009-0001-5045-1235ЕвстафьеваЛ. В.Evstaf’evaL. V.

Лилия Валерьевна Евстафьева, старший преподаватель, кафедра разведения, генетики и биотехнологии животных

127550, г. Москва, ул. Тимирязевская, 52

Liliya V. Evstaf’eva, Senior Lecturer, Department of Animal Breeding, Genetics and Biotechnology

49, Timiryazevskaya Str., Moscow, 127434

lilmo@inbox.ruhttps://orcid.org/0000-0002-2170-5259КоноваловаЕ. Н.KonovalovaE. N.

Елена Николаевна Коновалова, кандидат биол. наук, старший научный сотрудник лаборатории молекулярной генетики

142132, Московская область, городской округ Подольск, пос. Дубровицы, 60

Elena N. Konovalova, CSc (Bio), Senior Research Associate of the Laboratory of Molecular Genetics

60, Dubrovitsy v., Podolsk Urban District, Moscow Region, 142132

konoval-elena@yandex.ruhttps://orcid.org/0000-0003-1786-0341БелаяЕ. В.BelayaE. V.

Елена Валентиновна Белая, кандидат биол. наук, доцент

220030, г. Минск, ул. Советская, 18

Elena V. Belaya, CSc (Bio), Associate Professor

18, Sovetskaya Str., Minsk

Belaya005@rambler.ruРоссийский государственный аграрный университет – МСХА имени К.А. ТимирязеваРоссияRussian State Agrarian University – Moscow Timiryazev Agricultural AcademyRussian FederationФедеральный исследовательский центр животноводства – ВИЖ имени академика Л.К. ЭрнстаРоссияFederal Research Center for Animal Husbandry named after Academy Member L.K. ErnstRussian FederationБелорусский государственный педагогический университет имени М. ТанкаБеларусьBelarusian State Pedagogical University named after Maxim TankBelarus202310082023123748Copyright © Селионова М.И., Евстафьева Л.В., Коновалова Е.Н., Белая Е.В., 20232023Селионова М.И., Евстафьева Л.В., Коновалова Е.Н., Белая Е.В.Selionova M.I., Evstaf’eva L.V., Konovalova E.N., Belaya E.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.bioscience-journal.com/jour/article/view/67

В статье представлен обзор современных генетических технологий для совершенствования продуктивных качеств и прогнозирования племенной ценности мясного скота. В частности, в маркере ассоциированной селекции наиболее перспективным является отбор по желательным генотипам в генах миостатина (MSTN), кальпаина (CAPN), кальпастатина (CAST), гормона роста (GH), лептина (LEP), тириоглобулина (TG), белка, связывающего жирные кислоты (FABP), С-рецептора ретиновой кислоты (RORC), диацил-глицерол-ацилтрансферазы (DGATI), стерол-Со десатуразы (SCD). Современным и значительно более прогрессивным подходом является метод одношагового геномного наилучшего линейного несмещенного прогноза (Single Step Genomic Best Linear Unbiased Predictions, SSG- BLUP), с помощью которого рассчитывается геномная оценка племенной ценности (Genomic Estimated Breeding Value, GEBV) с использованием данных генотипирования ДНК-чипом, фенотипа и родословной. Для поиска новых генов продуктивности мясного скота в настоящее время более информативным признан полногеномный анализ ассоциаций (genome-wide association study, GWAS), основанный на использовании генетических маркеров, распределенных по всему геному и находящихся в неравновесном сцеплении, по меньшей мере – с одним из количественных признаков. Выявлены новые гены, ассоциированные с живой массой в разные периоды онтогенеза, среднесуточным приростом живой массы, остаточным потреблением корма, весом туши и содержания в ней мякоти. Большинство идентифицированных генов контролирует процессы клеточного деления, липидного и углеводного обмена. Накопление данных по полногеномным ассоциативным исследованиям и экзомному секвенированию способствовало развитию новых методов генетического анализа – генной онтологии и генных сетей. Использование генных сетей привело к первому детальному пониманию генетической основы формирования сложных фенотипических признаков на основе сложного взаимодействия регуляторных сетей «главных» и «периферических» генов, контролирующих развитие определенного признака.

This article provides an overview of modern genetic technologies for improving production traits and predicting breeding value in beef cattle. In particular, in marker-assisted selection the most promising is the selectionby desirable genotypes in the genes of myostatin (MSTN), calpain (CAPN), calpastatin (CAST), growth hormone (GH), leptin (LEP), thyroglobulin (TG), fatty acid binding protein (FABP), retinoic acid C-receptor (RORC), diacyl-glycerol acyltransferase (DGATI), sterol-Co desaturase (SCD). A modern and much more advanced approach is the Single Step Genomic Best Linear Unbiased Predictions (ssGBLUP) method, which calculates a Genomic Estimated Breeding Value (GEBV) using DNA chip genotyping, phenotype and pedigree data. Genome-wide association studies (GWAS), based on the use of genetic markers distributed throughout the genome and in non-equilibrium linkage with at least one of the quantitative traits, are currently recognised as more informative for finding new genes for beef cattle productivity. New genes associated with live weight at different stages of ontogenesis, average daily live weight gain, residual feed intake, carcass weight and flesh content have been identified. Most of the identified genes control cell division, lipid and carbohydrate metabolism. The accumulated data on full-genome association studies and exome sequencing led to new methods of genetic analysis – gene ontology and gene networks. The use of gene networks provided the first detailed understanding of the genetic basis for the formation of complex phenotypic traits based on the complex interaction of regulatory networks of «major» and «peripheral» genes controlling the development of a particular trait.

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