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Record W3136102254 · doi:10.33943/mms.2021.60.50.003

POLYMORPHISM OF THE BETA-CASEIN GENE IN HOLSTEIN COWS

2021· article· ru· W3136102254 on OpenAlexaboutno aff
Л.В. КАЛАШНИКОВА, В.Г. Труфанов, Я.А. ХАБИБРАХМАНОВА, Т.Б. ГАНЧЕНКОВА, N. V. Ryzhova, И.Ю. ПАВЛОВА

Bibliographic record

VenueMolochnoe i miasnoe skotovodstvo · 2021
Typearticle
Languageru
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsAllele frequencyHerdAlleleGenotypeGenotype frequencyAnimal sciencePopulationBiologyPolymorphism (computer science)GeneticsGeneDemography

Abstract

fetched live from OpenAlex

Представлены результаты исследований частоты встречаемости аллельных вариантов А1 и А2 гена бета-казеина (CSN2) у животных голштинской породы (n=510), принадлежащих пяти племенным хозяйствам Российской Федерации. Анализ ДНК проводился методом полимеразной цепной реакции с искусственно созданным сайтом рестрикции (ACRS-ПЦР). В среднем, по всему исследованному поголовью частота генотипов составила: A1A1—15% (n=78), A1A2—41 % (n=210), A2A2—44% (n=222). Частота желательного аллельного варианта А2 в среднем по всем стадам достигла 0,641 и превысила частоту аллеля А1 (0,359). Генотип А2А2 чаще встречается в группе племенных животных, импортированных из Дании. Им обладают 77% особей из 40 исследованных. Частота аллеля А2 в стаде исследуемого племенного хозяйства Курской области в 7 раз превысила частоту аллеля А1 и достигла 0,875. На 2-ом месте по частоте аллеля А2 находится маточное поголовье, завезенное из США. В племенном хозяйстве Московской области 42% животных из 354 исследованных имеют желательный генотип А2А2, частота аллеля А2 составила 0,640. В Камчатском крае отмечена сходная частота аллеля А2 (0,635) у племенных особей голштинской породы североамериканской селекции. В группе скота венгерской селекции, принадлежащей племенному хозяйству Рязанской области, частота аллеля А2 гена CSN2 ниже (0,611). В другом хозяйстве этого региона у голштинской породы канадского происхождения частота аллеля А1 (0,522) превысила частоту аллеля А2 (0,478). По исследованному поголовью оценки наблюдаемой (Ho) и ожидаемой (He) гетерозиготности имеют сходные значения и составляют 0,430 и 0,460 соответственно. The results of studies of the frequency of occurrence of allelic variants A1 and A2 of the beta-casein gene (CSN2) in Holstein animals (n=510) belonging to five breeding farms of the Russian Federation are presented. DNA analysis was performed by polymerase chain reaction with an artificially created restriction site (ACRS-PCR). On average, the frequency of genotypes for the entire studied population was: A1A1-15% (n=78), A1A2—41% (n=210), A2A2—44% (n=222). The frequency of the desired A2 allele variant reached 0.641 on average across all herds and exceeded the frequency of the A1 allele (0.359). The A2A2 genotype is more common in a group of breeding animals imported from Denmark. It is possessed by 77% of the 40 individuals studied. The frequency of the A2 allele in the herd of the studied breeding farm of the Kursk region was 7 times higher than the frequency of the A1 allele and reached 0.875. On the 2nd place in the frequency of the A2 allele is the breeding stock imported from the United States. In the breeding farm of the Moscow region, 42% of the 354 animals studied have the desired genotype A2A2, the frequency of the A2 allele was 0.640. In the Kamchatka Territory, a similar frequency of the A2 allele (0.635) was observed in cows of the Holstein breed of North American selection. In the group of Hungarian-bred cattle belonging to the Ryazan Region breeding farm, the frequency of the A2 allele of the CSN2 gene is lower (0.611). In another farm in this region, the Holstein breed of Canadian origin had the frequency of the A1 allele (0.522) higher than the frequency of the A2 allele (0.478). For the studied livestock, the estimates of observed (Ho) and expected (He) heterozygosity have similar values and are 0.430 and 0.460, respectively.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.230
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
Has abstractyes

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