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Genomic predictions for crossbred dairy cattle

机译:杂交奶牛的基因组预测

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摘要

Genomic evaluations are useful for crossbred as wellas purebred populations when selection is applied tocommercial herds. Dairy farmers had already spentmore than $1 million to genotype over 32,000 crossbredanimals before US genomic evaluations became availablefor those animals. Thus, new tools were neededto provide accurate genomic predictions for crossbreds.Genotypes for crossbreds are imputed more accuratelywhen the imputation reference population includespurebreds. Therefore, genotypes of 6,296 crossbredanimals were imputed from lower-density chips byincluding either 3,119 ancestors or 834,367 genotypedanimals in the reference population. Crossbreds inthe imputation study included 733 Jersey × HolsteinF_1 animals, 55 Brown Swiss × Holstein F_1 animals,2,300 Holstein backcrosses, 2,026 Jersey backcrosses,27 Brown Swiss backcrosses, and 502 other crossbredsof various breed combinations. Another 653 animalsappeared to be purebreds that owners had miscodedas a different breed. Genomic breed composition wasestimated from 60,671 markers using the known breedidentities for purebred, progeny-tested Holstein, Jersey,Brown Swiss, Ayrshire, and Guernsey bulls as the 5traits (breed fractions) to be predicted. Estimates ofbreed composition were adjusted so that no percentageswere negative or exceeded 100%, and breed percentagessummed to 100%. Another adjustment set percentagesabove 93.5% equal to 100%, and the resulting valuewas termed breed base representation (BBR). Largerpercentages of missing alleles were imputed by usinga crossbred reference population rather than only theclosest purebred reference population. Crossbred predictionswere averages of genomic predictions computedusing marker effects for each pure breed, which wereweighted by the animal’s BBR. Marker and polygeniceffects were estimated separately for each breed on theall-breed scale instead of within-breed scales. For crossbreds,genomic predictions weighted by BBR were moreaccurate than the average of parents’ breeding valuesand slightly more accurate than predictions using onlythe predominant breed. For purebreds, single-trait predictionsusing only within-breed data were as accurateas multi-trait predictions with allele effects in differentbreeds treated as correlated effects. Crossbred genomicpredicted transmitting abilities were implemented bythe Council on Dairy Cattle Breeding in April 2019 andwill aid producers in managing their breeding programsand selecting replacement heifers.
机译:基因组评估也适用于杂交as purebred populations when selection is applied to商业牛群。奶农已经花了超过100万美元到基因型超过32,000多次杂交在美国基因组评估之前的动物可以获得对于那些动物。因此,需要新工具为杂交提供准确的基因组预测。杂交的基因型更加准确地估算当归属参考人口包括纯种。因此,基因型为6,296克里德动物被低密度芯片避阻包括3,119祖先或834,367基因分型参考人群中的动物。杂交杂志估算研究包括733泽西×荷斯坦F_1动物,55棕色瑞士×荷斯坦F_1动物,2,300霍尔斯坦返回克罗斯,2,026泽西河口,27个棕色瑞士返回和502个其他杂交各种品种组合。另外653只动物似乎是业主错误等待的纯种血统作为一种不同的品种。基因组品种组合物是使用已知品种的60,671个标记估计纯种,后代测试的Holstein,泽西岛的身份,布朗瑞士,艾尔郡和根西岛公牛队作为5要预测的特征(品种分数)。估计调整品种组成,使得没有百分比是消极的或超过100%,并品种百分比总结到100%。另一个调整集百分比高于93.5%等于100%,并产生的值被称为品种基础代表(BBR)。较大使用缺失等位基因的百分比杂种参考人口而不是只有最近的纯种参考人口。杂交预测是基因组预测的平均值计算对每个纯品种的标记效应是由动物的BBR加权。标记和多基因对每个品种分别估计效果全品种,而不是品种范围。对于杂交,BBR加权的基因组预测更多比父母的育种价值的平均值准确并且仅比使用的预测稍微准确主要品种。对于纯种,单个特征预测仅使用内在的数据是准确的作为不同的多种特征预测,等位基因效应品种被视为相关效果。杂交基因组预测的传输能力是实现的2019年4月和奶牛育种委员会及将援助生产者管理他们的繁殖计划并选择替换小母牛。

著录项

  • 来源
    《Journal of dairy science》 |2020年第2期|1620-1631|共12页
  • 作者单位

    USDA Agricultural Research Service Animal Genomics and Improvement Laboratory Beltsville MD 20705-2350;

    USDA Agricultural Research Service Animal Genomics and Improvement Laboratory Beltsville MD 20705-2350;

    Departamento de Ciencias Exatas Universidade Estadual Paulista (Unesp) Faculdade de Ciencias Agrarias e Veterinarias Jaboticabal Sao Paulo CEP 14884-900 Brazil;

    Council on Dairy Cattle Breeding Bowie MD 20716;

    Council on Dairy Cattle Breeding Bowie MD 20716;

    Council on Dairy Cattle Breeding Bowie MD 20716;

    Council on Dairy Cattle Breeding Bowie MD 20716;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    genomic evaluation; multiple breed; crossbreeding; imputation;

    机译:基因组评估;多种品种;杂交育种;归档;

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