Animal Genomics and Improvement Laboratory, USDA, Agricultural Research Service, Beltsville, MD 20705-2350.
J Dairy Sci. 2020 Jun;103(6):5291-5301. doi: 10.3168/jds.2019-17684. Epub 2020 Apr 22.
Genomic selection was adopted very quickly in the 10 yr after first implementation, and breeders continue to find new uses for genomic testing. Breeding values with higher reliability earlier in life are estimated by combining DNA genotypes for many thousands of loci using existing identification, pedigree, and phenotype databases for millions of animals. Quality control for both new and previous data is greatly improved by comparing genomic and pedigree relationships to correct parent-progeny conflicts and discover many additional ancestors. Many quantitative trait loci and gene tests have been added to previous assays that used only evenly spaced, highly polymorphic markers. Imputation now combines genotypes from many assays of differing marker densities. Prediction models have gradually advanced from normal or Bayesian distributions within trait and breed to single-step, multitrait, or other more complex models, such as multibreed models that may be needed for crossbred prediction. Genomic selection was initially applied to males to predict progeny performance but is now widely applied to females or even embryos to predict their own later performance. The initial focus on additive merit has expanded to include mating programs, genomic inbreeding, and recessive alleles. Many producers now use DNA testing to decide which heifers should be inseminated with elite dairy, beef, or sex-sorted semen, which should be embryo donors or recipients, or which should be sold or kept for breeding. Because some of these decisions are expensive to delay, predictions are now provided weekly instead of every few months. Predictions from international genomic databases are often more accurate and cost-effective than those from within-country databases that were previously designed for progeny testing unless local breeds, conditions, or traits differ greatly from the larger database. Selection indexes include many new traits, often with lower heritability or requiring large initial investments to obtain phenotypes, which provide further incentive to cooperate internationally. The genomic prediction methods developed for dairy cattle are now applied widely to many animal, human, and plant populations and could be applied to many more.
基因组选择在首次实施后的 10 年内被迅速采用,育种者继续发现基因组测试的新用途。通过结合现有识别、系谱和表型数据库中数千个位置的 DNA 基因型,对数千个位置的 DNA 基因型进行组合,可以在生命早期更可靠地估计繁殖值。通过比较基因组和系谱关系,对新数据和旧数据进行质量控制,可以大大改善亲代-后代冲突的纠正和发现更多的祖先。许多数量性状基因座和基因测试已添加到以前仅使用均匀间隔、高度多态性标记的检测中。现在,通过组合来自不同标记密度的多个检测的基因型,实现了推断。预测模型逐渐从性状和品种内的正态或贝叶斯分布发展到单步、多性状或其他更复杂的模型,例如可能需要用于杂交预测的多品种模型。基因组选择最初应用于雄性以预测后代性能,但现在已广泛应用于雌性甚至胚胎,以预测其自身的后期性能。最初对加性优势的关注已扩展到包括交配计划、基因组近交和隐性等位基因。许多生产者现在使用 DNA 测试来决定哪些小母牛应该用优质奶牛、肉牛或性别分选精液进行配种,哪些应该成为胚胎供体或受体,或者哪些应该出售或保留用于繁殖。由于这些决策中的一些延迟成本很高,因此现在每周而不是每隔几个月提供预测。来自国际基因组数据库的预测通常比以前为后代测试设计的国内数据库更准确和更具成本效益,除非当地品种、条件或性状与较大的数据库有很大差异。选择指数包括许多新的性状,通常具有较低的遗传力或需要较大的初始投资来获得表型,这为国际合作提供了进一步的激励。为奶牛开发的基因组预测方法现在已广泛应用于许多动物、人类和植物群体,并且可能会应用于更多的群体。