Department of Animal Husbandry and Dairying, Ministry of Fisheries, Animal Husbandry and Dairying, New Delhi, India.
ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India.
Mol Biol Rep. 2020 Nov;47(11):9123-9133. doi: 10.1007/s11033-020-05919-0. Epub 2020 Oct 24.
Bull fertility is considered an indispensable trait, as far as farm economics is concerned since it is the successful conception in a cow that provides calf crop, along with the ensuing lactation. This ensures sustainability of a dairy farm. Traditionally, bull fertility did not receive much attention by the farm managers and breeding animals were solely evaluated based on phenotypic predictors, namely, sire conception rate and seminal parameters in bull. With the advent of the molecular era in animal breeding, attempts were made to unravel the genetic complexity of bull fertility by the identification of genetic markers related to the trait. Marker-Assisted Selection (MAS) is a methodology that aims at utilizing the genetic information at markers and selecting improved populations for important traits. Traditionally, MAS was pursued using a candidate gene approach for identifying markers related to genes that are already known to have a physiological function related to the trait but this approach had certain shortcomings like stringent criteria for significance testing. Now, with the availability of genome-wide data, the number of markers identified and variance explained in relation to bull fertility has gone up. So, this presents a unique opportunity to revisit MAS by selection based on the information of a large number of genome-wide markers and thus, improving the accuracy of selection.
公牛的繁殖力被认为是农场经济中不可或缺的特征,因为只有在母牛成功受孕并随后进行哺乳的情况下,才能提供牛犊。这确保了奶牛场的可持续性。传统上,农场管理者并没有过多关注公牛的繁殖力,而是仅仅根据表型预测因子(即公牛的受胎率和精液参数)来评估繁殖动物。随着动物育种的分子时代的到来,人们试图通过鉴定与该性状相关的遗传标记来揭示公牛繁殖力的遗传复杂性。标记辅助选择(MAS)是一种旨在利用标记的遗传信息并选择具有重要性状的改良群体的方法。传统上,MAS 是通过候选基因方法来识别与已知具有与该性状相关的生理功能的基因相关的标记,但这种方法存在一些缺点,例如对显著性检验的严格标准。现在,随着全基因组数据的可用性,与公牛繁殖力相关的标记数量和可解释的方差已经增加。因此,这为基于大量全基因组标记的信息进行选择的 MAS 提供了一个独特的机会,从而提高了选择的准确性。