截断历史数据对奶羊选育候选者预测能力的影响

IF 4 2区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
I. Granado-Tajada, E. Ugarte
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引用次数: 0

摘要

过去几十年来,遗传评估方法不断发展,改进了育种价值评估。许多育种计划都有历史表型记录和大量世代,但利用这些记录可能会带来更多的不便,而不是好处。本研究利用三个拉特萨奶羊种群 40 年的产奶量记录,通过同时评估去除历史数据、两种血统深度和两种方法(传统 BLUP 和单步基因组 BLUP 或 ssGBLUP)来评估基因分型幼畜的预测能力。使用线性回归法比较了后代测试前后对年轻公羊的预测,以 4 年为间隔(从 1992 年到 2012 年)设置了 6 个截断点,并计算了准确率、偏差和离散度的比率。在所有拉特萨种群中,包含基因组信息的候选品种预测准确率最高(全数据集在 0.54 至 0.69 之间)。然而,在数据量较大的种群中,删除历史表型数据会导致中等程度的准确率提高(平均提高 2.5%),而在数据量较小的种群中,删除 2004 年之前的数据会导致中等程度的准确率提高(提高 2.7%),当删除更多记录时,准确率会降低。根据基因组信息预测育种值时,验证个体的偏差较小(介于 2.1 和 13.9 之间),数据量大的种群删除数据量最大时偏差较小(减少 5.2%),而数据量较小的种群从 1996 年至 2008 年的数据删除中获益(偏差减少 3.8%)。同时,当包含的数据较少时,估计遗传趋势的斜率较低,并观察到未知亲本组估计值被高估。结果表明,与传统的 BLUP 评价相比,ssGBLUP 评价效果显著,而血统深度的影响很小,删除历史表型数据也有好处。因此,删除 2000 年至 2004 年的表型记录、使用两代血统以及实施 ssGBLUP 方法将有利于 Latxa 的常规遗传评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of truncating historical data on prediction ability of dairy sheep selection candidates

Along the last decades, the genetic evaluation methodology has evolved, improving breeding value estimates. Many breeding programmes have historical phenotypic records and large number of generations, but to make use of them could result in more inconveniences than benefits. In this study, the prediction ability of genotyped young animals was assessed by simultaneously evaluating the removal of historical data, two pedigree deepness and two methodologies (traditional BLUP and single−step genomic BLUP or ssGBLUP), using milk yield records of 40 years of three Latxa dairy sheep populations. The linear regression method was used to compare predictions of young rams before and after progeny testing, with six cut-off points, by intervals of 4 years (from 1992 to 2012), and statistics of ratio of accuracies, bias, and dispersion were calculated. The prediction accuracy of selection candidates, when genomic information was included, was the highest in all Latxa populations (between 0.54 and 0.69 with full data set). Nevertheless, the deletion of historical phenotypic data resulted on moderate accuracy gain in the bigger data size populations (mean gain 2.5%), and the smaller population took advantage of a moderate data deletion (2.7% gain by removing data until 2004), reducing accuracy when more records were removed. The bias of validation individuals was lower when the breeding value was predicted based on genomic information (between 2.1 and 13.9), being lower when the biggest amount of data was deleted in the bigger data size populations (5.2% reduction), and the smaller population was benefited from data deletion between 1996 and 2008 (3.8% bias reduction). Meanwhile, the slope of estimated genetic trend was lower when less data were included, and an overestimation of the unknown parent group estimates was observed. The results indicated that ssGBLUP evaluations were outstanding, compared with traditional BLUP evaluations, while the depth of pedigree had a very small influence, and deletion of historical phenotypic data was beneficial. Thus, Latxa routine genetic evaluations would benefit from truncating phenotypic records between 2000 and 2004, the use of two pedigree generations and the implementation of ssGBLUP methodology.

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来源期刊
Animal
Animal 农林科学-奶制品与动物科学
CiteScore
7.50
自引率
2.80%
发文量
246
审稿时长
3 months
期刊介绍: Editorial board animal attracts the best research in animal biology and animal systems from across the spectrum of the agricultural, biomedical, and environmental sciences. It is the central element in an exciting collaboration between the British Society of Animal Science (BSAS), Institut National de la Recherche Agronomique (INRA) and the European Federation of Animal Science (EAAP) and represents a merging of three scientific journals: Animal Science; Animal Research; Reproduction, Nutrition, Development. animal publishes original cutting-edge research, ''hot'' topics and horizon-scanning reviews on animal-related aspects of the life sciences at the molecular, cellular, organ, whole animal and production system levels. The main subject areas include: breeding and genetics; nutrition; physiology and functional biology of systems; behaviour, health and welfare; farming systems, environmental impact and climate change; product quality, human health and well-being. Animal models and papers dealing with the integration of research between these topics and their impact on the environment and people are particularly welcome.
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