Genotyping both live and dead animals to improve post-weaning survival of pigs in breeding programs

IF 3.6 1区农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE

Genetics Selection Evolution Pub Date : 2024-09-18 DOI:10.1186/s12711-024-00932-4

Md Sharif-Islam, Julius H. J. van der Werf, Mark Henryon, Thinh Tuan Chu, Benjamin J. Wood, Susanne Hermesch

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引用次数: 0

Abstract

In this study, we tested whether genotyping both live and dead animals (GSD) realises more genetic gain for post-weaning survival (PWS) in pigs compared to genotyping only live animals (GOS). Stochastic simulation was used to estimate the rate of genetic gain realised by GSD and GOS at a 0.01 rate of pedigree-based inbreeding in three breeding schemes, which differed in PWS (95%, 90% and 50%) and litter size (6 and 10). Pedigree-based selection was conducted as a point of reference. Variance components were estimated and then estimated breeding values (EBV) were obtained in each breeding scheme using a linear or a threshold model. Selection was for a single trait, i.e. PWS with a heritability of 0.02 on the observed scale. The trait was simulated on the underlying scale and was recorded as binary (0/1). Selection candidates were genotyped and phenotyped before selection, with only live candidates eligible for selection. Genotyping strategies differed in the proportion of live and dead animals genotyped, but the phenotypes of all animals were used for predicting EBV of the selection candidates. Based on a 0.01 rate of pedigree-based inbreeding, GSD realised 14 to 33% more genetic gain than GOS for all breeding schemes depending on PWS and litter size. GSD increased the prediction accuracy of EBV for PWS by at least 14% compared to GOS. The use of a linear versus a threshold model did not have an impact on genetic gain for PWS regardless of the genotyping strategy and the bias of the EBV did not differ significantly among genotyping strategies. Genotyping both dead and live animals was more informative than genotyping only live animals to predict the EBV for PWS of selection candidates, but with marginal increases in genetic gain when the proportion of dead animals genotyped was 60% or greater. Therefore, it would be worthwhile to use genomic information on both live and more than 20% dead animals to compute EBV for the genetic improvement of PWS under the assumption that dead animals reflect increased liability on the underlying scale.

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对活体和死体动物进行基因分型，提高育种项目中猪断奶后的存活率

在这项研究中，我们测试了与只对活体动物进行基因分型（GOS）相比，同时对活体动物和死体动物进行基因分型（GSD）是否能在猪的断奶后存活率（PWS）方面实现更多的遗传增益。随机模拟估算了在基于血统的近亲繁殖率为 0.01 的情况下，GSD 和 GOS 在三种育种方案中实现的遗传增益率，这三种育种方案在断奶后存活率（95%、90% 和 50%）和窝产仔数（6 和 10）方面存在差异。基于血统的选择是作为参考点进行的。每个育种方案都使用线性或阈值模型对变异成分进行估计，然后获得估计育种值（EBV）。选择针对的是单一性状，即在观察尺度上遗传率为 0.02 的 PWS。该性状在基础量表上进行模拟，并记录为二元性状（0/1）。候选品种在选育前要进行基因分型和表型分析，只有活体候选品种才有资格参加选育。基因分型策略在活体和死体动物的基因分型比例上有所不同，但所有动物的表型都用于预测候选样本的 EBV。基于 0.01 的近亲繁殖率，在所有育种方案中，GSD 实现的遗传增益比 GOS 高 14% 至 33%，具体取决于 PWS 和产仔数。与 GOS 相比，GSD 对 PWS 的 EBV 预测准确率至少提高了 14%。无论采用哪种基因分型策略，线性模型和阈值模型的使用对PWS的遗传增益都没有影响，而且不同基因分型策略的EBV偏差也没有显著差异。与只对活体动物进行基因分型相比，同时对死体动物和活体动物进行基因分型更能预测候选动物PWS的EBV，但当死体动物的基因分型比例达到或超过60%时，遗传增益的增加微乎其微。因此，值得使用活体动物和超过 20% 死体动物的基因组信息来计算 PWS 遗传改良的 EBV，前提是死体动物反映了基本规模上责任的增加。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Genetics Selection Evolution 生物-奶制品与动物科学

CiteScore

6.50

自引率

9.80%

发文量

审稿时长

1 months

期刊介绍： Genetics Selection Evolution invites basic, applied and methodological content that will aid the current understanding and the utilization of genetic variability in domestic animal species. Although the focus is on domestic animal species, research on other species is invited if it contributes to the understanding of the use of genetic variability in domestic animals. Genetics Selection Evolution publishes results from all levels of study, from the gene to the quantitative trait, from the individual to the population, the breed or the species. Contributions concerning both the biological approach, from molecular genetics to quantitative genetics, as well as the mathematical approach, from population genetics to statistics, are welcome. Specific areas of interest include but are not limited to: gene and QTL identification, mapping and characterization, analysis of new phenotypes, high-throughput SNP data analysis, functional genomics, cytogenetics, genetic diversity of populations and breeds, genetic evaluation, applied and experimental selection, genomic selection, selection efficiency, and statistical methodology for the genetic analysis of phenotypes with quantitative and mixed inheritance.