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分析与母猪泌乳饲料效率相关性状的因果结构。

Analysis of the causal structure of traits involved in sow lactation feed efficiency.

机构信息

Institute of Agrifood Research and Technology (IRTA)-Animal Breeding and Genetics, Caldes de Montbui, Barcelona, Spain.

GenPhySE, INRAE, INPT, Université de Toulouse, 31326, Castanet Tolosan, France.

出版信息

Genet Sel Evol. 2022 Jul 26;54(1):53. doi: 10.1186/s12711-022-00744-4.

Abstract

BACKGROUND

Feed efficiency during lactation involves a set of phenotypic traits that form a complex system, with some traits exerting causal effects on the others. Information regarding such interrelationships can be used to predict the effect of external interventions on the system, and ultimately to optimize management practices and multi-trait selection strategies. Structural equation models can be used to infer the magnitude of the different causes of such interrelationships. The causal network necessary to fit structural equation models can be inferred using the inductive causation (IC) algorithm. By implementing these statistical tools, we inferred the causal association between the main energy sources and sinks involved in sow lactation feed efficiency for the first time, i.e., daily lactation feed intake (dLFI) in kg/day, daily sow weight balance (dSWB) in kg/day, daily litter weight gain (dLWG) in kg/day, daily back fat thickness balance (dBFTB) in mm/day, and sow metabolic body weight (SMBW) in kg. Then, we tested several selection strategies based on selection indices, with or without dLFI records, to improve sow efficiency during lactation.

RESULTS

The IC algorithm using 95% highest posterior density (HPD) intervals resulted in a fully directed acyclic graph, in which dLFI and dLWG affected dSWB, the posterior mean of the corresponding structural coefficients (PM) being 0.12 and - 0.03, respectively. In turn, dSWB influenced dBFTB and SMBW, with PM equal to 0.70 and - 1.22, respectively. Multiple indirect effects contributed to the variances and covariances among the analyzed traits, with the most relevant indirect effects being those involved in the association between dSWB and dBFTB and between dSWB and SMBW. Selection strategies with or without phenotypic information on dLFI, or that hold this trait constant, led to the same pattern and similar responses in dLFI, dSWB, and dLWG.

CONCLUSIONS

Selection based on an index including only dBFTB and dLWG records can reduce dLFI, keep dSWB constant or increase it, and increase dLWG. However, a favorable response for all three traits is probably not achievable. Holding the amount of feed provided to the sows constant did not offer an advantage in terms of response over the other strategies.

摘要

背景

哺乳期的饲料效率涉及一组表型特征,这些特征构成了一个复杂的系统,其中一些特征对其他特征施加因果影响。关于这种相互关系的信息可用于预测外部干预对系统的影响,并最终优化管理实践和多性状选择策略。结构方程模型可用于推断这种相互关系的不同原因的大小。可以使用归纳因果(IC)算法来推断拟合结构方程模型所需的因果网络。通过实施这些统计工具,我们首次推断了参与母猪哺乳期饲料效率的主要能量源和汇之间的因果关系,即每天泌乳饲料摄入量(dLFI)、每天母猪体重平衡(dSWB)、每天仔猪体重增加(dLWG)、每天背脂厚度平衡(dBFTB)和母猪代谢体重(SMBW)。然后,我们测试了几种基于选择指数的选择策略,这些策略有或没有 dLFI 记录,以提高母猪哺乳期的效率。

结果

使用 95%最高后验密度(HPD)区间的 IC 算法产生了一个完全有向无环图,其中 dLFI 和 dLWG 影响 dSWB,相应结构系数的后验均值(PM)分别为 0.12 和-0.03。反过来,dSWB 影响 dBFTB 和 SMBW,PM 分别为 0.70 和-1.22。多个间接效应导致分析性状之间的方差和协方差,最相关的间接效应是那些涉及 dSWB 和 dBFTB 之间以及 dSWB 和 SMBW 之间的关联。具有或不具有 dLFI 表型信息的选择策略,或者保持该性状不变,导致 dLFI、dSWB 和 dLWG 呈现相同的模式和相似的反应。

结论

基于仅包括 dBFTB 和 dLWG 记录的指数进行选择,可以减少 dLFI,保持 dSWB 不变或增加 dSWB,并增加 dLWG。然而,可能无法实现所有三个性状的有利反应。保持给母猪提供的饲料量不变,在反应方面没有优于其他策略的优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/005a/9327305/fff068841429/12711_2022_744_Fig1_HTML.jpg

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