Investigating causal biological relationships between reproductive performance traits in high-performing gilts and sows1

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

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^0.75. 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_95%) intervals resulted in a fully directed acyclic graph, in which dLFI and dLWG affected dSWB, the posterior mean of the corresponding structural coefficients (PM_λ) being 0.12 and - 0.03, respectively. In turn, dSWB influenced dBFTB and SMBW, with PM_λ 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.

Utilization and reproductive performance of gilts in large-scale pig farming system with different production levels in China: a descriptive study

Background

This study was to investigate the utilization and reproductive performance of gilts in large-scale pig farms. Data of this descriptive study included 169,013 gilts of 1540 gilts’ batches on 105 large-scale pig farms from April 2020 to March 2021. According to the upper and lower 25th percentiles of piglets weaned per sow per year (PSY) during the research stage, pig farms were divided into three productivity groups: high-performing (HP), intermediate-performing (IP) and low-performing (LP) farms. On the basis of breeds, LP (LP-Total) farms was further divided into LP-breeding pig (LP-BP) and LP-commercial pig (LP-CP) groups. Average utilization, estrus and first mating data was collected from a total of 1540 gilts’ batches. The age-related factors (introduction age, age at first estrus and age at first mating) and litter production (total number of piglets, number of piglets born alive and number of weaned piglets, as well as their proportion distribution) among HP and LP groups were compared. The litter production in different age groups were also analyzed.

Results

The introduction age, mortality and culling rate of HP farms were lower compared with LP farms. Total number of piglets per litter, number of piglets born alive per litter and number of weaned piglets per litter in HP farms were significantly more than those of LP groups, respectively. The proportion distribution peaks of litter production in HP farms were shifted about two more than those in LP groups, respectively; and the proportion of low litter production (eight per litter or less) was lower than that in LP groups. The results of different age groups showed that total number of piglets per litter and number of piglets born alive per litter in 220–279 d were the most, while that of 370 d was the least.

Conclusions

The overall utilization and reproductive performance of gilts in HP farms was better than those of LP farms. The difference in utilization was reflected in introduction source, culling rate and mortality. While the age at first estrus and first mating, breeds and litter production were the main differences for reproductive performance.

Searching for causal relationships among latent variables concerning performance, carcass, and meat quality traits in broilers

In causal relationship studies, the latent variables may summarize the phenotypes in theoretical traits according to their phenotypic correlations, improving the understanding of causal relationships between broilers phenotypes. In this study, we aimed to investigate potential causal relationships among latent variables in broilers using a structural equation model in the context of genetic analysis. The data used in this study comprised 14 traits in broilers with 2,017 records each, and 104,154 animals in pedigree. Four latent variables (WEIGHT, LOSSES, COLOUR, and VISCERA) were defined and validated using Bayesian Confirmatory Factor Analysis. Subsequently, a search for causal linkage structures was performed, obtaining a single causal link structure between the latent variables. Then, this information was used to fit the structural equation model (SEM). The results from the SEM indicated positive causal effects of the variables WEIGHT and LOSSES on the variables VISCERA and COLOUR, respectively, with structural coefficient estimates of 1.006 and 0.040, respectively. On the other hand, an antagonist causal effect of the variable WEIGHT on the variable LOSSES was verified, with a structural coefficient estimate of -4.333. These results highlight the causal relationship between performance and meat quality traits, which may be associated with the natural processes involved in the conversion of muscle into meat and the structural changes in muscle tissues due to intense selection for high growth rates in broilers.

Inferring phenotypic causal structure among farrowing and weaning traits in pigs

Direct selection for litter size or weight at weaning in pigs is often hindered by external interventions such as cross-fostering. The objective of this study was to infer the causal structure among phenotypes of reproductive traits in pigs to enable subsequent direct selection for these traits. Examined traits included: number born alive (NBA), litter size on day 21 (LS21), and litter weight on day 21 (LW21). The study included 6,240 litters from 1,673 Landrace dams and 5,393 litters from 1,484 Large White dams. The inductive causation (IC) algorithm was used to infer the causal structure, which was then fitted to a structural equation model (SEM) to estimate causal coefficients and genetic parameters. Based on the IC algorithm and temporal and biological information, the causal structure among traits was identified as: NBA → LS21 → LW21 and NBA → LW21. Owing to the causal effect of NBA on LS21 and LW21, the genetic, permanent environmental, and residual variances of LS21 and LW21were much lower in the SEM than in the multiple-trait model for both breeds. Given the strong effect of NBA on LS21 and LW21, the SEM and causal information might assist with selective breeding for LS21 and LW21 when cross-fostering occurs.