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. 2021 Aug 4:12:707870.
doi: 10.3389/fgene.2021.707870. eCollection 2021.

Host Genetics of Response to Porcine Reproductive and Respiratory Syndrome in Sows: Reproductive Performance

Affiliations

Host Genetics of Response to Porcine Reproductive and Respiratory Syndrome in Sows: Reproductive Performance

Felipe M W Hickmann et al. Front Genet. .

Abstract

Porcine Reproductive and Respiratory Syndrome (PRRS) is historically the most economically important swine disease worldwide that severely affects the reproductive performance of sows. However, little is still known about the genetic basis of reproductive performance in purebred herds during a PRRS outbreak through the comparison of maternal and terminal breeds. Thus, the objective of this work was to explore the host genetics of response to PRRS in purebred sows from two breeds. Reproductive data included 2546 Duroc and 2522 Landrace litters from 894 and 813 purebred sows, respectively, which had high-density genotype data available (29,799 single nucleotide polymorphisms; SNPs). The data were split into pre-PRRS, PRRS, and post-PRRS phases based on standardized farrow-year-week estimates. Heritability estimates for reproductive traits were low to moderate (≤0.20) for Duroc and Landrace across PRRS phases. On the other hand, genetic correlations of reproductive traits between PRRS phases were overall moderate to high for both breeds. Several associations between MARC0034894, a candidate SNP for response to PRRS, with reproductive performance were identified (P-value < 0.05). Genomic analyses detected few QTL for reproductive performance across all phases, most explaining a small percentage of the additive genetic variance (≤8.2%, averaging 2.1%), indicating that these traits are highly polygenic. None of the identified QTL within a breed and trait overlapped between PRRS phases. Overall, our results indicate that Duroc sows are phenotypically more resilient to PRRS than Landrace sows, with a similar return to PRRS-free performance between breeds for most reproductive traits. Genomic prediction results indicate that genomic selection for improved reproductive performance under a PRRS outbreak is possible, especially in Landrace sows, by training markers using data from PRRS-challenged sows. On the other hand, the high genetic correlations with reproductive traits between PRRS phases suggest that selection for improved reproductive performance in a clean environment could improve performance during PRRS, but with limited efficiency due to their low heritability estimates. Thus, we hypothesize that an indicator trait that could be indirectly selected to increase the response to selection for these traits would be desirable and would also improve the reproductive performance of sows during a PRRS outbreak.

Keywords: GWAS; PRRS; QTL; SNP; disease outbreak; genomics; reproduction; swine.

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Conflict of interest statement

The authors declare that this study received funding from Smithfield Premium Genetics, NC, United States. The funder had the following involvement with the study: providing performance and genotype data and collection of blood samples. In addition, YH and KG are employed by the company Smithfield Premium Genetics, NC, United States. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Impact of Porcine Reproductive and Respiratory Syndrome (PRRS) on herd average reproductive performance. Standardized estimates of farrow-year-week (FYW) during 2018 for each reproductive trait for Duroc (A) and Landrace (B) sows. Thirty-day rolling averages (RA) of reproductive traits for Duroc (C) and Landrace (D) sows.
FIGURE 2
FIGURE 2
Genomic prediction accuracies of reproductive traits during a Porcine Reproductive and Respiratory Syndrome (PRRS) outbreak. Results are presented for Duroc (A) and Landrace (B) across genomic prediction scenarios (GPS) for total number born (TNB), number born alive (NBA), number born dead (NBD), number of stillborn (NSB), number born mummified (NBM), and number of piglets weaned (NW) using BayesB. The y-axis represents the genomic prediction accuracy and the x-axis the GPS. Details for the different GPS are available in Table 2. Results for all methods and standard deviations across folds are available in Supplementary Table 2.

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