Genetic associations between stayability and longevity in commercial crossbred sows, and stayability in multiplier sows

Abstract

Longevity in commercial sows is often selected for through stayability traits measured in purebred animals. However, this may not be justifiable because longevity and stayability may be subject to both genotype by environment interaction (G × E) and genotype by genotype interaction (G × G). This study tested the hypothesis that stayability to service after first parity is more strongly genetically correlated with longevity in commercial herds when stayability is measured in commercial herds rather than multiplier herds. The analysis was based on farrowing- and service-records from 470,824 sows (189,263 multiplier; 281,561 commercial) and 300 herds (156 multiplier; 144 commercial sows). Multiplier sows were either purebred Landrace or Yorkshire and commercial sows were mainly rotationally crossbreds between the two breeds. Commercial longevity was defined as age in days when culled (LongC), and stayability to service after first parity was defined for both commercial sows (StayC) and multiplier sows (StayM). The genetic correlations between LongC, StayC, and StayM were estimated by restricted maximum likelihood using linear mixed models. Genetic parameters were estimated separately for Landrace and Yorkshire. In Landrace, the genetic correlations between LongC and StayC, LongC and StayM, and StayC and StayM were 0.86 ± 0.02, 0.24 ± 0.05, and 0.34 ± 0.06, respectively. In Yorkshire, the genetic correlations between LongC and StayC, LongC and StayM, and StayC and StayM were 0.81 ± 0.03, 0.17 ± 0.05, and 0.18 ± 0.7, respectively. Conclusively, longevity in commercial herds is more strongly correlated with stayability when stayability is measured in commercial herds rather than multiplier herds.

Genetic analysis for sow stayability at different parities in purebred Landrace and Large White pigs

“…Previous studies have consistently estimated the heritability of purebred sow stayability to be low by using linear model (Aasmundstad et al, 2014 ; Le et al, 2016 ; López‐Serrano et al, 2000 ; Poulsen et al, 2020 ; Serenius et al, 2006 ; Tholen et al, 1996 ) and threshold model (Hong et al, 2019 ; Le et al, 2016 ). Aasmundstad et al ( 2014 ), López‐Serrano et al ( 2000 ), Poulsen et al ( 2020 ), and Tholen et al ( 1996 ) considered information on reproductive performance of sows, such as age at first farrowing and litter size, into model as macro‐environment effects.…”

“…Previous studies have consistently estimated the heritability of purebred sow stayability to be low by using linear model (Aasmundstad et al, 2014 ; Le et al, 2016 ; López‐Serrano et al, 2000 ; Poulsen et al, 2020 ; Serenius et al, 2006 ; Tholen et al, 1996 ) and threshold model (Hong et al, 2019 ; Le et al, 2016 ). Aasmundstad et al ( 2014 ), López‐Serrano et al ( 2000 ), Poulsen et al ( 2020 ), and Tholen et al ( 1996 ) considered information on reproductive performance of sows, such as age at first farrowing and litter size, into model as macro‐environment effects. Some studies considered the common litter environmental effects (Aasmundstad et al, 2014 ; Hong et al, 2019 ; López‐Serrano et al, 2000 ; Serenius et al, 2006 ), and Serenius et al ( 2006 ) reported that the heritability was estimated to be slightly lower when using model considering the common litter environmental effects than when using model ignoring it, which were also observed for several body measurement and meat production traits (Ogawa et al, 2021 ; Ohnishi & Satoh, 2018 ).…”

“…Previous studies have consistently estimated the heritability of purebred sow stayability to be low by using linear model (Aasmundstad et al, 2014;Le et al, 2016;L opez-Serrano et al, 2000;Poulsen et al, 2020;Tholen et al, 1996) and threshold model (Hong et al, 2019;Le et al, 2016). Aasmundstad model as macro-environment effects.…”

“…Genetic parameter estimation for stayability, which is defined as a binary trait relating to survivability in a given environment and is treated as an indicator trait for longevity, has been widely conducted in pigs (Aasmundstad et al, 2014 ; Abell et al, 2016 ; Hong et al, 2019 ; Le et al, 2016 ; López‐Serrano et al, 2000 ; Poulsen et al, 2020 ; Serenius et al, 2006 ; Tholen et al, 1996 ), dairy cattle (e.g., Abe et al, 2020 ; Bijma & Jensen, 1996 ; VanRaden et al, 2016 ), beef cattle (e.g., Fennewald et al, 2018 ; Martinez et al, 2005 ; Silva et al, 2003 ), horses (e.g., Albertsdóttir et al, 2012 ; Bugislaus et al, 2005 ; Langlois & Vrijenhoek, 2004 ), sheep (Lee et al, 2015 ; McIntyre et al, 2012 ), and goats (Scholtens et al, 2018 ). In pigs, genetic parameters for sow stayability from first parity to a particular time, especially second parity, have been estimated, and threshold model analyses have been performed (Abell et al, 2016 ; Hong et al, 2019 ; Le et al, 2016 ).…”

Predictive performances of animal models using different multibreed relationship matrices in systems with rotational crossbreeding

“…The objective of this study was to compare methods for relationship matrices in terms of ability to predict accurate and unbiased breeding values with phenotypes from rotationally crossbred animals. We compared the NRM as used by Poulsen et al [ 15 ], the partial relationship matrices by García-Cortés and Toro [ 9 ], the approximate partial relationship matrices by Strandén and Mäntysaari [ 12 ], and the relationship matrix with metafounders by Legarra et al [ 14 ].…”