Heritability and genetic correlation estimates of semen production traits with litter traits and pork production traits in purebred Duroc pigs

Abstract

We estimated heritabilities of semen production traits and their genetic correlations with litter traits and pork production traits in purebred Duroc pigs. Semen production traits were semen volume, sperm concentration, proportion of morphologically normal sperms, total number of sperm, and total number of morphologically normal sperm. Litter traits at farrowing were total number born, number born alive, number stillborn, total litter weight at birth, mean litter weight at birth, and piglet survival rate at birth. Litter traits at weaning were litter size at weaning, total litter weight at weaning, mean litter weight at weaning, and piglet survival rate from birth to weaning. Pork production traits were average daily gain, backfat thickness, and loin muscle area. We analyzed 45,913 semen collection records of 896 boars, 6,950 farrowing performance records of 1,400 sows, 2,237 weaning performance records of 586 sows, and individual growth performance records of 9,550 animals measured at approximately 5 months of age. Heritabilities were estimated using a single-trait animal model. Genetic correlations were estimated using a two-trait animal model. Estimated heritabilities of semen production traits ranged from 0.20 for sperm concentration to 0.29 for semen volume and were equal to or higher than those of litter traits, ranging from 0.06 for number stillborn and piglet survival rate at birth to 0.25 for mean litter weight at birth, but lower than those of pork production traits, ranging from 0.50 for average daily gain to 0.63 for backfat thickness. In many cases, the absolute values of estimated genetic correlations between semen production traits and other traits were smaller than 0.3. These estimated genetic parameters provide useful information for establishing a comprehensive pig breeding scheme.

Genetic relationship between superovulatory response traits and carcass traits in Japanese Black cattle

“…Indirect estimation could be useful when the initial value setting for REML iteration or direct REML estimation did not converge. For example, Ogawa et al (2022) reported the indirectly estimated value of the genetic correlation between survival rate at birth and survival rate from birth to weaning in pigs because it could not be directly estimated. On the other hand, indirect estimates obtained using other estimates could deviate from the true value.…”

“…Production efficiency in livestock production, including average daily gain, residual feed intake, and survival rate at birth (e.g., Homma et al, 2021; Ogawa et al, 2022; Yazaki et al, 2020), can be defined as the difference and ratio. For superovulatory response traits in dairy cattle, König et al (2007) estimated genetic parameters of ratio traits, such as the percentage of transferable embryos; such analyses were not performed in Zoda et al (2021).…”

Effect of ambient temperature on average daily gain of pigs evaluated using public weather data and a plateau‐linear regression model

“…In this study, an upper bound for thr was set to 25.0°C because the maximum value of expected T was estimated to be 26.3°C, and that for per was 50 according to the upper bound for ADG of 1500 g from 30 to 105 kg in this study (i.e., 75,000/1500 = 50). Note that the length of testing period (days from 30 to 105 kg) was different among animals, that the effect of heat stress could be more serious for heavier pigs (Katsumata et al, 1996 ; Quiniou et al, 2000 ) and that the definition of ADG and rearing conditions could be different among the studies (e.g., Homma et al, 2021 ; Ogawa, Kimata, et al, 2022 ; Yazaki et al, 2020 ). Therefore, a more sophisticated study to identify a suitable indicator of cumulative heat load and its impact on growth performance in pigs should be performed.…”

“…Studies to identify indicators of heat stress (e.g., Cross et al, 2018 ; Gourdine et al, 2019 ; Poullet et al, 2022 ) and those for predicting breeding values for heat tolerance using public weather data (e.g., Tiezzi et al, 2020 ; Usala et al, 2021 ; Zumbach et al, 2008 ) have also been performed. Thus, developing methodology to genetically improve pigs to cope with climate change would be valuable (e.g., Gourdine et al, 2021 ; Hara et al, 2022 ; Ogawa, Kimata, et al, 2022 ).…”

“…The data included phenotypic information on age at the start and end of testing (D30 and D105, respectively), ADG during the performance testing between 30 kg and 105 kg, and backfat thickness (BF) measured on the left side at 3 cm from the position of half the body length and loin muscle area (LMA) at D105 by ultrasound (B‐mode; Super eye meat 900; Fujihira Industry Co., Ltd., Tokyo, Japan). For the 2268 pigs, phenotypic records were between 0.5 and 5.0 cm for BF, between 80 kg and 130 kg for body weight at end of testing, between 15 and 60 cm 2 for LMA, and between 300 and 1500 g for ADG, alike with data editing implementing in the national genetic evaluation in Japan ( http://www.nlbc.go.jp/kachikukairyo/iden/buta/hyoka_meat2.pdf ) (Ogawa, Kimata, et al, 2022 ).…”