Genetic relationship between purebred and crossbred sow longevity

Background: The overall breeding objective for a nucleus swine selection program is to improve crossbred commercial performance. Most genetic improvement programs are based on an assumed high degree of positive relationship between purebred performance in a nucleus herd and their relatives' crossbred performance in a commercial herd. The objective of this study was to examine the relationship between purebred and crossbred sow longevity performance. Sow longevity was defined as a binary trait with a success occurring if a sow remained in the herd for a certain number of parities and including the cumulative number born alive as a measure of reproductive success. Heritabilities, genetic correlations, and phenotypic correlations were estimated using THRGIBBS1F90.Results: Results indicated little to no genetic correlations between crossbred and purebred reproductive traits.This indicates that selection for longevity or lifetime performance at the nucleus level may not result in improved longevity and lifetime performance at the crossbred level. Early parity performance was highly correlated with lifetime performance indicating that an indicator trait at an early parity could be used to predict lifetime performance. This would allow a sow to have her own record for the selection trait before she has been removed from the herd.Conclusions: Results from this study aid in quantifying the relationship between purebred and crossbred performance and provide information for genetic companies to consider when developing a selection program where the objective is to improve crossbred sow performance. Utilizing crossbred records in a selection program would be the best way to improve crossbred sow productivity.     

Genetics of length of productive life and lifetime prolificacy in the Finnish Landrace and Large White pig populations

The objective of this study was to estimate direct and indirect selection potential for length of productive life and lifetime prolificacy in Finnish Large White and Landrace swine populations. To study the direct selection potential, the heritabilities of these traits were estimated. The genetic correlations of length of productive life and lifetime prolificacy with prolificacy traits and overall leg conformation were estimated to evaluate whether selection for these traits could indirectly improve measures of sow longevity. In addition, correlations between length of productive life, lifetime prolificacy, ADG, and backfat thickness were estimated. Records were used from Finnish purebred Landrace (n = 26,744) and Large White (n = 24,007) sows born on operations that perform on-farm production tests on all females. Heritabilities were estimated using both a survival analysis procedure and a linear model. Due to computational limitations, correlations were estimated with the linear model only. Estimated length of productive life heritabilities obtained from linear model analyses were less (0.05 to 0.10) than those obtained from survival analyses (0.16 to 0.19). This may be indicative of the superiority of survival analysis compared with linear model analysis methods when evaluating longevity or similar types of data. All the prolificacy traits were genetically correlated with length of productive life and lifetime prolificacy, and the correlations were greater than 0.13. These results indicate that selection for increased number of piglets weaned in the first litter and for short first farrowing interval is beneficial for sow longevity and also for sow's lifetime prolificacy. The genetic correlations between length of productive life and leg conformation score also were favorable (0.32 in Landrace and 0.17 in Large White). The heritability estimates indicate that survival analysis is likely the most appropriate method of evaluating longevity traits in swine. Because of computational problems, simultaneous analysis of linear traits and longevity is not currently possible. More research is needed to develop methods for multiple linear and survival trait analyses.     

Relationships between longevity,lifetime productivity,carcass traits and conformation in Polish maternal pig breeds

The objective of this study was to obtain heritability estimates for longevity (length of life, length of productive life, number of litters) and lifetime productivity traits (lifetime pig production, lifetime pig efficiency, lifetime litter efficiency) and genetic correlation between them and litter size at first farrowing, growth (ADG), backfat thickness (BF), loin depth, lean meat percentage (LMP), phenotypic selection index (PSI), and exterior in 19423 Polish Landrace (L) and 16049 Polish Large White (LW) sows. Heritabilities for longevity and lifetime productivity traits were 0.10–0.13 for L sows and 0.09–0.11 for LW sows depending on the trait definition. The genetic correlations among these traits were all high and positive, ranging from 0.76 to 0.99. Antagonistic genetic correlations (?0.21 to ?0.26) were found between longevity traits and PSI and LMP in LW sows, while in L sows the respective parameters were lower and not significant for length of productive life. The number of live‐born piglets in the first litter was positively correlated with lifetime pig production and lifetime pig efficiency in both breeds. The genetic correlations of longevity and lifetime pig production with ADG, BF, loin depth and exterior were small, and in most cases, not significant.     

Genetic parameter estimates from joint evaluation of purebreds and crossbreds in swine using the crossbred model.

Records on lifetime daily gain and backfat from two purebred lines A (n = 6,022), B (n = 24,170), and their reciprocal crosses C (n = 6,135) were used to estimate genetic parameters using within-line and terminal-cross models. The models that were fitted included fixed (contemporary group and sex), random additive A and(or) random additive B, random dominance, and random litter effects. Model for purebreds included only one additive effect, whereas the model for crossbreds included two additive effects. End weight was included as a covariable for backfat. Heritability estimates for lifetime daily gain were 0.26, 0.28, and 0.23 with within-line models for lines A, B, and C, respectively, and 0.26, 0.30, and 0.27 with the crossbred model, respectively. Heritability estimates for backfat were 0.52, 0.35, and 0.29 with within-line models for lines A, B, and C, respectively, and 0.51, 0.38, and 0.29 with the crossbred model, respectively. The genetic correlations between purebreds and crossbreds (r(pc)) for lifetime daily gain were 0.99 (A-C) and 0.62 (B-C); for backfat the correlations were 0.32 (A-C) and 0.70 (B-C). The amount of dominance variance from the crossbred model expressed as a proportion of phenotypic variance for lifetime daily gain was 0.39, 0.16, and 0.29 for lines A, B, and C respectively. Dominance variance for backfat was estimated as 0. A joint evaluation of purebreds and crossbreds would be most efficient with the crossbred model. The dominance variation should be accounted for lifetime daily gain.     

Heat stress effects on farrowing rate in sows: genetic parameter estimation using within-line and crossbred models

The pork supply chain values steady and undisturbed piglet production. Fertilization and maintaining gestation in warm and hot climates is a challenge that can be potentially improved by selection. The objective of this study was to estimate 1) genetic variation for farrowing rate of sows in 2 dam lines and their reciprocal cross; 2) genetic variation for farrowing rate heat tolerance, which can be defined as the random regression slope of farrowing rate against increasing temperature at day of insemination, and the genetic correlation between farrowing rate and heat tolerance; 3) genetic correlation between farrowing rate in purebreds and crossbreds; and 4) genetic correlation between heat tolerance in purebreds and crossbreds. The estimates were based on 93,969 first insemination records per cycle from 24,456 sows inseminated between January 2003 and July 2008. These sows originated from a Dutch purebred Yorkshire dam line (D), an International purebred Large White dam line (ILW), and from their reciprocal crosses (RC) raised in Spain and Portugal. Within-line and crossbred models were used for variance component estimation. Heritability estimates for farrowing rate were 0.06, 0.07, and 0.02 using within-line models for D, ILW, and RC, respectively, and 0.07, 0.07, and 0.10 using the crossbred model, respectively. For farrowing rate, purebred-crossbred genetic correlations were 0.57 between D and RC and 0.50 between ILW and RC. When including heat tolerance in the within-line model, heritability estimates for farrowing rate were 0.05, 0.08, and 0.03 for D, ILW, and RC, respectively. Heritability for heat tolerance at 29.3°C was 0.04, 0.02, and 0.05 for D, ILW, and RC, respectively. Genetic correlations between farrowing rate and heat tolerance tended to be negative in crossbreds and ILW-line sows, implying selection for increased levels of production traits, such as growth and reproductive output, is likely to increase environmental sensitivity. This study shows that genetic selection for farrowing rate and heat tolerance is possible. However, when this selection is based solely on purebred information, the expected genetic progress on farrowing rate and heat tolerance in crossbreds (commercial animals) would be inconsequential.     

Estimation of genetic parameters on test stations using purebred and crossbred progeny of sires of the Bavarian Piétrain

Genetic parameters were estimated for purebred and crossbred progeny of Bavarian Piétrain sires on two test stations. The data set used contained 4276 purebred pigs and 13,980 crossbred pigs recorded between 2000 and 2004. In total 332 sires having purebred and crossbred progeny were available to estimate the genetic correlations between purebred and crossbred performances. Though the genetic correlations between purebred and crossbred pigs are fairly high (0.7–0.9), their performances have to be considered as genetically different traits, because variance components and heritabilities differ substantially. Therefore, purebred and crossbred breeding values of candidates are not identical, and thus result in different rankings. However, due to the high correlations purebred pigs provide a lot of information for estimating the crossbred breeding values of the real selection criterion. The Halothan locus, whose effects have been analyzed in detail, affects both purebred and crossbred parameters. To avoid detrimental effects on the efficiency of the breeding programme, the n-allele could be either eliminated or the genotypes of all test animals should be known. Differences in the variance components between the two test stations have been found and are problematic with respect to the breeding value estimation utilizing the pooled data set. Hence, it should be attempted to further improve the standardization of the performance test on both stations.     

Joint evaluation of purebreds and crossbreds in swine

Data from two purebred swine lines A (n = 6,022) and B (n = 24,170), and their reciprocal, cross C (n = 6,135), were used to examine gains in reliability of combined purebred and crossbred evaluation over conventional within-line evaluations using crossbred and pureline models. Random effects in the pureline model included additive, parental dominance, and litter. In the crossbred model, effects were as in the pureline model except traits of each line were treated as separate traits and two additive effects were present. The approximate model was the same as the pureline except it was used for all lines disregarding breed differences. The traits in the evaluation were lifetime daily gain (LDG) and backfat. When separate line evaluations were replaced by evaluations with crossbreds, mean reliabilities of predicted breeding values increased by 2 to 9% for purebreds and by 21 to 72% for crossbreds. Rank correlations between these breeding values were > 0.99 for purebreds but 0.85 to 0.87 for crossbreds. Rank correlations between predicted breeding values obtained from crossbred and approximate models were 0.98 to 0.99 for purebreds and 0.96 to 0.98 for crossbreds. When the number of crossbreds was small in comparison to purebreds, the increase in reliability by using the crossbred data and the crossbred model as opposed to purebred models was small for purebreds but large for crossbreds. The approximate model provided very similar rankings to the crossbred model for purebreds but rankings were less consistent for crossbreds.     

Genetic association between leg conformation in young pigs and sow longevity

Longevity is important in pig production with respect to both economic and ethical aspects. Direct selection for longevity might be ineffective because ‘true’ longevity can only be recorded when a sow has been culled or died. Thus, indirect selection for longevity using information from other traits that can be recorded early in life and are genetically correlated with longevity might be an alternative. Leg conformation has been included in many breeding schemes for a number of years. However, proving that leg conformation traits are good early indicators for longevity still remains. Our aim was to study genetic associations between leg conformation traits of young (5 months; 100 kg) Swedish Yorkshire pigs in nucleus herds and longevity traits of sows in nucleus and multiplier herds. Data included 97 533 animals with information on conformation (Movement and Overall score) recorded at performance testing and 26 962 sows with information on longevity. The longevity traits were as follows: stayability from 1st to 2nd parity, lifetime number of litters and lifetime number of born alive piglets. Genetic analyses were performed with both linear models using REML and linear‐threshold models using Bayesian methods. Heritabilities estimated using the Bayesian method were higher than those estimated using REML, ranging from 0.10 to 0.24 and 0.07 to 0.20, respectively. All estimated genetic correlations between conformation and longevity traits were significant and favourable. Heritabilities and genetic correlations between conformation and longevity indicate that selection on leg conformation should improve sow longevity.     

非洲猪瘟背景下企业留种三元与二元母猪产仔数的比较研究

主要通过比较丹系和加系纯种、二元和三元母猪的窝产健仔数,分析不同遗传背景及胎次的母猪产仔性能差异,为评估三元母猪产仔性能及其替代更新提供参考依据。研究整理了国内某规模化企业2019年5月至2020年8月间猪场的209 963条产仔记录,其中丹系56 016窝,加系153 947窝,通过单因素方差分析及邓肯检验对2个品系的母猪产健仔数进行统计分析。结果表明,丹系种猪平均窝产健仔数〔（12.54±2.61）头,n=56 016〕极显著高于加系种猪〔（11.21±2.89）头,n=153 947〕（P<0.01）。进一步分析品系内二元母猪与三元母猪的产仔性能差异,发现丹系二元母猪平均窝产健仔数〔（12.59±2.69）头,n=43 007〕显著高于丹系三元母猪〔（11.12±2.95）头,n=1 236〕（P<0.05）;加系二元母猪平均窝产健仔数〔（11.55±2.81）头,n=84 374〕显著高于加系三元母猪〔（10.73±2.87）头,n=47 185〕（P<0.05）。研究还对比了胎次对产仔性能的影响,丹系二元母猪第2胎产健仔数〔（13.28±2.32）头,n=9 396〕显著高于其他胎次,加系二元母猪第3胎产健仔数〔（12.11±2.61）头,n=12 208〕显著高于其他胎次。结果说明,三元母猪的产仔性能与二元母猪相比存在明显劣势,大规模使用三元母猪可能对企业的PSY和MSY有较大影响。当有足够二元后备母猪补充群体时,应该将使用过4~5胎的三元母猪主动淘汰,将其对企业的种猪成本及收益不利影响控制在最小范围,降低对企业经济效益的影响。     

Factors to adjust litter weight of pigs to a standard 21 days of age

Factors to standardize litter weights of nursing pigs to 21 d of age were calculated from daily weights measured on 64 crossbred litters from 10 to 32 d of age. The results were compared to published factors derived from a data set of Duroc pigs weighed every 3 to 4 d. Dams in the present study were white crossbred sows and gilts, and sires were maternal or terminal breed types. Multiplicative factors were calculated by dividing the mean 21-d litter weight (LW21) by mean daily litter weight. Linear and quadratic regression coefficients of LW21 on age at weighing were fitted to the factors (R2 = .997). The final equation for adjusting litter weights to a 21-d basis was 2.5246 - .1041 x (d of age) + .0015 x (d of age)2. There were good agreement with published factors for d 19 to 25, but divergence for younger and older litters resulted in significant differences between the linear coefficients. These differences may be due to departure from a linear growth curve, which daily measurements would incorporate, or differences in sow populations. Thus, use of the new factors should be considered for white crossbred sow populations. A least squares analysis indicated that LW21 was significantly altered by parity, not by the number of pigs allowed to nurse or by breed of sire. After adjustment to 21 d, litter weights also should be adjusted for differences in parity before evaluating sow productivity, by using additive factors such as those recommended by the National Swine Improvement Federation.     

A comparison of seasonal effects on fertility of Landrace and Large White sows in a subhumid tropical environment

Forty sows comprising equal numbers of Landrace (LR) and Large White (LW) breeds were studied for 20 months to determine the effects of dry and rainy seasons on their fertility traits in Nigeria. Ambient temperature (14.0-35.5 degrees C) was lowest at night and highest in the day and peaked during the dry season. A relative humidity range of 60-82% peaked during the late rainy season. LR produced 218 and 278 piglets from 23 and 28 litters, while LW produced 193 and 316 piglets from 20 and 30 litters during dry and rainy seasons respectively. Fertility traits did not differ significantly between the breeds due to season (P greater than 0.05). Mean post-weaning oestrus at 10.9 and 9.0 days and preweaning mortality rates of 14.2% and 16.9% during dry and rainy seasons for LR differed significantly (P less than 0.5) from those of LW which were 12.2 and 8.2 days and 14.5% and 18.9% respectively. The significance of seasonal effects on sow fertility and necessary management techniques are discussed.     

Analysis of longevity and exterior traits on Large White sows in Switzerland

Records of length of productive life, from first farrowing to culling, of 16,464 Large White purebred sows from SUISAG were studied using survival analysis. The major aims of the study were to model the risk of culling within parity and to assess the influence of exterior traits, such as the number of teats or feet and leg scores, on culling. Culling was concentrated at the first day after each farrowing or at the first day after weaning. Weaning itself was mostly between 21 and 49 d after farrowing, with an average weaning age of 35 d. Because of the definition of culling date used, there was practically no risk of culling from these periods. The culling rates at different periods suggested a modeling of the baseline hazard function within parity instead of over the entire productive life of the animals. A piecewise Weibull function and a simple graphical method to validate its adequacy were proposed for sow longevity analysis. The risk of culling increased with older parities (P < 0.001) and with decreasing litter size at weaning (P < 0.001). The exterior traits analyzed (number of teats, and feet and leg scores, on a scale from 1 to 7) had a moderate effect on the risk of culling compared with other factors but were still influential on survival, productive life expectancy, and annual replacement rate. Sows with less than 13 good teats had 1.35 times greater risk of being culled than sows with more good teats (P < 0.05). Sows with an X-O rear leg score of 2 had 1.4 times greater risk of being culled than sows with an intermediate score of 4 (P < 0.05). Sows at the optimum score of 4 for the size of inner claws of the rear leg had 0.83 times less risk of being culled (P < 0.01) than sows with scores of 2 and 3. Furthermore, when a phenotypic index for feet and legs was used to group these variables, the effect was highly significant (P < 0.001). Therefore, a means to improve longevity is through phenotypic selection of replacement gilts based on exterior traits: gilts with 13 or less good teats or with extreme feet and leg scores should be culled. From a genetic point of view, sows with the best value in the current index for exterior traits had a lower risk of culling (P < 0.01), and therefore, it is possible to obtain a response for sow longevity via indirect selection for exterior traits. From 1999 to 2003, the trend has been to eliminate extreme animals on exterior traits. This may partly explain the improvement of sow length of productive life longevity from 560 d in 2000 to nearly 710 d in 2003 observed in the data set.     

Bioeconomic evaluation of sow longevity and profitability

Sow production indicators, including litter size, litter weight, and the length of time that sows remained in the herd (sow longevity), were used to characterize sow performance and profitability. Sow longevity and production records from 148,568 sows in 32 commercial herds from Central Illinois from January 1995 to May 2001 were analyzed using survival and repeatability models, respectively. The factors studied included sow genetics (32 genetic lines), with eight major lines present in multiple herds, and the combination of herd and year of entry in the herd. The largest difference in longevity between the major genetic lines was approximately one parity. There were differences (P < 0.05) in the instantaneous sow removal rate or hazard from the major lines. These differences constitute evidence that sow longevity could be improved by using replacements from specific genetic lines. The net present value per sow (present value of future cash flows and the present value of the sow) was used to evaluate the effect of sow longevity and production traits on economic returns. Assuming a zero discount rate per parity, genetic lines with longer herd life resulted in greater profit than genetic lines with shorter herd life. This difference was reduced with increasing discount rates and was reversed with high discount rates and low net income per litter. These results suggest that the magnitude of the economic improvement attained through the use of sow genetic lines with longer longevity depends on the economic context under which the evaluation is made.     

Genetic analysis of age at first service, return rate, litter size, and weaning-to-first service interval of gilts and sows

The aim of this study was to estimate genetic parameters of seven traits related to sow reproductive performance. Data on all Norwegian Landrace pigs (NL) born in nucleus herds and raised in nucleus or multiplying herds from 1990 to 2000 were extracted from the Norwegian national recording scheme. Reproductive traits investigated were age at first service (AFS), return rate in gilts (RRg), age at first farrowing (AFF), live-born piglets in the first litter (NBA1), interval from weaning to first service after first litter (WTS1), return rate after first litter (RR1), live-born piglets in the second litter (NBA2), and interval from weaning to first service after second litter (WTS2). After editing, the data set comprised 12,583 to 56,042 records, depending on the trait. A mixed linear and a joint linear threshold animal model were used to estimate (co)variance components. A full Bayesian approach via Gibbs sampling was adopted. The statistical model used for analysis included contemporary groups of herd-year (-season), purebred or crossbred litter, single or double insemination, mating type, parity in which the animal was born, a regression on lactation length, and an additive genetic effect. Neither the estimated heritabilities nor the genetic correlations differed much between the two approaches, but there was a tendency for higher genetic correlations using the joint linear threshold model approach. Average heritabilities were as follows: AFS = 0.31; RRg = 0.03; RR1 = 0.02; NBA1 = 0.12; NBA2 = 0.14; WTS1 = 0.08; and WTS2 = 0.03. The highest genetic correlations were estimated between NBA1 and NBA2 (r(g) = 0.95), RR1 and WTS1 (r(g) = 0.93), and between WTS1 and WTS2 (r(g) = 0.78). The estimated genetic correlation between NBA and WTS were close to zero. Selection for increased NBA will slightly increase AFS and reduce the probability of a return. Selection for decreased AFS will have a favorable effect on WTS intervals; however, selection for decreased AFS seems to have an unfavorable effect on return rate both on gilts and sows. Conversely, selection for decreased WTS intervals will reduce the probability of a return. Potential selection candidates to include in a multivariate fertility index are AFS, NBA, and WTS1. Due to the low heritability and low, but favorable, genetic correlations to NBA and WTS, RR is not recommended as a selection candidate.     

Optimal definition of contemporary groups for crossbred pigs in a joint purebred and crossbred genetic evaluation

In the pig industry, purebred animals are raised in nucleus herds and selected to produce crossbred progeny to perform in commercial environments. Crossbred and purebred performances are different, correlated traits. All purebreds in a pen have their performance assessed together at the end of a performance test. However, only selected crossbreds are removed (based on visual inspection) and measured at different times creating many small contemporary groups (CGs). This may reduce estimated breeding value (EBV) prediction accuracies. Considering this sequential recording of crossbreds, the objective was to investigate the impact of different CG definitions on genetic parameters and EBV prediction accuracy for crossbred traits. Growth rate (G_P) and ultrasound backfat (BF_P) records were available for purebreds. Lifetime growth (G_X) and backfat (BF_X) were recorded on crossbreds. Different CGs were tested: CG_all included farm, sex, birth year, and birth week; CG_week added slaughter week; and CG_day used slaughter day instead of week. Data of 124,709 crossbreds were used. The purebred phenotypes (62,274 animals) included three generations of purebred ancestors of these crossbreds and their CG mates. Variance components for four-trait models with different CG definitions were estimated with average information restricted maximum likelihood. Purebred traits’ variance components remained stable across CG definitions and varied slightly for BF_X. Additive genetic variances (and heritabilities) for G_X fluctuated more: 812 ± 36 (0.28 ± 0.01), 257 ± 15 (0.17 ± 0.01), and 204 ± 13 (0.15 ± 0.01) for CG_all, CG_week, and CG_day, respectively. Age at slaughter (AAS) and hot carcass weight (HCW) adjusted for age were investigated as alternatives for G_X. Both have potential for selection but lower heritabilities compared with G_X: 0.21 ± 0.01 (0.18 ± 0.01), 0.16 ± 0.02 (0.16 + 0.01), and 0.10 ± 0.01 (0.14 ± 0.01) for AAS (HCW) using CG_all, CG_week, and CG_day, respectively. The predictive ability, linear regression (LR) accuracy, bias, and dispersion of crossbred traits in crossbreds favored CG_day, but correlations with unadjusted phenotypes favored CG_all. In purebreds, CG_all showed the best LR accuracy, while showing small relative differences in bias and dispersion. Different CG scenarios showed no relevant impact on BF_X EBV. This study shows that different CG definitions may affect evaluation stability and animal ranking. Results suggest that ignoring slaughter dates in CG is more appropriate for estimating crossbred trait EBV for purebred animals.     

Effect of leg conformation on survivability of Duroc, Landrace, and Large White sows

Sow longevity influences farm economy and can be considered an important indicator of animal welfare. Body features such as leg conformation can play a key role in sow longevity, although little is known about its effect on culling decisions. Within this context, longevity data from 587 Duroc, 239 Landrace, and 217 Large White sows were analyzed with special emphasis on the effect of leg conformation. Sow longevity was analyzed twice for each breed, testing the effect of a subjective overall score for leg conformation, or the presence or absence of 6 specific leg conformation defects. Each preliminary model also included a teat conformation score with 3 levels, farm or origin, backfat thickness at 6 mo of age, and 2 continuous sources of variation, namely the age at the first farrowing and the number of piglets born alive at each farrowing. Overall leg conformation score influenced (P < 0.01) sow longevity in Duroc, Landrace, and Large White sows, with a greater hazard ratio (HR) for poorly conformed sows (1.56, 2.16, and 1.79, respectively) than for well-conformed sows (0.32, 0.66, and 0.68, respectively). Abnormal hoof growth reduced survivability in Duroc (HR = 2.78; P < 0.001) and Landrace sows (HR = 1.88; P < 0.01); the presence of splayed feet (P < 0.05) or bumps and injuries (P < 0.001) increased the risk of culling in Duroc sows (HR = 2.08 and 3.57, respectively), whereas the incidence of straight pastern increased the HR in Large White sows (HR = 2.49; P < 0.01). In all 3 breeds, longevity decreased for plantigrade sows, with a greater HR in Duroc (HR = 3.38; P < 0.001) than in Landrace (HR = 1.53; P < 0.10) and Large White sows (HR = 1.73; P < 0.05). Teat conformation did not influence sow longevity (P > 0.10). Estimates of heritability for longevity in Duroc sows ranged from 0.05 to 0.07 depending on the algorithm applied. Leg conformation had a substantial effect on sow longevity, where an accurate removal of poorly leg-conformed candidate gilts before first mating could improve sow survival and reduce culling costs. These moderate estimates of heritability indicated that survivability of Duroc sows could be genetically improved by direct selection for leg conformation.     

Genetic analysis of sow longevity and sow lifetime reproductive traits using censored data

Sow longevity is a key component for efficient and profitable pig farming; however, approximately 50% of sows are removed annually from a breeding herd. There is no consensus in the scientific literature regarding a definition for sow longevity; however, it has been suggested that it can be measured using several methods such as stayability and economic indicators such as lifetime piglets produced. Sow longevity can be improved by genetic selection; however, it is rarely included in genetic evaluations. One reason is elongated time intervals required to collect complete lifetime data. The effect of genetic parameter estimation software in handling incomplete data (censoring) and possible early indicator traits were evaluated analysing a 30% censored data set (12 725 pedigreed Landrace × Large White sows that included approximately 30% censored data) with DMU6, THRGIBBS1F90 and GIBBS2CEN. Heritability estimates were low for all the traits evaluated. The results show that the binary stayability traits benefited from being analysed with a threshold model compared to analysing with a linear model. Sires were ranked very similarly regardless if the program handled censoring when all available data were included. Accumulated born alive and stayability were good indicators for lifetime born alive traits. Number of piglets born alive within each parity could be used as an early indicator trait for sow longevity.     

Survival analysis of preweaning piglet survival in a dry-cured ham-producing crossbred line

The aim of this study was to investigate piglet preweaning survival and its relationship with a total merit index (TMI) used for selection of Large White terminal boars for dry-cured ham production. Data on 13,924 crossbred piglets (1,347 litters), originated by 189 Large White boars and 328 Large White-derived crossbred sows, were analyzed under a frailty proportional hazards model, assuming different baseline hazard functions and including sire and nursed litter as random effects. Estimated hazard ratios (HR) indicated that sex, cross-fostering, year-month of birth, parity of the nurse sow, size of the nursed litter, and class of TMI were significant effects for piglet preweaning survival. Female piglets had less risk of dying than males (HR = 0.81), as well as cross-fostered piglets (HR = 0.60). Survival increased when piglets were nursed by sows of third (HR = 0.85), fourth (HR = 0.76), and fifth (HR = 0.79) parity in comparison with first and second parity sows. Piglets of small (HR = 3.90) or very large litters (HR >1.60) had less chance of surviving in comparison with litters of intermediate size. Class of TMI exhibited an unfavorable relationship with survival (HR = 1.20 for the TMI top class). The modal estimates of sire variance under different baseline hazard functions were 0.06, whereas the variance for the nursed litter was close to 0.7. The estimate of the nursed litter effect variance was greater than that of the sire, which shows the importance of the common environment generated by the nurse sow. Relationships between sire rankings obtained from different survival models were high. The heritability estimate in equivalent scale was low and reached a value of 0.03. Nevertheless, the exploitable genetic variation for this trait justifies the inclusion of piglet preweaning survival in the current breeding program for selection of Large White terminal boars for dry-cured ham production.     

Genomic estimation of additive and dominance effects and impact of accounting for dominance on accuracy of genomic evaluation in sheep populations

The objectives of this study were to estimate the additive and dominance variance component of several weight and ultrasound scanned body composition traits in purebred and combined cross‐bred sheep populations based on single nucleotide polymorphism (SNP) marker genotypes and then to investigate the effect of fitting additive and dominance effects on accuracy of genomic evaluation. Additive and dominance variance components were estimated in a mixed model equation based on “average information restricted maximum likelihood” using additive and dominance (co)variances between animals calculated from 48,599 SNP marker genotypes. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP), and the accuracy of prediction was assessed based on a random 10‐fold cross‐validation. Across different weight and scanned body composition traits, dominance variance ranged from 0.0% to 7.3% of the phenotypic variance in the purebred population and from 7.1% to 19.2% in the combined cross‐bred population. In the combined cross‐bred population, the range of dominance variance decreased to 3.1% and 9.9% after accounting for heterosis effects. Accounting for dominance effects significantly improved the likelihood of the fitting model in the combined cross‐bred population. This study showed a substantial dominance genetic variance for weight and ultrasound scanned body composition traits particularly in cross‐bred population; however, improvement in the accuracy of genomic breeding values was small and statistically not significant. Dominance variance estimates in combined cross‐bred population could be overestimated if heterosis is not fitted in the model.     

Between-breed variability of stillbirth and its relationship with sow and piglet characteristics

Litter characteristics at birth were recorded in 4 genetic types of sows with differing maternal abilities. Eighty-two litters from F(1) Duroc x Large White sows, 651 litters from Large White sows, 63 litters from Meishan sows, and 173 litters from Laconie sows were considered. Statistical models included random effects of sow, litter, or both; fixed effects of sow genetic type, parity, birth assistance, and piglet sex, as well as gestation length, farrowing duration, piglet birth weight, and litter size as linear covariates. The quadratic components of the last 2 factors were also considered. For statistical analyses, GLM were first considered, assuming a binomial distribution of stillbirth. Hierarchical models were also fitted to the data to take into account correlations among piglets from the same litter. Model selection was performed based on deviance and deviance information criterion. Finally, standard and robust generalized estimating equations (GEE) procedures were applied to quantify the importance of each effect on a piglet's probability of stillbirth. The 5 most important factors involved were, in decreasing order (contribution of each effect to variance reduction): difference between piglet birth weight and the litter mean (2.36%), individual birth weight (2.25%), piglet sex (1.01%), farrowing duration (0.99%), and sow genetic type (0.94%). Probability of stillbirth was greater for lighter piglets, for male piglets, and for piglets from small or very large litters. Probability of stillbirth increased with sow parity number and with farrowing duration. Piglets born from Meishan sows had a lower risk of stillbirth (P < 0.0001) and were little affected by the sources of variation mentioned above compared with the 3 other sow genetic types. Standard and robust GEE approaches gave similar results despite some disequilibrium in the data set structure highlighted with the robust GEE approach.