Parity changed fecal microbiota of sows and its correlation with milk long-chain fatty acid profiles

The goal of this study was to characterize the fecal microbiota profiles of gestating sows, along with the fecal microbiota and milk fatty acid contents of lactating sows and their correlations with reproductive performance at different parities. The results showed that the microbiota of third parity gestating sows contained a greater abundance of Prevotella compared to the other two parity groups, while lactating sows exhibiting higher reproductive performance at fifth parity exhibited a greater abundance of Lactobacillus species. The lactating sows with higher reproductive performance also exhibited higher total monounsaturated fatty acid (MUFA) and higher total polyunsaturated fatty acid (PUFA) levels relative to sows with lower reproductive performance at all three analyzed parities, especially sows at fifth parity produced the lowest total saturated fatty acid (SFA) levels, and showed the highest C18:1n9c and C18:2n6c concentrations. In correlational analyses, the abundance of Oligella, Lactobacillus, and Corynebacterium was highly positively correlated with C18:1n9c, C18:2n6c, and C20:4n6. Overall, these results provide a rational basis for efforts to improve sow reproductive performance through the provision of precisely regulated nutrition. KEY POINTS: • Clear differences in the fecal microbiota were evident between sows of different parities. • Lactating sows with high reproductive performance showed distinct milk fatty acid profiles.

Genetic parameter analysis of reproductive traits in Large White pigs

OBJECTIVE

The primary objective of this study was to determine the genetic parameters for reproductive traits among Large White pigs, including the following traits: total number born (TNB), number born alive (NBA), litter birth weight (LBW), average birth weight (ABW), gestation length (GL), age at first service (AFS) and age at first farrowing (AFF).

METHODS

The dataset consisted of 19,036 reproductive records from 4,986 sows, and a multi-trait animal model was used to estimate genetic variance components of seven reproductive traits.

RESULTS

The heritability estimates for these reproductive traits ranged from 0.09 to 0.26, with the highest heritability for GL and AFF, and the lowest heritability for NBA. The repeatabilities for TNB, NBA, LWB, ABW, and GL were ranged from 0.16 to 0.34. Genetic and phenotypic correlations ranged from -0.41 to 0.99, and -0.34 to 0.98, respectively. In particular, the correlations between TNB, NBA and LBW, between AFS and AFF, exhibited a strong positive correlation. Furthermore, for TNB, NBA, LBW, ABW, and GL, genetic correlations of the same trait between different parities were moderately to strongly correlated (0.32 to 0.97), and the correlations of adjacent parities were higher than those of nonadjacent parities.

CONCLUSION

All the results in the present study can be used as a basis for the genetic assessment of the target population. In the formulation of dam line selection index, AFS or AFF can be considered to combine with TNB in a multiple trait swine breeding value estimation system. Moreover, breeders are encouraged to increase the proportion of sows at parity 3-5 and reinforce the management of sows at parity 1 and parity ≥8.

Genetic aspects of piglet survival and related traits: a review

In livestock, mortality in general, and mortality of the young, is societal worries and is economically relevant for farm efficiency. Genetic change is cumulative; if it exists for survival of the young and genetic merit can be estimated with sufficient accuracy, it can help alleviate the pressure of mortality. Lack of survival is a moving target; livestock production is in continuous change and labor shortage is a given. There is now ample evidence of clear genetic variance and of models able to provide genomic predictions with enough accuracy for selection response. Underlying traits such as birth weight, uniformity in birth weight, gestation length, number of teats, and farrowing duration all show genetic variation and support selection for survival or, alternatively, be selected for on their own merit.

Parturition in Mammals: Animal Models, Pain and Distress

Parturition is a complex physiological process and involves many hormonal, morphological, physiological, and behavioural changes. Labour is a crucial moment for numerous species and is usually the most painful experience in females. Contrary to the extensive research in humans, there are limited pain studies associated with the birth process in domestic animals. Nonetheless, awareness of parturition has increased among the public, owners, and the scientific community during recent years. Dystocia is a significant factor that increases the level of parturition pain. It is considered less common in polytocous species because newborns' number and small size might lead to the belief that the parturition process is less painful than in monotocous animal species and humans. This review aims to provide elements of the current knowledge about human labour pain (monotocous species), the relevant contribution of the rat model to human labour pain, and the current clinical and experimental knowledge of parturition pain mechanisms in domestic animals that support the fact that domestic polytocous species also experience pain. Moreover, both for women and domestic animal species, parturition's pain represents a potential welfare concern, and information on pain indicators and the appropriate analgesic therapy are discussed.

Phenotypic and genomic relationships between vulva score categories and reproductive performance in first-parity sows

Background

One of the biggest challenges in the swine industry is to increase female reproductive efficiency. Recently, vulva score categories (VSC), assessed prior to puberty, has been proposed as an indicator trait of efficient reproductive performance in sows. The objective of this study was to validate the use of VSC as an indicator trait for reproductive performance, and to perform genetic and genomic analyses for VSC.

Methods

The phenotypic relationship of VSC, using a three-point scale: small (VSC-S), medium (VSC-M), and large (VSC-L), on reproductive performance was evaluated on three farms. VSC was measured at 15 weeks of age, for farms 1 and 2, and at 14 weeks of age for farm 3 on 3981 Yorkshire gilts, in which 1083 had genotypes (~ 50 K SNPs). Genetic parameters for VSC with reproductive traits were estimated using ssGBLUP. A Genome-wide association study (GWAS) for VSC was performed using BayesB.

Results

For the phenotypic analysis of VSC across datasets, differences in performance were identified there was a significant effect (P ≤ 0.05) for the interaction between Farm and VSC for total number dead (TND), and a trend (P < 0.10) for total number born (TNB). There were significant (P ≤ 0.05) pre-defined contrasts of VSC-S versus VSC-M + L on TNB, number born alive (NBA), TND, number of stillborn (NSB), and number of mummies (MUM). Heritability estimates for VSC as a categorical trait (VSCc) and a quantitative trait (VSCq) were 0.40 ± 0.02 and 0.83 ± 0.02, respectively, for across farm, 0.13 ± 0.07 and 0.20 ± 0.10, respectively, for Farm1, 0.07 ± 0.07 and 0.09 ± 0.09, respectively, for Farm2, and 0.20 ± 0.03 and 0.34 ± 0.05, respectively, for Farm3. For across farms, favorable genetic correlations estimates were found for TNB (0.28 ± 0.19) and NBA (0.26 ± 0.17). Within farms, moderate genetic correlations between VSC with reproductive traits were found for TNB (0.61 ± 0.47) and MUM (0.69 ± 0.47) for farm 1, for number of services until first farrow (NS; 0.69 ± 0.38) and unique service with successful first farrow (SFS; − 0.71 ± 0.38) for farm 3. Multiple genomic regions associated with VSC_c were identified. Of these, a QTL located on chromosome 3 at 33–34 Mb accounted for about 7.1% of the genetic variance for VSC_c and VSC_q. This region harbors the gene PRM1 that has been associated with early embryonic development in pigs.

Conclusions

The results support potential of VSC for improved reproductive efficiency on first-parity performance, but the results might depend on the interaction between environmental factors and VSC, as well as potentially additive genetics.

Mixing aggression intensity is associated with age at first service and floor type during gestation, with implications for sow reproductive performance

Aggression resulting from mixing to establish a dominance hierarchy is a major welfare concern for group-housed sows. The associated stress can negatively impact aspects of reproductive performance. Objectives of this study were to investigate associations between 1) age at first service (AFS) and mixing aggression intensity in first parity sows, 2) mixing aggression intensity and reproductive performance within and between parity one and parity two, and 3) mixing aggression intensity, floor type during gestation and reproductive performance. Gilts (n =160, hereafter referred to as sows) were mixed into stable groups of eight unfamiliar individuals approximately 4 days after artificial insemination, housed on fully slatted concrete (CON; n =80) floor uncovered or covered with rubber slat mats (RUB; n =80), and followed through two parities. Skin lesions (SLMIX; a proxy for the intensity of mixing aggression), were scored post mixing in each parity according to severity (0=no lesions to 5=severe lesions) on five body regions (ear, neck, hindquarter, rump, and belly) on the left and right sides, and at the tail/anogenital region. Total SLMIX score was calculated for each sow. Data on reproductive performance traits were acquired retrospectively from farm records for both parities. Two analyses were performed: 1) data from each parity were analysed separately and 2) SLMIX score in parity one was used to predict reproductive performance in parity two. Lower AFS was associated with a lower SLMIX score in parity one (P =0.031). There was no association between SLMIX score and reproductive performance in parity one, while sows with higher SLMIX score in parity two had a higher proportion of piglets dead during lactation (P =0.027) and a longer cycle length (P =0.003) in parity two. Sows with higher SLMIX scores in parity one had more non-productive days (P <0.001) in parity two. Concrete sows had a higher SLMIX score than RUB sows in parity one (P =0.015), but not in parity two. In addition, CON sows had a higher proportion of piglets born dead (P =0.013) compared with RUB sows in parity two. Mixing aggression has a negative influence on reproductive performance within parities, and it may also have a long-term negative carry-over effect on reproductive performance in subsequent parities. Serving gilts at younger ages could help to minimize the intensity of aggression at mixing, while housing on rubber flooring has beneficial implications for their reproductive performance.

Comparison of two models to estimate genetic parameters for number of born alive in pigs

The performance of the two‐trait animal model that regards the first parity and later parities as two different traits in estimating genetic parameters for number of born alive (NBA) was examined using real and simulated data. Genetic parameters for NBA were estimated in purebred Landrace and Large White pigs using a single‐trait repeatability model (Model 1) that regards all parities as the same trait and a two‐trait animal model (Model 2) that regards the first and the later parities as different traits. For Model 2, the permanent environmental effect was fitted to only the records of the later parities. Heritability for NBA estimated using Model 1 was 0.12 for Landrace and 0.11 for Large White. Estimated heritability for NBA of the first parity and the later parities was 0.21 and 0.16, respectively, for Landrace; 0.18 and 0.16, respectively, for Large White obtained using Model 2, and higher than those in both breeds obtained using Model 1. Further results based on data simulated using the Monte Carlo method suggest that estimated additive genetic variance could be more biased using Model 2 than Model 1.

Farm data analysis for lifetime performance components of sows and their predictors in breeding herds

Our objectives in this review are 1) to define the four components of sow lifetime performance, 2) to organize the four components and other key measures in a lifetime performance tree, and 3) to compile information about sow and herd-level predictors for sow lifetime performance that can help producers or veterinarians improve their decision making. First, we defined the four components of sow lifetime performance: lifetime efficiency, sow longevity, fertility and prolificacy. We propose that lifetime efficiency should be measured as annualized piglets weaned or annualized piglets born alive which is an integrated measure for sow lifetime performance, whereas longevity should be measured as sow life days and herd-life days which are the number of days from birth to removal and the number of days from date of first-mating to removal, respectively. We also propose that fertility should be measured as lifetime non-productive days, whereas prolificacy should be measured as lifetime pigs born alive. Second, we propose two lifetime performance trees for annualized piglets weaned and annualized piglets born alive, respectively, and show inter-relationships between the four components of the lifetime performance in these trees. Third, we describe sow and herd-level predictors for high lifetime performance of sows. An example of a sow-level predictor is that gilts with lower age at first-mating are associated with higher lifetime performance in all four components. Other examples are that no re-service in parity 0 and shorter weaning-to-first-mating interval in parity 1 are associated with higher fertility, whereas more piglets born in parity 1 is associated with higher prolificacy. It appears that fertility and prolificacy are independent each other. Furthermore, sows with high prolificacy and high fertility are more likely to have high longevity and high efficiency. Also, an increased number of stillborn piglets indicates that sows have farrowing difficulty or a herd health problem. Regarding herd-level predictors, large herd size is associated with higher efficiency. Also, herd-level predictors can interact with sow level predictors for sow lifetime performance. For example, sow longevity decreases more in large herds than small-to-mid herds, whereas gilt age at first-mating increases. So, it appears that herd size alters the impact of delayed gilt age at first-mating on sow longevity. Increased knowledge of these four components of sow lifetime performance and their predictors should help producers and veterinarians maximize a sow's potential and optimize her lifetime productivity in breeding herds.

Genetic Analysis of Antibody Response to Porcine Reproductive and Respiratory Syndrome Vaccination as an Indicator Trait for Reproductive Performance in Commercial Sows

We proposed to investigate the genomic basis of antibody response to porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) vaccination and its relationship to reproductive performance in non-PRRSV-infected commercial sows. Nine hundred and six F1 replacement gilts (139 ± 17 days old) from two commercial farms were vaccinated with a commercial modified live PRRSV vaccine. Blood samples were collected about 52 days after vaccination to measure antibody response to PRRSV as sample-to-positive (S/P) ratio and for single-nucleotide polymorphism (SNP) genotyping. Reproductive performance was recorded for up to 807 sows for number born alive (NBA), number of piglets weaned, number born mummified (MUM), number of stillborn (NSB), and number of pre-weaning mortality (PWM) at parities (P) 1-3 and per sow per year (PSY). Fertility traits such as farrowing rate and age at first service were also analyzed. BayesC0 was used to estimate heritability and genetic correlations of S/P ratio with reproductive performance. Genome-wide association study (GWAS) and genomic prediction were performed using BayesB. The heritability estimate of S/P ratio was 0.34 ± 0.05. High genetic correlations (r _g) of S/P ratio with farrowing performance were identified for NBA P1 (0.61), PWM P2 (-0.70), NSB P3 (-0.83), MUM P3 (-0.84), and NSB PSY (-0.90), indicating that genetic selection for increased S/P ratio would result in improved performance of these traits. A quantitative trait locus was identified on chromosome 7 (∼25 Mb), at the major histocompatibility complex (MHC) region, explaining ∼30% of the genetic variance for S/P ratio, mainly by SNPs ASGA0032113, H3GA0020505, and M1GA0009777. This same region was identified in the bivariate GWAS of S/P ratio and reproductive traits, with SNP H3GA0020505 explaining up to 10% (for NBA P1) of the genetic variance of reproductive performance. The heterozygote genotype at H3GA0020505 was associated with greater S/P ratio and NBA P1 (P = 0.06), and lower MUM P3 and NSB P3 (P = 0.07). Genomic prediction accuracy for S/P ratio was high when using all SNPs (0.67) and when using only those in the MHC region (0.59) and moderate to low when using all SNPs excluding those in the MHC region (0.39). These results suggest that there is great potential to use antibody response to PRRSV vaccination as an indicator trait to improve reproductive performance in commercial pigs.

Association between single-nucleotide polymorphisms within candidate genes and fertility in Landrace and Duroc pigs

Finding effective predictors of traits related to boar fertility is essential for increasing the efficiency of artificial insemination systems in pig breeding. The objective of this study was to find associations between single-nucleotide polymorphisms (SNPs) within candidate genes and fertility in the breeds Landrace and Duroc. Animals with breeding values for total number of piglets born, were re-sequenced for exonic regions of 14 candidate genes related to male and female fertility using samples from 16 Landrace boars and 16 Duroc boars (four with high and four with low breeding value of total number of piglets born for each breed for male fertility, and the same for female fertility) to detect genetic variants. Genotyping for the detected SNPs was done in 619 Landrace boars and 513 Duroc boars. Two SNPs in BMPR1 and one SNP in COX-2 were found significantly associated with the total number of piglets born in Landrace. In Duroc, two SNPs in PLC_z, one SNP in VWF and one SNP in ZP3 were found significantly associated with total number of piglets born. These SNPs explained between 0.27% and 1.18% of the genetic variance. These effects are too low for being used directly for selection purposes but can be of interest in SNP-panels used for genomic selection.

Effect of selection for residual feed intake during the grow/finish phase of production on sow reproductive performance and lactation efficiency

As feed costs continue to rise and efficiency during finishing is emphasized, the impact of selecting for more efficient grow/finish pigs on reproductive performance and feed efficiency of sows must be evaluated. Therefore, the objectives of this study were to evaluate correlated responses for sow reproductive performance and lactation feed efficiency to selection for residual feed intake (RFI) during the grow/finish phase of production (RFI) in 2 selection lines of pigs developed at Iowa State University (Ames, IA) and to estimate heritabilities of these traits. One line was selected over 7 generations for decreased RFIG/F (low RFI [LRFI] line) and the other line was randomly selected for 5 generations and then selected for increased RFIG/F (high RFI [HRFI] line). After 7 generations of selection, LRFI sows had 1.0 more piglets farrowed ( = 0.11) compared with HRFI sows, 1.3 more pigs born alive ( < 0.05), similar farrowing survival, 0.4 fewer mummies ( < 0.01), and more piglets weaned, both by litter (1.6 more; < 0.01) and by sow (1.1 more; < 0.01). Low RFI sows consumed 25 kg less feed and lost 9.8 kg more BW, 7.0 kg more fat mass, and 3.1 mm more backfat than HRFI sows ( < 0.001) during lactation. Although LRFI sows had a greater negative energy balance (-19.8 vs. -8.0 MJ ME/d; < 0.001), they had better RFI during lactation (-28.6 vs. 8.2 kg; < 0.0001), and the trend was for LRFI sows to have better lactation efficiency (61.3 vs. 57.8%; = 0.47) than HRFI sows. Heritabilities for sow weights, sow body composition, sow maintenance requirements (estimated from BW), and piglet birth weight were high ( > 0.4, SE < 0.07). Traits pertaining to piglet growth during lactation and mobilization of body tissue of the sow were moderately heritable (0.2 < < 0.4, SE < 0.07). In conclusion, selection for decreased RFI has favorably affected piglet performance and lactation efficiency but has unfavorably affected sow body condition loss and energy balance during lactation. These results indicate that pigs selected for increased efficiency during grow-finish are better able to direct resources where needed during other life history phases, that is, reproduction and lactation.

Technical note: A general transformation formula for interval traits connected with reproduction in pigs

A general transformation formula is presented for interval traits in pigs, such as weaning-to-first-service interval or farrowing interval. In the logarithmic transformation, only observations that are greater than the median of the trait as originally recorded are transformed. The transformation considerably reduces the skewness and kurtosis of the trait so that the distribution of transformed data approaches normality. In analysis of >12,000 records for weaning-to-first-service interval and >9,000 records for farrowing interval, heritability estimates for the transformed data were greater than for the data as recorded. Therefore, selection on the transformed trait should be more effective than selection on the original trait. The transformation formula is likely to be appropriate for other livestock species and traits.

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.

Environmental and genetic factors affecting the weaning-estrus interval in sows

We examined the effects of environmental and genetic factors on the weaning-to-estrus interval (WEI) in sows. In order to perform the analyses of the environmental factors, 8104 observations of the 1st to the 6th WEI were carried out, while 6548 observations of the 1st to the 3rd WEI were carried out for the analyses of genetic factors. The environmental model included as fixed effects, herd, genetic line, year and season of birth, as well as the covariates, age of sow at farrowing, litter size at birth and lactation length. Genetic analysis was performed by repeatability and multitrait models. The mean and coefficient of variation for WEI were 7.02 days and 100.6%, respectively. The linear effect of lactation length and the quadratic effect of the age of sow at farrowing affected the WEI. Herd, year and season of farrowing were significant sources of variation for WEI, and there was no influence of genetic line or of litter size at birth. Heritability estimated by the repeatability model was 0.04, while heritabilities obtained by the multitrait model were 0.07, 0.02 and 0.07 for the first three WEI, respectively. Estimates of genetic correlations among the different WEI were of moderate to low magnitude. It was concluded that environmental factors, such as year and season of farrowing, lactation length, age of sow at farrowing and herd, should be considered in the model for best estimation of genetic parameters for this trait. Although with only a small possible genetic gain, selection can be made based on the first WEI.

Phenotypic and genetic correlations between gilt estrus, puberty, growth, composition, and structural conformation traits with first-litter reproductive measures

The objective was to estimate correlations of gilt estrus, puberty, growth, composition, and structural conformation traits with first-litter reproductive measures. Four groups of gilts (n = 1,225; Genetic Improvement Services of NC, Newton Grove, NC) entered the NC Swine Evaluation Station (Clayton, NC) averaging 162 d of age and were observed daily for symptoms of estrus. Once symptoms of first estrus were observed in 70% of gilts, recording of symptoms of estrus in all gilts occurred every 12 h for 30 d, utilizing fence-line boar contact. Subjective estrous traits were maximum and total strength of standing reflex, as observed with and without the presence of a boar, and strength of vulva reddening and swelling. Objective estrous traits consisted of vulva redness, vulva width, length of estrus, and age at puberty. Growth and composition traits included BW at puberty, days to 114 kg, and 10th rib backfat and LM area at 114 kg and at puberty. Subjective structural conformation traits were muscle mass, rib width, front leg side view, rear leg side view, front legs front view, rear legs rear view, and locomotion. First-litter sow traits included if gilt farrowed (Stay), age at first farrowing (AFF), total number of piglets born (TNB), and weaning to conception interval (WCI). Variance components were estimated using an animal model with AIREMLF90 for linear traits and THRGIBBS1F90 for categorical traits. Heritability estimates for Stay, AFF, and TNB were 0.14, 0.22, and 0.02, respectively. Genetic correlations between length of estrus, the standing reflex traits, and age at puberty with Stay were 0.34, 0.34 to 0.74, and -0.27, respectively, and with AFF were -0.11, -0.04 to -0.41, and 0.76, respectively. Days to 114 kg had genetic associations with Stay, AFF, and TNB of 0.52, -0.25, and -0.08, respectively. Backfat at 114 kg had genetic correlations with Stay, AFF, and TNB of -0.29, 0.14, and 0.47, respectively. Vulva redness and TNB were negatively correlated phenotypically (r = -0.14) and genetically (r = -0.53). Associations between structural conformation traits with Stay, AFF, TNB, and WCI were generally low to moderate and favorable. Selection for longer length of estrus, stronger standing reflex, or younger age at puberty would increase the proportion of gilts that farrow and reduce age at first farrowing.

Genetic associations of sow longevity with age at first farrowing, number of piglets weaned, and wean to insemination interval in the Finnish Landrace swine population

The objective of the study was to estimate genetic parameters for length of productive life (LPL), and determine its genetic correlation with age at first farrowing (AFF), number of piglets weaned at first farrowing (NW), and first wean-to-insemination interval (W2I) in the Finnish Landrace swine population. Data from the Finnish national litter recording scheme were utilized to estimate the genetics of LPL, and genetic associations between LPL, AFF, NW, and W2I. Data from the Finnish Landrace sow records were utilized from farms that farrowed more than 20 gilts annually from 2000 through 2005. The data set included information from 11,222 sows, all of which had AFF and NW information available. The sows producing the records evaluated were daughters of 1,267 sires, and there were 3,684 animals in the pedigree when all of the sires were traced back to founder animals. All data were obtained from FABA Breeding (Vantaa, Finland). Multivariate Bayesian analysis of Gaussian, right censored Gaussian, and categorical traits was utilized to estimate (co)variance parameters of LPL, AFF, NW, and W2I of the sow. From these traits, AFF and NW were treated as Gaussian, LPL as right-censored Gaussian, and W2I as categorical traits. Estimated posterior means of heritabilities were 0.22, 0.16, 0.09, and 0.08 for LPL, AFF, NW, and W2I, respectively. A relatively large proportion of variance due to farm-year interaction was observed (posterior means of f(2) ranged between 0.03 and 0.26). The LPL was moderately genetically correlated with NW and AFF (posterior means were -0.20 and 0.36, respectively), whereas no clear association was found between W2I and LPL. Favorable genetic correlations between AFF and W2I and between NW and W2I were also observed. Additionally, an unfavorable genetic correlation between AFF and NW was observed in the present data set. Because LPL is genetically associated with other economically important prolificacy traits, it should be included in a multiple trait swine breeding value estimation system.

Selection for sow longevity1,2

Sow longevity plays an important role in economically efficient piglet production because sow longevity is related to the number of piglets produced during its productive lifetime; however, selection for sow longevity is not commonly practiced in any pig breeding program. There is relatively little scientific literature concerning the genetic parameters (genetic variation and genetic correlations) or methods available for breeding value estimation for effective selection for sow longevity. This paper summarizes the current knowledge about the genetics of sow longevity and discusses the available breeding value estimation methods for sow longevity traits. The studies in the literature clearly indicate that sow longevity is a complex trait, and even the definition of sow longevity is variable depending on the researcher and research objective. In general, the measures and analyses of sow longevity can be divided into 1) continuous traits (e.g., productive lifetime) analyzed with proportional hazard models; and 2) more simple binary traits such as stayability until some predetermined fixed parity. Most studies have concluded that sufficient genetic variation exists for effective selection on sow longevity, and heritability estimates have ranged between 0.02 and 0.25. Moreover, sow longevity has shown to be genetically associated with prolificacy and leg conformation traits. Variable results from previous research have led to a lack of consensus among swine breeders concerning the valid methodology of estimating breeding values for longevity traits. One can not deny the superiority of survival analysis in the modeling approach of longevity data; however, multiple-trait analyses are not possible using currently available survival analysis software. Less sophisticated approaches have the advantage of evaluating multiple traits simultaneously, and thus, can use the genetic associations between sow longevity and other traits. Additional research is needed to identify the most efficient selection methods for sow longevity. Future research needs to concentrate on multiple trait analysis of sow longevity traits. Moreover, because longevity is a fitness trait, the nonadditive genetic effects (e.g., dominance) may play important role in the inheritance of sow longevity. Currently, not a single estimate for dominance variance of sow longevity could be identified from the scientific literature.