Large scale sequence-based screen for recessive variants allows for identification and monitoring of rare deleterious variants in pigs.

Most deleterious variants are recessive and segregate at relatively low frequency. Therefore, high sample sizes are required to identify these variants. In this study we report a large-scale sequence based genome-wide association study (GWAS) in pigs, with a total of 120,000 Large White and 80,000 Synthetic breed animals imputed to sequence using a reference population of approximately 1,100 whole genome sequenced pigs. We imputed over 20 million variants with high accuracies (R2>0.9) even for low frequency variants (1-5% minor allele frequency). This sequence-based analysis revealed a total of 14 additive and 9 non-additive significant quantitative trait loci (QTLs) for growth rate and backfat thickness. With the non-additive (recessive) model, we identified a deleterious missense SNP in the CDHR2 gene reducing growth rate and backfat in homozygous Large White animals. For the Synthetic breed, we revealed a QTL on chromosome 15 with a frameshift variant in the OBSL1 gene. This QTL has a major impact on both growth rate and backfat, resembling human 3M-syndrome 2 which is related to the same gene. With the additive model, we confirmed known QTLs on chromosomes 1 and 5 for both breeds, including variants in the MC4R and CCND2 genes. On chromosome 1, we disentangled a complex QTL region with multiple variants affecting both traits, harboring 4 independent QTLs in the span of 5 Mb. Together we present a large scale sequence-based association study that provides a key resource to scan for novel variants at high resolution for breeding and to further reduce the frequency of deleterious alleles at an early stage in the breeding program.

Identification of the genetic basis of sow pelvic organ prolapse.

Introduction: Pelvic organ prolapse (POP) is one contributor to recent increases in sow mortality that have been observed in some populations and environments, leading to financial losses and welfare concerns. Methods: With inconsistent previous reports, the objective here was to investigate the role of genetics on susceptibility to POP, using data on 30,429 purebred sows, of which 14,186 were genotyped (25K), collected from 2012 to 2022 in two US multiplier farms with a high POP incidence of 7.1% among culled and dead sows and ranging from 2% to 4% of all sows present by parity. Given the low incidence of POP for parities 1 and >6, only data from parities 2 to 6 were retained for analyses. Genetic analyses were conducted both across parities, using cull data (culled for POP versus another reason), and by parity, using farrowing data. (culled for POP versus culled for another reason or not culled). Results and Discussion: Estimates of heritability from univariate logit models on the underlying scale were 0.35 ± 0.02 for the across-parity analysis and ranged from 0.41 ± 0.03 in parity 2 to 0.15 ± 0.07 in parity 6 for the by-parity analyses. Estimates of genetic correlations of POP between parities based on bivariate linear models indicated a similar genetic basis of POP across parities but less similar with increasing distance between parities. Genome wide association analyses revealed six 1 Mb windows that explained more than 1% of the genetic variance in the across-parity data. Most regions were confirmed in several by-parity analyses. Functional analyses of the identified genomic regions showed a potential role of several genes on chromosomes 1, 3, 7, 10, 12, and 14 in susceptibility to POP, including the Estrogen Receptor gene. Gene set enrichment analyses showed that genomic regions that explained more variation for POP were enriched for several terms from custom transcriptome and gene ontology libraries. Conclusion: The influence of genetics on susceptibility to POP in this population and environment was confirmed and several candidate genes and biological processes were identified that can be targeted to better understand and mitigate the incidence of POP.

Preimplantation Genetic Testing for Aneuploidy (PGT-A) Reveals High Levels of Chromosomal Errors in In Vivo-Derived Pig Embryos, with an Increased Incidence When Produced In Vitro.

Preimplantation genetic testing for aneuploidy (PGT-A) is widespread, but controversial, in humans and improves pregnancy and live birth rates in cattle. In pigs, it presents a possible solution to improve in vitro embryo production (IVP), however, the incidence and origin of chromosomal errors remains under-explored. To address this, we used single nucleotide polymorphism (SNP)-based PGT-A algorithms in 101 in vivo-derived (IVD) and 64 IVP porcine embryos. More errors were observed in IVP vs. IVD blastocysts (79.7% vs. 13.6% p < 0.001). In IVD embryos, fewer errors were found at blastocyst stage compared to cleavage (4-cell) stage (13.6% vs. 40%, p = 0.056). One androgenetic and two parthenogenetic embryos were also identified. Triploidy was the most common error in IVD embryos (15.8%), but only observed at cleavage, not blastocyst stage, followed by whole chromosome aneuploidy (9.9%). In IVP blastocysts, 32.8% were parthenogenetic, 25.0% (hypo-)triploid, 12.5% aneuploid, and 9.4% haploid. Parthenogenetic blastocysts arose from just three out of ten sows, suggesting a possible donor effect. The high incidence of chromosomal abnormalities in general, but in IVP embryos in particular, suggests an explanation for the low success of porcine IVP. The approaches described provide a means of monitoring technical improvements and suggest future application of PGT-A might improve embryo transfer success.

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.

Piglet survival is under genetic control and there are clear differences between individuals in their ability to live. Animals that do not survive their first weeks will obviously not reproduce as this is natural selection. Animals that survive still harbor relevant genetic differences. The genomic toolset, the use of genetic markers, makes it possible to link each animal to all others in the population, alive or dead, creating good opportunities for selection. Piglet survival depends on the genetic make-up of 1) the piglet itself, is it vital and heavy enough, 2) of the mother, are the piglets born at term, with low variation in birth weight, and 3) of the sow nursing the piglets, often the mother, does she allow the piglets to drink enough colostrum and milk of enough quality? This review explores the black box approach, complex statistical analysis of very large scale genomic recording of survival data, and it explores the biological approach, the influences of gestation length, birth weight, uniformity, number of teats, colostrum, etc., on birth weight. There is little doubt that genetic selection can increase survival of piglets. The challenge is to do this selection in balance with other production traits, such as litter size and body composition.

Effects of a Multi-Suckling System Combined With Enriched Housing Post-Weaning on Response and Cognitive Resilience to Isolation.

Improving welfare is still a critical issue in pig husbandry. Upgrades of the housing environment seem to be a promising solution to optimise resilience as a whole, and therefore improve animal welfare. The objective of this study was to evaluate the effect of an alternative housing system to enhance cognitive resilience and also to promote the pigs' welfare. A total of 96 piglets from two contrasted housing systems [alternative housing system (AHS) vs. conventional system (CONV)] was used. The major upgrades of the alternative system were multi-litter housing during lactation, delayed weaning, extra space allowance, and environmental enrichment from birth onwards. To estimate welfare, weight, and feed intake (as a general indicator of performances), the tear staining area (as a chronic stress indicator), behavioural postures, heart rate traits, and saliva cortisol concentration were measured over a 21 h-isolation. To assess cognitive resilience, the pigs were subjected to a maze with a social reward both before and after the isolation challenge and indicators of cognitive abilities were followed. The AHS pigs showed lower cortisol levels and tear staining area before the challenge, demonstrating overall better welfare due to the alternative housing conditions. During the challenge, AHS pigs had a lower heart rate, higher heart rate variability, and higher vagal activity than the CONV pigs, which might indicate a reduced sensitivity to the stressor. AHS pigs appeared to have a better long-term memory tested in a maze. Providing social and environmental enrichments, that fit the satisfaction of the essential needs of the pigs better, appears to be beneficial for pig welfare as a whole. Its effects on cognitive resilience still need to be proven.

Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs.

BACKGROUND: The genetic background of trait variability has captured the interest of ecologists and animal breeders because the genes that control it could be involved in buffering various environmental effects. Phenotypic variability of a given trait can be assessed by studying the heterogeneity of the residual variance, and the quantitative trait loci (QTL) that are involved in the control of this variability are described as variance QTL (vQTL). This study focuses on litter size (total number born, TNB) and its variability in a Large White pig population. The variability of TNB was evaluated either using a simple method, i.e. analysis of the log-transformed variance of residuals (LnVar), or the more complex double hierarchical generalized linear model (DHGLM). We also performed a single-SNP (single nucleotide polymorphism) genome-wide association study (GWAS). To our knowledge, this is only the second study that reports vQTL for litter size in pigs and the first one that shows GWAS results when using two methods to evaluate variability of TNB: LnVar and DHGLM. RESULTS: Based on LnVar, three candidate vQTL regions were detected, on Sus scrofa chromosomes (SSC) 1, 7, and 18, which comprised 18 SNPs. Based on the DHGLM, three candidate vQTL regions were detected, i.e. two on SSC7 and one on SSC11, which comprised 32 SNPs. Only one candidate vQTL region overlapped between the two methods, on SSC7, which also contained the most significant SNP. Within this vQTL region, two candidate genes were identified, ADGRF1, which is involved in neurodevelopment of the brain, and ADGRF5, which is involved in the function of the respiratory system and in vascularization. The correlation between estimated breeding values based on the two methods was 0.86. Three-fold cross-validation indicated that DHGLM yielded EBV that were much more accurate and had better prediction of missing observations than LnVar. CONCLUSIONS: The results indicated that the LnVar and DHGLM methods resulted in genetically different traits. Based on their validation, we recommend the use of DHGLM over the simpler method of log-transformed variance of residuals. These conclusions can be useful for future studies on the evaluation of the variability of any trait in any species.

Relationship between indirect genetic effects for growth, environmental enrichment, coping style and sex with the serum metabolome profile of pigs.

Including Indirect Genetic Effects (IGE) in breeding programs to reduce aggression in group housed animals has been proposed. However, the effect of selection for IGE for growth on animal metabolism and physiology is unknown. The purpose of this study was twofold: (1) To investigate the effects of this new breeding method along with two housing (barren and straw), coping style (high and low resisters) and sex (female and castrated males) options on the metabolome profile of pigs. (2) To identify and map biological processes associated with a regrouping test at 9 weeks of age. We used Nuclear Magnetic Resonance to quantify 49 serum metabolites at week 8, 9 and 22. Also, we quantified 3 catecholamines (tyramine, epinephrine, phenylethylamine) and serotonin and three water soluble vitamins (B2, B5 and B7). Overall, no significant differences were observed between negative and positive IGE animals. The magnitude of change (delta) of many metabolites as a response to the regrouping test was significantly affected by IGE, especially that of the amino acids (P < 0.05), being greater in positive IGE pigs. The regrouping test was associated with alteration in glycine, serine and threonine metabolism. In conclusion positive and negative IGE animals respond differently to the regrouping test.

Applying an association weight matrix in weighted genomic prediction of boar taint compounds.

Biological information regarding markers and gene association may be used to attribute different weights for single nucleotide polymorphism (SNP) in genome-wide selection. Therefore, we aimed to evaluate the predictive ability and the bias of genomic prediction using models that allow SNP weighting in the genomic relationship matrix (G) building, with and without incorporating biological information to obtain the weights. Firstly, we performed a genome-wide association studies (GWAS) in data set containing single- (SL) or a multi-line (ML) pig population for androstenone, skatole and indole levels. Secondly, 1%, 2%, 5%, 10%, 30% and 50% of the markers explaining the highest proportions of the genetic variance for each trait were selected to build gene networks through the association weight matrix (AWM) approach. The number of edges in the network was computed and used to derive weights for G (AWM-WssGBLUP). The single-step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used as standard scenarios. All scenarios presented predictive abilities different from zero; however, the great overlap in their confidences interval suggests no differences among scenarios. Most of scenarios of based on AWM provide overestimations for skatole in both SL and ML populations. On the other hand, the skatole and indole prediction were no biased in the ssGBLUP (S1) in both SL and ML populations. Most of scenarios based on AWM provide no biased predictions for indole in both SL and ML populations. In summary, using biological information through AWM matrix and gene networks to derive weights for genomic prediction resulted in no increase in predictive ability for boar taint compounds. In addition, this approach increased the number of analyses steps. Thus, we can conclude that ssGBLUP is most appropriate for the analysis of boar taint compounds in comparison with the weighted strategies used in the present work.

Quantifying Individual Response to PRRSV Using Dynamic Indicators of Resilience Based on Activity.

Pigs are faced with various perturbations throughout their lives, some of which are induced by management practices, others by natural causes. Resilience is described as the ability to recover from or cope with a perturbation. Using these data, activity patterns of an individual, as well as deviations from these patterns, can potentially be used to quantify resilience. Dynamic indicators of resilience (DIORs) may measure resilience on a different dimension by calculating variation, autocorrelation and skewness of activity from the absolute activity data. The aim of this study was to investigate the potential of using DIORs of activity, such as average, root mean square error (RMSE), autocorrelation or skewness as indicators of resilience to infection with the Porcine Reproductive and Respiratory Syndrome Virus (PRRSV). For this study, individual activity was obtained from 232 pigs equipped with ear tag accelerometers and inoculated with PRRSV between seven and 9 weeks of age. Clinical scores were assigned to each individual at 13 days post-challenge and used to distinguish between a resilient and non-resilient group. Mortality post-challenge was also recorded. Average, RMSE, autocorrelation and skewness of activity were calculated for the pre- and post-challenge phases, as well as the change in activity level pre- vs. post-challenge (i.e., delta). DIORs pre-challenge were expected to predict resilience to PRRSV in the absence of PRRSV infection, whereas DIORs post-challenge and delta were expected to reflect the effect of the PRRSV challenge. None of the pre-challenge DIORs predicted morbidity or mortality post-challenge. However, a higher RMSE in the 3 days post-challenge and larger change in level and RMSE of activity from pre- to post-challenge tended to increase the probability of clinical signs at day 13 post-infection (poor resilience). A higher skewness post-challenge (tendency) and a larger change in skewness from pre- to post-challenge increased the probability of mortality. A decrease in skewness post-challenge lowered the risk of mortality. The post-challenge DIOR autocorrelation was neither linked to morbidity nor to mortality. In conclusion, results from this study showed that post-challenge DIORs of activity can be used to quantify resilience to PRRSV challenge.

Estimation of litter size variability phenotypes in Large White sows.

The objective of this study was to obtain new phenotypes of phenotypic variability for the total number born (TNB) in pigs using the residual variance of TNB. The analysis was based on 246,799 Large White litter observations provided by Topigs Norsvin. Three animal models were used to obtain estimates of residual variance for TNB: the basic model (BM) containing fixed effects of farm-year and season and random effects of animal and permanent environmental sow, the basic model with an additional fixed effect of parity (BMP) and a random regression model (RRM). The within-individual variance of the residuals was calculated and log-transformed to obtain three new variability traits: LnVarBM, LnVarBMP and LnVarRRM. Then, (co)variance components, heritability, the genetic coefficient of variation at the standard deviation level (GCVSDe ) and genetic correlations between the three LnVar's and between the LnVar's and mean total number born (mTNB) were estimated with uni-, bi- and trivariate models. Results indicated that genetically LnVar's are the same trait and are positively correlated with the mTNB (~0.60). Thus, both traits should be included in breeding programmes to avoid an increase in TNB variability while selecting for increased TNB. Heritability of the LnVar's was estimated at 0.021. The GCVSDe for LnVar's showed that a change of 8% in residual standard deviation of TNB could be obtained per generation. Those results indicate that phenotypic variability of litter size is under genetic control, thus it may be improved by selection.

Genetic correlations between growth performance and carcass traits of purebred and crossbred pigs raised in tropical and temperate climates1.

In pig breeding, selection commonly takes place in purebred (PB) pigs raised mainly in temperate climates (TEMP) under optimal environmental conditions in nucleus farms. However, pork production typically makes use of crossbred (CB) animals raised in nonstandardized commercial farms, which are located not only in TEMP regions but also in tropical and subtropical regions (TROP). Besides the differences in the genetic background of PB and CB, differences in climate conditions, and differences between nucleus and commercial farms can lower the genetic correlation between the performance of PB in the TEMP (PBTEMP) and CB in the TROP (CBTROP). Genetic correlations (rg) between the performance of PB and CB growing-finishing pigs in TROP and TEMP environments have not been reported yet, due to the scarcity of data in both CB and TROP. Therefore, the present study aimed 1) to verify the presence of genotype × environment interaction (G × E) and 2) to estimate the rg for carcass and growth performance traits when PB and 3-way CB pigs are raised in 2 different climatic environments (TROP and TEMP). Phenotypic records of 217,332 PB and 195,978 CB, representing 2 climatic environments: TROP (Brazil) and TEMP (Canada, France, and the Netherlands) were available for this study. The PB population consisted of 2 sire lines, and the CB population consisted of terminal 3-way cross progeny generated by crossing sires from one of the PB sire lines with commercially available 2-way maternal sow crosses. G × E appears to be present for average daily gain, protein deposition, and muscle depth given the rg estimates between PB in both environments (0.64 to 0.79). With the presence of G × E, phenotypes should be collected in TROP when the objective is to improve the performance of CB in the TROP. Also, based on the rg estimates between PBTEMP and CBTROP (0.22 to 0.25), and on the expected responses to selection, selecting based only on the performance of PBTEMP would give limited genetic progress in the CBTROP. The rg estimates between PBTROP and CBTROP are high (0.80 to 0.99), suggesting that combined crossbred-purebred selection schemes would probably not be necessary to increase genetic progress in CBTROP. However, the calculated responses to selection show that when the objective is the improvement of CBTROP, direct selection based on the performance of CBTROP has the potential to lead to the higher genetic progress compared with indirect selection on the performance of PBTROP.

Weighted single-step GWAS and gene network analysis reveal new candidate genes for semen traits in pigs.

BACKGROUND: In recent years, there has been increased interest in the study of the molecular processes that affect semen traits. In this study, our aim was to identify quantitative trait loci (QTL) regions associated with four semen traits (motility, progressive motility, number of sperm cells per ejaculate and total morphological defects) in two commercial pig lines (L1: Large White type and L2: Landrace type). Since the number of animals with both phenotypes and genotypes was relatively small in our dataset, we conducted a weighted single-step genome-wide association study, which also allows unequal variances for single nucleotide polymorphisms. In addition, our aim was also to identify candidate genes within QTL regions that explained the highest proportions of genetic variance. Subsequently, we performed gene network analyses to investigate the biological processes shared by genes that were identified for the same semen traits across lines. RESULTS: We identified QTL regions that explained up to 10.8% of the genetic variance of the semen traits on 12 chromosomes in L1 and 11 chromosomes in L2. Sixteen QTL regions in L1 and six QTL regions in L2 were associated with two or more traits within the population. Candidate genes SCN8A, PTGS2, PLA2G4A, DNAI2, IQCG and LOC102167830 were identified in L1 and NME5, AZIN2, SPATA7, METTL3 and HPGDS in L2. No regions overlapped between these two lines. However, the gene network analysis for progressive motility revealed two genes in L1 (PLA2G4A and PTGS2) and one gene in L2 (HPGDS) that were involved in two biological processes i.e. eicosanoid biosynthesis and arachidonic acid metabolism. PTGS2 and HPGDS were also involved in the cyclooxygenase pathway. CONCLUSIONS: We identified several QTL regions associated with semen traits in two pig lines, which confirms the assumption of a complex genetic determinism for these traits. A large part of the genetic variance of the semen traits under study was explained by different genes in the two evaluated lines. Nevertheless, the gene network analysis revealed candidate genes that are involved in shared biological pathways that occur in mammalian testes, in both lines.

Fecal microbial composition associated with variation in feed efficiency in pigs depends on diet and sex.

Dietary fiber content and composition affect microbial composition and activity in the gut, which in turn influence energetic contribution of fermentation products to the metabolic energy supply in pigs. This may affect feed efficiency (FE) in pigs. The present study investigated the relationship between the fecal microbial composition and FE in individual growing-finishing pigs. In addition, the effects of diet composition and sex on the fecal microbiome were studied. Fecal samples were collected of 154 grower-finisher pigs (3-way crossbreeds) the day before slaughter. Pigs were either fed a diet based on corn/soybean meal (CS) or a diet based on wheat/barley/by-products (WB). Fecal microbiome was characterized by 16S ribosomal DNA sequencing, clustered by operational taxonomic unit (OTU), and results were subjected to a discriminant approach combined with principal component analysis to discriminate diets, sexes, and FE extreme groups (10 high and 10 low FE pigs for each diet by sex-combination). Pigs on different diets and males vs. females had a very distinct fecal microbiome, needing only 2 OTU for diet (P = 0.020) and 18 OTU for sex (P = 0.040) to separate the groups. The 2 most important OTU for diet, and the most important OTU for sex, were taxonomically classified as the same bacterium. In pigs fed the CS diet, there was no significant association between FE and fecal microbiota composition based on OTU (P > 0.05), but in pigs fed the WB diet differences in FE were associated with 17 OTU in males (P = 0.018) and to 7 OTU in females (P = 0.010), with 3 OTU in common for both sexes. In conclusion, our results showed a diet and sex-dependent relationship between FE and the fecal microbial composition at slaughter weight in grower-finisher pigs.

Relationship Between the Estimated Breeding Values for Litter Traits at Birth and Ovarian and Embryonic Traits and Their Additive Genetic Variance in Gilts at 35 Days of Pregnancy.

We investigated (1) the relationship between the estimated breeding values (EBVs) for litter traits at birth and ovulation rate (OR), average corpora luteal weight, uterine length and embryonic survival and development traits in gilts at 35 days of pregnancy by linear regression, (2) the genetic variance of OR, average corpora lutea (CL) weight, uterine length and embryonic survival and development traits at 35 days of pregnancy, and (3) the genetic correlations between these traits. Landrace (n = 86) and Yorkshire × Landrace (n = 304) gilts were inseminated and slaughtered at 35 days of pregnancy. OR was assessed by dissection of the CL on both ovaries. Individual CL was weighed and the average CL weight calculated. The number of embryos (total and vital) were counted and the vital embryos were individually weighed for calculation of within litter average and standard deviation (SD) of the embryo weight. Length of the uterine implantation site of the vital embryos was measured and the average per gilt calculated. Results suggests that increasing the EBV for total number of piglets born would proportionally increase OR and number of embryos, while decreasing the average CL weight. On the contrary, increasing the EBV for average piglet birth weight and for within litter birth weight standard deviation would increase the average CL weight. There was no relationship between the EBVs for BW and for BWSD and vital embryonic weight at 35 days of pregnancy. OR, average CL weight, number of embryos, average weight and implantation length of the vital embryos had all moderate to high heritabilities, ranging from 0.36 (±0.18) to 0.70 (±0.17). Thus, results indicate that there is ample genetic variation in OR, average CL weight and embryonic development traits. This knowledge could be used to optimize the balance between selection for litter size, average piglets birth weight and within litter birth weight uniformity.

Genetic Selection to Enhance Animal Welfare Using Meat Inspection Data from Slaughter Plants.

Animal health and welfare are monitored during meat inspection in many slaughter plants around the world. Carcasses are examined by meat inspectors and remarks are made with respect to different diseases, injuries, and other abnormalities. This is a valuable data resource for disease prevention and enhancing animal welfare, but it is rarely used for this purpose. Records on carcass remarks on 140,375 finisher pigs were analyzed to investigate the possibility of genetic selection to reduce the risk of the most prevalent diseases and indicators of suboptimal animal welfare. As part of this, effects of some non-genetic factors such as differences between farms, sexes, and growth rates were also examined. The most frequent remarks were pneumonia (15.4%), joint disorders (9.8%), pleuritis (4.7%), pericarditis (2.3%), and liver lesions (2.2%). Joint disorders were more frequent in boars than in gilts. There were also significant differences between farms. Pedigree records were available for 142,324 pigs from 14 farms and were used for genetic analysis. Heritability estimates for pneumonia, pleuritis, pericarditis, liver lesions, and joint disorders were 0.10, 0.09, 0.14, 0.24, and 0.17 on the liability scale, respectively, suggesting the existence of substantial genetic variation. This was further confirmed though genome wide associations using deregressed breeding values as phenotypes. The genetic correlations between these remarks and finishing traits were small but mostly negative, suggesting the possibility of enhancing pig health and welfare simultaneously with genetic improvement in finishing traits. A selection index based on the breeding values for these traits and their economic values was developed. This index is used to enhance animal welfare in pig farms.

Genetic control of complex traits, with a focus on reproduction in pigs.

Reproductive traits are complex, and desirable reproductive phenotypes, such as litter size or semen quality, are true polygenetic traits determined by multiple gene regulatory pathways. Each individual gene contributes to the overall variation in these traits, so genetic improvements can be achieved using conventional selection methodology. In the past, a pedigree-based-relationship matrix was used; this is now replaced by a combination of pedigree-based- and genomic-relationship matrices. The heritability of reproductive traits is low to moderate, so large-scale data recording is required to identify specific, selectable attributes. Male reproductive traits-including ejaculate volume and sperm progressive motility-are moderately heritable, and could be used in selection programs. A few high-merit artificial-insemination boars can impact many sow populations, so additional knowledge about male reproduction-specifically pre-pubertal detection of infertility and the technologies of semen cryopreservation and sex sorting-should further improve global breeding efforts. Conversely, female pig reproduction is currently a limiting factor of genetic improvement. Litter size and farrowing interval are the main obstacles to increasing selection intensity and to reducing generation interval in a breeding program. Age at puberty and weaning-to-estrus interval can be selected for, thereby reducing the number of non-productive days. The number of piglets born alive and litter weights are also reliably influenced by genetic selection. Characterization of genotype-environment interactions will provide opportunities to match genetics to specific farm systems. Continued investment to understand physiological models for improved phenotyping and the development of technologies to facilitate pig embryo production for genetic selection are warranted to ensure optimal breeding in future generations.

Gene networks for total number born in pigs across divergent environments.

For reproductive traits such as total number born (TNB), variance due to different environments is highly relevant in animal breeding. In this study, we aimed to perform a gene-network analysis for TNB in pigs across different environments using genomic reaction norm models. Thus, based on relevant single-nucleotide polymorphisms and linkage disequilibrium blocks across environments obtained from GWAS, different sets of candidate genes having biological roles linked to TNB were identified. Network analysis across environment levels resulted in gene interactions consistent with known mammal's fertility biology, captured relevant transcription factors for TNB biology and pointing out different sets of candidate genes for TNB in different environments. These findings may have important implication for animal production, as optimal breeding may vary depending on later environments. Based on these results, genomic diversity was identified and inferred across environments highlighting differential genetic control in each scenario.