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

Effect of SNPs on Litter Size in Swine

Although sows do not directly enter the market, they play an important role in piglet breeding on farms. They consume large amounts of feed, resulting in a significant environmental burden. Pig farms can increase their income and reduce environmental pollution by increasing the litter size (LS) of swine. PCR-RFLP/SSCP and GWAS are common methods to evaluate single-nucleotide polymorphisms (SNPs) in candidate genes. We conducted a systematic meta-analysis of the effect of SNPs on pig LS. We collected and analysed data published over the past 30 years using traditional and network meta-analyses. Trial sequential analysis (TSA) was used to analyse population data. Gene set enrichment analysis and protein-protein interaction network analysis were used to analyse the GWAS dataset. The results showed that the candidate genes were positively correlated with LS, and defects in PCR-RFLP/SSCP affected the reliability of candidate gene results. However, the genotypes with high and low LSs did not have a significant advantage. Current breeding and management practices for sows should consider increasing the LS while reducing lactation length and minimizing the sows' non-pregnancy period as much as possible.

A first characterization of the microbiota-resilience link in swine

BACKGROUND

The gut microbiome plays a crucial role in understanding complex biological mechanisms, including host resilience to stressors. Investigating the microbiota-resilience link in animals and plants holds relevance in addressing challenges like adaptation of agricultural species to a warming environment. This study aims to characterize the microbiota-resilience connection in swine. As resilience is not directly observable, we estimated it using four distinct indicators based on daily feed consumption variability, assuming animals with greater intake variation may face challenges in maintaining stable physiological status. These indicators were analyzed both as linear and categorical variables. In our first set of analyses, we explored the microbiota-resilience link using PERMANOVA, α-diversity analysis, and discriminant analysis. Additionally, we quantified the ratio of estimated microbiota variance to total phenotypic variance (microbiability). Finally, we conducted a Partial Least Squares-Discriminant Analysis (PLS-DA) to assess the classification performance of the microbiota with indicators expressed in classes.

RESULTS

This study offers four key insights. Firstly, among all indicators, two effectively captured resilience. Secondly, our analyses revealed robust relationship between microbial composition and resilience in terms of both composition and richness. We found decreased α-diversity in less-resilient animals, while specific amplicon sequence variants (ASVs) and KEGG pathways associated with inflammatory responses were negatively linked to resilience. Thirdly, considering resilience indicators in classes, we observed significant differences in microbial composition primarily in animals with lower resilience. Lastly, our study indicates that gut microbial composition can serve as a reliable biomarker for distinguishing individuals with lower resilience.

CONCLUSION

Our comprehensive analyses have highlighted the host-microbiota and resilience connection, contributing valuable insights to the existing scientific knowledge. The practical implications of PLS-DA and microbiability results are noteworthy. PLS-DA suggests that host-microbiota interactions could be utilized as biomarkers for monitoring resilience. Furthermore, the microbiability findings show that leveraging host-microbiota insights may improve the identification of resilient animals, supporting their adaptive capacity in response to changing environmental conditions. These practical implications offer promising avenues for enhancing animal well-being and adaptation strategies in the context of environmental challenges faced by livestock populations. Video Abstract.

Genome-wide single nucleotide polymorphism (SNP) data reveal potential candidate genes for litter traits in a Yorkshire pig population

The litter trait is one of the most important economic traits, and increasing litter size is of great economic value in the pig industry. However, the molecular mechanisms underlying pig litter traits remain elusive. To identify molecular markers and candidate genes for pig litter traits, a genome-wide association study (GWAS) and selection signature analysis were conducted in a Yorkshire pig population. A total of 518 producing sows were genotyped with Illumina Porcine SNP 50 BeadChip, and 1969 farrowing records for the total number born (TNB), the number born alive (NBA), piglets born dead (PBD), and litter weight born alive (LWB) were collected. Then, a GWAS was performed for the four litter traits using a repeatability model. Based on the estimated breeding values (EBVs) of TNB, 15 high- and 15 low-prolificacy individuals were selected from the 518 sows to implement selection signature analysis. Subsequently, the selection signatures affecting the litter traits of sows were detected by using two methods including the fixation index (FST) and θ π . Combining the results of the GWAS and selection signature analysis, 20 promising candidate genes (NKAIN2, IGF1R, KISS1R, TYRO3, SPINT1, ADGRF5, APC2, PTBP1, CLCN3, CBR4, HPF1, FAM174A, SCP2, CLIC1, ZFYVE9, SPATA33, KIF5C, EPC2, GABRA2, and GABRA4) were identified. These findings provide novel insights into the genetic basis of pig litter traits and will be helpful for improving the reproductive performances of sows in pig breeding.

Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs

BACKGROUND

There is a growing need to improve robustness of fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a proxy for robustness of fattening pigs by modelling the longitudinal energy allocation coefficient to growth, with the resulting environmental variance of this allocation coefficient considered as a proxy for robustness. The second objective was to estimate its genetic parameters and correlations with traits under selection and with phenotypes that are routinely collected. In total, 5848 pigs from a Pietrain NN paternal line were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm is equipped with an automatic feeding system that records individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between the available cumulative net energy, which was estimated from feed intake, and cumulative weight gain during the fattening period. Longitudinal energy allocation coefficients were analysed using a two-step approach to estimate both the genetic variance of the coefficients and the genetic variance in their residual variance, which will be referred to as the log-transformed squared residual (LSR).

RESULTS

The LSR trait, which could be interpreted as an indicator of the response of the animal to perturbations/stress, showed a low heritability (0.05 ± 0.01), a high favourable genetic correlation with average daily growth (- 0.71 ± 0.06), and unfavourable genetic correlations with feed conversion ratio (- 0.76 ± 0.06) and residual feed intake (- 0.83 ± 0.06). Segmentation of the population in four classes using estimated breeding values for LSR showed that animals with the lowest estimated breeding values were those with the worst values for phenotypic proxies of robustness, which were assessed using records routinely collected on farm.

CONCLUSIONS

Results of this study show that selection for robustness, based on estimated breeding values for environmental variance of the allocation coefficients to growth, can be considered in breeding programs for fattening pigs.

Genome-Wide Association Study on Reproductive Traits Using Imputation-Based Whole-Genome Sequence Data in Yorkshire Pigs

Reproductive traits have a key impact on production efficiency in the pig industry. It is necessary to identify the genetic structure of potential genes that influence reproductive traits. In this study, a genome-wide association study (GWAS) based on chip and imputed data of five reproductive traits, namely, total number born (TNB), number born alive (NBA), litter birth weight (LBW), gestation length (GL), and number of weaned (NW), was performed in Yorkshire pigs. In total, 272 of 2844 pigs with reproductive records were genotyped using KPS Porcine Breeding SNP Chips, and then chip data were imputed to sequencing data using two online software programs: the Pig Haplotype Reference Panel (PHARP v2) and Swine Imputation Server (SWIM 1.0). After quality control, we performed GWAS based on chip data and the two different imputation databases by using fixed and random model circulating probability unification (FarmCPU) models. We discovered 71 genome-wide significant SNPs and 25 potential candidate genes (e.g., SMAD4, RPS6KA2, CAMK2A, NDST1, and ADCY5). Functional enrichment analysis revealed that these genes are mainly enriched in the calcium signaling pathway, ovarian steroidogenesis, and GnRH signaling pathways. In conclusion, our results help to clarify the genetic basis of porcine reproductive traits and provide molecular markers for genomic selection in pig breeding.

The impact of Box-Cox transformation on phenotypic and genomic characteristics of litter size variability in Landrace pigs

The genetic background of variability remains of interest especially in traits of high economic importance, e.g. litter size in pigs. It has been indicated that the data transformation can affect the variability phenotype. This study aims to evaluate the phenotypic and genomic background of variability of litter size obtained from data before and after the Box-Cox transformation. In total, 67 500 records on the total number born (TNB) in Landrace pig population were used. Since the data presented skewness, the decision was made to perform Box-Cox transformation on TNB and obtain bcTNB. Next, the phenotypic variability was estimated as log-transformed variance of residuals (LnVar) for both TNB (LnVar_TNB) and bcTNB (LnVar_bcTNB). The variability traits were further used in the genome-wide association study (GWAS) performed on 10 688 sows genotyped with Axiom porcine 660 K or imputed to 660 K SNP-chip. The substantial difference in skewness was observed after data transformation, represented as a change from -0.46 to -0.02. Heritability for TNB was 0.118 vs 0.125 for bcTNB. The heritability for LnVar_TNB was 0.0025 vs 0.0037 for LnVar_bcTNB. The change in the genetic variance was confirmed when genetic coefficients on SD level were compared: 2% for LnVar_TNB vs 4% for LnVar_bcTNB. In bivariate analysis, the genetic correlation between the additive genetic effects of the mean TNB and its variability changed from 0.38 to 0.63. The observed positive genetic correlations indicated that selection focused on increasing the litter size will simultaneously cause an increase in litter size variability. Based on GWAS, 14 SNPs were detected for LnVar_TNB and eight for LnVar_bcTNB, with two of them indicating the most promising candidate genes. First candidate gene located on Sus scrofa chromosome (SSC) 3 is STAG3, which plays an essential role in gametogenesis. Second gene located on SSC 10 is ESRRG, which affects placenta development. The additional post-GWAS analysis indicated even more candidate genes for LnVar_TNB and LnVar_bcTNB. The most promising candidate gene was located on SSC 13 - MFN1, which is involved in embryonic development. The results of this study indicated a substantial change in variance components for variability when the Box-Cox transformation was applied to data presenting skewness. Moreover, the data transformation changed the phenotype substantially enough that only part of SNP overlapped between two variability traits. Our investigation shows that it is essential to perform Box-Cox transformation for skewed data in order to properly describe phenotypic and genomic properties of litter size variability in Landrace pigs.

Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat

Individuals within a common environment experience variations due to unique and non-identifiable micro-environmental factors. Genetic sensitivity to micro-environmental variation (i.e. micro-environmental sensitivity) can be identified in residuals, and genotypes with lower micro-environmental sensitivity can show greater resilience towards environmental perturbations. Micro-environmental sensitivity has been studied in animals; however, research on this topic is limited in plants and lacking in wheat. In this article, we aimed to (i) quantify the influence of genetic variation on residual dispersion and the genetic correlation between genetic effects on (expressed) phenotypes and residual dispersion for wheat grain yield using a double hierarchical generalized linear model (DHGLM); and (ii) evaluate the predictive performance of the proposed DHGLM for prediction of additive genetic effects on (expressed) phenotypes and its residual dispersion. Analyses were based on 2,456 advanced breeding lines tested in replicated trials within and across different environments in Denmark and genotyped with a 15K SNP-Illumina-BeadChip. We found that micro-environmental sensitivity for grain yield is heritable, and there is potential for its reduction. The genetic correlation between additive effects on (expressed) phenotypes and dispersion was investigated, and we observed an intermediate correlation. From these results, we concluded that breeding for reduced micro-environmental sensitivity is possible and can be included within breeding objectives without compromising selection for increased yield. The predictive ability and variance inflation for predictions of the DHGLM and a linear mixed model allowing heteroscedasticity of residual variance in different environments (LMM-HET) were evaluated using leave-one-line-out cross-validation. The LMM-HET and DHGLM showed good and similar performance for predicting additive effects on (expressed) phenotypes. In addition, the accuracy of predicting genetic effects on residual dispersion was sufficient to allow genetic selection for resilience. Such findings suggests that DHGLM may be a good choice to increase grain yield and reduce its micro-environmental sensitivity.

Retrotransposon Insertion Polymorphisms (RIPs) in Pig Reproductive Candidate Genes

Retrotransposons account for more than one-third of the pig reference genome. On account of the genome variability in different breeds, structural variation (SV) caused by retrotranspos-on-generated deletion or insertion (indel) may have a function in the genome. Litter size is one of the most important reproductive traits and significantly impacts profitability in terms of pig production. We used the method of bioinformatics, genetics, and molecular biology to make an analysis among different pig genomes. Predicted 100 SVs were annotated as retrotransposon indel in 20 genes related to reproductive performance. The PCR detection based on these predicted SVs revealed 20 RIPs in 20 genes, that most RIPs (12) were generated by SINE indel, and eight RIPs were generated by the ERV indel. We selected 12 RIPs to make the second round PCR detection in 24 individuals among nine pig breeds. The PCR detection results revealed that the RIP-A1CF-4 insertion in the breed of Bama, Large White, and Meishan only had the homozygous genotype but low to moderately polymorphisms were present in other breeds. We found that RIP-CWH43-9, RIP-IDO2-9, RIP-PRLR-6, RIP-VMP1-12, and RIP-OPN-1 had a rich polymorphism in the breed of Large White pigs. The statistical analysis revealed that RIP-CWH43-9 had a SINE insertion profitable to the reproductive traits of TNB and NBA but was significantly affected (p < 0.01) and (p < 0.05) in the reproductive traits of litter birthweight (LW) in Large White. On the other hand, the SINE insertion in IDO2-9 may be a disadvantage to the reproductive traits of LW, which was significantly affected (p < 0.05) in Large White. These two RIPs are significant in pig genome research and could be useful molecular markers in the breeding system.

Identification of Genomic Regions and Candidate Genes for Litter Traits in French Large White Pigs Using Genome-Wide Association Studies

The reproductive traits of sows are one of the important economic traits in pig production, and their performance directly affects the economic benefits of the entire pig industry. In this study, a total of 895 French Large White pigs were genotyped by GeneSeek Porcine 50K SNP Beadchip and four phenotypic traits of 1407 pigs were recorded, including total number born (TNB), number born alive (NBA), number healthy piglets (NHP) and litter weight born alive (LWB). To identify genomic regions and genes for these traits, we used two approaches: a single-locus genome-wide association study (GWAS) and a single-step GWAS (ssGWAS). Overall, a total of five SNPs and 36 genomic regions were identified by single-locus GWAS and ssGWAS, respectively. Notably, fourof all five significant SNPs were located in 10.72–11.06 Mb on chromosome 7, were also identified by ssGWAS. These regions explained the highest or second highest genetic variance in the TNB, NBA and NHP traits and harbor the protein coding gene ENSSSCG00000042180. In addition, several candidate genes associated with litter traits were identified, including JARID2, PDIA6, FLRT2 and DICER1. Overall, these novel results reflect the polygenic genetic architecture of the litter traits and provide a theoretical reference for the following implementation of molecular breeding.

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.