Investigating pig survival in different production phases using genomic models

Genome-wide scans for selection signatures in indigenous chickens reveal candidate genes associated with local adaptation

Population growth and climate change pose challenges to the sustainability of poultry farming. The emphasis on high-yield traits in commercialized breeds has led to a decline in their adaptability. Chicken varieties adapted to the local environment, possessing traits that facilitate adaptation to climate change, such as disease resistance and tolerance to extreme weather conditions, can improve hybridization outcomes. In this study, we conducted an analysis of the population structure and genetic diversity of 110 chickens representing indigenous breeds from southern China and two different commercial breeds. Further, we performed comparative population genomics, utilizing nucleotide diversity and fixation statistics, to characterize genomic features of natural selection and to identify unique genetic traits and potential selection markers developed by indigenous breeds after adapting to the local environment. Results based on genetic diversity and population structure analyses showed that indigenous varieties exhibited high levels of genetic diversity. Commercial breeds that have been indigenously bred demonstrated higher levels of genetic diversity than those that have not, and breeds with different selection practices displayed significant differences in genetic structure. Additionally, we further searched for potential genomic regions in native chicken ecotypes, uncovering several candidate genes related to ecological adaptations affecting local breeds, such as IKBKB, S1PR1, TSHR, IL1RAPL1 and AMY2A, which are involved in disease resistance, heat tolerance, immune regulation and behavioral traits. This work provides important insights into the genomic characterization of ecotypes of native chicken in southern China. The identification of candidate genes associated with traits such as disease resistance, heat tolerance, immunomodulation, and behavioral traits is a significant outcome. These candidate genes may contribute to the understanding of the molecular basis of the adaptation of the southern native chicken to the local environment. It is recommended that these genes be integrated into chicken breeding programs to enhance sustainable agriculture and promote effective conservation and utilization strategies.

Selection Signal Analysis Reveals Hainan Yellow Cattle Are Being Selectively Bred for Heat Tolerance

Hainan yellow cattle are indigenous Zebu cattle from southern China known for their tolerance of heat and strong resistance to disease. Generations of adaptation to the tropical environment of southern China and decades of artificial breeding have left identifiable selection signals in their genomic makeup. However, information on the selection signatures of Hainan yellow cattle is scarce. Herein, we compared the genomes of Hainan yellow cattle with those of Zebu, Qinchuan, Nanyang, and Yanbian cattle breeds by the composite likelihood ratio method (CLR), Tajima's D method, and identifying runs of homozygosity (ROHs), each of which may provide evidence of the genes responsible for heat tolerance in Hainan yellow cattle. The results showed that 5210, 1972, and 1290 single nucleotide polymorphisms (SNPs) were screened by the CLR method, Tajima's D method, and ROH method, respectively. A total of 453, 450, and 325 genes, respectively, were identified near these SNPs. These genes were significantly enriched in 65 Gene Ontology (GO) functional terms and 11 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (corrected p < 0.05). Five genes-Adenosylhomocysteinase-like 2, DnaJ heat shock protein family (Hsp40) member C3, heat shock protein family A (Hsp70) member 1A, CD53 molecule, and zinc finger and BTB domain containing 12-were recognized as candidate genes associated with heat tolerance. After further functional verification of these genes, the research results may benefit the understanding of the genetic mechanism of the heat tolerance in Hainan yellow cattle, which lay the foundation for subsequent studies on heat stress in this breed.

Review: Improving pig survival with a focus on birthweight: a practical breeding perspective

Survival of growing pigs through to slaughter age is not only a key driver of profitability but also has implications for animal welfare. Changing preweaning mortality by over 3% gives a similar change in profit per pig as changing postweaning mortality by 1%. There is significant scope to improve both traits through management and breeding to improve survival. The aim of this literature review was to explore the relationship between litter size and piglet birthweight and the detrimental impact this negative association has had on pig survival, along with genetic strategies that have been implemented in breeding programmes. It is suggested that the primary effect of litter size on mortality was indirect, through the effects of litter size on individual piglet birthweights. The circumstances affecting the litter a piglet was born into were the most important for determining the birthweight of individual piglets, rather than the genetic make-up of the individual piglet itself. Therefore, breeding programmes should include the average piglet birthweight of a litter (i.e., a sow trait) rather than individual piglet birthweight to maintain the weight of piglets at birth. The relative weighting of litter size and average piglet birthweight should be done in a manner that avoids selecting heavy pigs from small litters. Additional genetic strategies to improve survival include survival at the litter level, or survival of individual piglets or enhanced through the use of genomic information. At the litter level, litter size at day 5 and weaning can be considered as sow traits, but the use of these traits depends on the recording environment. At the individual piglet level, pre- and postweaning survival can be recorded as 0/1 traits and analysed directly. Although heritabilities are low for all these traits, genetic improvements can be made. For preweaning survival, the genes of the nurse sow are more important than the genes of the individual piglet. The nurse sow model captures both the lactation and gestation effects, and the information obtained when piglets born from different litters are reared together. However, once a piglet is weaned, its own genes became more important for the expression of postweaning mortality outcomes. Finally, for a successful selection programme, combining the average piglet birthweight at the litter level and mortality data based on individual piglet records (not solely birthweight) might yield the best response in piglet survival.

Functional genomics of reproduction in pigs: Are we there yet?

Reproductive failure is the main reason for culling females in swine herds and is both a financial and sustainability issue. Because reproductive traits are complex and lowly to moderately heritable, genomic selection within populations can achieve substantial genetic gain in reproductive efficiency. A better understanding of the physiological components affecting the expression of these traits will facilitate greater understanding of the genes affecting reproductive traits and is necessary to improve and optimize management strategies to maximize reproductive success of gilts and sows. Large-scale genotyping with single-nucleotide polymorphism (SNP) arrays are used for genome-wide association studies (GWAS) and have facilitated identification of positional candidate genes. Transcriptomic data can be used to weight SNP for GWAS and could lead to previously unidentified candidate genes. Resequencing and fine mapping of candidate genes are necessary to identify putative functional variants and some of these have been incorporated into new genotyping arrays. Sequence imputation and genotype by sequence are newer strategies that could reveal novel functional mutations. In this study, these approaches are discussed. Advantages and limitations are highlighted where additional research is needed.

Genome-wide scans for selection signatures in indigenous pigs revealed candidate genes relating to heat tolerance

Heat stress is a major problem that constrains pig productivity. Understanding and identifying adaptation to heat stress has been the focus of recent studies, and the identification of genome-wide selection signatures can provide insights into the mechanisms of environmental adaptation. Here, we generated whole-genome re-sequencing data from six Chinese indigenous pig populations to identify genomic regions with selection signatures related to heat tolerance using multiple methods: three methods for intra-population analyses (Integrated Haplotype Score, Runs of Homozygosity and Nucleotide diversity Analysis) and three methods for inter-population analyses (Fixation index (F_ST), Cross-population Composite Likelihood Ratio and Cross-population Extended Haplotype Homozygosity). In total, 1 966 796 single nucleotide polymorphisms were identified in this study. Genetic structure analyses and F_ST indicated differentiation among these breeds. Based on information on the location environment, the six breeds were divided into heat and cold groups. By combining two or more approaches for selection signatures, outlier signals in overlapping regions were identified as candidate selection regions. A total of 163 candidate genes were identified, of which, 29 were associated with heat stress injury and anti-inflammatory effects. These candidate genes were further associated with 78 Gene Ontology functional terms and 30 Kyoto Encyclopedia of Genes and Genomes pathways in enrichment analysis (P < 0.05). Some of these have clear relevance to heat resistance, such as the AMPK signalling pathway and the mTOR signalling pathway. The results improve our understanding of the selection mechanisms responsible for heat resistance in pigs and provide new insights of introgression in heat adaptation.

Genetic parameters for early piglet weight, litter traits and number of functional teats in organic pigs

Knowledge remains limited on genetic variation and genetic correlations for traits in sows and piglets that are reared in an organic or outdoor setting. Here, we estimated genetic variance components for individual piglet weight, litter weight, litter size traits, and number of functional teats in a pig population raised under outdoor organic conditions. Data were collected from the largest organic multiplier farm in Denmark. Individual piglet weight was recorded at birth and on day 10. Number of live and dead piglets were recorded at birth, day 4, and day 11. Mean and total litter weight were calculated based on the individual weight of living piglets at birth and on day 10. The estimated heritability was highest for the number of functional teats (0.49), mean weight of a litter at birth (0.33) and on day 10 (0.25). In contrast, heritability was lowest for litter size traits (0.04-0.08) and piglet weight (0.06-0.07). Maternal heritability was much higher for individual piglet weight than direct heritability. The results showed that selection for higher mean weight results in smaller litters. Also, selection for individual birth weight of piglets results in heavier piglets at 10 days. In conclusion, this study confirmed that there is genetic variation in individual piglet weight, litter traits, and number of functional teats in organically and outdoor-reared pigs.

The impact of genotyping strategies and statistical models on accuracy of genomic prediction for survival in pigs

BACKGROUND

Survival from birth to slaughter is an important economic trait in commercial pig productions. Increasing survival can improve both economic efficiency and animal welfare. The aim of this study is to explore the impact of genotyping strategies and statistical models on the accuracy of genomic prediction for survival in pigs during the total growing period from birth to slaughter.  RESULTS: We simulated pig populations with different direct and maternal heritabilities and used a linear mixed model, a logit model, and a probit model to predict genomic breeding values of pig survival based on data of individual survival records with binary outcomes (0, 1). The results show that in the case of only alive animals having genotype data, unbiased genomic predictions can be achieved when using variances estimated from pedigree-based model. Models using genomic information achieved up to 59.2% higher accuracy of estimated breeding value compared to pedigree-based model, dependent on genotyping scenarios. The scenario of genotyping all individuals, both dead and alive individuals, obtained the highest accuracy. When an equal number of individuals (80%) were genotyped, random sample of individuals with genotypes achieved higher accuracy than only alive individuals with genotypes. The linear model, logit model and probit model achieved similar accuracy.

CONCLUSIONS

Our conclusion is that genomic prediction of pig survival is feasible in the situation that only alive pigs have genotypes, but genomic information of dead individuals can increase accuracy of genomic prediction by 2.06% to 6.04%.

Leveraging low-density crossbred genotypes to offset crossbred phenotypes and their impact on purebred predictions

The objectives of this study were to 1) investigate the predictability and bias of genomic breeding values (GEBV) of purebred (PB) sires for CB performance when CB genotypes imputed from a low-density panel are available, 2) assess if the availability of those CB genotypes can be used to partially offset CB phenotypic recording, and 3) investigate the impact of including imputed CB genotypes in genomic analyses when using the algorithm for proven and young (APY). Two pig populations with up to 207,375 PB and 32,893 CB phenotypic records per trait and 138,026 PB and 32,893 CB genotypes were evaluated. PB sires were genotyped for a 50K panel, whereas CB animals were genotyped for a low-density panel of 600 SNP and imputed to 50K. The predictability and bias of GEBV of PB sires for backfat thickness (BFX) and average daily gain recorded (ADGX) recorded on CB animals were assessed when CB genotypes were available or not in the analyses. In the first set of analyses, direct inverses of the genomic relationship matrix (G) were used with phenotypic datasets truncated at different time points. In the next step, we evaluated the APY algorithm with core compositions differing in the CB genotype contributions. After that, the performance of core compositions was compared with an analysis using a random PB core from a purely PB genomic set. The number of rounds to convergence was recorded for all APY analyses. With the direct inverse of G in the first set of analyses, adding CB genotypes imputed from a low-density panel (600 SNP) did not improve predictability or reduce the bias of PB sires' GEBV for CB performance, even for sires with fewer CB progeny phenotypes in the analysis. That indicates that the inclusion of CB genotypes primarily used for inferring pedigree in commercial farms is of no benefit to offset CB phenotyping. When CB genotypes were incorporated into APY, a random core composition or a core with no CB genotypes reduced bias and the number of rounds to convergence but did not affect predictability. Still, a PB random core composition from a genomic set with only PB genotypes resulted in the highest predictability and the smallest number of rounds to convergence, although bias increased. Genotyping CB individuals for low-density panels is a valuable identification tool for linking CB phenotypes to pedigree; however, the inclusion of those CB genotypes imputed from a low-density panel (600 SNP) might not benefit genomic predictions for PB individuals or offset CB phenotyping for the evaluated CB performance traits. Further studies will help understand the usefulness of those imputed CB genotypes for traits with lower PB-CB genetic correlations and traits not recorded in the PB environment, such as mortality and disease traits.

Effective Selection for Lower Mortality in Organic Pigs through Selection for Total Number Born and Number of Dead Piglets

Simple Summary

Breeders use breeding goals to guide genetic gain in a population in a desired direction. Breeding goals consist of economically interesting traits, in which each trait receives an economic value. For example, to increase the size of a piglet litter, breeders use a breeding goal that includes the trait “number of live piglets in a litter” for a specific day after birth. While the litter size is selected using the trait “number of live piglets,” it is composed of two traits: “total number born” and “number of dead piglets.” The current study used simulations to illustrate that selection for litter size could be improved by selecting for the latter two traits rather than the former. This approach corrects for the fact that these two traits are genetically related to each other, but they also have genetic differences. Further, splitting one trait into two traits allows breeders to focus on the specific elements of a trait. For example, organic pig breeders could select for better piglet welfare by splitting “number of live piglets” into two traits, giving a negative economic value to the number of dead piglets.

Abstract

Selection for the number of living pigs on day 11 (L11) aims to reduce piglet mortality and increase litter size simultaneously. This approach could be sub-optimal, especially for organic pig breeding. This study evaluated the effect of selecting for a trait by separating it into two traits. Genetic parameters for L11, the total number born (TNB), and the number of dead piglets at day 11 (D11) were estimated using data obtained from an organic pig population in Denmark. Based on these estimates, two alternative breeding schemes were simulated. Specifically, selection was made using: (1) a breeding goal with L11 only versus (2) a breeding goal with TNB and D11. Different weightings for TNB and D11 were tested. The simulations showed that selection using the first breeding scheme (L11) produced lower annual genetic gain (0.201) compared to the second (TNB and D11; 0.207). A sensitivity analysis showed that the second scheme performed better because it exploited differences in heritability, and accounted for genetic correlations between the two traits. When the second breeding scheme placed more emphasis on D11, D11 declined, whereas genetic gain for L11 remained high (0.190). In conclusion, selection for L11 could be optimized by separating it into two correlated traits with different heritability, reducing piglet mortality and enhancing L11.

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.

The Effect of Using Organic or Conventional Sires on Genetic Gain in Organic Pigs: A Simulation Study

Simple Summary

Breeding programs are used for the selection and breeding of animals that maximize a breeding objective in a specific production environment. Currently, breeders use pigs from conventional populations to breed organic pigs. This could be problematic, because pigs that perform best in an indoor and controlled conventional environment may not perform as well in the outdoor and less-controlled organic environment. To test this theory, we simulated different breeding programs for organic pigs. We used our knowledge on the genetics of the Danish pig population to make the simulations as realistic as possible. The first simulated breeding program used conventional boars to breed organic pigs. The second simulated breeding program used only organic pigs to breed for organic pigs. The results of the current study illustrate the importance of using pigs from an organic breeding population to breed organic pigs. If conventional pigs are used instead, the organic pigs will be adapted to suit a conventional production system.

Abstract

Current organic pig-breeding programs use pigs from conventional breeding populations. However, there are considerable differences between conventional and organic production systems. This simulation study aims to evaluate how the organic pig sector could benefit from having an independent breeding program. Two organic pig-breeding programs were simulated: one used sires from a conventional breeding population (conventional sires), and the other used sires from an organic breeding population (organic sires). For maintaining the breeding population, the conventional population used a conventional breeding goal, whereas the organic population used an organic breeding goal. Four breeding goals were simulated: one conventional breeding goal, and three organic breeding goals. When conventional sires were used, genetic gain in the organic population followed the conventional breeding goal, even when an organic breeding goal was used to select conventional sires. When organic sires were used, genetic gain followed the organic breeding goal. From an economic point of view, using conventional sires for breeding organic pigs is best, but only if there are no genotype-by-environment interactions. However, these results show that from a biological standpoint, using conventional sires biologically adapts organic pigs for a conventional production system.