Genetic analysis of within-litter variation in piglets' birth weight using genomic or pedigree relationship matrices

Traits Defining Sow Lifetime Maternal Performance

Declining sow performance with increasing parity or an increase in the number of poor- quality pigs potentially impacts on farm productivity. This study investigated the phenotypic and genetic background of the sow's influence on (i) the number of pigs not meeting the industry standards (tail-enders) and (ii) changes in performance with parity. Data were available for 3592 sows and their litters (13,976 litters) from a pig production system in NSW, Australia. The mean, standard deviation (SD), and slope for trait values over time were estimated for the sow characteristic traits: number of born-alive (NBA) and stillborn (SB) piglets and body condition of sow recorded with a caliper (CAL), along with maternal effects on piglet performance, represented by: average piglet birth weight (APBW), number of weaned piglets (WEAN), and tail-enders (TEND). Traits were analyzed in ASReml 4.2, by using an animal model. The number of tail-enders produced by a sow is a heritable trait, with a heritability estimate of 0.14 ± 0.04. Sow characteristics and maternal effects on piglet performance expressed by mean and slope had similar heritability estimates, ranging from 0.10 ± 0.03 to 0.38 ± 0.05, whereas estimates for SD traits were generally not different from zero. The latter suggests individual variability in sow characteristics or maternal performance between parities is largely not genetic in origin. This study demonstrated that more attention is required to identify contributions to the problem of tail-enders, and that slope traits could potentially be useful in the breeding program to maximize lifetime performance.

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.

Selection for litter size and litter birthweight in Large White pigs: Maximum, mean and variability of reproduction traits

Gradually increasing trend of litter size poses a challenge to pig farmers in terms of managing larger litters. Therefore, it seems that a balanced approach that optimises litter size, litter birthweight, and uniformity of those traits is needed in order to address animal welfare and farm management concerns. This study aimed to investigate this issue by defining several traits for total number born (TNB), number born alive (NBA) and litter birthweight (LW). First, the highest value from at least five records per sow was selected as maximum (max) value for each reproduction trait. Second, a mean (mean) for each reproduction trait was calculated per sow. Last, the variability of reproduction traits between parities of the sow was calculated as log-transformed variance of residuals of all observations per sow for each reproduction trait (LnVar). In total, 23 193 Large White sows from Topigs Norsvin with 152 282 litter records were used for analysis in ASReml 4.1. Also, a simulation of breeding schemes was performed with the use of SelAction 2.1 and estimates from genetic analysis. Maximum value of reproductive traits had a much higher heritability than repeated observations or mean of reproduction traits, e.g., 0.31 for maxTNB vs. 0.12 for TNB and 0.07 for meanTNB, which allows for a faster response under selection. The maximum value traits, however, were found to carry more risks, i.e. higher ratio of stillborn (not for maxNBA) and increased variability of traits. Thus, using them in breeding programme should be carefully considered. The genetic coefficient of variation on SD level estimated to indicate the genetic magnitude for variability phenotypes indicated a maximum change of 6-9% in genetic SD of TNB, NBA and LW. The genetic correlations between mean and corresponding variability traits varied from 0.66 to 0.74, whereas the correlation between other mean and variability traits ranged from 0.33 to 0.99. The simulation indicated that even with selection targeted against the variability of reproduction traits, a very limited change should be expected due to a complex genetic and phenotypic relationship between the traits. In the scenarios with selection against LnVarTNB and LnVarLW, this was a decrease of 0.1-0.6% per year, whereas in scenario with selection against LnVarNBA, the range was 0.6-1.1% per year. It is still possible to increase litter size and birthweight further, however, a balance between mean and variability of reproduction traits is required, which can be obtained only by a very well designed breeding programme.

Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review

Genetic control of residual variance offers opportunities to increase uniformity and resilience of livestock and aquaculture species. Improving uniformity and resilience of animals will improve health and welfare of animals and lead to more homogenous products. Our aims in this review were to summarize the current models and methods to study genetic control of residual variance, genetic parameters and genomic results for residual variance and discuss future research directions. Typically, the genetic coefficient of variation is high (median = 0.27; range 0-0.86) and the heritability of residual variance is low (median = 0.01; range 0-0.10). Higher heritabilities can be achieved when increasing the number of records per animal. Divergent selection experiments have supported the feasibility of selecting for high or low residual variance. Genomic studies have revealed associations in regions related to stress, including those from the heat shock protein family. Although the number of studies is growing, genetic control of residual variance is still poorly understood, but big data and genomics offer great opportunities.

The Statistical Scale Effect as a Source of Positive Genetic Correlation Between Mean and Variability: A Simulation Study

The selection objective for animal production is the highest income with the lowest production cost, while ensuring the highest animal welfare. A selection experiment for environmental variability of birth weight in mice showed a correlated response in the mean after 20 generations starting from a crossed panmictic population. The relationship between the birth weight and its environmental variability explained the correlated response. The scale effect represents a potential cause of this correlation. The relationship between the mean and the variability implies: the higher the mean, the higher the variability. The study was to quantify by simulation the genetic correlation between a trait and its environmental variability. This can be attributable to the scale effect in a range of coefficients of variation and heritabilities between 0.05 and 0.50. The resulting genetic correlation ranged from 0.1335 to 0.7021 being the highest for the highest heritability and the lowest CV. The scale effect for a trait with heritability between 0.25 and 0.35 and CV between 0.15 and 0.25 generated a genetic correlation between 0.43 and 0.57. The genetic coefficient of variation (GCV) affecting residual variability was modulated by the strength reducing the impact of the scale effect. GCV ranged from 0.0050 to 1.4984. The strength of the scale effect might be in the range between 0 and 1. The scale effect would explain many reported genetic correlation and the additive genetic variance for the variability. This is relevant when increasing the mean of a trait jointly with the reduction of its variability.

Invited review: Piglet survival: benefits of the immunocompetence

Piglet mortality has a negative impact on animal welfare and public acceptance. Moreover, the number of weaned piglets per sow mainly determines the profitability of piglet production. Increased litter sizes are associated with lower birth weights and piglet survival. Decreased survival rates and performance of piglets make the control of diseases and infections within pig production even more crucial. Consequently, selection for immunocompetence becomes an important key aspect within modern breeding programmes. However, the phenotypic recording of immune traits is difficult and expensive to realize within farm routines. Even though immune traits show genetic variability, only few examples exist on their respective suitability within a breeding programme and their relationships to economically important production traits. The analysis of immune traits for an evaluation of immunocompetence to gain a generally improved immune response is promising. Generally, in-depth knowledge of the genetic background of the immune system is needed to gain helpful insights about its possible incorporation into breeding programmes. Possible physiological drawbacks for enhanced immunocompetence must be considered with regards to the allocation theory and possible trade-offs between the immune system and performance. This review aims to discuss the relationships between the immunocompetence of the pig, piglet survival as well as the potential of these traits to be included into a breeding strategy for improved robustness.

Estimation of breeding values for uniformity of growth in Atlantic salmon (Salmo salar) using pedigree relationships or single-step genomic evaluation

Background

In farmed Atlantic salmon, heritability for uniformity of body weight is low, indicating that the accuracy of estimated breeding values (EBV) may be low. The use of genomic information could be one way to increase accuracy and, hence, obtain greater response to selection. Genomic information can be merged with pedigree information to construct a combined relationship matrix (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{H}}$$\end{document}H matrix) for a single-step genomic evaluation (ssGBLUP), allowing realized relationships of the genotyped animals to be exploited, in addition to numerator pedigree relationships (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{A}}$$\end{document}A matrix). We compared the predictive ability of EBV for uniformity of body weight in Atlantic salmon, when implementing either the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{A}}$$\end{document}A or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{H}}$$\end{document}H matrix in the genetic evaluation. We used double hierarchical generalized linear models (DHGLM) based either on a sire-dam (sire-dam DHGLM) or an animal model (animal DHGLM) for both body weight and its uniformity.

Results

With the animal DHGLM, the use of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{H}}$$\end{document}H instead of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{A}}$$\end{document}A significantly increased the correlation between the predicted EBV and adjusted phenotypes, which is a measure of predictive ability, for both body weight and its uniformity (41.1 to 78.1%). When log-transformed body weights were used to account for a scale effect, the use of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{H}}$$\end{document}H instead of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{A}}$$\end{document}A produced a small and non-significant increase (1.3 to 13.9%) in predictive ability. The sire-dam DHGLM had lower predictive ability for uniformity compared to the animal DHGLM.

Conclusions

Use of the combined numerator and genomic relationship matrix (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{H}}$$\end{document}H) significantly increased the predictive ability of EBV for uniformity when using the animal DHGLM for untransformed body weight. The increase was only minor when using log-transformed body weights, which may be due to the lower heritability of scaled uniformity, the lower genetic correlation of transformed body weight with its uniformity compared to the untransformed traits, and the small number of genotyped animals in the reference population. This study shows that ssGBLUP increases the accuracy of EBV for uniformity of body weight and is expected to increase response to selection in uniformity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-017-0308-3) contains supplementary material, which is available to authorized users.

Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population

Background

In many traits, not only individual trait levels are under genetic control, but also the variation around that level. In other words, genotypes do not only differ in mean, but also in (residual) variation around the genotypic mean. New statistical methods facilitate gaining knowledge on the genetic architecture of complex traits such as phenotypic variability. Here we study litter size (total number born) and its variation in a Large White pig population using a Double Hierarchical Generalized Linear model, and perform a genome-wide association study using a Bayesian method.

Results

In total, 10 significant single nucleotide polymorphisms (SNPs) were detected for total number born (TNB) and 9 SNPs for variability of TNB (varTNB). Those SNPs explained 0.83 % of genetic variance in TNB and 1.44 % in varTNB. The most significant SNP for TNB was detected on Sus scrofa chromosome (SSC) 11. A possible candidate gene for TNB is ENOX1, which is involved in cell growth and survival. On SSC7, two possible candidate genes for varTNB are located. The first gene is coding a swine heat shock protein 90 (HSPCB = Hsp90), which is a well-studied gene stabilizing morphological traits in Drosophila and Arabidopsis. The second gene is VEGFA, which is activated in angiogenesis and vasculogenesis in the fetus. Furthermore, the genetic correlation between additive genetic effects on TNB and on its variation was 0.49. This indicates that the current selection to increase TNB will also increase the varTNB.

Conclusions

To the best of our knowledge, this is the first study reporting SNPs associated with variation of a trait in pigs. Detected genomic regions associated with varTNB can be used in genomic selection to decrease varTNB, which is highly desirable to avoid very small or very large litters in pigs. However, the percentage of variance explained by those regions was small. The SNPs detected in this study can be used as indication for regions in the Sus scrofa genome involved in maintaining low variability of litter size, but further studies are needed to identify the causative loci.