On the relationship between an Asian haplotype on chromosome 6 that reduces androstenone levels in boars and the differential expression of SULT2A1 in the testis

Endocrine Fertility Parameters-Genomic Background and their Genetic Relationship to Boar Taint in German Landrace and Large White

The surgical castration of young male piglets without anesthesia is no longer allowed in Germany from 2021. One alternative is breeding against boar taint, but shared synthesis pathways of androstenone (AND) and several endocrine fertility parameters (EFP) indicate a risk of decreasing fertility. The objective of this study was to investigate the genetic background between AND, skatole (SKA), and six EFP in purebred Landrace (LR) and Large White (LW) populations. The animals were clustered according to their genetic relatedness because of their different origins. Estimated heritabilities (h²) of AND and SKA ranged between 0.52 and 0.34 in LR and LW. For EFP, h² differed between the breeds except for follicle-stimulating hormone (FSH) (h²: 0.28-0.37). Both of the breeds showed unfavorable relationships between AND and testosterone, 17-β estradiol, and FSH. The genetic relationships (r_g) between SKA and EFP differed between the breeds. A genome-wide association analysis revealed 48 significant associations and confirmed a region for SKA on S us S crofa chromosome (SSC) 14. For EFP, the results differed between the clusters. In conclusion, r_g partly confirmed physiologically expected antagonisms between AND and EFP. Particular attention should be spent on fertility traits that are based on EFP when breeding against boar taint to balance the genetic progress in both of the trait complexes.

Genomic background and genetic relationships between boar taint and fertility traits in German Landrace and Large White

BACKGROUND

Due to ethical reasons, surgical castration of young male piglets in their first week of life without anesthesia will be banned in Germany from 2021. Breeding against boar taint is already implemented in sire breeds of breeding organizations but in recent years a low demand made this trait economically less important. The objective of this study was to estimate heritabilities and genetic relationships between boar taint compounds androstenone and skatole and maternal/paternal reproduction traits in 4'924 Landrace (LR) and 4'299 Large White (LW) animals from nucleus populations. Additionally, genome wide association analysis (GWAS) was performed per trait and breed to detect SNP marker with possible pleiotropic effects that are associated with boar taint and fertility.

RESULTS

Estimated heritabilities (h²) were 0.48 (±0.08) for LR (0.39 ± 0.07 for LW) for androstenone and 0.52 (±0.08) for LR (0.32 ± 0.07 for LW) for skatole. Heritabilities for reproduction did not differ between breeds except age at first insemination (LR: h² = 0.27 (±0.05), LW: h² = 0.34 (±0.05)). Estimates of genetic correlation (r_g) between boar taint and fertility were different in LR and LW breeds. In LR an unfavorable r_g of 0.31 (±0.15) was observed between androstenone and number of piglets born alive, whereas this r_g in LW (- 0.15 (±0.16)) had an opposite sign. A similar breed-specific difference is observed between skatole and sperm count. Within LR, the r_g of 0.08 (±0.13) indicates no relationship between the traits, whereas the r_g of - 0.37 (±0.14) in LW points to an unfavorable relationship. In LR GWAS identified QTL regions on SSC5 (21.1-22.3 Mb) for androstenone and on SSC6 (5.5-7.5 Mb) and SSC14 (141.1-141.6 Mb) for skatole. For LW, one marker was found on SSC17 at 48.1 Mb for androstenone and one QTL on SSC14 between 140.5 Mb and 141.6 Mb for skatole.

CONCLUSION

Knowledge about such genetic correlations could help to balance conventional breeding programs with boar taint in maternal breeds. QTL regions with unfavorable pleiotropic effects on boar taint and fertility could have deleterious consequences in genomic selection programs. Constraining the weighting of these QTL in the genomic selection formulae may be a useful strategy to avoid physiological imbalances.

Using markers with large effect in genetic and genomic predictions

The first attempts of applying marker-assisted selection (MAS) in animal breeding were not very successful because the identification of markers closely linked to QTL using low-density microsatellite panels was difficult. More recently, the use of high-density SNP panels in genome-wide association studies (GWAS) have increased the power and precision of identifying markers linked to QTL, which offer new possibilities for MAS. However, when GWAS started to be performed, the focus of many breeders had already shifted from the use of MAS to the application of genomic selection (using all available markers without any preselection of markers linked to QTL). In this study, we aimed to evaluate the prediction accuracy of a MAS approach that accounts for GWAS findings in the prediction models by including the most significant SNP from GWAS as a fixed effect in the marker-assisted BLUP (MA-BLUP) and marker-assisted genomic BLUP (MA-GBLUP) prediction models. A second aim was to compare the prediction accuracies from the marker-assisted models with those obtained from a Bayesian variable selection (BVS) model. To compare the prediction accuracies of traditional BLUP, MA-BLUP, genomic BLUP (GBLUP), MA-GBLUP, and BVS, we applied these models to the trait "number of teats" in 4 distinct pig populations, for validation of the results. The most significant SNP in each population was located at approximately 103.50 Mb on chromosome 7. Applying MAS by accounting for the most significant SNP in the prediction models resulted in improved prediction accuracy for number of teats in all evaluated populations compared with BLUP and GBLUP. Using MA-BLUP instead of BLUP, the increase in prediction accuracy ranged from 0.021 to 0.124, whereas using MA-GBLUP instead of GBLUP, the increase in prediction accuracy ranged from 0.003 to 0.043. The BVS model resulted in similar or higher prediction accuracies than MA-GBLUP. For the trait number of teats, BLUP resulted in the lowest prediction accuracies whereas the highest were observed when applying MA-GBLUP or BVS. In the same data set, MA-BLUP can yield similar or superior accuracies compared with GBLUP. The superiority of MA-GBLUP over traditional GBLUP is more pronounced when training populations are smaller and when relationships between training and validation populations are smaller. Marker-assisted GBLUP did not outperform BVS but does have implementation advantages in large-scale evaluations.

Assigning breed origin to alleles in crossbred animals

BACKGROUND

For some species, animal production systems are based on the use of crossbreeding to take advantage of the increased performance of crossbred compared to purebred animals. Effects of single nucleotide polymorphisms (SNPs) may differ between purebred and crossbred animals for several reasons: (1) differences in linkage disequilibrium between SNP alleles and a quantitative trait locus; (2) differences in genetic backgrounds (e.g., dominance and epistatic interactions); and (3) differences in environmental conditions, which result in genotype-by-environment interactions. Thus, SNP effects may be breed-specific, which has led to the development of genomic evaluations for crossbred performance that take such effects into account. However, to estimate breed-specific effects, it is necessary to know breed origin of alleles in crossbred animals. Therefore, our aim was to develop an approach for assigning breed origin to alleles of crossbred animals (termed BOA) without information on pedigree and to study its accuracy by considering various factors, including distance between breeds.

RESULTS

The BOA approach consists of: (1) phasing genotypes of purebred and crossbred animals; (2) assigning breed origin to phased haplotypes; and (3) assigning breed origin to alleles of crossbred animals based on a library of assigned haplotypes, the breed composition of crossbred animals, and their SNP genotypes. The accuracy of allele assignments was determined for simulated datasets that include crosses between closely-related, distantly-related and unrelated breeds. Across these scenarios, the percentage of alleles of a crossbred animal that were correctly assigned to their breed origin was greater than 90 %, and increased with increasing distance between breeds, while the percentage of incorrectly assigned alleles was always less than 2 %. For the remaining alleles, i.e. 0 to 10 % of all alleles of a crossbred animal, breed origin could not be assigned.

CONCLUSIONS

The BOA approach accurately assigns breed origin to alleles of crossbred animals, even if their pedigree is not recorded.

Hybrid origin of European commercial pigs examined by an in-depth haplotype analysis on chromosome 1

Although all farm animals have an original source of domestication, a large variety of modern breeds exist that are phenotypically highly distinct from the ancestral wild population. This phenomenon can be the result of artificial selection or gene flow from other sources into the domesticated population. The Eurasian wild boar (Sus scrofa) has been domesticated at least twice in two geographically distinct regions during the Neolithic revolution when hunting shifted to farming. Prior to the establishment of the commercial European pig breeds we know today, some 200 years ago Chinese pigs were imported into Europe to improve local European pigs. Commercial European domesticated pigs are genetically more diverse than European wild boars, although historically the latter represents the source population for domestication. In this study we examine the cause of the higher diversity within the genomes of European commercial pigs compared to their wild ancestors by testing two different hypotheses. In the first hypothesis we consider that European commercial pigs are a mix of different European wild populations as a result of movement throughout Europe, hereby acquiring haplotypes from all over the European continent. As an alternative hypothesis, we examine whether the introgression of Asian haplotypes into European breeds during the Industrial Revolution caused the observed increase in diversity. By using re-sequence data for chromosome 1 of 136 pigs and wild boars, we show that an Asian introgression of about 20% into the genome of European commercial pigs explains the majority of the increase in genetic diversity. These findings confirm that the Asian hybridization, that was used to improve production traits of local breeds, left its signature in the genome of the commercial pigs we know today.

Asian low-androstenone haplotype on pig chromosome 6 does not unfavorably affect production and reproduction traits

European pigs that carry Asian haplotypes of a 1.94-Mbp region on pig chromosome 6 have lower levels of androstenone, one of the two main compounds causing boar taint. The objective of our study was to examine potential pleiotropic effects of the Asian low-androstenone haplotypes. A single nucleotide polymorphism marker, rs81308021, distinguishes the Asian from European haplotypes and was used to investigate possible associations of androstenone with production and reproduction traits. Eight traits were available from three European commercial breeds. For the two sow lines studied, a favorable effect on number of teats was detected for the low-androstenone haplotype. In one of these sow lines, a favorable effect on number of spermatozoa per ejaculation was detected for the low-androstenone haplotype. No unfavorable pleiotropic effects were found, which suggests that selection for low-androstenone haplotypes within the 1.94 Mbp would not unfavorably affect the other eight relevant traits.

Boar taint in entire male pigs: a genomewide association study for direct and indirect genetic effects on androstenone

Androstenone is one of the compounds causing boar taint of pork and is highly heritable (approximately 0.6). Recently, indirect genetic effects (IGE; also known as associative effects or social genetic effects) were found for androstenone, meaning that pen mates (boars) affect each other's androstenone level genetically. Similar to estimating variance components with a direct-indirect animal model, direct and indirect genetic SNP effects can be estimated for androstenone. This study aims to detect SNP with significant direct genetic effects and IGE on androstenone. The dataset consisted of 1,282 noncastrated boars (993 boars genotyped) from 184 groups of pen members. After quality control, 46,421 SNP were included in the analysis. One model for single-SNP regression was fitted, where both the direct SNP effect of the individual itself and the indirect SNP effects of its pen mates were included. None of the SNP (direct or indirect) were found genomewide significant. One QTL on SSC6 was chromosome-wide significant for the direct effect. A single SNP on SSC9 and 2 regions and a single SNP on SSC14 were found for the indirect effect. A backwards elimination method and haplotype analysis were used to quantify the variance explained by the SNP. The backwards elimination method identified 4 independent regions affecting androstenone. The QTL on SSC6 explained 2.1 and 2.6% of the phenotypic variance using the backwards elimination method or the haplotype analysis. The QTL on SSC14 explained 3.4 and 2.7% of the phenotypic variance using the backwards elimination method or the haplotype analysis. The single association on SSC9 explained 2.2% of the phenotypic variance. All significant QTL together explained 7 to 8% of phenotypic variance and 40 to 44% of the total genetic variance available for response to selection. Besides the newly discovered QTL and the confirmation of known QTL, this study also presents a methodology to model SNP for IGE.