Multi-trait genomic prediction in pigs using single and multistep methods based on the absorption of ungenotyped animals.

Many quantitative traits measured in breeding programs are genetically correlated. The genetic correlations between the traits indicate that the measurement of one trait carries information on others. To benefit from this information, multi-trait genomic prediction (MTGP) is preferable to use. However, MTGP is more difficult to implement compared to single-trait genomic prediction (STGP), and even more challenging for the goal to exploit not only the information on other traits but also the information on ungenotyped animals. This could be accomplished using both single and multistep methods. The single-step method was achieved by implementing a single-step genomic best linear unbiased prediction (ssGBLUP) approach using a multi-trait model. Here, we examined a multistep analysis based on an approach called "Absorption" to achieve this goal. The Absorption approach absorbed all available information including the phenotypic information on ungenotyped animals as well as the information on other traits if applicable, into mixed model equations of genotyped animals. The multistep analysis included (1) to apply the Absorption approach that exploits all available information and (2) to implement genomic BLUP (GBLUP) prediction on the absorbed dataset. In this study, the ssGBLUP and multistep analysis were applied to 5 traits in Duroc pigs, which were slaughter percentage, feed consumption from 40 to 120 kg (FC40_120), days of growth from 40 to 120 kg (D40_120), age at 40 kg (A40) and lean meat percentage. The results showed that MTGP yielded higher accuracy than STGP, which on average was 0.057 higher for the multistep method and 0.045 higher for ssGBLUP. The multistep method achieved similar prediction accuracy as ssGBLUP. However, the prediction bias of the multistep method was in general lower than that of ssGBLUP.

Association between single-nucleotide polymorphisms within candidate genes and fertility in Landrace and Duroc pigs.

Finding effective predictors of traits related to boar fertility is essential for increasing the efficiency of artificial insemination systems in pig breeding. The objective of this study was to find associations between single-nucleotide polymorphisms (SNPs) within candidate genes and fertility in the breeds Landrace and Duroc. Animals with breeding values for total number of piglets born, were re-sequenced for exonic regions of 14 candidate genes related to male and female fertility using samples from 16 Landrace boars and 16 Duroc boars (four with high and four with low breeding value of total number of piglets born for each breed for male fertility, and the same for female fertility) to detect genetic variants. Genotyping for the detected SNPs was done in 619 Landrace boars and 513 Duroc boars. Two SNPs in BMPR1 and one SNP in COX-2 were found significantly associated with the total number of piglets born in Landrace. In Duroc, two SNPs in PLCz, one SNP in VWF and one SNP in ZP3 were found significantly associated with total number of piglets born. These SNPs explained between 0.27% and 1.18% of the genetic variance. These effects are too low for being used directly for selection purposes but can be of interest in SNP-panels used for genomic selection.

Sperm DNA integrity in Landrace and Duroc boar semen and its relationship to litter size.

The sperm chromatin structure assay is a method for assessment of sperm DNA fragmentation, a parameter reported to be negatively related to field fertility in several mammal species. This method calculates a DNA fragmentation index (DFI) whose high values indicate abnormal chromatin structure. In this study, running from March 2010 until June 2017, the aim was to assess sperm DFI in stored liquid extended semen from two different pig breeds, Norwegian Landrace (NL; n = 693) and Norwegian Duroc (ND; n = 655), and to evaluate the influence on total number of piglets born (TNB). There was a significantly higher median DFI (p < 0.0001) in ejaculates from the 478 ND boars compared to the 452 NL boars. Data from 19,496 NL litters and 3,877 ND litters of the same boars were retrieved. For either breed, sow herd (p < 0.0001), parity (p < 0.05) and DFI (p < 0.05) showed significant effects on TNB. The DFI was negatively correlated to TNB in both breeds. The boars with the 5% lowest TNB had a least square means DFI of 3.05% and 2.24% in NL and ND, respectively, compared to 1.67% and 1.23% for the boars with the 5% highest TNB (p < 0.01). The DFI and the motility of the same semen samples were negatively correlated (p < 0.0001), and the high and low TNB groups showed significant differences in motility. However, this difference could not be used for practical prediction of TNB group (92.1% vs. 89.7%; p = 0.0038 and 92.3% vs. 89.5%; p = 0.018; NL and ND, respectively). In conclusion, our results indicate that sperm DNA integrity in semen with good motility and morphology may be an additional prediction parameter for fertility in pigs.

Relationship between sperm motility characteristics and ATP concentrations, and association with fertility in two different pig breeds.

Boar fertility has a major impact on overall pig reproductive efficiency. Using accurate and objective in vitro sperm variables for predicting in vivo fertility from a single ejaculate, however, is challenging. Motility is the most widely used indicator of sperm quality, and a computer assisted sperm analysis (CASA) system is now available for objective assessment of sperm motility characteristics. In this study sperm motility characteristics and semen ATP concentrations were investigated and the effect of both were evaluated on total number of piglets born (TNB) when Norwegian Landrace (NL) and Norwegian Duroc (ND) boar semen was used for AI. In addition, breed differences for semen storage capacity were investigated. The results from CASA analysis indicated there were differences between NL and ND sperm motility variables. The percentage of motile sperm cells decreased in both NL (P = 0.01) and ND (P < 0.0001) during storage. A large proportion of sperm cells with a hyperactive motility pattern were detected in ND semen on the day of collection, with no significant changes as a result of storage. Inconsistent with this finding, there was greater degree of hyper-activation in sperm motility pattern for NL because of semen storage. There was a significant decrease in semen ATP concentration during storage (P < 0.0001) in both breeds. The linearity of sperm movement at the day of collection and the wobble after storage influenced TNB in NL, while the percentage of motile cells, curvilinear velocity and lateral head amplitude on the day of semen collection and linearity after storage influenced TNB in ND.

On the distance of genetic relationships and the accuracy of genomic prediction in pig breeding.

BACKGROUND: With the advent of genomic selection, alternative relationship matrices are used in animal breeding, which vary in their coverage of distant relationships due to old common ancestors. Relationships based on pedigree (A) and linkage analysis (GLA) cover only recent relationships because of the limited depth of the known pedigree. Relationships based on identity-by-state (G) include relationships up to the age of the SNP (single nucleotide polymorphism) mutations. We hypothesised that the latter relationships were too old, since QTL (quantitative trait locus) mutations for traits under selection were probably more recent than the SNPs on a chip, which are typically selected for high minor allele frequency. In addition, A and GLA relationships are too recent to cover genetic differences accurately. Thus, we devised a relationship matrix that considered intermediate-aged relationships and compared all these relationship matrices for their accuracy of genomic prediction in a pig breeding situation. METHODS: Haplotypes were constructed and used to build a haplotype-based relationship matrix (GH), which considers more intermediate-aged relationships, since haplotypes recombine more quickly than SNPs mutate. Dense genotypes (38 453 SNPs) on 3250 elite breeding pigs were combined with phenotypes for growth rate (2668 records), lean meat percentage (2618), weight at three weeks of age (7387) and number of teats (5851) to estimate breeding values for all animals in the pedigree (8187 animals) using the aforementioned relationship matrices. Phenotypes on the youngest 424 to 486 animals were masked and predicted in order to assess the accuracy of the alternative genomic predictions. RESULTS: Correlations between the relationships and regressions of older on younger relationships revealed that the age of the relationships increased in the order A, GLA, GH and G. Use of genomic relationship matrices yielded significantly higher prediction accuracies than A. GH and G, differed not significantly, but were significantly more accurate than GLA. CONCLUSIONS: Our hypothesis that intermediate-aged relationships yield more accurate genomic predictions than G was confirmed for two of four traits, but these results were not statistically significant. Use of estimated genotype probabilities for ungenotyped animals proved to be an efficient method to include the phenotypes of ungenotyped animals.