The transcriptomic insight into the differential susceptibility of African Swine Fever in inbred pigs

African swine fever (ASF) is a global threat to animal health and food security. ASF is typically controlled by strict biosecurity, rapid diagnosis, and culling of affected herds. Much progress has been made in developing modified live virus vaccines against ASF. There is host variation in response to ASF infection in the field and under controlled conditions. To better understand the dynamics underlying this host differential morbidity, whole transcriptome profiling was carried out in twelve immunized and five sham immunized pigs. Seventeen MHC homozygous inbred Large white Babraham pigs were sampled at three time points before and after the challenge. The changes in the transcriptome profiles of infected animals were surveyed over time. In addition, the immunization effect on the host response was studied as well among the contrasts of all protection subgroups. The results showed two promising candidate genes to distinguish between recovered and non-recovered pigs after infection with a virulent African swine fever virus (ASFV) pre-infection: HTRA3 and GFPT2 (padj < 0.05). Variant calling on the transcriptome assemblies showed a two-base pair insertion into the ACOX3 gene closely located to HTRA3 that may regulate its expression as a putative genomic variant for ASF. Several significant DGEs, enriched gene ontology (GO) terms, and KEGG pathways at 1 day and 7 days post-infection, compared to the pre-infection, indicate a significant inflammation response immediately after ASF infection. The presence of the virus was confirmed by the mapping of RNA-Seq reads on two whole viral genome sequences. This was concordant with a higher virus load in the non-recovered animals 7 days post-infection. There was no transcriptome signature on the immunization at pre-infection and 1 day post-infection. More samples and data from additional clinical trials may support these findings

The Genetic Architecture of Economically Important Traits Provides Major Challenges for the Implementation of Gene Editing in Livestock

Gene editing has been hyped as a game-changer in many biological fields including medicine and agriculture. This includes the promise to manipulate the DNA of livestock animals at sufficient throughput, both in terms of number of loci and animals, to consider gene editing as a routine component of livestock breeding programmes. In this essay I will argue that the application of gene editing for complex traits in livestock will prove extremely challenging for a number of reasons: 1) our understanding of the genetic control of complex traits remains sketchy; 2) even with cutting edge ‘omics technologies, the identification of functional mutations remains very challenging; 3) before selecting certain mutations for gene editing, we need to capture the pleiotropic effects of the mutation and test whether its effects are truly additive. With the current understanding of complex traits there is a risk that gene editing will revert to a candidate gene approach without knowledge or understanding of where the important mutations reside. This means that it will be some time before we can really benefit from gene editing for truly complex traits in livestock. In the meantime gene editing could deliver quick wins by ‘repairing’ lethal recessive defects that are present in many elite breeding animals. Furthermore I will outline how gene editing can have an important role in the identification of QTN via in-vitro genetics

Insights from the early generations of the Swedish rainbow trout (Oncorhynchus mykiss) breeding program

This study aimed to gain insights about the status of the Swedish breeding program through studying key phenotypic traits. In total, 133 and 73 full-sib families were formed during the reproductive seasons of 2016 and 2019, respectively. Growth-related recordings were available from two occasions: & SIM; 9 and 24-27 months post-hatch. A preliminary descriptive analysis of the fecundity and early embryo survival identified substantial differences in favor of G0 (year class 2016) which was partly explained by the fact that first-time 3-year-old spawners were used in G1 (year class 2019). Moderate to high heritability values (0.23-0.49) were obtained for body length and weight across both time points and generations. Furthermore, the prediction accuracy of the estimated breeding values was & SIM;0.65, while the expected genetic gain was 36 g per year. Overall, our results demonstrate the positive performance of the Swedish rainbow trout breeding program

Seasonal and age-related changes in sperm quality of farmed arctic charr (Salvelinus alpinus)

Background Substantial variation in male fertility is regularly observed in farmed Arctic charr. However, detailed investigations of its fluctuation during a reproductive season and across years are lacking. Furthermore, information about the effect of underlying genetic factors influencing sperm quality is scarce. The current study focused on seasonal and age-related factors that may affect sperm quality characteristics in males reared in natural and delayed photoperiods. Animals were sampled three times for two consecutive years, and sperm quality parameters were recorded using a computer-assisted sperm analysis (CASA) system. Thereafter, high-throughput sequencing technologies were applied, aiming to identify genomic regions related to the variation of sperm quality throughout the reproductive season.Results An across-season variation in the recorded sperm quality parameters was evident. Overall, 29% and 42% of males from the natural and delayed spawning groups had a highly variable total progressive motility. Males at four years of age showed significantly higher sperm motility and velocities during the early October and November recordings compared to the following year when the same animals were five years of age. On the other hand, the opposite was observed regarding sperm concentration during the last sampling. A genome-wide F-ST scan detected SNP differentiation among males with high and low variability in total progressive motility (PM) on eight chromosomes (F-ST > 0.17), Genome wide windows with the highest F-ST contained SNPs in proximity (within 250 kb up- and downstream distance) to 16 genes with sperm quality biological functions in mammalian species.Conclusion Our findings provide a detailed view of seasonal, age-related, and genetic effects on sperm quality and can be used to guide decisions on broodstock selection and hatchery management

Evaluating the potential of improving sperm quality traits in farmed Arctic charr (Salvelinus alpinus) using selective breeding

Arctic charr (Salvelinus alpinus) is a high-value species for the Nordic aquaculture. The highly variable reproductive performance that is commonly observed in commercial farms is hindering the expansion of the Arctic charr industry in Sweden. Traits related to sperm motility (total motility; curvilinear velocity; average path velocity; straight-line velocity) and concentration can play a pivotal role in male fertility. Selective breeding practices could offer solutions and contribute to improving male fertility. The current study aimed to investigate the magnitude of genetic variance for sperm quality traits in a selectively bred population of Arctic charr from Sweden and evaluate the possibility of their improvement through selection. Sperm motility and concentration were recorded using a computer-assisted semen analysis (CASA) system and a NucleoCounter, respectively, in over 400 males from year-class 2017. Double digest restriction-site associated DNA sequencing (ddRAD-seq) was applied in a subset of the recorded animals (n = 329), resulting in the detection of over 5000 single nucleotide polymorphisms (SNPs). Moderate heritability estimates were obtained for the recorded semen traits using both pedigree (0.21-0.32; SE 0.09) and genomic (0.23-0.26; SE 0.09) relationship matrices. A genome-wide association study (GWAS) detected a single SNP significantly associated (P < 1e-05) with total sperm motility on chromosome LG7 in relatively close proximity (500 Kb) to PTPN11 a gene previously associated with sperm quality traits in mammals. Moreover, weighted single-step genomic best linear unbiased prediction (WssGBLUP) pinpointed genomic regions explaining more than 3 % of the additive genetic variance for both the motility traits and the sperm concentration. Finally, the efficiency of genomic prediction was tested using a 3-fold cross-validation scheme. Higher prediction accuracy for total motility and velocities (both curvilinear and average path) was obtained using genomic information (0.26-0.29, SE 0.03-0.06) compared to pedigree (0.20-0.28, SE 0.04-0.07), while for sperm concentration a pedigree-based model (0.22 SE 0.03) was more efficient than the genomic model (0.14 SE 0.04). Overall, our results indicate that the recorded sperm quality traits are heritable, and could be improved through selective breeding practices

The efficiency of mapping of quantitative trait loci using cofactor analysis in half-sib design

This simulation study was designed to study the power and type I error rate in QTL mapping using cofactor analysis in half-sib designs. A number of scenarios were simulated with different power to identify QTL by varying family size, heritability, QTL effect and map density, and three threshold levels for cofactor were considered. Generally cofactor analysis did not increase the power of QTL mapping in a half-sib design, but increased the type I error rate. The exception was with small family size where the number of correctly identified QTL increased by 13% when heritability was high and 21% when heritability was low. However, in the same scenarios the number of false positives increased by 49% and 45% respectively. With a liberal threshold level of 10% for cofactor combined with a low heritability, the number of correctly identified QTL increased by 14% but there was a 41% increase in the number of false positives. Also, the power of QTL mapping did not increase with cofactor analysis in scenarios with unequal QTL effect, sparse marker density and large QTL effect (25% of the genetic variance), but the type I error rate tended to increase. A priori, cofactor analysis was expected to have higher power than individual chromosome analysis especially in experiments with lower power to detect QTL. Our study shows that cofactor analysis increased the number of false positives in all scenarios with low heritability and the increase was up to 50% in low power experiments and with lower thresholds for cofactors