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

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

Comparison of analyses of the QTLMAS XII common dataset. II: genome-wide association and fine mapping

As part of the QTLMAS XII workshop, a simulated dataset was distributed and participants were invited to submit analyses of the data based on genome-wide association, fine mapping and genomic selection. We have evaluated the findings from the groups that reported fine mapping and genome-wide association (GWA) efforts to map quantitative trait loci (QTL). Generally the power to detect QTL was high and the Type 1 error was low. Estimates of QTL locations were generally very accurate. Some methods were much better than others at estimating QTL effects, and with some the accuracy depended on simulated effect size or minor allele frequency. There were also indications of bias in the effect estimates. No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses. Although this study is based on a single dataset, it indicates that there is a need to improve fine mapping and GWA methods with respect to estimation of genetic effects, appropriate choice of significance thresholds and analysis of epistasis

Inbreeding and pedigree analysis of the European red dairy cattle

Background Red dairy cattle breeds have an important role in the European dairy sector because of their functional characteristics and good health. Extensive pedigree information is available for these breeds and provides a unique opportunity to examine their population structure, such as effective population size, depth of the pedigree, and effective number of founders and ancestors, and inbreeding levels. Animals with the highest genetic contributions were identified. Pedigree data included 9,073,403 animals that were born between 1900 and 2019 from Denmark, Finland, Germany, Latvia, Lithuania, the Netherlands, Norway, Poland, and Sweden, and covered 32 breeds. The numerically largest breeds were Red Dairy Cattle and Meuse-Rhine-Yssel. Results The deepest average complete generation equivalent (9.39) was found for Red Dairy Cattle in 2017. Mean pedigree completeness ranged from 0.6 for Finncattle to 7.51 for Red Dairy Cattle. An effective population size of 166 animals was estimated for the total pedigree and ranged from 35 (Rotes Hohenvieh) to 226 (Red Dairy Cattle). Average generation intervals were between 5 and 7 years. The mean inbreeding coefficient for animals born between 1960 and 2018 was 1.5%, with the highest inbreeding coefficients observed for Traditional Angler (4.2%) and Rotes Hohenvieh (4.1%). The most influential animal was a Dutch Meuse-Rhine-Yssel bull born in 1960. The mean inbreeding level for animals born between 2016 and 2018 was 2% and highest for the Meuse-Rhine-Yssel (4.64%) and Rotes Hohenvieh breeds (3.80%). Conclusions We provide the first detailed analysis of the genetic diversity and inbreeding levels of the European red dairy cattle breeds. Rotes Hohenvieh and Traditional Angler have high inbreeding levels and are either close to or below the minimal recommended effective population size, thus it is necessary to implement tools to monitor the selection process in order to control inbreeding in these breeds. Red Dairy Cattle, Vorderwalder, Swedish Polled and Hinterwalder hold more genetic diversity. Regarding the Meuse-Rhine-Yssel breed, given its decreased population size, increased inbreeding and low effective population size, we recommend implementation of a breeding program to prevent further loss in its genetic diversity