Novel development of biotechnology opens up for new possibilities to change the genetics of farm animals. Through the use of so called molecular knives and genome editing the cows can be edited to be born without horns, and thus will not have to be dehorned. It is also possible to lower the risk of mastitis in cows by transferring genes from humans through genetic modification (GM). Those are just a few examples on possible applications for new technologies in animal breeding.
To date there are no genetically modified or genome edited farm animals in food production and it is uncertain how the future legislation will regulate the application of those techniques. If such applications will be permitted, the techniques will probably be used in large-scale breeding programmes, which will raise new questions – both practical and ethical. Can we, do we need to, and should we use these techniques in commercial breeding? Does it matter what reproduction technology we use, how we handle cows and embryos, or what trait we change? Are there alternatives ways to reach the same goals?
In a new interdisciplinary article, the researchers describe the scientific literature, and contribute with ethical reflections on this topic. Ethical questions regarding animal welfare are important. Values and views on the integrity of animals, naturalness and risk assessments are other factors that affect how the new technologies could be assessed and could be received in society.
The knowledge in the area of genetics is expanding in a fast pace but we still have limited understanding of how complex traits are regulated on the genetic level. To change individual genes with known effect is currently closer at hand than changing genes at several places in the genome to affect complex traits. The techniques can be used to different extent and for different purposes, which will determine to which degree the animals are affected. In the article the researchers suggest that the new techniques should be evaluated within the breeding context they are to be applied in, before being used on a larger scale. In this process an advisory committee with researchers, industry representatives, representatives of the public, as well as ethically or philosophically educated persons, could provide valuable views.
– These are complex issues, but they will be easier to manage if we focus on specific applications, says Susanne Eriksson..
There is a mutation that results in a missing bit of chromosome number 23 in individuals of the Swedish Red White Cattle (SRB) dairy breed. The mutation is a so called deletion, which means that a larger chunk of the chromosome (about 525,000 bases in this case) is missing. This specific deletion can result in stillborn calves.
Researcher Dirk Jan de Koning and his colleagues discovered the deletion as they studied the genomes of cattle of the Holstein, Jersey and SRB breeds. In the article they report that they found 8,480 deletions in 175 cattle, of which most (82 percent) are not in registered in the databases of previously discovered deletions.
In the various missing chromosome pieces there are several inheritance sites that are important for animal health and fertility. In contrast, there are few genes that are related with high milk production in these deletions. The knowledge is intended to be used in the breeding of dairy cows, and the researchers have created a "deletion catalog" that will make it easier to find out if an animal has a certain deletion or not, and what it means for the animal.
The study also revealed that the RGB breed has a greater genetic diversity, compared to the Holstein and Jersey breeds.
Mesbah-Uddin, M., Guldbrandtsen, B., Iso-Touru, T., Vilkki, J., De Koning, D.J., Boichard, D., Lund, M.S., & Sahana, G. 2018. Genome-wide mapping of large deletions and their population-genetic properties in dairy cattle. DNA Research 25: 49-59
In both animal and plant breeding, the main goal is to select individuals that will improve future generations with respect to desired traits. For that purpose, statistical methods are applied to predict the genetic values of the individuals, and to rank the candidate progenitors of the future generation. In the last decade genomic selection, which is a selection method based on the availability of hundreds of thousands of DNA markers, has been used more and more. Today this method is applied routinely for selection in several livestock species like dairy cattle, pig, chicken and beef cattle. Nonetheless, the applications of strategies based on genomic selection are not always straightforward for all species and populations, and studies are needed to determine the specific requirements for each case.
The most common statistical method used to predict the genomic breeding values (GBLUP) assumes that the markers are independent and all have some effect on the analysed trait. However, this is rarely the case. As an example, there are studies indicating that markers located within or near regions rich in genes on a chromosome can explain more variance than markers located in intergenic regions.
Presently, knowledge of the genetic architecture of complex traits is available, and the amount of information on the DNA markers is continuously increasing in the form of accurately annotated genomes, Quantitative Trait Loci (QTLs) databases, gene expression and gene pathway studies. Now attempt has been made to include these types of information in the prediction models in order to improve the accuracy of the genomic breeding values.
The researchers have looked into the possibilities to include explanatory variables for marker-specific variances in a general BLUP analysis method. Starting with simulated data they created different scenarios of the genetic architecture of a trait which were then analysed using the traditional GBLUP model and the alternative model that includes the extra information on the markers. The models were evaluated in terms of their predictive accuracy. Their results indicate that the alternative model tends to perform better compared to the traditional GBLUP, and yield higher accuracies in most of the scenarios tested. Moreover, the researchers identified two factors that influence how well the alternative model will work: 1. The genetic architecture of the trait. The alternative model performed better than GBLUP when the number of genes controlling the trait was low, but its predictive ability decreased with an increasing number of genes involved. 2. The external information. In the study the researchers assumed that the location of the genes was known, and they defined windows of markers around the genes to have higher weights than the rest of the markers. The results show that larger windows tend to decrease the accuracy because the information provided to the model was vaguer in this case. Including biological information into the model is beneficial as it can increase the accuracy compared to the standard GBLUP. Nonetheless, this benefit depends upon the underlying genetic architecture of the trait and on the quality of the external information.
Mouresan, E.F., Selle, M. & Rönnegård, L. 2019. Genomic prediction including SNP-specific variance predictors. G3: Genes, Genomes, Genetics 9:3333-3343 DOI: 10.1534/g3.119.400381