Introduction

New biotechnologies often enhance the potential of other technologies and are usually themselves dependent on other technologies for useful application. This is amply illustrated in applications to genetic improvement of livestock.

Main Article

The basic principle of animal breeding, "to get the best, breed from the best", has not changed since humans first domesticated the wild ancestors of modern livestock species. What has changed are the tools used to identify the best and then to breed from them. Gone are the days when the only information available was the animal's own performance and selection simply meant keeping the better animals longer.

Modern livestock improvement utilizes an increasingly complex web of technologies. Extensive recording schemes are used to capture information on many different economically important performance characters of many thousands, sometimes millions of animals. This information is processed using complex statistical procedures on high powered computers. Reproductive technologies are used to spread genes from the best animals more widely and to turn over generations more rapidly. Dairy cattle improvement programs, for example, are entirely dependent on the technology of artificial insemination and the freezing of semen. This allows a bull to be evaluated accurately based on the performance of a large family of daughters. If he is selected on the basis of this information he can then produce many thousands of high merit daughters. Similarly, multiple ovulation and embryo transfer allow elite cows to produce many more progeny than would be possible through natural breeding.

The success of modern genetic improvement has been achieved without being able to look into the genetic black box at the more than 70,000 genes that determine the unique genetic makeup of each animal. The explosive development of molecular genetic technologies are, however, beginning to give us our first glimpse inside that black box. With rare exceptions we are still a long way from being able to say what are the functions of each of the 70,000 genes, and even farther from being able to define what the optimum combination of variants at each of those genes should be. The rare exceptions involve mutations of genes that cause major deleterious effects. An example is the discovery of the gene that causes porcine stress syndrome, where afflicted pigs are extremely sensitive to stress and have carcasses with poor meat quality. The genetic test developed for this gene was the result of a collaborative effort between Dr. David MacLennan at the University of Toronto and Dr. Peter O'Brian at the University of Guelph, and is being used worldwide to improve pig welfare and meat quality by eliminating the mutant form from the population.

Since defining the roles of the 70,000 plus genes is a long way off, animal scientists are currently hanging their hopes on genetic markers. These markers have no function of their own, they simply identify a particular region of genetic instructions. This region will contain hundreds of genes that do have a function, but we do not generally know which genes they are or what is their function. But, we can follow the inheritance of many different markers in families of animals and see whether inheritance of any of these markers is associated with improved performance. If they are, we then know that one or more genes in the region of the marker are having a beneficial effect. We do not need to discover which genes are involved, but can go to use the information on the genetic markers to make future selection decisions, since animals that inherit the marker will also inherit the useful effects associated with it. This is known as marker assisted selection, and the first Canadian project in this area, a collaborative venture between the scientists at the University of Guelph and the Saskatchewan Research Council, is just starting. The prospect of adding this marker information to the complex web of information already used in selection, is spurring further developments in information gathering, statistical analysis and computing technologies.

The genetic marker tests can be done at any age, even on early embryos, so it is possible to select animals or embryos on the basis of this information prior to entry to conventional performance testing programs. Existing reproductive technologies, such as artificial insemination and embryo transfer will help here by producing larger families than currently required, which can then be selected for the best marker combinations. Emerging reproductive technologies such as IVF (see article by Dr. Don Reiger) should allow large families to be produced from very young females, which opens the door to having one or two generations of marker selection prior to conventional selection with little increase in time.

New technologies expand the web of complexity in the search for the best possible rate of genetic improvement.