Abstract

Animal improvement is a complex process which involves modification of the genotype and the environment in an harmonious fashion. Unequivocal successes have generally involved either improvement of relatively simple traits like milk yield or weight gain per unit feed consumed, or improvement of a complex of characters by breed substitution. Quantitative genetics is the basic tool of animal improvement but it is statistical in nature and not well adapted to traits which are hard to measure. It is also very difficult to incorporate modern molecular genetics into the quantitative framework. These novel methods are also difficult to use effectively, partly because so much remains to be learnt about the regulation of all genes, whether part of an animal’s normal genome or alien material incorporated by genetical engineering, and partly because there are as yet few candidate genes suitable for manipulation. Nevertheless, the outlook has never been brighter for the achievement of worthwhile animal improvement by genetical means. These apparently conflicting views are discussed by means of examples.

Introduction

In discussing animal improvement, I shall mainly be concerned with genetics, but want to emphasise at the outset that genetics alone cannot yield genuine improvement. As different genotypes are chosen for breeding, so their environment is altered, not necessarily for the better, so that alteration of the environment can be just as important as alteration of the gene pool. To take a very simple example, if a producer chooses to try to increase the productivity of his or her Merino flock in the 250 mm rainfall country in South Australia by increasing the frequency of twinning and hence allowing increased selection intensity for fleece traits, then the neonatal environment is automatically made more difficult for all lambs in the flock, if nothing else is changed.

A second point to bear in mind, because I shall not refer to it in every instance, is that when one moves from animal genetics to animal breeding, one must define the breeding objectives completely (e.g. Ponzoni and Newman, 1989). The process of such definition should identify all of the relevant, measurable or assessable traits and their interactions. The history of breeding is full of examples of failure to identify breeding objectives; I shall not labour the point. But I make it because one frequently hears the argument that it is accuracy in selection that matters more than anything else. Further, the objectives change over time so that they require regular re-evaluation. The Australian Milking Zebu was developed as a tropical dairy breed for Australia for example, but the industry changed before the breed was ready.

I want firstly to discuss some past successes, then to consider some current practices, then to look to the future.

Industry Successes

In general terms, Table 1, modified from Lerner and Donald (1964), illustrates progress in the application of genetical and reproductive methodology to straight forward increases in quantitative traits. As can be seen, progress has occurred at very different rates in different species, the main brakes on the pace of change being generation interval and the worth of individual animals. But Table 1 also highlights the almost completely statistical approach of animal breeding from the reconciliation of Mendelian and quantitative genetics in 1918 onwards. While much is known of particular genes in particular species, few useful genes (as opposed to genes influencing colour, pattern and other visually interesting traits) have been deliberately introduced into domestic animals and most of the major gene changes which have been made were carried out before breeders knew anything of the work of Gregor Mendel.

Table 1 A comparison of the main classes of farm livestock

X = widely and effectively used;

0 = minor use as yet in 1964;

• used in 1989 (modified from Lerner and Donald, 1964)

		Dairy Cattle	Beef Cattle	Pigs	Sheep	Poultry for Eggs	Poultry for Meat
Closed Population	Eye judgment Breed Society Performance test Artificial insemination Family testing MOET	X X X X X •	X X 0 0 • •	X X X 0 0	X X 0 • •	X 0 X	X X X X
Organised crossing	Eye judgement Performance test Large scale breeding Inbred lines Control population	X • •	X • •	• • • •	• • • •	X X X X	0 0 0 0
Approximate heritability	Amount of product Quality of product Reproduction and viability	30 45 5	40 30 5	40 50 10	35 40 5	20 50 10	40 30 10
Ranking	Value of individual animals Reproductive rate	5 5	6 6	3 3	4 4	1 1	1 1

We can also gain some perspective of overall change in the various animal industries by comparing the 1964 and 1989 entries in Table 1. These show how the adoption of applied quantitative genetics has continued over time. What they do not show is the rate of improvement in traits of interest. Table 2 gives an indication of how much room for improvement there still is. But equally, there have, at times, been changes in the rate of improvement. For example, in the poultry industry changes in eggs laid per bird per year have increased steadily over about thirty years from less than 200 (feed conversion ratio (FCR) 4) to more than 280 (FCR 2.8) and this progress may continue for some time. In broilers, FCR fell over the same period from more than 3 to less than 2, but the proportion of fat in the carcase rose unacceptably in recent years. Selection designed to produce leaner birds will not lower FCR greatly overall, but will lower FCR for lean meat, which is what is wanted.

Table 2 Possible genetic responses to selection for milk yield (N) in dairy cattle, with various new technologies (from Smith, 1988)

Genetic response (sd units per year)
Progeny testing schemes
Rates currently achieved Rates possible Current systems Efficient systems Efficient systems, with females bred by MOET Efficient systems, screening young bulls in MOET breed full sibships, using an indicator traitb Sperm fusion	0.01 - 0.07 0.1 0a 0.13 0.13 - 0.15 0.14 - 0.15 0.14 - 0.18
MOU nucleus schemes
Rates possible Adult nucleus scheme Juvenile nucleus scheme With embryo splitting x 2 With embryo splitting x 16 Adult nucleus scheme with an indicator traitb Juvenile nucleus scheme with an indicator traitb	0.12 - 0.16 0.16 - 0.22 0.17 - 0.24 0.18 - 0.27 0.14 - 0.18 0.25 - 0.32
Cloning
Genetic lift possible (requires commercial embryo transfer)	1.8 sd

^a 0.1 sd = 1.5% of the mean (CV = 0.15).

^b Indicator trait (T). Coheritability (r_Gh_Mh_T) with milk yield (M) = 0.25.

Present State Of Application Of Knowledge

Quantitative genetics has been well described as a mature technology. It works: a trait with reasonable heritability can readily be increased by simple truncation selection; linear genetic selection indexes can modify sets of traits in predictable ways; crossing schemes perform approximately as expected; and so on. The widespread adoption of mainly BLUP-based industry breeding schemes (BREEDPLAN, GROUPBREEDPLAN, LAMBPLAN, etc) makes improvement of individual flocks or herds a straightforward matter, provided that users do not forget that they are dealing with animals which have to perform under normal conditions of husbandry. (With the implication that they cannot neglect traits related to viability and fertility).

However, quantitative genetics is incomplete in three different ways. Firstly, it lacks a bridge to molecular genetics. While this may be built on the work of Kacser (e.g. Keightley and Kacser, 1987) and others, it nevertheless does not exist (Mayo, 1989). secondly, and importantly for the future success of cross-breeding schemes, there is no satisfactory, complete theoretical basis for epistasis and heterosis (Mayo,1987; Geiger, 1988). Thirdly, and most importantly, all of the applied quantitative genetics in widespread use is based on linear relationships between traits.

Linear dependence is rare in biological systems but is a valid and successful first approximation for single trait selection and for the combination of a few traits. What happens when we need to select for a combination of traits which are certainly not linearly related? Consider the components of clean scoured fleece weight: diameter, fibre length, density of fibres, distribution of fibre diameter, specific gravity of fibre. These are certainly not linearly related and approximate linear relationships which may be achieved by scaling and appropriate mathematical manipulations may not be genetically or physiologically valid. And what happens when we also consider fleece quality, as assessed by part-qualitative, incompletely objective techniques (Table 3)?

Table 3 Genetic correlations between raw wool quality characteristics (modified from Ryan, 1989)

Y	VM	MFD	VFD	SL	VSL	SS	CR	RC	C
V	-0.2	0.2	-	0.4	-	0.2a	-0.4	0.5	05b
VM		-0.2	-	-	-	-	-	-	-
MFD			0.6a	0.2	-	0.5a	-0.2	0.5	0.5
VFD				0.4a	-	-	-0.6a	-	-
SL					-	-0.1a	-0.4	-0.5	0.4
VSL						-	-	-	-
ss							-	-	-
CR								0.4	-
RC									-
C									-

(Y yield, VM vegetable matter, MFD mean fibre diameter, VFD variability of fibre diameter, SL staple length, VSL variability of staple length, SS staple strength, CR/RC crimp/resistance to compression, C colour, D dark fibre colour)

Values presented are mid range values from published estimates. See Mortimer (1987). Turner and Young (1969), Lewer, Rae and Wickham (1983), Watson, Jackson and Whiteley (1977), Bigham, Meyer and Smeaton (1983).

a = from Rogan unpublished

b = from P J James personal communication

a Refers to method of discovery: M, discovered by observation of difference between marker phenotypes; M-f, “measured genotype” response; 5, discovered by segregating genotypes.

Table 4 Major gene effects on quantitative traits of domestic animals (modified from Pirchner, 1988)

Species	Locus	Quantitative Trait	Discoverya
Cattle	Blood group M Double muscling Lysozyme level Twinning β- Lg K-Cn	Milk yield, mastitis Leanness, dystokia Disease resistance? Litter size Whey protein level, milk yield Cheese quality	M M M M M- f M- f
Pigs	Halothane resistance Susceptibility to E. Coli-K88 SLA	Leanness, stress susceptibility Resistance to E. coli infection, antibody formation Litter size, piglet viability, testis size	M M M
Sheep	Scrapie resistance Helminth resistance White coat colour Booroola F	Litter size Ovulation rate, litter size	M M M
Poultry	RSV resistance MHC Naked neck	Resistance to leucosis infection Resistance to Marek’s disease, progression! regression of tumours Heat tolerance	M M M
Horse	ELA	Progression/regression of tumours	M

Mendelian Genetics

In most species of livestock, a few genes with alleles which cause significant economic loss have been identified. Genes with alleles which yield significant economic advantage arc much rarer. Table 4 shows some genes which fall into one or other category. The reason why few major advantageous genes have been identified in modern times is obvious yet frequently unremarked. It is that artificial selection is an ancient process in human terms. Hence, most genes with alleles which are advantageous to production in any straightforward sense have had those alleles fixed. This does not mean that major genes will be irrelevant in animal improvement; far from it - what it means is that new approaches to identification and utilisation are required, including those of genetic engineering.

Firstly, however, consider one identified gene which is at a low frequency in one breed of one species and which is of demonstrable utility - the Booroola F gene. This gene increases the ovulation rate by about 1 for each dose of the F allele. Thus, it provides a way of permanently increasing fertility. The question one nust ask, however, is whether a permanent increase in fertility is what is wanted. The environment becomes the determinant. What if one has a choice? Should one choose Fecundin or Multivac or Booroola? The fertility-enhancing vaccines provide a short-term, variable means of increasing the fertility of a flock; in many cases this may be the method of choice. perhaps in WA, where flock marking percentages are so low, but the summer feed problem has not been solved, graziers might initially be most relucant to develop a permanent increase in reproductive potential, until they had determined that they could manage higher fertility achieved through use of one of the vaccines on a small scale. Again one sees how the environment is as important as the genetical change.

Although very extensive studies of the F gene have been carried out (e.g. Piper and Bindon, 1988), the actual nature of the gene has not been identified, nor has its chrontosomal location been determined. This means that some of the work of using the gene is still in the form of progeny testing, as if the trait of interest were quantitative. Once the gene is identified, its function defined, and its DNA sequence determined, it can be cloned and manipulated by recombinant DNA techniques. Thus, even though it has been identified for many years as a major gene with additive effects, it is still being used through traditional techniques, such as backcrossing to incorporate it into the Border Leicester breed.

Genetical Engineering

Useful manipulation of major genes by traditional genetical means, while straightforward is, as we have seen, very rare in current animal breeding practice. The main reason for this, as again we have seen, is the rarity of useful genes. Accordingly, we cannot expect the impact of genetical engineering on animal breeding to be great for quite a few years yet. Techniques necessary for genetical engineering, such as artificial insemination, embryo storage and MOET, are well established and can have major effects in altering rates of progress (Table 2) and, in due course, industry structure. Cloning will also be very important when it becomes a practical tool, rather than a rare, usually unrepeatable process.

Because of the lack of target genes in species of economic importance, most workers have attempted to manipulate genes from unrelated species. There has, however, been one gene which has been targeted in several species, the growth hormone gene. This hormone is normally produced in very small quantities under regulation by the pituitary, and the aim has been to have more growth hormone produced under external regulation, since it has been shown that a small dose of growth hormone injected into an animal can increase growth rate dramatically and can also change carcase composition so that the amount of fat is very much less than in normal growth on the same diet.

In mice, the gene can be activated by use of the promoter (the sequence of DNA that initiates transcription, the first stage of the set of processes that leads to the synthesis of a gene product) for a metallothionein gene. This gene is responsible for certain aspects of the metabolism which involve heavy metals, so that its promoter is responsive to above-normal levels of zinc in an aninal’s tissues. Hence, if this promoter is attached to another gene, that gene should also be activated by high levels of zinc in the tissues. This hypothesis has been borne out in practice in mice, and was then tested in Merino sheep by Kevin Ward and his group (Ward et al. 1988; Nancarrow et al. 1988). Unfortunately, the same stretch of DNA which could be regulated by added zinc in the mouse proved to express growth hormone in the sheep but in an unregulated way, so that the animal had excessively high circulating levels of growth hormone which changed many aspects of its metabolism. In particular, excessive glucose was produced in the blood, which challenged the pancreas to produce more insulin than normal. After a few months of this excessive activity, the pancreas failed and the animal became severely diabetic.

Much has been learnt from this work, both about how to incorporate alien genes into different species and about the effects of such alien genes, so that these experiments have been very successful in a scientific sense. They have not, however, been useful as yet in animal breeding. It is possible that subsequent work, which aims to reduce biological limitations to wool production, will be equally successful scientifically but will not yield economically important results for quite a few years. Eventually, however, it will pay dividends. At the moment, the molecular biologists are navigating in uncharted waters.

There is a second approach to DNA manipulation which might seem more logical and that is the modification of an animal’s own genes. After all, minor changes in a given gene can produce major changes in phenotype. Also, changing a gene which is already part of a harmoniously regulated set of genes, as is the case for all of a species’s own genes, should produce less gross disturbances than I have described for the alien growth hormone gene. Indeed, when we think of changing just one gene in a really top animal, then cloning that animal many-fold, we perhaps see the real hope of genetical engineering and the reason why it is so eagerly pursued by so many.

What is needed is a much larger repertoire of target genes. This requires other approaches, in particular the mapping of the whole genome of a given species. In Australia, we have a major programme of gene mapping coordinated by Jay Hetzel of the CSIRO Division of Tropical Animal Production, the aim being to produce maps of where the genes lie on the chromosomes in both sheep and cattle. When these maps are completed, or even rather less sparse than is now the case, they will allow the location of genes recognised in other species, because the chromosomes of mammals are rather alike and iron the location of genes in one species one can make inferences about their location in other species. Hence, if a useful gene is found in one species, it can be tracked down in another. So the AMLRDC is funding this very long term work because it will certainly be of great eventual benefit to both the sheep and cattle industries.

Conclusion

Animal improvement in all of the major species of domestic livestock should be occurring at a faster rate than ever before, except where physiological limits have been reached. This may not be far off in broiler or layer poultry, but is a long way off in Merino, in beef cattle, in dairy cattle, and in prime lamb production. Where a single trait is the target for selection, or just a few traits, the problems are well defined, the solutions available are in sight.

But most animal production is not like that. Wool is the most obvious example of a very complex trait, in fact one where quantity of product and quality of product are both complex in determination and possibly negatively associated at both the phenotypic and genotypic level. So any simple approach to modification of wool production is likely to create problems. Not insoluble problems, but difficult and time-consuming to investigate: we can say that WOOLPLAN is a good tool, in need of sharpening, but do we know exactly how to sharpen it?

Take molecular approaches to wool quality. Can we improve the properties of wool which make it such an excellent fibre for clothing - softness, water absorption, compressibility - by altering the proteins which make up the wool? Probably, but where do we start? And can we add desirable properties like insect resistance and shrink-proofing in the follicle rather than the factory? Perhaps, but if so, will the other properties remain?

To repeat: we have the tools for accelerating animal improvement, but we still lack a great deal of basic knowledge of what we want to improve.

Acknowledgements

I thank I.R. Franklin, N. Jackson, L.R. Piper, B.L. Sheldon and K.A. Ward for advice.

References

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