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The Better Breeding Blog will be an informative series of posts that will explain in layman’s terms the science of animal breeding and how to apply this to your own animals. This information is evergreen and applicable to all species, and if I can cut through the rather intense maths and genetics behind successful breeding such that pretty much anyone can understand it, then I will regard this as mission accomplished!

There are two planned sections for this site. This section, the blog, will be more “freeform”. I don’t want to be caught in analysis-paralysis working out the best structure for my posts, or how to best organise them from day one. I’d never get started! Rather, I’ll post on whatever comes to mind first, and allow things to develop naturally from there. The second section is intended to be more book-like, and will be the repository for a more organised structure where posts will ultimately be collated into chapters. This section doesn’t exist yet.

There may be an assumption that this site is biased towards alpacas, but I cannot stress enough that this blog is for all breeders of all animals. The science is exactly the same regardless of species, and I welcome and encourage all feedback and suggestions from serious breeders looking to improve their own stock and future revenues.

Knowledge and Understanding of Genetics and Inheritance
To know and understand the fundamentals of genetics and inheritance—and ultimately, better breeding—is half the battle. The aim of this blog is to give you these.

Information
It is crucial to have good information on your animals, whether those you already have or those you wish to acquire. Good information requires meticulous record keeping.

Application of Theory
To know and understand a concept is all well and good, but the best breeders are the ones that apply that knowledge.

Commitment
Have a goal in mind, and stick with it. Determine what to you is the perfect animal, and set out to breed it.

Patience
Genetic change is slower than you may realise. Your perfect sire may not be born overnight, but if you commit to your plan and follow through one step at a time, you will create that animal with time.

Perseverance
There will be setbacks, and the desire to jump on trends and fads of the day will be strong. Just think of the hare and the tortoise and do not deviate. A sound plan will always prevail, though it may appear dull and boring to flighty trendsetters!

Another word common in breeding is trait. People often use the words trait and phenotype interchangeably, but they are really two distinct things.
A trait is something that can be measured or observed.
A phenotype is the value for that trait.

Examples:

• Staple length in wool animals is a trait, “110mm/year” is a phenotype
• Temperament is a trait, “placid” is a phenotype
• Milk production is a trait, “6,000 litres/year” is a phenotype

Another thing worth clarifying is that people also tend to use the word “phenotype” to mean appearance. Coat colour is a classic example of this type of phenotype, and probably the one “everyone” learns in high school biology. But phenotype can also refer to performance. Speed (eg of Thoroughbreds and greyhounds) is an example of this type of phenotype. So is milk yield. The words phenotype and performance are interchangeable when referring to measurable traits as opposed to observable ones.

To summarise:
If it can be observed, it’s a trait.
If it can be measured, it’s a trait as well.
The value of a trait is a phenotype, and a phenotpye can refer to appearance or performance.

One last point to make: an animal has as many phenotypes as it has measurable and observable traits.

P = G + E

Scarey, isn’t it?! Though I stress that this is an oversimplified equation.

In words:
Phenotype = Genotype + Environment

In other words:
Phenotype is the result of genetic factors (the genotype) and nongenetic factors (the environment).

Not all phenotypes have nongenetic influences—a pup born with brindle coat genes is going to be brindle whether born in a temperate zone or the heart of a desert. But many phenotypes do, and this should be considered when embarking on a breeding programme.

Consider exceptional stud stock, known to produce high quality progeny over and over. Now consider those progeny raised during extensive drought with no supplementary feeding. Those babies have to-die-for genotypes (as evidenced by their pedigrees), but those genes won’t manifest as to-die-for phenotypes. Those babies will probably grow to be stunted, malnourished adults underperforming in the traits they were bred for.

An example is medium wool sheep raised in harsh conditions so as to grow (force) a lower micron fleece than their genotype would otherwise code for. The animals are not at peak performance, and while the wool will be fine[r], it will probably be brittle and susceptible to breakage when processed on high-speed industrial machinery. Compare this to genetically-fixed fine wool sheep consistently producing a low micron fibre of even diametre and strength in less harsh conditions.

E in the simple formula above is often taken to be physical environment. This includes availability of feed, feed quality, water quality, altitude, soil, and the weather and seasons.

But E includes more abstract factors which are also environmental (nongenetic) influences on phenotype. These are: fixed resources, management, and economics.

Fixed resources are such things as farm size, workable land and labour - can additional, more nutritional feed be grown to boost phenotypes for example?

Management is the farmer’s policies. Policies could include supplementary feeding, vaccinations and deworming to increase animal health, which in turn leads to more productive phenotypes. Or a breeder may introduce animals with better genotypes to his herd to improve future generations’ phenotypes.

Economics covers all farm inputs (costs) and outputs (prices for animals and their products). Feed, labour and husbandry inputs can all affect phenotypic outputs, which in turn influence prices obtained.

The goal of breeding is to improve phenotype by improving genotype, but there is more to this than simply cobbling together a pedigree with desirable ancestors. Remember that P = G + E, and neither G nor E can be taken in isolation on your quest for P nirvana. Rather, you need to consider an animal as part of a system, something we’ll go into more in the next post.

Consider a farm. It has:

• animals
• farmer(s)
• a physical environment (altitude, soils, seasons, climate, water and pasture availability and quality)
• fixed resources (land size, workable area, labour)
• management policies
• economic inputs and outputs

The animals are the genotypic input, with the rest environmental inputs. All these parts interact and ultimately influence the end phenotypes.

When you have a group of parts interacting to create a whole, you have a system. Here, the farm is a system and the animals and environmental inputs are the parts of that system.

 A breeder strives to improve phenotype by improving genotype. But for maximum genotypic improvement, a breeder must also consider his animals as part of a system, subject to the interactions of other parts, and make decisions accordingly. And these decisions could be different for different breeders, as their personal systems also are different.

For example, alpacas in Australia occupy a wide range of temperate climates, and management practices change to suit. Paralysis tick can be fatal on the east coast if not controlled for, but not in the drier inland where it doesn’t exist. Shearing and birthing are timed, for the most part, earlier with decreasing latitude. It was not uncommon for breeders located in the subtropical north in the early days of the industry to relocate to the temperate south, having lost valuable breeding stock (genotypes) and their young to ticks and the vagaries of hot humid weather that set them back several years.

Nowadays most alpacas are found in the traditional wool sheep regions of the country. The environments favourable to wool sheep genotypes would appear to also be favourable to alpaca genotypes. The next four posts will go more deeply into genotype-environment interactions, and how knowledge of these can improve breeding programmes.

It wouldn’t be surprising for alpacas to do better in temperate regions than subtropical regions. Their native habitat after all is the Andes at altitudes of 3,500m to 5,000m. You may expect alpacas to do even better in their native habitat again. Yet this isn’t necessarily the case. Many alpacas have succumbed to unusually cold winters in the Andes ^[4] that would probably have been completely unaffected in warmer winters elsewhere.

You may think from all this and the title of this post that the location of an alpaca and its performance in that location is a genotype-environment interaction. (Also called genotype by environment, or G × E) But it isn’t—it is simply an example of one environmental effect on genotype, ie a nongenetic influence on animal performance.

A genotype-environment interaction is one where there is a difference in performance between two or more genotypes in two or more environments.

Going back to alpacas. There are two alpaca fleece genotypes: the huacaya and the suri. Huacayas have sheep-like crimpy wool that grows outwards from the body, while suris have fibre “dreadlocks” that hang like curtains on the body. Pictures can be seen here.

 Suris are much rarer than huacayas in the Andes. One possible reason—though not a sole one—could be because the suri fleece type, with dreadlocks hanging either side of the body, results in an exposed line along the back. This exposure would make them more susceptible to cold, wet, windy conditions at high altitudes than the more evenly covered huacayas. Yet both suris and huacayas thrive equally well in Australia where temperatures can hover around freezing during winter in many parts—though without shelter suris are still adversely affected by cold, wet and windy weather ^[5].

If this is the case, we do now have a potential genotype-environment interaction. The two genotypes are suri and huacaya, and the two environments are an Andes winter above 3,500m and a still cold, though milder in comparison, Australian winter.

Please note the following is merely to illustrate a point. It is not based on real data and in no way implies a real situation.

We could represent this with a graph like this:

An example of a genotype-environment interaction
© Optimate Group Pty Ltd

where the huacaya geneotype performs better in the more extreme environment, but both genotypes do well in a less extreme environment.

But note how the difference between huacaya and suri performance in the Andes is greater than the difference between the same two genotypes’ performance in Australia. Thus a G × E interaction is evident, as the difference in performance between the two genotypes is also different between the two environments.

It’s important to realise that graphs of G × E interactions always have the lines converging, diverging, or even crossing. A G × E interaction does not exist if the lines are parallel. This is because there is no difference between the genotypes in different environments—each genotype has been negatively, positively, or neutrally affected equally.

Let’s rework the graph above to make this point clearer. Again:
The following is merely to illustrate a point. It is not based on real data and in no way implies a real situation.

An example of no genotype-environment interaction
© Optimate Group Pty Ltd

Here, the two lines representing susceptibility of each genotype to cold weather are parallel. In other words, the difference in performance is the same for each environment. A huacaya genotype appears equally less susceptible to cold weather than a suri genotype in both climates. There is no G × E interaction here, as both genotypes changed their performance by equal amounts when the environment changed. This graph simply describes a simple environmental effect on two genotypes, not an interaction between genotypes and environments.

You must always have at least two genotypes and at least two environments to show a genotype-environment interaction. Graphing these makes visualising the interaction so much easier. The vertical axis represents the outcome you are measuring, and the horizontal axis represents the environments you are comparing. You will have a G × E interaction only if the lines are never parallel.

The next few posts will cover more examples of graphed interactions, some real examples in the animal world, and how knowledge of G × E interactions can help develop suitable breeding objectives.

References:

1. Meat & Livestock Australia. 2016. Cattle Herd and Sheep Flock Population Changes: 2015-16 on 2012-13 percentage change. p2. Retrieved 9th February, 2018.
2. Meat & Livestock Australia. 2017. Sheep Numbers Map 2016-17. Retrieved 9th February, 2018.
3. AgriFutures Australia. 2017. Alpaca Current and Potential Growing Areas. Retrieved 9th February, 2018.
4. Vice News. 22nd July, 2016. Llamas and Alpacas are Dying because of an Unusually Cold Winter in the Andes. Retrieved 9th February, 2018.
5. Shangrilah Alpacas. 2012. Dispelling Myths About Suri. Retrieved 9th February, 2018.

It’s a lot easier to visualise these interactions with graphs, so here goes!
“Environment” on the x-axis could refer to temperate vs tropical, low altitude vs high, or any other variable a breed is exposed to.
“Peformance” on the y-axis could refer to such things as length of wool, rate of growth, milk production, or any other measurement of animal performance.

The two graphs below both show no interaction, as both lines in each are parallel. The orientation or slope of the two lines may differ from graph to graph: the key distinction is that the lines are parallel.

Example 1: no genotype-environment interaction. The lines are parallel, indicating no change in the performance difference of two genotypes in different environments.
© Optimate Group Pty Ltd

Example 2: no genotype-environment interaction. The lines in this graph slope in the opposite direction to the graph above, but are still parallel.
© Optimate Group Pty Ltd

If the lines are parallel, then they are an equal distance apart. Thus there is no change in performance difference between each genotype in each environment. The performance itself may differ for each genotype in different environments—as evidenced by a slope upwards or downwards on the graph— but the difference in performance between the genotypes remains the same.

The two graphs below show a weak interaction. There is a small change in performance difference. In the second graph, the lines cross—this indicates a reranking of genotype when environment changes.

Example 1: a weak genotype-environment interaction. Performance difference between two genotypes is slightly larger in one environment than in another.
© Optimate Group Pty Ltd

Example 2: a weak genotype-environment interaction, with reranking.
© Optimate Group Pty Ltd

The two graphs below show a strong interaction. The difference in performance between the two genotypes is quite large. Again we can see a reranking of genotype in the second graph.

Example 1: a strong genotype-environment interaction. The difference in performance between the two genotypes in one environment is much larger than in another environment.
© Optimate Group Pty Ltd

Example 2: a strong genotype-environment interaction. Here, not only is the difference in performance between two genotypes larger in one environment than the other, but there is a reranking as well.
© Optimate Group Pty Ltd

The next post will cover some real-life examples of G × E interactions in animals.

G × E Interactions Between Subspecies of Bos taurus
Genotypes: Bos taurus taurus and Bos taurus indicus
Environments: Temperate and tropical

Bos taurus is the binomial (scientific) name for cattle. There are two subspecies of cattle: Bos taurus taurus (temperate cattle, eg the Angus and Hereford breeds) and Bos taurus indicus (tropical cattle, eg the Brahman and Afrikaner breeds). Photos of the many breeds within each subspecies can be found here.

The Bos taurus indicus subspecies is genetically adapted for tick resistance and extremes in temperature and humidity. The Bos taurus taurus subspecies is not, but is genetically adapted to cooler regions, and fattens readily on forage in preparation for cold winters.

The temperate subspecies performs better in temperate environments than the tropical subspecies, but the tropical one still performs well. In tropical environments however, the tropical subspecies outperforms the temperate one, though the heat, humidity and parasites are stressful conditions for both groups.

We could graph this interaction like this:

G x E interaction: two cattle subspecies in temperate and tropical environments. Both do relatively well in temperate environments, but a tropically-adapted genotype better handles the parasite, humidity and temperature stresses of a tropical environment.
© Optimate Group Pty Ltd

Note the reranking that occurs too.

G × E Interactions Between Breeds
Genotypes: Brown Swiss, Jersey, Ayrshire, Guernsey and Holstein-Friesian dairy cow breeds
Environments: High and low temperatures

High temperatures are well-known in the dairy industry to affect milk production. Australia has both temperate and tropical dairy breeds, but the temperate European breeds predominate in the industry. Summers in Australian temperate regions are hotter on average then Europe’s, so let us consider summer temperature variation in Australia as our E.

Of the popular temperate breeds in Australia, the Brown Swiss and Jersey cope best with heat stress, followed by the Ayrshire and the Guernsey, with the Holstein-Friesian coping least well of all. ^[1]

Please note that the following is not based on real data and is for illustrative purposes only.
We might graph this interaction like this:

An example of a genotype-environment interaction between breeds
© Optimate Group Pty Ltd

G × E Interactions Between Individuals Within a Breed
Genotypes: High pulmonary arterial pressure and low pulmonary arterial pressure
Environments: Altitude above and below 1.5 km (5,000 ft)

Brisket disease, or high mountain disease, affects cattle at altitudes above 1,500 m (5,000 ft) and has major economic costs in the USA.

The low oxygen content at high altitude causes the arterial walls in the lungs of susceptible animals to thicken, which decreases the diameter of, and increases blood pressure in, the pulmonary artery (the artery that carries deoxygenated blood from the right ventricle of the heart to the lungs). The heart has to work harder to pump this blood to the lungs, resulting in the right ventricle enlarging. Eventually the right ventricle becomes so enlarged that it cannot contract any more. Fluid may leak out through the blood vessels and into the brisket and up into the neck and jaw, and/or across into the belly. The animal is lethargic and eventually dies from lack of blood flow to vital organs. Alternatively the animal dies more quickly from heart failure when the buildup of pressure becomes so great that the right ventricle’s valves blow out.

The disease can be selected against by only using bulls with low pulmonary arterial pressure, which is a reliable and heritable measure against the disease.

Because the disease only manifests at high altitudes, we might graph this G × E interaction like this:

An example of a genotype-environment interaction between individuals within a breed.
© Optimate Group Pty Ltd

The next post will cover how knowledge of G × E interactions can help develop suitable breeding objectives.

Reference:

1. Dairy Australia Cool Cows. 2016. Dairy Cattle Herd Susceptibility. Retrieved 26th February, 2018.