Evolving notes, images and sounds by Luis Apiolaza

Category: research (Page 2 of 7)

Essentially

A month ago I said “trees are much more than wood and one of our PhD students at the School of Forestry, University of Canterbury had a look at production of Eucalypts essential oils, particularly cineole”. We could easily see seasonality of production (peak in Spring & Summer) and the difference between juvenile and mature foliage. (Eucalypts essential oil)

Today, fresh from the oven, we have our new article moving from phenotypic variability to the genetic control of 20 essential-oil compounds with exciting names like 1,8-cineole and aromadendrene. A tale of alternative species and products, progeny trials and gas chromatography.

Of the 85 tested E. bosistoana families, seven families possessed breeding values indicating that their oil would meet the British Pharmacopoeia standard specification of a minimum of 70% 1,8-cineole. However, the negative correlation between total oil content and 1,8-cineole concentration indicates that families with higher-quality oil have less oil in their leaves.

Chamira Rajapaksha, Luis A. Apiolaza, Marie A. Squire and Clemens Altaner. 2023. Genetic parameters of essential-oil traits for Eucalyptus bosistoana, Australian Forestry DOI: 10.1080/00049158.2023.2270681 (Open Access)

Negative genetic relationship between cineole yield and total oil production.

At the core of your breeding programme

Surely you have been in this situation: meeting, there is coffee and biscuits at the back, some fruit if you’re lucky, and people with ideas flying about how to improve or change the breeding programme. Some of the ideas look easy to implement, others need a huge amount of work, and everyone has a different favourite.

Breeding programmes are vehicles to deliver genetic gain, which results on extra profit (or, at least, on maintaining your competitive position). So it makes sense to evaluate the effect of every suggested change to the programme under the lens of the Breeder’s Equation.

The Breeder’s Equation

Typically books present the simplest formula:

\(Gain = i h^2 \sigma_p\)

which works fine when dealing with a single trait, during the first round of selection based on an individual’s own record.

In reality, we are likely dealing with multiple traits and a more advance breeding programme. We move to \(G = i r_{IH} \sigma_H\). Now H is a total genetic economic value involving multiple traits, targeted by a selection index I, that takes into account all the genetic and economic information. This is great for calculations but harder to communicate.

It is probably easier to keep in mind that
 
Gain = (selection intensity x accuracy x variability) / time.

The meaning of each equation term is much easier to relate to something tangible. Focusing the meeting on these terms seems much more productive to me.

Did you develop a new way of phenotyping a hard-to-assess characteristic? We can now push selection intensity. Are we bringing new material to the breeding programme? That will increase variability. Are we paying for a new chip with N thousand SNP? That shortens time, maybe at lower accuracy but may also affect selection intensity.

We can look at the ideas, have basic discussions and later simulate those ideas (work in silico if you want to be posh).

A few ways of thinking of genetic gain and the Breeder’s Equation

In between plant and animal breeding 1: economic weights

Unless you live in paradise and you have a single objective trait—so your whole breeding objective is “more X”—you have to take into account trade-offs between traits. On the genetics front we deal with this via genetic correlations, but on the value front we have to figure out how much is an extra unit of X worth compared to an extra unit of Y (at least in relative terms).

You might be thinking “I don’t use no stinking economic weights, mate”. Instead you could used desired gains, independent culling levels, etc. You are not using explicit economic weights, but there are undeclared, implicit weights in your selections.

There are a few things that make tree breeding economic weights a bit different from the ones used in crop and animal breeding:

  1. Time or, better, TIME! We are selecting trees to go to the breeding programme, which later will be deployed and go for a full rotation (7 years for pulp in the tropics or 15 in temperate environments, 25~40 years for solid wood). This create huge uncertainty for the value of traits in 15~50 years in the future. We did some work on dealing with economic uncertainty here:

Evison, D.C. and Apiolaza, L.A. 2015. Incorporating economic weights into Radiata pine breeding selection decisions. Canadian Journal of Forest Research 45(1): 135-140 (PDF).

  1. The relationship between wood properties and end products is harder to quantify, particularly if dealing with solid wood products. If you come from the animal world, imagine the difference between breeding for milk (that can be homogenized, a bit like pulp) and for meat (where mixing filet with rump is a major loss of value, a bit like solid timber).

Even worse than pedigree errors: selecting for the wrong thing

Imagine this: you have been patiently assessing trees for wood density (selection criterion) thinking that you’re improving wood stiffness (objective trait). Stiffer trees produce stiffer and more dimensionally stable wood, higher value, more profit. Your choice of density sounds reasonable, on average denser wood tends to be stiffer… except that wood is not getting stiffer.

If you look in the literature, wood density is this sort of universal, canonical trait: it is correlated to pretty much everything, it’s easy to assess and highly heritable. However, there is a big problem, as the relationship between density and stiffness changes with age. This points to something important: it is essential to have a good understanding of the traits we want to breed for.

Wood nerdiness: Stiffness depends on the product of density by velocity^2. Velocity is a proxy for the angle of cellulose microfibrils in the secondary cell walls (MFA); the faster the velocity, the lower the angle, a small angle leads to higher stiffness. The variability of stiffness in young trees is dominated by the variability of MFA, while the variability of stiffness in older trees is dominated by the variability of density. This is the root of the problem:

We were selecting for density when to improve the stiffness for the first rings of the trees we needed to select for velocity. Wasted time, wasted effort. On the plus side, trees got denser, which was good news for carbon sequestration. A positive, unintended consequence.

In between plant and animal breeding 1

Let’s start with the obvious: trees are plants and—unless you are breeding Ents—they do not walk around. Therefore, the first obvious statement is that tree breeding heavily relies on experimental designs to account for environmental variability.

But trees are much larger and long-lived than corn or potatoes, so we need much larger trials, in land that’s often not flat, because if it were flat you’d be growing a plant crop. So our trials often need incomplete blocks, and within-trial spatial analysis tends to make sense.

Trees are also much closer to the wild populations. Most tree breeding programmes are not beyond a 4th generation from undomesticated trees, so we have shallow pedigrees, unlike plants and animals.

Another difference is that we normally don’t test a few cultivars, but we are testing thousands of genotypes, which puts us closer to animal breeding. Animal breeding breeders often assume a set of “standard” genetic parameters for genetic evaluation; instead, like other plant-people, we estimate genetic parameters from the trials under analysis. This means we may end up trying to run a multivariate analysis with, say, 100 trials (sites) with half a million genotypes, using an tree (animal) model BLUP, which doesn’t work, unless we move to a factor analytic, reduced tree (animal) model, etc with all sort of compromises. We are still looking for the right level of complexity for genetic analyses.

Trees also have a very clear distinction between the objective traits and selection criteria. Traits have an effect on profit and normally are expressed at rotation age (say ~25 years in radiata pine), like volume per ha, wood stiffness, stem form, etc. Criteria are easier, earlier and cheaper to assess, usually at 1/4 to 1/3 of rotation, like stem diameter and height, acoustic velocity, etc.

Trees are heterogeneous, anisotropic, hard to quantify sometimes. We invest substantial efforts developing phenotyping tools to describe these gigantic organisms. Sometimes, in the wind, they look just like Ents.

« Older posts Newer posts »

© 2024 Palimpsest

Theme by Anders NorenUp ↑