Evolving notes, images and sounds by Luis Apiolaza

Category: research (Page 4 of 8)

Do I have pedigree errors?

We just finished a genetic analysis, got breeding values for all our selection criteria, combined them with genetic parameters and economic weights in a selection index (I) that predicts the total genetic-economic value in dollars (H). We rank our trees from best to worst, perhaps with some constraint on relatedness, and go to the field to collect genetic material.

  1. Perhaps the trial is not well labelled, so we are in the wrong block.
  2. Or we are entering perpendicular to it, so the rows are the columns.
  3. Or the tree labels are gone, so we are picking up the wrong tree.
  4. Or we identified the right tree, but set the wrong label when collecting seeds or cuttings.
  5. We grafted the cuttings and set a new, incorrect label. Or we sowed the seeds in the wrong part of the nursery.
  6. Maybe the grafts ended up in the wrong part of our orchard, or with the wrong labels.
  7. Did we properly label the pollen we just collected. And dried. And stored.
  8. Is the pollen from the male parent going to the right female?
  9. Once we collected the cones (or capsules, or fruits) are they separate in the kiln, and the machine that removes the seeds?
  10. Are the seeds sown with the right labels?
  11. Are the seedlings going to the right place in the trial? Go to 1.

We could genotype the trees to check for pedigree errors. But see 1, 2, 3 & 4. Is the pedigree wrong, or the sample for genotyping, or both?

The question is not if we have errors in the pedigree. One would need superhuman luck to avoid having any errors in a small breeding programme and a real miracle if you have close to a million trees with records. The real questions are:

  • Can we reduce future errors?
  • Do the pedigree errors we have make a significant (in the sense of important, not statistically) difference to the breeding and deployment programmes?

For example, over the years I have seen huge improvements in Proseed (the largest tree seed producer in New Zealand). Multiple validation steps, QR codes for orchard blocks, pollen, cones under processing, etc. There are similar changes going in parts of the breeding programme.

What’s the percentage of errors that remain in the system? I don’t know. Does it make a substantial difference? Maybe.

Are there other problems that could be even bigger? Yes. Maybe breeding for the wrong thing, but that’s another post.

All that glitters is not GxE interaction

We are going over the fifth version(*) of a manuscript with a Ph.D. student and colleagues, polishing some details before submission, thinking about one of the results: basically there is little, if any, Genotype by Environment (GxE) interaction.

There is a long history of studying GxE interaction in radiata pine, with parallels in the development of statistical techniques. From basic models with a few sites with common parents, to massive multivariate, factor analytic, animal (tree) model BLUP with huge levels of imbalance, showing substantial GxE in some cases.

This time, however, we have a number of extremely well connected sites, clonal replication, SNP-based pedigree, factor analytic covariance matrices, etc. And there is almost no GxE interaction for stem diameter (a low heritability trait) and wood basic density (a high heritability trait).

Could it be that a large-proportion of reported GxE interaction relates to data structure? If that’s the case, Is there much point on trying to explain the “interaction” with environmental variables? 🤔 I’m not saying that this happens all the times, but I have seen the issues quite a few times already.

(*) One of those cases where the journey—learning how to pitch the problem, emphasising the most relevant parts, etc.—is almost more important than the destination. We are training researchers 😉.

Early selection: how early is early enough? Part 4

In the previous post we were able to screen trees for wood properties at 2 years of age, separating normal and compression wood by leaning the trees. We obtained genetic parameters, breeding values, etc. However, we also discovered that planting the trees directly in the ground was still subject to too much environmental variability. So, take a guess… we planted another trial.

This time we had 90 families and 10 clones, for a total of 3,000 trees growing in 75 litre bags, leaning, with slow-release fertiliser, an irrigation system. Not only that, but we tested 3 different populations: clonal (derived from trees selected at ‘traditional’ 8 years of age for growth and stiffness), seed orchard (derived from trees selected at ‘traditional’ 8 years of age for growth and basic density), and new selections (selected for a combination of traits).

As a start, heritabilities increased substantially (sometimes doubling it), which is not surprising considering that the trees were in bags. One cool thing was that we could observe differences between populations at 2 years of age; so selecting at ‘traditional’ 1/4-1/3 of rotation does actually pick up trees on different wood properties trajectories. This was 1- confirmation of something we hinted some years ago (coming post) and 2- guiding other work I am writing up at the moment.

This “bagged trial” was published Open Access as: Apiolaza LA and Sharma M. 2023. Selection history affects very early expression of wood properties in Pinus radiata. New Forests DOI: 10.1007/s11056-023-09997-3.

Early selection: how early is early enough? Part 3

In my previous post we were able to detect extremes of wood density and stiffness with leaning trees at 8 months of age, vertically splitting the sample to separate normal and compression wood. This was doable, but the size of the wood samples was a tad small to screen large numbers of trees, so we proposed screening parents of a seed orchard with a trial including 49 controlled-pollinated families at ages 2, 3, 4 and 5 (harvesting one quarter of the trees per year).

After 2 years, we processed the first quarter of the trial (492 trees) extracting a 200 mm long stem bolt from the leaning trees (see photo below) and found that:

  • Wood properties were under moderate genetic control at 2 years of age, so we felt we could, at least, screen out the worst families.
  • There was more environmental variation than desired (particularly soil) and lots of wind, which altered the leaning angle of the trees.
  • This variability left us feeling that there was no point on continuing with the experiment in that site (sometimes you win, sometimes you lose) and that we needed to setup an alternative experiment. TO BE CONTINUED. YES, OF COURSE THERE IS ANOTHER EXPERIMENT.
Composite image showing leaning radiata pine trees, a stem bolt with compression and normal wood, and a matrix of genetic parameters.
Composite image showing leaning radiata pine trees, a stem bolt with compression and normal wood, and a matrix of genetic parameters.

More details in: Apiolaza, L.A., Chauhan, S. and Walker, J.C.F. 2011. Genetic
control of very early compression and opposite wood in Pinus radiata and its implications for selection. Tree Genetics & Genomes 7(3): 563-571. PDF available at https://luis.apiolaza.net/publications/

Early selection: how early is early enough? Part 2

In the previous post I mentioned that we wanted to screen trees for wood properties as early as possible, BUT there is a lot of “noise” with the mix of normal and reaction wood (compression in softwoods or tension in hardwoods). The main problems for running a glasshouse experiment were:

  • How to separate normal and reaction wood? Here the good-old-leaning-trees approach was handy.
  • Trees move a lot in real life, what’s the effect of thigmomorphogenesis (fancy name for response to movement)? How can we move them? Build a rocking machine: having good technicians help.
  • How good are the screening methods? Before embarking in a big experiment, better look first in a few clones with contrasting wood properties (4 Arborgen varieties). If that doesn’t work, pull the plug.

So we got a glasshouse with four clones, some ramets standing, some leaning and some rocking for eight months. Standing trees and rocking trees had random arcs of compression wood, but rocking reduced wood stiffness by 20%, which is similar to what happens to mature trees on the edge of stands. Leaning trees nicely separated normal and compression wood, which now could be analyzed separately; not only that, but they magnified the differences between the clones. TO BE CONTINUED.

Read more details here https://rdcu.be/donFJ

Luis A. Apiolaza, Brian Butterfield, Shakti S. Chauhan & John C. F. Walker. 2011. Characterization of mechanically perturbed young stems: can it be used for wood quality screening? Annals of Forest Science 68: 407–414.

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