Evolving notes, images and sounds by Luis Apiolaza

Category: breeding (Page 2 of 5)

Why are you complicating the analysis?

Progeny trials (or progeny testing or genetic tests or whatever you call them) are a real money pit. They are super useful, with many functions(*) but they are expensive as hell. Their establishment, maintenance and assessment are a constant money sink.

Progeny trials follow an experimental design, through which we try to isolate signal from environmental noise. They also follow a mating design that we keep track of via the pedigree (either through a list of crosses or using marker information). Putting those two designs together starts producing a more complex analysis, which becomes even more complicated as we also include multiple environments, multiple traits, etc.

So, Why am I complicating the analysis? Because I want to squeeze as much value of those bloody expensive trials. Over 25 years ago, 1997 to be precise, I read a very cool article: “Accounting for Natural and Extraneous Variation in the Analysis of Field Experiments” by Arthur Gilmour, Brian Cullis and ArĹ«nas Verbyla (available for free here). It is a beautiful example of model building AND value extraction from a single trial. What is the point of leaving money on the table (or genetic signal in the trial)? None.

These days there are multiple options for statistical software for running those “complex” analyses. I use ASReml-R, you may use something else. There are diminishing returns, there are simplifications that are a good idea, but please, keep on polishing those analyses.

(*) That’s another post, of course.

Have you visited the trials?

I was having a chat with analysts that just had a project dumped on their lap. They were questioning previous analyses as complex and were thinking of doing something much simpler and effective to make some progress on the project. There had been many delays due to personnel issues, so it was the time to move faster. They were chatting with me/asking my opinion because I was familiar with one of the data sets.

I was struggling with some of the assumptions they were making until I asked “Have you visited the trials? At least one of them…”. The answer was “no, we haven’t”.

It may sound like a silly question, as an analysis of an experiment is just applying a recipe, isn’t it? Nevertheless, this is not your typical textbook analysis, where everything is nice and square and tidy. These are forestry experiments, growing on the crappy end of land use classes, on hilly terrain, where different parts of the trial have different aspect, slope, fertility, drainage, etc… That’s why we care about the experimental design and anything else that helps us reduce environmental noise (x/y positions, spatial modelling of residuals, etc).

Becoming familiar with the site, the material being tested and the history of the experiments is not optional.

Breeding: simple interfaces, complex strategies

I found this text I wrote 20 years ago(*), part of a discussion document I prepared for a review of the radiata pine breeding strategy. Fixed a couple of typos, but I guess we still need pretty much the same thing. 🤔

“Different breeders value different things or, better put, they emphasise different values when developing breeding strategies. One of the reasons why many breeding programs struggle to achieve results is that they face an extremely complex list of activities, which are almost impossible to complete.

“A knee-jerk reaction from some breeders has been to recur to the KISS principle when developing breeding strategies. Unfortunately, the typical reaction has been “let’s create this dumb down strategy because it is simple to apply”. Bzzz. Wrong answer! What they have often done is to create a glorified “deployment strategy” that has almost no chance of surviving in the long term: that is, short term gain based on long term disappointment.

“Breeders need to realise that what needs to be simple is the _interface_ of the strategy. This means that we need a smooth interaction between the “theoretical animal” and the people that will be implementing it. This does not mean that the strategy is theoretically simple, but that the day-to-day activities are a breeze to complete.

“This type of interface requires the development—either in-house or through contracting the service—of tools that make life easy. For example:

  • Easy access to predicted breeding values, including desktop and online access. In addition, there needs to be an idea of the reliability of those predicted values if we are going to use them for deployment purposes.
  • Tools that make easy deciding what to select and which trees should be mated with each other (mate selection and allocation).
  • Protocols for deployment and tools for keeping control of the availability of genetic material.
  • Easy management of the interaction between improvement and deployment objectives.

“In summary, breeders need tools for dealing with the huge amount of data created by breeding and deployment activities, so it can be transformed into information.”

(*) Well, 19 years ago, this was 2005, but twenty sounds much better.

Why is this trait I like getting worse in the breeding programme?

The short answer: because the trait you like is not part of the breeding objective and, therefore, has not an economic weight assigned to it. And if it doesn’t have an economic weight it has 0 (zero) economic importance.

A longer answer: in breeding there is a distinction between objective traits (which have an impact on profit), and the selection criteria (variables that are easy and cheap to assess, and that are correlated with the objective traits). They may even happen at different ages. For example, in forestry stem volume and wood stiffness at rotation age (say 25 years) can be objective traits for the production of structural timber. Stem diameter, wood density and standing tree velocity at age 8 can be selection criteria.

Some of the confusion may come from when people like a selection criterion (like wood density) and think the breeding programme is trying to improve that. In this example, we weren’t (at least at that time). We cared about volume and stiffness. Sacrificing levels of some selection criterion while pursuing the objective traits is perfectly fine if I am maximising value. And in a modern breeding programme you are pretty much always looking at value, not at a single trait.

If you find this interesting, you may also like Why did my breeding values go down?

Do you remember your first time?

You were nervous. Would they like it as much as you did? Would you make the cut? Your first manuscript as a senior author tends to be a memorable experience. On one side, you have been working a long time, coming to terms with the problem, learning, building models, polishing the words [insert a few iterations here] until you submit the manuscript. It is a hopeful act.

Do you remember the feeling of the first acceptance? Your work was judged good enough to be published in that special journal, the one you like. The one were so and so, the authors you admire, published their work. Later you’ll understand that there are diminishing returns, so your tenth article will not provoke the same reaction, and your fiftieth article… you get the idea.

Do you remember your first rejection? Was it just a “desk-rejection”, wrong journal, no big deal? Or was it a “we hate the manuscript, what a turd”? This one can hurt, but there are diminishing returns too: your tenth rejection is more like “meh, what do they know?”.

Both the acceptances and rejections are of that particular piece of work. They are not about you, although some referees (typically referee No2) sometimes manage to make it feel personal. You are not a better or worse person because of the comments of a random set of referees. It is good to remember that a different sample of referees could have told you something very different about the manuscript.

I do remember the first acceptance; I barely remember the first rejection. I do look at those experiences with older eyes, thinking that in both cases I would write the manuscript very differently today.

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