One of the cool things about Quantitative Genetics is that it works everywhere. As a forester, I work with trees and my analyses reflect that, accounting for the biological constraints of our species (long-lived, usually, but not always, monoecious species—both sexes in the same individual), experimental designs (often incomplete-block), relatively shallow pedigrees (we started a few generations ago), etc.

However, as a Forestry undergrad I chose to take a Quantitative Genetics course in the Department of Animal Science at the Universidad de Chile. The examples used rabbits, sheep, etc. but the equations were directly applicable to trees. As a postgrad, I was, again, in the Department of Animal Science (at Massey this time) and the courses and discussions were mostly about cows. Unsurprisingly, the equations were directly applicable to trees.

Last week, I was fitting a multivariate animal-model BLUP with trees but, with small changes, you could use the code for cows, or rabbits, or wheat, or potatoes. This means that we, quantitative geneticists, get to be interested in the developments in other industries.

That was a long preamble! The thing is that I came across these article in Radio New Zealand: What’s the model sheep of the future? where there was a link to the nProve system “a free online tool for farmers wanting to identify breeders producing rams suitable for their own operation” developed by Beef+Lamb New Zealand. I HAD to look at nProve, of course, and there was one thing that really grabbed my attention: there is a very large number of traits that can be used to select rams, including multiple terminal indices, health indices, or just play directly with the breeding values for specific traits. There are regions in the country too.

It looks like a great tool to help farmers and I imagine that there must be substantial work communicating the tool to farmers. Just in case, here is a sort of equivalent tool for radiata pine in New Zealand: TopTree.

I am often fascinated by people who can explain something that I already know but in a *much better* way. For example, Howie Hua is great at looking at mathematical issues (geometric mean, in this example) and coming up with a simple, straightforward way of presenting it; a way that feels fresh.

If we can explain things (here is my attempt at “changes of rankings”), if we can socialise, if we can share a common understanding with colleagues, customers, community; then we can do a much better job in our projects. This is true in forestry, breeding and, I guess, pretty much any activity.

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 Arūnas 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.

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.