Morning coffee: against validation and optimization

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It appears like I’m accumulating pet peeves at an alarming rate. In all probability, I am guilty of most of them myself, but that is no reason not to complain about them on the internet. For example: Spend some time in a genetics lab, and you will probably hear talk of ”validation” and ”optimization”. But those things rarely happen in a lab.

According to a dictionary, to ”optimize” means to make something as good as possible. That is almost never possible, nor desirable. What we really do is change things until they work according to some accepted standard. That is not optimization; that is tweaking.

To ”validate” means to confirm to that something is true, which is rarely possible. Occasionally we have something to compare to that you are really sure about, so that if a method agrees with it, we can be pretty certain that it works. But a lot of time, we don’t know the answer. The best we can do is to gather additional evidence.

Additional evidence, ideally from some other method with very different assumptions, is great. So is adjusting a protocol until it performs sufficiently well. So why not just say what we mean?

”You keep using that word. I do not think that it means what you think it means.”

A year ago in Lund: the panel discussion at Evolution in Sweden 2016

This meeting took place on the 13th and 14th of January 2016 in Lund. It feels a bit odd to write about it now, but my blog is clearly in a state of anachronistic anarchy as well as ett upphöjt tillstånd av språklig förvirring, so that’s okay. It was a nice meeting, spanning quite a lot of things, from mosasaurs to retroviruses. It ended with a panel discussion of sorts that made me want to see more panel discussions at meetings.

The panel consisted of Anna-Liisa Laine, Sergey Gavrilets, Per Lundberg, Niklas Wahlberg, and Charlie Cornwallis, and a lot of people joined in with comments. I don’t know how the participants were chosen (Anna-Liisa Laine and Sergey Gavrilets were the invited speakers, so they seem like obvious choices), or how they were briefed; Per Lundberg served as a moderator and asked the other participants about their predictions about the future of the field (if memory serves me right).

I thought some of the points were interesting. One of Sergey Gavrilets’ three anticipated future developments was links between different levels of organisation; he mentioned systems biology and community ecology in the same breath. This sounded interesting to me, who not so secretly dreams of the day when systems biology, quantitative genetics, and populations genetics can all be brought to bear on the same phenotypes. (The other two directions of research he brought up were cliodynamics and human evolution.) He himself had, earlier in his talk, provided an example where a model of human behaviour shows the possibility of something interesting — that a kind of cooperation or drive for equality can be favoured without anything like kin or group selection. That is, in some circumstances it pays to protect the weak, and thus make sure that they bullies do not get too much ahead. He said something to the effect that now is the time to apply evolutionary biology to humans. I would disagree with that. On the one hand, if you are interested in studying humans, any time is the time. On the other hand, if the claim is that now, evolutionary biology is mature and solid, so one can go out and apply it to help other disciplines to sort out their problems … I think that would be overly optimistic.

A lot of the discussion was about Mats Björklund‘s talk about predicting evolution, or failing to do so. Unfortunately, I think he had already left, and this was the one talk of the conference that I missed (due to dull practical circumstances stemming from a misplaced wallet), so this part of the discussion mostly passed me by.

A commonplace that recurred a few times was jokes about sequencing … this or that will not be solved by sequencing thousands of genomes, or by big data — you know the kind. This is true, of course; massively parallel sequencing is good when you want to 1) make a new reference genome sequence; 2) get lots and lots of genetic markers or 3) quantify sequences in some library. That certainly doesn’t cover all of evolutionary biology, but it is still quite useful. Every time this came up part of me felt like putting my hand up to declare that I do in fact think that sequencing thousands of individuals is a good idea. But I didn’t, so I write it here where even fewer people will read it.

This is (according to my notes) what the whiteboard said at the end of the session:

”It’s complicated …”
”We need more data …”
”Predictions are difficult/impossible”
”We need more models”

Business as usual
Eventually we’ll get there (where?)
Revise assumptions, models, theories, methods, what to measure

Nothing in evolutionary biology makes sense except in the light of ecology phylogeny disease

Everything in evolution makes sense in the light of mangled Dobzhansky quotes.

(Seriously, I get why pastiches of this particular quote are so common: It’s a very good turn of phrase, and one can easily substitute the scientific field and the concept one thinks is particularly important. Nothing in behavioural ecology makes sense except in the light of Zahavi’s handicap principle etc. It is a fun internal joke, but at the same time sounds properly authoritative. Michael Lynch’s version sometimes seems to be quoted in the latter way.)

Linköping–Edinburgh–Uppsala

If you are the kind of person who reads the lists of decisions from Formas, you may already know this. In March, I’m starting a new postdoc position, in collaboration with John Hickey’s AlphaGenes group at the Roslin Institute in Edinburgh and Dirk-Jan de Koning’s group at the Swedish University of Agriculture in Uppsala, funded by a mobility starting grant for young researchers from the research council Formas. Hurrah!

The project involves using huge datasets from livestock animals to search for genes and variants underlying quantitative traits. In that sense, for me, this is both a new direction (animal breeding research) and a natural continuation (the genetic basis of quantitative traits). So, in the coming years I anticipate, among other things, learning a ton about computational quantitative genetics; meeting and working with great people; travelling more than ever (relative to my relatively low baseline); writing a poem or two about the scenic environs of Edinburgh and the Royal Mounds of Uppsala; figuring out the across-borders relationship thing; discovering new and useful things about quantitative traits; and hopefully picking up a bit of a Scottish tone in my otherwise Swenglish accent.

Linköping has been very good to me, and so have my colleagues in the Wright lab and AVIAN Behavioural Genetics and Physiology group. So, naturally, I’m both happy and sad to leave. Friends in Linköping, we will meet again.

Also, happy new year!

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(Me holding a sign that says (in Swedish): ”Thank you, Formas! I will do my very best.”)