2020 blog recap

Dear diary,

During 2020, ”On unicorns and genes” published a total of 29 posts (not including this one, because it’s scheduled for 2021). This means that I kept on schedule for the beginning of the year, then had an extended blog vacation in the fall. I did write a little bit more in Swedish (about an attempt at Crispr debate, a course I took in university pedagogy, and some more about that course) which was one of the ambitions.

Let’s pick one post per month to represent the blogging year of 2020:

January: Things that really don’t matter: megabase or megabasepair. This post deals with a pet peeve of mine: should we write physical distances in genetics as base pairs (bp) or bases?

February: Using R: from plyr to purrr, part 0 out of however many. (Part one might appear at some point, I’m sure.) Finally, the purrr tidyverse package has found a place in my code. It’s still not the first tool I reach for when I need to apply a function, but it’s getting there.

March: Preprint: ”Genetics of recombination rate variation in the pig”. Preprint post about our work with genetic mapping of recombination rate in the pig.

April: Virtual animal breeding journal club: ”An eQTL in the cystathionine beta synthase gene is linked to osteoporosis in laying hens”. The virtual animal breeding journal club, organised by John Cole, was one of the good things that happened in 2020. I don’t know if it will live on in 2021, but if not, it was a treat as long as it lasted. This post contains my slides from when I presented a recent paper, from some colleagues, about the genetics of bone quality in chickens.

May: Robertson on genetic correlation and loss of variation. A post about a paper by Alan Robertson from 1959. This paper is reasonably often cited as a justification for 0.80 as some kind of cut-off for when a genetic correlation is sufficiently different enough from 1 to be important. That is not at really what the paper says.

June: Journal club of one: ”Genomic predictions for crossbred dairy cattle”. My reading on a paper about genomic evaluation for crossbred cattle in the US.

July: Twin lambs with different fathers. An all too brief methods description prompted me to write some R code. This might be my personal favourite of the year.

August: Journal club of one: ”Chromosome-level and haplotype-resolved genome assembly enabled by high-throughput single-cell sequencing of gamete genomes”. Journal club post about a preprint with a neat-looking genome assembly strategy. This is where the posts start becoming sparse.

December: One notebook’s worth of work. Introspective post about my attempts to organise my work.

In other news, trips were cancelled, Zoom teaching happened, and I finally got the hang of working from home. We received funding for a brand new research project about genome dynamics during animal breeding. There will be lots of sequence data. There will be simulations. It starts next year, and I will write more about it later.

Also, Uppsala is sometimes quite beautiful:

The next notebook of work

Dear diary,

The last post was about my attempt to use the Getting Things Done method to bring some more order to research, work, and everything. This post will contain some more details about my system, at a little less than a year into the process, on the off chance that anyone wants to know. This post will use some Getting Things Done jargon without explaining it. There are many useful guides online, plus of course the book itself.

Medium

Most of my system lives in paper notebooks. The main notebook contains my action list, projects list, waiting for list and agendas plus a section for notes. I quickly learned that the someday/maybe lists won’t fit, so I now have a separate (bigger) notebook for those. My calendar is digital. I also use a note taking app for project support material, and as an extra inbox for notes I jot down on my phone. Thus, I guess it’s a paper/digital hybrid.

Contexts

I have five contexts: email/messaging, work computer, writing, office and home. There were more in the beginning, but I gradually took out the ones I didn’t use. They need to be few enough and map cleanly to situations, so that I remember to look at them. I added the writing context because I tend to treat, and schedule, writing tasks separately from other work tasks. The writing context also includes writing-adjacent support tasks such as updating figures, going through reviewer comments or searching for references.

Inboxes

I have a total of nine inboxes, if you include all the email accounts and messenger services where people might contact me about things I need to do. That sounds excessive, but only three of those are where I put things for myself (physical inbox, notes section of notebook, and notes app), and so far they’re all getting checked regularly.

Capture

I do most of my capture in the notes app on my phone (when not at a desk) or on piece of paper (when at my desk). When I get back to having in-person meetings, I assume more notes are going to end up in the physical notebook, because it’s nicer to take meeting notes on paper than on a phone.

Agendas

The biggest thing I changed in the new notebook was to dedicate much more space to agendas, but it’s already almost full! It turns out there are lots of things ”I should talk to X about the next time we’re speaking”, rather than send X an email immediately. Who knew?

Waiting for

This is probably my favourite. It is useful to have a list of who have said they will get back to me, when, and about what. That little date next to their name helps me not feel like a nag when I ask them again after a reasonable time, and makes me appreciate them more when they respond quickly.

Weekly review

I already had the habit of scheduling an appointment with myself on Fridays (or otherwise towards the end of the week) to go over some recurring items. I’ve expanded this appointment to do a weekly review of the notebook, calendar, someday/maybe list, and some other bespoke checklist items. I bribe myself with sweets to support this habit.

Things I’d like to improve

Here are some of the things I want to improve:

  • The project list. A project sensu Getting Things Done can be anything from purchase new shoes to taking over the world. The project list is supposed to keep track of what you’ve undertaken to do, and make sure you have come up with actions that progress them. My project list isn’t very complete, and doesn’t spark new actions very often.
  • Project backlogs. On the other hand, I have some things on the project list that are projects in a greater sense, and will have literally thousands of actions, both from me and others. These obviously need planning ahead beyond the next thing to do. I haven’t yet figured out the best way to keep a backlog of future things to do in a project, potentially with dependencies, and feed them into my list of things to do when they become current.
  • Notes. I have a strong note taking habit, but a weak note reading habit. Essentially, many of my notes are write-only; this feels like a waste. I’ve started my attempts to improve the situation with meeting notes: trying to take five minutes right after a meeting (if possible) to go over the notes, extract any calendar items, actions and waiting-fors, and decide whether I need to save the note or if I can throw it away. What to do about research notes from reading and from seminars is another matter.

One notebook’s worth of work

Image: an Aviagen sponsored notebook from the 100 Years of Genetics meeting in Edinburgh, with post-its sticking out, next to a blue Ballograf pen

Dear diary,

”If could just spend more time doing stuff instead of worrying about it …” (Me, at several points over the years.)

I started this notebook in spring last year and recently filled it up. It contains my first implementation of the system called ”Getting Things Done” (see the book by David Allen with the same name). Let me tell you a little about how it’s going.

The way I organised my work, with to-do lists, calendar, work journal, and routines for dealing with email had pretty much grown organically up until the beginning of this year. I’d gotten some advice, I’d read the odd blog post and column about email and calendar blocking, but beyond some courses in project management (which are a topic for another day), I’d gotten myself very little instruction on how to do any of this. How does one actually keep a good to-do list? Are there principles and best practices? I was aware that Getting Things Done was a thing, and last spring, a mention in passing on the Teaching in Higher Ed podcast prompted me to give it a try.

I read up a little. The book was right there in the university library, unsurprisingly. I also used a blog post by Alberto Taiuti about doing Getting Things Done in a notebook, and read some other writing by researchers about how they use the method (Robert Talbert and Veronika Cheplygina).

There is enough out there about this already that I won’t make my own attempt to explain the method in full, but here are some of the interesting particulars:

You are supposed to be careful about how you organise your to-do lists. You’re supposed to make sure everything on the list is a clear, unambiguous next action that you can start doing when you see it. Everything else that needs thinking, deciding, mulling over, reflecting etc, goes somewhere else, not on your list of thing to do. This means that you can easily pick something off your list and start work on it.

You are supposed to be careful about your calendar. You’re supposed to only put things in there that have a fixed date and time attached, not random reminders or aspirational scheduling of things you would like to do. This means that you can easily look at your calendar and know what your day, week and month look like.

You are supposed to be careful to record everything you think about that matters. You’re supposed to take a note as soon as you have a potentially important thought and put it in a dedicated place that you will check and go through regularly. This means that you don’t have to keep things in your head.

This sounds pretty straightforward, doesn’t it? Well, despite having to-do lists, calendars and a habit of note-taking for years, I’ve not been very disciplined about any of this before. My to-do list items have often been vague, too big tasks that are hard to get started on. My calendar has often contained aspirational planning entries that didn’t survive contact with the realities of the workday. I often delude myself that I’ll remember an idea or a decision, to have quietly it slip out of my mind.

Have I become more productive, or less stressed? The honest answer is that I don’t know. I don’t have a reliable way to track either productivity or stress levels, and even if I did: the last year has not really been comparable to the year before, for several reasons. However, I feel like thinking more about how I organise my work makes a difference, and I’ve felt a certain joy working on the process, as well as a certain dread when looking at it all organised in one place. Let’s keep going and see where this takes us.

Reflektioner om högskolepedagogik, tagning 2

Kära dagbok,

Mer tankar från fortsättningskurs i högskolepedagogik.

På det andra kurstillfället ägnade vi rätt mycket tid åt ett romantiskt ideal för universitetet: både passet om pedagogisk utveckling i ett större sammanhang och passet om forskningsanknuten undervisning drog mycket inspiration från Humboldts ideal om högre utbildning som en helhet utbildning och forskning, som ska bibringa studenterna bildning och en generell färdighet att tänka självständigt, snarare än ämnes- och yrkeskunskaper.

Det står i högskolelagen och allt:

Verksamheten skall bedrivas så att det finns ett nära samband mellan forskning och utbildning.

Utbildning på grundnivå ska utveckla studenternas
– förmåga att göra självständiga och kritiska bedömningar,
– förmåga att självständigt urskilja, formulera och lösa problem, och
– beredskap att möta förändringar i arbetslivet.

Inom det område som utbildningen avser ska studenterna, utöver kunskaper och färdigheter, utveckla förmåga att
– söka och värdera kunskap på vetenskaplig nivå,
– följa kunskapsutvecklingen, och
– utbyta kunskaper även med personer utan specialkunskaper inom området.

Men lagen skriver också om arbetslivet, yrkesverksamhet och så vidare. Det låter som en blandning av den ovanstående visionen och andra hänsyn, och det överensstämmer kanske med universitetets historia som innehåller både holistisk Humboldt och en start som glorifierat prästseminarium.

Sådant pratade vi alltså om. Vad är ett universitet egentligen? Vad är poängen med den typ av utbildning som vi driver? Jag fick några gånger intrycket att den här delen av kursen handlade om att övertyga oss om att bildningsideal är bra och viktigt.

Men den lokala miljön är förmodligen ännu viktigare än den större inramningen. En bra miljö med kollegor som bryr sig och stöttar varandra hjälper förstås att göra undervisningen bättre. Mårtensson & Roxå (2011) tittade på fyra ”starka akademiska mikrokulturer”, det vill säga ställen på universitet där undervisning och forskning ansågs fungera väldigt bra. Som man kanske kan vänta sig är det här grupper där man håller undervisning för väldigt viktigt, har förtroende för varandra, har goda relationer med andra, och upplever ett gemensamt ärende.

Ärenden ifråga verkade vara långsiktiga mål som var riktade utåt. Poängen var att forma fältet, utbilda studenter som kommer att utveckla yrket, påverka omvärlden. Inte ”att bli en excellent utbildningsmiljö”, eller något sådant som förmodligen står i dokument på högre nivå i organisationen. Författarna använder ordet ”enterprise”. Det påminner om skillnaden mellan extern och intern motivation hos den som lär sig. Klart läraren också blir mer driven av av intern motivation som en önskan att förändra sitt fält, än av extern motivation som att universitetet har en strategi att bli bäst på undervisning.

Det kom också fram att i de här miljöerna såg man undervisning inte bara som väldigt viktigt, utan som något som är oskiljaktigt från forskning. På så sätt ligger de också i linje med Humboldt.

Sätten att göra forskningsanknuten undervisning kan (såklart, som allting annat här i världen, beskrivas med en fyrfälting):

Det vill säga, undervisningen kan vara inriktad på forskningsresultat eller på forskningsprocesser, och det kan vara läraren som berättar eller studenten som gör. Forskningsanknytning kan alltså bestå i uppdatera materialet med det senaste från forskningsfronten (eller i alla fall något senare än det som står i läroboken), avslöja insider-information om hur forskningen går till, låta studenter själva läsa och analysera primär forskningslitteratur, eller låta studenter öva forskningsmetoder. Jag gillade särskilt en formulering som Göran Hartman använde om uppgifter där det inte finns en färdig lösning ”som ni lärare sitter och tjuvhåller på”.

Har jag jobbat något med forskningsanknytning? Ja, mest i det lärarledda innehållsinriktade hörnet. Det är ju bland det roliga med att göra en ny föreläsning, att försöka hitta någon forskningsartikel att passa in. Förstå min glädje när jag såg ett Tinbergen-citat mitt i en tung Drosophila-genetikstudie (Hoopfer et al 2015) och hittade en ursäkt att ta med den i en gästföreläsning om beteendegenetik. Det var ett litet exempel, men ändå.

Jag lärde mig också ett nytt fint ord. Jag hade ingen aning om att det rätt prosaiska svenska ”forskningsanknuten undervisning” på engelska heter ”the research–teaching nexus”. Fint ska det vara.

Reflektion om högskolepedagogik, apropå kurs

Kära dagbok,

Det finns väl inget utryck som för en att låta så mycket som en universitetslärare som ”reflektion”. Det skulle vara om ordet står i plural och stavas med x. Jag går en fortsättningskurs i högskolepedagogik just nu, och det får mig att tänka på lärande.

En positiv bieffekt är att den låter mig resa till Alnarp, ett av SLU:s andra campusområden. Det är fint där.

För det första är jag förvånad över hur mycket som sitter kvar från grundkursen jag gick som doktorand. Men för säkerhets skull, för jag hade känslan av att jag lärde mig ganska lite då, skummade jag igenom Biggs & Tang (2011), kursboken för SLU:s grundkurs i högskolepedagogik. Den håller hårt på constructive alignment av mål, aktiviteter och examination: idén att en bra kurs först definierar var studenterna ska kunna i slutet av den, och sedan övar och examinerar den just detta. Det är viktigt att precisera just vad ”kunna” betyder i sammanhanget: ska studenterna kunna förklara begrepp, ska de kunna lösa problem, eller ska de kunna bygga en maskin? I så fall bör de få öva sig på att förklara, eller bygga maskiner, och examineras på sin förmåga att förklara, eller bygga maskiner. Det är en av de där förrädiskt enkla idéerna som kan leda till dramatiska förändringar om man tar den på allvar.

Jag tänker förstås tillbaka på när jag var student. En del kurser var exemplariska i att sätta oss i basgrupper där vi fick förklara begrepp för varandra eller laborationer där vi fick lösa problem. En gästföreläsare (vars namn jag glömt; hon kom från Rättsmedicinalverket, tror jag) gav en 15 minuters introduktion till sitt ämne. Sedan lät hon oss tänka ut vad vi ville veta, formulera frågor, och sedan byggde hon resten av föreläsningen kring dem. Intressant sätt att både ge studenterna ansvar och hjälpa henne tackla vår förförståelse av ämnet. Modigt också. Annat var mindre exemplariskt; många sömniga föreläsningar blev det.

Själv har jag ofta tänkt, och sagt, ungefär ”tänk om jag kunde gå tillbaka och göra om de där första årens kurser med allt jag lärt mig om att lära mig”. I viss mån är det säkert sant att jag blev bättre på att lära mig. De sista åren som student fick jag till exempel för mig att organisera mina anteckningar som färgglada tankekartor, och repetera genom att titta på delar av dem och förklara dem för mig själv. Det var säkert inte helt tokigt. Å andra sidan, mycket av det jag lärde mig om att lära mig var nog också att strategiskt och ytligt ta in det som behövdes för att lyckas med tentan. På så sätt är det kanske till och med möjligt att lära sig sämre sämre med mer erfarenhet.

Som deltagare i en kurs om pedagogik fäster en naturligtvis extra uppmärksamhet vid hur undervisningen går till. Lever lärarna som de lär? Och vilka aktiviteter kan jag snappa upp och själv använda, helst de som inte kräver att kursen designas om i grunden utan bara annan användning av den tid och de resurser som finns? Kursen har inte gjort mig besviken så långt. Den fungerar verkligen som demonstration av sitt eget innehåll, med massor av små think–pair–share-moment, tillverkning av konceptkartor i grupper, gott om tid till diskussion, och aktiviteter som låter oss använda det vi läst in oss på i förväg, så att det får förberedelsen att kännas meningsfull.

Ett par höjdpunkter från de första kurstillfällena:

Linn Areskoug från enheten för pedagogisk utveckling samt från Uppsala universitet pratade om normkritik. Rubriken var ”könsmedveten undervisning”, men som sig bör var innehållet ifrågasättande även mot den idén. Det slog mig också under föreläsningen att föreläsningsstilen till stod del bestod av att problematisera de begrepp som just introducerats med en serie exempel och öppna frågor. När syftet var att åhörarna skulle tänka över identitet och ifrågasätta saker som känns uppenbara för oss, är det kanske en bra strategi. Jag undrar om det skulle gå att presentera svåra problem inom genetik på ett liknande sätt, med betoningen på frågorna, inte svaren.

Alexis Engström från Uppsala universitet gav oss, bland annat, bokstavligt talat en lista på aktiviteter som bryter med traditionell undervisning. Det jag tyckte lät mest spännande var ”Det saknade perspektivet”: att lämna ett pass i schemat öppet och ge studenterna i uppgift att fylla det med innehåll och aktiviteter som de tycker saknas i kursen, lite som en storskalig variant av vad gästföreläsaren från Rättsmedicinalverket gjorde.

Jag hoppas få med mig några saker jag kan göra för att vara en bättre lärare, och förstå lite bättre hur lärande fungerar. Som bonus har jag i alla fall roligt.

Litteratur

Biggs J & Tang C. (2011) Teaching for quality learning at university. 4th edition. Open University Press.

Self-indulgent meta-post of the year

Dear diary,

Time for a recap of the On unicorns and genes blogging year. During 2019, your friendly neighbourhood genetics blog mostly kept to its schedule of four posts per month with some blog vacation in summer and in December.

This added up to a total of 43 posts (including this one), only one of them in Swedish (Gener påverkar ditt och datt, reflecting on how genome-wide association is often reported as if it was something else), and three of them posts about three first-author papers that came out in 2019:

Paper: ”Integrating selection mapping with genetic mapping and functional genomics”
Paper: ”Sequence variation, evolutionary constraint, and selection at the CD163 gene in pigs”
Paper: ”Removal of alleles by genome editing (RAGE) against deleterious load”

Now, let’s pick one post per month to represent the blogging year of 2019:

January: Showing a difference in means between two groups. This is one of those hard easy problems: Imagine we have an experiment comparing the means of two groups; how do we show both the data and the estimate of the difference, with uncertainty, in the same plot? In this little post, I try a more and a less radical version. I’ve since used the less radical version a couple of times.

February: ”We have reached peak gene and passed it”. Comment on an opinion piece that argued that revisions to the gene concept have important implications for modern genetics, and people need to be told. I agreed about a lot of the criticisms, but thought they should have nothing to do with gene concepts.

March: Journal club of one: ”Biological relevance of computationally predicted pathogenicity of noncoding variants”. Journal club post about a then recent paper about how variant effect prediction, especially for noncoding variants, is really hard, and not easy to evaluate fairly either.

April: Greek in biology. Comment on a stimulating essay about multilingualism in biology.

May: What single step does with relationship. A simulation and a couple of heatmaps to try to understand how the single step method describes relationship between individuals by blending genomic and pedigree relatedness.

June: Simulating genetic data with R: an example with deleterious variants Post from my talk at the Edinburgh R users group.

July: Using R: Correlation heatmap with ggplot2. I thought I’d update one of my most viewed posts, which was becoming too embarrassingly outdated. Unfortunately, I also broke its old address, and that wasn’t so smart.

August: Blog vacation.

September: Genes do not form networks. They just don’t. It seems DiFrisco & Jaeger (2019) don’t think so either. I would like to return to this later.

October: Using R: Animal model with simulated data. What it says on the tin: example of simulating a pedigree with AlphaSimR and fitting an animal model.

November: Using R: from gather to pivot. Introduction to, and celebration of, one of the best changes in the tidyverse.

December: Interpreting genome scans, with wisdom. Opinions about genome-wide association, precipitated by Eric Faumann’s Twitter account.

This is also the year I moved from the Roslin in Scotland to Uppsala, Sweden, for the second phase of the mobility grant-supported postdoc. Here are many of my worldly possessions, being hauled:

What will happen on here in 2020? The ambition is to keep a reasonably regular schedule, post about papers as they come out, and maybe write some more in Swedish.

I’ll also have a blog anniversary. The blog, admittedly rather different the first years, started in earnest in June 2010. I’m not sure how to celebrate, but I feel like I should follow up on one of the first posts (about linkage mapping and the probably spurious ”Gay Gene” at Xq28), now that Ganna et al. (2019) put genetic mapping of sexual orientation is in the news again.

Computational Genetics Discussion Cookies

The Computational Genetics Discussion Group is an informal seminar series on anything quantitative genetics, genomics and breeding run by the Hickey group at the Roslin Institute. Over the last year and a half or so, I’ve been the one emailing people and bringing biscuits, and at some point, I got fed up with the biscuits available at my local Tesco. Here is my recipe for computational genetics discussion cookies.

CGDC
Makes ca 50 cookies

1. Melt and brown 250 g butter.

2. Mix 100 g white sugar, 100 g Demerara sugar, 65 g of golden syrup, and 2 teaspoons of vanilla extract.

3. Add the melted butter and two eggs and whisk together.

4. Mix 375 g of flour and 0.75 teaspoons of bicarbonate. Add this to the butter, egg and sugar mix.

5. Split the batter into two halves. To each half, add one of:

  • 300 g chopped chocolate
  • 5 crushed digestive biscuits and 2 teaspoons of ground cinnamon
  • 50 g of crushed mini pretzels and 200 g of chopped fruit jellies
  • 50 g of oats and 120 g of raisins (weigh the raisins dry and then soak in hot water)
  • 75 g of desiccated coconut and 120 g of raisins
  • 125 g of granola mix

6. Bake for 7.5 minutes at 200 degrees Celsius.

7. Let rest for at least two minutes before moving off of the tray.

Temple Grandin at Roslin: optimisation and overselection

A couple of months ago (16 May to be precise), I listened to a talk by Temple Grandin at the Roslin Institute.

Grandin is a captivating speaker, and as an animal scientist (of some kind), I’m happy to have heard her talk at least once. The lecture contained a mix of:

  • practical experiences from a career of troubleshooting livestock management and systems,
  • how thinking differently (visually) helps in working with animal behaviour,
  • terrific personal anecdotes, among other things about starting up her business as a livestock management consultant from a student room,
  • a recurring theme, throughout the talk, of unintended side-effects in animal breeding, framed as a risk of ”overselecting” for any one trait, uncertainty about ”what is optimal”, and the importance of measuring and soberly evaluating many different things about animals and systems.

This latter point interests me, because it concerns genetics and animal breeding. Judging by the question in the Q&A, it also especially interested rest of the audience, mostly composed of vet students.

Grandin repeatedly cautioned against ”overselecting”. She argued that if you take one trait, any trait, and apply strong directional selection, bad side-effects will emerge. As a loosely worded biological principle, and taken to extremes, this seems likely to be true. If we assume that traits are polygenic, that means both that variants are likely to be pleiotropic (because there are many causal variants and a limited number of genes; this one argument for the omnigenic model) and that variants are likely to be linked to other variants that affect other traits. So changing one trait a lot is likely to affect other traits. And if we assume that the animal was in a pretty well-functioning state before selection, we should expect that if some trait that we’re not consciously selecting on changes far enough from that state, that is likely to cause problems.

We can also safely assume that there are always more traits that we care about than we can actually measure, either because they haven’t become a problem yet, or because we don’t have a good way to measure them. Taken together, this sound like a case for being cautious, measuring a lot of things about animal performance and welfare, and continuously re-evaluating what one is doing. Grandin emphasised the importance of measurement, drumming in that: ”you will manage what you measure”, ”this happens gradually”, and therefore, there is a risk that ”the bad becomes the new normal” if one does not keep tabs on the situation by recording hard quantitative data.

Doesn’t this sound a lot like the conventional view of mainstream animal breeding? I guess it depends: breeding is a big field, covering a lot of experiences and views, from individual farmers’ decisions, through private and public breeding organisations, to the relative Castalia of academic research. However, the impression from my view of the field, is Grandin and mainstream animal breeders are in agreement about the importance of:

  1. recording lots of traits about all aspects of the performance and functioning of the animal,
  2. optimising them with good performance on the farm as the goal,
  3. constantly re-evaluating practice and updating the breeding goals and management to keep everything on track.

To me, what Grandin presented as if it was a radical message (and maybe it was, some time ago, or maybe it still is, in some places) sounded much like singing the praises of economic selection indices. I had expected something more controversial. Then again, that depends on what assumptions are built into words like ”good performance”, ”on track”, ”functioning of the animal” etc. For example, she talked a bit about the strand of animal welfare research that aims to quantify positive emotions in animals; one could take the radical stance that we should measure positive emotions and include that in the breeding goal.

”Overselection” as a term also carries connotations that I don’t agree with, because I don’t think that the framing as biological overload is helpful. To talk about overload and ”overselection” makes one think of selection as a force that strains the animal in itself, and the alternative as ”backing off” (an expression term Grandin repeatedly used in the talk). But if the breeding goal is off the mark, in the sense that it doesn’t get towards what’s actually optimal for the animal on the farm, breeding less efficiently is not getting you to a better outcome; it only gets towards the same, suboptimal, outcome more slowly. The problem isn’t efficiency in itself, but misspecification, and uncertainty about what the goal should be.

Grandin expands on this idea in the introductory chapter to ”Are we pushing animals to their biological limits? Welfare and ethical applications” (Grandin & Whiting 2018, eds). (I don’t know much about the pig case used as illustration, but I can think of a couple of other examples that illustrate the same point.) It ends with this great metaphor about genomic power tools, that I will borrow for later:

We must be careful not to repeat the mistakes that were made with conventional breeding where bad traits were linked with desirable traits. One of the best ways to prevent this is for both animal and plant breeders to do what I did in the 1980s and 1990s: I observed many different pigs from many places and the behaviour problems became obvious. This enabled me to compare animals from different lines in the same environment. Today, both animal and plant breeders have ‘genomic power tools’ for changing an organism’s genetics. Power tools are good things, but they must be used carefully because changes gan be made more quickly. A circular saw can chop your hand off much more easily than a hand saw. It has to be used with more care.

Kauai field trip 2018

Let’s keep the tradition of delayed travel posts going!

In August last year, I joined Dom Wright, Rie Henriksen, and Robin Abbey-Lee, as part of Dom’s FERALGEN project, on their field work on Kauai. I did some of my dissertation work on the Kauai feral chickens, but I never saw them live until now. Our collaborator Eben Gering was also on the islands, but the closest we got to each other was Skyping between the islands. It all went smoothly until the end of the trip, when a hurricane came uncomfortably close to the island for a while. Here are some pictures. In time, I promise to blog about the actual research too.

Look! Chickens by the sea, chickens on parking lots, a sign telling people not to feed the chickens on a sidewalk in central Kapaa! Lots of chickens.

I’m not kidding: lots of chickens.

Links

An old Nature News feature from a previous field trip (without me)

My post about our 2016 paper on Kauai feralisation genomics

Various positions

What use is there in keeping a blog if you can’t post your arbitrary idiosyncratic opinions as if you were an authority? Here is a list of opinions about life in the scientific community.

Social media for scientists

People who promote social media for scientists by humblebragging about how they got a glam journal paper because of Twitter should stop. An unknown PhD student from the middle of nowhere must be a lot more likely to get into trouble than get on a paper because of Twitter.

Speaking of that, who thinks that that writing an angry letter to someone’s boss is the appropriate response to disagreeing with someone on Twitter? Please stop with that.

Poster sessions

Poster sessions are a pain. Not only do you suffer the humiliation of not begin cool enough to give a talk, you also get to haul a poster tube to the conference. The trouble is that we can’t do away with poster sessions, because they fulfill the important function of letting a lot of people contribute to the conference so that they have a reason to go there.

Now cue comments of this kind: ”That’s not true! I’ve had some of my best conference conversations at poster sessions. Maybe you just don’t know how to make a poster …” It is true that I don’t know how to make a good poster. Regardless, my ad hoc hypothesis for why people say things like this is that they’re already known and connected enough to have good conversations anywhere at the conference, and that the poster served as a signpost for their colleagues to find them.

How can one make a poster session as good as possible? Try to make lots of space so people won’t have to elbow each other. Try to find a room that won’t be incredibly noisy and full of echos. Try to avoid having some posters hidden behind pillars and in corners.

Also, don’t organize a poster competition unless you also organize a keynote competition.

Theory

There is way way way too little theory in biology education, as far as I can tell. Much like computer programming — a little of which is recognized as a useful skill to have even for empirically minded biologists who are not going to be programming experts — it is very useful to be able to read a paper without skipping the equations, or tell whether a paper is throwing dust when it states that ”[unspecified] Theory predicts …” this or that. But somehow, materials for theory manage to be even more threatening than computer documentation, which is an impressive feat. If you disagree, please post in the comments a reference to an introduction to coalescent theory that is accessible for, say, a biology PhD student who hasn’t taken a probability course in a few years.

Language corrections

That thing when reviewers suggest that a paper be checked by a native English speaker, when they mean that it needs language editing, is rude. Find a way of phrasing it that won’t offend that one native English speaker who invariably is on the paper, but doesn’t have an English enough name and affiliation for you!