‘Approaches to genetics for livestock research’ at IASH, University of Edinburgh

A couple of weeks ago, I was at a symposium on the history of genetics in animal breeding at the Institute of Advanced Studies in the Humanities, organized by Cheryl Lancaster. There were talks by two geneticists and two historians, and ample time for discussion.

First geneticists:

Gregor Gorjanc presented the very essence of quantitative genetics: the pedigree-based model. He illustrated this with graphs (in the sense of edges and vertices) and by predicting his own breeding value for height from trait values, and from his personal genomics results.

Then, yours truly gave this talk: ‘Genomics in animal breeding from the perspectives of matrices and molecules’. Here are the slides (only slightly mangled by Slideshare). This is the talk I was preparing for when I collected the quotes I posted a couple of weeks ago.

I talked about how there are two perspectives on genomics: you can think of genomes either as large matrices of ancestry indicators (statistical perspective) or as long strings of bases (sequence perspective). Both are useful, and give animal breeders and breeding researchers different tools (genomic selection, reference genomes). I also talked about potential future breeding strategies that use causative variants, and how they’re not about stopping breeding and designing the perfect animal in a lab, but about supplementing genomic selection in different ways.

Then, historians:

Cheryl Lancaster told the story of how ABGRO, the Animal Breeding and Genetics Research Organisation in Edinburgh, lost its G. The organisation was split up in the 1950s, separating fundamental genetics research and animal breeding. She said that she had expected this split to be do to scientific, methodological or conceptual differences, but instead found when going through the archives, that it all was due to personal conflicts. She also got into how the ABGRO researchers justified their work, framing it as ”fundamental research”, and aspired to do long term research projects.

Jim Lowe talked about the pig genome sequencing and mapping efforts, how it was different from the human genome project in organisation, and how it used comparisons to the human genome a lot. Here he’s showing a photo of Alan Archibald using the gEVAL genome browser to quality-check the pig genome. He also argued that the infrastructural outcomes of a project like the human genome project, such as making it possible for pig genome scientists to use the human genome for comparisons, are more important and less predictable than usually assumed.

The discussion included comments by some of the people who were there (Chris Haley, Bill Hill), discussion about the breed concept, and what scientists can learn from history.

What is a breed? Is it a genetical thing, defined by grouping individuals based on their relatedness, a historical thing, based on what people think a certain kind of animal is supposed to look like, or a marketing tool, naming animals that come from a certain system? It is probably a bit of everything. (I talked with Jim Lowe during lunch; he had noticed how I referred to Griffith & Stotz for gene concepts, but omitted the ”post-genomic” gene concept they actually favour. This is because I didn’t find it useful for understanding how animal breeding researchers think. It is striking how comfortable biologists are with using fuzzy concepts that can’t be defined in a way that cover all corner cases, because biology doesn’t work that way. If the nominal gene concept is broken by trans-splicing, practicing genomicists will probably think of that more as a practical issue with designing gene databases than a something that invalidates talking about genes in principle.)

What would researchers like to learn from history? Probably how to succeed with large research endeavors and how to get funding for them. Can one learn that from history? Maybe not, but there might be lessons about thinking of research as ”basic”, ”fundamental”, ”applied” etc, and about what the long term effects of research might be.

Greek in biology

This is a fun essay about biological terms borrowed from or inspired by Greek, written by a group of (I presume) Greek speakers: Iliopoulos & al (2019), Hypothesis, analysis and synthesis, it’s all Greek to me.

We hope that this contribution will encourage scientists to think about the terminology used in modern science, technology and medicine (Wulff, 2004), and to be more careful when seeking to introduce new words and phrases into our vocabulary.

First, I like how they celebrate the value of knowing more than one language. I feel like bi- and multilingualism in science is most often discussed as a problem: Either we non-native speakers have problems catching up with the native speakers, or we’re burdening them with our poor writing. Here, the authors seem to argue that knowing another language (Greek) helps both your understanding of scientific language, and the style and grace with which you use it.

I think this is the central argument:

Non-Greek speakers will, we are sure, be surprised by the richness and structure of the Greek language, despite its often inept naturalization in English or other languages, and as a result be better able to understand their own areas of science (Snell, 1960; Montgomery, 2004). Our favorite example is the word ‘analysis’: everyone uses it, but few fully understand it. ‘Lysis’ means ‘breaking up’, while ‘ana-‘ means ‘from bottom to top’ but also ‘again/repetitively’: the subtle yet ingenious latter meaning of the term implies that if you break up something once, you might not know how it works; however, if you break up something twice, you must have reconstructed it, so you must understand the inner workings of the system.

I’m sure it is true that some of the use of Greek-inspired terms in scientific English is inept, and would benefit from checking by someone who knows Greek. However, this passage invites two objections.

First, why would anyone think that the Greek language has less richness and structure then English? Then again, if I learned Greek, it is possible that I would find that the richness would be even more than I expected.

Second, does knowing Greek mean that you have a deeper appreciation for the nuances of a concept like analysis? Maybe ‘analysis’ as understood without those double meanings of the ‘ana-‘ prefix is less exciting, but if it is true that most people don’t know about this subtlety, this can’t be what they mean by ‘analysis’. So, if that etymological understanding isn’t part of how most people use the word, do we really understand it better by learning that story? It sounds like they think that the word is supposed to have a true meaning separate from how it is used, and I’m not sure that is helpful.

So what are some less inept uses of Greek? They like the term ‘epigenomics’, writing that it is being ‘introduced in a thoughtful and meaningful way’. To me, this seems like an unfortunate example, because I can think of few terms in genomics that cause more confusion. ‘Epigenomics’ is the upgraded version of ‘epigenetics’, a word which was, unfortunately, coined at least twice with different meanings. And now, epigenetics is this two-headed beast that feeds on geneticists’s energy as they try to understand what on earth other geneticists are saying.

First, Conrad Waddington glued ‘epigenesis’ and ‘genetics’ together to define epigenetics as ‘the branch of biology that studies the causal interactions between genes and their products which bring the phenotype into being’ (Waddington 1942, quoted in Deans & Maggert 2015). That is, it is what we today might call developmental genetics. Later, David Nanney connected it to gene regulatory mechanisms that are stable through cell division, and we get the modern view of epigenetics as a layer of regulatory mechanisms on top of the DNA sequence. I would be interested to know which of these two intertwined meanings it is that the authors like.

Judging by the affiliations of the authors, the classification of the paper (by the way, how is this ‘computational and systems biology, genetics and genomics’, eLife?), and the citations (16 of 27 to medicine and science journals, a lot of which seems to be similar opinion pieces), this feels like a missed opportunity to connect with language scholarship. I’m no better myself–I’m not a scholar of language, and I haven’t tried to invite one to co-write this blog post with me … But there must be scholarship and expertise outside biomedicine relevant to this topic, and language sources richer than an etymological online dictionary?

Finally, the table of new Greek-inspired terms that ‘might be useful’ is a fun thought exercise, and if it serves as inspiration for someone to have an eureka moment about a concept they need to investigate, great (‘… but what is a katagenome, really? Oh, maybe …’). But I think that telling scientists to coin new words is inviting catastrophe. I’d much rather take the lesson that we need fewer new tortured terms borrowed from Greek, rather than more of them. It’s as if I, driven by the nuance and richness I recognise in my own first language, set out to coin övergenome, undergenome and pågenome.

Neutral citation again

Here is a piece of advice about citation:

Rule 4: Cite transparently, not neutrally

Citing, even in accordance with content, requires context. This is especially important when it happens as part of the article’s argument. Not all citations are a part of an article’s argument. Citations to data, resources, materials, and established methods require less, if any, context. As part of the argument, however, the mere inclusion of a citation, even when in the right spot, does not convey the value of the reference and, accordingly, the rationale for including it. In a recent editorial, the Nature Genetics editors argued against so-called neutral citation. This citation practice, they argue, appears neutral or procedural yet lacks required displays of context of the cited source or rationale for including [11]. Rather, citations should mention assessments of value, worth, relevance, or significance in the context of whether findings support or oppose reported data or conclusions.

This flows from the realisation that citations are political, even though that term is rarely used in this context. Researchers can use them to accurately represent, inflate, or deflate contributions, based on (1) whether they are included and (2) whether their contributions are qualified. Context or rationale can be qualified by using the right verbs. The contribution of a specific reference can be inflated or deflated through the absence of or use of the wrong qualifying term (‘the authors suggest’ versus ‘the authors establish’; ‘this excellent study shows’ versus ‘this pilot study shows’). If intentional, it is a form of deception, rewriting the content of scientific canon. If unintentional, it is the result of sloppy writing. Ask yourself why you are citing prior work and which value you are attributing to it, and whether the answers to these questions are accessible to your readers.

When Nature Genetics had an editorial condemning neutral citation, I took it to be a demand that authors show that they’ve read and thought about the papers they cite.

This piece of advice seems to ask something different: that authors be honest about their opinions about a work they cite. That is a radical suggestion, because if people were, I believe readers would get offended. That is, if the paper wasn’t held back by offended peer reviewers before it reached any readers. Honestly, as a reviewer, I would probably complain if I saw a value-laden and vacuous statement like ‘this excellent study’ in front of a citation. It would seem to me an rude attempt to tell the reader what to think.

So how are we to cite a study? On the one hand, we can’t just drop the citation in a sentence, but are obliged to ‘mention assessments of value, worth, relevance or significance’. On the other hand, we must make sure that they are ‘qualified by using the right verbs’. And if citation is political, then whether a study ‘suggests’ or ‘establishes’ conclusions is also political.

Disclaimer: I don’t like the 10 simple rules format at all. I find that they belong on someone’s personal blog and not in a scientific journal, given that their evidence for their assertions usually amounts to nothing more than my own meandering experience … This one is an exception, because Bart Penders does research on how scientists collaborate and communicate (even if he cites no research in this particular part of the text).

Penders B (2018) Ten simple rules for responsible referencing. PLoS Computional Biology

Preprints and conference tweets

I’m sure the perceived need for speedy science communication and putting everything online can seem a bit shallow. To paraphrase various comments: ‘How self-important can one be? Do I really think that other people can’t wait to read my latest research paper? Do they need to know that I went to @someperson’s talk and it was #great?’ It may seem like this is all vanity. But it’s not.

The answer to this straw man’s questions are obvious, once I’ve thought about my own relationship to the preprint server Biorxiv, from which I read a lot of papers nowadays: I don’t know whether there is anyone out there waiting for my research papers to be released. (And in fact, I doubt it.) But I know for a fact that I, myself, am waiting for other people’s papers. I’ve found that I really like to read what other people in my field are working on, with as little delay as possible, even with potential errors and unclarities that peer review may help iron out.

As for conference tweets, behind the paper blog posts, and Twitter discussions about talks, preprints, and published papers–if you are part of a tight-knit community of researchers, you probably already know what a lot of the other members are working on, and what their opinions are. You already go to the same meetings, occasionally review each others papers, maybe you’re even on terms where you can just ask each other, maybe even send previews of manuscripts to each other.

But preprints and conference tweets expand that circle to include students, researchers in remote places, those new to the field, those who do not dare to ask. It helps keep us in the loop too. Or draw us a little closer to the loop, at any rate. It may all be vanity, but it has some nice side effects.

Peer review glossary

‘Misleading’ — not exactly as I would have written it

‘Somewhat confusing’ — using terminology from adjacent sub-subfield

‘Confusing’ — completely illegible

‘Poorly structured’ — not exactly as I would have written it

‘Conversational’ — in need of adjectives

‘Descriptive’ — using technology that isn’t fashionable anymore

‘Potentially’ — definitely

‘by a native English speaker’ — by the Microsoft Word spell checker

‘due to insufficient enthusiasm’ — because it’s trite

‘gratefully’ — begrudgingly

‘adequate’ — perfunctory

‘constructive’ — fairly polite

Different worlds

Some time ago, I gave a seminar about some work involving chicken combs, and I made some offhand remark about how I don’t think that the larger combs of modern layer chickens are the result of direct selection. I think it is more likely to be be a correlated response to selection on reproductive traits. During question time, someone disagreed, proposing that ornamental traits should be very likely to have been under artificial selection.

I choose this example partly because the stakes are so low. I may very well be wrong, but it doesn’t matter for the work at hand. Clearly, I should be more careful to acknowledge all plausible possibilities, and not speculate so much for no reason. But I think this kind of thing is an example of something quite common.

That is: researchers, even those who fit snugly into the same rather narrow sub-field, may make quite different default assumptions about the world. I suspect, for instance, that we were both, in the absence of hard evidence, trying to be conservative in falling back on the most parsimonious explanation. I know that I think of a trait being under direct selection as a strong claim, and ”it may just be hitch-hiking on something else” as a conservative attitude. But on the other hand, one could think of direct artificial selection as a simpler explanation as opposed to a scenario that demands pleiotropy.

I can see a point to both attitudes, and in different contexts, I’d probably think of either direct selection and pleiotropy as the more far-fetched. For example, I am hard pressed to believe that reductions in fearfulness and changes in pigmentation of domestic animals are generally explained by pleiotropic variants.

This is why I think that arguments about Occam’s razor, burdens of proof, and what the appropriate ”null” hypothesis for a certain field is supposed to be, while they may be appealing (especially so when they support your position), are fundamentally unhelpful. And this is why I think incommensurability is not that outlandish a notion. Sometimes, researchers might as well be living in different worlds.