‘Simulating genetic data with R: an example with deleterious variants (and a pun)’

A few weeks ago, I gave a talk at the Edinburgh R users group EdinbR on the RAGE paper. Since this is an R meetup, the talk concentrated on the mechanics of genetic data simulation and with the paper as a case study. I showed off some of what Chris Gaynor’s AlphaSimR can do, and how we built on that to make the specifics of this simulation study. The slides are on the EdinbR Github.

Genetic simulation is useful for all kinds of things. Sure, they’re only as good as the theory that underpins them, but the willingness to try things out in simulations is one of the things I always liked about breeding research.

This is my description of the logic of genetic simulation: we think of the genome as a large table of genotypes, drawn from some distribution of allele frequencies.

To make an utterly minimal simulation, we could draw allele frequencies from some distribution (like a Beta distribution), and then draw the genotypes from a binomial distribution. Done!

However, there is a ton of nuance we would like to have: chromosomes, linkage between variants, sexes, mating, selection …

AlphaSimR addresses all of this, and allows you to throw individuals and populations around to build pretty complicated designs. Here is the small example simulation I used in the talk.


library(AlphaSimR)
library(ggplot2)

## Generate founder chromsomes

FOUNDERPOP <- runMacs(nInd = 1000,
                      nChr = 10,
                      segSites = 5000,
                      inbred = FALSE,
                      species = "GENERIC")


## Simulation parameters

SIMPARAM <- SimParam$new(FOUNDERPOP)
SIMPARAM$addTraitA(nQtlPerChr = 100,
                   mean = 100,
                   var = 10)
SIMPARAM$addSnpChip(nSnpPerChr = 1000)
SIMPARAM$setGender("yes_sys")


## Founding population

pop <- newPop(FOUNDERPOP,
              simParam = SIMPARAM)

pop <- setPheno(pop,
                varE = 20,
                simParam = SIMPARAM)


## Breeding

print("Breeding")
breeding <- vector(length = 11, mode = "list")
breeding[[1]] <- pop

for (i in 2:11) {
    print(i)
    sires <- selectInd(pop = breeding[[i - 1]],
                       gender = "M",
                       nInd = 25,
                       trait = 1,
                       use = "pheno",
                       simParam = SIMPARAM)

    dams <- selectInd(pop = breeding[[i - 1]],
                      nInd = 500,
                      gender = "F",
                      trait = 1,
                      use = "pheno",
                      simParam = SIMPARAM)

    breeding[[i]] <- randCross2(males = sires,
                                females = dams,
                                nCrosses = 500,
                                nProgeny = 10,
                                simParam = SIMPARAM)
    breeding[[i]] <- setPheno(breeding[[i]],
                              varE = 20,
                              simParam = SIMPARAM)
}



## Look at genetic gain and shift in causative variant allele frequency

mean_g <- unlist(lapply(breeding, meanG))
sd_g <- sqrt(unlist(lapply(breeding, varG)))

plot_gain <- qplot(x = 1:11,
                   y = mean_g,
                   ymin = mean_g - sd_g,
                   ymax = mean_g + sd_g,
                   geom = "pointrange",
                   main = "Genetic mean and standard deviation",
                   xlab = "Generation", ylab = "Genetic mean")

start_geno <- pullQtlGeno(breeding[[1]], simParam = SIMPARAM)
start_freq <- colSums(start_geno)/(2 * nrow(start_geno))

end_geno <- pullQtlGeno(breeding[[11]], simParam = SIMPARAM)
end_freq <- colSums(end_geno)/(2 * nrow(end_geno))

plot_freq_before <- qplot(start_freq, main = "Causative variant frequency before") 
plot_freq_after <- qplot(end_freq, main = "Causative variant frequency after") 

This code builds a small livestock population, breeds it for ten generations, and looks at the resulting selection response in the form of a shift of the genetic mean, and the changes in the underlying distribution of causative variants. Here are the resulting plots:

‘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.

What single step does with relationship

We had a journal club about the single step GBLUP method for genomic evaluation a few weeks ago. In this post, we’ll make a few graphs of how the single step method models relatedness between individuals.

Imagine you want to use genomic selection in a breeding program that already has a bunch of historical pedigree and trait information. You could use some so-called multistep evaluation that uses one model for the classical pedigree + trait quantitative genetics and one model for the genotype + trait genomic evaluation, and then mix the predictions from them together. Or you could use the single-step method, which combines pedigree, genotypes and traits into one model. It does this by combining the relationship estimates from pedigree and genotypes. That matrix can then go into your mixed model.

We’ll illustrate this with a tiny simulated population: five generations of 100 individuals per generation, where ten random pairings produce the next generation, with families of ten individuals. (The R code is on Github and uses AlphaSimR for simulation and AGHmatrix for matrices). Here is a heatmap of the pedigree-based additive relationship matrix for the population:

What do we see? In the lower left corner are the founders, and not knowing anything about their heritage, the matrix has them down as unrelated. The squares of high relatedness along the diagonal are the families in each generation. As we go upwards and to the right, relationship is building up.

Now, imagine the last generation of the population also has been genotyped with a SNP chip. Here is a heatmap of their genomic relationship matrix:

Genomic relationship is more detailed. We can still discern the ten families within the last generation, but no longer are all the siblings equally related to each other and to their ancestors. The genotyping helps track segregation within families, pointing out to us when relatives are more or less related than the average that we get from the pedigree.

Enter the single-step relationship matrix. The idea is to put in the genomic relationships for the genotyped individuals into the big pedigree-based relationship matrix, and then adjust the rest of the matrix to propagate that extra information we now have from the genotyped individuals to their ungenotyped relatives. Here is the resulting heatmap:

You can find the matrix equations in Legarra, Aguilar & Misztal (2009). The matrix, called H, is broken down into four partitions called H11, H12, H21, and H22. H22 is the part that pertains to the genotyped animals, and it’s equal to the genomic relationship matrix G (after some rescaling). The others are transformations of G and the corresponding parts of the additive relationship matrix, spreading G onto A.

To show what is going on, here is the difference between the additive relationship matrix and the single-step relationship matrix, with lines delineating the genotyped animals and breaking the matrix into the four partitions:

What do we see? In the top right corner, we have a lot of difference, where the genomic relationship matrix has been plugged in. Then, fading as we go from top to bottom and from right to left, we see the influence of the genomic relationship on relatives, diminishing the further we get from the genotyped individuals.

Literature

Legarra, Andres, I. Aguilar, and I. Misztal. ”A relationship matrix including full pedigree and genomic information.” Journal of dairy science 92.9 (2009): 4656-4663.

Excerpts about genomics in animal breeding

Here are some good quotes I’ve come across while working on something.

Artificial selection on the phenotypes of domesticated species has been practiced consciously or unconsciously for millennia, with dramatic results. Recently, advances in molecular genetic engineering have promised to revolutionize agricultural practices. There are, however, several reasons why molecular genetics can never replace traditional methods of agricultural improvement, but instead they should be integrated to obtain the maximum improvement in economic value of domesticated populations.

Lande R & Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics.

Smith and Smith suggested that the way to proceed is to map QTL to low resolution using standard mapping methods and then to increase the resolution of the map in these regions in order to locate more closely linked markers. In fact, future developments should make this approach unnecessary and make possible high resolution maps of the whole genome, even, perhaps, to the level of the DNA sequence. In addition to easing the application of selection on loci with appreciable individual effects, we argue further that the level of genomic information available will have an impact on infinitesimal models. Relationship information derived from marker information will replace the standard relationship matrix; thus, the average relationship coefficients that this represents will be replaced by actual relationships. Ultimately, we can envisage that current models combining few selected QTL with selection on polygenic or infinitesimal effects will be replaced with a unified model in which different regions of the genome are given weights appropriate to the variance they explain.

Haley C & Visscher P. (1998) Strategies to utilize marker–quantitative trait loci associations. Journal of Dairy Science.

Instead, since the late 1990s, DNA marker genotypes were included into the conventional BLUP analyses following Fernando and Grossman (1989): add the marker genotype (0, 1, or 2, for an animal) as a fixed effect to the statistical model for a trait, obtain the BLUP solutions for the additive polygenic effect as before, and also obtain the properly adjusted BLUE solution for the marker’s allele substitution effect; multiply this BLUE by 0, 1, or 2 (specic for the animal) and add the result to the animal’s BLUP to obtain its marker-enhanced EBV. A logical next step was to treat the marker genotypes as semi-random effects, making use of several different shrinkage strategies all based on the marker heritability (e.g., Tsuruta et al., 2001); by 2007, breeding value estimation packages such as PEST (Neumaier and Groeneveld, 1998) supported this strategy as part of their internal calculations. At that time, a typical genetic evaluation run for a production trait would involve up to 30 markers.

Knol EF, Nielsen B, Knap PW. (2016) Genomic selection in commercial pig breeding. Animal Frontiers.

Although it has not caught the media and public imagination as much as transgenics and cloning, genomics will, I believe, have just as great a long-term impact. Because of the availability of information from genetically well-researched species (humans and mice), genomics in farm animals has been established in an atypical way. We can now see it as progressing in four phases: (i) making a broad sweep map (~20 cM) with both highly informative (microsatellite) and evolutionary conserved (gene) markers; (ii) using the informative markers to identify regions of chromosomes containing quantitative trait loci (QTL) controlling commercially important traits–this requires complex pedigrees or crosses between phenotypically anc genetically divergent strains; (iii) progressing from the informative markers into the QTL and identifying trait genes(s) themselves either by complex pedigrees or back-crossing experiments, and/or using the conserved markers to identify candidate genes from their position in the gene-rich species; (iv) functional analysis of the trait genes to link the genome through physiology to the trait–the ‘phenotype gap’.

Bulfield G. (2000) Biotechnology: advances and impact. Journal of the Science of Food and Agriculture.

I believe animal breeding in the post-genomic era will be dramatically different to what it is today. There will be massive research effort to discover the function of genes including the effect of DNA polymorphisms on phenotype. Breeding programmes will utilize a large number of DNA-based tests for specific genes combined with new reproductive techniques and transgenes to increase the rate of genetic improvement and to produce for, or allocate animals to, the product line to which they are best suited. However, this stage will not be reached for some years by which time many of the early investors will have given up, disappointed with the early benefits.

Goddard M. (2003). Animal breeding in the (post-) genomic era. Animal Science.

Genetics is a quantitative subject. It deals with ratios, with measurements, and with the geometrical relationships of chromosomes. Unlike most sciences that are based largely on mathematical techniques, it makes use of its own system of units. Physics, chemistry, astronomy, and physiology all deal with atoms, molecules, electrons, centimeters, seconds, grams–their measuring systems are all reducible to these common units. Genetics has none of these as a recognizable component in its fundamental units, yet it is a mathematically formulated subject that is logically complete and self-contained.

Sturtevant AH & Beadle GW. (1939) An introduction to genetics. W.B. Saunders company, Philadelphia & London.

We begin by asking why genes on nonhomologous chromosomes assort independently. The simple cytological story rehearsed above answers the questions. That story generates further questions. For example, we might ask why nonhomologous chromosomes are distributed independently at meiosis. To answer this question we could describe the formation of the spindle and the migration of chromosomes to the poles of the spindle just before meiotic division. Once again, the narrative would generate yet further questions. Why do the chromosomes ”condense” at prophase? How is the spindle formed? Perhaps in answering these questions we would begin to introduce the chemical details of the process. Yet simply plugging a molecular account into the narratives offered at the previous stages would decrease the explanatory power of those narratives.

Kitcher, P. (1984) 1953 and all that. A tale of two sciences. Philosophical Review.

And, of course, this great quote by Jay Lush.

There is no breeder’s equation for environmental change

This post is about why heritability coefficients of human traits can’t tell us what to do. Yes, it is pretty much an elaborate subtweet.

Let us begin in a different place, where heritability coefficients are useful, if only a little. Imagine there is selection going on. It can be natural or artificial, but it’s selection the old-fashioned way: there is some trait of an individual that makes it more or less likely to successfully reproduce. We’re looking at one generation of selection: there is one parent generation, some of which reproduce and give rise to the offspring generation.

Then, if we have a well-behaved quantitative trait, no systematic difference between the environments that the two generations experience (also, no previous selection; this is one reason I said ‘if only a little’), we can get an estimate of the response to selection, that is how the mean of the trait will change between the generations:

R = h^2S

R is the response. S, the selection differential, is the difference between the mean all of the parental generation and the selected parents, and thus measures the strength of the selection. h2 is the infamous heritability, which measures the accuracy of the selection.

That is, the heritability coefficient tells you how well the selection of parents reflect the offspring traits. A heritability coefficient of 1 would mean that selection is perfect; you can just look at the parental individuals, pick the ones you like, and get the whole selection differential as a response. A heritability coefficient of 0 means that looking at the parents tells you nothing about what their offspring will be like, and selection thus does nothing.

Conceptually, the power of the breeder’s equation comes from the mathematical properties of selection, and the quantitative genetic assumptions of a linear parent–offspring relationship. (If you’re a true connoisseur of theoretical genetics or a glutton for punishment, you can derive it from the Price equation; see Walsh & Lynch (2018).) It allows you to look (one generation at a time) into the future only because we understand what selection does and assume reasonable things about inheritance.

We don’t have that kind machinery for environmental change.

Now, another way to phrase the meaning of the heritability coefficient is that it is a ratio of variances, namely the additive genetic variance (which measures the trait variation that runs in families) divided by the total variance (which measures the total variation in the population, duh). This is equally valid, more confusing, and also more relevant when we’re talking about something like a population of humans, where no breeding program is going on.

Thus, the heritability coefficient is telling us, in a specific highly geeky sense, how much of trait variation is due to inheritance. Anything we can measure about a population will have a heritability coefficient associated with it. What does this tell us? Say, if drug-related crime has yay big heritability, does that tell us anything about preventing drug-related crime? If heritability is high, does that mean interventions are useless?

The answers should be evident from the way I phrased those rhetorical questions and from the above discussion: There is no theoretical genetics machinery that allows us to predict the future if the environment changes. We are not doing selection on environments, so the mathematics of selection don’t help us. Environments are not inherited according to the rules of quantitative genetics. Nothing prevents a trait from being eminently heritable and respond even stronger to changes in the environment, or vice versa.

(There is also the argument that quantitative genetic modelling of human traits matters because it helps control for genetic differences when estimating other factors. One has to be more sympathetic towards that, because who can argue against accurate measurement? But ought implies can. For quantitative genetic models to be better, they need to solve the problems of identifying variance components and overcoming population stratification.)

Much criticism of heritability in humans concern estimation problems. These criticisms may be valid (estimation is hard) or silly (of course, lots of human traits have substantial heritabilities), but I think they miss the point. Even if accurately estimated, heritabilities don’t do us much good. They don’t help us with the genetic component, because we’re not doing breeding. They don’t help us with the environmental component, because there is no breeder’s equation for environmental change.

Paper: ‘Removal of alleles by genome editing (RAGE) against deleterious load’

Our new paper is about using predicted deleterious variants in animal breeding. We use simulation to look at the potential to improve livestock fitness by either selecting on detected deleterious variants or removing deleterious alleles by genome editing.

Summary

Deleterious variants occur when errors in DNA replication that disrupt the function of a gene. Such errors are frequent enough that all organisms carry mildly deleterious variants. Geneticists describe this as a deleterious load, that cause organisms to be less healthy and fit than they could have been if these errors didn’t happen. Load is especially pertinent to livestock populations, because of their relatively small population sizes and inbreeding.

Historically, it has not been possible to observe deleterious variants directly, but as genome sequencing becomes cheaper and new bioinformatic methods are being developed, we can now sequence livestock and detect variants that are likely to be deleterious.

In this study, we used computer simulation to see how future breeding strategies involving selection or genome editing could be used to reduce deleterious load. We tested selection against deleterious load and genome editing strategy we call RAGE (Removal of Alleles by Genome Editing) in simulated livestock populations to see how it improved fitness. The simulations suggest that selecting on deleterious variants identified from genome sequencing may help improve fitness of livestock populations, and that genome editing to remove deleterious variants could improve them even more.

For these strategies to be effective, it is important that detection of deleterious variants is accurate, and genome editing of more than one variant per animal would need to become possible without damaging side-effects. Future research on how to measure deleterious load in large sequence datasets from livestock animals, and how to perform genome editing safely and effectively will be important.

Figure 2 from the paper, showing the average fitness of simulated populations (y-axis) over the generations of breeding (x-axis) with different types of future breeding against deleterious variants.

‘RAGE against …’, what’s with the acronym?

We are very happy with the acronym. In addition to making at least two pop culture references, it’s also a nod to Promotion of Alleles by Genome Editing (PAGE) from Jenko et al. (2015). I like that the acronyms, both PAGE and RAGE, emphasises that we’re dealing with alleles that already exist within a population. We propose using genome editing as a way to promote alleles we like and remove alleles we don’t like in addition to classical breeding. The fancy new biotechnology does not replace selection, but supplements it.

Do you really think one should genome edit farm animals?

Yes, if all the bio- and reproductive technology can be made to work! Currently, genome editing methods like Crispr/Cas9 require many attempts to get precise editing to the desired allele at one place, and it doesn’t scale to multiple edits in the same animal … Not yet. But lots of smart people are competing to make it happen.

Genome editing of farm animals would also need a lot of reproductive technology, that currently isn’t really there (but probably more so for cattle than for other species). Again, lots of clever people work on it.

If it can be made to work, genome editing could be a useful breeding method.

What about the ethics of genome editing?

We don’t discuss ethics much in the paper. In one simple sense, that is because ethics isn’t our expertise. I also think a discussion of the ethics of RAGE, much like an informed discussion about the economics of it, requires empirical knowledge that we don’t have yet.

I am not of the opinion that there is a dignity or integrity to the genome that would prohibit genome editing as a rule. So the question is not ‘genome editing or not’, but ‘under what circumstances and for what applications is genome editing useful and justified?’ and ‘are the benefits of RAGE, PAGE, or whatever -GE, enough to outweigh the risks and costs?’. There is room for uncertainty and disagreement about those questions.

For a good discussion of the ethics of genome editing that is likely to raise more questions than it answers, see Eriksson et al. (2018). Among other things, they make the point that advanced reproductive technologies is a precondition for genome editing, but kind of slips out of the discussion sometimes. I think the most pressing question, both from the ethical and economical perspective, is whether the benefits of genome editing are enough to justify widespread use of reproductive technologies (in species where that isn’t already commonplace). I also like how they make the point that one needs to look at the specific applications of genome editing, in context, when evaluating them.

The simulation looks nifty! I want to simulate breeding programs like that!

You can! The simulations used the quantitative genetic simulation R package AlphaSimR with some modifications for simulating the fitness traits. There is code with the paper. Here are also the slides from when I talked about the paper at the Edinburgh R user group.

You make a ton of assumptions!

We do. Some of them are extremely uncontroversial (the basic framework of segregation and recombination during inheritance), some we can get some idea about by looking at the population genetics literature (we’ve taken inspiration from estimates of deleterious mutation rates and effect distributions estimated from humans), and some we don’t have much knowledge about at all (how does load of deleterious variants relate to the production, reproduction and health traits that are important to breeding? The only way to know is to measure). If you read the paper, don’t skip that part of the Discussion.

Would this work in plants?

Yes, probably! Plant breeding programs are a bit different, so I guess one should simulate them to really know. RAGE would be a part of the ‘Breeding 4.0’ logic of Wallace, Rodgers-Melnick & Butler (2018). In many ways the problems with plants are smaller, with less unknown reproductive technology that needs to be invented first, and an easier time field testing edited individuals.

Literature

Johnsson M, Gaynor RC, Jenko J, Gorjanc G, de Koning D-J, Hickey, JM. (2019) Removal of alleles by genome editing (RAGE) against deleterious load. Genetics Selection Evolution.

Jenko J, Gorjanc G, Cleveland MA, Varshney RK, Whitelaw CBA, Woolliams JA, Hickey JM. (2015). Potential of promotion of alleles by genome editing to improve quantitative traits in livestock breeding programs. Genetics Selection Evolution.

Eriksson, S., Jonas, E., Rydhmer, L., & Röcklinsberg, H. (2018). Invited review: Breeding and ethical perspectives on genetically modified and genome edited cattle. Journal of dairy science, 101(1), 1-17.

Wallace, J. G., Rodgers-Melnick, E., & Buckler, E. S. (2018). On the road to Breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics. Annual review of genetics, 52, 421-444.

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.