Journal club of one: ”Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses”

This paper (Wallace 2020) is about improvements to the colocalisation method for genome-wide association studies called coloc. If you have an association to trait 1 in a region, and another association with trait 2, coloc investigates whether they are caused by the same variant or not. I’ve never used coloc, but I’m interested because setting reasonable priors is related to getting reasonable parameters for genetic architecture.

The paper also looks at how coloc is used in the literature (with default settings, unsurprisingly), and extends coloc to relax the assumption of only one causal variant per region. In that way, it’s a solid example of thoughtfully updating a popular method.

(A note about style: This isn’t the clearest paper, for a few reasons. The structure of the introduction is indirect, talking a lot about Mendelian randomisation before concluding that coloc isn’t Mendelian randomisation. The paper also uses numbered hypotheses H1-H4 instead of spelling out what they mean … If you feel a little stupid reading it, it’s not just you.)

coloc is what we old QTL mappers call a pleiotropy versus linkage test. It tries to distinguish five scenarios: no association, trait 1 only, trait 2 only, both traits with linked variants, both traits with the same variant.

This paper deals with the priors: What is the prior probability of a causal association to trait 1 only p_1, trait 2 only p_2, or both traits p_{12} , and are the defaults good?

They reparametrise the priors so that it becomes possible to get some estimates from the literature. They work with the probability that a SNP is causally associated with each trait (which means adding the probabilities of association q_1 = p_1 + p_{12} ) … This means that you can look at single trait association data, and get an idea of the number of marginal associations, possibly dependent on allele frequency. The estimates from a gene expression dataset and a genome-wide association catalog work out to a prior around 10 ^ {-4} , which is the coloc default. So far so good.

How about p_{12} ?

If traits were independent, you could just multiply q_1 and q_2. But not all of the genome is functional. If you could straightforwardly define a functional proportion, you could just divide by it.

You could also look at the genetic correlation between traits. It makes sense that the overall genetic relationship between two traits should inform the prior that you see overlap at this particular locus. This gives a lower limit for p_{12} . Unfortunately, this still leaves us dependent on what kinds of traits we’re analysing. Perhaps, it’s not so surprising that there isn’t one prior that universally works for all kinds of pairs of trait:

Attempts to colocalise disease and eQTL signals have ranged from underwhelming to positive. One key difference between outcomes is the disease-specific relevance of the cell types considered, which is consistent with variable chromatin state enrichment in different GWAS according to cell type. For example, studies considering the overlap of open chromatin and GWAS signals have convincingly shown that tissue relevance varies by up to 10 fold, with pancreatic islets of greatest relevance for traits like insulin sensitivity and immune cells for immune-mediated diseases. This suggests that p_{12} should depend explicitly on the specific pair of traits under consideration, including cell type in the case of eQTL or chromatin mark studies. One avenue for future exploration is whether fold change in enrichment of open chromatin/GWAS signal overlap between cell types could be used to modulate p_{12} and select larger values for more a priori relevant tissues.


Wallace, Chris. ”Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses.” PLoS Genetics 16.4 (2020): e1008720.

Adrian Bird on genome ecology

I recently read this essay by Adrian Bird on ”The Selfishness of Law-Abiding Genes”. That is a colourful title in itself, but it doesn’t stop there; this is an extremely metaphor-rich piece. In terms of the theoretical content, there is not much new under the sun. Properties of the organism like complexity, redundancy, and all those exquisite networks of developmental gene regulation may be the result of non-adaptive processes, like constructive neutral evolution and intragenomic conflict. As the title suggests, Bird argues that this kind of thinking is generally accepted about things like transposable elements (”selfish DNA”), but that the same logic applies to regular ”law-abiding” genes. They may also be driven by other evolutionary forces than a net fitness gain at the organismal level.

He gives a couple of possible examples: toxin–antitoxin gene pairs, RNA editing and MeCP2 (that’s probably Bird’s favourite protein that he has done a lot of work on). He gives this possible description of MeCP2 evolution:

Loss of MeCP2 via mutation in humans leads to serious defects in the brain, which might suggest that MeCP2 is a fundamental regulator of nervous system development. Evolutionary considerations question this view, however, as most animals have nervous systems, but only vertebrates, which account for a small proportion of the animal kingdom, have MeCP2. This protein therefore appears to be a late arrival in evolutionary terms, rather than being a core ancestral component of brain assembly. A conventional view of MeCP2 function is that by exerting global transcriptional restraint it tunes gene expression in neurons to optimize their identity, but it is also possible to devise a scenario based on self-interest. Initially, the argument goes, MeCP2 was present at low levels, as it is in non-neuronal tissues, and therefore played little or no role in creating an optimal nervous system. Because DNA methylation is sparse in the great majority of the genome, sporadic mutations that led to mildly increased MeCP2 expression would have had a minimal dampening effect on transcription that may initially have been selectively neutral. If not eliminated by drift, further chance increases might have followed, with neuronal development incrementally adjusting to each minor hike in MeCP2-mediated repression through compensatory mutations in other genes. Mechanisms that lead to ‘constructive neutral evolution’ of this kind have been proposed. Gradually, brain development would accommodate the encroachment of MeCP2 until it became an essential feature. So, in response to the question ‘why do brains need MeCP2?’, the answer under this speculative scenario would be: ‘they do not; MeCP2 has made itself indispensable by stealth’.

I think this is a great passage, and it can be read both as a metaphorical reinterpretation, and as substantive hypothesis. The empirical question ”Did MeCP2 offer an important innovation to vertebrate brains as it arose?”, is a bit hard to answer with data, though. On the other hand, if we just consider the metaphor, can’t you say the same about every functional protein? Sure, it’s nice to think of p53 as the Guardian of the Genome, but can’t it also be viewed as a gangster extracting protection money from the organism? ”Replicate me, or you might get cancer later …”

The piece argues for a gene-centric view, that thinks of molecules and the evolutionary pressures they face. This doesn’t seem so be the fashionable view (sorry, extended synthesists!) but Bird argues that it would be healthy for molecular cell biologists to think more about the alternative, non-adaptive, bottom-up perspective. I don’t think the point is to advocate that way of thinking to the exclusion of the all other. To me, the piece reads more like an invitation to use a broader set of metaphors and verbal models to aid hypothesis generation.

There are too may good quotes in this essay, so I’ll just quote one more from the end, where we’ve jumped from the idea of selfish law-abiding genes, over ”genome ecology” — not in the sense of using genomics in ecology, but in the sense of thinking of the genome as some kind of population of agents with different niches and interactions, I guess — to ”Genetics Meets Sociology?”

Biologists often invoke parallels between molecular processes of life and computer logic, but a gene-centered approach suggests that economics or social science may be a more appropriate model …

I feel like there is a circle of reinforcing metaphors here. Sometimes when we have to explain how something came to be, for example a document, a piece of computer code or a the we do things in an organisation, we say ”it grew organically” or ”it evolved”. Sometimes we talk about the genome as a computer program, and sometimes we talk about our messy computer program code as an organism. Like viruses are just like computer viruses, only biological.


Bird, Adrian. ”The Selfishness of Law-Abiding Genes.” Trends in Genetics 36.1 (2020): 8-13.

Journal club of one: ”Genomic predictions for crossbred dairy cattle”

A lot of dairy cattle is crossbred, but genomic evaluation is often done within breed. What about the crossbred individuals? This paper (VanRaden et al. 2020) describes the US Council on Dairy Cattle Breeding’s crossbred genomic prediction that started 2019.

In short, the method goes like this: They describe each crossbred individual in terms of their ”genomic breed composition”, get predictions for each them based on models from all the breeds separately, and then combine the results in proportion to the genomic breed composition. The paper describes how they estimate the genomic breed composition, and evaluated accuracy by predicting held-out new data from older data.

The genomic breed composition is a delightfully elegant hack: They treat ”how much breed X is this animal” as a series of traits and run a genomic evaluation on them. The training set: individuals from sets of reference breeds with their trait value set to 100% for the breed they belong to and 0% for other breeds. ”Marker effects for GBC [genomic breed composition] were then estimated using the same software as for all other traits.” Neat. After some adjustment, they can be interpreted as breed percentages, called ”base breed representation”.

As they already run genomic evaluations from each breed, they can take these marker effects and then animal’s genotypes, and get one estimate for each breed. Then they combine them, weighting by the base breed representation.

Does it work? Yes, in the sense that it provides genomic estimates for animals that otherwise wouldn’t have any, and that it beats parent average estimates.

Accuracy of GPTA was higher than that of [parent average] for crossbred cows using truncated data from 2012 to predict later phenotypes in 2016 for all traits except productive life. Separate regressions for the 3 BBR categories of crossbreds suggest that the methods perform equally well at 50% BBR, 75% BBR, and 90% BBR.

They mention in passing comparing these estimates to estimates from a common set of marker effects for all breeds, but there is no detail about that model or how it compared in accuracy.

The discussion starts with this sentence:

More breeders now genotype their whole herds and may expect evaluations for all genotyped animals in the future.

That sounds like a reasonable expectation, doesn’t it? Before what they could do with crossbred genotypes was to throw it away. There are lots of other things that might be possible with crossbred evaluation in the future (pulling in crossbred data into the evaluation itself, accounting for ancestry in different parts of the genome, estimating breed-of-origin of alleles, looking at dominance etc etc).

My favourite result in the paper is Table 8, which shows:

Example BBR for animals from different breeding systems are shown in Table 8. The HO cow from a 1964 control line had 1960s genetics from a University of Minnesota experimental selection project and a relatively low relationship to the current HO population because of changes in breed allele frequencies over the past half-century. The Danish JE cow has alleles that differ somewhat from the North American JE population. Other examples in the table show various breed crosses, and the example for an animal from a breed with no reference population shows that genetic contributions from some other breed may be evenly distributed among the included breeds so that BBR percentages sum to 100. These examples illustrate that GBC can be very effective at detecting significant percentages of DNA contributed by another breed.


VanRaden, P. M., et al. ”Genomic predictions for crossbred dairy cattle.” Journal of Dairy Science 103.2 (2020): 1620-1631.

A partial success

In 2010, Poliseno & co published some results on the regulation of a gene by a transcript from a pseudogene. Now, Kerwin & co have published a replication study, the protocol for which came out in 2015 (Khan et al). An editor summarises it like this in an accompanying commentary (Calin 2020):

The partial success of a study to reproduce experiments that linked pseudogenes and cancer proves that understanding RNA networks is more complicated than expected.

I guess he means ”partial success” in the sense that they partially succeeded in performing the replication experiments they wanted. These experiments did not reproduce the gene regulation results from 2010.

Seen from the outside — I have no insight in what is going on here or who the people involved are — something is not working here. If it takes five years from paper to replication effort, and then another five years to replication study accompanied by an editorial commentary that subtly undermines it, we can’t expect replication studies to update the literature, can we?


What’s the moral of the story, according to Calin?

What are the take-home messages from this Replication Study? One is the importance of fruitful communication between the laboratory that did the initial experiments and the lab trying to repeat them. The lack of such communication – which should extend to the exchange of protocols and reagents – was the reason why the experiments involving microRNAs could not be reproduced. The original paper did not give catalogue numbers for these reagents, so the wrong microRNA reagents were used in the Replication Study. The introduction of reporting standards at many journals means that this is less likely to be an issue for more recent papers.

There is something right and something wrong about this. On the one hand, talking to your colleagues in the field obviously makes life easier. We would like researchers to put all pertinent information in writing, and we would like there to be good communication channels in cases where the information turns out not to be what the reader needed. On the other hand, we don’t want science to be esoteric. We would like experiments to be reproducible without the special artifact or secret sauce. If nothing else, because the people’s time and willingness to provide tech support for their old papers might be limited. Of course, this is hard, in a world where the reproducibility of an experiment might depend on the length of digestion (Hines et al 2014) or that little plastic thingamajig you need for the washing step.

Another take-home message is that it is finally time for the research community to make raw data obtained with quantitative real-time PCR openly available for papers that rely on such data. This would be of great benefit to any group exploring the expression of the same gene/pseudogene/non-coding RNA in the same cell line or tissue type.

This is true. You know how doctored, or just poor, Western blots are a notorious issue in the literature? I don’t think that’s because Western blot as a technique is exceptionally bad, but because there is a culture of showing the raw data (the gel), so people can notice problems. However, even if I’m all for showing real-time PCR amplification curves (as well as melting curves, standard curves, and the actual batch and plate information from the runs), I doubt that it’s going to be possible to trouble-shoot PCR retrospectively from those curves. Maybe sometimes one would be able to spot a PCR that looks iffy, but beyond that, I’m not sure what we would learn. PCR issues are likely to have to do with subtle things like primer design, reaction conditions and handling that can only really be tackled in the lab.

The world is messy, alright

Both the commentary and the replication study (Kerwin et al 2020) are cautious when presenting their results. I think it reads as if the authors themselves either don’t truly believe their failure to replicate or are bending over backwards to acknowledge everything that could have gone wrong.

The original study reported that overexpression of PTEN 3’UTR increased PTENP1 levels in DU145 cells (Figure 4A), whereas the Replication Study reports that it does not. …

However, the original study and the Replication Study both found that overexpression of PTEN 3’UTR led to a statistically significant decrease in the proliferation of DU145 cells compared to controls.

In the original study Poliseno et al. reported that two microRNAs – miR-19b and miR-20a – suppress the transcription of both PTEN and PTENP1 in DU145 prostate cancer cells (Figure 1D), and that the depletion of PTEN or PTENP1 led to a statistically significant reduction in the corresponding pseudogene or gene (Figure 2G). Neither of these effects were seen in the Replication Study. There are many possible explanations for this. For example, although both studies used DU145 prostate cancer cells, they did not come from the same batch, so there could be significant genetic differences between them: see Andor et al. (2020) for more on cell lines acquiring mutations during cell cultures. Furthermore, one of the techniques used in both studies – quantitative real-time PCR – depends strongly on the reagents and operating procedures used in the experiments. Indeed, there are no widely accepted standard operating procedures for this technique, despite over a decade of efforts to establish such procedures (Willems et al., 2008; Schwarzenbach et al., 2015).

That is both commentary and replication study seem to subscribe to a view of the world where biology is so rich and complex that both might be right, conditional on unobserved moderating variables. This is true, but it throws us into a discussion of generalisability. If a result only holds in some genotypes of DU145 prostate cancer cells, which might very well be the case, does it generalise enough to be useful for cancer research?

Power underwhelming

There is another possible view of the world, though … Indeed, biology rich and complicated, but in the absence of accurate estimates, we don’t know which of all these potential moderating variables actually do anything. First order, before we start imagining scenarios that might explain the discrepancy, is to get a really good estimate of it. How do we do that? It’s hard, but how about starting with a cell size greater than N = 5?

The registered report contains power calculations, which is commendable. As far as I can see, it does not describe how they arrived at the assumed effect sizes. Power estimates for a study design depend on the assumed effect sizes. Small studies tend to exaggerate effect sizes (because, if an estimate is small the difference can’t be significant). This means that taking the estimates as staring effect sizes might leave you with a design that is still unable to detect a true effect of reasonable size.

I don’t know what effect sizes one should expect in these kinds of experiments, but my intuition would be that even if you think that you can get good power with a handful of samples per cell, can’t you please run a couple more? We are all limited by resources and time, but if you’re running something like a qPCR, the cost per sample must be much smaller than the cost for doing one run of the experiment in the first place. It’s really not as simple as adding one row on a plate, but almost.


Calin, George A. ”Reproducibility in Cancer Biology: Pseudogenes, RNAs and new reproducibility norms.” eLife 9 (2020): e56397.

Hines, William C., et al. ”Sorting out the FACS: a devil in the details.” Cell reports 6.5 (2014): 779-781.

Kerwin, John, and Israr Khan. ”Replication Study: A coding-independent function of gene and pseudogene mRNAs regulates tumour biology.” eLife 9 (2020): e51019.

Khan, Israr, et al. ”Registered report: a coding-independent function of gene and pseudogene mRNAs regulates tumour biology.” Elife 4 (2015): e08245.

Poliseno, Laura, et al. ”A coding-independent function of gene and pseudogene mRNAs regulates tumour biology.” Nature 465.7301 (2010): 1033-1038.

Journal club of one: ”Evolutionary stalling and a limit on the power of natural selection to improve a cellular module”

This is a relatively recent preprint on how correlations between genetic variants can limit the response to selection, with experimental evolution in bacteria.

Experimental evolution and selection experiments live on the gradient from modelling to observations in the wild. Experimental evolution researchers can design the environments and the genotypes to pose problems for evolution, and then watch in real time as organisms solve them. With sequencing, they can also watch how the genome responds to selection.

In this case, the problem posed is how to improve a particular cellular function (”module”). The researcher started out with engineered Escherichia coli that had one component of their translation machinery manipulated: they started out with only one copy of an elongation factor gene (where E.coli normally has two) that could be either from another species, an reconstructed ancestral form, or the regular E.coli gene as a control.

Then, they sequenced samples from replicate populations over time, and looked for potentially adaptive variants: that is, operationally, variants that had large changes in frequency (>20%) and occurred in genes that had more than one candidate adaptive variant.

Finally, because they looked at what genes these variants occurred in. Were they related to translation (”TM-specific” as they call it) or not (”generic”). That gave them trajectories of potentially adaptive variants like this. The horizontal axis is time and the vertical frequency of the variant. The letters are populations of different origin, and the numbers replicates thereof. The colour shows the classification of variants. (”fimD” and ”trkH” in the figure are genes in the ”generic” category that are of special interest for other reasons. The orange–brown shading signifies structural variation at the elongation factor gene.)

This figure shows their main observations:

  • V, A and P populations had more adaptive variants in translation genes, and also worse fitness at the start of the experiment. This goes together with improving more during the experiment. If a population has poor translation, a variant in a translation gene might help. If it has decent translation efficiency already, there is less scope for improvement, and adaptive variants in other kinds of genes happen more often.

    We found that populations whose TMs were initially mildly perturbed (incurring ≲ 3% fitness cost) adapted by acquiring mutations that did not directly affect the TM. Populations whose TM had a moderately severe defect (incurring ~19% fitness cost) discovered TM-specific mutations, but clonal interference often prevented their fixation. Populations whose TMs were initially severely perturbed (incurring ~35% fitness cost) rapidly discovered and fixed TM-specific beneficial mutations.

  • Adaptive variants in translation genes tended to increase fast and early during the experiment and often get fixed, suggesting that they have larger effects than. Again, the your translation capability is badly broken, a large-effect variant in a translation gene might help.

    Out of the 14 TM-specific mutations that eventually fixed, 12 (86%) did so in the first selective sweep. As a result, an average TM-specific beneficial mutation reached fixation by generation 300 ± 52, and only one (7%) reached fixation after generation 600 … In contrast, the average fixation time of generic mutations in the V, A and P populations was 600 ± 72 generations, and 9 of them (56%) fixed after the first selective sweep

  • After one adaptive variant in a translation gene, it seems to stop at that.

The question is: when there aren’t further adaptive variants in translation genes, is that because it’s impossible to improve translation any further, or because of interference from other variants? They use the term ”evolutionary stalling”, kind of asexual linked selection. Because variants occur together, selection acts on the net effect of all the variants in an individual. Adaptation in a certain process (in this case translation) might stall, if there are large-effect adaptive variants in other, potentially completely unrelated processes, that swamp the effect on translation.

They argue for three kinds of indirect evidence that the adaptation in translation has stalled in at least some of the populations:

  1. Some of the replicate populations of V didn’t fix adaptive translation variants.
  2. In some populations, there were a second adaptive translation variant, not yet fixed.
  3. There have been adaptive translation mutations in the Long Term Evolution Experiment, which is based on E.coli with unmanipulated translation machinery.

Stalling depends on large-effect variants, but after they have fixed, adaptation might resume. They use the metaphor of natural selection ”shifting focus”. The two non-translation genes singled out in the above figure might be examples of that:

While we did not observe resumption of adaptive evolution in [translation] during the duration of this experiment, we find evidence for a transition from stalling to adaptation in trkH and fimD genes. Mutations in these two genes appear to be beneficial in all our genetic backgrounds (Figure 4). These mutations are among the earliest to arise and fix in E, S and Y populations where the TM does not adapt … In contrast, mutations in trkH and fimD arise in A and P populations much later, typically following fixations of TM-specific mutations … In other words, natural selection in these populations is initially largely focused on improving the TM, while adaptation in trkH and fimD is stalled. After a TM-specific mutation is fixed, the focus of natural selection shifts away from the TM to other modules, including trkH and fimD.

This is all rather indirect, but interesting. Both ”the focus of natural selection shifting” and ”coupling of modules by the emergent neutrality threshold” are inspiring ways to think about the evolution of genetic architecture, and new to me.


Venkataram, Sandeep, et al. ”Evolutionary Stalling and a Limit on the Power of Natural Selection to Improve a Cellular Module.” bioRxiv (2019): 850644.

Better posters are nice, but we need better poster session experiences

Fear and loathing in the conference centre lobby

Let me start from a negative place, because my attitude to poster sessions is negative. Poster sessions are neither good ways to communicate science, nor to network at conferences. Moreover, they are unpleasant.

The experience of going to a poster session, as an attendant or a presenter goes something like this: You have to stand in a crowded room that is too loud and try to either read technical language or hold a conversation in about a difficult topic. Even without anxiety, mobility, or hearing difficulties, a poster session is unlikely to be enjoyable or efficient.

Poster sessions are bad because of necessities of conference organisation. We want to invite many people, but we can’t fit in many talks; we get crowded poster sessions.

They are made worse by efforts to make them better, such as mandating presenters to stand by their posters, in some cases on pain of some sanction by the organisers, or to have the poster presenters act as dispensers of alcohol. If you need to threaten or drug people to participate in an activity, that might be a sign.

They are made not worse but a bit silly, by assertions that poster sessions are of utmost importance for conferencing. Merely stating that the poster session is vibrant and inspiring, or that you want to emphasise the poster as an important form of communication, sadly, does not make it so, if the poster sessions are still business as usual.

Mike Morrison’s ”Better Scientific Poster” design

As you can see above, my diagnosis of the poster session problem is part that you’re forced to read walls of text or listen to mini-lectures, and part that it happens in an overcrowded space. The walls of text and mini-lecture might be improved by poster design.

Enter the Better Scientific Poster. I suggest clicking on that link and looking at the poster templates directly. I waited too long to look at the actual template files, because I expected a bunch of confusing designer stuff. It’s not. They contain their own documentation and examples.

There is also a video on YouTube expanding on the thinking behind the design, but I think this conversation on the Everyting Hertz podcast is the best introduction, if you need an introduction beyond the template. The YouTube video doesn’t go into enough detail, and is also a bit outdated. The poster template has gone through improvements since.

If you want to hear the criticisms of the design, here’s a blog post summarising some of it. In short, it is unscientific and intellectually arrogant to put a take home message in too large a font, and it would be boring if all posters used the same template. Okay.

The caveats

I am not a designer, which should be abundantly clear to everyone. I don’t really know what good graphic design principles for a poster are.

There is also no way to satisfy everyone. Some people will think you’ve put too little on the poster unless it ”tells the full story” and a has self-contained description of the methods with all caveats. Some people, like me, will think you’ve put way too much on it long before that.

What I like, however, is that Morrison’s design is based on an analysis of the poster session experience that aligns with mine, and that it is based on a goal for the poster that makes sense. The features of the design flow from that goal. If you listen to the video or the Hertz episode: Morrison has thought about the purpose of the poster.

He’s not just expressing some wisdom his PhD supervisor told him in stern voice, or what his gut feeling tells him, which I suspect is the two sources that scientists’ advice on communication is usually based on. We all think that poster sessions are bad, because we’ve been to poster sessions. We usually don’t have thought-through ideas about what to do better.

Back to a place of negativity

For those reasons, I think the better poster is likely to be an improvement. I was surprised that I didn’t see it sweep through poster sessions at the conferences I went to last summer, but there were a few. I was going to try it for TAGGC 2020 (here is my poster aboutthe genetics of recombination rate in the pig), but that moved online, which made poster presentations a little different.

However, changing up poster layout can only get you so far. Unless someone has a stroke of genius to improve the poster viewing experience or change the economics of poster attendance, there no bright future for the poster session. Individually, the rational course of action isn’t to fiddle with the design and spend time to squeeze marginal improvements out of our posters. It is to spend as little time as possible on posters, ignoring our colleagues’ helpful advice on how to make them prettier and more scientific, and lowering our expectations.

Robertson on genetic correlation and loss of variation

It’s not too uncommon to see animal breeding papers citing a paper by Alan Robertson (1959) to support a genetic correlation of 0.8 as a cut-off point for what is a meaningful difference. What is that based on?

The paper is called ”The sampling variance of the genetic correlation coefficient” and, as the name suggests, it is about methods for estimating genetic correlations. It contains a section about the genetic correlation between environments as a way to measure gene-by-environment interaction. There, Robertson discusses experimental designs for detecting gene-by-environment interaction–that is, estimating whether a genetic correlation between different environments is less than one. He finds that you need much larger samples than for estimating heritabilities. It is in this context that the 0.8 number comes up. Here is the whole paragraph:

No interaction means a genetic correlation of unity. How much must the correlation fall before it has biological or agricultural importance? I would suggest that this figure is around 0.8 and that no experiment on genotype-environment interaction would have been worth doing unless it could have detected, as a significant deviation from unity, a genetic correlation of 0.6. In the first instance, I propose to argue from the standpoint of a standard error of 0.2 as an absolute minimum.

That is, in the context of trying to make study design recommendations for detecting genotype-by-environment interactions, Robertson suggests that a genetic correlation of 0.8 might be a meaningful difference from 1. The paper does not deal with designing breeding programs for multiple environments or the definition of traits, and it has no data on any of that. It seems to be a little bit like Fisher’s p < 0.05: Suggest a rule of thumb, and risk it having a life of its own in the future.

In the process of looking up this quote, I also found this little gem, from ”The effect of selection on the estimation of genetic parameters” (Robertson 1977). It talks about the problems that arise with estimating genetic parameters in populations under selection, when many quantitative genetic results, in one way or another, depend on random mating. Here is how it ends:

This perhaps points the moral of this paper. The individuals of one generation are the parents of the next — if they are accurately evaluated and selected in the first generation, the variation between families will be reduced in the next. You cannot have your cake and eat it.


Robertson, A. ”The sampling variance of the genetic correlation coefficient.” Biometrics 15.3 (1959): 469-485.

Robertson, A. ”The effect of selection on the estimation of genetic parameters.” Zeitschrift für Tierzüchtung und Züchtungsbiologie 94.1‐4 (1977): 131-135.

Using R: setting a colour scheme in ggplot2

Note to self: How to quickly set a colour scheme in ggplot2.

Imagine we have a series of plots that all need a uniform colour scale. The same category needs to have the same colour in all graphics, made possibly with different packages and by different people. Instead of hard-coding the colours and the order of categories, we can put them in a file, like so:

colours <- read_csv("scale_colours.csv")
# A tibble: 5 x 2
  name   colour 
1 blue   #d4b9da
2 red    #c994c7
3 purple #df65b0
4 green  #dd1c77
5 orange #980043

Now a plot with default colours, using some made-up data:

x <- 1:100

beta <- rnorm(5, 1, 0.5)

stroop <- data.frame(x,
                     sapply(beta, function(b) x * b + rnorm(100, 1, 10)))
colnames(stroop)[2:6] <- c("orange", "blue", "red", "purple", "green") 

data_long <- pivot_longer(stroop, -x)

plot_y <- qplot(x = x,
                y = value,
                colour = name,
                data = data_long) +
  theme_minimal() +
  theme(panel.grid = element_blank())

Now we can add the custom scale like this:

plot_y_colours <- plot_y + 
  scale_colour_manual(limits = colours$name,
                      values = colours$colour)

Virtual animal breeding journal club: ”An eQTL in the cystathionine beta synthase gene is linked to osteoporosis in laying hens”

The other day the International Virtual Animal Breeding Journal Club, organised by John Cole, had its second meeting. I presented a recent paper about using genetic mapping and gene expression to find a putative causative gene for a region associated with bone strength in layer chickens. This from colleauges I know and work with, but I wasn’t involved in this work myself.

Here is the paper:

De Koning, Dirk-Jan, et al. ”An eQTL in the cystathionine beta synthase gene is linked to osteoporosis in laying hens.” Genetics Selection Evolution 52.1 (2020): 1-17.

Here are my slides:

Ian Dunn and DJ de Koning were both on the call to answer some questions and give the authors’ perspective, which, again, I thought was very useful. I hope this becomes a recurring theme of the journal club.

I chose the paper because I think it’s a good example of the QTL–eQTL paradigm of causative gene identification. We got some discussion about that. Conclusions: You never really know whether an association with gene expression is causal or reactive, unless there’s some kind of experimental manipulation. We all want more annotation, more functional genomics and more genome sequences. I can’t argue with that.

Here is the a review of layer chicken bone biology referred to in the slides, if you want to look into that:

Whitehead, C. C. ”Overview of bone biology in the egg-laying hen.” Poultry science 83.2 (2004): 193-199.

If you want to follow the journal club, see the Google group and Twitter account for announcements.

Virtual animal breeding journal club: ”Structural equation models to disentangle the biological relationship between microbiota and complex traits …”

The other day was the first Virtual breeding and genetics journal club organised by John Cole. This was the first online journal club I’ve attended (shocking, given how many video calls I’ve been on for other sciencey reasons), so I thought I’d write a little about it: both the format and the paper. You can look the slide deck from the journal club here (pptx file).

The medium

We used Zoom, and that seemed to work, as I’m sure anything else would, if everyone just mute their microphone when they aren’t speaking. As John said, the key feature of Zoom seems to be the ability for the host to mute everyone else. During the call, I think we were at most 29 or so people, but only a handful spoke. It will probably get more intense with the turn taking if more people want to speak.

The format

John started the journal club with a code of conduct, which I expect helped to set what I felt was a good atmosphere. In most journal clubs I’ve been in, I feel like the atmosphere has been pretty good, but I think we’ve all heard stories about hyper-critical and hostile journal clubs, and that doesn’t sound particularly fun or useful. On that note, one of the authors, Oscar González-Recio, was on the call and answered some questions.

The paper

Saborío‐Montero, Alejandro, et al. ”Structural equation models to disentangle the biological relationship between microbiota and complex traits: Methane production in dairy cattle as a case of study.” Journal of Animal Breeding and Genetics 137.1 (2020): 36-48.

The authors measured methane emissions (by analysing breath with with an infrared gas monitor) and abundance of different microbes in the rumen (with Nanopore sequencing) from dairy cows. They genotyped the animals for relatedness.

They analysed the genetic relationship between breath methane and abundance of each taxon of microbe, individually, with either:

  • a bivariate animal model;
  • a structural equations model that allows for a causal effect of abundance on methane, capturing the assumption that the abundance of a taxon can affect the methane emission, but not the other way around.

They used them to estimate heritabilities of abundances and genetic correlations between methane and abundances, and in the case of the structural model: conditional on the assumed causal model, the effect of that taxon’s abundance on methane.

My thoughts

It’s cool how there’s a literature building up on genetic influences on the microbiome, with some consistency across studies. These intense high-tech studies on relatively few cattle might build up to finding new traits and proxies that can go into larger scale phenotyping for breeding.

As the title suggests, the paper advocates for using the structural equations model: ”Genetic correlation estimates revealed differences according to the usage of non‐recursive and recursive models, with a more biologically supported result for the recursive model estimation.” (Conclusions)

While I agree that a priori, it makes sense to assume a structural equations model with a causal structure, I don’t think the results provide much evidence that it’s better. The estimates of heritabilities and genetic correlations from the two models are near indistinguishable. Here is the key figure 4, comparing genetic correlation estimates:


As you can see, there are a couple of examples of genetic correlations where the point estimate switches sign, and one of them (Succinivibrio sp.) where the credible intervals don’t overlap. ”Recursive” is the structural equations model. The error bars are 95% credible intervals. This is not strong evidence of anything; the authors are responsible about it and don’t go into interpreting this difference. But let us speculate! They write:

All genera in this case, excepting Succinivibrio sp. from the Proteobacteria phylum, resulted in overlapped genetic cor- relations between the non‐recursive bivariate model and the recursive model. However, high differences were observed. Succinivibrio sp. showed the largest disagreement changing from positively correlated (0.08) in the non‐recursive bivariate model to negatively correlated (−0.20) in the recursive model.

Succinivibrio are also the taxon with the estimated largest inhibitory effect on methane (from the structural equations model).

While some taxa, such as ciliate protozoa or Methanobrevibacter sp., increased the CH4 emissions …, others such as Succinivibrio sp. from Proteobacteria phylum decreased it

Looking at the paper that first described these bacteria (Bryan & Small 1955),  Succinivibrio were originally isolated from the cattle rumen, and their name is because ”they ferment glucose with the production of a large amount of succinic acid”. Bryant & Small made a fermentation experiment to see what came out, and it seems that the bacteria don’t produce methane:


This is also in line with a rRNA sequencing study of high and low methane emitting cows (Wallace & al 2015) that found lower Succinivibrio abundance in high methane emitters.

We may speculate that Succinivibrio species could be involved in diverting energy from methanogens, and thus reducing methane emissions. If that is true, then the structural equations model estimate (larger genetic negative correlation between Succinivibrio abundance and methane) might be better than one from the animal model.

Finally, while I’m on board with the a priori argument for using a structural equations model, as with other applications of causal modelling (gene networks, Mendelian randomisation etc), it might be dangerous to consider only parts of the system independently, where the microbes are likely to have causal effects on each other.


Saborío‐Montero, Alejandro, et al. ”Structural equation models to disentangle the biological relationship between microbiota and complex traits: Methane production in dairy cattle as a case of study.” Journal of Animal Breeding and Genetics 137.1 (2020): 36-48.

Wallace, R. John, et al. ”The rumen microbial metagenome associated with high methane production in cattle.” BMC genomics 16.1 (2015): 839.

Bryant, Marvin P., and Nola Small. ”Characteristics of two new genera of anaerobic curved rods isolated from the rumen of cattle.” Journal of bacteriology 72.1 (1956): 22.