# Theory in genetics

A couple of years ago, Brian Charlesworth published this essay about the value of theory in Heredity. He liked the same Sturtevant & Beadle quote that I liked.

Two outstanding geneticists, Alfred Sturtevant and George Beadle, started their splendid 1939 textbook of genetics (Sturtevant and Beadle 1939) with the remark ‘Genetics is a quantitative subject. It deals with ratios, 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’.

This statement may surprise the large number of contemporary workers in genetics, who use high-tech methods to analyse the functions of genes by means of qualitative experiments, and think in terms of the molecular mechanisms underlying the cellular or developmental processes, in which they are interested. However, for those who work on transmission genetics, analyse the genetics of complex traits, or study genetic aspects of evolution, the core importance of mathematical approaches is obvious.

Maybe this comes a surprise to some molecularly minded biologists; I doubt those working adjacent to a field called ”biophysics” or trying to understand what on Earth a ”t-distributed stochastic neighbor embedding” does to turn single-cell sequences into colourful blobs will have missed that there are quantitative aspects to genetics.

Anyways, Sturtevant & Beadle (and Charlesworth) are thinking of another kind of quantitation: they don’t just mean that maths is useful to geneticists, but of genetics as a particular kind of abstract science with its own concepts. It’s the distinction between viewing genetics as chemistry and genetics as symbols. In this vein, Charlesworth makes the distinction between statistical estimation and mathematical modelling in genetics, and goes on to give examples of the latter by an anecdotal history models of genetic variation, eventually going deeper into linkage disequilibrium. It’s a fun read, but it doesn’t really live up to the title by spelling out actual arguments for mathematical models, other than the observation that they have been useful in population genetics.

The hypothetical recurring reader will know this blog’s position on theory in genetics: it is useful, not just for theoreticians. Consequently, I agree with Charlesworth that formal modelling in genetics is a good thing, and that there is (and ought to be more of) constructive interplay between data and theory. I like that he suggests that mathematical models don’t even have to be that sophisticated to be useful; even if you’re not a mathematician, you can sometimes improve your understanding by doing some sums. He then takes that back a little by telling a joke about how John Maynard Smith’s paper on hitch-hiking was so difficult that only two researchers in the country could be smart enough to understand it. The point still stands. I would add that this applies to even simpler models than I suspect that Charlesworth had in mind. Speaking from experience, a few pseudo-random draws from a binomial distribution can sometimes clear your head about a genetic phenomenon, and while this probably won’t amount to any great advances in the field, it might save you days of fruitless faffing.

As it happens, I also recently read this paper (Robinaugh et al. 2020) about the value of formal theory in psychology, and in many ways, it makes explicit some things that Charlesworth’s essay doesn’t spell out, but I think implies: We want our scientific theories to explain ”robust, generalisable features of the world” and represent the components of the world that give rise to those phenomena. Formal models, expressed in precise languages like maths and computational models are preferable to verbal models, that express the structure of a theory in words, because these precise languages make it easier to deduce what behaviour of the target system that the model implies. Charlesworth and Robinaugh et al. don’t perfectly agree. For one thing, Robinaugh et al. seem to suggest that a good formal model should be able to generate fake data that can be compared to empirical data summaries and give explanations of computational models, while Charlesworth seems to view simulation as an approximation one sometimes has to resort to.

However, something that occurred to me while reading Charlesworth’s essay was the negative framing of why theory is useful. This is how Charlesworth recommends mathematical modelling in population genetic theory, by approvingly repeating this James Crow quote:

I hope to have provided evidence that the mathematical modelling of population genetic processes is crucial for a proper understanding of how evolution works, although there is of course much scope for intuition and verbal arguments when carefully handled (The Genetical Theory of Natural Selection is full of examples of these). There are many situations in which biological complexity means that detailed population genetic models are intractable, and where we have to resort to computer simulations, or approximate representations of the evolutionary process such as game theory to produce useful results, but these are based on the same underlying principles. Over the past 20 years or so, the field has moved steadily away from modelling evolutionary processes to developing statistical tools for estimating relevant parameters from large datasets (see Walsh and Lynch 2017 for a comprehensive review). Nonetheless, there is still plenty of work to be done on improving our understanding of the properties of the basic processes of evolution.

The late, greatly loved, James Crow used to say that he had no objection to graduate students in his department not taking his course on population genetics, but that he would like them to sign a statement that they would not make any pronouncements about evolution. There are still many papers published with confused ideas about evolution, suggesting that we need a ‘Crow’s Law’, requiring authors who discuss evolution to have acquired a knowledge of basic population genetics.

This is one of the things I prefer about Robinaugh et al.’s account: To them, theory is not mainly about clearing up confusion and wrongness, but about developing ideas by checking their consistency with data, and exploring how they can be modified to be less wrong. And when we follow Charlesworth’s anecdotal history of linked selection, it can be read as sketching a similar path. It’s not a story about some people knowing ”basic population genetics” and being in the right, and others now knowing it and being confused (even if that surely happens also); it’s about a refinement of models in the face of data — and probably vice versa.

If you listen to someone talking about music theory, or literary theory, they will often defend themselves against the charge that theory drains their domain of the joy and creativity. Instead, they will argue that theory helps you appreciate the richness of music, and gives you tools to invent new and interesting music. You stay ignorant of theory at your own peril, not because you risk doing things wrong, but because you risk doing uninteresting rehashes, not even knowing what you’re missing. Or something like that. Adam Neely (”Why you should learn music theory”, YouTube video) said it better. Now, the analogy is not perfect, because the relationship between empirical data and theory in genetics is such that the theory really does try to say true or false things about the genetics in a way that music theory (at least as practiced by music theory YouTubers) does not. I still think there is something to be said for theory as a tool for creativity and enjoyment in genetics.

Literature

Charlesworth, B. (2019). In defence of doing sums in genetics. Heredity, 123(1), 44-49.

Robinaugh, D., Haslbeck, J., Ryan, O., Fried, E. I., & Waldorp, L. (2020). Invisible hands and fine calipers: A call to use formal theory as a toolkit for theory construction. Paper has since been published in a journal, but I read the preprint.

# A model of polygenic adaptation in an infinite population

How do allele frequencies change in response to selection? Answers to that question include ”it depends”, ”we don’t know”, ”sometimes a lot, sometimes a little”, and ”according to a nonlinear differential equation that actually doesn’t look too horrendous if you squint a little”. Let’s look at a model of the polygenic adaptation of an infinitely large population under stabilising selection after a shift in optimum. This model has been developed by different researchers over the years (reviewed in Jain & Stephan 2017).

Here is the big equation for allele frequency change at one locus:

$\dot{p}_i = -s \gamma_i p_i q_i (c_1 - z') - \frac{s \gamma_i^2}{2} p_i q_i (q_i - p_i) + \mu (q_i - p_i )$

That wasn’t so bad, was it? These are the symbols:

• the subscript i indexes the loci,
• $\dot{p}$ is the change in allele frequency per time,
• $\gamma_i$ is the effect of the locus on the trait (twice the effect of the positive allele to be precise),
• $p_i$ is the frequency of the positive allele,
• $q_i$ the frequency of the negative allele,
• $s$ is the strength of selection,
• $c_1$ is the phenotypic mean of the population; it just depends on the effects and allele frequencies
• $\mu$ is the mutation rate.

This breaks down into three terms that we will look at in order.

# The directional selection term

$-s \gamma_i p_i q_i (c_1 - z')$

is the term that describes change due to directional selection.

Apart from the allele frequencies, it depends on the strength of directional selection $s$, the effect of the locus on the trait $\gamma_i$ and how far away the population is from the new optimum $(c_1 - z')$. Stronger selection, larger effect or greater distance to the optimum means more allele frequency change.

It is negative because it describes the change in the allele with a positive effect on the trait, so if the mean phenotype is above the optimum, we would expect the allele frequency to decrease, and indeed: when

$(c_1 - z') < 0$

this term becomes negative.

If you neglect the other two terms and keep this one, you get Jain & Stephan's "directional selection model", which describes behaviour of allele frequencies in the early phase before the population has gotten close to the new optimum. This approximation does much of the heavy lifting in their analysis.

# The stabilising selection term

$-\frac{s \gamma_i^2}{2} p_i q_i (q_i - p_i)$

is the term that describes change due to stabilising selection. Apart from allele frequencies, it depends on the square of the effect of the locus on the trait. That means that, regardless of the sign of the effect, it penalises large changes. This appears to make sense, because stabilising selection strives to preserve traits at the optimum. The cubic influence of allele frequency is, frankly, not intuitive to me.

# The mutation term

Finally,

$\mu (q_i - p_i )$

is the term that describes change due to new mutations. It depends on the allele frequencies, i.e. how of the alleles there are around that can mutate into the other alleles, and the mutation rate. To me, this is the one term one could sit down and write down, without much head-scratching.

# Walking in allele frequency space

Jain & Stephan (2017) show a couple of examples of allele frequency change after the optimum shift. Let us try to draw similar figures. (Jain & Stephan don’t give the exact parameters for their figures, they just show one case with effects below their threshold value and one with effects above.)

First, here is the above equation in R code:

pheno_mean <- function(p, gamma) {
sum(gamma * (2 * p - 1))
}

allele_frequency_change <- function(s, gamma, p, z_prime, mu) {
-s * gamma * p * (1 - p) * (pheno_mean(p, gamma) - z_prime) +
- s * gamma^2 * 0.5 * p * (1 - p) * (1 - p - p) +
mu * (1 - p - p)
}


With this (and some extra packaging; code on Github), we can now plot allele frequency trajectories such as this one, which starts at some arbitrary point and approaches an optimum:

Animation of alleles at two loci approaching an equilibrium. Here, we have two loci with starting frequencies 0.2 and 0.1 and effect size 1 and 0.01, and the optimum is at 0. The mutation rate is 10-4 and the strength of selection is 1. Animation made with gganimate.

# Resting in allele frequency space

The model describes a shift from one optimum to another, so we want want to start at equilibrium. Therefore, we need to know what the allele frequencies are at equilibrium, so we solve for 0 allele frequency change in the above equation. The first term will be zero, because

$(c_1 - z') = 0$

when the mean phenotype is at the optimum. So, we can throw away that term, and factor the rest equation into:

$(1 - 2p) (-\frac{s \gamma ^2}{2} p(1-p) + \mu) = 0$

Therefore, one root is $p = 1/2$. Depending on your constitution, this may or may not be intuitive to you. Imagine that you have all the loci, each with a positive and negative allele with the same effect, balanced so that half the population has one and the other half has the other. Then, there is this quadratic equation that gives two other equilibria:

$\mu - \frac{s\gamma^2}{2}p(1-p) = 0$
$\implies p = \frac{1}{2} (1 \pm \sqrt{1 - 8 \frac{\mu}{s \gamma ^2}})$

These points correspond to mutation–selection balance with one or the other allele closer to being lost. Jain & Stephan (2017) show a figure of the three equilibria that looks like a semicircle (from the quadratic equation, presumably) attached to a horizontal line at 0.5 (their Figure 1). Given this information, we can start our loci out at equilibrium frequencies. Before we set them off, we need to attend to the effect size.

# How big is a big effect? Hur långt är ett snöre?

In this model, there are big and small effects with qualitatively different behaviours. The cutoff is at:

$\hat{\gamma} = \sqrt{ \frac{8 \mu}{s}}$

If we look again at the roots to the quadratic equation above, they can only exist as real roots if

$\frac {8 \mu}{s \gamma^2} < 1$

because otherwise the expression inside the square root will be negative. This inequality can be rearranged into:

$\gamma^2 > \frac{8 \mu}{s}$

This means that if the effect of a locus is smaller than the threshold value, there is only one equilibrium point, and that is at 0.5. It also affects the way the allele frequency changes. Let us look at two two-locus cases, one where the effects are below this threshold and one where they are above it.

threshold <- function(mu, s) sqrt(8 * mu / s)

threshold(1e-4, 1)

[1] 0.02828427

With mutation rate of 10-4 and strength of selection of 1, the cutoff is about 0.028. Let our ”big” loci have effect sizes of 0.05 and our small loci have effect sizes of 0.01, then. Now, we are ready to shift the optimum.

The small loci will start at an equilibrium frequency of 0.5. We start the large loci at two different equilibrium points, where one positive allele is frequent and the other positive allele is rare:

get_equilibrium_frequencies <- function(mu, s, gamma) {
c(0.5,
0.5 * (1 + sqrt(1 - 8 * mu / (s * gamma^2))),
0.5 * (1 - sqrt(1 - 8 * mu / (s * gamma^2))))
}

(eq0.05 <- get_equilibrium_frequencies(1e-4, 1, 0.05))

[1] 0.50000000 0.91231056 0.08768944
get_equlibrium_frequencies(1e-4, 1, 0.01)

[1] 0.5 NaN NaN

# Look at them go!

These animations show the same qualitative behaviour as Jain & Stephan illustrate in their Figure 2. With small effects, there is gradual allele frequency change at both loci:

However, with large effects, one of the loci (the one on the vertical axis) dramatically changes in allele frequency, that is it’s experiencing a selective sweep, while the other one barely changes at all. And the model will show similar behaviour when the trait is properly polygenic, with many loci, as long as effects are large compared to the (scaled) mutation rate.

Here, I ran 10,000 time steps; if we look at the phenotypic means, we can see that they still haven’t arrived at the optimum at the end of that time. The mean with large effects is at 0.089 (new optimum of 0.1), and the mean with small effects is 0.0063 (new optimum: 0.02).

Let’s end here for today. Maybe another time, we can return how this model applies to actually polygenic architectures, that is, with more than two loci. The code for all the figures is on Github.

Literature

Jain, K., & Stephan, W. (2017). Modes of rapid polygenic adaptation. Molecular biology and evolution, 34(12), 3169-3175.

# Journal club of one: ”Versatile simulations of admixture and accurate local ancestry inference with mixnmatch and ancestryinfer”

Admixture is the future of every sub-field of genetics, just in case you didn’t know. Both in the wild and domestic animals, populations or even species sometimes cross. This causes different patterns of relatedness than in well-mixed populations. Often we want to estimate ”local ancestry”, that is: what source population a piece of chromosome in an individual originates from. It is one of those genetics problems that is made harder by the absence of any way to observe it directly.

This recent paper (Schumer et al 2020; preprint version, which I read, here) presents a method for simulating admixed sequence data, and a method for inferring local ancestry from it. It does something I like, namely to pair analysis with fake-data simulation to check methods.

The simulation method is a built from four different simulators:

1. macs (Chen, Majoram & Wall 2009), which creates polymorphism data under neutral evolution from a given population history. They use macs to generate starting chromosomes from two ancestral populations.

2. Seq-Gen (Rambaut & Grassly 1997). Chromosomes from macs are strings of 0s and 1s representing the state at biallelic markers. If you want DNA-level realism, with base composition, nucleotide substitution models and so on, you need something else. I don’t really follow how they do this. You can tell from the source code that they use the local trees that macs spits out, which Seq-Gen can then simulate nucleotides from. As they put it, the resulting sequence ”lacks other complexities of real genome sequences such as repetitive elements and local variation in base composition”, but it is a step up from ”0000110100”.

3. SELAM (Corbett-Detig & Jones 2016), which simulates admixture between populations with population history and possibly selection. Here, SELAM‘s role is to simulate the actual recombination and interbreeding to create the patterns of local ancestry, that they will then fill with the sequences they generated before.

4. wgsim, which simulates short reads from a sequence. At this point, mixnmatch has turned a set of population genetic parameters into fasta files. That is pretty cool.

On the one hand, building on tried and true tools seems to be the right thing to do, less wheel-reinventing. It’s great that the phylogenetic simulator Seq-Gen from 1997 can be used in a paper published in 2020. On the other hand, looking at the dependencies for running mixnmatch made me a little pale: seven different bioinformatics or population genetics softwares (not including the dependencies you need to compile them), R, Perl and Python plus Biopython. Computational genetics is an adventure of software installation.

They use the simulator to test the performance of a hidden Markov model for inferring local ancestry (Corbett-Detig & Nielsen 2017) with different population histories and settings, and then apply it to swordtail fish data. In particular, one needs to set thresholds for picking ”ancestry informative” (i.e. sufficiently differentiated) markers between the ancestral populations, and that depends on population history and diversity.

In passing, they use the estimate the swordtail recombination landscape:

We used the locations of observed ancestry transitions in 139 F2 hybrids that we generated between X. birchmanni and X. malinche … to estimate the recombination rate in 5 Mb windows. … We compared inferred recombination rates in this F2 map to a linkage disequilibrium based recombination map for X. birchmanni that we had previously generated (Schumer et al., 2018). As expected, we observed a strong correlation in estimated recombination rate between the linkage disequilibrium based and crossover maps (R=0.82, Figure 4, Supporting Information 8). Simulations suggest that the observed correlation is consistent with the two recombination maps being indistinguishable, given the low resolution of the F2 map (Supporting Information 8).