Simulating shared segments between relatives

A few months ago I saw this nice figure from Amy Williams of the number of DNA segments that are expected to be shared between relatives. I thought it would be fun to simulate segment sharing with AlphaSimR.

Because DNA comes in chromosomes that don’t break up and recombine that much, the shared DNA between relatives tends to come in long chunks — segments that are identical by descent. The distribution of segment lengths can sometimes be used to tell apart relationships that would otherwise give the same average (e.g., Yengo et al. 2019, Qiao et al. 2021).

But let’s not do anything sophisticated. Instead, we take three very simple pedigrees — anyone who’s taken introductory genetics will recognize these ones — and look at relationships between full-sibs, half-sibs and cousins. We’ll also look at the inbred offspring of matings between full-sibs, half-sibs and cousins to see that the proportion that they share between their two copies of the genome lines up with the expected inbreeding.

There won’t be any direct comparison to the values that Williams’ simulation, because it simulated more distant relationships than this, starting with cousins and then moving further away. This is probably more interesting, especially for human genealogical genetics.

The code is on GitHub if you wants to follow along.

The pedigrees

Here are the three pedigrees, drawn with the kinship2 package:

A pedigree, here, is really a table of individuals, where each column tells us their identifier, their parents, and optionally their sex, like this:

id, mother, father, sex
1, NA, NA, M
2, NA, NA, F
3, NA, NA, M
4, 2, 1, F
5, 2, 1, M
6, NA, NA, F
7, 4, 3, M
8, 6, 5, F
9, 8, 7, F

We can use GeneticsPed to check the relatedness and inbreeding if we don’t trust that I’ve entered the pedigrees right.


ped_fullsib <- read_csv("pedigrees/inbreeding_fullsib.txt")
ped_halfsib <- read_csv("pedigrees/inbreeding_halfsib.txt")
ped_cousin <- read_csv("pedigrees/inbreeding_cousin.txt")

inbreeding_ped <- function(ped) {



print(map(list(ped_fullsib, ped_halfsib, ped_cousin), inbreeding_ped))
   1    2    3    4    5 
0.00 0.00 0.00 0.00 0.25 

    1     2     3     4     5     6 
0.000 0.000 0.000 0.000 0.000 0.125 

     1      2      3      4      5      6      7      8      9 
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0625

Comparing haplotypes

We need some functions to compare haplotypes and individuals:


## Find shared segments between two haplotypes expressed as vectors
## map is a vector of marker positions

compare_haplotypes <- function(h1, h2, map) {
    sharing <- h1 == h2

    runs <- rle(sharing)
    end <- cumsum(runs$lengths)
    start <- c(1, end[-length(end)] + 1)

    segments <- tibble(start = start,
                       end = end,
                       start_pos = map[start],
                       end_pos = map[end],
                       segment_length = end_pos - start_pos,
                       value = runs$values)


We will have haplotypes of the variants that go together on a chromosome, and we want to find segments that are shared between them. We do this with a logical vector that tests each variant for equality, and then use the rle to turn this into run-length encoding. We extract the start and end position of the runs and then keep only the runs of equality.

Building on that function, we want to find the shared segments on a chromosome between two individuals. That is, we make all the pairwise comparisons between the haplotypes they carry and combine them.

## Find shared segments between two individuals (expressed as
## matrices of haplotypes) for one chromosome

compare_individuals_chr <- function(ind1, ind2, map) {

    h1_1 <- as.vector(ind1[1,])
    h1_2 <- as.vector(ind1[2,])

    h2_1 <- as.vector(ind2[1,])
    h2_2 <- as.vector(ind2[2,])

    sharing1 <- compare_haplotypes(h1_1, h2_1, map)
    sharing2 <- compare_haplotypes(h1_1, h2_2, map)
    sharing3 <- compare_haplotypes(h1_2, h2_1, map)
    sharing4 <- compare_haplotypes(h1_2, h2_2, map)

    bind_rows(sharing1, sharing2, sharing3, sharing4)

Finally, we use that function to compare individuals along all the chromosomes.

This function takes in a population and simulation parameter object from AlphaSimR, and two target individuals to be compared.

We use AlphaSimR‘s pullIbdHaplo function to extract tracked founder haplotypes (see below) and then loop over chromosomes to apply the above comparison functions.

## Find shared segments between two target individuals in a
## population

compare_individuals <- function(pop,
                                simparam) {

    n_chr <- simparam$nChr

    ind1_ix <- paste(target_individuals[1], c("_1", "_2"), sep = "")
    ind2_ix <- paste(target_individuals[2], c("_1", "_2"), sep = "")

    ibd <- pullIbdHaplo(pop,
                        simParam = simparam)

    map <- simparam$genMap
    loci_per_chr <- map_dbl(map, length)

    chr_ends <- cumsum(loci_per_chr)
    chr_starts <- c(1, chr_ends[-n_chr] + 1)

    results <- vector(mode = "list",
                      length = n_chr)

    for (chr_ix in 1:n_chr) {

        ind1 <- ibd[ind1_ix, chr_starts[chr_ix]:chr_ends[chr_ix]]
        ind2 <- ibd[ind2_ix, chr_starts[chr_ix]:chr_ends[chr_ix]]

        results[[chr_ix]] <- compare_individuals_chr(ind1, ind2, map[[chr_ix]])
        results[[chr_ix]]$chr <- chr_ix


(You might think it would be more elegant, when looping over chromosomes, to pull out the identity-by-descent data for each chromosome at a time. This won’t work on version 1.0.4 though, because of a problem with pullIbdHaplo which has been fixed in the development version.)

We use an analogous function to compare the haplotypes carried by one individual. See the details on GitHub if you’re interested.

Building the simulation

We are ready to run our simulation: This code creates a few founder individuals that will initiate the pedigree, and sets up a basic simulation. The key simulation parameter is to set setTrackRec(TRUE) to turn on tracking of recombinations and founder haplotypes.


## Set up simulation

founders <- runMacs(nInd = 10,
                    nChr = 25)

simparam <- SimParam$new(founders)


founderpop <- newPop(founders,
                     simParam = simparam)

To simulate a pedigree, we use pedigreeCross, a built-in function to simulate a given pedigree, and then apply our comparison functions to the resulting simulated population.

## Run the simulation for a pedigree one replicate

simulate_pedigree <- function(ped,
                              simparam) {
    pop <- pedigreeCross(founderPop = founderpop,
                         id = ped$id,
                         mother = ped$mother,
                         father = ped$father,
                         simParam = simparam)
    shared_parents <- compare_individuals(pop,
    shared_inbred <- compare_self(pop,
    list(population = pop,
         shared_segments_parents = shared_parents,
         shared_segments_self_inbred = shared_inbred)


First we can check how large proportion of the genome of our inbred individuals is shared between their two haplotypes, averaged over 100 replicates. That is, how much of the genome is homozygous identical by descent — what is their genomic inbreeding? It lines up with the expectation form pedigree: 0.25 for the half-sib pedigree, close to 0.125 for the full-sib pedigree and close to 0.0625 for the cousin pedigree. The proportion shared by the parents is, as it should, about double that.

      case inbred_self_sharing parent_sharing
  full-sib        0.25 (0.052)    0.5 (0.041)
  half-sib        0.13 (0.038)   0.25 (0.029)
    cousin       0.064 (0.027)   0.13 (0.022)

Table of the mean proportion of genome shared between the two genome copies in inbred individuals and between their parents. Standard deviations in parentheses.

This is a nice consistency check, but not really what we wanted. The point of explicitly simulating chromosomes and recombinations is to look at segments, not just total sharing.

With a little counting and summarisation, we can plot the distributions of segment lengths. The horizontal axis is the length of the segments expressed in centimorgan. The vertical axis is the number of shared
segments of this length or longer. Each line is a replicate.

If we look at the summaries (table below), full-sibs share on average 74 segments greater than 1 cM in length, half-sibs 37, and cousins 29.

In real data, short segments might be harder to detect, but because we’re using simulated fake data, we don’t have to worry about phasing errors or false positive sharing.

If we look only at long segments (> 20 cM), full-sibs share on average 46 segments, half-sibs 23, and cousins 13. (Also, similar to Williams’ simulations, none of the cousins simulated here had less than five long segments shared.)

  case     `1 cM`   `10 cM`  `20 cM`  `30 cM`  `40 cM`  
  full-sib 74 (5.2) 60 (4.2) 46 (3.6) 34 (4)   24 (3.8) 
  half-sib 37 (3.4) 30 (3.1) 23 (3.3) 17 (2.8) 13 (2.6) 
  cousin   29 (3.8) 20 (3.3) 13 (3.2) 7.6 (2)  4.3 (1.8)

Table of the mean number of shared segments of different minimum length. Standard deviations in parentheses.

We an also look at the average length of the segments shared, and note that while full-sibs and half-sibs differ in the number of segments, and total segment length shared (above), the length of individual segments is about the same:

  case     mean_length_sd
  full-sib 0.33 (0.032)  
  half-sib 0.34 (0.042)  
  cousin   0.21 (0.03)

Table of the mean length shared segments. Standard deviations in parentheses.


Williams’ simulation, using the ped-sim tool, had a more detailed model of recombination in the human genome, with different interference parameters for each chromosome, sex-specific recombination and so on. In that way, it is much more realistic.
We’re not modelling any one genome in particular, but a very generic genome. Each chromosome is 100 cM long for example; one can imagine that a genome with many short chromosomes would give a different distribution. This can be changed, though; the chromosome size is the easiest, if we just pick a species.


Yengo, L., Wray, N. R., & Visscher, P. M. (2019). Extreme inbreeding in a European ancestry sample from the contemporary UK population. Nature communications, 10(1), 1-11.

Qiao, Y., Sannerud, J. G., Basu-Roy, S., Hayward, C., & Williams, A. L. (2021). Distinguishing pedigree relationships via multi-way identity by descent sharing and sex-specific genetic maps. The American Journal of Human Genetics, 108(1), 68-83.

Estimating recent population history from linkage disequilibrium with GONE and SNeP

In this post, we will look at running two programs that infer population history — understood as changes in linkage disequilibrium over time — from genotype data. The post will chronicle running them on some simulated data; it will be light on theory, and light on methods evaluation.

Linkage disequilibrium, i.e. correlation between alleles at different genetic variants, breaks down over time when alleles are shuffled by recombination. The efficiency of that process depends on the distance between the variants (because variants close to each other on the same chromosome will recombine less often) and the population size (because more individuals means more recombinations). Those relationships mean that the strength of linkage disequilibrium at a particular distance between variants is related to the population size at a particular time. (Roughly, and assuming a lot.) There are several methods that make use of the relationship between effective population size, recombination rate and linkage disequilibrium to estimate population history.

The programs

The two programs we’ll look at are SNeP and GONE. They both first calculate different statistics of pairwise linkage disequilibrium between markers. SNeP groups pairs of markers into groups based on the distance between them, and estimates the effective population size for each group and how many generations ago each group represents. GONE goes further: it uses a formula for the expected linkage disequilibrium from a sequence of effective population sizes and a genetic algorithm to find such a sequence that fits the observed linkage disequilibrium at different distances.

Paper about GONE: Santiago, E., Novo, I., Pardiñas, A. F., Saura, M., Wang, J., & Caballero, A. (2020). Recent demographic history inferred by high-resolution analysis of linkage disequilibrium. Molecular Biology and Evolution, 37(12), 3642-3653.

Paper about SNeP: Barbato, M., Orozco-terWengel, P., Tapio, M., & Bruford, M. W. (2015). SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data. Frontiers in genetics, 6, 109.

These methods are suited for estimating recent population history in single closed populations. There are other methods, e.g. the Pairwise Markovian coealescent and methods based on Approximate Bayesian Computation, that try to reach further back in time or deal with connected populations.

(Humorously, barring one capitalisation difference, GONE shares it’s name with an unrelated program related to effective population sizes, GONe … There are not enough linkage disequilibrium puns to go around, I guess.)

Some fake data

First, let us generate some fake data to run the programs on. We will use the Markovian coalescent simulator MaCS inside AlphaSimR. That is, we won’t really make use of any feature of AlphaSimR except that it’s a convenient way to run MaCS.

There is a GitHub repo if you want to follow along.

We simulate a constant population, a population that decreased in size relatively recently, a population that increased in size recently, and a population that decreased in longer ago. The latter should be outside of what these methods can comfortably estimate. Finally, let’s also include a population that has migration from an other (unseen) population. Again, that should be a case these methods struggle with.

Simulated true population histories. Note that the horizontal axis is generations ago, meaning that if you read left to right, it runs backwards in time. This is typical when showing population histories like this, but can be confusing. Also not the different scales on the horizontal axis.


## Population histories

recent_decrease <- tibble(generations = c(1, 50, 100, 150),
                          Ne = c(1000, 1500, 2000, 3000))

recent_increase <- tibble(generations = c(1, 50, 100, 150),
                          Ne = c(3000, 2000, 1500, 1000))

ancient_decrease <- tibble(generations = recent_decrease$generations + 500,
                           Ne = recent_decrease$Ne)

We can feed these population histories (almost) directly into AlphaSimR’s runMacs2 function. The migration case is a little bit more work because we will to modify the command, but AlphaSimR still helps us. MaCS takes a command line written using the same arguments as the paradigmatic ms program. The runMacs2 function we used above generates the MaCS command line for us; we can ask it to just return the command for us to modify. The split argument tells us that we want two populations that split 100 generations ago.

runMacs2(nInd = 100,
         Ne = recent_decrease$Ne[1],
         histGen = recent_decrease$generations[-1],
         histNe = recent_decrease$Ne[-1],
         split = 100,
         returnCommand = TRUE)

The resulting command looks like this:

"1e+08 -t 1e-04 -r 4e-05 -I 2 100 100  -eN 0.0125 1.5 -eN 0.025 2 -eN 0.0375 3 -ej 0.025001 2 1"

The first part is the number of basepairs on the chromosome, -t flag is for the population mutation rate \theta = 4 N_e \mu, -r for the recombination rate (also multiplied by four times the effective population size). The -eN arguments change the population size, and the -ej argument is for splitting and joining populations.

We can check that these numbers make sense: The population mutation rate of 10-4 is the typical per nucleotide mutation rate of 2.5 × 10-8 multiplied by four times the final population size of 1000. The scaled recombination rate of 4 × 10-5 is typical per nucleotide recombination rate of 10-8 multiplied by the same.

The population size changes (-eN arguments) are of the format scaled time followed by a scaling of the final population size. Times are scaled by four times the final population size, again. This means that 0.0125 followed by 1.5 refers to that 4 × 0.0125 × 1000 = 50 generations ago population size was 1.5 times the final population size, namely 1500. And so on.

-I (I assume for ”island” as in the ”island model”) sets up the two populations, each with 100 individuals and a migration rate between them. We modify this by setting it to 200 for the first population (because we want diploid individuals, so we need double the number of chromosomes) and 0 for the other; that is, this ghost population will not be observed, only used to influence the first one by migration. Then we set the third parameter, that AlphaSimR has not used: the migration rate. Again, this is expressed as four times the final population size, so for a migration rate of 0.05 we put 200.

Now we can run all cases to generate samples of 100 individuals.

migration_command <- "1e+08 -t 1e-04 -r 4e-05 -I 2 200 0 200  -eN 0.0125 1.5 -eN 0.025 2 -eN 0.0375 3 -ej 0.025001 2 1"

pops <- list(pop_constant = runMacs2(nInd = 100,
                                     nChr = 5,
                                     histNe = NULL,
                                     histGen = NULL,
                                     Ne = 1000),
             pop_recent = runMacs2(nInd = 100,
                                   nChr = 5,
                                   Ne = recent_decrease$Ne[1],
                                   histGen = recent_decrease$generations[-1],
                                   histNe = recent_decrease$Ne[-1]),
             pop_increase = runMacs2(nInd = 100,
                                     nChr = 5,
                                     Ne = recent_increase$Ne[1],
                                     histGen = recent_increase$generations[-1],
                                     histNe = recent_increase$Ne[-1]),
             pop_ancient = runMacs2(nInd = 100,
                                    nChr = 5,
                                    Ne = ancient_decrease$Ne[1],
                                    histGen = ancient_decrease$generations[-1],
                                    histNe = ancient_decrease$Ne[-1]),
             pop_migration = runMacs(nInd = 100,
                                     nChr = 5,
                                     manualCommand = migration_command,
                                     manualGenLen = 1))

Both GONE and SNeP work with text files in Plink ped/map format. Look in the repository if you want to see the not too exciting details of how we extract 10 000 markers and save them to Plink format together with their positions.

Running GONE

GONE source code and binaries are found in their GitHub repository, which we can clone or simply download from the web. As I’m running on Linux, we will use the binaries in the Linux subfolder. Before doing anything else, we will have to set the permissions for each of the binaries stored in the PROGRAMMES directory, with chmod u+x to make them executable.

GONE consists of a set of binaries and a bash script that runs them in order. As the as the script assumes it’s always running from the directory where you put GONE and always writes the output files into the same directory, the easiest way to handle it is to duplicate the entire GONE directory for every run. This also duplicates the INPUT_PARAMETERS_FILE file that one would modify to change settings. Then, invoking GONE with default settings would be as simple as opening a terminal, moving to the working directory and running the script, feeding it the base name of the Plink file:

./ pop_constant

Thus, we write a prep script like this that copies the entire folder, puts the data into it, and then runs GONE:


## Gone needs all the content of the operating system-specific subfolder to be copied
## into a working directory to run from. Therefore we create the "gone" directory
## and copy in the Linux version of the software from the tools directory.

mkdir gone

cd gone

cp -r ../tools/GONE/Linux/* .

## Loop over all the cases and invoke the GONE runscript. Again, because GONE
## needs the data to be in the same directory, we copy the data files into the
## working directory.

for CASE in pop_constant pop_recent pop_ancient pop_migration pop_increase; do

  cp ../simulation/${CASE}.* .
  ./ ${CASE}

GONE puts its output files (named with the prefixes Output_Ne_, Output_d2_ and OUTPUT_ followed by the base name of the input files) in the same directory. The most interesting is Output_Ne_ which contains the estimates and the all caps OUTPUT_ file that contains logging information about the run.

Estimates look like this:

Ne averages over 40 independent estimates.
Generation      Geometric_mean
1       1616.29
2       1616.29
3       1616.29
4       1616.29
5       1291.22
6       1221.75
7       1194.16
8       1157.95

And the log looks like this:


-0.009012       Hardy-Weinberg deviation (sample)
-0.003987       Hardy-Weinberg deviation (population)

 NIND(real sample)=100
 F_dev_HW (sample)=-0.009017
 F_dev_HW (pop)=-0.003992
 Genetic distances available in map file

I will now discuss a couple of issues I ran into. Note, this should not be construed as any kind of criticism of the programs or their authors. Everyone is responsible for their own inability to properly read manuals; I just leave them here in case they are helpful to someone else.

  • If you forget to set the permissions of the binaries, the error message will look like this:/
./ line 96: ./PROGRAMMES/MANAGE_CHROMOSOMES2: Permission denied
cp: cannot stat 'chromosome*': No such file or directory
bash: ./PROGRAMMES/LD_SNP_REAL3: Permission denied
  • Whereas Plink allows various different kinds of allele symbols in .ped files, GONE does not like allele codes that don’t look like A, C, G or T. The error message for other symbols looks like this:
Running GONE
 Format error in file outfileLD
 Format error in file outfileLD
 Format error in file outfileLD
 Format error in file outfileLD
 Format error in file outfileLD

Running SNeP

SNeP is only available as binaries on its Sourceforge page. Again, I’m using Linux binaries, so I downloaded the Linux binary from there and put it into a tools folder. The binary can be run from any directory, controlling the settings with command line flags. This would run SNeP on one of our simulated datasets, using the Haldane mapping function and correction of linkage disequilibrium values for sample size; these seem to be reasonable defaults:

./SNeP1.1 -ped simulation/pop_constant.ped -out snep/pop_constant -samplesize 2 -haldane

Thus, we write a run script like this:


## As opposed to GONE, SNeP comes as one binary that can run from any directory. We still create
## a working directory to keep the output files in.

mkdir snep

## We loop over all cases, reading the data from the "simulation" directory,
## and directing the output to the "snep" directory. The settings are to
## correct r-squared for sample size using the factor 2, and to use the Haldane
## mapping function. We direct the output to a text file for logging purposes.

for CASE in pop_constant pop_recent pop_ancient pop_migration pop_increase; do

  ./tools/snep/SNeP1.1 \
    -ped simulation/${CASE}.ped \
    -out snep/${CASE} \
    -samplesize 2 \
    -haldane > snep/${CASE}_out.txt

SNeP creates two ouptut files with the given prefix, one with the extension .NeAll with the estimates a log file with the suffix SNeP.log file. Above, we also save the standard output to another log file.

Estimates look like this:

GenAgo	Ne	dist	r2	r2SD	items
13	593	3750544	0.0165248	0.0241242	37756
15	628	3272690	0.017411	0.0256172	34416
18	661	2843495	0.0184924	0.0281098	30785
20	681	2460406	0.0200596	0.0310288	27618
24	721	2117017	0.0214468	0.0313662	24898

Issues I ran into:

  • There are two versions of SNeP on Sourceforge, version 1.1 and version 11.1. According to the readme, SNeP requires ”GCC 4.8.2 or newer”, which I guess is a way to say that it needs a recent enough version of GLIBC, the runtime library that includes the C++ standard library. SNeP 1.11 appears to depend on GLIBC 2.29, and because I have 2.27, I had to use SNeP version 1.1 instead. It might be possible that it doesn’t require the new version of glibc, and that one could build from source on my system — but the source is not available, so who knows; this is a problem with distributing software as binaries only.
  • You cannot specify just a file name for your output. It needs to be a directory followed by a file name; that is, ”snep_constant” is not okay, but ”./snep_constant” is. The error looks like this:
/tools/snep/SNeP1.1 -ped constant.ped -out snep_constant
*                SNeP               *
*                v1.1               *
*      *

Sat Dec 25 13:09:38 2021

The -out path provided gives an error, aborting.

The moment you’ve been waiting for

Let’s read the results and look at the estimates!

Estimates from GONE, with default settings, and SNeP, with reasonable setting used in the SNeP paper, applied to simulated data from different scenarios. Grey dots are estimates, and black lines the true simulated population history. The estimates go on for a while, but as per the GONE paper’s recommendations, we should not pay attention to estimates further back in time where these methods are not accurate. That is, we should probably concentrate on the first 100 generations or so.

Again, this isn’t a systematic methods evaluation, so this shouldn’t be taken as a criticism of the programs or methods. But we can note that in these circumstances, the estimates capture some features of the simulated population histories but gets other features quite wrong. GONE estimates a recent decrease in the scenario with a recent decrease, but not the further decreases before, and a recent increase when there was a recent increase, but overestimates its size by a few thousand. In the migration scenario, GONE shows the kind of artefact the authors tell us to expect, namely a drastic population size drop. Unexpectedly, though, it estimates a similar drastic drop in the scenario with constant population size. SNeP captures the recent decrease, but underestimates its size. In fact, SNeP estimates a similar shape in all scenarios, both for increased, decreased and constant populations.

The plotting code looks something like this (see GitHub for the details): we create the file names, read all the output files into the same data frame with map_dfr, label them by what case they belong to by joining with the data frame of labels (with inner_join from dplyr) and make a plot with ggplot2. The true_descriptions data frame contains the true population histories used to simulate the data.


cases <- tibble(case = c("pop_constant",
                description = c("Constant",
                                "Recent decrease",
                                "Ancient decrease",
                                "Recent decrease with migration",
                                "Recent increase"))

snep_file_names <- paste("snep/", cases$case, ".NeAll", sep = "")
names(snep_file_names) <- cases$case

snep <- map_dfr(snep_file_names, read_tsv, .id = "case")

snep_descriptions <- inner_join(snep, cases)
snep_descriptions$description <- factor(snep_descriptions$description,
                                        levels = cases$description)

## Make both a plot of the entire range of estimates, and a plot of the
## first 200 generations, which is the region where estimates are expected
## to be of higher quality
plot_snep_unconstrained <- ggplot() +
  geom_point(aes(x = GenAgo, y = Ne),
             data = snep_descriptions,
             colour = "grey") +
  facet_wrap(~ description,
             scale = "free_y",
             ncol = 2) +
  geom_segment(aes(x = start,
                   y = Ne,
                   xend = end,
                   yend = Ne),
               data = true_descriptions) +
  theme_bw() +
  theme(panel.grid = element_blank(),
        strip.background = element_blank()) +
  xlab("Generations ago")

plot_snep <- plot_snep_unconstrained +
  coord_cartesian(xlim = c(0, 200), ylim = c(0, 3000))

När kartan inte stämmer med terrängen gäller terrängen

When the results of a method don’t agree with the parameters of simulated data, the problem can either lie with the method or with the simulated data. And in this case, coalescent simulation is known to have problems with linkage disequilibrium. Here is a figure (Fig A, of appendix 2) of Nelson et al. (2020) who study the problems with coalescent simulations over long regions (such as the ones we simulated here).

The problem occurs for variants that are far apart (e.g. at around 100 = 1 expected recombinations between loci), where there should still be linkage disequilibrium, whereas the coalescent simulator (in this figure, ”msprime (Hudson)”) gives too low linkage disequilibrium. When we try to estimate effective population size late in population history, the methods rely on linkage equilibrium between markers far apart, and if they have too low linkage disequilibrium, the estimated population size should be too large. This does not appear to be what is going on here, but there might be more subtle problems with the simulated linkage disequilibrium that fools these methods; we could try something like Nelson et al.’s hybrid method or a forward simulation instead.


Barbato, M., Orozco-terWengel, P., Tapio, M., & Bruford, M. W. (2015). SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data. Frontiers in genetics, 6, 109.

Nelson, D., Kelleher, J., Ragsdale, A. P., Moreau, C., McVean, G., & Gravel, S. (2020). Accounting for long-range correlations in genome-wide simulations of large cohorts. PLoS genetics, 16(5), e1008619.

Santiago, E., Novo, I., Pardiñas, A. F., Saura, M., Wang, J., & Caballero, A. (2020). Recent demographic history inferred by high-resolution analysis of linkage disequilibrium. Molecular Biology and Evolution, 37(12), 3642-3653.

Using R: plyr to purrr, part 1

This is the second post about my journey towards writing more modern Tidyverse-style R code; here is the previous one. We will look at the common case of taking subset of data out of a data frame, making some complex R object from them, and then extracting summaries from those objects.

More nostalgia about plyr

I miss the plyr package. Especially ddply, ldply, and dlply, my favourite trio of R functions of all times. Yes, the contemporary Tidyverse package dplyr is fast and neat. And those plyr functions operating on arrays were maybe overkill; I seldom used them. But plyr was so smooth, so beautiful, and — after you’ve bashed your head against it for some time and it changed your mind — so intuitive. The package still exists, but it’s retired, and we shouldn’t keep writing R code like it’s 2009, should we?

I used to like to do something like this: take a data frame, push it through a function that returns some complex object, store those objects in a list, and then map various functions over that list to extract the parts I care about. What is the equivalent in the new idiom? To do the same thing but with the purrr package, of course! purrr replaces the list-centric parts of plyr, while dplyr covers data frame-centric summarisation, mutation and so on.

For this example we will be using the lm function on subsets of data and store the model object. It’s the simple case that everyone reaches for to demonstrate these features, but it’s a bit dubious. If you find yourself fitting a lot of linear models to subsets, maybe there are other models you should think about Especially here, when the fake data just happens to come from a multilevel model with varying intercepts … But in this case, let’s simulate a simple linear regression and look at the coefficients we get out.


n_groups <- 10
group_sizes <- rpois(n_groups, 10)
n <- sum(group_sizes)

fake_data <- tibble(id = 1:n,
                    group = rep(1:n_groups,
                                times = group_sizes),
                    predictor = runif(n, 0, 1))
group_intercept <- rnorm(n_groups, 0, 1)

fake_data$response <- fake_data$predictor * 10 +
  group_intercept[fake_data$group] +

And here is the plyr code: First, dlply takes us from a data frame, splitting it by group, to a list of linear models. Then, ldply takes us from the list of models to a data frame of coefficients. tidy is a function from the wonderful broom package which extracts the same information as you would get in the rather unwieldy object from summary(lm), but as a data frame.


fit_model <- function(data) {
  lm(response ~ predictor, data)

models <- dlply(fake_data,
result <- ldply(models, tidy)

This is what the results might looks like. Notice how ldply adds the split labels nicely into the group column, so we know which rows came from which subset.

   group        term   estimate std.error  statistic      p.value
1      1 (Intercept) -0.2519167 0.5757214 -0.4375670 6.732729e-01
2      1   predictor 10.6136902 1.0051490 10.5593207 5.645878e-06
3      2 (Intercept)  3.1528489 0.6365294  4.9531864 7.878498e-04
4      2   predictor  8.2075766 1.1458702  7.1627452 5.292586e-05
5      3 (Intercept) -0.8103777 0.6901212 -1.1742542 2.786901e-01

split/map: The modern synthesis

If we pull out purrr, we can get the exact same table like so. The one difference is that we get a tibble (that is, a contemporary, more well-behaved data frame) out of it instead of a base R data.frame.


models <- map(split(fake_data,
result <- map_df(models,
                 .id = "group")
# A tibble: 80 x 6
   group term        estimate std.error statistic  p.value
 1 1     (Intercept)     1.67     0.773      2.16 6.32e- 2
 2 1     predictor       8.67     1.36       6.39 2.12e- 4
 3 2     (Intercept)     4.11     0.566      7.26 4.75e- 5
 4 2     predictor       8.19     1.11       7.36 4.30e- 5
 5 3     (Intercept)    -7.50     0.952     -7.89 9.99e- 5
 6 3     predictor      11.5      1.75       6.60 3.03e- 4
 7 4     (Intercept)   -19.8      0.540    -36.7  7.32e-13
 8 4     predictor      11.5      0.896     12.8  5.90e- 8
 9 5     (Intercept)   -12.4      1.03     -12.0  7.51e- 7
10 5     predictor       9.69     1.82       5.34 4.71e- 4
# … with 70 more rows

First, the base function split lets us break the data into subsets based on the values of a variable, which in this case is our group variable. The output of this function is a list of data frames, one for each group.

Second, we use map to apply a function to each element of that list. The function is the same modelling function that we used above, which shoves the data into lm. We now have our list of linear models.

Third, we apply the tidy function to each element of that list of models. Because we want the result to be one single data frame consolidating the output from each element, we use map_df, which will combine the results for us. (If we’d just use map again, we would get a list of data frames.) The .id argument tells map to add the group column that indicates what element of the list of models each row comes from. We want this to be able to identify the coefficients.

If we want to be fancy, we can express with the Tidyverse-related pipe and dot notation:


result <- fake_data %>%
  split(.$group) %>%
  map(fit_model) %>%
  map_df(tidy, .id = "group")

Nesting data into list columns

This (minus the pipes) is where I am at in most of my R code nowadays: split with split, apply with map and combine with map_dfr. This works well and looks neater than the lapply/Reduce solution discussed in part 0.

We can push it a step further, though. Why take the linear model out of the data frame, away from its group labels any potential group-level covariates — isn’t that just inviting some kind of mix-up of the elements? With list columns, we could store the groups in a data frame, keeping the data subsets and any R objects we generate from them together. (See Wickham’s & Grolemund’s R for Data Science for a deeper case study of this idea.)


fake_data_nested <- nest(group_by(fake_data, group),
                         data = c(id, predictor, response))

fake_data_models <- mutate(fake_data_nested,
                           model = map(data,
                           estimates = map(model,

result <- unnest(fake_data_models, estimates)

First, we use the nest function to create a data frame where each row is a group, and the ”data” column contains the data for that subset.

Then, we add two list columns to that data frame: our list of models, and then our list of data frames with the coefficients.

Finally, we extract the estimates into a new data frame with unnest. The result is the same data frame of coefficients and statistics, also carrying along the data subsets, linear models and coefficents.

The same code with pipes:

fake_data %>% 
  group_by(group) %>% 
  nest(data = c(id, predictor, response)) %>% 
  mutate(model = map(data, fit_model),
         estimates = map(model, tidy)) %>% 
  unnest(estimates) -> ## right way assignment just for kicks

I’m still a list column sceptic, but I have to concede that this is pretty elegant. It gets the job done, it keeps objects that belong together together so that there is no risk of messing up the order, and it is not that much more verbose. I especially like that we can run the models and extract the coefficients in the same mutate call.

Coda: mixed model

Finally, I can’t resist comparing the separate linear models to a linear mixed model of all the data.

We use lme4 to fit a varying-intercept model, a model that puts the same coefficient on the slope between the response and predictor in each group, but allows the intercepts to vary between groups, assuming them to be drawn from the same normal distribution. We put the coefficients from the linear models fit in each group separately and the linear mixed model together in the same data frame to be able to plot them.


model <- lmer(response ~ (1|group) + predictor,

lm_coef <- pivot_wider(result,
                       names_from = term,
                       values_from = estimate,
                       id_cols = group)

lmm_coef <- cbind(group = levels(model@flist$group),

model_coef <- rbind(transform(lm_coef, model = "lm"),
                    transform(lmm_coef, model = "lmm"))

colnames(model_coef)[2] <- "intercept"

ggplot() +
  geom_point(aes(x = predictor,
                 y = response,
                 colour = factor(group)),
             data = fake_data) +
  geom_abline(aes(slope = predictor,
                  intercept = intercept,
                  colour = factor(group),
                  linetype = model),
              data = model_coef) +
  theme_bw() +
  theme(panel.grid = element_blank())

As advertised, the linear mixed model has the same slope in every group, and intercepts pulled closer to the mean. Now, we know that this is a good model for these fake data because we created them, and in the real world, that is obviously not the case … The point is: if we are going to fit a more complex model of the whole data, we want to be able to explore alternatives and plot them together with the data. Having elegant ways to transform data frames and summarise models at our disposal makes that process smoother.

A plot of genes on chromosomes

Marta Cifuentes and Wayne Crismani asked on Twitter if there is a web tool similar to the Arabidopsis Chromosome Map Tool that makes figures of genes on chromosomes for humans. This will not really be an answer to the question — not a web tool, not conveniently packaged — but I thought that would be a nice plot to make in R with ggplot2. We will use the ggrepel package to help with labelling, and get information from the Ensembl REST API with httr and jsonlite.

The plot and the final code to generate it

Below I will go through the functions that get us there, but here is the end product. The code is on GitHub as usual.

## Some Ensembl genes to test with

ensembl_genes <- c("ENSG00000125845", ## BMP2
                   "ENSG00000181690", ## PLAG1
                   "ENSG00000177508", ## IRX3
                   "ENSG00000140718") ## FTO

chr_sizes <- get_chromosome_sizes_from_ensembl(species = "homo_sapiens")

coords <- get_coordinates_from_ensembl(ensembl_genes)

plot_genes_test <- plot_genes(coords,

We will use Ensembl and access the genes via Ensembl Gene IDs. Here, I’ve looked up the Ensembl Gene IDs for four genes I like (in humans).

We need to know how long human chromosomes are in order to plot them, so we have a function for that; we also need to get coordinates for the genes, and we have a function for that. They are both below. These functions call up the Ensembl REST API to get the data from the Ensembl database.

Finally, there is a plotting function that takes the coordinates and the chromosome sizes as input and return a ggplot2 plot.

Getting the data out of the Ensembl REST API

Now, starting from the top, we will need to define those functions to interact with the Ensembl REST API. This marvellous machine allows us to get data out of the Ensembl database over HTTP, writing our questions in the URL. It is nicely described with examples from multiple languages on the Ensembl REST API website.

An alternative to using the REST API would be to download gene locations from BioMart. This was my first thought. BioMart is more familiar to me than the REST API, and it also has the nice benefit that it is easy to download every gene and store it away for the future. However, there isn’t a nice way to get chromosome lengths from BioMart, so we would have to download them from somewhere else. This is isn’t hard, but I thought using the REST API for both tasks seemed nicer.

## Plot showing the location of a few genomes on chromosomes


## Get an endpoint from the Ensembl REST api and return parsed JSON

get_from_rest_api <- function(endpoint_string,
                              server = "") {
  rest <- GET(paste(server, endpoint_string, sep = ""),

This first function gets content by sending a request, checking whether it worked (and stopping with an error if it didn’t), and then unpacking the content into an R object.

## Get chromosomes sizes from the Ensembl REST API

get_chromosome_sizes_from_ensembl <- function(species) {

  json <- get_from_rest_api(paste("info/assembly/", species, sep = ""))

  data.frame(name = as.character(json$top_level_region$name),
             length = as.numeric(json$top_level_region$length),
             stringsAsFactors = FALSE)

This second function asks for the genome assembly information for a particular species with the GET info/assembly/:species endpoint, and extracts the chromosome lengths into a data frame. The first part of data gathering is done, now we just need the coordinates fort the genes of interest.

## Get coordinates from Ensembl ids

get_coordinates_from_ensembl <- function(ensembl_ids) {
          function(ei) {
            json <- get_from_rest_api(paste("lookup/id/", ei, sep = ""))
            data.frame(position = (json$start + json$end)/2,
                       chr = json$seq_region_name,
                       display_name = json$display_name,
                       stringsAsFactors = FALSE)

This function asks for the gene information for each gene ID we’ve given it with the GET lookup/id/:id endpoint, and extracts the rough position (mean of start and end coordinate), chromosome name, and the ”display name”, which in the human case will be a gene symbol. (For genes that don’t have a gene symbol, we would need to set up this column ourselves.)

At this point, we have the data we need in two data frames. That means it’s time to make the plot.

Plotting code

We will build a plot with two layers: first the chromosomes (as a geom_linerange) and then the gene locations (as a geom_text_repel from the ggrepel package). The text layer will move the labels around so that they don’t overlap even when the genes are close to each other, and by setting the nudge_x argument we can move them to the side of the chromosomes.

Apart from that, we change the scale to set he order of chromosomes and reverse the scale of the y-axis so that chromosomes start at the top of the plot.

The function returns a ggplot2 plot object, so one can do some further customisation after the fact — but for some features one would have to re-write things inside the function.

plot_genes <- function(coordinates,
                       chromosome_sizes) {

  ## Restrict to chromosomes that are in data  
  chrs_in_data <-
    chromosome_sizes[chromosome_sizes$name %in% coordinates$chr,]
  chr_order <- order(as.numeric(chrs_in_data$name))
  ggplot() +
    geom_linerange(aes(x = name,
                       ymin = 1,
                       ymax = length/1e6),
                   size = 2,
                   colour = "grey",
                   data = chrs_in_data) +
    geom_text_repel(aes(x = chr,
                        y = position/1e6,
                        label = display_name),
                    nudge_x = 0.33,
                    data = coordinates) +
    scale_y_reverse() +
    ## Fix ordering of chromosomes on x-axis
    scale_x_discrete(limits = chrs_in_data$name[chr_order],
                     labels = chrs_in_data$name[chr_order]) +
    theme_bw() +
    theme(panel.grid = element_blank()) +
    xlab("Chromosome") +
    ylab("Position (Mbp)")

Possible extensions

One feature from the Arabidopsis inspiration that is missing here is the position of centromeres. We should be able to use the option ?bands=1 in the GET info/assembly/:species to get cytogenetic band information and separate p and q arms of chromosomes. This will not be universal though, i.e. not available for most species I care about.

Except to make cartoons of gene positions, I think this might be a nice way to make plots of genome regions with very course resolution, i.e. linkage mapping results, where one could add lines to show genomic confidence intervals, for example.

Convincing myself about the Monty Hall problem

Like many others, I’ve never felt that the solution to the Monty Hall problem was intuitive, despite the fact that explanations of the correct solution are everywhere. I am not alone. Famously, columnist Marilyn vos Savant got droves of mail from people trying to school her after she had published the correct solution.

The problem goes like this: You are a contestant on a game show (based on a real game show hosted by Monty Hall, hence the name). The host presents you with three doors, one of which contains a prize — say, a goat — and the others are empty. After you’ve made your choice, the host opens one of the doors, showing that it is empty. You are now asked whether you would like to stick to your initial choice, or switch to the other door. The right thing to do is to switch, which gives you 2/3 probability of winning the goat. This can be demonstrated in a few different ways.

A goat is a great prize. Image: Casey Goat by Pete Markham (CC BY-SA 2.0)

So I sat down to do 20 physical Monty Hall simulations on paper. I shuffled three cards with the options, picked one, and then, playing the role of the host, took away one losing option, and noted down if switching or holding on to the first choice would have been the right thing to do. The results came out 13 out of 20 (65%) wins for the switching strategy, and 7 out of 20 (35%) for the holding strategy. Of course, the Monty Hall Truthers out there must question whether this demonstration in fact happened — it’s too perfect, isn’t it?

The outcome of the simulation is less important than the feeling that came over me as I was running it, though. As I was taking on the role of the host and preparing to take away one of the losing options, it started feeling self-evident that the important thing is whether the first choice is right. If the first choice is right, holding is the right strategy. If the first choice is wrong, switching is the right option. And the first choice, clearly, is only right 1/3 of the time.

In this case, it was helpful to take the game show host’s perspective. Selvin (1975) discussed the solution to the problem in The American Statistician, and included a quote from Monty Hall himself:

Monty Hall wrote and expressed that he was not ”a student of statistics problems” but ”the big hole in your argument is that once the first box is seen to be empty, the contestant cannot exchange his box.” He continues to say, ”Oh, and incidentally, after one [box] is seen to be empty, his chances are no longer 50/50 but remain what they were in the first place, one out of three. It just seems to the contestant that one box having been eliminated, he stands a better chance. Not so.” I could not have said it better myself.

A generalised problem

Now, imagine the same problem with a number d number of doors, w number of prizes and o number of losing doors that are opened after the first choice is made. We assume that the losing doors are opened at random, and that switching entails picking one of the remaining doors at random. What is the probability of winning with the switching strategy?

The probability of picking the a door with or without a prize is:

\Pr(\text{pick right first}) = \frac{w}{d}

\Pr(\text{pick wrong first}) = 1 - \frac{w}{d}

If we picked a right door first, we have w – 1 winning options left out of d – o – 1 doors after the host opens o doors:

\Pr(\text{win\textbar right first}) = \frac{w - 1}{d - o - 1}

If we picked the wrong door first, we have all the winning options left:

\Pr(\text{win\textbar wrong first}) = \frac{w}{d - o - 1}

Putting it all together:

\Pr(\text{win\textbar switch}) = \Pr(\text{pick right first}) \cdot \Pr(\text{win\textbar right first}) + \\   + \Pr(\text{pick wrong first}) \cdot \Pr(\text{win\textbar wrong first}) = \\  = \frac{w}{d} \frac{w - 1}{d - o - 1} + (1 - \frac{w}{d}) \frac{w}{d - o - 1}

As before, for the hold strategy, the probability of winning is the probability of getting it right the first time:

\Pr(\text{win\textbar hold}) = \frac{w}{d}

With the original Monty Hall problem, w = 1, d = 3 and o = 1, and therefore

\Pr(\text{win\textbar switch}) = \frac{1}{3} \cdot 0 + \frac{2}{3} \cdot 1

Selvin (1975) also present a generalisation due to Ferguson, where there are n options and p doors that are opened after the choice. That is, w = 1, d = 3 and o = 1. Therefore,

\Pr(\text{win\textbar switch}) = \frac{1}{n} \cdot 0 + (1 - \frac{1}{n}) \frac{1}{n - p - 1} =  \frac{n - 1}{n(n - p - 1)}

which is Ferguson’s formula.

Finally, in Marilyn vos Savant’s column, she used this thought experiment to illustrate why switching is the right thing to do:

Here’s a good way to visualize what happened. Suppose there are a million doors, and you pick door #1. Then the host, who knows what’s behind the doors and will always avoid the one with the prize, opens them all except door #777,777. You’d switch to that door pretty fast, wouldn’t you?

That is, w = 1 still, d = 106 and o = 106 – 2.

\Pr(\text{win\textbar switch}) = 1 - \frac{1}{10^6}

It turns out that the solution to the generalised problem is that it is always better to switch, as long as there is a prize, and as long as the host opens any doors. One can also generalise it to choosing sets of more than one door. This makes some intuitive sense: as long as the host takes opens some doors, taking away losing options, switching should enrich for prizes.

Some code

To be frank, I’m not sure I have convinced myself of the solution to the generalised problem yet. However, using the code below, I did try the calculation for all combinations of total number of doors, prizes and doors opened up to 100, and in all cases, switching wins. That inspires some confidence, should I end up on a small ruminant game show.

The code below first defines a wrapper around R’s sampling function, which has a very annoying alternative behaviour when fed a vector of length one, to be able to build a computational version of my physical simulation. Finally, we have a function for the above formulae. (See whole thing on GitHub if you are interested.)

## Wrap sample into a function that avoids the "convenience"
## behaviour that happens when the length of x is one

sample_safer <- function(to_sample, n) {
  assert_that(n <= length(to_sample))
  if (length(to_sample) == 1)
  else {
    return(sample(to_sample, n))

## Simulate a generalised Monty Hall situation with
## w prizes, d doors and o doors that are opened.

sim_choice <- function(w, d, o) {
  ## There has to be less prizes than unopened doors
  assert_that(w < d - o) 
  wins <- rep(1, w)
  losses <- rep(0, d - w)
  doors <- c(wins, losses)
  ## Pick a door
  choice <- sample_safer(1:d, 1)
  ## Doors that can be opened
  to_open_from <- which(doors == 0)
  ## Chosen door can't be opened
  to_open_from <- to_open_from[to_open_from != choice]
  ## Doors to open
  to_open <- sample_safer(to_open_from, o)
  ## Switch to one of the remaining doors
  possible_switches <- setdiff(1:d, c(to_open, choice))
  choice_after_switch <- sample_safer(possible_switches , 1)
  result_hold <- doors[choice]
  result_switch <- doors[choice_after_switch]

## Formulas for probabilities

mh_formula <- function(w, d, o) {
  ## There has to be less prizes than unopened doors
  assert_that(w < d - o) 
  p_win_switch <- w/d * (w - 1)/(d - o - 1) +
                     (1 - w/d) * w / (d - o - 1) 
  p_win_hold <- w/d

## Standard Monty Hall

mh <- replicate(1000, sim_choice(1, 3, 1))
> mh_formula(1, 3, 1)
[1] 0.3333333 0.6666667
> rowSums(mh)/ncol(mh)
[1] 0.347 0.653

The Monty Hall problem problem

Guest & Martin (2020) use this simple problem as their illustration for computational model building: two 12 inch pizzas for the same price as one 18 inch pizza is not a good deal, because the 18 inch pizza contains more food. Apparently this is counter-intuitive to many people who have intuitions about inches and pizzas.

They call the risk of having inconsistencies in our scientific understanding because we cannot intuitively grasp the implications of our models ”The pizza problem”, arguing that it can be ameliorated by computational modelling, which forces you to spell out implicit assumptions and also makes you actually run the numbers. Having a formal model of areas of circles doesn’t help much, unless you plug in the numbers.

The Monty Hall problem problem is the pizza problem with a vengeance; not only is it hard to intuitively grasp what is going on in the problem, but even when presented with compelling evidence, the mental resistance might still remain and lead people to write angry letters and tweets.


Guest, O, & Martin, AE (2020). How computational modeling can force theory building in psychological science. Preprint.

Selvin, S (1975) On the Monty Hall problem. The American Statistician 29:3 p.134.

Showing a difference in mean between two groups, take 2

A couple of years ago, I wrote about the paradoxical difficulty of visualising a difference in means between two groups, while showing both the data and some uncertainty interval. I still feel like many ills in science come from our inability to interpret a simple comparison of means. Anything with more than two groups or a predictor that isn’t categorical makes things worse, of course. It doesn’t take much to overwhelm the intuition.

My suggestion at the time was something like this — either a panel that shows the data an another panel with coefficients and uncertainty intervals; or a plot that shows the with lines that represent the magnitude of the difference at the upper and lower limit of the uncertainty interval.

Alternative 1 (left), with separate panels for data and coefficient estimates, and alternative 2 (right), with confidence limits for the difference shown as vertical lines. For details, see the old post about these graphs.

Here is the fake data and linear model we will plot. If you want to follow along, the whole code is on GitHub. Group 0 should have a mean of 4, and the difference between groups should be two, and as the graphs above show, our linear model is not too far off from these numbers.


data <- data.frame(group = rep(0:1, 20))
data$response <- 4 + data$group * 2 + rnorm(20)

model <- lm(response ~ factor(group), data = data)
result <- tidy(model)

Since the last post, a colleague has told me about the Gardner-Altman plot. In a paper arguing that confidence intervals should be used to show the main result of studies, rather than p-values, Gardner & Altman (1986) introduced plots for simultaneously showing confidence intervals and data.

Their Figure 1 shows (hypothetical) systolic blood pressure data for a group of diabetic and non-diabetic people. The left panel is a dot plot for each group. The right panel is a one-dimensional plot (with a different scale than the right panel; zero is centred on the mean of one of the groups), showing the difference between the groups and a confidence interval as a point with error bars.

There are functions for making this kind of plot (and several more complex alternatives for paired comparisons and analyses of variance) in the R package dabestr from Ho et al. (2019). An example with our fake data looks like this:

Alternative 3: Gardner-Altman plot with bootstrap confidence interval.


bootstrap <- dabest(data,
                    idx = c("0", "1"),
                    paired = FALSE)

bootstrap_diff <- mean_diff(bootstrap)


While this plot is neat, I think it is a little too busy — I’m not sure that the double horizontal lines between the panels or the shaded density for the bootstrap confidence interval add much. I’d also like to use other inference methods than bootstrapping. I like how the scale of the right panel has the same unit as the left panel, but is offset so the zero is at the mean of one of the groups.

Here is my attempt at making a minimalistic version:

Alternative 4: Simplified Garner-Altman plot.

This piece of code first makes the left panel of data using ggbeeswarm (which I think looks nicer than the jittering I used in the first two alternatives), then the right panel with the estimate and approximate confidence intervals of plus/minus two standard errors of the mean), adjusts the scale, and combines the panels with patchwork.


ymin <- min(data$response)
ymax <- max(data$response)

plot_points_ga <- ggplot() +
  geom_quasirandom(aes(x = factor(group), y = response),
                   data = data) +
  xlab("Group") +
  ylab("Response") +
  theme_bw() +
  scale_y_continuous(limits = c(ymin, ymax))

height_of_plot <- ymax-ymin

group0_fraction <- (coef(model)[1] - ymin)/height_of_plot

diff_min <- - height_of_plot * group0_fraction

diff_max <- (1 - group0_fraction) * height_of_plot

plot_difference_ga <- ggplot() +
  geom_pointrange(aes(x = term, y = estimate,
                      ymin = estimate - 2 * std.error,
                      ymax = estimate + 2 * std.error),
                  data = result[2,]) +
  scale_y_continuous(limits = c(diff_min, diff_max)) +
  ylab("Difference") +
  xlab("Comparison") +

(plot_points_ga | plot_difference_ga) +
    plot_layout(widths = c(0.75, 0.25))


Gardner, M. J., & Altman, D. G. (1986). Confidence intervals rather than P values: estimation rather than hypothesis testing. British Medical Journal

Ho, J., Tumkaya, T., Aryal, S., Choi, H., & Claridge-Chang, A. (2019). Moving beyond P values: data analysis with estimation graphics. Nature methods.

A genetic mapping animation in R

Cullen Roth posted a beautiful animation of quantitative trait locus mapping on Twitter. It is pretty amazing. I wanted to try to make something similar in R with gganimate. It’s not going to be as beautiful as Roth’s animation, but it will use the same main idea of showing both a test statistic along the genome, and the underlying genotypes and trait values. For example, Roth’s animation has an inset scatterplot that appears above the peak after it’s been reached; to do that I think we would have to go a bit lower-level than gganimate and place our plots ourselves.

First, we’ll look at a locus associated with body weight in chickens (with data from Henriksen et al. 2016), and then a simulated example. We will use ggplot2 with gganimate and a magick trick for combining the two animations. Here are some pertinent snippets of the code; as usual, find the whole thing on Github.

LOD curve

We will use R/qtl for the linkage mapping. We start by loading the data file (Supplementary Dataset from Henriksen et al. 2016). A couple of individuals have missing covariates, so we won’t be able to use them. This piece of code first reads the cross file, and then removes the two offending rows.


## Read cross file
cross <- read.cross(format = "csv",
                    file = "41598_2016_BFsrep34031_MOESM83_ESM.csv")

cross <- subset(cross, ind = c("-34336", "-34233"))

For nice plotting, let’s restrict ourselves to fully informative markers (that is, the ones that tell the two founder lines of the cross apart). There are some partially informative ones in the dataset too, and R/qtl can get some information out of them thanks to genotype probability calculations with its Hidden Markov Model. They don’t make for nice scatterplots though. This piece of code extracts the genotypes and identifies informative markers as the ones that only have genotypes codes 1, 2 or 3 (homozygote, heterozygote and other homozygote), but not 5 and 6, which are used for partially informative markers.

## Get informative markers and combine with phenotypes for plotting

geno <-,
                                chr = 1))

geno_values <- lapply(geno, unique)
informative <- unlist(lapply(geno_values,
    function(g) all(g %in% c(1:3, NA))))

geno_informative <- geno[informative]

Now for the actual scan. We run a single QTL scan with covariates (sex, batch that the chickens were reared in, and principal components of genotypes), and pull out the logarithm of the odds (LOD) across chromosome 1. This piece of code first prepares a design matrix of the covariates, and then runs a scan of chromosome 1.

## Prepare covariates
pheno <- pull.pheno(cross)

covar <- model.matrix(~ sex_number + batch + PC1 + PC2 + PC3 + PC4 + 
                        PC5 + PC6 + PC7 + PC8 + PC9 + PC10,
                      na.action = na.exclude)[,-1]

scan <- scanone(cross = cross,
                pheno.col = "weight_212_days",
                method = "hk",
                chr = 1,
                addcovar = covar)

Here is the LOD curve along chromosome 1 that want to animate. The peak is the biggest-effect growth locus in this intercross, known as ”growth1”.

With gganimate, animating the points is as easy as adding a transition layer. This piece of code first makes a list of some formatting for our graphics, then extracts the LOD scores from the scan object, and makes the plot. By setting cumulative in transition_manual the animation will add one data point at the time, while keeping the old ones.


formatting <- list(theme_bw(base_size = 16),
                   theme(panel.grid = element_blank(),
                         strip.background = element_blank(),
                         legend.position = "none"),
                   scale_colour_manual(values =
                         c("red", "purple", "blue")))

lod <-
lod <- lod[informative,]
lod$marker_number <- 1:nrow(lod)

plot_lod <- qplot(x = pos,
                  y = lod,
                  data = lod,
                  geom = c("point", "line")) +
  ylab("Logarithm of odds") +
  xlab("Position") +
  formatting +
                    cumulative = TRUE)

Plot of the underlying data

We also want a scatterplot of the data. Here what a jittered scatterplot will look like at the peak. The horizontal axes are genotypes (one homozygote, heterozygote in the middle, the other homozygote) and the vertical axis is the body mass in grams. We’ve separated the sexes into small multiples. Whether to give both sexes the same vertical axis or not is a judgement call. The hens weigh a lot less than the roosters, which means that it’s harder to see patterns among them when put on the same axis as the roosters. On the other hand, if we give the sexes different axes, it will hide that difference.

This piece of code builds a combined data frame with informative genotypes and body mass. Then, it makes the above plot for each marker into an animation.


## Combined genotypes and weight
geno_informative$id <- pheno$id
geno_informative$w212 <- pheno$weight_212_days
geno_informative$sex <- pheno$sex_number

melted <- pivot_longer(geno_informative,
                       -c("id", "w212", "sex"))

melted <- na.exclude(melted)

## Add marker numbers
marker_numbers <- data.frame(name = rownames(scan),
                             marker_number = 1:nrow(scan),
                             stringsAsFactors = FALSE)

melted <- inner_join(melted, marker_numbers)

## Recode sex to words
melted$sex_char <- ifelse(melted$sex == 1, "male", "female")

plot_scatter <- qplot(x = value,
                     geom = "jitter",
                     y = w212,
                     colour = factor(value),
                     data = melted) +
  facet_wrap(~ factor(sex_char),
             ncol = 1) +
  xlab("Genotype") +
  ylab("Body mass") +
  formatting +

Combining the animations

And here is the final animation:

To put the pieces together, we use this magick trick (posted by Matt Crump). That is, animate the plots, one frame for each marker, and then use the R interface for ImageMagick to put them together and write them out.

gif_lod <- animate(plot_lod,
                   fps = 2,
                   width = 320,
                   height = 320,
                   nframes = sum(informative))

gif_scatter <- animate(plot_scatter,
                       fps = 2,
                       width = 320,
                       height = 320,
                       nframes = sum(informative))

## Magick trick from Matt Crump

mgif_lod <- image_read(gif_lod)
mgif_scatter <- image_read(gif_scatter)

new_gif <- image_append(c(mgif_lod[1], mgif_scatter[1]))
for(i in 2:sum(informative)){
  combined <- image_append(c(mgif_lod[i], mgif_scatter[i]))
  new_gif <- c(new_gif, combined)

image_write(new_gif, path = "out.gif", format = "gif")


Henriksen, Rie, et al. ”The domesticated brain: genetics of brain mass and brain structure in an avian species.” Scientific reports 6.1 (2016): 1-9.

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


\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) {
    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.


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

Twin lambs with different fathers

I just learned that in sheep, lambs from the same litter pretty often have different fathers, if the ewe has mated with different males. Berry et al. (2020) looked at sheep flocks on Irland that used more than one ram, and:

Of the 539 pairs of twins included in the analysis, 160 (i.e. 30%) were sired by two different rams. Of the 137 sets of triplets included in the analysis, 73 (i.e. 53%) were sired by more than one ram. Of the nine sets of quadruplets, eight were sired by two rams with the remaining litter being mono‐paternal. The overall incidence of heteropaternal superfecundation among litters was therefore 35%. Given that the incidence of multiple births in these flocks was 65%, heteropaternal superfecundation is expected to be relatively common in sheep; this is especially true as all but two of the litter‐mates were polyzygotic.

They figured this out by looking at individuals genotyped on SNP chips with tens of thousands of SNPs, with both lambs and the potential parents genotyped, so there can’t be much uncertainty in the assignment. You don’t need that many genotyped markers to get a confident assignment, and they don’t have that many rams to choose from.

Time for some Mendelian inheritance

Let’s simulate a situation like this: We set up a population and a marker panel for genotyping, split them into ewes and rams, and make some lambs.


founderpop <- runMacs(nInd = 105,
                      nChr = 10,
                      segSites = 100)

simparam <- SimParam$new(founderpop)

simparam$addSnpChip(nSnpPerChr = 100)

parents <- newPop(founderpop,
                  simParam = simparam)

ewes <- parents[1:100]
rams <- parents[101:105]

lambs <- randCross2(females = ewes,
                    males = rams,
                    nCrosses = 100,
                    nProgeny = 2,
                    simParam = simparam)

Now, if we have the genotypes of a lamb and its mother, how do we know the father? In this paper, they use exclusion methods: They compared the genotypes from the offspring with the parents and used inheritance rules to exclude rams that can't be the father because if they were, the offspring couldn't have the genotypes it had. Such breaking of regular inheritance patterns would be a "Mendelian inconsistency". This is the simplest kind of parentage assignment; fancier methods will calculate the probabilities of different genotypes, and allow you to reconstruct unknown relationships.

We can do this in two ways:

  • ignore the ewe’s genotypes and look for opposite homozygotes between lamb and ram, which are impossible regardless of the mother’s genotype
  • use both the ewe’s and ram’s genotypes to look what lamb genotypes are possible from a cross between them; this adds a few more cases where we can exclude a ram even if the lamb is heterozygous

To do the first, we count the number of opposite homozygous markers. In this genotype coding, 0 and 2 are homozygotes, and 1 is a heterozygous marker.

opposite_homozygotes <- function(ram,
                                 lamb) {
    sum(lamb == 0 & ram == 2) +
        sum(lamb == 2 & ram == 0)


When we include the ewe's genotype, there are a few more possible cases. We could enumerate all of them, but here is some R code to generate them. We first get all possible gametes from each parent, we combine the gametes in all possible combinations, and that gives us the possible lamb genotypes at that marker. If the lamb does, in fact, not have any of those genotypes, we declare the marker inconsistent. Repeat for all markers.

## Generate the possible gametes from a genotype

possible_gametes <- function(genotype) {

    if (genotype == 0) {
        gametes <- 0
    } else if (genotype == 1) {
        gametes <- c(0, 1)
    } else if (genotype == 2) {
        gametes <- 1


## Generate the possible genotypes for an offspring from
## parent possible gametes

possible_genotypes <- function(father_gametes,
                               mother_gametes) {

    possible_combinations <- expand.grid(father_gametes, mother_gametes)
    resulting_genotypes <- rowSums(possible_combinations)

## Check offspring genotypes for consistency with parent genotypes

mendelian_inconsistency <- function(ewe,
                                    lamb) {

    n_markers <- length(ewe)
    inconsistent <- logical(n_markers)

    for (marker_ix in 1:n_markers) {

        possible_lamb_genotypes <-

        inconsistent[marker_ix] <-
          !lamb[marker_ix] %in% possible_lamb_genotypes


(These functions assume that we have genotypes in vectors. The full code that extracts this information from the simulated data and repeats for all markers is on Gitbhub.)

Here is the outcome for a set of random lambs. The red dots point out the true fathers: because we have perfect genotype data simulated without errors, the true father always has 100% consistent markers.

If we compare how many markers are found inconsistent with the two methods, we get a pattern like this graph. Including the ewe’s genotypes lets us discover a lot more inconsistent markers, but in this case, with plentiful and error-free markers, it doesn’t make a difference.

Thresholds and errors

If I have any complaint with the paper, it’s that the parentage analysis isn’t really described in the methods. This is what it says:

Parentage testing using simple exclusion‐based approaches is determined by the proportion of opposing homozygotes in putative sire–offspring pairs.


Maternal verification was undertaken using the exclusion method (Double et al . 1997) comparing the genotype of the dam with that of her putative progeny and only validated dam–offspring pairs were retained. Genotypes of the mature rams in the flock were compared with all lambs born in that flock using the exclusion method.

(The reference is related to exclusion methods, but it’s describing how to calculate exclusion probabilities in a certain circumstance. That is, it’s part of a methodological conversation about exclusion methods, but doesn’t actually describe what they did.)

I don’t doubt that they did it well. Still, it would be interesting to know the details, because in the absence of perfect genotype data, they must have had some thresholds for error and some criterion for deciding which ram was right, even if it seemed obvious.


Berry, D. P., et al. ”Heteropaternal superfecundation frequently occurs in multiple‐bearing mob‐mated sheep.” Animal Genetics (2020).

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)