Introduction

ADMIXTOOLS is a widely used software package for calculating admixture statistics and testing population admixture hypotheses. However, although powerful and comprehensive, it is not exactly known for being user-friendly.

A typical ADMIXTOOLS workflow generally involves a combination of sed/awk/shell scripting and manual editing to create text configuration files. These are then passed as command-line arguments to one of ADMIXTOOLS commands, and control how to run a particular analysis. The results are then redirected to another file, which has to be parsed by the user to extract values of interest, often using command-line utilities again or (worse) by manual copy-pasting. Finally, the processed results are analysed in R, Excel or another program.

This workflow is quite cumbersome, especially if one wants to explore many hypotheses involving different combinations of populations. Most importantly, however, it makes it difficult to coduct reproducible research, as it is nearly impossible to construct fully automated “pipelines” that don’t require user intervention.

This R package makes it possible to perform all stages of ADMIXTOOLS analyses entirely from R, completely removing the need for “low level” configuration of individual ADMIXTOOLS programs.

Installation

Note that in order to use the admixr package, you need a working installation of ADMIXTOOLS! You can find installation instructions here. The software runs on Linux and macOS and these are the two systems that admixr is tested on.

Furthermore, you need to make sure that R can find ADMIXTOOLS binaries on the $PATH. If this is not the case, running library(admixr) will show a warning message with instructions on how to fix this.

To install admixr from GitHub you need to install the package devtools first. To do this, you can simply run (in R):

Furthermore, if you want to follow the examples in this vignette, you will need the tidyverse collection of packages for data manipulation manipulation and plotting, which you can install with:

You definitely don’t need tidyverse for working with admixr but it really makes data manipulation and plotting things much easier. I recommend at least giving it a shot.

When everything is ready, you can run the following code to load both packages:

A note about EIGENSTRAT format

ADMIXTOOLS software uses a peculiar set of genetic file formats, which may seem strange if you are used to working with VCF files. However, the basic idea remains the same: we want to store and access SNP data (REF/ALT alleles) of a set of individuals at a defined set of genomic positions.

EIGENSTRAT datasets always contain three kinds of files:

  • ind file - specifies a unique name, sex (optional - can be simply “U” for “undefined”) and label (such as population assignment) of each sample;
  • snp file - specifies the positions of SNPs, REF/ALT alleles etc.;
  • geno file - contains SNP data (one row per site, one character per sample) in a dense string-based format:
    • 0: individual is homozygous ALT
    • 1: individual is a heterozygote
    • 2: individual is homozygous REF
    • 9: missing data

Therefore, a VCF file is essentially a combination of all three files in a single package.

Let’s first download a small testing SNP dataset using a built-in admixr function download_data(). This function downloads the data into a temporary directory (you can specify the destination using its dirname argument, in case you want to place it elsewhere). In addition to this, the function returns a shared path/prefix of the whole dataset.

We can verify that there are indeed three files with this prefix:

Let’s look at their contents:

ind file

Chimp        U  Chimp
Mbuti        U  Mbuti
Yoruba       U  Yoruba
Khomani_San  U  Khomani_San
Han          U  Han
Dinka        U  Dinka
Sardinian    U  Sardinian
Papuan       U  Papuan
French       U  French
Vindija      U  Vindija
Altai        U  Altai
Denisova     U  Denisova

The first column (sample name) and the third column (population label) are generally not the same (sample names often have numerical suffixes to make them unique, etc.), but were kept the same here for simplicity. Importantly, when specifying population/sample names in admixr functions, the information in the third column is what is used. For example, if you have individuals such as “French1”, “French2”, “French3” in the first column of an ind file, all three sharing a “French” population label in the third column, specifying “French” in an admixr function will combine all three samples in a single population, instead of working with each individual separately.

snp file (first 3 lines)

1_832756    1   0.008328    832756  T   G
1_838931    1   0.008389    838931  A   C
1_843249    1   0.008432    843249  A   T

The columns of this file are, in order:

  1. SNP string ID
  2. chromosome
  3. genetic distance
  4. position along a chromosome
  5. reference allele
  6. alternative allele

geno file (first 3 lines)

902021012000
922221211222
922222122222

Each row is one genomic site, each column is a genotype in one individual.

Philosophy of admixr

The goal of admixr is to make ADMIXTOOLS analyses as trivial to run as possible, without having to worry about par/pop/left/right configuration files (as they are known in the jargon of ADMIXTOOLS) and other low-level details.

The only interface between you and ADMIXTOOLS is the following set of R functions:

Anything that would normally require dozens of lines of shell scripts can be often accomplished by running a single line of R code.

Internal representation of EIGENSTRAT data

As we saw above, each EIGENSTRAT dataset has three components. The way this data is internally represented in admixr is using a small S3 R object created using the eigenstrat constructor function. This function accepts the path and prefix of a trio of EIGENSTRAT snp/ind/geno files and returns an R object of the class EIGENSTRAT:

This object encapsulates the paths to all three EIGENSTRAT components and makes it easy to pass the data to different admixr functions.

The following couple of sections describe how to use the admixr package using simple example analyses.

\(D\) statistic

Let’s say we are interested in the following question: "Which populations today show evidence of Neanderthal admixture?

One way of looking at this is using the following D statistic: \[D(\textrm{present-day human W}, \textrm{African}, \textrm{Neanderthal}, \textrm{Chimp}).\]

\(D\) statistics are based on comparing the proportions of BABA and ABBA sites patterns observed in the data:

\[D = \frac{\textrm{# BABA sites - # ABBA sites}}{\textrm{# BABA sites + # ABBA sites}}.\]

Significant departure of \(D\) from zero indicates an excess of allele sharing between the first and the third population (positive \(D\)), or an excess of allele sharing between the second and the third population (negative \(D\)). If we get \(D\) that is not significantly different from 0, this suggests that the first and second populations form a clade, and don’t differ in the rate of allele sharing with the third population (this is the null hypothesis that the data is compared against).

Therefore, our \(D\) statistic above tests whether some modern humans today admixed with Neanderthals, which would increase their genetic affinity to this archaic group compared to Africans (whose ancestors never met Neanderthals).

Let’s save some population names first to make our code more concise:

Using the admixr package we can then calculate our \(D\) statistic simply by running:

result <- d(W = pops, X = "Yoruba", Y = "Vindija", Z = "Chimp", data = snps)

The result is a following data frame:

W X Y Z D stderr Zscore BABA ABBA nsnps
French Yoruba Vindija Chimp 0.0313 0.006933 4.510 15802 14844 487753
Sardinian Yoruba Vindija Chimp 0.0287 0.006792 4.222 15729 14852 487646
Han Yoruba Vindija Chimp 0.0278 0.006609 4.199 15780 14928 487925
Papuan Yoruba Vindija Chimp 0.0457 0.006571 6.953 16131 14721 487694
Khomani_San Yoruba Vindija Chimp 0.0066 0.006292 1.051 16168 15955 487564
Mbuti Yoruba Vindija Chimp -0.0005 0.006345 -0.074 15751 15766 487642

We can see that in addition to the specified population names, the output table contains additional columns:

  • D - \(D\) statistic value
  • stderr - standard error of the \(D\) statistic calculated using the block jackknife
  • Zscore - \(Z\)-zscore value (number of standard errors the \(D\) is from 0, i.e. how strongly do we reject the null hypothesis of no admixture)
  • BABA, ABBA - counts of observed site patterns
  • nsnps - number of SNPs used for a given calculation

While we could certainly make inferences by looking at the \(Z\)-scores, tables in general are not the best representation of this kind of data, especially as the number of samples increases. Instead, we can use the ggplot2 package to plot the results:

(If you want to more know about data analysis using R, including plotting with ggplot2, I highly recommend this free book.)

We can see that the \(D\) values for Africans are not significantly different from 0, meaning that the data is consistent with the null hypothesis of no Neanderthal ancestry in Africans. On the other hand, the test rejects the null hypothesis for all non-Africans today, suggesting that Neanderthals admixed with the ancestors of present-day non-Africans.

\(f_4\) statistic

An alternative way of addressing the previous question is to use the \(f_4\) statistic, which is very similar to \(D\) statistic and can be calculated as:

\[ f_4 = \frac{\textrm{# BABA sites - # ABBA sites}}{\textrm{# sites}}\]

Again, significant departure of \(f_4\) from 0 can be interpreted as evidence of gene flow.

To repeat the previous analysis using \(f_4\) statistic, we can run the function f4():

result <- f4(W = pops, X = "Yoruba", Y = "Vindija", Z = "Chimp", data = snps)
W X Y Z f4 stderr Zscore BABA ABBA nsnps
French Yoruba Vindija Chimp 0.001965 0.000437 4.501 15802 14844 487753
Sardinian Yoruba Vindija Chimp 0.001798 0.000427 4.209 15729 14852 487646
Han Yoruba Vindija Chimp 0.001746 0.000418 4.178 15780 14928 487925
Papuan Yoruba Vindija Chimp 0.002890 0.000417 6.924 16131 14721 487694
Khomani_San Yoruba Vindija Chimp 0.000436 0.000415 1.051 16168 15955 487564
Mbuti Yoruba Vindija Chimp -0.000030 0.000410 -0.074 15751 15766 487642

By comparing this result to the \(D\) statistic analysis above, we can make the same conclusions.

You might be wondering why we have both \(f_4\) and \(D\) if they are so similar. The truth is that \(f_4\) is, among other things, directly informative about the amount of shared genetic drift (“branch length”) between pairs of populations, which is a very useful theoretical property. Other than that, it’s often a matter of personal preference and so admixr provides functions for calculating both.

\(f_4\)-ratio statistic

Now we know that non-Africans today carry some Neanderthal ancestry. But what if we want to know how much Neanderthal ancestry they have? What proportion of their genomes is of Neanderthal origin? To answer questions like this, we can use the \(f_4\)-ratio statistic, which can be formulated in the following way (using a notation of Patterson et al., 2012, who formally described its properties).

\[f_4\textrm{-ratio} = \frac{f_4(A, O; X, C)}{f_4(A, O; B, C)}.\]

Using amidxr, we can calculate \(f_4\)-ratios using the following code (X being a vector of samples which we want to estimate the Neanderthal ancestry in):

result <- f4ratio(X = pops, A = "Altai", B = "Vindija", C = "Yoruba", O = "Chimp", data = snps)

The ancestry proportion (a number between 0 and 1) is given in the alpha column:

A B X C O alpha stderr Zscore
Altai Vindija French Yoruba Chimp 0.023774 0.006173 3.851
Altai Vindija Sardinian Yoruba Chimp 0.024468 0.006079 4.025
Altai Vindija Han Yoruba Chimp 0.022117 0.005901 3.748
Altai Vindija Papuan Yoruba Chimp 0.037311 0.005821 6.410
Altai Vindija Khomani_San Yoruba Chimp 0.003909 0.005923 0.660
Altai Vindija Mbuti Yoruba Chimp 0.000319 0.005721 0.056

We can make several observations:

  • Again, we don’t see any significant Neanderthal ancestry in present-day Africans (proportion is consistent with 0%), which is what we confirmed using \(D\) and \(f_4\) above.
  • Present-day non-Africans carry between 2-3% of Neanderthal ancestry.
  • We see a much higher proportion of Neanderthal ancestry in people from Papua New Guinea - more than 4%. This is consistent with earlier studies that suggest additional archaic admixture events in the ancestors of present-day Papuans.

\(f_3\) statistic

The \(f_3\) statistic, also known as the 3-population statistic, is useful whenever we want to:

  1. Estimate the branch length (shared genetic drift) between a pair of populations \(A\) and \(B\) with respect to a common outgroup \(C\). In this case, the higher the \(f_3\) value, the longer the shared evolutionary time between \(A\) and \(B\).
  2. Test whether population \(C\) is a mixture of two populations \(A\) and \(B\). Significantly negative values of the \(f_3\) statistic are then a statistical evidence of this admixture.

As an example, imagine we are interested in relative divergence times between pairs of present-day human populations, and want to know in which approximate order they split of from each other. To address this problem, we could use \(f_3\) statistic by fixing the \(C\) outgroup as San, and calculating pairwise \(f_3\) statistics between all present-day modern humans.

A B C f3 stderr Zscore nsnps
French French Khomani_San 0.000000 -1.000000 0.000 -1
French Sardinian Khomani_San 0.353447 0.012527 28.215 249760
French Han Khomani_San 0.316964 0.011914 26.604 253158
French Papuan Khomani_San 0.306962 0.011708 26.218 251648
French Mbuti Khomani_San 0.119283 0.008448 14.119 271501
French Dinka Khomani_San 0.190141 0.010049 18.922 276964

We can see that when we order the heatmap labels based on values of pairwise \(f_3\) statistics, the (already known) order of population splits pops up nicely (i.e. San separated first, followed by Mbuti, etc.).

qpWave and qpAdm

Both qpWave and qpAdm can be though of as more complex and powerful extensions of the basic ideas behind a simple \(f_4\) statistic. Building upon the \(f_4\) theory and generalizing it, qpWave makes it possible to find the lowest number of “streams of ancestry” between two groups of populations that is consistent with the data. Extending the concept of \(f_4\) statistics even further, qpAdm allows to find the proportions of ancestry from a set of ancestral populations that contributed ancestry to our population of interest.

Unfortunately, both methods represent a rather advanced topic that still lacks proper documentation and beginner-friendly tutorials, and explaining them in detail is beyond the scope of this vignette. If you want to use them, it’s crucial that you read the official documentation decribing the basic ideas of both methods (distributed with ADMIXTOOLS), and that you read the relevant supplementary sections of papers published by David Reich’s group. At the very least, I recommend reading:

In the remainder of this section, I will assume that you are familiar with both methods, and will only explain how to use admixr for running them from R.

qpWave

To run qpWave, you must provide a list of left and right populations (using the terminology of Haak et al. 2015 above). The aim of the method is to get an idea about the number of migration waves from right to left (with no back-migration from left to right!). This is done by estimating the rank of a matrix of all possible \(f_4\) statistics

\[f_4(\textrm{left}_1, \textrm{left}_i; \textrm{right}_1, \textrm{right}_i),\]

where \(\textrm{left}_1\) and \(\textrm{right}_1\) are some fixed populations and the \(i\) and \(j\) indices run over all other possible choices of populations.

As an example, let’s try to find the number of admixture waves from right = {Yoruba, Mbuti, Alta} into left = {French, Sardinian, Han} populations. We can do this using the function qpWave(), setting its arguments appropriately:

The qpWave() function returns a data frame which shows the results of a series of matrix rank tests. The rank column is the matrix rank tested, df, chisq and tail give the degrees of freedom, \(\chi^2\) value and \(p\)-value for the comparison with the saturated model (the \(p\)-value then indicates which matrix rank is consistent with the data - see example below), and dfdiff, chisqdiff and taildiff give the same, but always comparing a model to the model with one rank less.

rank df chisq tail dfdiff chisqdiff taildiff
0 4 1.758 0.7801969 0 0.000 1.0000000
1 1 0.192 0.6614221 3 1.566 0.6671280
2 0 0.000 1.0000000 1 0.192 0.6614221

In this example, we see that matrix \(r = 0\) cannot be rejected (tail \(p\)-value = 0.78). Because Reich et al. 2012 showed that \(r + 1 \le n\), where \(n\) is the number of admixture waves, we can interpret this as left populations having at least \(n = 1\) streams of ancestry from the set of right populations. In this case, the most likely explanation is Neandertal admixture into non-Africans today.

Now, what happens if we add Papuans to the left group?

rank df chisq tail dfdiff chisqdiff taildiff
0 6 29.150 0.0000570 0 0.000 1.0000000
1 2 0.603 0.7395638 4 28.547 0.0000097
2 0 0.000 1.0000000 2 0.603 0.7395638

We can now clearly reject rank \(r = 0\), but we see that the data is consistent with rank \(r = 1\), meaning that there must have been at least \(n = 2\) streams of ancestry from right to left populations (\(r + 1 \le n\)). Because this happened after we introduced Papuans to the left set, this could indicate a separate pulse of archaic introgression into Papuans, which is not surprising given what we know about significantly more archaic ancestry in Papuans than in any other present-day population.

qpAdm

The qpAdm method can be used to find, for a given target population, the proportions of ancestry coming from a set of source populations. Importantly, since we often lack accurate representatives of the true ancestral populations, we can use a set of reference populations instead, under a crucial assumption that the references set is phylogenetically closer to true source populations than to a set of specified outgroups. For example, coming back to our example of estimating the proportions of Neandertal ancestry in people today, we could define:

  • a set of European individuals as the target;
  • Vindija Neanderthal and an African as two source populations;
  • outgroup populations as Chimp, Altai Neanderthal and Denisovan (which are all further from the true ancestral populations than the specified sources).

Having defined all three population sets, we can run qpAdm with:

The qpAdm() function has an argument details (default TRUE) which makes the function return a list of three elements:

  • proportions - data frame with admixture proportions - this is what we mostly care about;
  • ranks - results of rank tests performed by qpWave - these evaluate how well does the assumed traget-sources-outgroups population model match the data;
  • subsets - results of the “all subsets” analysis (see the documentation for more details.

If details is set to FALSE, only the proportions components is returned by the qpAdm() function.

Let’s start with the ranks element:

target rank df chisq tail dfdiff chisqdiff taildiff
Sardinian 1 1 0.006 0.9362605 3 -0.006 1.0000000
Sardinian 2 0 0.000 1.0000000 1 0.006 0.9362605
Han 1 1 2.144 0.1431157 3 -2.144 1.0000000
Han 2 0 0.000 1.0000000 1 2.144 0.1431157
French 1 1 3.814 0.0508171 3 -3.814 1.0000000
French 2 0 0.000 1.0000000 1 3.814 0.0508171

The row with rank = 1 represents a qpWave test with all \(n\) source populations set as the left set and all outgroups as the right set. This test evaluates whether the ancestral populations are descended from \(n\) independent streams of ancestry. In our case, \(n = 2\) (Mbuti and Vindija), which means that the data would have to be consistent with rank \(r = 1\) to satisfy the inequality \(r + 1 \le n\) proved by Reich et al., 2012. We see that this is true for all three target populations (\(p\)-value > 0.05 for all targets), and the simple model of Neandertal admixture thus seems to be reasonably consistent with the data.

The rank = 2 row represents a qpWave test after adding a target population to the left group together with the sources. This test makes sure that including the target population does not increase the rank of the \(f_4\) matrix, meaning that the target can be really modelled as a mixture of ancestries from the sources. If the \(p\)-values turn out to be very low, this indicates that the assumed model does not fit the data and that a part of the ancestry in a target possibly cannot be traced to any of the sources. In our case, however, all rank = 2 test \(p\)-values are not significant, and we can be reasonably sure that the target samples can be fully modelled as a mixtures of all specified references.

Having made sure that our model is reasonably correct, we can now take a look at proportions, which contains admixture proportion estimates from all specified sources, as well as standard errors for those proportions using a block jackknife:

target Vindija Yoruba stderr_Vindija stderr_Yoruba nsnps
Sardinian 0.025 0.975 0.006 0.006 499314
Han 0.021 0.979 0.006 0.006 499654
French 0.022 0.978 0.006 0.006 499434

If we compare this result to the \(f_4\)-ratio values calculated above, we see that the qpAdm estimates are very close to what we got earlier.

The third element in the list of results shows the outcome of an “all subsets” analysis, which involves testing all subsets of potential source populations. Each 1 in the “pattern” column means that the proportion of ancestry from that particular source population (in the order as specified by the user) was forced to 0.0.

target pattern wt dof chisq tail Vindija Yoruba
Sardinian 00 0 1 0.006 0.9362610 0.025 0.975
Sardinian 01 1 2 15953.171 0.0000000 1.000 0.000
Sardinian 10 1 2 16.564 0.0002530 0.000 1.000
Han 00 0 1 2.144 0.1431160 0.021 0.979
Han 01 1 2 14965.791 0.0000000 1.000 0.000
Han 10 1 2 14.454 0.0007269 0.000 1.000
French 00 0 1 3.814 0.0508171 0.022 0.978
French 01 1 2 15441.258 0.0000000 1.000 0.000
French 10 1 2 16.028 0.0003308 0.000 1.000

Grouping samples

What we’ve been doing so far was calculating statistics for individual samples. However, it is often useful to treat multiple samples as a single group or population. admixr provides a function called relabel() that does just that.

Here is an example: let’s say we want to run a similar analysis to the one described in the \(D\) statistic section, but we want to treat Europeans, Africans and archaics as combined populations, and not as separate individuals. But the ind file that we have does not contain grouped labels - each sample stands on its own:

Chimp        U  Chimp
Mbuti        U  Mbuti
Yoruba       U  Yoruba
Khomani_San  U  Khomani_San
Han          U  Han
Dinka        U  Dinka
Sardinian    U  Sardinian
Papuan       U  Papuan
French       U  French
Vindija      U  Vindija
Altai        U  Altai
Denisova     U  Denisova

To merge several individual samples under a combined label we can call relabel() like this:

We can see that the function relabel returned a modified EIGENSTRAT object, which contains a new item in the “modifiers” section - the path to a new ind file. Let’s look at its contents:

Chimp        U  Chimp
Mbuti        U  African
Yoruba       U  African
Khomani_San  U  African
Han          U  Han
Dinka        U  African
Sardinian    U  European
Papuan       U  Papuan
French       U  European
Vindija      U  Archaic
Altai        U  Archaic
Denisova     U  Archaic

Having the modified EIGENSTRAT object ready, we can then use “European”, “African” and “Archaic” names in any of the admixr wrapper functions described above. For example:

result <- d(W = "European", X = "African", Y = "Archaic", Z = "Chimp", data = modif_snps)

Here is the result, showing again Europeans show genetic affinity to archaic humans compared to Africans today:

W X Y Z D stderr Zscore BABA ABBA nsnps
European African Archaic Chimp 0.0225 0.004404 5.117 15487 14805 489003

Note that the d() function correctly picks up the “group modifier” ind file from the provided EIGENSTRAT object and uses it in place of the original ind file.

Counting present/missing SNPs

The count_snps function can be useful for quality control, weighting of admixture statistics (\(D\), \(f_4\), etc.) in regression analyses etc. There are two optional arguments:

  • prop - changes whether to report SNP counts or proportions (set to FALSE by default),
  • missing - controls whether to count missing SNPs instead of present SNPs (set to FALSE by default).

For each sample, count the SNPs present in that sample:

id sex label present
Chimp U Chimp 491273
Mbuti U Mbuti 499334
Yoruba U Yoruba 499246
Khomani_San U Khomani_San 499250
Han U Han 499654
Dinka U Dinka 499362
Sardinian U Sardinian 499314
Papuan U Papuan 499377
French U French 499434
Vindija U Vindija 497544
Altai U Altai 497729
Denisova U Denisova 497398

Data filtering

Filtering based on a BED file

It is quite common to repeat a particular analysis only on a subset of the genome (such as intergenic sites, etc). However, EIGENSTRAT is a rather obscure file format which is generally not supported by standard bioinformatics tools. Luckily, admixr includes a function filter_bed() that takes an EIGENSTRAT object and a BED file as its inputs and produces a new object that contains a modifier called “excluded”, linking to a snp file with coordinates of sites that did not pass the filtering and will be excluded from later analyses.

If we want to run the whole analysis in a single pipeline, we can use the %>% pipe operator and do the following:

(The %>% operator takes what is on its left side and puts it as a first argument of a function on the right side. While it takes some time to get used to, it is very useful in longer multi-step “pipelines” because it makes more pipelines much more readable. In fact, the resulting code often reads almost like English! The %>% pipe is automatically imported when you load the tidyverse library, and you can read about it more here.)

snps %>%
  filter_bed("regions.bed") %>%
  d(W = "French", X = "Mbuti", Y = "Vindija", Z = "Chimp")

This is because in the formal definitions of admixr function, data = is always the argument, so we don’t have to specify it manually.

Important: The filter_bed() function makes it very easy to do filtering without worrying about locations of intermediate files, but it is important to keep in mind that the function still creates temporary files under the hood. If you plan to run many independent calculations on a filtered subset of the data, it’s better to save the new EIGENSTRAT object to a variable first and re-use the same object multiple times, rather than running the whole pipeline for each analysis separately (which would create new copies of intermediate files for each iteration).

Filtering out potential ancient DNA damage SNPs

In the field of ancient DNA, we often need to repeat an analysis on a subset of data that is less likely to be influenced by ancient DNA damage, to verify that our results are not caused by artifacts in the data (due to biochemical properties of DNA degradation, ancient DNA damage will lead to an increase in C→T and G→A substitutions). Using a similar method described in the BED filtering section above, we can use the transversions_only() function to generate a snp file with positions that carry transitions (C→T and G→A sites):

new_snps <- transversions_only(snps)

# perform the calculation only on transversions
d(W = "French", X = "Dinka", Y = "Altai", Z = "Chimp", data = new_snps)

Again, we could combine several filtering steps into one pipeline:

snps %>%                                    # take the original data
  filter_bed("regions.bed", remove = TRUE) %>%  # remove sites not in specified regions
  transversions_only() %>%                      # remove potential false SNPs due to aDNA damage
  d(W = "French", X = "Dinka", Y = "Altai", Z = "Chimp") # calculate D on the filtered dataset

Merging EIGENSTRAT datasets

Another useful data processing function is merge_eigenstrat(). This function takes two EIGENSTRAT datasets and merges them, producing a union of samples and intersection of SNPs from both of them and returning a new EIGENSTRAT object.


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