Computational population genetics, clearly explained

Yes. For two qpAdm models with the same target, the same right groups, and the same settings, the model with the higher p-value is the better statistical fit. qpAdm calculates a covariance-weighted discrepancy between the observed and fitted f4-statistics. The p-value reflects how well the model explains the used f4-statistics. A higher p-value means the discrepancy between the observed and fitted values is less unusual under the model. This does not mean that the model with the highest p-value for a target is automatically the best one, because qpAdm results depend on the selected right groups. Uninformative right-groups can lack the power to detect a bad model, while overly restrictive ones can make a plausible model appear to fit badly. Therefore, p-values are more comparable when models for the same target are compared using the same groups and settings. Models with different numbers of sources are also comparable since p-values account for different degrees of freedom. Z-scores can be used to assess whether an additional source is justified. ...

A Vahaduo generated PCA model for Sardinians gives: 82.8% Barcin Neolithic 11.6% Loschbour 5.6% Yamnaya Distance: 3.4303% Ganj Dareh was included in the sources but gets a weight of zero. This seems to imply that Sardinians don’t have any eastern-Farmer related ancestry. When Sardinians are modelled with qpAdm using Barcin Neolithic, Loschbour, Yamnaya, and Ganj Dareh, the model fits well: 68.6% Barcin Neolithic 11.9% Loschbour 10.2% Yamnaya 9.4% Ganj Dareh Neolithic p = 0.769 When Ganj Dareh is dropped, the model fails (p=1.18×10−12p = 1.18 \times 10^{-12}p=1.18×10−12). ...

This post covers how to run pairwise f2-statistics and FST in AdmixPy. They are simple to interpret, and are also useful computationally. Once f2 blocks have been computed and cached, many downstream analyses can reuse them without repeatedly reading and converting the original genotype data. What does f2 measure? The f2-statistic quantifies allele-frequency differentiation between two populations, AAA and BBB, and is defined as: f2(A,B)=E[(pA−pB)2] f_2(A, B) = E[(p_A - p_B)^2] f2​(A,B)=E[(pA​−pB​)2]where pAp_ApA​ and pBp_BpB​ are the allele frequencies of populations AAA and BBB at a SNP, and the squared allele-frequency differences are averaged across SNPs. ...

I recently published AdmixPy on GitHub, a fast implementation of f-statistics, qpAdm, and qpWave in Python that runs on Linux, macOS, and Windows. It works directly on the new AADR TGENO distribution format and is faster than ADMIXTOOLS 2 on most workloads. Supported input formats: EIGENSTRAT (.geno/.snp/.ind), packed AncestryMap (.geno/.snp/.ind), TGENO (.tgeno/.snp/.ind), and SNP-major PLINK binary (.bed/.bim/.fam). AdmixPy is implemented in Python and depends only on NumPy, SciPy, and pandas. Installation is handled through pip, and it should behave the same on every platform. ...

Commercial raw DNA exports are not provided in the file formats normally used by ADMIXTOOLS, ADMIXTOOLS 2, AADR-based workflows, or PLINK. Files from 23andMe, AncestryDNA, FamilyTreeDNA, MyHeritage, and Living DNA are usually plain-text vendor exports, while downstream workflows often require PLINK PACKEDPED or EIGENSTRAT/PACKEDANCESTRYMAP files. EIGENSTRAT is often used loosely to refer to the .geno/.snp/.ind triplet. Strictly speaking, EIGENSTRAT is the plain-text version of that triplet; PACKEDANCESTRYMAP is the packed binary form of the same three files. ADMIXTOOLS and ADMIXTOOLS 2 work with either, but PACKEDANCESTRYMAP takes far less disk space and loads much faster, which is why it’s the practical default used here. ...

The origins of the Proto-Anatolians are often treated as one of the more obscure problems, but the genetic data may be not that ambigous. Anatolian is regarded as the earliest-splitting branch of “Indo-European”, and its divergence is deep enough that some linguists distinguish a pre–Proto-Indo-European stage, sometimes called “Indo-Anatolian”, from the Proto-Indo-European reconstructed from the non-Anatolian branches. Under either framing, the relevant question is the same: whether the earlier Eneolithic steppe-related ancestry behind Yamnaya, particularly the Caucasus–Lower Volga (CLV) component, also moved south of the Caucasus into Anatolia. For this purpose, I use Progress-2 specifically as proxy for the north Caucasus-facing part of this Eneolithic steppe-related ancestry, since it sits directly at the northern end of the Caucasus and therefore serves as a good proxy for groups that may have passed through the region. ...

Recently, in April 2026, new AADR versions were released on Harvard Dataverse. Among the more important additions are the new compatibility datasets introduced for reducing platform-specific bias when co-analyzing ancient DNA generated with different experimental setups. This is especially relevant when combining data produced with different capture reagents such as Agilent (AG), Twist (TW), and shotgun (SG), because these can introduce systematic differences that may affect downstream analyses. The compatibility panels were added to minimize that problem and make mixed-platform datasets more directly comparable. ...

I recently published dt, a modern data transformation tool designed to make the awk workflows commonly used on this blog more intuitive, expressive, and fast. Dt is written in Rust because it compiles to a single binary that runs anywhere, and it uses Polars for the actual data processing, giving you columnar operations that handle large files efficiently. The syntax uses explicit functions (filter(), select(), mutate()) chained together with pipes, making common transformations easier to read and modify. There’s also an interactive REPL that shows you the result after each operation, letting you build complex pipelines step-by-step, catch mistakes early, and undo errors with .undo. ...

In this post, I will build a transparent admixture-screening workflow from scratch in R using f4-statistics and constrained regression. The main advantage is automation: instead of hand-writing every candidate model, the script tests many 2-way, 3-way, and 4-way source combinations in one pass and ranks them by fit. ADMIXTOOLS 2 already includes batch tools such as qpadm_multi() and qpadm_rotate(), so the point is not that qpAdm cannot be automated. The point is that this custom workflow is compact, transparent, easy to modify, and useful for exploratory model search before you validate the strongest candidates more formally. ...

mergeit is part of the EIGENSOFT package and can be used to merge exactly two EIGENSTRAT/PACKEDANCESTRYMAP datasets without converting to PACKEDPED format first. Setting up EIGENSOFT mergeit is part of the EIGENSOFT package. You can install it via conda: conda install -c bioconda eigensoft Alternatively, if you prefer to compile from source, see: From EIGENSTRAT to PACKEDPED. Setting Up A Parameter File Like other EIGENSOFT tools, mergeit requires a parameter file: ...

In this post, I’ll cover pseudohaploid genotype calling using pileupCaller and converting the output to EIGENSTRAT format for use with ADMIXTOOLS. Since we just created this BAM ourselves in the previous post, we already know it’s aligned to hs37d5. However, if you’re starting with a BAM file, you’ll need to verify the reference genome first. I’ll start by showing how to check BAM headers to identify the reference genome. Identifying the Reference Genome from BAM Headers Before processing any BAM file, you should verify which reference genome it was aligned against. This is critical because AADR compatibility requires hs37d5 specifically. BAMs aligned to other GRCh37-based references like hg19 are also compatible (since they share the same coordinate system, differing only in chromosome naming conventions), but hg38/GRCh38 BAMs would require realignment from FASTQs. ...

This post covers using qpAdm in R to test ancestry models and estimate admixture proportions. qpAdm builds on f4-statistics and provides a framework for evaluating whether proposed source populations can explain a target population’s genetic makeup. For R and admixtools setup instructions on Debian/Ubuntu, see my previous post: Running f4-Statistics with Admixtools in R. Windows users can find R installation instructions on the R website. What is qpAdm? qpAdm is a method for testing ancestry models and estimating admixture proportions. It determines whether a target population can be modeled as a mixture of specified source populations (“left populations”), and if the model fits, calculates the contribution from each source. The method builds on f4-statistics (covered in my previous post) to evaluate these ancestry models. ...

This post covers how to run f4-statistics using the admixtools package for R. Compared with the original ADMIXTOOLS workflow, the R implementation is more convenient for testing multiple population combinations because it can be used interactively, without repeatedly editing parameter files. It is also much faster in practice, which makes it possible to work directly with the full AADR dataset without creating subsets. What are f4-statistics? F4-statistics measure asymmetry in allele sharing among four populations. For four populations AAA, BBB, CCC, and DDD, the statistic is written as: ...

Recently, MyHeritage quietly announced that all new DNA kits will be processed using low-pass whole-genome sequencing (WGS) instead of traditional genotyping arrays, for just $30 per kit. The coverage depth will be roughly 2x (as announced in their blog post), compared to the 30x used in clinical sequencing. It’s nowhere near diagnostic quality, but it is whole-genome data nevertheless. The surprising part isn’t the technology, it’s the price. Getting an entire genome, even at shallow depth, for less than what MyHeritage charges to “unlock” an uploaded SNP file (~$35) seems almost too cheap to be real. Below are a few of the issues I see: technical, economic, and data-security related. ...

In an earlier post, PLINK PCA Tutorial: Running PCA in PLINK (Commands + Output), I showed the manual way to build a subset from the .ind/.fam. That works, but if you want to keep thousands of samples it gets tedious fast. Below is a one-liner using awk that generates a PLINK --keep file automatically from a list of populations. Prepare a list of populations to keep: Create a text file (e.g. pops) in the same directory as your reference .ind and .fam. Put one population label per line: ...

This post is a short follow-up to the previous one on Estimating Ancestry Components Using ADMIXTURE. Here, we’ll explore supervised ADMIXTURE, a mode that allows you to explicitly define ancestral populations and infer the ancestry proportions of unassigned individuals based on those references. What Is a Supervised Run? In supervised mode, ADMIXTURE skips the component discovery step and instead uses user-defined groupings to represent ancestral components. The benefit: if you already have solid candidates for reference populations, you can use them to quickly infer ancestry proportions for target or admixed individuals. ...

In this post, I’ll demonstrate how to estimate ancestry proportions using one of the most widely used tools in population genetics: ADMIXTURE. ADMIXTURE is a model-based clustering algorithm that estimates individual ancestry proportions and ancestral allele frequencies from multilocus SNP genotypes. Preparing the Dataset Download the appropriate ADMIXTURE binary and either place it in your dataset directory or make it globally accessible. For this run, I included a subset of West Asian populations along with a few adjacent populations (around 150 samples in total). Linkage Disequilibrium (LD) pruning was applied beforehand. If you’re unsure how to prune your dataset, refer to the previous post. ...

This post is a continuation of the previous one, where I demonstrated how to perform PCA with PLINK. While PLINK’s PCA is great for quick, exploratory analysis, smartpca (part of the EIGENSOFT toolset) is more commonly used in published genetic studies. Smartpca needs to be compiled on Linux or macOS, or alternatively installed via conda. I covered both methods in this earlier post: From EIGENSTRAT to PACKEDPED. As before, I’ll use a small subset. The focus here is on the technical process. One key difference in this post is that I’ll perform Linkage Disequilibrium (LD) pruning, which helps reduce SNP redundancy and improves the detection of population structure in PCA. ...

In this post, I’ll demonstrate how to perform a PCA on a PLINK dataset. Before we begin, we need to prepare a subset of samples we’re interested in analyzing. To do this, we’ll extract sample information from the .fam file. But first, we need to identify the samples of interest. For example, those from a specific population such as Sardinians. The easiest way is to open the corresponding .ind file and look at the population column, which is the third column in each row. Open the file in a text editor, and search for the population name, in this case, Sardinian. ...

The files downloaded in the previous blog post are distributed as an EIGENSTRAT-style .geno/.snp/.ind dataset. This naming can be confusing: the .snp and .ind files are the usual EIGENSTRAT metadata files, but the .geno file may either be plain-text EIGENSTRAT or binary PACKEDANCESTRYMAP. PACKEDPED format allows for easier downstream processing using the PLINK toolset. With PLINK, it becomes straightforward to extract sample subsets, filter SNPs, and perform a wide range of analyses. ...

Note: This post uses an older AADR release and parts of it may now be outdated. For the latest AADR v66 download, including TGENO conversion and ADMIXTOOLS2 compatibility notes, see Downloading and Converting AADR v66. A Linux environment is unavoidable when it comes to bioinformatical data processing and preparation. You can use your favorite distribution. For Windows users, the Windows Subsystem for Linux (WSL) provides a good alternative to dual booting or setting up a full virtual machine. ...