Computational population genetics, clearly explained

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 matters 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 population genetic 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. The result is not a replacement for qpAdm, but a fast screening layer that can help you identify promising models before you validate them more formally. ADMIXTOOLS 2 already includes batch tools such as qpadm_multi() and qpadm_rotate(), so the point here 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. ...

mergeit is part of the EIGENSOFT package and can be used to merge exactly two EIGENSTRAT/PACKEDANCESTRYMAP datasets without converting to PACKEDPED format first. In this post, I’ll show how to merge the sample we created in Pseudohaploid Genotyping for Ancient DNA: BAM to EIGENSTRAT with the AADR dataset. Setting up EIGENSOFT mergeit is part of the EIGENSOFT package. You can install it via conda: conda install -c bioconda eigensoft If you haven’t installed conda yet, see the Miniconda setup in the pseudohaploid genotyping post. ...