Practical tutorials in computational population genetics

In this post I’ll cover how to run f4-statistics using the admixtools package for R. While I don’t typically use R for other tasks, I prefer this implementation over the original one because working in a REPL environment is more practical than editing parameter files, especially when you’re testing different population combinations. The interactive workflow makes programmatic model testing straightforward. Beyond these workflow improvements, the R version is also significantly faster, not due to any inherent language advantage, but simply better implementation. You can work with the full AADR dataset without creating subsets. ...

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 infers individual ancestries from multilocus SNP genotype datasets. 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. ...