Awk

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: ...

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. ...

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. I covered how to install and prepare the toolchain on Linux in this earlier post: From EIGENSTRAT to PACKEDPED. As before, I’ll use a small subset. The focus here is on the technical process, not on interpreting the results. 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. ...