PCA

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 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 particularly common in population-genetic and ancient-DNA studies. Smartpca can be compiled from the EIGENSOFT source or installed through conda. I covered the installation process 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 reduces redundancy between correlated SNPs before 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. ...