Relationships among evolutionary distance, the variance–covariance matrix, multidimensional scaling, and principal component analysis

Abstract excerpt

Principal component analyses (PCAs) are often used to visualize patterns of genetic variation in human populations. Previous studies showed a close correspondence between genetic and geographic distances. In such PCAs, the principal components are eigenvectors of the data’s variance-covariance matrix, which is obtained by a genetic relationship matrix (GRM). However, it is difficult to apply GRM to multiallelic si...

Topics

Open a Topic to create a Post that cites this publication.

• Computational Biology
• Emigration and Immigration
• Gene Frequency
• Genetics, Population
• Genome, Human
• Models, Genetic
• Polymorphism, Single Nucleotide
• Principal Component Analysis

Identifiers and source

Literature Corpus work

c294ff3f-77a7-5e8f-8f9c-b90623812e34

DOI

10.1101/2022.03.02.482744