Exploring Population Structure with Admixture Models and Principal Component Analysis

Chi-Chun Liu; Suyash Shringarpure; Kenneth Lange; John Novembre

doi:10.1007/978-1-0716-0199-0_4

Exploring Population Structure with Admixture Models and Principal Component Analysis

Methods Mol Biol. 2020:2090:67-86. doi: 10.1007/978-1-0716-0199-0_4.

Authors

Chi-Chun Liu¹, Suyash Shringarpure², Kenneth Lange^{3

4

5}, John Novembre⁶

Affiliations

¹ Department of Human Genetics, University of Chicago, Chicago, IL, USA.
² 23andMe Inc., Sunnyvale, CA, USA.
³ Department of Computational Medicine, University of California, Los Angeles, CA, USA.
⁴ Department of Human Genetics, University of California, Los Angeles, CA, USA.
⁵ Department of Statistics, University of California, Los Angeles, CA, USA.
⁶ Department of Human Genetics, Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA. jnovembre@uchicago.edu.

Abstract

Population structure is a commonplace feature of genetic variation data, and it has importance in numerous application areas, including evolutionary genetics, conservation genetics, and human genetics. Understanding the structure in a sample is necessary before more sophisticated analyses are undertaken. Here we provide a protocol for running principal component analysis (PCA) and admixture proportion inference-two of the most commonly used approaches in describing population structure. Along with hands-on examples with CEPH-Human Genome Diversity Panel and pragmatic caveats, readers will learn to analyze and visualize population structure on their own data.

Keywords: Admixture; Population stratification; Population structure; Principal component analysis.

MeSH terms

Computational Biology
Genetics, Population / methods*
Genome, Human
Humans
Models, Genetic
Polymorphism, Single Nucleotide*
Principal Component Analysis

Abstract

MeSH terms

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