Principals about principal components in statistical genetics

Fentaw Abegaz, Kridsadakorn Chaichoompu, Emmanuelle Génin, David W. Fardo, Inke R. König, Jestinah M.Mahachie John, Kristel Van Steen

Research output: Contribution to journalReview articlepeer-review

14 Scopus citations


Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.

Original languageEnglish
Pages (from-to)2200-2216
Number of pages17
JournalBriefings in Bioinformatics
Issue number6
StatePublished - Nov 1 2019

Bibliographical note

Publisher Copyright:
© 2018 The Author(s) 2018. Published by Oxford University Press. All rights reserved.


  • exploration and prediction
  • population stratification
  • principal component analysis
  • statistical genetics

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology


Dive into the research topics of 'Principals about principal components in statistical genetics'. Together they form a unique fingerprint.

Cite this