Principals about principal components in statistical genetics.
exploration and prediction
population stratification
principal component analysis
statistical genetics
Journal
Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837
Informations de publication
Date de publication:
27 11 2019
27 11 2019
Historique:
received:
18
01
2018
revised:
21
07
2018
accepted:
12
08
2018
pubmed:
17
9
2018
medline:
2
9
2020
entrez:
17
9
2018
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 30219892
pii: 5095727
doi: 10.1093/bib/bby081
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
2200-2216Informations de copyright
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.