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Bioinformatics. 2017 Sep 1;33(17):2776-2778. doi: 10.1093/bioinformatics/btx299.

FlashPCA2: principal component analysis of Biobank-scale genotype datasets.

Author information

1
Centre for Systems Genomics, School of BioSciences.
2
Department of Pathology, University of Melbourne, Parkville, VIC 3010, Australia.
3
Department of Statistics, Purdue University, West Lafayette, IN 47907-2066, USA.

Abstract

Motivation:

Principal component analysis (PCA) is a crucial step in quality control of genomic data and a common approach for understanding population genetic structure. With the advent of large genotyping studies involving hundreds of thousands of individuals, standard approaches are no longer feasible. However, when the full decomposition is not required, substantial computational savings can be made.

Results:

We present FlashPCA2, a tool that can perform partial PCA on 1 million individuals faster than competing approaches, while requiring substantially less memory.

Availability and implementation:

https://github.com/gabraham/flashpca .

Contact:

gad.abraham@unimelb.edu.au.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28475694
DOI:
10.1093/bioinformatics/btx299
[Indexed for MEDLINE]

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