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

Author information

Affiliations

  • 1. Centre for Systems Genomics, School of BioSciences.
      Authors
    • Abraham G 1
    • Inouye M 1
    (2 authors)
  • 2. Department of Statistics, Purdue University, West Lafayette, IN 47907-2066, USA.
      Authors
    • Qiu Y 2
    (1 author)

ORCIDs linked to this article

Bioinformatics (Oxford, England), 01 Sep 2017, 33(17):2776-2778
https://doi.org/10.1093/bioinformatics/btx299 PMID: 28475694 

This article is based on a previously available preprint.

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

[email protected].

Supplementary information

Supplementary data are available at Bioinformatics online.

Full text links 

Read article at publisher's site: https://doi.org/10.1093/bioinformatics/btx299

Read article for free, from open access legal sources, via Unpaywall: https://academic.oup.com/bioinformatics/article-pdf/33/17/2776/25163845/btx299.pdfUnpaywall

References 

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Funding 

Funders who supported this work.

NHMRC

    National Health and Medical Research Council