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FlashPCA2: principal component analysis of biobank-scale genotype datasets

View ORCID ProfileGad Abraham, Yixuan Qiu, View ORCID ProfileMichael Inouye
doi: https://doi.org/10.1101/094714
Gad Abraham
1Centre for Systems Genomics, School of BioSciences, University of Melbourne, Parkville, VIC 3010, Australia
2Department of Pathology, University of Melbourne, Parkville, VIC 3010, Australia
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Yixuan Qiu
3Department of Statistics, Purdue University, West Lafayette, IN 47907-2066, USA
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Michael Inouye
1Centre for Systems Genomics, School of BioSciences, University of Melbourne, Parkville, VIC 3010, Australia
2Department of Pathology, University of Melbourne, Parkville, VIC 3010, Australia
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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 computationally feasible. We present FlashPCA2, a tool that can perform PCA on 1 million individuals faster than competing approaches, while requiring substantially less memory.

Availability https://github.com/gabraham/ashpca

Contact gad.abraham{at}unimelb.edu.au

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 17, 2016.
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FlashPCA2: principal component analysis of biobank-scale genotype datasets
Gad Abraham, Yixuan Qiu, Michael Inouye
bioRxiv 094714; doi: https://doi.org/10.1101/094714
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FlashPCA2: principal component analysis of biobank-scale genotype datasets
Gad Abraham, Yixuan Qiu, Michael Inouye
bioRxiv 094714; doi: https://doi.org/10.1101/094714

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