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  FaST linear mixed models for genome-wide association studies

Lippert, C., Listgarten, J., Liu, Y., Kadie, C., Davidson, R., & Heckerman, D. (2011). FaST linear mixed models for genome-wide association studies. Nature Methods, 8(10), 833-835. doi:10.1038/nmeth.1681.

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https://www.nature.com/articles/nmeth.1681.pdf (Publisher version)
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Lippert, C1, Author           
Listgarten, J, Author
Liu, Y, Author
Kadie , CM, Author
Davidson, RI, Author
Heckerman, D, Author
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1Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497664              

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 Abstract: We describe factored spectrally transformed linear mixed models (FaST-LMM), an algorithm for genome-wide association studies (GWAS) that scales linearly with cohort size in both run time and memory use. On Wellcome Trust data for 15,000 individuals, FaST-LMM ran an order of magnitude faster than current efficient algorithms. Our algorithm can analyze data for 120,000 individuals in just a few hours, whereas current algorithms fail on data for even 20,000 individuals (http://mscompbio.codeplex.com/).

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 Dates: 2011-10
 Publication Status: Published in print
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 Identifiers: DOI: 10.1038/nmeth.1681
BibTex Citekey: LippertLLKDH2011
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Title: Nature Methods
  Other : Nature methods
Source Genre: Journal
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Publ. Info: New York, NY : Nature Pub. Group
Pages: - Volume / Issue: 8 (10) Sequence Number: - Start / End Page: 833 - 835 Identifier: ISSN: 1548-7091
CoNE: https://pure.mpg.de/cone/journals/resource/111088195279556