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Journal Article

FaST linear mixed models for genome-wide association studies

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Lippert,  C
Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B97E-4
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/).