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Empirical Inference Article 2011

FaST linear mixed models for genome-wide association studies

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/).

Author(s): Lippert, C. and Listgarten, J. and Liu, Y. and Kadie, CM. and Davidson, RI. and Heckerman, D.
Journal: Nature Methods
Volume: 8
Number (issue): 10
Pages: 833–835
Year: 2011
Month: October
Day: 0
Bibtex Type: Article (article)
DOI: 10.1038/nmeth.1681
Digital: 0
Electronic Archiving: grant_archive
Links:

BibTex 

@article{LippertLLKDH2011,
  title = {FaST linear mixed models for genome-wide association studies},
  journal = {Nature Methods},
  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/).},
  volume = {8},
  number = {10},
  pages = {833–835},
  month = oct,
  year = {2011},
  slug = {lippertllkdh2011},
  author = {Lippert, C. and Listgarten, J. and Liu, Y. and Kadie, CM. and Davidson, RI. and Heckerman, D.},
  month_numeric = {10}
}