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}
}