Asset Details
Do you wish to reserve the book?
FaST linear mixed models for genome-wide association studies
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
FaST linear mixed models for genome-wide association studies
Do you wish to request the book?
FaST linear mixed models for genome-wide association studies
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
FaST linear mixed models for genome-wide association studies
2011
Overview
An algorithm for linear mixed models substantially reduces memory usage and run time for genome-wide association studies. The improved algorithm scales linearly in cohort size, allowing the application of these models to much larger samples.
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/
).
Publisher
Nature Publishing Group US,Nature Publishing Group
