Efficient Bayesian mixed-model analysis increases association power in large cohorts

Efficient Bayesian mixed-model analysis increases association power in large cohorts

Linear mixed models are a powerful statistical tool for identifying genetic associations and avoiding confounding. However, existing methods are computationally intractable in large cohorts and may not optimize power. All existing methods require time cost O(MN2) (where N is the number of samples an...

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Bibliographic Details
Main Authors: Loh, Po-Ru, Bulik-Sullivan, Brendan K, Vilhjálmsson, Bjarni J, Salem, Rany M, Chasman, Daniel I, Ridker, Paul M, Neale, Benjamin M, Patterson, Nick, Price, Alkes L, Tucker, George Jay, Finucane, Hilary Kiyo, Berger Leighton, Bonnie
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:en_US
Published: Nature Publishing Group 2017
Online Access:http://hdl.handle.net/1721.1/110185
https://orcid.org/0000-0003-3864-9828
https://orcid.org/0000-0002-2724-7228

Internet

http://hdl.handle.net/1721.1/110185
https://orcid.org/0000-0003-3864-9828
https://orcid.org/0000-0002-2724-7228

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