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Zhou, Xiang, Stephens, Matthew (2014) Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nature Methods, 11 (4). 407-409 doi:10.1038/nmeth.2848

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Reference TypeJournal (article/letter/editorial)
TitleEfficient multivariate linear mixed model algorithms for genome-wide association studies
JournalNature Methods
AuthorsZhou, XiangAuthor
Stephens, MatthewAuthor
Year2014 (April)Volume11
Issue4
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1038/nmeth.2848Search in ResearchGate
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Mindat Ref. ID7942750Long-form Identifiermindat:1:5:7942750:8
GUID0
Full ReferenceZhou, Xiang, Stephens, Matthew (2014) Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nature Methods, 11 (4). 407-409 doi:10.1038/nmeth.2848
Plain TextZhou, Xiang, Stephens, Matthew (2014) Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nature Methods, 11 (4). 407-409 doi:10.1038/nmeth.2848
In(2014, April) Nature Methods Vol. 11 (4) Springer Science and Business Media LLC

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Guan, Yongtao, Stephens, Matthew (2011) Bayesian variable selection regression for genome-wide association studies and other large-scale problems. The Annals of Applied Statistics, 5 (3) 1780-1815 doi:10.1214/11-aoas455
Zhou, Xiang (2017) A unified framework for variance component estimation with summary statistics in genome-wide association studies. The Annals of Applied Statistics, 11 (4) 2027-2051 doi:10.1214/17-aoas1052
 
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