Fast computation for genome-wide association studies using boosted one-step statistics | Zendy

Arend Voorman | Zendy; Kenneth Rice | Zendy; Thomas Lumley | Zendy

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Fast computation for genome-wide association studies using boosted one-step statistics

Author(s) -

Arend Voorman,

Kenneth Rice,

Thomas Lumley

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts291

Subject(s) - genome wide association study , computation , computer science , statistical power , statistical model , computational statistics , boss , genetic association , r package , regression , data mining , association (psychology) , statistics , computational biology , algorithm , artificial intelligence , machine learning , mathematics , biology , genetics , computational science , single nucleotide polymorphism , philosophy , materials science , epistemology , metallurgy , gene , genotype

Statistical analyses of genome-wide association studies (GWAS) require fitting large numbers of very similar regression models, each with low statistical power. Taking advantage of repeated observations or correlated phenotypes can increase this statistical power, but fitting the more complicated models required can make computation impractical.

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