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Author (up) Zhou, W.; Nielsen, J.B.; Fritsche, L.G.; Dey, R.; Gabrielsen, M.E.; Wolford, B.N.; LeFaive, J.; VandeHaar, P.; Gagliano, S.A.; Gifford, A.; Bastarache, L.A.; Wei, W.-Q.; Denny, J.C.; Lin, M.; Hveem, K.; Kang, H.M.; Abecasis, G.R.; Willer, C.J.; Lee, S. url  doi
  Title Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies Type Journal Article
  Year 2018 Publication Nature Genetics Abbreviated Journal Nat Genet  
  Volume 50 Issue 9 Pages 1335-1341  
  Abstract In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, the linear mixed model and the recently proposed logistic mixed model, perform poorly; they produce large type I error rates when used to analyze unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE (Scalable and Accurate Implementation of GEneralized mixed model), provides accurate P values even when case-control ratios are extremely unbalanced. SAIGE uses state-of-art optimization strategies to reduce computational costs; hence, it is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 samples from white British participants with European ancestry for > 1,400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.  
  Address Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1061-4036 ISBN Medium  
  Area Expedition Conference  
  Notes PMID:30104761; PMCID:PMC6119127 Approved no  
  Call Number HUNT @ maria.stuifbergen @ Serial 2192  
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