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@ARTICLE{Su:132947,
      author       = {Y.-R. Su and C. Di and S. Bien and L. Huang and X. Dong and
                      G. Abecasis and S. Berndt and S. Bezieau and H. Brenner$^*$
                      and B. Caan and G. Casey and J. Chang-Claude$^*$ and S.
                      Chanock and S. Chen and C. Connolly and K. Curtis and J.
                      Figueiredo and M. Gala and S. Gallinger and T. Harrison and
                      M. Hoffmeister$^*$ and J. Hopper and J. R. Huyghe and M.
                      Jenkins and A. Joshi and L. Le Marchand and P. Newcomb and
                      D. Nickerson and J. Potter and R. Schoen and M. Slattery and
                      E. White and B. Zanke and U. Peters and L. Hsu},
      title        = {{A} {M}ixed-{E}ffects {M}odel for {P}owerful {A}ssociation
                      {T}ests in {I}ntegrative {F}unctional {G}enomics.},
      journal      = {The American journal of human genetics},
      volume       = {102},
      number       = {5},
      issn         = {0002-9297},
      address      = {New York, NY [u.a.]},
      publisher    = {Cell Press},
      reportid     = {DKFZ-2018-00586},
      pages        = {904 - 919},
      year         = {2018},
      abstract     = {Genome-wide association studies (GWASs) have successfully
                      identified thousands of genetic variants for many complex
                      diseases; however, these variants explain only a small
                      fraction of the heritability. Recently, genetic association
                      studies that leverage external transcriptome data have
                      received much attention and shown promise for discovering
                      novel variants. One such approach, PrediXcan, is to use
                      predicted gene expression through genetic regulation.
                      However, there are limitations in this approach. The
                      predicted gene expression may be biased, resulting from
                      regularized regression applied to moderately sample-sized
                      reference studies. Further, some variants can individually
                      influence disease risk through alternative functional
                      mechanisms besides expression. Thus, testing only the
                      association of predicted gene expression as proposed in
                      PrediXcan will potentially lose power. To tackle these
                      challenges, we consider a unified mixed effects model that
                      formulates the association of intermediate phenotypes such
                      as imputed gene expression through fixed effects, while
                      allowing residual effects of individual variants to be
                      random. We consider a set-based score testing framework,
                      MiST (mixed effects score test), and propose two data-driven
                      combination approaches to jointly test for the fixed and
                      random effects. We establish the asymptotic distributions,
                      which enable rapid calculation of p values for genome-wide
                      analyses, and provide p values for fixed and random effects
                      separately to enhance interpretability over GWASs. Extensive
                      simulations demonstrate that our approaches are more
                      powerful than existing ones. We apply our approach to a
                      large-scale GWAS of colorectal cancer and identify two
                      genes, POU5F1B and ATF1, which would have otherwise been
                      missed by PrediXcan, after adjusting for all known loci.},
      cin          = {C070 / G110 / C020},
      ddc          = {570},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)G110-20160331 /
                      I:(DE-He78)C020-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29727690},
      doi          = {10.1016/j.ajhg.2018.03.019},
      url          = {https://inrepo02.dkfz.de/record/132947},
}