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@ARTICLE{Hieke:128777,
      author       = {S. Hieke and A. Benner$^*$ and R. F. Schlenk and M.
                      Schumacher and L. Bullinger and H. Binder},
      title        = {{I}dentifying {P}rognostic {SNP}s in {C}linical {C}ohorts:
                      {C}omplementing {U}nivariate {A}nalyses by {R}esampling and
                      {M}ultivariable {M}odeling.},
      journal      = {PLoS one},
      volume       = {11},
      number       = {5},
      issn         = {1932-6203},
      address      = {Lawrence, Kan.},
      publisher    = {PLoS},
      reportid     = {DKFZ-2017-04792},
      pages        = {e0155226 -},
      year         = {2016},
      abstract     = {Clinical cohorts with time-to-event endpoints are
                      increasingly characterized by measurements of a number of
                      single nucleotide polymorphisms that is by a magnitude
                      larger than the number of measurements typically considered
                      at the gene level. At the same time, the size of clinical
                      cohorts often is still limited, calling for novel analysis
                      strategies for identifying potentially prognostic SNPs that
                      can help to better characterize disease processes. We
                      propose such a strategy, drawing on univariate testing ideas
                      from epidemiological case-controls studies on the one hand,
                      and multivariable regression techniques as developed for
                      gene expression data on the other hand. In particular, we
                      focus on stable selection of a small set of SNPs and
                      corresponding genes for subsequent validation. For
                      univariate analysis, a permutation-based approach is
                      proposed to test at the gene level. We use regularized
                      multivariable regression models for considering all SNPs
                      simultaneously and selecting a small set of potentially
                      important prognostic SNPs. Stability is judged according to
                      resampling inclusion frequencies for both the univariate and
                      the multivariable approach. The overall strategy is
                      illustrated with data from a cohort of acute myeloid
                      leukemia patients and explored in a simulation study. The
                      multivariable approach is seen to automatically focus on a
                      smaller set of SNPs compared to the univariate approach,
                      roughly in line with blocks of correlated SNPs. This more
                      targeted extraction of SNPs results in more stable selection
                      at the SNP as well as at the gene level. Thus, the
                      multivariable regression approach with resampling provides a
                      perspective in the proposed analysis strategy for SNP data
                      in clinical cohorts highlighting what can be added by
                      regularized regression techniques compared to univariate
                      analyses.},
      cin          = {C060},
      ddc          = {500},
      cid          = {I:(DE-He78)C060-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:27159447},
      pmc          = {pmc:PMC4861340},
      doi          = {10.1371/journal.pone.0155226},
      url          = {https://inrepo02.dkfz.de/record/128777},
}