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@ARTICLE{Lin:142471,
      author       = {H.-Y. Lin and P.-Y. Huang and D.-T. Chen and H.-Y. Tung and
                      T. A. Sellers and J. M. Pow-Sang and R. Eeles and D. Easton
                      and Z. Kote-Jarai and A. Amin Al Olama and S. Benlloch and
                      K. Muir and G. G. Giles and F. Wiklund and H. Gronberg and
                      C. A. Haiman and J. Schleutker and B. G. Nordestgaard and R.
                      C. Travis and F. Hamdy and D. E. Neal and N. Pashayan and
                      K.-T. Khaw and J. L. Stanford and W. J. Blot and S. N.
                      Thibodeau and C. Maier and A. S. Kibel and C. Cybulski and
                      L. Cannon-Albright and H. Brenner$^*$ and R. Kaneva and J.
                      Batra and M. R. Teixeira and H. Pandha and Y.-J. Lu and J.
                      Y. Park},
      collaboration = {P. Consortium},
      title        = {{AA}9int: {SNP} interaction pattern search using
                      non-hierarchical additive model set.},
      journal      = {Bioinformatics},
      volume       = {34},
      number       = {24},
      issn         = {1460-2059},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2019-00190},
      pages        = {4141-4150},
      year         = {2018},
      abstract     = {The use of single nucleotide polymorphism (SNP)
                      interactions to predict complex diseases is getting more
                      attention during the past decade, but related statistical
                      methods are still immature. We previously proposed the SNP
                      Interaction Pattern Identifier (SIPI) approach to evaluate
                      45 SNP interaction patterns/patterns. SIPI is statistically
                      powerful but suffers from a large computation burden. For
                      large-scale studies, it is necessary to use a powerful and
                      computation-efficient method. The objective of this study is
                      to develop an evidence-based mini-version of SIPI as the
                      screening tool or solitary use and to evaluate the impact of
                      inheritance mode and model structure on detecting SNP-SNP
                      interactions.We tested two candidate approaches: the
                      Five-Full and AA9int method. The Five-Full approach is
                      composed of the five full interaction models considering
                      three inheritance modes (additive, dominant and recessive).
                      The AA9int approach is composed of nine interaction models
                      by considering non-hierarchical model structure and the
                      additive mode. Our simulation results show that AA9int has
                      similar statistical power compared to SIPI and is superior
                      to the Five-Full approach, and the impact of the
                      non-hierarchical model structure is greater than that of the
                      inheritance mode in detecting SNP-SNP interactions. In
                      summary, it is recommended that AA9int is a powerful tool to
                      be used either alone or as the screening stage of a
                      two-stage approach (AA9int+SIPI) for detecting SNP-SNP
                      interactions in large-scale studies.The AA9int and parAA9int
                      functions (standard and parallel computing version) are
                      added in the SIPI R package, which is freely available at
                      $https://linhuiyi.github.io/LinHY_Software/.Supplementary$
                      data are available at Bioinformatics online.},
      cin          = {C070 / G110 / L101},
      ddc          = {570},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)G110-20160331 /
                      I:(DE-He78)L101-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29878078},
      pmc          = {pmc:PMC6289141},
      doi          = {10.1093/bioinformatics/bty461},
      url          = {https://inrepo02.dkfz.de/record/142471},
}