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@ARTICLE{Karunamuni:157070,
      author       = {R. A. Karunamuni and M.-P. Huynh-Le and C. C. Fan and R. A.
                      Eeles and D. F. Easton and Z. Kote-Jarai and A. Amin Al
                      Olama and S. Benlloch Garcia and K. Muir and H. Gronberg and
                      F. Wiklund and M. Aly and J. Schleutker and C. Sipeky and T.
                      L. J. Tammela and B. G. Nordestgaard and T. J. Key and R. C.
                      Travis and D. E. Neal and J. L. Donovan and F. C. Hamdy and
                      P. Pharoah and N. Pashayan and K.-T. Khaw and S. N.
                      Thibodeau and S. K. McDonnell and D. J. Schaid and C. Maier
                      and W. Vogel and M. Luedeke and K. Herkommer and A. S. Kibel
                      and C. Cybulski and D. Wokolorczyk and W. Kluzniak and L.
                      Cannon-Albright and H. Brenner$^*$ and B. Schöttker$^*$ and
                      B. Holleczek$^*$ and J. Y. Park and T. A. Sellers and H.-Y.
                      Lin and C. Slavov and R. Kaneva and V. Mitev and J. Batra
                      and J. A. Clements and A. Spurdle and M. R. Teixeira and P.
                      Paulo and S. Maia and H. Pandha and A. Michael and I. G.
                      Mills and O. A. Andreassen and A. M. Dale and T. M. Seibert},
      collaboration = {A. P. C. BioResource and P. Consortium},
      title        = {{T}he effect of sample size on polygenic hazard models for
                      prostate cancer.},
      journal      = {European journal of human genetics},
      volume       = {28},
      number       = {10},
      issn         = {1476-5438},
      address      = {Basingstoke},
      publisher    = {Stockton Press},
      reportid     = {DKFZ-2020-01361},
      pages        = {1467-1475},
      year         = {2020},
      note         = {2020 Oct;28(10):1467-1475},
      abstract     = {We determined the effect of sample size on performance of
                      polygenic hazard score (PHS) models in prostate cancer. Age
                      and genotypes were obtained for 40,861 men from the
                      PRACTICAL consortium. The dataset included 201,590 SNPs per
                      subject, and was split into training and testing sets.
                      Established-SNP models considered 65 SNPs that had been
                      previously associated with prostate cancer. Discovery-SNP
                      models used stepwise selection to identify new SNPs. The
                      performance of each PHS model was calculated for random
                      sizes of the training set. The performance of a
                      representative Established-SNP model was estimated for
                      random sizes of the testing set. Mean HR98/50 (hazard ratio
                      of top $2\%$ to average in test set) of the Established-SNP
                      model increased from 1.73 $[95\%$ CI: 1.69-1.77] to 2.41
                      [2.40-2.43] when the number of training samples was
                      increased from 1 thousand to 30 thousand. Corresponding
                      HR98/50 of the Discovery-SNP model increased from 1.05
                      [0.93-1.18] to 2.19 [2.16-2.23]. HR98/50 of a representative
                      Established-SNP model using testing set sample sizes of 0.6
                      thousand and 6 thousand observations were 1.78 [1.70-1.85]
                      and 1.73 [1.71-1.76], respectively. We estimate that a study
                      population of 20 thousand men is required to develop
                      Discovery-SNP PHS models while 10 thousand men should be
                      sufficient for Established-SNP models.},
      cin          = {C070 / C120 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)C120-20160331 /
                      I:(DE-He78)HD01-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32514134},
      doi          = {10.1038/s41431-020-0664-2},
      url          = {https://inrepo02.dkfz.de/record/157070},
}