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@ARTICLE{Brigante:182090,
      author       = {G. Brigante and C. Lazzaretti and E. Paradiso and F. Nuzzo
                      and M. Sitti and F. Tüttelmann and G. Moretti and R.
                      Silvestri and F. Gemignani and A. Försti$^*$ and K.
                      Hemminki$^*$ and R. Elisei and C. Romei and E. A. Zizzi and
                      M. A. Deriu and M. Simoni and S. Landi and L. Casarini},
      title        = {{G}enetic signature of differentiated thyroid carcinoma
                      susceptibility: a machine learning approach.},
      journal      = {European thyroid journal},
      volume       = {11},
      number       = {5},
      issn         = {2235-0640},
      address      = {Basel},
      publisher    = {Karger},
      reportid     = {DKFZ-2022-02408},
      pages        = {e220058},
      year         = {2022},
      abstract     = {To identify a peculiar genetic combination predisposing to
                      differentiated thyroid carcinoma (DTC), we selected a set of
                      single nucleotide polymorphisms (SNPs) associated with DTC
                      risk, considering polygenic risk score (PRS), Bayesian
                      statistics and a machine learning (ML) classifier to
                      describe cases and controls in three different datasets.
                      Dataset 1 (649 DTC, 431 controls) has been previously
                      genotyped in a genome-wide association study (GWAS) on
                      Italian DTC. Dataset 2 (234 DTC, 101 controls) and dataset 3
                      (404 DTC, 392 controls) were genotyped. Associations of 171
                      SNPs reported to predispose to DTC in candidate studies were
                      extracted from the GWAS of dataset 1, followed by
                      replication of SNPs associated with DTC risk (P < 0.05) in
                      dataset 2. The reliability of the identified SNPs was
                      confirmed by PRS and Bayesian statistics after merging the
                      three datasets. SNPs were used to describe the case/control
                      state of individuals by ML classifier. Starting from 171
                      SNPs associated with DTC, 15 were positive in both datasets
                      1 and 2. Using these markers, PRS revealed that individuals
                      in the fifth quintile had a seven-fold increased risk of DTC
                      than those in the first. Bayesian inference confirmed that
                      the selected 15 SNPs differentiate cases from controls.
                      Results were corroborated by ML, finding a maximum AUC of
                      about 0.7. A restricted selection of only 15 DTC-associated
                      SNPs is able to describe the inner genetic structure of
                      Italian individuals, and ML allows a fair prediction of case
                      or control status based solely on the individual genetic
                      background.},
      keywords     = {differentiated thyroid cancer (Other) / machine learning
                      (Other) / single nucleotide polymorphism (Other)},
      cin          = {B062 / HD01 / C020},
      ddc          = {610},
      cid          = {I:(DE-He78)B062-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)C020-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35976137},
      pmc          = {pmc:PMC9513665},
      doi          = {10.1530/ETJ-22-0058},
      url          = {https://inrepo02.dkfz.de/record/182090},
}