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  • Author: Rossella Elisei x
  • Genetics and omics in thyroidology x
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Giulia Brigante Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria, Modena, Italy

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Clara Lazzaretti Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Elia Paradiso Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Federico Nuzzo Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Martina Sitti Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Frank Tüttelmann Institute of Reproductive Genetics, University of Münster, Münster, Germany

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Gabriele Moretti Department of Biology, University of Pisa, Pisa, Italy

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Roberto Silvestri Department of Biology, University of Pisa, Pisa, Italy

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Federica Gemignani Department of Biology, University of Pisa, Pisa, Italy

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Asta Försti Hopp Children’s Cancer Center (KiTZ), Heidelberg, Germany
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany

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Kari Hemminki Biomedical Center, Faculty of Medicine and Biomedical Center in Pilsen, Charles University in Prague, Pilsen, Czech Republic
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

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Rossella Elisei Department of Endocrinology, University Hospital, Pisa, Italy

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Cristina Romei Department of Endocrinology, University Hospital, Pisa, Italy

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Eric Adriano Zizzi PolitoBIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy

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Marco Agostino Deriu PolitoBIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy

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Manuela Simoni Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria, Modena, Italy
Center for Genomic Research, University of Modena and Reggio Emilia, Modena, Italy

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Stefano Landi Department of Biology, University of Pisa, Pisa, Italy

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Livio Casarini Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
Center for Genomic Research, University of Modena and Reggio Emilia, Modena, Italy

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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.

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