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Assessing thyroid cancer risk using polygenic risk scores

Liyanarachchi, S; Gudmundsson, J; Ferkingstad, E; He, H; Jonasson, JG; Tragante, V; Asselbergs, FW; ... de la Chapelle, A; + view all (2020) Assessing thyroid cancer risk using polygenic risk scores. Proceedings of the National Academy of Sciences (PNAS) , 117 (11) pp. 5997-6002. 10.1073/pnas.1919976117. Green open access

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Genome-wide association studies (GWASs) have identified at least 10 single-nucleotide polymorphisms (SNPs) associated with papillary thyroid cancer (PTC) risk. Most of these SNPs are common variants with small to moderate effect sizes. Here we assessed the combined genetic effects of these variants on PTC risk by using summarized GWAS results to build polygenic risk score (PRS) models in three PTC study groups from Ohio (1,544 patients and 1,593 controls), Iceland (723 patients and 129,556 controls), and the United Kingdom (534 patients and 407,945 controls). A PRS based on the 10 established PTC SNPs showed a stronger predictive power compared with the clinical factors model, with a minimum increase of area under the receiver-operating curve of 5.4 percentage points (P ≤ 1.0 × 10−9). Adding an extended PRS based on 592,475 common variants did not significantly improve the prediction power compared with the 10-SNP model, suggesting that most of the remaining undiscovered genetic risk in thyroid cancer is due to rare, moderate- to high-penetrance variants rather than to common low-penetrance variants. Based on the 10-SNP PRS, individuals in the top decile group of PRSs have a close to sevenfold greater risk (95% CI, 5.4–8.8) compared with the bottom decile group. In conclusion, PRSs based on a small number of common germline variants emphasize the importance of heritable low-penetrance markers in PTC.

Type: Article
Title: Assessing thyroid cancer risk using polygenic risk scores
Open access status: An open access version is available from UCL Discovery
DOI: 10.1073/pnas.1919976117
Publisher version: http://dx.doi.org/10.1073/pnas.1919976117
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery.ucl.ac.uk/id/eprint/10096981
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