Genetic Polymorphism Predisposing to Differentiated Thyroid Cancer: A Review of Major Findings of the Genome-Wide Association Studies.
10.3803/EnM.2018.33.2.164
- Author:
Vladimir A SAENKO
1
;
Tatiana I ROGOUNOVITCH
Author Information
1. Department of Radiation Molecular Epidemiology, Atomic Bomb Disease Institute, Nagasaki University, Nagasaki, Japan. saenko@nagasaki-u.ac.jp
- Publication Type:Review
- Keywords:
Thyroid neoplasms;
Genetic loci;
Genome-wide association study;
Polymorphism, single nucleotide;
Genetic predisposition to disease;
Genetic testing
- MeSH:
Counseling;
Epidemiology;
Genetic Loci;
Genetic Predisposition to Disease;
Genetic Testing;
Genetic Variation;
Genome-Wide Association Study*;
Humans;
Japan;
Korea;
Polymorphism, Genetic*;
Polymorphism, Single Nucleotide;
Thyroid Gland*;
Thyroid Neoplasms*
- From:Endocrinology and Metabolism
2018;33(2):164-174
- CountryRepublic of Korea
- Language:English
-
Abstract:
Thyroid cancer has one of the highest hereditary component among human malignancies as seen in medical epidemiology investigations, suggesting the potential meaningfulness of genetic studies. Here we review researches into genetic variations that influence the chance of developing non-familial differentiated thyroid cancer (DTC), focusing on the major findings of the genome-wide association studies (GWASs) of common single-nucleotide polymorphisms (SNPs). To date, eight GWAS have been performed, and the association of a number of SNPs have been reproduced in dozens of replication investigations across different ethnicities, including Korea and Japan. Despite the cumulative effect of the strongest SNPs demonstrates gradual increase in the risk for cancer and their association signals are statistically quite significant, the overall prediction ability for DTC appears to be very limited. Thus, genotyping of common SNPs only would be insufficient for evidence-based counseling in clinical setting at present. Further studies to include less significant and rare SNPs, non-SNP genetic information, gene-gene interactions, ethnicity, non-genetic and environmental factors, and development of more advanced computational algorithms are warranted to approach to personalized disease risk prediction and prognostication.
