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Detalle de: Correction: An Epistatic Interaction between the PAX8 and STK17B Genes in Papillary Thyroid Cancer Susceptibility

Title Correction: An Epistatic Interaction between the PAX8 and STK17B Genes in Papillary Thyroid Cancer Susceptibility
Abstract

Papillary Thyroid Cancer (PTC) is a heterogeneous and complex disease; susceptibility to PTC is influenced by the joint effects of multiple common, low-penetrance genes, although relatively few have been identified to date.Here we applied a rigorous combined approach to assess both the individual and epistatic contributions of genetic factors to PTC susceptibility, based on one of the largest series of thyroid cancer cases described to date.In addition to identifying the involvement of TSHR variation in classic PTC, our pioneer study of epistasis revealed a significant interaction between variants in STK17B and PAX8.The interaction was detected by MD-MBR (p = 0.00010) and confirmed by other methods, and then replicated in a second independent series of patients (MD-MBR p = 0.017).Furthermore, we demonstrated an inverse correlation between expression of PAX8 and STK17B in a set of cell lines derived from human thyroid carcinomas.Overall, our work sheds additional light on the genetic basis of thyroid cancer susceptibility, and suggests a new direction for the exploration of the inherited genetic contribution to disease using association studies.

Apa Reference Landa, I., Boullosa, C., Inglada-Pérez, L., Sastre-Perona, A., Pastor, S., Velázquez, A., Mancikova, V., Ruiz-Llorente, S., Schiavi, F., Marcos, R., Malats, N., Opocher, G., Diaz-Uriarte, R., Santisteban, P., Valencia, A., & Robledo, M. (2013). Correction: An Epistatic Interaction between the PAX8 and STK17B Genes in Papillary Thyroid Cancer Susceptibility. PLoS ONE, 8(10). 10.1371/annotation/cd94c3eb-70f2-4dfa-85be-b3fc41e495c3
Doi 10.1371/annotation/cd94c3eb-70f2-4dfa-85be-b3fc41e495c3
Publication Date 21 Oct 2013
Publication Type Article
Researcher Ramon Díaz Uriarte
Ontology
Research Groups Bioinformatics and Computational Biology in Cancer Evolution
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