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Endocrine Abstracts (2016) 41 GP198 | DOI: 10.1530/endoabs.41.GP198

ECE2016 Guided Posters Thyroid - Basic (10 abstracts)

Differences in gene–gene interactions in Graves’ disease patients stratified by the age of diagnosis

Beata Jurecka-Lubieniecka 1 , Tomasz Bednarczuk 2 , Rafal Ploski 3 , Dorota Kula 1 , Andrzej Tukiendorf 4 , Zofia Kolosza 4 & Barbara Jarzab 1


1Department of Nuclear Medicine and Endocrine Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Poland, Gliwice, Poland; 2Department of Internal Medicine and Endocrinology, Medical University of Warsaw, Warsaw, Poland; 3Department of Medical Genetics, Forensic Medicine, Medical University of Warsaw, Warsaw, Poland; 4Department of Epidemiology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland.


Background: The genetic predisposition of Graves’ disease (GD) was proved by the identification of genes with substantial, non-interactive effects on the disease process. It is known, however, that genetic interactions significantly increase the likelihood of immune-tolerance-related complex diseases like allergic asthma and rheumatoid arthritis. In the present study we analyzed the effects of interactions of multiple loci on the genetic predisposition to GD patients.

Material and methods: A total of 709 patients with GD and 1216 healthy control persons were included in the study. The patients were stratified according to age at the time of the GD onset. Further, association analyses were performed for genes with a known influence on the development of GD – HLADRB1, PTPN22, CTLA4 and TSHR. The interactions among polymorphisms were analyzed using the multiple logistic regression and multifactor dimensionality reduction (MDR) methods.

Results: GD patients stratified by the age of onset differed in allele frequencies of the HLADRB1*03 and 1858T polymorphisms of the PTPN22 gene (OR=1.7, P=0.003; OR=1.49, P=0.01 respectively).

We tested the genetic interactions of four SNPs in a pairwise fashion with regard to the disease risk. The interactions between the HLADRB1/PTPN22 and HLADRB1/CTLA4 genes in young patients were found by the usage of multiple logistic regression. The coexistence of HLADRB1 with CTLA4 and HLADRB1 with PTPN22 showed interactions on more than additive levels (OR=3.64, P=0.002; OR=4.20, P<0.001 respectively), suggesting that interactions between these pairs of genes contribute to the development of GD. The MDR analysis confirmed these interactions.

Conclusion: In contrast with a single gene effect, we observed that interactions between the HLADRB1/PTPN22 and HLADRB1/CTLA4 genes provided a better match for determining the risk of young patients for GD onset.

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