Genetic interaction analysis of VEGF-A rs3025039 and VEGFR-2 rs2071559 identifies a genetic profile at higher risk to develop nodular goiter

CONTEXT

Nodular goiter in patients from areas of iodine deficiency is due to the growth of follicular and endothelial cells, involving different vascular-related growth factors in its pathogenesis.

OBJECTIVE

The aim of our study was to examine the association of known single polymorphisms of vascular endothelial growth factor-A [VEGF-A], VEGF receptor-2 [VEGFR-2] and hypoxia-inducible factor-1α [HIF-1α] genes or their genetic interactions with the risk of nodular goiter development.

PATIENTS AND METHODS

116 normal subjects, without any thyroid disease, and 108 subjects with nodular goiter [subjects with goiter and at least one thyroid nodule of > 1 cm of maximum size and in absence of signs of autoimmunity] were selected from a homogeneous population living in a mild iodine deficiency geographic area. Analyses were performed on germline DNA obtained from blood samples and VEGF-A rs3025039, VEGFR-2 rs2071559, and HIF-1αrs11549465 SNPs were investigated by real-time PCR technique. The multifactor dimensionality reduction [MDR] methodology was applied to investigate the genetic interaction between SNPs. Hardy-Weinberg equilibrium was performed.

RESULTS

None of the studied polymorphisms were individually associated with a higher risk to develop nodular goiter [P > 0.05]. The combination of the VEGF-A rs3025039 and VEGFR-2 rs2071559 polymorphisms had the highest accuracy of 0.58 [P = 0.018] and the interaction of some genotypes was significantly associated with the risk of nodular goiter development.

CONCLUSIONS

Our results support a genetic interaction between the VEGF-A rs3025039 and VEGFR-2 rs2071559 polymorphisms as a predictor of the risk to develop nodular goiter in subjects coming from an area with mild iodine deficiency.

Learning directed acyclic graphs from large-scale genomics data

In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.