alexa Novel genome-wide association study-based candidate loci for differentiated thyroid cancer risk.
Genetics

Genetics

Molecular Biology: Open Access

Author(s): Figlioli G, Khler A, Chen B, Elisei R, Romei C,

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Abstract CONTEXT: Genome-wide association studies (GWASs) on differentiated thyroid cancer (DTC) have identified robust associations with single nucleotide polymorphisms (SNPs) at 9q22.33 (FOXE1), 14q13.3 (NKX2-1), and 2q35 (DIRC3). Our recently published GWAS suggested additional susceptibility loci specific for the high-incidence Italian population. OBJECTIVE: The purpose of this study was to identify novel Italian-specific DTC risk variants based on our GWAS and to test them further in low-incidence populations. DESIGN: We investigated 45 SNPs selected from our GWAS first in an Italian population. SNPs that showed suggestive evidence of association were investigated in the Polish and Spanish cohorts. RESULTS: The combined analysis of the GWAS and the Italian replication study (2260 case patients and 2218 control subjects) provided strong evidence of association with rs10136427 near BATF (odds ratio [OR] =1.40, P = 4.35 × 10(-7)) and rs7267944 near DHX35 (OR = 1.39, P = 2.13 × 10(-8)). A possible role in DTC susceptibility in the Italian populations was also found for rs13184587 (ARSB) (P = 8.54 × 10(-6)) and rs1220597 (SPATA13) (P = 3.25 × 10(-6)). Only the associations between rs10136427 and rs7267944 and DTC risk were replicated in the Polish and the Spanish populations with little evidence of population heterogeneity (GWAS and all replications combined, OR = 1.30, P = 9.30 × 10(-7) and OR = 1.32, P = 1.34 × 10(-8), respectively). In silico analyses provided new insights into the possible functional consequences of the SNPs that showed the strongest association with DTC. CONCLUSIONS: Our findings provide evidence for novel DTC susceptibility variants. Further studies are warranted to identify the specific genetic variants responsible for the observed associations and to functionally validate our in silico predictions. This article was published in J Clin Endocrinol Metab and referenced in Molecular Biology: Open Access

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