Dataset Information

Combination testing using a single MSH5 variant alongside HLA haplotypes improves coeliac disease risk prediction in the Polish population

ABSTRACT: Background. Assessment of non-HLA variants alongside standard HLA testing was previously shown to improve the identification of potential coeliac disease (CD) patients. We intended to identify new genetic variants associated with CD in the Polish population that would improve CD risk prediction when used alongside HLA haplotype analysis. Results. Association analysis using four HLA-tagging SNPs showed that, as was found in other populations, positive predicting genotypes (HLA-DQ2.5/DQ2.5, HLA-DQ2.5/DQ2.2, and HLA-DQ2.5/DQ8) were found at higher frequencies in CD patients than in healthy control individuals in the Polish population. Both CD-associated SNPs discovered by GWAS were found in the CD susceptibility region, confirming the previously-determined association of the major histocompatibility (MHC) region with CD pathogenesis. The two most significant SNPs from the GWAS were rs9272346 (HLA-dependent; localized within 1 Kb of DQA1) and rs3130484 (HLA-independent; mapped to MSH5). Specificity of CD prediction using the four HLA-tagging SNPs achieved 92.9%, but sensitivity was only 45.5%. However, when a testing combination of the HLA-tagging SNPs and the MSH5 SNP was used, specificity decreased to 80%, and sensitivity increased to 74%.

ORGANISM(S): Homo sapiens

PROVIDER: GSE65424 | GEO | 2015/10/15

SECONDARY ACCESSION(S): PRJNA273961

REPOSITORIES: GEO

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Project description:Background: Current reports suggest that a certain minimum plasma concentration of (Z)-endoxifen is required for breast cancer patient to benefit from tamoxifen therapy. A reliable prediction of which patients are at risk for insufficient exposure to (Z)-endoxifen would be relevant for optimizing treatment, e.g. by adjusting the dose, from the very beginning. The objective of this study was to search for new DNA variants that could be helpful in the preemptive prediction of impaired tamoxifen to endoxifen metabolism. Methods: The molecular ratio (MR) was defined as (Z)-endoxifen plasma concentration divided by the sum of concentrations of tamoxifen and other measured metabolites. The MR of 0.0146, previously delineated as corresponding to (Z)-endoxifen 6 ng/ml efficacy threshold level, was adopted as a cut-off value in a genome-wide association study (GWAS) comprising 287 breast cancer patients receiving tamoxifen 20 mg daily. Multivariate binary logistic regression was used for the pre-selection of variables that showed independent impact on MR and to develop models predictive for MR value below the threshold. Results: In total, 13 GWAS-selected single nucleotide polymorphisms (SNPs) located outside the CYP2D6 gene and two known CYP2D6 variants were significantly associated with the MR value below 0.0146. The strongest association was observed for rs8138080 in WBP2NL (p = 1.78 x 10-15). The minor allele of six SNPs was associated with a decreased risk of impaired tamoxifen metabolism. CYP2D6 genotype was found by Nagelkerke pseudo-R2 to explain 42.7% of a total variation observed in MR, while rs8138080 alone explained 33.7% of this variability. Two alternative models for MR prediction have been developed. The prediction accuracy of Model 1, including rs7245, rs6950784 and rs1320308 in addition to CYP2D6 genotype, was considerably higher than predictive performance of CYP2D6 genotype alone (AUC increase from 0.758 to 0.879). The AUC of 0.830 achieved for the Model 2, developed with the same three SNPs as in Model 1 and rs8138080 instead of CYP2D6 genotype, makes it an interesting, simplified alternative to the costly and time-consuming testing of full CYP2D6 genotype. Conclusions: The four novel SNPs, tested alone or in addition to CYP2D6 genotype, improved the prediction of impaired tamoxifen to endoxifen metabolism, allowing for adjustments in dosing regimen before the treatment begins.

Dataset Information

Combination testing using a single MSH5 variant alongside HLA haplotypes improves coeliac disease risk prediction in the Polish population

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