Author(s): Hebner C, Petermann I, Browning BL, Shelling AN, Ferguson LR
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Abstract Accurate measurement of allele frequencies between population groups with differing sensitivities to disease is fundamental to genetic epidemiology. Genotyping errors can markedly influence the biological conclusions of a study. This issue may be especially important now there is increasing recognition of triallelic single nucleotide polymorphisms (SNPs) in the genome and their possible role in diseases like inflammatory bowel disease. For example, the MDR1 (ABCB1) SNP G2677/T/A was, like many other triallelic SNPs, originally described as diallelic. Here, we report a comprehensive analyses of estimated allele frequencies of this SNP in a set of 73 human DNA samples, comparing six commonly used genotyping methods (Applied Biosystems Taqman, Roche LightCycler melting analysis, allelic discrimination PCR, DNA sequencing, Sequenom, and RFLP) from the angle of their error potential. Only Sequenom and DNA sequencing provided accurate measurements, if we had not had prior knowledge of the triallelic nature of this SNP. The other tested methods (with the exception of LightCycler) failed to show any indication of the presence of the rare third A- allele in a diallelic assay. Although most of the errors were due to the inability to detect the third allele, all methods except Sequenom and sequencing produced errors for the detection of the two common alleles G and T (LightCycler, 6 errors; PCR, 4 errors; RFLP, 2 errors; Taqman, 1 error). There is considerable variability in the reported frequencies of the different alleles of the MDR1 G2677/T/A SNP, and the role of this SNP in the etiology of inflammatory bowel disease has been controversial. Our data emphasize the importance of choosing the appropriate method for SNP detection and lead us to suggest that part of the previously reported variation may reflect artifacts associated with the different genotyping methodologies used. The failure to recognize the triallic nature of a SNP may lead to underestimations of real genetic associations.
This article was published in Cancer Epidemiol Biomarkers Prev
and referenced in Molecular Biology: Open Access