Algorithm for automatic genotype calling of single nucleotide polymorphisms using the full course of TaqMan real-time data

Single nucleotide polymorphisms (SNPs) are often determined using TaqMan real-time PCR assays (Applied Biosystems) and commercial software that assigns genotypes based on reporter probe signals at the end of amplification. Limitations to the large-scale application of this approach include the need for positive controls or operator intervention to set signal thresholds when one allele is rare. In the interest of optimizing real-time PCR genotyping, we developed an algorithm for automatic genotype calling based on the full course of real-time PCR data. Best cycle genotyping algorithm (BCGA), written in the open source language R, is based on the assumptions that classification depends on the time (cycle) of amplification and that it is possible to identify a best discriminating cycle for each SNP assay. The algorithm is unique in that it classifies samples according to the behavior of blanks (no DNA samples), which cluster with heterozygous samples. This method of classification eliminates the need for positive controls and permits accurate genotyping even in the absence of a genotype class, for example when one allele is rare. Here, we describe the algorithm and test its validity, compared to the standard end-point method and to DNA sequencing

Genetic and acquired prothrombotic risk factors and sudden hearing loss

OBJECTIVES: Idiopathic sudden sensorineural hearing loss (ISSNHL) is a frequently encountered condition, and various pathogenetic mechanisms have been hypothesized, such as viral infections, autoimmune processes, and ischemic events; however, whatever the cause, impaired cochlear perfusion appears to be the most important event. A number of inherited prothrombotic risk factors and their related genetic alterations have recently been correlated with vascular disorders. METHODS: To investigate the mechanisms of vascular thrombosis of the inner ear leading to sudden HL, we examined 100 patients with SSNHL for the presence of acquired or inherited prothrombotic risk factors and 200 healthy volunteers as controls. All of the subjects underwent hematologic examinations, including MTHFR C677T and A1298C, prothrombin G20210A, platelet GlyIIIaA1/A2, V Leiden G1691A genotyping, fibrinogenemia, cholesterolemia, homocystinemia, and folatemia. Genomic DNA was isolated from peripheral blood leukocytes using standard methods, and gene mutations were investigated using a LightCycler DNA analyser and polymerase chain reaction. RESULTS: A statistically significant association was found between SSNHL and the MTHFR C677T/A1298C polymorphisms, the prothrombin G20210A transition, and the platelet GlyIIIa and V Leiden G1691A mutations. Furthermore, the SSNHL patients had significantly higher levels of fibrinogenemia, cholesterolemia, and homocystinemia and lower levels of folatemia than the controls (P < .0001). CONCLUSIONS: The association between inherited and acquired prothrombotic factors and sudden HL suggests that the microvascular impairment causing SSNHL may be caused by a multifactorial mechanism. All patients with ISSNHL should undergo a comprehensive hematologic investigation of inherited and acquired prothrombotic factors, including MTHFR polymorphisms, the prothrombin transition, and the platelet and V Leiden mutations, to identify a subset of patients at high risk of recurrent HL

Silhouette width s(i) histograms for 32 samples and 2–3 blanks (speckled bars), classified according to partitioning around medoids (PAM) ()

<p><b>Copyright information:</b></p><p>Taken from "Algorithm for automatic genotype calling of single nucleotide polymorphisms using the full course of TaqMan real-time data"</p><p>Nucleic Acids Research 2006;34(7):e56-e56.</p><p>Published online 14 Apr 2006</p><p>PMCID:PMC1440877.</p><p>© The Author 2006. Published by Oxford University Press. All rights reserved</p> For each SNP analyzed, the plots report the number of samples, n, assigned to each generic genotype cluster (excluding blanks), and the mean silhouette width, S, for each cluster