SeqEM: an adaptive genotype-calling approach for next-generation sequencing studies.

MOTIVATION: Next-generation sequencing presents several statistical challenges, with one of the most fundamental being determining an individual's genotype from multiple aligned short read sequences at a position. Some simple approaches for genotype calling apply fixed filters, such as calling a heterozygote if more than a specified percentage of the reads have variant nucleotide calls. Other genotype-calling methods, such as MAQ and SOAPsnp, are implementations of Bayes classifiers in that they classify genotypes using posterior genotype probabilities. RESULTS: Here, we propose a novel genotype-calling algorithm that, in contrast to the other methods, estimates parameters underlying the posterior probabilities in an adaptive way rather than arbitrarily specifying them a priori. The algorithm, which we call SeqEM, applies the well-known Expectation-Maximization algorithm to an appropriate likelihood for a sample of unrelated individuals with next-generation sequence data, leveraging information from the sample to estimate genotype probabilities and the nucleotide-read error rate. We demonstrate using analytic calculations and simulations that SeqEM results in genotype-call error rates as small as or smaller than filtering approaches and MAQ. We also apply SeqEM to exome sequence data in eight related individuals and compare the results to genotypes from an Illumina SNP array, showing that SeqEM behaves well in real data that deviates from idealized assumptions. CONCLUSION: SeqEM offers an improved, robust and flexible genotype-calling approach that can be widely applied in the next-generation sequencing studies. AVAILABILITY AND IMPLEMENTATION: Software for SeqEM is freely available from our website: www.hihg.org under Software Download.

Moyamoya following cranial irradiation for primary brain tumors in children.

OBJECTIVE: To study the risk factors for the development of moyamoya syndrome after cranial irradiation for primary brain tumors in children. METHODS: We reviewed neuroimaging studies and dosimetry data for 456 children who were treated with radiation for a primary brain tumor and who were prospectively evaluated with serial neuroimaging studies and neurologic evaluations. A total of 345 patients had both adequate neuroimaging and radiation dosimetry data for further analysis. We used survival analysis techniques to examine the relationship of clinically important variables as risk factors for the development of moyamoya over time. RESULTS: Overall, 12 patients (3.5%) developed evidence of moyamoya. The onset of moyamoya was more rapid for patients with neurofibromatosis type 1 (NF1) (median of 38 vs 55 months) and for patients who received >5,000 cGy of radiation (median of 42 vs 67 months). In a multiple Cox proportional hazards regression analysis controlling for age at start of radiation, each 100-cGy increase in radiation dose increased the rate of moyamoya by 7% (hazard ratio [HR] = 1.07, 95% CI: 1.02 to 1.13, p = 0.01) and the presence of NF1 increased the rate of moyamoya threefold (HR = 3.07, 95% CI: 0.90 to 10.46, p = 0.07). CONCLUSIONS: Moyamoya syndrome is a potentially serious complication of cranial irradiation in children, particularly for those patients with tumors in close proximity to the circle of Willis, such as optic pathway glioma. Patients who received higher doses of radiation to the circle of Willis and with neurofibromatosis type 1 have increased risk of the development of moyamoya syndrome.