Dataset Information

Multisite de novo mutations in human offspring after paternal exposure to ionizing radiation.

ABSTRACT: A genome-wide evaluation of the effects of ionizing radiation on mutation induction in the mouse germline has identified multisite de novo mutations (MSDNs) as marker for previous exposure. Here we present the results of a small pilot study of whole genome sequencing in offspring of soldiers who served in radar units on weapon systems that were emitting high-frequency radiation. We found cases of exceptionally high MSDN rates as well as an increased mean in our cohort: While a MSDN mutation is detected in average in 1 out of 5 offspring of unexposed controls, we observed 12 MSDNs in altogether 18 offspring, including a family with 6 MSDNs in 3 offspring. Moreover, we found two translocations, also resulting from neighboring mutations. Our findings indicate that MSDNs might be suited in principle for the assessment of DNA damage from ionizing radiation also in humans. However, as exact person-related dose values in risk groups are usually not available, the interpretation of MSDNs in single families would benefit from larger molecular epidemiologic studies on this new biomarker.

SUBMITTER: Holtgrewe M

PROVIDER: S-EPMC6168503 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Multisite de novo mutations in human offspring after paternal exposure to ionizing radiation.

Holtgrewe Manuel M Knaus Alexej A Hildebrand Gabriele G Pantel Jean-Tori JT Santos Miguel Rodriguez de Los MRL Neveling Kornelia K Goldmann Jakob J Schubach Max M Jäger Marten M Coutelier Marie M Mundlos Stefan S Beule Dieter D Sperling Karl K Krawitz Peter Michael PM

Scientific reports 20181002 1

A genome-wide evaluation of the effects of ionizing radiation on mutation induction in the mouse germline has identified multisite de novo mutations (MSDNs) as marker for previous exposure. Here we present the results of a small pilot study of whole genome sequencing in offspring of soldiers who served in radar units on weapon systems that were emitting high-frequency radiation. We found cases of exceptionally high MSDN rates as well as an increased mean in our cohort: While a MSDN mutation is d ...[more]

PMID: 30279461

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Project description:MotivationWhole-genome and -exome sequencing on parent-offspring trios is a powerful approach to identifying disease-associated genes by detecting de novo mutations in patients. Accurate detection of de novo mutations from sequencing data is a critical step in trio-based genetic studies. Existing bioinformatic approaches usually yield high error rates due to sequencing artifacts and alignment issues, which may either miss true de novo mutations or call too many false ones, making downstream validation and analysis difficult. In particular, current approaches have much worse specificity than sensitivity, and developing effective filters to discriminate genuine from spurious de novo mutations remains an unsolved challenge.ResultsIn this article, we curated 59 sequence features in whole genome and exome alignment context which are considered to be relevant to discriminating true de novo mutations from artifacts, and then employed a machine-learning approach to classify candidates as true or false de novo mutations. Specifically, we built a classifier, named De Novo Mutation Filter (DNMFilter), using gradient boosting as the classification algorithm. We built the training set using experimentally validated true and false de novo mutations as well as collected false de novo mutations from an in-house large-scale exome-sequencing project. We evaluated DNMFilter's theoretical performance and investigated relative importance of different sequence features on the classification accuracy. Finally, we applied DNMFilter on our in-house whole exome trios and one CEU trio from the 1000 Genomes Project and found that DNMFilter could be coupled with commonly used de novo mutation detection approaches as an effective filtering approach to significantly reduce false discovery rate without sacrificing sensitivity.AvailabilityThe software DNMFilter implemented using a combination of Java and R is freely available from the website at http://humangenome.duke.edu/software.

Dataset Information

Multisite de novo mutations in human offspring after paternal exposure to ionizing radiation.

Publications

Multisite de novo mutations in human offspring after paternal exposure to ionizing radiation.

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