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BMC Genomics. 2017 Feb 14;18(1):155. doi: 10.1186/s12864-017-3522-z.

De novo mutational profile in RB1 clarified using a mutation rate modeling algorithm.

Author information

1
Genomics and Computational Biology Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
2
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. ganguly@mail.med.upenn.edu.
3
Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. ganguly@mail.med.upenn.edu.
4
Perelman School of Medicine, University of Pennsylvania, 415 Anatomy Chemistry Building, 3620 Hamilton Walk, Philadelphia, PA, 19104, USA. ganguly@mail.med.upenn.edu.
5
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. bvoight@upenn.edu.
6
Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. bvoight@upenn.edu.
7
Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. bvoight@upenn.edu.
8
Perelman School of Medicine, 10-126 Smilow Center for Translational Research, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA. bvoight@upenn.edu.

Abstract

BACKGROUND:

Studies of de novo mutations offer great promise to improve our understanding of human disease. After a causal gene has been identified, it is natural to hypothesize that disease relevant mutations accumulate within a sub-sequence of the gene - for example, an exon, a protein domain, or at CpG sites. These assessments are typically qualitative, because we lack methodology to assess the statistical significance of sub-gene mutational burden ultimately to infer disease-relevant biology.

METHODS:

To address this issue, we present a generalized algorithm to grade the significance of de novo mutational burden within a gene ascertained from affected probands, based on our model for mutation rate informed by local sequence context.

RESULTS:

We applied our approach to 268 newly identified de novo germline mutations by re-sequencing the coding exons and flanking intronic regions of RB1 in 642 sporadic, bilateral probands affected with retinoblastoma (RB). We confirm enrichment of loss-of-function mutations, but demonstrate that previously noted 'hotspots' of nonsense mutations in RB1 are compatible with the elevated mutation rates expected at CpG sites, refuting a RB specific pathogenic mechanism. Our approach demonstrates an enrichment of splice-site donor mutations of exon 6 and 12 but depletion at exon 5, indicative of previously unappreciated heterogeneity in penetrance within this class of substitution. We demonstrate the enrichment of missense mutations to the pocket domain of RB1, which contains the known Arg661Trp low-penetrance mutation.

CONCLUSION:

Our approach is generalizable to any phenotype, and affirms the importance of statistical interpretation of de novo mutations found in human genomes.

KEYWORDS:

Mutation Rate; Retinoblastoma; Variability in Mutation Rate; Variant Prioritization; de novo mutations

PMID:
28193182
PMCID:
PMC5307739
DOI:
10.1186/s12864-017-3522-z
[Indexed for MEDLINE]
Free PMC Article

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