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

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 t

SUBMITTER: Aggarwala V 

PROVIDER: S-EPMC5307739 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature