De Novo Truncating Mutations in WASF1 Cause Intellectual Disability with Seizures.

Next-generation sequencing has been invaluable in the elucidation of the genetic etiology of many subtypes of intellectual disability in recent years. Here, using exome sequencing and whole-genome sequencing, we identified three de novo truncating mutations in WAS protein family member 1 (WASF1) in five unrelated individuals with moderate to profound intellectual disability with autistic features and seizures. WASF1, also known as WAVE1, is part of the WAVE complex and acts as a mediator between Rac-GTPase and actin to induce actin polymerization. The three mutations connected by Matchmaker Exchange were c.1516C>T (p.Arg506Ter), which occurs in three unrelated individuals, c.1558C>T (p.Gln520Ter), and c.1482delinsGCCAGG (p.Ile494MetfsTer23). All three variants are predicted to partially or fully disrupt the C-terminal actin-binding WCA domain. Functional studies using fibroblast cells from two affected individuals with the c.1516C>T mutation showed a truncated WASF1 and a defect in actin remodeling. This study provides evidence that de novo heterozygous mutations in WASF1 cause a rare form of intellectual disability

Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.

Telomere length is a risk factor in disease and the dynamics of telomere length are crucial to our understanding of cell replication and vitality. The proliferation of whole genome sequencing represents an unprecedented opportunity to glean new insights into telomere biology on a previously unimaginable scale. To this end, a number of approaches for estimating telomere length from whole-genome sequencing data have been proposed. Here we present Telomerecat, a novel approach to the estimation of telomere length. Previous methods have been dependent on the number of telomeres present in a cell being known, which may be problematic when analysing aneuploid cancer data and non-human samples. Telomerecat is designed to be agnostic to the number of telomeres present, making it suited for the purpose of estimating telomere length in cancer studies. Telomerecat also accounts for interstitial telomeric reads and presents a novel approach to dealing with sequencing errors. We show that Telomerecat performs well at telomere length estimation when compared to leading experimental and computational methods. Furthermore, we show that it detects expected patterns in longitudinal data, repeated measurements, and cross-species comparisons. We also apply the method to a cancer cell data, uncovering an interesting relationship with the underlying telomerase genotype