Population-specific genetic variation in large sequencing data sets: why more data is still better.

We have generated a next-generation whole-exome sequencing data set of 2628 participants of the population-based Rotterdam Study cohort, comprising 669 737 single-nucleotide variants and 24 019 short insertions and deletions. Because of broad and deep longitudinal phenotyping of the Rotterdam Study, this data set permits extensive interpretation of genetic variants on a range of clinically relevant outcomes, and is accessible as a control data set. We show that next-generation sequencing data sets yield a large degree of population-specific variants, which are not captured by other available large sequencing efforts, being ExAC, ESP, 1000G, UK10K, GoNL and DECODE.

Huvariome: a web server resource of whole genome next-generation sequencing allelic frequencies to aid in pathological candidate gene selection.

Background: Next generation sequencing provides clinical research scientists with direct read out of innumerable variants, including personal, pathological and common benign variants. The aim of resequencing studies is to determine the candidate pathogenic variants from individual genomes, or from family-based or tumor/normal genome comparisons. Whilst the use of appropriate controls within the experimental design will minimize the number of false positive variations selected, this number can be reduced further with the use of high quality whole genome reference data to minimize false positives variants prior to candidate gene selection.

Absence of common somatic alterations in genes on 1p and 19q in oligodendrogliomas.

A common and histologically well defined subtype of glioma are the oligodendroglial brain tumors. Approximately 70% of all oligodendrogliomas have a combined loss of the entire 1p and 19q chromosomal arms. This remarkably high frequency suggests that the remaining arms harbor yet to be identified tumor suppressor genes.