The majority of children with Dravet syndrome (DS) are caused by de novo SCN1A mutations. To investigate the origin of the mutations, we developed and applied a new method that combined deep amplicon resequencing with a Bayesian model to detect and quantify allelic fractions with improved sensitivity. Of 174 SCN1A mutations in DS probands which were considered “de novo” by Sanger sequencing, we identified 15 cases (8.6%) of parental mosaicism. We identified another five cases of parental mosaicism that were also detectable by Sanger sequencing. Fraction of mutant alleles in the 20 cases of parental mosaicism ranged from 1.1% to 32.6%. Thirteen (65% of 20) mutations originated paternally and seven (35% of 20) maternally. Twelve (60% of 20) mosaic parents did not have any epileptic symptoms. Their mutant allelic fractions were significantly lower than those in mosaic parents with epileptic symptoms (P = 0.016). We identified mosaicism with varied allelic fractions in blood, saliva, urine, hair follicle, oral epithelium, and semen, demonstrating that postzygotic mutations could affect multiple somatic cells as well as germ cells. Our results suggest that more sensitive tools for detecting low‐level mosaicism in parents of families with seemingly “de novo” mutations will allow for better informed genetic counseling.

Long noncoding RNAs (lncRNAs) are emerging as key regulators of multiple essential biological processes involved in physiology and pathology. By analyzing the largest compendium of 14,166 samples from normal and tumor tissues, we significantly expand the landscape of human long noncoding RNA with a high-quality atlas: RefLnc (Reference catalog of LncRNA). Powered by comprehensive annotation across multiple sources, RefLnc helps to pinpoint 275 novel intergenic lncRNAs correlated with sex, age or race as well as 369 novel ones associated with patient survival, clinical stage, tumor metastasis or recurrence. Integrated in a user-friendly online portal, the expanded catalog of human lncRNAs provides a valuable resource for investigating lncRNA function in both human biology and cancer development.

We analyzed 131 human brains (44 neurotypical, 19 with Tourette syndrome, 9 with schizophrenia, and 59 with autism) for somatic mutations after whole genome sequencing to a depth of more than 200×. Typically, brains had 20 to 60 detectable single-nucleotide mutations, but ~6% of brains harbored hundreds of somatic mutations. Hypermutability was associated with age and damaging mutations in genes implicated in cancers and, in some brains, reflected in vivo clonal expansions. Somatic duplications, likely arising during development, were found in ~5% of normal and diseased brains, reflecting background mutagenesis. Brains with autism were associated with mutations creating putative transcription factor binding motifs in enhancer-like regions in the developing brain. The top-ranked affected motifs corresponded to MEIS (myeloid ectopic viral integration site) transcription factors, suggesting a potential link between their involvement in gene regulation and autism.

Genomic mosaicism arising from postzygotic mutations has long been associated with cancer and more recently with non-cancer diseases. It has also been detected in healthy individuals including healthy parents of children affected with genetic disorders, highlighting its critical role in the origin of genetic mutations. However, most existing software for the genome-wide identification of single-nucleotide mosaicisms (SNMs) requires a paired control tissue obtained from the same individual which is often unavailable for non-cancer individuals and sometimes missing in cancer studies. Here, we present MosaicHunter (http://mosaichunter.cbi.pku.edu.cn), a bioinformatics tool that can identify SNMs in whole-genome and whole-exome sequencing data of unpaired samples without matched controls using Bayesian genotypers. We evaluate the accuracy of MosaicHunter on both simulated and real data and demonstrate that it has improved performance compared with other somatic mutation callers. We further demonstrate that incorporating sequencing data of the parents can be an effective approach to significantly improve the accuracy of detecting SNMs in an individual when a matched control sample is unavailable. Finally, MosaicHunter also has a paired mode that can take advantage of matched control samples when available, making it a useful tool for detecting SNMs in both non-cancer and cancer studies.