Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies.

Autism Spectrum Disorder (ASD) demonstrates high heritability and familial clustering, yet the genetic causes remain only partially understood as a result of extensive clinical and genomic heterogeneity. Whole-genome sequencing (WGS) shows promise as a tool for identifying ASD risk genes as well as unreported mutations in known loci, but an assessment of its full utility in an ASD group has not been performed. We used WGS to examine 32 families with ASD to detect de novo or rare inherited genetic variants predicted to be deleterious (loss-of-function and damaging missense mutations). Among ASD probands, we identified deleterious de novo mutations in six of 32 (19%) families and X-linked or autosomal inherited alterations in ten of 32 (31%) families (some had combinations of mutations). The proportion of families identified with such putative mutations was larger than has been previously reported; this yield was in part due to the comprehensive and uniform coverage afforded by WGS. Deleterious variants were found in four unrecognized, nine known, and eight candidate ASD risk genes. Examples include CAPRIN1 and AFF2 (both linked to FMR1, which is involved in fragile X syndrome), VIP (involved in social-cognitive deficits), and other genes such as SCN2A and KCNQ2 (linked to epilepsy), NRXN1, and CHD7, which causes ASD-associated CHARGE syndrome. Taken together, these results suggest that WGS and thorough bioinformatic analyses for de novo and rare inherited mutations will improve the detection of genetic variants likely to be associated with ASD or its accompanying clinical symptoms.

De novo mutations (DNMs) are important in autism spectrum disorder (ASD), but so far analyses have mainly been on the ~1.5% of the genome encoding genes. Here, we performed whole-genome sequencing (WGS) of 200 ASD parent–child trios and characterised germline and somatic DNMs. We confirmed that the majority of germline DNMs (75.6%) originated from the father, and these increased significantly with paternal age only (P=4.2×10−10). However, when clustered DNMs (those within 20 kb) were found in ASD, not only did they mostly originate from the mother (P=7.7×10−13), but they could also be found adjacent to de novo copy number variations where the mutation rate was significantly elevated (P=2.4×10−24). By comparing with DNMs detected in controls, we found a significant enrichment of predicted damaging DNMs in ASD cases (P=8.0×10−9; odds ratio=1.84), of which 15.6% (P=4.3×10−3) and 22.5% (P=7.0×10−5) were non-coding or genic non-coding, respectively. The non-coding elements most enriched for DNM were untranslated regions of genes, regulatory sequences involved in exon-skipping and DNase I hypersensitive regions. Using microarrays and a novel outlier detection test, we also found aberrant methylation profiles in 2/185 (1.1%) of ASD cases. These same individuals carried independently identified DNMs in the ASD-risk and epigenetic genes DNMT3A and ADNP. Our data begins to characterize different genome-wide DNMs, and highlight the contribution of non-coding variants, to the aetiology of ASD.

BackgroundStructural variants (SVs) are less common than single nucleotide polymorphisms and indels in the population, but collectively account for a significant fraction of genetic polymorphism and diseases. Base pair differences arising from SVs are on a much higher order (>100 fold) than point mutations; however, none of the current detection methods are comprehensive, and currently available methodologies are incapable of providing sufficient resolution and unambiguous information across complex regions in the human genome. To address these challenges, we applied a high-throughput, cost-effective genome mapping technology to comprehensively discover genome-wide SVs and characterize complex regions of the YH genome using long single molecules (>150 kb) in a global fashion.ResultsUtilizing nanochannel-based genome mapping technology, we obtained 708 insertions/deletions and 17 inversions larger than 1 kb. Excluding the 59 SVs (54 insertions/deletions, 5 inversions) that overlap with N-base gaps in the reference assembly hg19, 666 non-gap SVs remained, and 396 of them (60%) were verified by paired-end data from whole-genome sequencing-based re-sequencing or de novo assembly sequence from fosmid data. Of the remaining 270 SVs, 260 are insertions and 213 overlap known SVs in the Database of Genomic Variants. Overall, 609 out of 666 (90%) variants were supported by experimental orthogonal methods or historical evidence in public databases. At the same time, genome mapping also provides valuable information for complex regions with haplotypes in a straightforward fashion. In addition, with long single-molecule labeling patterns, exogenous viral sequences were mapped on a whole-genome scale, and sample heterogeneity was analyzed at a new level.ConclusionOur study highlights genome mapping technology as a comprehensive and cost-effective method for detecting structural variation and studying complex regions in the human genome, as well as deciphering viral integration into the host genome.Electronic supplementary materialThe online version of this article (doi:10.1186/2047-217X-3-34) contains supplementary material, which is available to authorized users.

MicroRNAs (miRNAs) are a class of small, well-conserved noncoding RNAs that regulate gene expression post-transcriptionally. They have been demonstrated to regulate a lot of biological pathways and cellular functions. Many miRNAs are dynamically regulated during central nervous system (CNS) development and are spatially expressed in adult brain indicating their essential roles in neural development and function. In addition, accumulating evidence strongly suggests that dysfunction of miRNAs contributes to neurological diseases. These observations, together with their gene regulation property, implicated miRNAs to be the key regulators in the complex genetic network of the CNS. In this review, we first focus on the ways through which miRNAs exert the regulatory function and how miRNAs are regulated in the CNS. We then summarize recent findings that highlight the versatile roles of miRNAs in normal CNS physiology and their association with several types of neurological diseases. Subsequently we discuss the limitations of miRNAs research based on current studies as well as the potential therapeutic applications and challenges of miRNAs in neurological disorders. We endeavor to provide an updated description of the regulatory roles of miRNAs in normal CNS functions and pathogenesis of neurological diseases.