Over the past decade, the Database of Genomic Variants (DGV; http://dgv.tcag.ca/) has provided a publicly accessible, comprehensive curated catalogue of structural variation (SV) found in the genomes of control individuals from worldwide populations. Here, we describe updates and new features, which have expanded the utility of DGV for both the basic research and clinical diagnostic communities. The current version of DGV consists of 55 published studies, comprising >2.5 million entries identified in >22 300 genomes. Studies included in DGV are selected from the accessioned data sets in the archival SV databases dbVar (NCBI) and DGVa (EBI), and then further curated for accuracy and validity. The core visualization tool (gbrowse) has been upgraded with additional functions to facilitate data analysis and comparison, and a new query tool has been developed to provide flexible and interactive access to the data. The content from DGV is regularly incorporated into other large-scale genome reference databases and represents a standard data resource for new product and database development, in particular for copy number variation testing in clinical labs. The accurate cataloguing of variants in DGV will continue to enable medical genetics and genome sequencing research.

When embarking on a microarray-based study of genomic copy number variation, what's helpful for navigating the myriad of available array platforms and data analysis approaches? Pinto et al. evaluate six samples from healthy controls in triplicate on commonly used combinations of commercial arrays and analytic tools, providing realistic comparisons of performance. We have systematically compared copy number variant (CNV) detection on eleven microarrays to evaluate data quality and CNV calling, reproducibility, concordance across array platforms and laboratory sites, breakpoint accuracy and analysis tool variability. Different analytic tools applied to the same raw data typically yield CNV calls with <50% concordance. Moreover, reproducibility in replicate experiments is <70% for most platforms. Nevertheless, these findings should not preclude detection of large CNVs for clinical diagnostic purposes because large CNVs with poor reproducibility are found primarily in complex genomic regions and would typically be removed by standard clinical data curation. The striking differences between CNV calls from different platforms and analytic tools highlight the importance of careful assessment of experimental design in discovery and association studies and of strict data curation and filtering in diagnostics. The CNV resource presented here allows independent data evaluation and provides a means to benchmark new algorithms.

Here we describe the SweGen data set, a comprehensive map of genetic variation in the Swedish population. These data represent a basic resource for clinical genetics laboratories as well as for sequencing-based association studies by providing information on genetic variant frequencies in a cohort that is well matched to national patient cohorts. To select samples for this study, we first examined the genetic structure of the Swedish population using high-density SNP-array data from a nation-wide cohort of over 10 000 Swedish-born individuals included in the Swedish Twin Registry. A total of 1000 individuals, reflecting a cross-section of the population and capturing the main genetic structure, were selected for whole-genome sequencing. Analysis pipelines were developed for automated alignment, variant calling and quality control of the sequencing data. This resulted in a genome-wide collection of aggregated variant frequencies in the Swedish population that we have made available to the scientific community through the website https://swefreq.nbis.se. A total of 29.2 million single-nucleotide variants and 3.8 million indels were detected in the 1000 samples, with 9.9 million of these variants not present in current databases. Each sample contributed with an average of 7199 individual-specific variants. In addition, an average of 8645 larger structural variants (SVs) were detected per individual, and we demonstrate that the population frequencies of these SVs can be used for efficient filtering analyses. Finally, our results show that the genetic diversity within Sweden is substantial compared with the diversity among continental European populations, underscoring the relevance of establishing a local reference data set.

CRISPR-Cas9 genome editing has potential to cure diseases without current treatments, but therapies must be safe. Here we show that CRISPR-Cas9 editing can introduce unintended mutations in vivo, which are passed on to the next generation. By editing fertilized zebrafish eggs using four guide RNAs selected for off-target activity in vitro, followed by long-read sequencing of DNA from >1100 larvae, juvenile and adult fish across two generations, we find that structural variants (SVs), i.e., insertions and deletions ≥50 bp, represent 6% of editing outcomes in founder larvae. These SVs occur both at on-target and off-target sites. Our results also illustrate that adult founder zebrafish are mosaic in their germ cells, and that 26% of their offspring carries an off-target mutation and 9% an SV. Hence, pre-testing for off-target activity and SVs using patient material is advisable in clinical applications, to reduce the risk of unanticipated effects with potentially large implications.

Background: De novo mutations are a frequent cause of disorders related to brain development. We report the results of screening patients diagnosed with both epilepsy and intellectual disability (ID) using exome sequencing to identify known and new causative de novo mutations relevant to these conditions. Methods: Exome sequencing was performed on 39 patient–parent trios to identify de novo mutations. Clinical significance of de novo mutations in genes was determined using the American College of Medical Genetics and Genomics standard guidelines for interpretation of coding variants. Variants in genes of unknown clinical significance were further analysed in the context of previous trio sequencing efforts in neurodevelopmental disorders. Results: In 39 patient–parent trios we identified 29 de novo mutations in coding sequence. Analysis of de novo and inherited variants yielded a molecular diagnosis in 11 families (28.2%). In combination with previously published exome sequencing results in neurodevelopmental disorders, our analysis implicates HECW2 as a novel candidate gene in ID and epilepsy. Conclusions: Our results support the use of exome sequencing as a diagnostic approach for ID and epilepsy, and confirm previous results regarding the importance of de novo mutations in this patient group. The results also highlight the utility of network analysis and comparison to previous large-scale studies as strategies to prioritise candidate genes for further studies. This study adds knowledge to the increasingly growing list of causative and candidate genes in ID and epilepsy and highlights HECW2 as a new candidate gene for neurodevelopmental disorders.

The current human reference sequence (GRCh38) is a foundation for large-scale sequencing projects. However, recent studies have suggested that GRCh38 may be incomplete and give a suboptimal representation of specific population groups. Here, we performed a de novo assembly of two Swedish genomes that revealed over 10 Mb of sequences absent from the human GRCh38 reference in each individual. Around 6 Mb of these novel sequences (NS) are shared with a Chinese personal genome. The NS are highly repetitive, have an elevated GC-content, and are primarily located in centromeric or telomeric regions. Up to 1 Mb of NS can be assigned to chromosome Y, and large segments are also missing from GRCh38 at chromosomes 14, 17, and 21. Inclusion of NS into the GRCh38 reference radically improves the alignment and variant calling from short-read whole-genome sequencing data at several genomic loci. A re-analysis of a Swedish population-scale sequencing project yields > 75,000 putative novel single nucleotide variants (SNVs) and removes > 10,000 false positive SNV calls per individual, some of which are located in protein coding regions. Our results highlight that the GRCh38 reference is not yet complete and demonstrate that personal genome assemblies from local populations can improve the analysis of short-read whole-genome sequencing data.