article · Genome Research
Fewer than half of individuals suspected of having a monogenic condition receive a precise molecular diagnosis through routine clinical testing. While long-read sequencing could improve testing, clinical adoption has been limited by the lack of baseline control datasets needed to filter and prioritise variants. To address this limitation, high-coverage nanopore sequencing was applied to an initial set of 100 diverse human genomes representing five global superpopulations. The approach identified an average of 24,543 structural variants per genome, revealing gene-disrupting variants and pathogenic repeat expansions that were missed by short-read sequencing technologies. The sequencing data also captured methylation profiles, identifying novel differentially methylated regions alongside expected patterns at imprinted loci and skewed X-inactivation. All raw and processed data are publicly accessible to provide a reference resource for discovering pathogenic structural variants.
Most individuals tested for suspected monogenic disorders fail to receive a precise molecular diagnosis. Long-read sequencing captures complex genomic alterations that standard methods miss, but clinical interpretation requires reference datasets from diverse populations to separate harmless variation from disease-causing mutations. Making these high-coverage datasets publicly available gives diagnostic teams the control data required to interpret complex structural variants accurately.
The release of this public reference dataset directly supports clinical diagnostics laboratories and genomic software developers. It enables the tertiary analysis and filtering of long-read sequencing data used to diagnose suspected monogenic conditions. As the dataset and summary statistics are already publicly available, the resource is immediately ready for integration into clinical bioinformatics pipelines and variant prioritisation tools without further commercial translation steps.
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Fewer than half of individuals with a suspected Mendelian or monogenic condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control data sets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project (1KGP) Oxford Nanopore Technologies Sequencing Consortium aims to generate LRS data from at least 800 of the 1KGP samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37× and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs.
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DOI: 10.1101/gr.279273.124
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