preprint
Standard clinical genetic tests fail to provide a definitive molecular diagnosis for more than half of individuals suspected of having a Mendelian disorder. While long-read sequencing can capture complex genetic variation, the lack of reference control datasets has restricted its use for variant filtering in diagnostics. To resolve this bottleneck, nanopore sequencing was applied to samples from diverse global populations within the 1000 Genomes Project. Analysis of the initial one hundred genomes detected tens of thousands of structural variants per individual, successfully uncovering pathogenic repeat expansions and gene-disrupting alterations that short-read methods miss. The dataset also profiles base modifications, identifying expected and novel DNA methylation patterns across the genome. This publicly accessible catalog provides a critical baseline of standard human genetic variation to support clinical genomic testing and disease research.
Identifying the genetic causes of rare inherited disorders remains difficult with standard technologies. By creating a comprehensive reference catalogue of structural and epigenetic variations across diverse human populations, this work equips medical geneticists with the baseline data needed to distinguish benign natural variations from disease-causing mutations.
This research provides reference data to advance clinical genomics and diagnostic test development. The direct users are diagnostic laboratories, clinical genetics services, and bioinformatics tool developers who require baseline datasets to filter and prioritise variants. As an open-access foundational resource with early data already public, it is immediately usable for tertiary analysis pipelines, though clinical diagnostic integration remains an ongoing, applied process.
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Less than half of individuals with a suspected Mendelian 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 datasets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project ONT Sequencing Consortium aims to generate LRS data from at least 800 of the 1000 Genomes Project 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 37x 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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1101/2024.03.05.24303792
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