High-coverage nanopore sequencing of samples from the 1000 Genomes Project to build a comprehensive catalog of human genetic variation

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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Publication Details

Journal
Genome Research
Published
2024-10-02
DOI
https://doi.org/10.1101/gr.279273.124
Citations
67
Primary Topic
Genetic Syndromes and Imprinting
Type
article

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article

High-coverage nanopore sequencing of samples from the 1000 Genomes Project to build a comprehensive catalog of human genetic variation

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67 citations
Genome Research
Genetic Syndromes and Imprinting
article

High-coverage nanopore sequencing of samples from the 1000 Genomes Project to build a comprehensive catalog of human genetic variation

Joy Goffena, Erik Garrison, Anna O. Basile, Evan E. Eichler, Miten Jain, Cate Paschal, Mikhail Kolmogorov, Stephen B. Montgomery, Andrea Guarracino, Phillip A. Richmond, Karynne Patterson, Lei Yang, Adrienne Helland, Richard N. McLaughlin, Fritz Joachim Sedlazeck, Michael C. Zody, Mahler Revsine, Rajeeva Musunuri, S. Ward, André Corvelo, Zachery Anderson, Angela L Miller, Qiuhui Li, Michael C. Schatz, Justin M. Zook, Danny E. Miller, Kendra Hoekzema, Nikhita Damaraju, Nathan D. Olson, Matthew Loose, Anthony A. Snead, W. Richard McCombie, Jonas A. Gustafson, Wouter De Coster, Sara Goodwin, E. Robb, Sophie H. R. Storz, Claudia Gonzaga‐Jauregui, Wayne E. Clarke, Christina Zakarian, Harriet Dashnow, Sophia B Gibson, Catherine Reeves, Tanner Jensen, David Twesigomwe, Maisha Sinha, Miranda Pg Zalusky
article en
67 citations

Abstract

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.

Genome ResearchVol. 34(11)
Louisiana State Department of Health and Hospitals (US), Pacific Northwest Diabetes Research Institute (US), Northeastern University (US), Seattle Children's Hospital (US), National Institute of Standards and Technology (US), Howard Hughes Medical Institute (US), University of Nottingham (GB), Johns Hopkins University (US), University of Antwerp (BE), University of Tennessee Health Science Center (US), Baylor College of Medicine (US), University of the Witwatersrand (ZA), University of Washington (US), University of Utah (US), Cold Spring Harbor Laboratory (US), Cancer Institute (WIA) (IN), Canadian Bulletin of Medical History (CA), Institute of Translational Health Sciences (US), Seattle Children's Hospital (US), Human Technopole (IT), National Institute of Standards (EG), VIB-UAntwerp Center for Molecular Neurology (BE), Brotman Baty Institute (US), National Cancer Institute (US), Material Measurement Laboratory (US), New York Genome Center (US), Nottingham Trent University (GB), New York University (US), Rice University (US), Universidad del Noreste (MX), Universidad Nacional Autónoma de México (MX), University of Colorado Denver (US), Stanford University (US)
National Science Foundation, Fonds Wetenschappelijk Onderzoek, National Institutes of Health, National Institute of Standards and Technology
Partnerships for the goals
Genetic Syndromes and Imprinting
DOIOpen Access PDFOpenAlex
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