Gustafson, Jonas A, Gibson, Sophia B, Damaraju, Nikhita, Zalusky, Miranda Pg, Hoekzema, Kendra, Twesigomwe, David, Yang, Lei, Snead, Anthony A, Richmond, Phillip A, De Coster, Wouter, Olson, Nathan D, Guarracino, Andrea, Li, Qiuhui, Miller, Angela L, Goffena, Joy, Anderson, Zachery, Storz, Sophie Hr, Ward, Sydney A, Sinha, Maisha, Gonzaga-Jauregui, Claudia, Clarke, Wayne E, Basile, Anna O, Corvelo, Andre, Reeves, Catherine E, Helland, Adrienne, Musunuri, Rajeeva Lochan, Revsine, Mahler, Patterson, Karynne E, Paschal, Cate, Zakarian, Christina, Goodwin, Sara, Jensen, Tanner D, Robb, Esther, 1000 Genomes ONT Sequencing Consortium, University of Washington Center for Rare Disease Research (UW-CR, Genomics Research to Elucidate the Genetics of Rare Diseases (GR, McCombie, W Richard, Sedlazeck, Fritz J, Zook, Justin M, Montgomery, Stephen B, Garrison, Erik, Kolmogorov, Mikhail, Schatz, Michael C, McLaughlin, Richard N, Dashnow, Harriet, Zody, Michael C, Loose, Matthew, Jain, Miten, Eichler, Evan E, Miller, Danny E (March 2024) Nanopore sequencing of 1000 Genomes Project samples to build a comprehensive catalog of human genetic variation. medRxiv. (Public Dataset) (Submitted)
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Abstract
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.
| Item Type: | Paper |
|---|---|
| Subjects: | bioinformatics bioinformatics > genomics and proteomics > genetics & nucleic acid processing bioinformatics > genomics and proteomics bioinformatics > genomics and proteomics > genetics & nucleic acid processing > genomes |
| CSHL Authors: | |
| Communities: | CSHL labs > McCombie lab CSHL labs > Goodwin lab |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 7 March 2024 |
| Date Deposited: | 19 Mar 2024 16:47 |
| Last Modified: | 19 Mar 2024 16:47 |
| PMCID: | PMC10942501 |
| Related URLs: | |
| Dataset ID: |
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| URI: | https://repository.cshl.edu/id/eprint/41468 |
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