High-coverage nanopore sequencing of samples from the 1000 Genomes Project to build a comprehensive catalog of human genetic variation
- Jonas A Gustafson1,
- Sophia B Gibson1,
- Nikhita Damaraju2,
- Miranda PG Zalusky1,
- Kendra Hoekzema1,
- David Twesigomwe3,
- Lei Yang4,
- Anthony A Snead5,
- Phillip A Richmond6,
- Wouter De Coster7,
- Nathan D Olson8,
- Andrea Guarracino9,
- Qiuhui Li10,
- Angela L Miller1,
- Joy Goffena1,
- Zachery B Anderson1,
- Sophie HR Storz1,
- Sydney A Ward1,
- Maisha Sinha1,
- Claudia Gonzaga-Jauregui11,
- Wayne E Clarke12,
- Anna O Basile13,
- Andre Corvelo13,
- Catherine E Reeves13,
- Adrienne Helland13,
- Rajeeva Lochan Musunuri13,
- Mahler Revsine10,
- Karynne E Patterson1,
- Cate Paschal14,
- Christina Zakarian1,
- Sara Goodwin15,
- Tanner D Jensen16,
- Esther Robb16,
- 1000 Genomes ONT Sequencing Consortium17,
- University of Washington Center for Rare Disease Research (UW-CRDR)17,
- Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium17,
- W. Richard McCombie15,
- Fritz J Sedlazeck18,
- Justin M Zook8,
- Stephen B Montgomery16,
- Erik Garrison19,
- Mikhail Kolmogorov20,
- Michael C Schatz10,
- Richard N McLaughlin Jr.21,
- Harriet Dashnow22,
- Michael C Zody13,
- Matthew Loose23,
- Miten Jain24,
- Evan E Eichler25 and
- Danny E. Miller26,27
- 1 University of Washington;
- 2 Institute for Public Health Genetics, University of Washington;
- 3 Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand;
- 4 Pacific Northwest Research Institute;
- 5 New York University;
- 6 Alamya Health;
- 7 VIB Center for Molecular Neurology, University of Antwerp;
- 8 National Institute of Standards and Technology;
- 9 University of Tennessee Health Science Center, Human Technopole;
- 10 Johns Hopkins University;
- 11 International Laboratory for Human Genome Research, Laboratorio Internacional de Investigacion sobre el Genoma Humano, Universidad Nacional Autonoma de Mexico;
- 12 New York Genome Center, Outlier Informatics Inc.;
- 13 New York Genome Center;
- 14 Seattle Children's Hospital, University of Washington;
- 15 Cold Spring Harbor Laboratory;
- 16 Stanford University;
- 17 -;
- 18 Baylor College of Medicine, Rice University;
- 19 University of Tennessee Health Science Center;
- 20 Cancer Data Science Laboratory, National Cancer Institute, NIH;
- 21 University of Washington, Pacific Northwest Research Institute;
- 22 University of Utah, University of Colorado School of Medicine;
- 23 Deep Seq, University of Nottingham;
- 24 Northeastern University;
- 25 Brotman Baty Institute for Precision Medicine, Howard Hughes Medical Institute, University of Washington;
- 26 Seattle Children's Hospital
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 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.
- Received March 4, 2024.
- Accepted September 16, 2024.
- Published by Cold Spring Harbor Laboratory Press
This manuscript is Open Access.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International license), as described at http://creativecommons.org/licenses/by-nc/4.0/.
