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Dou J, Tan Y, Kock KH, Wang J, Cheng X, Tan LM, Han KY, Hon CC, Park WY, Shin JW, Jin H, Wang Y, Chen H, Ding L, Prabhakar S, Navin N, Chen R, Chen K. Single-nucleotide variant calling in single-cell sequencing data with Monopogen. Nat Biotechnol 2024; 42:803-812. [PMID: 37592035 PMCID: PMC11098741 DOI: 10.1038/s41587-023-01873-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/21/2023] [Indexed: 08/19/2023]
Abstract
Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.
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Affiliation(s)
- Jinzhuang Dou
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yukun Tan
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kian Hong Kock
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Jun Wang
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Xuesen Cheng
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Le Min Tan
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Kyung Yeon Han
- Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea
| | - Chung-Chau Hon
- Laboratory for Genome Information Analysis, RIKEN center for Integrative Medical Sciences, Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea
| | - Jay W Shin
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Laboratory for Advanced Genomics Circuit, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Haijing Jin
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yujia Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA
| | - Li Ding
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Shyam Prabhakar
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Nicholas Navin
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rui Chen
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Alexander N, Illius S, Feyerabend D, Wacker J, Liszkowski U. Don't miss the chance to reap the fruits of recent advances in behavioral genetics. Behav Brain Sci 2023; 46:e208. [PMID: 37694995 DOI: 10.1017/s0140525x22002497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
In her target article, Burt revives a by now ancient debate on nature and nurture, and the ways to measure, disentangle, and ultimately trust one or the other of these forces. Unfortunately, she largely dismisses recent advances in behavior genetics and its huge potential in contributing to a better prediction and understanding of complex traits in social sciences.
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Affiliation(s)
- Nina Alexander
- Department of Psychiatry and Psychotherapy, Philipps University Marburg, Marburg, Germany. ; UKGM Gießen/Marburg-Team
- Center for Mind, Brain and Behavior, Philipps University Marburg, Marburg, Germany
| | - Sabrina Illius
- Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Hamburg, Germany
- ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Hamburg, Germany
| | - Dennis Feyerabend
- Department of Developmental Psychology, University of Hamburg, Hamburg, Germany
| | - Jan Wacker
- Department of Differential Psychology and Psychological Assessment, University of Hamburg, Hamburg, Germany ; https://www.koku.uni-hamburg.de/en/koku-team/liszkowski.html
| | - Ulf Liszkowski
- Department of Developmental Psychology, University of Hamburg, Hamburg, Germany
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Reddi HV, Wand H, Funke B, Zimmermann MT, Lebo MS, Qian E, Shirts BH, Zou YS, Zhang BM, Rose NC, Abu-El-Haija A. Laboratory perspectives in the development of polygenic risk scores for disease: A points to consider statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2023; 25:100804. [PMID: 36971772 DOI: 10.1016/j.gim.2023.100804] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 01/27/2023] [Indexed: 03/29/2023] Open
Affiliation(s)
- Honey V Reddi
- Department of Pathology & Laboratory Medicine, Medical College of Wisconsin, Milwaukee, WI
| | - Hannah Wand
- Division of Cardiovascular Medicine, Department of Medicine, Stanford Medicine, Stanford, CA
| | | | - Michael T Zimmermann
- Bioinformatics Research and Development Laboratory, Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI
| | - Matthew S Lebo
- Laboratory for Molecular Medicine, Mass General Brigham, Cambridge, MA
| | - Emily Qian
- Department of Genetics, Yale University, New Haven, CT
| | - Brian H Shirts
- Department of Laboratory Medicine & Pathology, UW Medicine, University of Washington, Seattle, WA
| | - Ying S Zou
- Department of Genomic Medicine and Pathology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Bing M Zhang
- Department of Pathology, Stanford University School of Medicine, Stanford, CA
| | - Nancy C Rose
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Utah Health, Salt Lake City, UT
| | - Aya Abu-El-Haija
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA; Harvard Medical School, Boston, MA
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Genomics and Epigenomics of Gestational Diabetes Mellitus: Understanding the Molecular Pathways of the Disease Pathogenesis. Int J Mol Sci 2022; 23:ijms23073514. [PMID: 35408874 PMCID: PMC8998752 DOI: 10.3390/ijms23073514] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/01/2022] [Accepted: 03/04/2022] [Indexed: 11/16/2022] Open
Abstract
One of the most common complications during pregnancy is gestational diabetes mellitus (GDM), hyperglycemia that occurs for the first time during pregnancy. The condition is multifactorial, caused by an interaction between genetic, epigenetic, and environmental factors. However, the underlying mechanisms responsible for its pathogenesis remain elusive. Moreover, in contrast to several common metabolic disorders, molecular research in GDM is lagging. It is important to recognize that GDM is still commonly diagnosed during the second trimester of pregnancy using the oral glucose tolerance test (OGGT), at a time when both a fetal and maternal pathophysiology is already present, demonstrating the increased blood glucose levels associated with exacerbated insulin resistance. Therefore, early detection of metabolic changes and associated epigenetic and genetic factors that can lead to an improved prediction of adverse pregnancy outcomes and future cardio-metabolic pathologies in GDM women and their children is imperative. Several genomic and epigenetic approaches have been used to identify the genes, genetic variants, metabolic pathways, and epigenetic modifications involved in GDM to determine its etiology. In this article, we explore these factors as well as how their functional effects may contribute to immediate and future pathologies in women with GDM and their offspring from birth to adulthood. We also discuss how these approaches contribute to the changes in different molecular pathways that contribute to the GDM pathogenesis, with a special focus on the development of insulin resistance.
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Emde AK, Phipps-Green A, Cadzow M, Gallagher CS, Major TJ, Merriman ME, Topless RK, Takei R, Dalbeth N, Murphy R, Stamp LK, de Zoysa J, Wilcox PL, Fox K, Wasik KA, Merriman TR, Castel SE. Mid-pass whole genome sequencing enables biomedical genetic studies of diverse populations. BMC Genomics 2021; 22:666. [PMID: 34719381 PMCID: PMC8559369 DOI: 10.1186/s12864-021-07949-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 08/25/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Historically, geneticists have relied on genotyping arrays and imputation to study human genetic variation. However, an underrepresentation of diverse populations has resulted in arrays that poorly capture global genetic variation, and a lack of reference panels. This has contributed to deepening global health disparities. Whole genome sequencing (WGS) better captures genetic variation but remains prohibitively expensive. Thus, we explored WGS at "mid-pass" 1-7x coverage. RESULTS Here, we developed and benchmarked methods for mid-pass sequencing. When applied to a population without an existing genomic reference panel, 4x mid-pass performed consistently well across ethnicities, with high recall (98%) and precision (97.5%). CONCLUSION Compared to array data imputed into 1000 Genomes, mid-pass performed better across all metrics and identified novel population-specific variants with potential disease relevance. We hope our work will reduce financial barriers for geneticists from underrepresented populations to characterize their genomes prior to biomedical genetic applications.
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Affiliation(s)
| | | | - Murray Cadzow
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | | | - Tanya J Major
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | | | - Ruth K Topless
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Riku Takei
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nicola Dalbeth
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Rinki Murphy
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Lisa K Stamp
- University of Otago Christchurch, Christchurch, New Zealand
| | - Janak de Zoysa
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Philip L Wilcox
- Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand
| | - Keolu Fox
- Departments of Anthropology and Global Health, University of California, San Diego, CA, USA
| | | | - Tony R Merriman
- Department of Biochemistry, University of Otago, Dunedin, New Zealand.
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA.
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Paskov K, Jung JY, Chrisman B, Stockham NT, Washington P, Varma M, Sun MW, Wall DP. Estimating sequencing error rates using families. BioData Min 2021; 14:27. [PMID: 33892748 PMCID: PMC8063364 DOI: 10.1186/s13040-021-00259-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 03/29/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample. RESULTS We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method's versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites. CONCLUSION Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.
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Affiliation(s)
- Kelley Paskov
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
| | - Jae-Yoon Jung
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.,Department of Pediatrics (Systems Medicine), Stanford University, Stanford, CA, USA
| | - Brianna Chrisman
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Nate T Stockham
- Department of Neuroscience, Stanford University, Stanford, CA, USA
| | - Peter Washington
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Maya Varma
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Min Woo Sun
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Dennis P Wall
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. .,Department of Pediatrics (Systems Medicine), Stanford University, Stanford, CA, USA.
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Díaz-de Usera A, Lorenzo-Salazar JM, Rubio-Rodríguez LA, Muñoz-Barrera A, Guillen-Guio B, Marcelino-Rodríguez I, García-Olivares V, Mendoza-Alvarez A, Corrales A, Íñigo-Campos A, González-Montelongo R, Flores C. Evaluation of Whole-Exome Enrichment Solutions: Lessons from the High-End of the Short-Read Sequencing Scale. J Clin Med 2020; 9:jcm9113656. [PMID: 33202991 PMCID: PMC7696786 DOI: 10.3390/jcm9113656] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 11/10/2020] [Accepted: 11/10/2020] [Indexed: 12/13/2022] Open
Abstract
Whole-exome sequencing has become a popular technique in research and clinical settings, assisting in disease diagnosis and increasing the understanding of disease pathogenesis. In this study, we aimed to compare common enrichment capture solutions available in the market. Peripheral blood-purified DNA samples were enriched with SureSelectQXT V6 (Agilent) and various Illumina solutions: TruSeq DNA Nano, TruSeq DNA Exome, Nextera DNA Exome, and Illumina DNA Prep with Enrichment, and sequenced on a HiSeq 4000. We found that their percentage of duplicate reads was as much as 2 times higher than previously reported values for the previous HiSeq series. SureSelectQXT and Illumina DNA Prep with Enrichment showed the best average on-target coverage, which improved when off-target regions were included. At high coverage levels and in shared bases, these two solutions and TruSeq DNA Exome provided three of the best performances. With respect to the number of small variants detected, SureSelectQXT presented the lowest number of detected variants in target regions. When off-target regions were considered, its ability equalized to other solutions. Our results show SureSelectQXT and Illumina DNA Prep with Enrichment to be the best enrichment capture solutions.
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Affiliation(s)
- Ana Díaz-de Usera
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Jose M. Lorenzo-Salazar
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Luis A. Rubio-Rodríguez
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Adrián Muñoz-Barrera
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Beatriz Guillen-Guio
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, 38010 Santa Cruz de Tenerife, Spain; (B.G.-G.); (I.M.-R.); (A.M.-A.); (A.C.)
| | - Itahisa Marcelino-Rodríguez
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, 38010 Santa Cruz de Tenerife, Spain; (B.G.-G.); (I.M.-R.); (A.M.-A.); (A.C.)
- Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Spain
| | - Víctor García-Olivares
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Alejandro Mendoza-Alvarez
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, 38010 Santa Cruz de Tenerife, Spain; (B.G.-G.); (I.M.-R.); (A.M.-A.); (A.C.)
| | - Almudena Corrales
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, 38010 Santa Cruz de Tenerife, Spain; (B.G.-G.); (I.M.-R.); (A.M.-A.); (A.C.)
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Antonio Íñigo-Campos
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Rafaela González-Montelongo
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Carlos Flores
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, 38010 Santa Cruz de Tenerife, Spain; (B.G.-G.); (I.M.-R.); (A.M.-A.); (A.C.)
- Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Correspondence: ; Tel.: +34-922-602938
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