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Tume CE, Chick SL, Holmans PA, Rees E, O’Donovan MC, Cameron D, Bray NJ. Genetic Implication of Specific Glutamatergic Neurons of the Prefrontal Cortex in the Pathophysiology of Schizophrenia. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100345. [PMID: 39099730 PMCID: PMC11295574 DOI: 10.1016/j.bpsgos.2024.100345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/03/2024] [Accepted: 05/19/2024] [Indexed: 08/06/2024] Open
Abstract
Background The prefrontal cortex (PFC) has been strongly implicated in the pathophysiology of schizophrenia. Here, we combined high-resolution single-nuclei RNA sequencing data from the human PFC with large-scale genomic data for schizophrenia to identify constituent cell populations likely to mediate genetic liability to the disorder. Methods Gene expression specificity values were calculated from a single-nuclei RNA sequencing dataset comprising 84 cell populations from the human PFC, spanning gestation to adulthood. Enrichment of schizophrenia common variant liability and burden of rare protein-truncating coding variants were tested in genes with high expression specificity for each cell type. We also explored schizophrenia common variant associations in relation to gene expression across the developmental trajectory of implicated neurons. Results Common risk variation for schizophrenia was prominently enriched in genes with high expression specificity for a population of mature layer 4 glutamatergic neurons emerging in infancy. Common variant liability to schizophrenia increased along the developmental trajectory of this neuronal population. Fine-mapped genes at schizophrenia genome-wide association study risk loci had significantly higher expression specificity than other genes in these neurons and in a population of layer 5/6 glutamatergic neurons. People with schizophrenia had a higher rate of rare protein-truncating coding variants in genes expressed by cells of the PFC than control individuals, but no cell population was significantly enriched above this background rate. Conclusions We identified a population of layer 4 glutamatergic PFC neurons likely to be particularly affected by common variant genetic risk for schizophrenia, which may contribute to disturbances in thalamocortical connectivity in the condition.
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Affiliation(s)
- Claire E. Tume
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Sophie L. Chick
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Peter A. Holmans
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Elliott Rees
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Michael C. O’Donovan
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Darren Cameron
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Nicholas J. Bray
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
- Neuroscience & Mental Health Innovation Institute, Cardiff University, Cardiff, Wales, United Kingdom
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2
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Yuan K, Longchamps RJ, Pardiñas AF, Yu M, Chen TT, Lin SC, Chen Y, Lam M, Liu R, Xia Y, Guo Z, Shi W, Shen C, Daly MJ, Neale BM, Feng YCA, Lin YF, Chen CY, O'Donovan MC, Ge T, Huang H. Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases. Nat Genet 2024; 56:1841-1850. [PMID: 39187616 DOI: 10.1038/s41588-024-01870-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 07/15/2024] [Indexed: 08/28/2024]
Abstract
Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestry has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping. SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and linkage disequilibrium patterns, accounts for multiple causal variants in a genomic region and can be applied to GWAS summary statistics. We comprehensively assessed the performance of SuSiEx using simulations. We further showed that SuSiEx improves the fine-mapping of a range of quantitative traits available in both the UK Biobank and Taiwan Biobank, and improves the fine-mapping of schizophrenia-associated loci by integrating GWAS across East Asian and European ancestries.
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Affiliation(s)
- Kai Yuan
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ryan J Longchamps
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Antonio F Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Mingrui Yu
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Tzu-Ting Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Shu-Chin Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Yu Chen
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Max Lam
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Human Genetics, Genome Institute of Singapore, Singapore, Singapore
- Division of Psychiatry Research, the Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Research Division Institute of Mental Health Singapore, Singapore, Singapore
| | - Ruize Liu
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Yan Xia
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Zhenglin Guo
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wenzhao Shi
- Digital Health China Technologies Corp. Ltd, Beijing, China
| | - Chengguo Shen
- Digital Health China Technologies Corp. Ltd, Beijing, China
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yen-Chen A Feng
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
- Department of Public Health & Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | | | - Michael C O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Tian Ge
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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Baytar AA, Yanar EG, Frary A, Doğanlar S. Association mapping and candidate gene identification for yield traits in European hazelnut ( Corylus avellana L.). PLANT DIRECT 2024; 8:e625. [PMID: 39170862 PMCID: PMC11336203 DOI: 10.1002/pld3.625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 07/10/2024] [Accepted: 07/11/2024] [Indexed: 08/23/2024]
Abstract
European hazelnut (Corylus avellana L.) is an important nut crop due to its nutritional benefits, culinary uses, and economic value. Türkiye is the leading producer of hazelnut, followed by Italy and the United States. Quantitative trait locus studies offer promising opportunities for breeders and geneticists to identify genomic regions controlling desirable traits in hazelnut. A genome-wide association analysis was conducted with 5,567 single nucleotide polymorphisms on a Turkish core set of 86 hazelnut accessions, revealing 189 quantitative trait nucleotides (QTNs) associated with 22 of 31 traits (p < 2.9E-07). These QTNs were associated with plant and leaf, phenological, reproductive, nut, and kernel traits. Based on the close physical distance of QTNs associated with the same trait, we identified 23 quantitative trait loci. Furthermore, we identified 23 loci of multiple QTs comprising chromosome locations associated with more than one trait at the same position or in close proximity. A total of 159 candidate genes were identified for 189 QTNs, with 122 of them containing significant conserved protein domains. Some candidate matches to known proteins/domains were highly significant, suggesting that they have similar functions as their matches. This comprehensive study provides valuable insights for the development of breeding strategies and the improvement of hazelnut and enhances the understanding of the genetic architecture of complex traits by proposing candidate genes and potential functions.
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Affiliation(s)
- Asena Akköse Baytar
- Department of Molecular Biology and Genetics, Faculty of ScienceIzmir Institute of TechnologyIzmirTürkiye
| | - Ertuğrul Gazi Yanar
- Department of Molecular Biology and Genetics, Faculty of ScienceIzmir Institute of TechnologyIzmirTürkiye
| | - Anne Frary
- Department of Molecular Biology and Genetics, Faculty of ScienceIzmir Institute of TechnologyIzmirTürkiye
| | - Sami Doğanlar
- Department of Molecular Biology and Genetics, Faculty of ScienceIzmir Institute of TechnologyIzmirTürkiye
- Plant Science and Technology Application and Research CenterIzmir Institute of TechnologyIzmirTürkiye
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Liu Y, Kuang W, Yue B, Zhou C. Genomic diversity and demographic history of the endangered Sichuan hill-partridge (Arborophila rufipectus). J Hered 2024; 115:532-540. [PMID: 38635970 DOI: 10.1093/jhered/esae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 04/17/2024] [Indexed: 04/20/2024] Open
Abstract
Species conservation can be improved by knowledge of genetic diversity and demographic history. The Sichuan hill-partridge (Arborophila rufipectus, SP) is an endangered species endemic to the mountains in southwestern China. However, little is known about this species' genomic variation and demographic history. Here, we present a comprehensive whole-genome analysis of six SP individuals from the Laojunshan National Nature Reserve in Sichuan Province, China. We observe a relatively high genetic diversity and low level of recent inbreeding in the studied SP individuals. This suggests that the current population carries genetic variability that may benefit the long-term survival of this species, and that the present population may be larger than currently recognized. Analyses of demographic history showed that fluctuations in the effective population size of SP are inconsistent with changes of the historical climate. Strikingly, evidence from demographic modeling suggests SPs population decreased dramatically 15,100 years ago after the Last Glacial Maximum, possibly due to refugial isolation and later human interference. These results provide the first detailed and comprehensive genomic insights into genetic diversity, genomic inbreeding levels, and demographic history of the Sichuan hill-partridge, which are crucial for the conservation and management of this endangered species.
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Affiliation(s)
- Yi Liu
- Fishes Conservation and Utilization in the Upper Reaches of the Yangtze River Key Laboratory of Sichuan Province, Neijiang Normal University, Neijiang, China
- Key Laboratory of Bioresources and Eco-environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu, China
| | - Weimin Kuang
- State Key Laboratory for Conservation and Utilization of Bio-Resource in Yunnan, School of Life Sciences, Yunnan University, Kunming, China
| | - Bisong Yue
- Key Laboratory of Bioresources and Eco-environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu, China
| | - Chuang Zhou
- Key Laboratory of Bioresources and Eco-environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu, China
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5
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Baumann A, Ruckert C, Meier C, Hutschenreiter T, Remy R, Schnur B, Döbel M, Fankep RCN, Skowronek D, Kutz O, Arnold N, Katzke AL, Forster M, Kobiela AL, Thiedig K, Zimmer A, Ritter J, Weber BHF, Honisch E, Hackmann K, Schmidt G, Sturm M, Ernst C. Limitations in next-generation sequencing-based genotyping of breast cancer polygenic risk score loci. Eur J Hum Genet 2024; 32:987-997. [PMID: 38907004 PMCID: PMC11291653 DOI: 10.1038/s41431-024-01647-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/17/2024] [Accepted: 06/10/2024] [Indexed: 06/23/2024] Open
Abstract
Considering polygenic risk scores (PRSs) in individual risk prediction is increasingly implemented in genetic testing for hereditary breast cancer (BC) based on next-generation sequencing (NGS). To calculate individual BC risks, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) with the inclusion of the BCAC 313 or the BRIDGES 306 BC PRS is commonly used. The PRS calculation depends on accurately reproducing the variant allele frequencies (AFs) and, consequently, the distribution of PRS values anticipated by the algorithm. Here, the 324 loci of the BCAC 313 and the BRIDGES 306 BC PRS were examined in population-specific database gnomAD and in real-world data sets of five centers of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), to determine whether these expected AFs can be reproduced by NGS-based genotyping. Four PRS loci were non-existent in gnomAD v3.1.2 non-Finnish Europeans, further 24 loci showed noticeably deviating AFs. In real-world data, between 11 and 23 loci were reported with noticeably deviating AFs, and were shown to have effects on final risk prediction. Deviations depended on the sequencing approach, variant caller and calling mode (forced versus unforced) employed. Therefore, this study demonstrates the necessity to apply quality assurance not only in terms of sequencing coverage but also observed AFs in a sufficiently large cohort, when implementing PRSs in a routine diagnostic setting. Furthermore, future PRS design should be guided by the technical reproducibility of expected AFs across commonly used genotyping methods, especially NGS, in addition to the observed effect sizes.
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Affiliation(s)
- Alexandra Baumann
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at TUD Dresden University of Technology and Faculty of Medicine of TUD Dresden University of Technology, Dresden, Germany
- ERN GENTURIS, Hereditary Cancer Syndrome Center Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), NCT/UCC Dresden, a partnership between German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology and Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- German Cancer Consortium (DKTK), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Christian Ruckert
- Department of Medical Genetics, University Hospital Münster, Münster, Germany
| | - Christoph Meier
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
| | - Tim Hutschenreiter
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at TUD Dresden University of Technology and Faculty of Medicine of TUD Dresden University of Technology, Dresden, Germany
- ERN GENTURIS, Hereditary Cancer Syndrome Center Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), NCT/UCC Dresden, a partnership between German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology and Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- German Cancer Consortium (DKTK), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Robert Remy
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University of Cologne and University Hospital Cologne, Cologne, Germany
| | - Benedikt Schnur
- Department of Human Genetics, Hannover Medical School (MHH), Hannover, Germany
| | - Marvin Döbel
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Rudel Christian Nkouamedjo Fankep
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University of Cologne and University Hospital Cologne, Cologne, Germany
| | - Dariush Skowronek
- Department of Human Genetics, University Medicine Greifswald and Interfaculty Institute of Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Oliver Kutz
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at TUD Dresden University of Technology and Faculty of Medicine of TUD Dresden University of Technology, Dresden, Germany
- ERN GENTURIS, Hereditary Cancer Syndrome Center Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), NCT/UCC Dresden, a partnership between German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology and Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- German Cancer Consortium (DKTK), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
- Department of Gynecology and Obstetrics, University Hospital Carl Gustav Carus at TUD Dresden University of Technology and Faculty of Medicine of TUD Dresden University of Technology, Dresden, Germany
| | - Norbert Arnold
- Department of Gynecology and Obstetrics, Institute of Clinical Chemistry Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Anna-Lena Katzke
- Department of Human Genetics, Hannover Medical School (MHH), Hannover, Germany
| | - Michael Forster
- Department of Gynecology and Obstetrics, Institute of Clinical Chemistry Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Anna-Lena Kobiela
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University of Cologne and University Hospital Cologne, Cologne, Germany
| | - Katharina Thiedig
- Division of Gynaecology and Obstetrics, Klinikum rechts der Isar der Technischen Universität München, München, Germany
| | - Andreas Zimmer
- Institute for Human Genetics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Julia Ritter
- Department of Human Genetics, Labor Berlin - Charité Vivantes GmbH, Berlin, Germany
| | - Bernhard H F Weber
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
- Institute of Clinical Human Genetics, University Hospital Regensburg, Regensburg, Germany
| | - Ellen Honisch
- Department of Gynaecology and Obstetrics, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Karl Hackmann
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at TUD Dresden University of Technology and Faculty of Medicine of TUD Dresden University of Technology, Dresden, Germany
- ERN GENTURIS, Hereditary Cancer Syndrome Center Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), NCT/UCC Dresden, a partnership between German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology and Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- German Cancer Consortium (DKTK), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Gunnar Schmidt
- Department of Human Genetics, Hannover Medical School (MHH), Hannover, Germany
| | - Marc Sturm
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Corinna Ernst
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University of Cologne and University Hospital Cologne, Cologne, Germany.
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Doublet M, Degalez F, Lagarrigue S, Lagoutte L, Gueret E, Allais S, Lecerf F. Variant calling and genotyping accuracy of ddRAD-seq: Comparison with 20X WGS in layers. PLoS One 2024; 19:e0298565. [PMID: 39058708 PMCID: PMC11280156 DOI: 10.1371/journal.pone.0298565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/23/2024] [Indexed: 07/28/2024] Open
Abstract
Whole Genome Sequencing (WGS) remains a costly or unsuitable method for routine genotyping of laying hens. Until now, breeding companies have been using or developing SNP chips. Nevertheless, alternatives methods based on sequencing have been developed. Among these, reduced representation sequencing approaches can offer sequencing quality and cost-effectiveness by reducing the genomic regions covered by sequencing. The aim of this study was to evaluate the ability of double digested Restriction site Associated DNA sequencing (ddRAD-seq) to identify and genotype SNPs in laying hens, by comparison with a presumed reliable WGS approach. Firstly, the sensitivity and precision of variant calling and the genotyping reliability of ddRADseq were determined. Next, the SNP Call Rate (CRSNP) and mean depth of sequencing per SNP (DPSNP) were compared between both methods. Finally, the effect of multiple combinations of thresholds for these parameters on genotyping reliability and amount of remaining SNPs in ddRAD-seq was studied. In raw form, the ddRAD-seq identified 349,497 SNPs evenly distributed on the genome with a CRSNP of 0.55, a DPSNP of 11X and a mean genotyping reliability rate per SNP of 80%. Considering genomic regions covered by expected enzymatic fragments (EFs), the sensitivity of the ddRAD-seq was estimated at 32.4% and its precision at 96.4%. The low CRSNP and DPSNP values were explained by the detection of SNPs outside the EFs theoretically generated by the ddRAD-seq protocol. Indeed, SNPs outside the EFs had significantly lower CRSNP (0.25) and DPSNP (1X) values than SNPs within the EFs (0.7 and 17X, resp.). The study demonstrated the relationship between CRSNP, DPSNP, genotyping reliability and the number of SNPs retained, to provide a decision-support tool for defining filtration thresholds. Severe quality control over ddRAD-seq data allowed to retain a minimum of 40% of the SNPs with a CcR of 98%. Then, ddRAD-seq was defined as a suitable method for variant calling and genotyping in layers.
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Affiliation(s)
| | | | | | | | - Elise Gueret
- MGX-Montpellier GenomiX, Univ. Montpellier, CNRS, INSERM, Montpellier, France
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7
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Junjun R, Zhengqian Z, Ying W, Jialiang W, Yongzhuang L. A comprehensive review of deep learning-based variant calling methods. Brief Funct Genomics 2024; 23:303-313. [PMID: 38366908 DOI: 10.1093/bfgp/elae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/14/2024] [Accepted: 01/18/2023] [Indexed: 02/18/2024] Open
Abstract
Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning-based approaches for variation detection have gained attention due to their ability to automatically learn genomic features that distinguish between variants. In our review, we discuss the recent advancements in deep learning-based algorithms for detecting small variations and structural variations in genomic data, as well as their advantages and limitations.
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Affiliation(s)
- Ren Junjun
- Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China
| | - Zhang Zhengqian
- Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China
| | - Wu Ying
- Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China
| | - Wang Jialiang
- Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China
| | - Liu Yongzhuang
- Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China
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8
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Heczko L, Liška V, Vyčítal O, Fiala O, Šůsová S, Hlaváč V, Souček P. Targeted panel sequencing of pharmacogenes and oncodrivers in colorectal cancer patients reveals genes with prognostic significance. Hum Genomics 2024; 18:83. [PMID: 39030589 PMCID: PMC11264515 DOI: 10.1186/s40246-024-00644-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/26/2024] [Indexed: 07/21/2024] Open
Abstract
BACKGROUND Colorectal cancer is still the second leading cause of cancer-related deaths and thus biomarkers allowing prediction of the resistance of patients to therapy and estimating their prognosis are needed. We designed a panel of 558 genes with pharmacogenomics records related to 5-fluorouracil resistance, genes important for sensitivity to other frequently used drugs, major oncodrivers, and actionable genes. We performed a target enrichment sequencing of DNA from tumors and matched blood samples of patients, and compared the results with patient prognosis stratified by systemic adjuvant chemotherapy. RESULTS The median number of detected variants per tumor sample was 18.5 with 4 classified as having a high predicted functional effect and 14.5 moderate effect. APC, TP53, and KRAS were the most frequent mutated genes (64%, 59%, and 42% of mutated samples, respectively) followed by FAT4 (23%), FBXW7, and PIK3CA (16% for both). Patients with advanced stage III had more frequently APC, TP53, or KRAS mutations than those in stages I or II. KRAS mutation counts followed an increasing trend with grade (G1 < G2 < G3). The response to adjuvant therapy was worse in carriers of frameshift mutations in APC or 12D variant in KRAS, but none of these oncodrivers had prognostic value. Carriage of somatic mutations in any of the genes ABCA13, ANK2, COL7A1, NAV3, or UNC80 had prognostic relevance for worse overall survival (OS) of all patients. In contrast, mutations in FLG, GLI3, or UNC80 were prognostic in the same direction for patients untreated, and mutations in COL6A3, LRP1B, NAV3, RYR1, RYR3, TCHH, or TENM4 for patients treated with adjuvant therapy. The first association was externally validated. From all germline variants with high or moderate predicted functional effects (median 326 per patient), > 5% frequency and positive Manhattan plot based on 3-year RFS, rs72753407 in NFACS, rs34621071 in ERBB4, and rs2444274 in RIF1 were significantly associated with RFS, OS or both. CONCLUSIONS The present study identified several putative somatic and germline genetic events with prognostic potential for colorectal cancer that should undergo functional characterization.
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Affiliation(s)
- Lucie Heczko
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, alej Svobody 1655/76, Pilsen, 323 00, Czech Republic
| | - Václav Liška
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, alej Svobody 1655/76, Pilsen, 323 00, Czech Republic
- Department of Surgery, Faculty of Medicine and University Hospital in Pilsen, Charles University, Pilsen, Czech Republic
| | - Ondřej Vyčítal
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, alej Svobody 1655/76, Pilsen, 323 00, Czech Republic
- Department of Surgery, Faculty of Medicine and University Hospital in Pilsen, Charles University, Pilsen, Czech Republic
| | - Ondřej Fiala
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, alej Svobody 1655/76, Pilsen, 323 00, Czech Republic
- Department of Oncology and Radiotherapeutics, Faculty of Medicine and University Hospital in Pilsen, Charles University, Pilsen, Czech Republic
| | - Simona Šůsová
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, alej Svobody 1655/76, Pilsen, 323 00, Czech Republic
- Toxicogenomics Unit, National Institute of Public Health, Prague, Czech Republic
| | - Viktor Hlaváč
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, alej Svobody 1655/76, Pilsen, 323 00, Czech Republic.
- Toxicogenomics Unit, National Institute of Public Health, Prague, Czech Republic.
| | - Pavel Souček
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, alej Svobody 1655/76, Pilsen, 323 00, Czech Republic.
- Toxicogenomics Unit, National Institute of Public Health, Prague, Czech Republic.
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9
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Lassen FH, Venkatesh SS, Baya N, Hill B, Zhou W, Bloemendal A, Neale BM, Kessler BM, Whiffin N, Lindgren CM, Palmer DS. Exome-wide evidence of compound heterozygous effects across common phenotypes in the UK Biobank. CELL GENOMICS 2024; 4:100602. [PMID: 38944039 PMCID: PMC11293579 DOI: 10.1016/j.xgen.2024.100602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 03/11/2024] [Accepted: 06/07/2024] [Indexed: 07/01/2024]
Abstract
The phenotypic impact of compound heterozygous (CH) variation has not been investigated at the population scale. We phased rare variants (MAF ∼0.001%) in the UK Biobank (UKBB) exome-sequencing data to characterize recessive effects in 175,587 individuals across 311 common diseases. A total of 6.5% of individuals carry putatively damaging CH variants, 90% of which are only identifiable upon phasing rare variants (MAF < 0.38%). We identify six recessive gene-trait associations (p < 1.68 × 10-7) after accounting for relatedness, polygenicity, nearby common variants, and rare variant burden. Of these, just one is discovered when considering homozygosity alone. Using longitudinal health records, we additionally identify and replicate a novel association between bi-allelic variation in ATP2C2 and an earlier age at onset of chronic obstructive pulmonary disease (COPD) (p < 3.58 × 10-8). Genetic phase contributes to disease risk for gene-trait pairs: ATP2C2-COPD (p = 0.000238), FLG-asthma (p = 0.00205), and USH2A-visual impairment (p = 0.0084). We demonstrate the power of phasing large-scale genetic cohorts to discover phenome-wide consequences of compound heterozygosity.
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Affiliation(s)
- Frederik H Lassen
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Samvida S Venkatesh
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Nikolas Baya
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Barney Hill
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Wei Zhou
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alex Bloemendal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Novo Nordisk Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin M Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Benedikt M Kessler
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nicola Whiffin
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Cecilia M Lindgren
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK.
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK.
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10
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Phillips AR. Variant calling in polyploids for population and quantitative genetics. APPLICATIONS IN PLANT SCIENCES 2024; 12:e11607. [PMID: 39184203 PMCID: PMC11342233 DOI: 10.1002/aps3.11607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/03/2024] [Accepted: 04/10/2024] [Indexed: 08/27/2024]
Abstract
Advancements in genome assembly and sequencing technology have made whole genome sequence (WGS) data and reference genomes accessible to study polyploid species. Compared to popular reduced-representation sequencing approaches, the genome-wide coverage and greater marker density provided by WGS data can greatly improve our understanding of polyploid species and polyploid biology. However, biological features that make polyploid species interesting also pose challenges in read mapping, variant identification, and genotype estimation. Accounting for characteristics in variant calling like allelic dosage uncertainty, homology between subgenomes, and variance in chromosome inheritance mode can reduce errors. Here, I discuss the challenges of variant calling in polyploid WGS data and discuss where potential solutions can be integrated into a standard variant calling pipeline.
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Affiliation(s)
- Alyssa R. Phillips
- Department of Evolution and EcologyUniversity of California, DavisDavis95616CaliforniaUSA
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11
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Martin CA, Sheppard EC, Ali HAA, Illera JC, Suh A, Spurgin LG, Richardson DS. Genomic landscapes of divergence among island bird populations: Evidence of parallel adaptation but at different loci? Mol Ecol 2024; 33:e17365. [PMID: 38733214 DOI: 10.1111/mec.17365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 03/01/2024] [Indexed: 05/13/2024]
Abstract
When populations colonise new environments, they may be exposed to novel selection pressures but also suffer from extensive genetic drift due to founder effects, small population sizes and limited interpopulation gene flow. Genomic approaches enable us to study how these factors drive divergence, and disentangle neutral effects from differentiation at specific loci due to selection. Here, we investigate patterns of genetic diversity and divergence using whole-genome resequencing (>22× coverage) in Berthelot's pipit (Anthus berthelotii), a passerine endemic to the islands of three north Atlantic archipelagos. Strong environmental gradients, including in pathogen pressure, across populations in the species range, make it an excellent system in which to explore traits important in adaptation and/or incipient speciation. First, we quantify how genomic divergence accumulates across the speciation continuum, that is, among Berthelot's pipit populations, between sub species across archipelagos, and between Berthelot's pipit and its mainland ancestor, the tawny pipit (Anthus campestris). Across these colonisation timeframes (2.1 million-ca. 8000 years ago), we identify highly differentiated loci within genomic islands of divergence and conclude that the observed distributions align with expectations for non-neutral divergence. Characteristic signatures of selection are identified in loci associated with craniofacial/bone and eye development, metabolism and immune response between population comparisons. Interestingly, we find limited evidence for repeated divergence of the same loci across the colonisation range but do identify different loci putatively associated with the same biological traits in different populations, likely due to parallel adaptation. Incipient speciation across these island populations, in which founder effects and selective pressures are strong, may therefore be repeatedly associated with morphology, metabolism and immune defence.
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Affiliation(s)
- Claudia A Martin
- School of Biological Sciences, University of East Anglia, Norfolk, UK
- Terrestrial Ecology Unit, Biology Department, Ghent University, Ghent, Belgium
- School of Biological Sciences, The University of Edinburgh, Edinburgh, UK
| | | | - Hisham A A Ali
- Department of Biology, Edward Grey Institute of Field Ornithology, University of Oxford, Oxford, UK
| | - Juan Carlos Illera
- Biodiversity Research Institute (CSIC-Oviedo University-Principality of Asturias), University of Oviedo, Mieres, Asturias, Spain
| | - Alexander Suh
- School of Biological Sciences, University of East Anglia, Norfolk, UK
- Department of Organismal Biology - Systematic Biology, Evolutionary Biology Centre (EBC), Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Lewis G Spurgin
- School of Biological Sciences, University of East Anglia, Norfolk, UK
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12
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Wu H, Jia Y, Chen X, Jiang N, Zhang Z, Chai S. Novel Allelic Gene Variations in CmCLAVATA3 ( CmCLV3) Were Identified in a Genetic Population of Melon ( Cucumis melo L.). Int J Mol Sci 2024; 25:6011. [PMID: 38892198 PMCID: PMC11173160 DOI: 10.3390/ijms25116011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 05/20/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
Carpel number (CN) is an important trait affecting the fruit size and shape of melon, which plays a crucial role in determining the overall appearance and market value. A unique non-synonymous single nucleotide polymorphism (SNP) in CmCLAVATA3 (CmCLV3) is responsible for the variation of CN in C. melo ssp. agrestis (hereafter agrestis), but it has been unclear in C. melo ssp. melo (hereafter melo). In this study, one major locus controlling the polymorphism of 5-CN (multi-CN) and 3-CN (normal-CN) in melo was identified using bulked segregant analysis (BSA-seq). This locus was then fine-mapped to an interval of 1.8 Mb on chromosome 12 using a segregating population containing 1451 progeny. CmCLV3 is still present in the candidate region. A new allele of CmCLV3, which contains five other nucleotide polymorphisms, including a non-synonymous SNP in coding sequence (CDS), except the SNP reported in agrestis, was identified in melo. A cis-trans test confirmed that the candidate gene, CmCLV3, contributes to the variation of CNs in melo. The qRT-PCR results indicate that there is no significant difference in the expression level of CmCLV3 in the apical stem between the multi-CN plants and the normal-CN plants. Overall, this study provides a genetic resource for melon fruit development research and molecular breeding. Additionally, it suggests that melo has undergone similar genetic selection but evolved into an independent allele.
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Affiliation(s)
| | | | | | | | | | - Sen Chai
- Engineering Laboratory of Genetic Improvement of Horticultural Crops of Shandong Province, College of Horticulture, Qingdao Agricultural University, Qingdao 266109, China; (H.W.); (Y.J.); (X.C.); (N.J.); (Z.Z.)
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13
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Gunasekaran D, Ardell DH, Nobile CJ. SNP-SVant: A Computational Workflow to Predict and Annotate Genomic Variants in Organisms Lacking Benchmarked Variants. Curr Protoc 2024; 4:e1046. [PMID: 38717471 PMCID: PMC11081530 DOI: 10.1002/cpz1.1046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2024]
Abstract
Whole-genome sequencing is widely used to investigate population genomic variation in organisms of interest. Assorted tools have been independently developed to call variants from short-read sequencing data aligned to a reference genome, including single nucleotide polymorphisms (SNPs) and structural variations (SVs). We developed SNP-SVant, an integrated, flexible, and computationally efficient bioinformatic workflow that predicts high-confidence SNPs and SVs in organisms without benchmarked variants, which are traditionally used for distinguishing sequencing errors from real variants. In the absence of these benchmarked datasets, we leverage multiple rounds of statistical recalibration to increase the precision of variant prediction. The SNP-SVant workflow is flexible, with user options to tradeoff accuracy for sensitivity. The workflow predicts SNPs and small insertions and deletions using the Genome Analysis ToolKit (GATK) and predicts SVs using the Genome Rearrangement IDentification Software Suite (GRIDSS), and it culminates in variant annotation using custom scripts. A key utility of SNP-SVant is its scalability. Variant calling is a computationally expensive procedure, and thus, SNP-SVant uses a workflow management system with intermediary checkpoint steps to ensure efficient use of resources by minimizing redundant computations and omitting steps where dependent files are available. SNP-SVant also provides metrics to assess the quality of called variants and converts between VCF and aligned FASTA format outputs to ensure compatibility with downstream tools to calculate selection statistics, which are commonplace in population genomics studies. By accounting for both small and large structural variants, users of this workflow can obtain a wide-ranging view of genomic alterations in an organism of interest. Overall, this workflow advances our capabilities in assessing the functional consequences of different types of genomic alterations, ultimately improving our ability to associate genotypes with phenotypes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Predicting single nucleotide polymorphisms and structural variations Support Protocol 1: Downloading publicly available sequencing data Support Protocol 2: Visualizing variant loci using Integrated Genome Viewer Support Protocol 3: Converting between VCF and aligned FASTA formats.
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Affiliation(s)
- Deepika Gunasekaran
- Quantitative and Systems Biology Graduate Program, University of California, Merced, CA, USA
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, CA, USA
| | - David H. Ardell
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, CA, USA
| | - Clarissa J. Nobile
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, CA, USA
- Health Science Research Institute, University of California, Merced, CA, USA
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14
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Zhang S, Wang P, Shi L, Wang C, Zhu Z, Qi C, Xie Y, Yuan S, Cheng L, Yin X, Zhang X. Exploring COVID-19 causal genes through disease-specific Cis-eQTLs. Virus Res 2024; 342:199341. [PMID: 38403000 PMCID: PMC10904281 DOI: 10.1016/j.virusres.2024.199341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024]
Abstract
Genome-wide association study (GWAS) analysis has exposed that genetic factors play important roles in COVID-19. Whereas a deeper understanding of the underlying mechanism of COVID-19 was hindered by the lack of expression of quantitative trait loci (eQTL) data specific for disease. To this end, we identified COVID-19-specific cis-eQTLs by integrating nucleotide sequence variations and RNA-Seq data from COVID-19 samples. These identified eQTLs have different regulatory effect on genes between patients and controls, indicating that SARS-CoV-2 infection may cause alterations in the human body's internal environment. Individuals with the TT genotype in the rs1128320 region seemed more susceptible to SARS-CoV-2 infection and developed into severe COVID-19 due to the abnormal expression of IFITM1. We subsequently discovered potential causal genes, of the result, a total of 48 genes from six tissues were identified. siRNA-mediated depletion assays in SARS-CoV-2 infection proved that 14 causal genes were directly associated with SARS-CoV-2 infection. These results enriched existing research on COVID-19 causal genes and provided a new sight in the mechanism exploration for COVID-19.
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Affiliation(s)
- Sainan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Ping Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Lei Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Chao Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Zijun Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yubin Xie
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam 999077, Hong Kong Special Administrative Region of China; State Key Laboratory of Emerging Infectious Diseases, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Pokfulam 999077, Hong Kong Special Administrative Region of China
| | - Shuofeng Yuan
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam 999077, Hong Kong Special Administrative Region of China; State Key Laboratory of Emerging Infectious Diseases, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Pokfulam 999077, Hong Kong Special Administrative Region of China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, Heilongjiang 150028, China.
| | - Xin Yin
- State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150040, China
| | - Xue Zhang
- NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, Heilongjiang 150028, China; McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
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15
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Woerner AE, Novroski NM, Mandape S, King JL, Crysup B, Coble MD. Identifying distant relatives using benchtop-scale sequencing. Forensic Sci Int Genet 2024; 69:103005. [PMID: 38171224 DOI: 10.1016/j.fsigen.2023.103005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/20/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024]
Abstract
The genetic component of forensic genetic genealogy (FGG) is an estimate of kinship, often conducted at genome scales between a great number of individuals. The promise of FGG is substantial: in concert with genealogical records and other nongenetic information, it can indirectly identify a person of interest. A downside of FGG is cost, as it is currently expensive and requires chemistries uncommon to forensic genetic laboratories (microarrays and high throughput sequencing). The more common benchtop sequencers can be coupled with a targeted PCR assay to conduct FGG, though such approaches have limited resolution for kinship. This study evaluates low-pass sequencing, an alternative strategy that is accessible to benchtop sequencers and can produce resolutions comparable to high-pass sequencing. Samples from a three-generation pedigree were augmented to include up to 7th degree relatives (using whole genome pedigree simulations) and the ability to recover the true kinship coefficient was assessed using algorithms qualitatively similar to those found in GEDmatch. We show that up to 7th degree relatives can be reliably inferred from 1 × whole genome sequencing obtainable from desktop sequencers.
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Affiliation(s)
- August E Woerner
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, TX, USA; Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX, USA.
| | - Nicole M Novroski
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, TX, USA; Department of Anthropology, University of Toronto, Mississauga, ON, Canada
| | - Sammed Mandape
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Jonathan L King
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Benjamin Crysup
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, TX, USA; Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Michael D Coble
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, TX, USA; Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX, USA
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16
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Gu LH, Wu RR, Zheng XL, Fu A, Xing ZY, Chen YY, He ZC, Lu LZ, Qi YT, Chen AH, Zhang YP, Xu TS, Peng MS, Ma C. Genomic insights into local adaptation and phenotypic diversity of Wenchang chickens. Poult Sci 2024; 103:103376. [PMID: 38228059 PMCID: PMC10823079 DOI: 10.1016/j.psj.2023.103376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/02/2023] [Accepted: 12/08/2023] [Indexed: 01/18/2024] Open
Abstract
Wenchang chicken, a prized local breed in Hainan Province of China renowned for its exceptional adaptability to tropical environments and good meat quality, is deeply favored by the public. However, an insufficient understanding of its population architecture and the unclear genetic basis that governs its typical attributes have posed challenges in the protection and breeding of this precious breed. To address these gaps, we conducted whole-genome resequencing on 200 Wenchang chicken samples derived from 10 distinct strains, and we gathered data on an array of 21 phenotype traits. Population genomics analysis unveiled distinctive population structures in Wenchang chickens, primarily attributed to strong artificial selection for different feather colors. Selection sweep analysis identified a group of candidate genes, including PCDH9, DPF3, CDIN1, and SUGCT, closely linked to adaptations that enhance resilience in tropical island habitats. Genome-wide association studies (GWAS) highlighted potential candidate genes associated with diverse feather color traits, encompassing TYR, RAB38, TRPM1, GABARAPL2, CDH1, ZMIZ1, LYST, MC1R, and SASH1. Through the comprehensive analysis of high-quality genomic and phenotypic data across diverse Wenchang chicken resource groups, this study unveils the intricate genetic backgrounds and population structures of Wenchang chickens. Additionally, it identifies multiple candidate genes linked to environmental adaptation, feather color variations, and production traits. These insights not only provide genetic reference for the purification and breeding of Wenchang chickens but also broaden our understanding of the genetic basis of phenotypic diversity in chickens.
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Affiliation(s)
- Li-Hong Gu
- Institute of Animal Science and Veterinary Medicine, Hainan Academy of Agricultural Sciences, Haikou 571199, China
| | - Ran-Ran Wu
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin-Li Zheng
- Institute of Animal Science and Veterinary Medicine, Hainan Academy of Agricultural Sciences, Haikou 571199, China
| | - An Fu
- Wenchang City Wenchang Chicken Research Institute, Wenchang 571300, China
| | - Zeng-Yang Xing
- Wenchang Long-quan Wenchang Chicken Industrial Co., Ltd., Wenchang 571346, China
| | - Yi-Yong Chen
- Hainan Chuang Wen Wenchang Chicken Industry Co., Ltd., Wenchang 571321, China
| | - Zhong-Chun He
- Institute of Animal Science and Veterinary Medicine, Hainan Academy of Agricultural Sciences, Haikou 571199, China
| | - Li-Zhi Lu
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Yan-Tao Qi
- Institute of Animal Science and Veterinary Medicine, Hainan Academy of Agricultural Sciences, Haikou 571199, China
| | - An-Hong Chen
- Institute of Animal Science and Veterinary Medicine, Hainan Academy of Agricultural Sciences, Haikou 571199, China
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming 650091, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tie-Shan Xu
- Tropical Crops Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
| | - Min-Sheng Peng
- Wenchang City Wenchang Chicken Research Institute, Wenchang 571300, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Cheng Ma
- Wenchang City Wenchang Chicken Research Institute, Wenchang 571300, China.
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17
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Zhao Y, Qin L, Pan H, Song T, Wang Y, Zhou X, Xiang Y, Li J, Liu Z, Sun Q, Guo J, Yan X, Tang B, Xu Q. Genetic analysis of transcription factors in dopaminergic neuronal development in Parkinson's disease. Chin Med J (Engl) 2024; 137:450-456. [PMID: 37341647 PMCID: PMC10876230 DOI: 10.1097/cm9.0000000000002743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND Genetic variants of dopaminergic transcription factor-encoding genes are suggested to be Parkinson's disease (PD) risk factors; however, no comprehensive analyses of these genes in patients with PD have been undertaken. Therefore, we aimed to genetically analyze 16 dopaminergic transcription factor genes in Chinese patients with PD. METHODS Whole-exome sequencing (WES) was performed using a Chinese cohort comprising 1917 unrelated patients with familial or sporadic early-onset PD and 1652 controls. Additionally, whole-genome sequencing (WGS) was performed using another Chinese cohort comprising 1962 unrelated patients with sporadic late-onset PD and 1279 controls. RESULTS We detected 308 rare and 208 rare protein-altering variants in the WES and WGS cohorts, respectively. Gene-based association analyses of rare variants suggested that MSX1 is enriched in sporadic late-onset PD. However, the significance did not pass the Bonferroni correction. Meanwhile, 72 and 1730 common variants were found in the WES and WGS cohorts, respectively. Unfortunately, single-variant logistic association analyses did not identify significant associations between common variants and PD. CONCLUSIONS Variants of 16 typical dopaminergic transcription factors might not be major genetic risk factors for PD in Chinese patients. However, we highlight the complexity of PD and the need for extensive research elucidating its etiology.
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Affiliation(s)
- Yuwen Zhao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Lixia Qin
- Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Hongxu Pan
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Tingwei Song
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Yige Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Xiaoxia Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Yaqin Xiang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jinchen Li
- Bioinformatics Center National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Centre for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Zhenhua Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Qiying Sun
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jifeng Guo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Centre for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, Hunan 410008, China
| | - Xinxiang Yan
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Beisha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Centre for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, Hunan 410008, China
| | - Qian Xu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Centre for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, Hunan 410008, China
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18
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Duan Z, Li X, Li S, Zhou H, Hu L, Xia H, Xie L, Xie F. Nosocomial surveillance of multidrug-resistant Acinetobacter baumannii: a genomic epidemiological study. Microbiol Spectr 2024; 12:e0220723. [PMID: 38197661 PMCID: PMC10846281 DOI: 10.1128/spectrum.02207-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 12/14/2023] [Indexed: 01/11/2024] Open
Abstract
Acinetobacter baumannii is a major opportunistic pathogen causing hospital-acquired infections, and it is imperative to comprehend its evolutionary and epidemiological dynamics in hospitals to prevent and control nosocomial transmission. Here, we present a comprehensive genomic epidemiological study involving the genomic sequencing and antibiotic resistance profiling of 634 A. baumannii strains isolated from seven intensive care units (ICUs) of a Chinese general hospital over 2 consecutive years. Our study reveals that ST2 is highly dominant (90.54%) in the ICUs, with 98.90% of the ST2 exhibiting multidrug resistant or extensively drug resistant. Phylogenetic analyses of newly sequenced genomes and public data suggest that nosocomial isolates originated outside the hospital but evolved inside. The major lineages appear to be stable, with 9 of the 28 identified nosocomial epidemic clones infecting over 60% of the affected patients. However, outbreaks of two highly evolved clones have been observed in different hospitals, suggesting significant inter-hospital transmission chains. By coupling patient medical records and genomic divergence of the ST2, we found that cross-ward patient transfer played a crucial role in pathogen's nosocomial transmission. Additionally, we identified 831 potential adaptive evolutionary loci and 44 associated genes by grouping and comparing the genomes of clones with different prevalence. Overall, our study provides a comprehensive and contemporary survey on the epidemiology and genomic evolution of A. baumannii in a large Chinese general hospital. These findings shed light on the nosocomial evolution and transmission of A. baumannii and offers valuable information for transmission prevention and antibiotic therapy.IMPORTANCEThis study delved into the genomic evolution and transmission of nosocomial Acinetobacter baumannii on a large scale, spanning both an extended time period and the largest sample size to date. Through molecular epidemiological investigations based on genomics, we can directly trace the origin of the pathogen, detecting and monitoring outbreaks of infectious diseases in a timely manner, and ensuring public health safety. In addition, this study also collects a large amount of genomic and antibiotic resistance detection data, which is helpful for phenotype prediction based on genomic sequencing. It enables patients to receive personalized antibiotic treatment quickly, helps doctors select antibiotics more accurately, and contributes to reducing the use of antibiotics and lowering the risk of antibiotic resistance development.
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Affiliation(s)
- Zhimei Duan
- College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Xuming Li
- Department of Scientific Affairs, Hugobiotech Co., Ltd., Beijing, China
| | - Song Li
- Department of Scientific Affairs, Hugobiotech Co., Ltd., Beijing, China
| | - Hui Zhou
- Department of Scientific Affairs, Hugobiotech Co., Ltd., Beijing, China
| | - Long Hu
- Department of Scientific Affairs, Hugobiotech Co., Ltd., Beijing, China
| | - Han Xia
- Department of Scientific Affairs, Hugobiotech Co., Ltd., Beijing, China
| | - Lixin Xie
- College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Fei Xie
- College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
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19
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Saba KH, Difilippo V, Kovac M, Cornmark L, Magnusson L, Nilsson J, van den Bos H, Spierings DC, Bidgoli M, Jonson T, Sumathi VP, Brosjö O, Staaf J, Foijer F, Styring E, Nathrath M, Baumhoer D, Nord KH. Disruption of the TP53 locus in osteosarcoma leads to TP53 promoter gene fusions and restoration of parts of the TP53 signalling pathway. J Pathol 2024; 262:147-160. [PMID: 38010733 DOI: 10.1002/path.6219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/24/2023] [Accepted: 09/19/2023] [Indexed: 11/29/2023]
Abstract
TP53 is the most frequently mutated gene in human cancer. This gene shows not only loss-of-function mutations but also recurrent missense mutations with gain-of-function activity. We have studied the primary bone malignancy osteosarcoma, which harbours one of the most rearranged genomes of all cancers. This is odd since it primarily affects children and adolescents who have not lived the long life thought necessary to accumulate massive numbers of mutations. In osteosarcoma, TP53 is often disrupted by structural variants. Here, we show through combined whole-genome and transcriptome analyses of 148 osteosarcomas that TP53 structural variants commonly result in loss of coding parts of the gene while simultaneously preserving and relocating the promoter region. The transferred TP53 promoter region is fused to genes previously implicated in cancer development. Paradoxically, these erroneously upregulated genes are significantly associated with the TP53 signalling pathway itself. This suggests that while the classical tumour suppressor activities of TP53 are lost, certain parts of the TP53 signalling pathway that are necessary for cancer cell survival and proliferation are retained. In line with this, our data suggest that transposition of the TP53 promoter is an early event that allows for a new normal state of genome-wide rearrangements in osteosarcoma. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Karim H Saba
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Valeria Difilippo
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Michal Kovac
- Bone Tumour Reference Centre at the Institute of Pathology, University Hospital and University of Basel, Basel, Switzerland
- Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia
| | - Louise Cornmark
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Linda Magnusson
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Jenny Nilsson
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Hilda van den Bos
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Diana Cj Spierings
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Mahtab Bidgoli
- Department of Clinical Genetics and Pathology, Laboratory Medicine, Medical Services, Skåne University Hospital, Lund, Sweden
| | - Tord Jonson
- Department of Clinical Genetics and Pathology, Laboratory Medicine, Medical Services, Skåne University Hospital, Lund, Sweden
| | - Vaiyapuri P Sumathi
- Department of Musculoskeletal Pathology, Royal Orthopaedic Hospital, Birmingham, UK
| | - Otte Brosjö
- Department of Orthopedics, Karolinska University Hospital, Stockholm, Sweden
| | - Johan Staaf
- Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Lund, Sweden
| | - Floris Foijer
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Emelie Styring
- Department of Orthopedics, Lund University, Skåne University Hospital, Lund, Sweden
| | - Michaela Nathrath
- Children's Cancer Research Centre and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- Department of Pediatric Oncology, Klinikum Kassel, Kassel, Germany
| | - Daniel Baumhoer
- Bone Tumour Reference Centre at the Institute of Pathology, University Hospital and University of Basel, Basel, Switzerland
| | - Karolin H Nord
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
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20
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Barbitoff YA, Ushakov MO, Lazareva TE, Nasykhova YA, Glotov AS, Predeus AV. Bioinformatics of germline variant discovery for rare disease diagnostics: current approaches and remaining challenges. Brief Bioinform 2024; 25:bbad508. [PMID: 38271481 PMCID: PMC10810331 DOI: 10.1093/bib/bbad508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/18/2023] [Accepted: 12/12/2023] [Indexed: 01/27/2024] Open
Abstract
Next-generation sequencing (NGS) has revolutionized the field of rare disease diagnostics. Whole exome and whole genome sequencing are now routinely used for diagnostic purposes; however, the overall diagnosis rate remains lower than expected. In this work, we review current approaches used for calling and interpretation of germline genetic variants in the human genome, and discuss the most important challenges that persist in the bioinformatic analysis of NGS data in medical genetics. We describe and attempt to quantitatively assess the remaining problems, such as the quality of the reference genome sequence, reproducible coverage biases, or variant calling accuracy in complex regions of the genome. We also discuss the prospects of switching to the complete human genome assembly or the human pan-genome and important caveats associated with such a switch. We touch on arguably the hardest problem of NGS data analysis for medical genomics, namely, the annotation of genetic variants and their subsequent interpretation. We highlight the most challenging aspects of annotation and prioritization of both coding and non-coding variants. Finally, we demonstrate the persistent prevalence of pathogenic variants in the coding genome, and outline research directions that may enhance the efficiency of NGS-based disease diagnostics.
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Affiliation(s)
- Yury A Barbitoff
- Dpt. of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya line 3, 199034, St. Petersburg, Russia
- Bioinformatics Institute, Kentemirovskaya st. 2A, 197342, St. Petersburg, Russia
| | - Mikhail O Ushakov
- Dpt. of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya line 3, 199034, St. Petersburg, Russia
| | - Tatyana E Lazareva
- Dpt. of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya line 3, 199034, St. Petersburg, Russia
| | - Yulia A Nasykhova
- Dpt. of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya line 3, 199034, St. Petersburg, Russia
| | - Andrey S Glotov
- Dpt. of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya line 3, 199034, St. Petersburg, Russia
| | - Alexander V Predeus
- Bioinformatics Institute, Kentemirovskaya st. 2A, 197342, St. Petersburg, Russia
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21
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Vado Y, Manero-Azua A, Pereda A, Perez de Nanclares G. Choosing the Best Tissue and Technique to Detect Mosaicism in Fibrous Dysplasia/McCune-Albright Syndrome (FD/MAS). Genes (Basel) 2024; 15:120. [PMID: 38255009 PMCID: PMC10815810 DOI: 10.3390/genes15010120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 01/24/2024] Open
Abstract
GNAS-activating somatic mutations give rise to Fibrous Dysplasia/McCune-Albright syndrome (FD/MAS). The low specificity of extra-skeletal signs of MAS and the mosaic status of the mutations generate some difficulties for a proper diagnosis. We studied the clinical and molecular statuses of 40 patients referred with a clinical suspicion of FD/MAS to provide some clues. GNAS was sequenced using both Sanger and Next-Generation Sequencing (NGS). We were able to identify the pathogenic variants in 25% of the patients. Most of them were identified in the affected tissue, but not in blood. Additionally, NGS demonstrated the ability to detect more patients with mosaicism (8/34) than Sanger sequencing (4/39). Even if in some cases, the clinical information was not complete, we confirmed that, as in previous works, when the patients were young children with a single manifestation, such as hyperpigmented skin macules or precocious puberty, the molecular diagnosis was usually negative. In conclusion, as FD/MAS is caused by mosaic variants, it is essential to use sensitive techniques that allow for the detection of low percentages and to choose the right tissue to study. When not possible, and due to the low positive genetic rate, patients with FD/MAS should only be genetically tested when the clinical diagnosis is really uncertain.
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Affiliation(s)
| | | | | | - Guiomar Perez de Nanclares
- Rare Disease Research Group, Molecular (Epi) Genetics Laboratory, Bioaraba Health Research Institute, Araba University Hospital, 01009 Vitoria-Gasteiz, Spain; (Y.V.); (A.M.-A.); (A.P.)
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22
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Hariprakash JM, Salviato E, La Mastra F, Sebestyén E, Tagliaferri I, Silva RS, Lucini F, Farina L, Cinquanta M, Rancati I, Riboni M, Minardi SP, Roz L, Gorini F, Lanzuolo C, Casola S, Ferrari F. Leveraging Tissue-Specific Enhancer-Target Gene Regulatory Networks Identifies Enhancer Somatic Mutations That Functionally Impact Lung Cancer. Cancer Res 2024; 84:133-153. [PMID: 37855660 PMCID: PMC10758689 DOI: 10.1158/0008-5472.can-23-1129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/29/2023] [Accepted: 10/17/2023] [Indexed: 10/20/2023]
Abstract
Enhancers are noncoding regulatory DNA regions that modulate the transcription of target genes, often over large distances along with the genomic sequence. Enhancer alterations have been associated with various pathological conditions, including cancer. However, the identification and characterization of somatic mutations in noncoding regulatory regions with a functional effect on tumorigenesis and prognosis remain a major challenge. Here, we present a strategy for detecting and characterizing enhancer mutations in a genome-wide analysis of patient cohorts, across three lung cancer subtypes. Lung tissue-specific enhancers were defined by integrating experimental data and public epigenomic profiles, and the genome-wide enhancer-target gene regulatory network of lung cells was constructed by integrating chromatin three-dimensional architecture data. Lung cancers possessed a similar mutation burden at tissue-specific enhancers and exons but with differences in their mutation signatures. Functionally relevant alterations were prioritized on the basis of the pathway-level integration of the effect of a mutation and the frequency of mutations on individual enhancers. The genes enriched for mutated enhancers converged on the regulation of key biological processes and pathways relevant to tumor biology. Recurrent mutations in individual enhancers also affected the expression of target genes, with potential relevance for patient prognosis. Together, these findings show that noncoding regulatory mutations have a potential relevance for cancer pathogenesis and can be exploited for patient classification. SIGNIFICANCE Mapping enhancer-target gene regulatory interactions and analyzing enhancer mutations at the level of their target genes and pathways reveal convergence of recurrent enhancer mutations on biological processes involved in tumorigenesis and prognosis.
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Affiliation(s)
| | - Elisa Salviato
- IFOM-ETS, the AIRC Institute of Molecular Oncology, Milan, Italy
| | | | - Endre Sebestyén
- IFOM-ETS, the AIRC Institute of Molecular Oncology, Milan, Italy
| | | | | | - Federica Lucini
- IFOM-ETS, the AIRC Institute of Molecular Oncology, Milan, Italy
| | - Lorenzo Farina
- IFOM-ETS, the AIRC Institute of Molecular Oncology, Milan, Italy
| | | | - Ilaria Rancati
- IFOM-ETS, the AIRC Institute of Molecular Oncology, Milan, Italy
| | | | | | - Luca Roz
- Fondazione IRCCS—Istituto Nazionale Tumori, Milan, Italy
| | - Francesca Gorini
- INGM, National Institute of Molecular Genetics “Romeo ed Enrica Invernizzi,” Milan, Italy
| | - Chiara Lanzuolo
- INGM, National Institute of Molecular Genetics “Romeo ed Enrica Invernizzi,” Milan, Italy
- Institute of Biomedical Technologies, National Research Council (ITB-CNR), Segrate, Italy
| | - Stefano Casola
- IFOM-ETS, the AIRC Institute of Molecular Oncology, Milan, Italy
| | - Francesco Ferrari
- IFOM-ETS, the AIRC Institute of Molecular Oncology, Milan, Italy
- Institute of Molecular Genetics “Luigi Luca Cavalli-Sforza,” National Research Council (IGM-CNR), Pavia, Italy
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23
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Chen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alföldi J, Watts NA, Vittal C, Gauthier LD, Poterba T, Wilson MW, Tarasova Y, Phu W, Grant R, Yohannes MT, Koenig Z, Farjoun Y, Banks E, Donnelly S, Gabriel S, Gupta N, Ferriera S, Tolonen C, Novod S, Bergelson L, Roazen D, Ruano-Rubio V, Covarrubias M, Llanwarne C, Petrillo N, Wade G, Jeandet T, Munshi R, Tibbetts K, O'Donnell-Luria A, Solomonson M, Seed C, Martin AR, Talkowski ME, Rehm HL, Daly MJ, Tiao G, Neale BM, MacArthur DG, Karczewski KJ. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 2024; 625:92-100. [PMID: 38057664 DOI: 10.1038/s41586-023-06045-0] [Citation(s) in RCA: 102] [Impact Index Per Article: 102.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 04/03/2023] [Indexed: 12/08/2023]
Abstract
The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders1-4, but attempts to assess constraint for non-protein-coding regions have proved more difficult. Here we aggregate, process and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD)-the largest public open-access human genome allele frequency reference dataset-and use it to build a genomic constraint map for the whole genome (genomic non-coding constraint of haploinsufficient variation (Gnocchi)). We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation. As expected, the average constraint for protein-coding sequences is stronger than that for non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants that are implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, which in turn suggests that non-coding constraint can aid the identification of constrained genes that are as yet unrecognized by current gene constraint metrics. We demonstrate that this genome-wide constraint map improves the identification and interpretation of functional human genetic variation.
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Affiliation(s)
- Siwei Chen
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
| | - Laurent C Francioli
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Julia K Goodrich
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ryan L Collins
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Qingbo Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Jessica Alföldi
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Nicholas A Watts
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Christopher Vittal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Laura D Gauthier
- Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Timothy Poterba
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael W Wilson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Yekaterina Tarasova
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - William Phu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Riley Grant
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mary T Yohannes
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zan Koenig
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yossi Farjoun
- Richards Lab, Lady Davis Institute, Montreal, Quebec, Canada
| | - Eric Banks
- Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Stacey Gabriel
- Broad Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Namrata Gupta
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Broad Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven Ferriera
- Broad Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Charlotte Tolonen
- Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sam Novod
- Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Louis Bergelson
- Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - David Roazen
- Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Miguel Covarrubias
- Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Nikelle Petrillo
- Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gordon Wade
- Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Thibault Jeandet
- Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ruchi Munshi
- Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathleen Tibbetts
- Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Matthew Solomonson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Cotton Seed
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alicia R Martin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael E Talkowski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mark J Daly
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
| | - Grace Tiao
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Benjamin M Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel G MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Konrad J Karczewski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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24
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Stuart KC, Johnson RN, Major RE, Atsawawaranunt K, Ewart KM, Rollins LA, Santure AW, Whibley A. The genome of a globally invasive passerine, the common myna, Acridotheres tristis. DNA Res 2024; 31:dsae005. [PMID: 38366840 PMCID: PMC10917472 DOI: 10.1093/dnares/dsae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 02/13/2024] [Accepted: 02/15/2024] [Indexed: 02/18/2024] Open
Abstract
In an era of global climate change, biodiversity conservation is receiving increased attention. Conservation efforts are greatly aided by genetic tools and approaches, which seek to understand patterns of genetic diversity and how they impact species health and their ability to persist under future climate regimes. Invasive species offer vital model systems in which to investigate questions regarding adaptive potential, with a particular focus on how changes in genetic diversity and effective population size interact with novel selection regimes. The common myna (Acridotheres tristis) is a globally invasive passerine and is an excellent model species for research both into the persistence of low-diversity populations and the mechanisms of biological invasion. To underpin research on the invasion genetics of this species, we present the genome assembly of the common myna. We describe the genomic landscape of this species, including genome wide allelic diversity, methylation, repeats, and recombination rate, as well as an examination of gene family evolution. Finally, we use demographic analysis to identify that some native regions underwent a dramatic population increase between the two most recent periods of glaciation, and reveal artefactual impacts of genetic bottlenecks on demographic analysis.
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Affiliation(s)
- Katarina C Stuart
- School of Biological Sciences, University of Auckland, Auckland, Aotearoa, New Zealand
- Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, Australia
| | - Rebecca N Johnson
- National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
| | - Richard E Major
- Australian Museum Research Institute, Australian Museum, Sydney, Australia
| | | | - Kyle M Ewart
- Australian Museum Research Institute, Australian Museum, Sydney, Australia
- School of Life and Environmental Sciences,University of Sydney, Sydney, Australia
| | - Lee A Rollins
- Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, Australia
| | - Anna W Santure
- School of Biological Sciences, University of Auckland, Auckland, Aotearoa, New Zealand
| | - Annabel Whibley
- School of Biological Sciences, University of Auckland, Auckland, Aotearoa, New Zealand
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25
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Robic A, Hadlich F, Costa Monteiro Moreira G, Louise Clark E, Plastow G, Charlier C, Kühn C. Innovative construction of the first reliable catalogue of bovine circular RNAs. RNA Biol 2024; 21:52-74. [PMID: 38989833 PMCID: PMC11244336 DOI: 10.1080/15476286.2024.2375090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/26/2024] [Indexed: 07/12/2024] Open
Abstract
The aim of this study was to compare the circular transcriptome of divergent tissues in order to understand: i) the presence of circular RNAs (circRNAs) that are not exonic circRNAs, i.e. originated from backsplicing involving known exons and, ii) the origin of artificial circRNA (artif_circRNA), i.e. circRNA not generated in-vivo. CircRNA identification is mostly an in-silico process, and the analysis of data from the BovReg project (https://www.bovreg.eu/) provided an opportunity to explore new ways to identify reliable circRNAs. By considering 117 tissue samples, we characterized 23,926 exonic circRNAs, 337 circRNAs from 273 introns (191 ciRNAs, 146 intron circles), 108 circRNAs from small non-coding genes and nearly 36.6K circRNAs classified as other_circRNAs. Furthermore, for 63 of those samples we analysed in parallel data from total-RNAseq (ribosomal RNAs depleted prior to library preparation) with paired mRNAseq (library prepared with poly(A)-selected RNAs). The high number of circRNAs detected in mRNAseq, and the significant number of novel circRNAs, mainly other_circRNAs, led us to consider all circRNAs detected in mRNAseq as artificial. This study provided evidence of 189 false entries in the list of exonic circRNAs: 103 artif_circRNAs identified by total RNAseq/mRNAseq comparison using two circRNA tools, 26 probable artif_circRNAs, and 65 identified by deep annotation analysis. Extensive benchmarking was performed (including analyses with CIRI2 and CIRCexplorer-2) and confirmed 94% of the 23,737 reliable exonic circRNAs. Moreover, this study demonstrates the effectiveness of a panel of highly expressed exonic circRNAs (5-8%) in analysing the tissue specificity of the bovine circular transcriptome.
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Affiliation(s)
- Annie Robic
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet-Tolosan, France
| | - Frieder Hadlich
- Institute of Genome Biology, Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany
| | | | | | - Graham Plastow
- Department of Agricultural, Food and Nutritional Science, Livestock Gentec, University of Alberta, Edmonton, AB, Canada
| | - Carole Charlier
- Unit of Animal Genomics, GIGA Institute, University of Liège, Liège, Belgium
- Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Christa Kühn
- Institute of Genome Biology, Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany
- Faculty of Agricultural and Environmental Sciences, University of Rostock, Rostock, Germany
- Friedrich Loeffler Institute, Federal Research Institute for Animal Health, Greifswald – Insel Riems, Germany
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26
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Ousmael K, Whetten RW, Xu J, Nielsen UB, Lamour K, Hansen OK. Identification and high-throughput genotyping of single nucleotide polymorphism markers in a non-model conifer (Abies nordmanniana (Steven) Spach). Sci Rep 2023; 13:22488. [PMID: 38110478 PMCID: PMC10728141 DOI: 10.1038/s41598-023-49462-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 12/08/2023] [Indexed: 12/20/2023] Open
Abstract
Single nucleotide polymorphism (SNP) markers are powerful tools for investigating population structures, linkage analysis, and genome-wide association studies, as well as for breeding and population management. The availability of SNP markers has been limited to the most commercially important timber species, primarily due to the cost of genome sequencing required for SNP discovery. In this study, a combination of reference-based and reference-free approaches were used to identify SNPs in Nordmann fir (Abies nordmanniana), a species previously lacking genomic sequence information. Using a combination of a genome assembly of the closely related Silver fir (Abies alba) species and a de novo assembly of low-copy regions of the Nordmann fir genome, we identified a high density of reliable SNPs. Reference-based approaches identified two million SNPs in common between the Silver fir genome and low-copy regions of Nordmann fir. A combination of one reference-free and two reference-based approaches identified 250 shared SNPs. A subset of 200 SNPs were used to genotype 342 individuals and thereby tested and validated in the context of identity analysis and/or clone identification. The tested SNPs successfully identified all ramets per clone and five mislabeled individuals via identity and genomic relatedness analysis. The identified SNPs will be used in ad hoc breeding of Nordmann fir in Denmark.
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Affiliation(s)
- Kedra Ousmael
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Denmark.
| | - Ross W Whetten
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, 27606, USA
| | - Jing Xu
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Denmark
| | - Ulrik B Nielsen
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Denmark
| | - Kurt Lamour
- Department of Entomology and Plant Pathology, University of Tennessee, Knoxville, TN, USA
| | - Ole K Hansen
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Denmark
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27
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Su J, Lu Z, Zeng J, Zhang X, Yang X, Wang S, Zhang F, Jiang J, Chen F. Multi-locus genome-wide association study and genomic prediction for flowering time in chrysanthemum. PLANTA 2023; 259:13. [PMID: 38063918 DOI: 10.1007/s00425-023-04297-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023]
Abstract
MAIN CONCLUSION Multi-locus GWAS detected several known and candidate genes responsible for flowering time in chrysanthemum. The associations could greatly increase the predictive ability of genome selection that accelerates the possible application of GS in chrysanthemum breeding. Timely flowering is critical for successful reproduction and determines the economic value for ornamental plants. To investigate the genetic architecture of flowering time in chrysanthemum, a multi-locus genome-wide association study (GWAS) was performed using a collection of 200 accessions and 330,710 single-nucleotide polymorphisms (SNPs) via 3VmrMLM method. Five flowering time traits including budding (FBD), visible colouring (VC), early opening (EO), full-bloom (OF) and senescing (SF) stages, plus five derived conditional traits were recorded in two environments. Extensive phenotypic variations were observed for these flowering time traits with coefficients of variation ranging from 6.42 to 38.27%, and their broad-sense heritability ranged from 71.47 to 96.78%. GWAS revealed 88 stable quantitative trait nucleotides (QTNs) and 93 QTN-by-environment interactions (QEIs) associated with flowering time traits, accounting for 0.50-8.01% and 0.30-10.42% of the phenotypic variation, respectively. Amongst the genes around these stable QTNs and QEIs, 21 and 10 were homologous to known flowering genes in Arabidopsis; 20 and 11 candidate genes were mined by combining the functional annotation and transcriptomics data, respectively, such as MYB55, FRIGIDA-like, WRKY75 and ANT. Furthermore, genomic selection (GS) was assessed using three models and seven unique marker datasets. We found the prediction accuracy (PA) using significant SNPs identified by GWAS under SVM model exhibited the best performance with PA ranging from 0.90 to 0.95. Our findings provide new insights into the dynamic genetic architecture of flowering time and the identified significant SNPs and candidate genes will accelerate the future molecular improvement of chrysanthemum.
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Affiliation(s)
- Jiangshuo Su
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Zhaowen Lu
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Junwei Zeng
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Xuefeng Zhang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Xiuwei Yang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Siyue Wang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Fei Zhang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
- Zhongshan Biological Breeding Laboratory, No. 50 Zhongling Street, Nanjing, 210014, China
| | - Jiafu Jiang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China
- Zhongshan Biological Breeding Laboratory, No. 50 Zhongling Street, Nanjing, 210014, China
| | - Fadi Chen
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People's Republic of China.
- Zhongshan Biological Breeding Laboratory, No. 50 Zhongling Street, Nanjing, 210014, China.
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28
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Liu NQ, Paassen I, Custers L, Zeller P, Teunissen H, Ayyildiz D, He J, Buhl JL, Hoving EW, van Oudenaarden A, de Wit E, Drost J. SMARCB1 loss activates patient-specific distal oncogenic enhancers in malignant rhabdoid tumors. Nat Commun 2023; 14:7762. [PMID: 38040699 PMCID: PMC10692191 DOI: 10.1038/s41467-023-43498-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 11/10/2023] [Indexed: 12/03/2023] Open
Abstract
Malignant rhabdoid tumor (MRT) is a highly malignant and often lethal childhood cancer. MRTs are genetically defined by bi-allelic inactivating mutations in SMARCB1, a member of the BRG1/BRM-associated factors (BAF) chromatin remodeling complex. Mutations in BAF complex members are common in human cancer, yet their contribution to tumorigenesis remains in many cases poorly understood. Here, we study derailed regulatory landscapes as a consequence of SMARCB1 loss in the context of MRT. Our multi-omics approach on patient-derived MRT organoids reveals a dramatic reshaping of the regulatory landscape upon SMARCB1 reconstitution. Chromosome conformation capture experiments subsequently reveal patient-specific looping of distal enhancer regions with the promoter of the MYC oncogene. This intertumoral heterogeneity in MYC enhancer utilization is also present in patient MRT tissues as shown by combined single-cell RNA-seq and ATAC-seq. We show that loss of SMARCB1 activates patient-specific epigenetic reprogramming underlying MRT tumorigenesis.
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Affiliation(s)
- Ning Qing Liu
- Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Hematology, Erasmus Medical Center (MC) Cancer Institute, Rotterdam, the Netherlands
| | - Irene Paassen
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Lars Custers
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Peter Zeller
- Oncode Institute, Utrecht, the Netherlands
- Hubrecht Institute-KNAW, Utrecht, the Netherlands
- University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hans Teunissen
- Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Dilara Ayyildiz
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Jiayou He
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Juliane Laura Buhl
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | | | - Alexander van Oudenaarden
- Oncode Institute, Utrecht, the Netherlands
- Hubrecht Institute-KNAW, Utrecht, the Netherlands
- University Medical Center Utrecht, Utrecht, the Netherlands
| | - Elzo de Wit
- Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, the Netherlands.
| | - Jarno Drost
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands.
- Oncode Institute, Utrecht, the Netherlands.
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29
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Eren KK, Çınar E, Karakurt HU, Özgür A. Improving the filtering of false positive single nucleotide variations by combining genomic features with quality metrics. Bioinformatics 2023; 39:btad694. [PMID: 38019945 PMCID: PMC10692869 DOI: 10.1093/bioinformatics/btad694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 10/16/2023] [Accepted: 11/28/2023] [Indexed: 12/01/2023] Open
Abstract
MOTIVATION Technical errors in sequencing or bioinformatics steps and difficulties in alignment at some genomic sites result in false positive (FP) variants. Filtering based on quality metrics is a common method for detecting FP variants, but setting thresholds to reduce FP rates may reduce the number of true positive variants by overlooking the more complex relationships between features. The goal of this study is to develop a machine learning-based model for identifying FPs that integrates quality metrics with genomic features and with the feature interpretability property to provide insights into model results. RESULTS We propose a random forest-based model that utilizes genomic features to improve identification of FPs. Further examination of the features shows that the newly introduced features have an important impact on the prediction of variants misclassified by VEF, GATK-CNN, and GARFIELD, recently introduced FP detection systems. We applied cost-sensitive training to avoid errors in misclassification of true variants and developed a model that provides a robust mechanism against misclassification of true variants while increasing the prediction rate of FP variants. This model can be easily re-trained when factors such as experimental protocols might alter the FP distribution. In addition, it has an interpretability mechanism that allows users to understand the impact of features on the model's predictions. AVAILABILITY AND IMPLEMENTATION The software implementation can be found at https://github.com/ideateknoloji/FPDetect.
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Affiliation(s)
- Kazım Kıvanç Eren
- Department of Computer Engineering, Kocaeli University, Kocaeli 41000, Turkey
| | - Esra Çınar
- R&D Department, Idea Technology Solutions LLC., Istanbul 34396, Turkey
| | - Hamza U Karakurt
- R&D Department, Idea Technology Solutions LLC., Istanbul 34396, Turkey
- Department of Bioengineering, Gebze Technical University, Kocaeli 41400, Turkey
| | - Arzucan Özgür
- Department of Computer Engineering, Boğaziçi University, Istanbul 34342, Turkey
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30
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Black JG, van Rooyen ARJ, Heinze D, Gaffney R, Hoffmann AA, Schmidt TL, Weeks AR. Heterogeneous patterns of heterozygosity loss in isolated populations of the threatened eastern barred bandicoot (Perameles gunnii). Mol Ecol 2023. [PMID: 38013623 DOI: 10.1111/mec.17224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/06/2023] [Accepted: 11/14/2023] [Indexed: 11/29/2023]
Abstract
Identifying and analysing isolated populations is critical for conservation. Isolation can make populations vulnerable to local extinction due to increased genetic drift and inbreeding, both of which should leave imprints of decreased genome-wide heterozygosity. While decreases in heterozygosity among populations are frequently investigated, fewer studies have analysed how heterozygosity varies among individuals, including whether heterozygosity varies geographically along lines of discrete population structure or with continuous patterns analogous to isolation by distance. Here we explore geographical patterns of differentiation and individual heterozygosity in the threatened eastern barred bandicoot (Perameles gunnii) in Tasmania, Australia, using genomic data from 85 samples collected between 2008 and 2011. Our analyses identified two isolated demes undergoing significant genetic drift, and several areas of fine-scale differentiation across Tasmania. We observed discrete genetic structures across geographical barriers and continuous patterns of isolation by distance, with little evidence of recent or historical migration. Using a recently developed analytical pipeline for estimating autosomal heterozygosity, we found individual heterozygosities varied within demes by up to a factor of two, and demes with low-heterozygosity individuals also still contained those with high heterozygosity. Spatial interpolation of heterozygosity scores clarified these patterns and identified the isolated Tasman Peninsula as a location where low-heterozygosity individuals were more common than elsewhere. Our results provide novel insights into the relationship between isolation-driven genetic structure and local heterozygosity patterns. These may help improve translocation efforts, by identifying populations in need of assistance, and by providing an individualised metric for identifying source animals for translocation.
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Affiliation(s)
- John G Black
- School of Biosciences, The University of Melbourne, Melbourne, Victoria, Australia
| | | | - Dean Heinze
- Research Centre of Applied Alpine Ecology, La Trobe University, Melbourne, Victoria, Australia
| | - Robbie Gaffney
- Department of Natural Resources and Environment, Hobart, Tasmania, Australia
| | - Ary A Hoffmann
- School of Biosciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Thomas L Schmidt
- School of Biosciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew R Weeks
- School of Biosciences, The University of Melbourne, Melbourne, Victoria, Australia
- Cesar Australia, Brunswick, Victoria, Australia
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31
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Heczko L, Hlaváč V, Holý P, Dvořák P, Liška V, Vyčítal O, Fiala O, Souček P. Prognostic potential of whole exome sequencing in the clinical management of metachronous colorectal cancer liver metastases. Cancer Cell Int 2023; 23:295. [PMID: 38008721 PMCID: PMC10676609 DOI: 10.1186/s12935-023-03135-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 11/09/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND Colorectal cancer is a highly prevalent and deadly. The most common metastatic site is the liver. We performed a whole exome sequencing analysis of a series of metachronous colorectal cancer liver metastases (mCLM) and matched non-malignant liver tissues to investigate the genomic profile of mCLM and explore associations with the patients' prognosis and therapeutic modalities. METHODS DNA samples from mCLM and non-malignant liver tissue pairs (n = 41) were sequenced using whole exome target enrichment and their germline and somatic genetic variability, copy number variations, and mutational signatures were assessed for associations with relapse-free (RFS) and overall survival (OS). RESULTS Our genetic analysis could stratify all patients into existing targeted therapeutic regimens. The most commonly mutated genes in mCLM were TP53, APC, and KRAS together with PIK3CA and several passenger genes like ABCA13, FAT4, PCLO, and UNC80. Patients with somatic alterations in genes from homologous recombination repair, Notch, and Hedgehog pathways had significantly prolonged RFS, while those with altered MYC pathway genes had poor RFS. Additionally, alterations in the JAK-STAT pathway were prognostic of longer OS. Patients bearing somatic variants in VIPR2 had significantly shorter OS and those with alterations in MUC16 prolonged OS. Carriage of the KRAS-12D variant was associated with shortened survival in our and external datasets. On the other hand, tumor mutation burden, mismatch repair deficiency, microsatellite instability, mutational signatures, or copy number variation in mCLM had no prognostic value. CONCLUSIONS The results encourage further molecular profiling for personalized treatment of colorectal cancer liver metastases discerning metachronous from synchronous scenarios.
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Affiliation(s)
- Lucie Heczko
- Laboratory of Pharmacogenomics, Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, 306 05, Czech Republic
| | - Viktor Hlaváč
- Laboratory of Pharmacogenomics, Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, 306 05, Czech Republic
- Toxicogenomics Unit, National Institute of Public Health, Prague, Czech Republic
| | - Petr Holý
- Laboratory of Pharmacogenomics, Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, 306 05, Czech Republic
- Toxicogenomics Unit, National Institute of Public Health, Prague, Czech Republic
| | - Pavel Dvořák
- Laboratory of Pharmacogenomics, Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, 306 05, Czech Republic
- Department of Biology, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czech Republic
| | - Václav Liška
- Laboratory of Pharmacogenomics, Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, 306 05, Czech Republic
- Department of Surgery, Faculty of Medicine and University Hospital in Pilsen, Charles University, Pilsen, Czech Republic
| | - Ondřej Vyčítal
- Laboratory of Pharmacogenomics, Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, 306 05, Czech Republic
- Department of Surgery, Faculty of Medicine and University Hospital in Pilsen, Charles University, Pilsen, Czech Republic
| | - Ondřej Fiala
- Laboratory of Pharmacogenomics, Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, 306 05, Czech Republic
- Department of Oncology and Radiotherapeutics, Faculty of Medicine and University Hospital in Pilsen, Charles University, Pilsen, Czech Republic
| | - Pavel Souček
- Laboratory of Pharmacogenomics, Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, 306 05, Czech Republic.
- Toxicogenomics Unit, National Institute of Public Health, Prague, Czech Republic.
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32
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Zhao S, Mekbib KY, van der Ent MA, Allington G, Prendergast A, Chau JE, Smith H, Shohfi J, Ocken J, Duran D, Furey CG, Hao LT, Duy PQ, Reeves BC, Zhang J, Nelson-Williams C, Chen D, Li B, Nottoli T, Bai S, Rolle M, Zeng X, Dong W, Fu PY, Wang YC, Mane S, Piwowarczyk P, Fehnel KP, See AP, Iskandar BJ, Aagaard-Kienitz B, Moyer QJ, Dennis E, Kiziltug E, Kundishora AJ, DeSpenza T, Greenberg ABW, Kidanemariam SM, Hale AT, Johnston JM, Jackson EM, Storm PB, Lang SS, Butler WE, Carter BS, Chapman P, Stapleton CJ, Patel AB, Rodesch G, Smajda S, Berenstein A, Barak T, Erson-Omay EZ, Zhao H, Moreno-De-Luca A, Proctor MR, Smith ER, Orbach DB, Alper SL, Nicoli S, Boggon TJ, Lifton RP, Gunel M, King PD, Jin SC, Kahle KT. Mutation of key signaling regulators of cerebrovascular development in vein of Galen malformations. Nat Commun 2023; 14:7452. [PMID: 37978175 PMCID: PMC10656524 DOI: 10.1038/s41467-023-43062-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 10/30/2023] [Indexed: 11/19/2023] Open
Abstract
To elucidate the pathogenesis of vein of Galen malformations (VOGMs), the most common and most severe of congenital brain arteriovenous malformations, we performed an integrated analysis of 310 VOGM proband-family exomes and 336,326 human cerebrovasculature single-cell transcriptomes. We found the Ras suppressor p120 RasGAP (RASA1) harbored a genome-wide significant burden of loss-of-function de novo variants (2042.5-fold, p = 4.79 x 10-7). Rare, damaging transmitted variants were enriched in Ephrin receptor-B4 (EPHB4) (17.5-fold, p = 1.22 x 10-5), which cooperates with p120 RasGAP to regulate vascular development. Additional probands had damaging variants in ACVRL1, NOTCH1, ITGB1, and PTPN11. ACVRL1 variants were also identified in a multi-generational VOGM pedigree. Integrative genomic analysis defined developing endothelial cells as a likely spatio-temporal locus of VOGM pathophysiology. Mice expressing a VOGM-specific EPHB4 kinase-domain missense variant (Phe867Leu) exhibited disrupted developmental angiogenesis and impaired hierarchical development of arterial-capillary-venous networks, but only in the presence of a "second-hit" allele. These results illuminate human arterio-venous development and VOGM pathobiology and have implications for patients and their families.
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Affiliation(s)
- Shujuan Zhao
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kedous Y Mekbib
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Martijn A van der Ent
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Garrett Allington
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Andrew Prendergast
- Yale Zebrafish Research Core, Yale School of Medicine, New Haven, CT, USA
| | - Jocelyn E Chau
- Department of Molecular Biophysics and Biochemistry, Yale School of Medicine, New Haven, CT, USA
| | - Hannah Smith
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - John Shohfi
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Jack Ocken
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Daniel Duran
- Department of Neurosurgery, University of Mississippi Medical Center, Jackson, MS, USA
| | - Charuta G Furey
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, AZ, USA
- Ivy Brain Tumor Center, Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Le Thi Hao
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Phan Q Duy
- Department of Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Benjamin C Reeves
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Junhui Zhang
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | | | - Di Chen
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Boyang Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Timothy Nottoli
- Yale Genome Editing Center, Department of Comparative Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Suxia Bai
- Yale Genome Editing Center, Department of Comparative Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Myron Rolle
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Xue Zeng
- Department of Molecular Biophysics and Biochemistry, Yale School of Medicine, New Haven, CT, USA
- Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, NY, USA
| | - Weilai Dong
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, NY, USA
| | - Po-Ying Fu
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Yung-Chun Wang
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Shrikant Mane
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Paulina Piwowarczyk
- Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Katie Pricola Fehnel
- Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alfred Pokmeng See
- Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Bermans J Iskandar
- Department of Neurological Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Beverly Aagaard-Kienitz
- Department of Neurological Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Quentin J Moyer
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Evan Dennis
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Emre Kiziltug
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Adam J Kundishora
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Tyrone DeSpenza
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Ana B W Greenberg
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Andrew T Hale
- Department of Neurosurgery, University of Alabama School of Medicine, Birmingham, AL, USA
| | - James M Johnston
- Department of Neurosurgery, University of Alabama School of Medicine, Birmingham, AL, USA
| | - Eric M Jackson
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Phillip B Storm
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Shih-Shan Lang
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - William E Butler
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bob S Carter
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul Chapman
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christopher J Stapleton
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Aman B Patel
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Georges Rodesch
- Service de Neuroradiologie Diagnostique et Thérapeutique, Hôpital Foch, Suresnes, France
- Department of Interventional Neuroradiology, Hôpital Fondation A. de Rothschild, Paris, France
| | - Stanislas Smajda
- Department of Interventional Neuroradiology, Hôpital Fondation A. de Rothschild, Paris, France
| | - Alejandro Berenstein
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tanyeri Barak
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | | | - Hongyu Zhao
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Andres Moreno-De-Luca
- Department of Radiology, Autism & Developmental Medicine Institute, Genomic Medicine Institute, Geisinger, Danville, PA, USA
| | - Mark R Proctor
- Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Edward R Smith
- Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Darren B Orbach
- Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurointerventional Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Seth L Alper
- Division of Nephrology and Center for Vascular Biology Research, Beth Israel Deaconess Medical Center, and Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Stefania Nicoli
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Department of Pharmacology, Yale School of Medicine, New Haven, CT, USA
- Yale Cardiovascular Research Center, Department of Internal Medicine, Section of Cardiology, Yale School of Medicine, New Haven, CT, USA
| | - Titus J Boggon
- Department of Molecular Biophysics and Biochemistry, Yale School of Medicine, New Haven, CT, USA
- Department of Pharmacology, Yale School of Medicine, New Haven, CT, USA
| | - Richard P Lifton
- Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, NY, USA
| | - Murat Gunel
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Philip D King
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
| | - Sheng Chih Jin
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA.
| | - Kristopher T Kahle
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA.
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, US.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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33
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Fleming AM, Zhu J, Done VK, Burrows CJ. Advantages and challenges associated with bisulfite-assisted nanopore direct RNA sequencing for modifications. RSC Chem Biol 2023; 4:952-964. [PMID: 37920399 PMCID: PMC10619145 DOI: 10.1039/d3cb00081h] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/23/2023] [Indexed: 11/04/2023] Open
Abstract
Nanopore direct RNA sequencing is a technology that allows sequencing for epitranscriptomic modifications with the possibility of a quantitative assessment. In the present work, pseudouridine (Ψ) was sequenced with the nanopore before and after the pH 7 bisulfite reaction that yields stable ribose adducts at C1' of Ψ. The adducted sites produced greater base call errors in the form of deletion signatures compared to Ψ. Sequencing studies on E. coli rRNA and tmRNA before and after the pH 7 bisulfite reaction demonstrated that using chemically-assisted nanopore sequencing has distinct advantages for minimization of false positives and false negatives in the data. The rRNA from E. coli has 19 known U/C sequence variations that give similar base call signatures as Ψ, and therefore, are false positives when inspecting base call data; however, these sites are refractory to reacting with bisulfite as is easily observed in nanopore data. The E. coli tmRNA has a low occupancy Ψ in a pyrimidine-rich sequence context that is called a U representing a false negative; partial occupancy by Ψ is revealed after the bisulfite reaction. In a final study, 5-methylcytidine (m5C) in RNA can readily be observed after the pH 5 bisulfite reaction in which the parent C deaminates to U and the modified site does not react. This locates m5C when using bisulfite-assisted nanopore direct RNA sequencing, which is otherwise challenging to observe. The advantages and challenges of the overall approach are discussed.
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Affiliation(s)
- Aaron M Fleming
- Department of Chemistry, University of Utah 315 S. 1400 East Salt Lake City UT 84112-0850 USA
| | - Judy Zhu
- Department of Chemistry, University of Utah 315 S. 1400 East Salt Lake City UT 84112-0850 USA
| | - Vilhelmina K Done
- Department of Chemistry, University of Utah 315 S. 1400 East Salt Lake City UT 84112-0850 USA
| | - Cynthia J Burrows
- Department of Chemistry, University of Utah 315 S. 1400 East Salt Lake City UT 84112-0850 USA
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34
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Bhowmick A, Bhakta K, Roy M, Gupta S, Das J, Samanta S, Patranabis S, Ghosh A. Heat shock response in Sulfolobus acidocaldarius and first implications for cross-stress adaptation. Res Microbiol 2023; 174:104106. [PMID: 37516156 DOI: 10.1016/j.resmic.2023.104106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 07/15/2023] [Accepted: 07/21/2023] [Indexed: 07/31/2023]
Abstract
Sulfolobus acidocaldarius, a thermoacidophilic crenarchaeon, frequently encounters temperature fluctuations, oxidative stress, and nutrient limitations in its environment. Here, we employed a high-throughput transcriptomic analysis to examine how the gene expression of S. acidocaldarius changes when exposed to high temperatures (92 °C). The data obtained was subsequently validated using quantitative reverse transcription-PCR (qRT-PCR) analysis. Our particular focus was on genes that are involved in the heat shock response, type-II Toxin-Antitoxin systems, and putative transcription factors. To investigate how S. acidocaldarius adapts to multiple stressors, we assessed the expression of these selected genes under oxidative and nutrient stresses using qRT-PCR analysis. The results demonstrated that the gene thβ encoding the β subunit of the thermosome, as well as hsp14 and hsp20, play crucial roles in the majority of stress conditions. Furthermore, we observed overexpression of at least eight different TA pairs belonging to the type II TA systems under all stress conditions. Additionally, four common transcription factors: FadR, TFEβ, CRISPR loci binding protein, and HTH family protein were consistently overexpressed across all stress conditions, indicating their significant role in managing stress. Overall, this work provides the first insight into molecular players involved in the cross-stress adaptation of S. acidocaldarius.
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Affiliation(s)
- Arghya Bhowmick
- Department of Biological Sciences, Bose Institute, EN Block, Sector-V, Kolkata-700091, India
| | - Koustav Bhakta
- Department of Biological Sciences, Bose Institute, EN Block, Sector-V, Kolkata-700091, India
| | - Mousam Roy
- Department of Biological Sciences, Bose Institute, EN Block, Sector-V, Kolkata-700091, India
| | - Sayandeep Gupta
- Department of Biological Sciences, Bose Institute, EN Block, Sector-V, Kolkata-700091, India
| | - Jagriti Das
- Department of Biological Sciences, Bose Institute, EN Block, Sector-V, Kolkata-700091, India
| | - Shirsha Samanta
- Department of Biological Sciences, Bose Institute, EN Block, Sector-V, Kolkata-700091, India
| | | | - Abhrajyoti Ghosh
- Department of Biological Sciences, Bose Institute, EN Block, Sector-V, Kolkata-700091, India.
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35
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Wang YW, McKeon MC, Elmore H, Hess J, Golan J, Gage H, Mao W, Harrow L, Gonçalves SC, Hull CM, Pringle A. Invasive Californian death caps develop mushrooms unisexually and bisexually. Nat Commun 2023; 14:6560. [PMID: 37875491 PMCID: PMC10598064 DOI: 10.1038/s41467-023-42317-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 10/05/2023] [Indexed: 10/26/2023] Open
Abstract
Canonical sexual reproduction among basidiomycete fungi involves the fusion of two haploid individuals of different mating types, resulting in a heterokaryotic mycelial body made up of genetically different nuclei. Using population genomics data and experiments, we discover mushrooms of the invasive and deadly Amanita phalloides can also be homokaryotic; evidence of sexual reproduction by single, unmated individuals. In California, genotypes of homokaryotic mushrooms are also found in heterokaryotic mushrooms, implying nuclei of homokaryotic mycelia are also involved in outcrossing. We find death cap mating is controlled by a single mating type locus, but the development of homokaryotic mushrooms appears to bypass mating type gene control. Ultimately, sporulation is enabled by nuclei able to reproduce alone as well as with others, and nuclei competent for both unisexuality and bisexuality have persisted in invaded habitats for at least 17 but potentially as long as 30 years. The diverse reproductive strategies of invasive death caps are likely facilitating its rapid spread, suggesting a profound similarity between plant, animal and fungal invasions.
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Affiliation(s)
- Yen-Wen Wang
- Department of Botany, University of Wisconsin-Madison, Madison, WI, USA.
| | - Megan C McKeon
- Department of Genetics, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Jacob Golan
- Department of Botany, University of Wisconsin-Madison, Madison, WI, USA
| | - Hunter Gage
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - William Mao
- Department of Botany, University of Wisconsin-Madison, Madison, WI, USA
| | - Lynn Harrow
- Department of Botany, University of Wisconsin-Madison, Madison, WI, USA
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Susana C Gonçalves
- Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, Coimbra, Portugal
| | - Christina M Hull
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Medical Microbiology & Immunology, University of Wisconsin-Madison, Madison, WI, USA
| | - Anne Pringle
- Department of Botany, University of Wisconsin-Madison, Madison, WI, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
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36
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Youngblood MW, Erson-Omay Z, Li C, Najem H, Coșkun S, Tyrtova E, Montejo JD, Miyagishima DF, Barak T, Nishimura S, Harmancı AS, Clark VE, Duran D, Huttner A, Avşar T, Bayri Y, Schramm J, Boetto J, Peyre M, Riche M, Goldbrunner R, Amankulor N, Louvi A, Bilgüvar K, Pamir MN, Özduman K, Kilic T, Knight JR, Simon M, Horbinski C, Kalamarides M, Timmer M, Heimberger AB, Mishra-Gorur K, Moliterno J, Yasuno K, Günel M. Super-enhancer hijacking drives ectopic expression of hedgehog pathway ligands in meningiomas. Nat Commun 2023; 14:6279. [PMID: 37805627 PMCID: PMC10560290 DOI: 10.1038/s41467-023-41926-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/25/2023] [Indexed: 10/09/2023] Open
Abstract
Hedgehog signaling mediates embryologic development of the central nervous system and other tissues and is frequently hijacked by neoplasia to facilitate uncontrolled cellular proliferation. Meningiomas, the most common primary brain tumor, exhibit Hedgehog signaling activation in 6.5% of cases, triggered by recurrent mutations in pathway mediators such as SMO. In this study, we find 35.6% of meningiomas that lack previously known drivers acquired various types of somatic structural variations affecting chromosomes 2q35 and 7q36.3. These cases exhibit ectopic expression of Hedgehog ligands, IHH and SHH, respectively, resulting in Hedgehog signaling activation. Recurrent tandem duplications involving IHH permit de novo chromatin interactions between super-enhancers within DIRC3 and a locus containing IHH. Our work expands the landscape of meningioma molecular drivers and demonstrates enhancer hijacking of Hedgehog ligands as a route to activate this pathway in neoplasia.
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Affiliation(s)
- Mark W Youngblood
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Department of Neurological Surgery, Malnati Brain Tumor Institute of the Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Zeynep Erson-Omay
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Chang Li
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Sun Yat-sen University Cancer Center, 510060, Guangzhou, P. R. China
| | - Hinda Najem
- Department of Neurological Surgery, Malnati Brain Tumor Institute of the Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Süleyman Coșkun
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Biological Sciences, Middle East Technical University, 06800, Ankara, Turkey
| | - Evgeniya Tyrtova
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, University of Washington, Seattle, WA, USA
| | - Julio D Montejo
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Danielle F Miyagishima
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Tanyeri Barak
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Sayoko Nishimura
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Akdes Serin Harmancı
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Victoria E Clark
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Daniel Duran
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Anita Huttner
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Timuçin Avşar
- Department of Neurosurgery, Bahcesehir University, School of Medicine, Istanbul, Turkey
| | - Yasar Bayri
- Department of Neurosurgery, Marmara University School of Medicine, 34854, Istanbul, Turkey
| | | | - Julien Boetto
- Department of Neurosurgery, Hopital Pitie-Salpetriere, AP-HP & Sorbonne Université, F-75103, Paris, France
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, Montpellier, France
| | - Matthieu Peyre
- Department of Neurosurgery, Hopital Pitie-Salpetriere, AP-HP & Sorbonne Université, F-75103, Paris, France
| | - Maximilien Riche
- Department of Neurosurgery, Hopital Pitie-Salpetriere, AP-HP & Sorbonne Université, F-75103, Paris, France
| | - Roland Goldbrunner
- Center for Neurosurgery, University Hospital of Cologne, 50937, Cologne, Germany
| | - Nduka Amankulor
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Angeliki Louvi
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Kaya Bilgüvar
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Genome Analysis, Yale University West Campus, Orange, CT, USA
- Department of Medical Genetics Acibadem Mehmet Ali Aydınlar University, School of Medicine, Istanbul, 34848, Turkey
| | - M Necmettin Pamir
- Department of Neurosurgery, Acibadem Mehmet Ali Aydınlar University, School of Medicine, Istanbul, 34848, Turkey
| | - Koray Özduman
- Department of Neurosurgery, Acibadem Mehmet Ali Aydınlar University, School of Medicine, Istanbul, 34848, Turkey
| | - Türker Kilic
- Department of Neurosurgery, Bahcesehir University, School of Medicine, Istanbul, Turkey
| | - James R Knight
- Yale Center for Genome Analysis, Yale University West Campus, Orange, CT, USA
| | - Matthias Simon
- University of Bonn Medical School, 53105, Bonn, Germany
- Department of Neurosurgery, Bethel Clinic, University of Bielefeld Medical Center OWL, Bielefeld, Germany
| | - Craig Horbinski
- Department of Neurological Surgery, Malnati Brain Tumor Institute of the Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Michel Kalamarides
- Department of Neurosurgery, Hopital Pitie-Salpetriere, AP-HP & Sorbonne Université, F-75103, Paris, France
| | - Marco Timmer
- Center for Neurosurgery, University Hospital of Cologne, 50937, Cologne, Germany
| | - Amy B Heimberger
- Department of Neurological Surgery, Malnati Brain Tumor Institute of the Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ketu Mishra-Gorur
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Jennifer Moliterno
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Katsuhito Yasuno
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA.
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA.
| | - Murat Günel
- Yale Program in Brain Tumor Research, Yale School of Medicine, New Haven, CT, USA.
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA.
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA.
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
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37
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Peng MS, Liu YH, Shen QK, Zhang XH, Dong J, Li JX, Zhao H, Zhang H, Zhang X, He Y, Shi H, Cui C, Ouzhuluobu, Wu TY, Liu SM, Gonggalanzi, Baimakangzhuo, Bai C, Duojizhuoma, Liu T, Dai SS, Murphy RW, Qi XB, Dong G, Su B, Zhang YP. Genetic and cultural adaptations underlie the establishment of dairy pastoralism in the Tibetan Plateau. BMC Biol 2023; 21:208. [PMID: 37798721 PMCID: PMC10557253 DOI: 10.1186/s12915-023-01707-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/20/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Domestication and introduction of dairy animals facilitated the permanent human occupation of the Tibetan Plateau. Yet the history of dairy pastoralism in the Tibetan Plateau remains poorly understood. Little is known how Tibetans adapted to milk and dairy products. RESULTS We integrated archeological evidence and genetic analysis to show the picture that the dairy ruminants, together with dogs, were introduced from West Eurasia into the Tibetan Plateau since ~ 3600 years ago. The genetic admixture between the exotic and indigenous dogs enriched the candidate lactase persistence (LP) allele 10974A > G of West Eurasian origin in Tibetan dogs. In vitro experiments demonstrate that - 13838G > A functions as a LP allele in Tibetans. Unlike multiple LP alleles presenting selective signatures in West Eurasians and South Asians, the de novo origin of Tibetan-specific LP allele - 13838G > A with low frequency (~ 6-7%) and absence of selection corresponds - 13910C > T in pastoralists across eastern Eurasia steppe. CONCLUSIONS Results depict a novel scenario of genetic and cultural adaptations to diet and expand current understanding of the establishment of dairy pastoralism in the Tibetan Plateau.
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Affiliation(s)
- Min-Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yan-Hu Liu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Quan-Kuan Shen
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiao-Hua Zhang
- State Key Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming, 650091, China
- Institute of Medical Biology, Chinese Academy of Medical Science, Peking Union Medical College, Kunming, 650118, China
| | - Jiajia Dong
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Jin-Xiu Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Hui Zhao
- State Key Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming, 650091, China
| | - Hui Zhang
- State Key Laboratory of Primate Biomedical Research (LPBR), School of Primate Translational Medicine, Kunming University of Science and Technology (KUST), Kunming, 650000, China
| | - Xiaoming Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yaoxi He
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hong Shi
- State Key Laboratory of Primate Biomedical Research (LPBR), School of Primate Translational Medicine, Kunming University of Science and Technology (KUST), Kunming, 650000, China
| | - Chaoying Cui
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa, 850000, China
| | - Ouzhuluobu
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa, 850000, China
| | - Tian-Yi Wu
- National Key Laboratory of High Altitude Medicine, High Altitude Medical Research Institute, Xining, 810000, China
| | - Shi-Ming Liu
- National Key Laboratory of High Altitude Medicine, High Altitude Medical Research Institute, Xining, 810000, China
| | - Gonggalanzi
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa, 850000, China
| | - Baimakangzhuo
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa, 850000, China
| | - Caijuan Bai
- The First People's Hospital of Gansu Province, Lanzhou, 730000, China
| | - Duojizhuoma
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa, 850000, China
| | - Ti Liu
- State Key Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming, 650091, China
| | - Shan-Shan Dai
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Robert W Murphy
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Centre for Biodiversity and Conservation Biology, Royal Ontario Museum, Toronto, ON, M5S 2C6, Canada
| | - Xue-Bin Qi
- State Key Laboratory of Primate Biomedical Research (LPBR), School of Primate Translational Medicine, Kunming University of Science and Technology (KUST), Kunming, 650000, China.
- Tibetan Fukang Hospital, Lhasa, 850000, China.
| | - Guanghui Dong
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
- Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming, 650091, China.
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38
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Choi D, Hahn Y. Quantitative Analysis of RNA Polymerase Slippages for Production of P3N-PIPO Trans-frame Fusion Proteins in Potyvirids. J Microbiol 2023; 61:917-927. [PMID: 37843796 DOI: 10.1007/s12275-023-00083-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 09/02/2023] [Accepted: 09/19/2023] [Indexed: 10/17/2023]
Abstract
Potyvirids, members of the family Potyviridae, produce the P3N-PIPO protein, which is crucial for the cell-to-cell transport of viral genomic RNAs. The production of P3N-PIPO requires an adenine (A) insertion caused by RNA polymerase slippage at a conserved GAAAAAA (GA6) sequence preceding the PIPO open reading frame. Presently, the slippage rate of RNA polymerase has been estimated in only a few potyvirids, ranging from 0.8 to 2.1%. In this study, we analyzed publicly available plant RNA-seq data and identified 19 genome contigs from 13 distinct potyvirids. We further investigated the RNA polymerase slippage rates at the GA6 motif. Our analysis revealed that the frequency of the A insertion variant ranges from 0.53 to 4.07% in 11 potyviruses (genus Potyvirus). For the two macluraviruses (genus Macluravirus), the frequency of the A insertion variant was found to be 0.72% and 10.96% respectively. Notably, the estimated RNA polymerase slippage rates for 12 out of the 13 investigated potyvirids were reported for the first time in this study. Our findings underscore the value of plant RNA-seq data for quantitative analysis of potyvirid genome variants, specifically at the GA6 slippage site, and contribute to a more comprehensive understanding of the RNA polymerase slippage phenomenon in potyvirids.
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Affiliation(s)
- Dongjin Choi
- Department of Life Science, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Yoonsoo Hahn
- Department of Life Science, Chung-Ang University, Seoul, 06974, Republic of Korea.
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39
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Schroeder C, Faust U, Krauße L, Liebmann A, Abele M, Demidov G, Schütz L, Kelemen O, Pohle A, Gauß S, Sturm M, Roggia C, Streiter M, Buchert R, Armenau-Ebinger S, Nann D, Beschorner R, Handgretinger R, Ebinger M, Lang P, Holzer U, Skokowa J, Ossowski S, Haack TB, Mau-Holzmann UA, Dufke A, Riess O, Brecht IB. Clinical trio genome sequencing facilitates the interpretation of variants in cancer predisposition genes in paediatric tumour patients. Eur J Hum Genet 2023; 31:1139-1146. [PMID: 37507557 PMCID: PMC10545765 DOI: 10.1038/s41431-023-01423-8] [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: 01/28/2023] [Revised: 05/19/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
The prevalence of pathogenic and likely pathogenic (P/LP) variants in genes associated with cancer predisposition syndromes (CPS) is estimated to be 8-18% for paediatric cancer patients. In more than half of the carriers, the family history is unsuspicious for CPS. Therefore, broad genetic testing could identify germline predisposition in additional children with cancer resulting in important implications for themselves and their families. We thus evaluated clinical trio genome sequencing (TGS) in a cohort of 72 paediatric patients with solid cancers other than retinoblastoma or CNS-tumours. The most prevalent cancer types were sarcoma (n = 26), neuroblastoma (n = 15), and nephroblastoma (n = 10). Overall, P/LP variants in CPS genes were identified in 18.1% of patients (13/72) and P/LP variants in autosomal-dominant CPS genes in 9.7% (7/72). Genetic evaluation would have been recommended for the majority of patients with P/LP variants according to the Jongmans criteria. Four patients (5.6%, 4/72) carried P/LP variants in autosomal-dominant genes known to be associated with their tumour type. With the immediate information on variant inheritance, TGS facilitated the identification of a de novo P/LP in NF1, a gonadosomatic mosaic in WT1 and two pathogenic variants in one patient (DICER1 and PALB2). TGS allows a more detailed characterization of structural variants with base-pair resolution of breakpoints which can be relevant for the interpretation of copy number variants. Altogether, TGS allows comprehensive identification of children with a CPS and supports the individualised clinical management of index patients and high-risk relatives.
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Affiliation(s)
- Christopher Schroeder
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
- Centre for Personalized Cancer Prevention, University Hospital Tübingen, Tübingen, Germany
| | - Ulrike Faust
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Luisa Krauße
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Alexandra Liebmann
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Michael Abele
- Department of Paediatric Haematology and Oncology, University Children's Hospital Tübingen, Tübingen, Germany
| | - German Demidov
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Leon Schütz
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Olga Kelemen
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Alexandra Pohle
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Silja Gauß
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Marc Sturm
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Cristiana Roggia
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Monika Streiter
- Department of Paediatric Haematology and Oncology, Children's Hospital Heilbronn, Heilbronn, Germany
| | - Rebecca Buchert
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Sorin Armenau-Ebinger
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Dominik Nann
- Institute of Pathology and Neuropathology, University Hospital Tübingen, Tübingen, Germany
| | - Rudi Beschorner
- Institute of Pathology and Neuropathology, University Hospital Tübingen, Tübingen, Germany
| | - Rupert Handgretinger
- Department of Paediatric Haematology and Oncology, University Children's Hospital Tübingen, Tübingen, Germany
| | - Martin Ebinger
- Department of Paediatric Haematology and Oncology, University Children's Hospital Tübingen, Tübingen, Germany
| | - Peter Lang
- Department of Paediatric Haematology and Oncology, University Children's Hospital Tübingen, Tübingen, Germany
| | - Ursula Holzer
- Department of Paediatric Haematology and Oncology, University Children's Hospital Tübingen, Tübingen, Germany
| | - Julia Skokowa
- Department of Oncology, Haematology, Immunology, Rheumatology, and Pulmonology, University Hospital Tübingen, Tübingen, Germany
| | - Stephan Ossowski
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Tobias B Haack
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Ulrike A Mau-Holzmann
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Andreas Dufke
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Olaf Riess
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
- Centre for Personalized Cancer Prevention, University Hospital Tübingen, Tübingen, Germany
- NGS Core Centre Tübingen, University Tübingen, Tübingen, Germany
| | - Ines B Brecht
- Department of Paediatric Haematology and Oncology, University Children's Hospital Tübingen, Tübingen, Germany.
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40
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Paskov K, Chrisman B, Stockham N, Washington PY, Dunlap K, Jung JY, Wall DP. Identifying crossovers and shared genetic material in whole genome sequencing data from families. Genome Res 2023; 33:1747-1756. [PMID: 37879861 PMCID: PMC10691535 DOI: 10.1101/gr.277172.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/12/2023] [Indexed: 10/27/2023]
Abstract
Large, whole-genome sequencing (WGS) data sets containing families provide an important opportunity to identify crossovers and shared genetic material in siblings. However, the high variant calling error rates of WGS in some areas of the genome can result in spurious crossover calls, and the special inheritance status of the X Chromosome presents challenges. We have developed a hidden Markov model that addresses these issues by modeling the inheritance of variants in families in the presence of error-prone regions and inherited deletions. We call our method PhasingFamilies. We validate PhasingFamilies using the platinum genome family NA1281 (precision: 0.81; recall: 0.97), as well as simulated genomes with known crossover positions (precision: 0.93; recall: 0.92). Using 1925 quads from the Simons Simplex Collection, we found that PhasingFamilies resolves crossovers to a median resolution of 3527.5 bp. These crossovers recapitulate existing recombination rate maps, including for the X Chromosome; produce sibling pair IBD that matches expected distributions; and are validated by the haplotype estimation tool SHAPEIT. We provide an efficient, open-source implementation of PhasingFamilies that can be used to identify crossovers from family sequencing data.
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Affiliation(s)
- Kelley Paskov
- Department of Biomedical Data Science, Stanford University, Stanford, California 94305, USA;
| | - Brianna Chrisman
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Nathaniel Stockham
- Department of Neuroscience, Stanford University, Stanford, California 94305, USA
| | | | - Kaitlyn Dunlap
- Department of Biomedical Data Science, Stanford University, Stanford, California 94305, USA
- Department of Pediatrics, Stanford University, Stanford, California 94305, USA
| | - Jae-Yoon Jung
- Department of Biomedical Data Science, Stanford University, Stanford, California 94305, USA
- Department of Pediatrics, Stanford University, Stanford, California 94305, USA
| | - Dennis P Wall
- Department of Biomedical Data Science, Stanford University, Stanford, California 94305, USA
- Department of Pediatrics, Stanford University, Stanford, California 94305, USA
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41
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Yan C, Xie HB, Adeola AC, Fu Y, Liu X, Zhao S, Han J, Peng MS, Zhang YP. Inference of ancestral alleles in the pig reference genome. Anim Genet 2023; 54:649-651. [PMID: 37329125 DOI: 10.1111/age.13337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 06/06/2023] [Accepted: 06/06/2023] [Indexed: 06/18/2023]
Affiliation(s)
- Chen Yan
- State Key Laboratory of Genetic Resources and Evolution and Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Hai-Bing Xie
- State Key Laboratory of Genetic Resources and Evolution and Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Adeniyi C Adeola
- State Key Laboratory of Genetic Resources and Evolution and Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Kunming, China
| | - Yuhua Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, China
| | - Xiaolei Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Jianlin Han
- International Livestock Research Institute, Nairobi, Kenya
- CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Min-Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution and Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Kunming, China
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution and Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Kunming, China
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, School of Life Sciences, Yunnan University, Kunming, China
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42
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Li Z, Liu X, Wang C, Li Z, Jiang B, Zhang R, Tong L, Qu Y, He S, Chen H, Mao Y, Li Q, Pook T, Wu Y, Zan Y, Zhang H, Li L, Wen K, Chen Y. The pig pangenome provides insights into the roles of coding structural variations in genetic diversity and adaptation. Genome Res 2023; 33:1833-1847. [PMID: 37914227 PMCID: PMC10691484 DOI: 10.1101/gr.277638.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 09/12/2023] [Indexed: 11/03/2023]
Abstract
Structural variations have emerged as an important driving force for genome evolution and phenotypic variation in various organisms, yet their contributions to genetic diversity and adaptation in domesticated animals remain largely unknown. Here we constructed a pangenome based on 250 sequenced individuals from 32 pig breeds in Eurasia and systematically characterized coding sequence presence/absence variations (PAVs) within pigs. We identified 308.3-Mb nonreference sequences and 3438 novel genes absent from the current reference genome. Gene PAV analysis showed that 16.8% of the genes in the pangene catalog undergo PAV. A number of newly identified dispensable genes showed close associations with adaptation. For instance, several novel swine leukocyte antigen (SLA) genes discovered in nonreference sequences potentially participate in immune responses to productive and respiratory syndrome virus (PRRSV) infection. We delineated previously unidentified features of the pig mobilome that contained 490,480 transposable element insertion polymorphisms (TIPs) resulting from recent mobilization of 970 TE families, and investigated their population dynamics along with influences on population differentiation and gene expression. In addition, several candidate adaptive TE insertions were detected to be co-opted into genes responsible for responses to hypoxia, skeletal development, regulation of heart contraction, and neuronal cell development, likely contributing to local adaptation of Tibetan wild boars. These findings enhance our understanding on hidden layers of the genetic diversity in pigs and provide novel insights into the role of SVs in the evolutionary adaptation of mammals.
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Affiliation(s)
- Zhengcao Li
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China;
| | - Xiaohong Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Chen Wang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Zhenyang Li
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Bo Jiang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Ruifeng Zhang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Lu Tong
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Youping Qu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Sheng He
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Haifan Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Yafei Mao
- Bio-X Institutes, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Qingnan Li
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Torsten Pook
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen 6700 AH, The Netherlands
| | - Yu Wu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Yanjun Zan
- Key Laboratory of Tobacco Improvement and Biotechnology, Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao 266000, China
| | - Hui Zhang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Lu Li
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Keying Wen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China
| | - Yaosheng Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, 510006 Guangzhou, China;
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43
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Tremmel R, Pirmann S, Zhou Y, Lauschke VM. Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance. Br J Clin Pharmacol 2023. [PMID: 37759374 DOI: 10.1111/bcp.15915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/20/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
Abstract
The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.
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Affiliation(s)
- Roman Tremmel
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - Sebastian Pirmann
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
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44
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Baker JL. Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research. FEMS Microbiol Rev 2023; 47:fuad051. [PMID: 37667515 PMCID: PMC10503653 DOI: 10.1093/femsre/fuad051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 08/02/2023] [Accepted: 09/01/2023] [Indexed: 09/06/2023] Open
Abstract
The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.
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Affiliation(s)
- Jonathon L Baker
- Department of Oral Rehabilitation & Biosciences, School of Dentistry, Oregon Health & Science University, 3181 Sam Jackson Park Road, Portland, OR 97202, United States
- Genomic Medicine Group, J. Craig Venter Institute, La Jolla, CA 92037, United States
- Department of Pediatrics, UC San Diego School of Medicine, La Jolla, CA 92093, United States
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45
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Das S, Biswas NK, Basu A. Mapinsights: deep exploration of quality issues and error profiles in high-throughput sequence data. Nucleic Acids Res 2023; 51:e75. [PMID: 37378434 PMCID: PMC10415152 DOI: 10.1093/nar/gkad539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 05/16/2023] [Accepted: 06/27/2023] [Indexed: 06/29/2023] Open
Abstract
High-throughput sequencing (HTS) has revolutionized science by enabling super-fast detection of genomic variants at base-pair resolution. Consequently, it poses the challenging problem of identification of technical artifacts, i.e. hidden non-random error patterns. Understanding the properties of sequencing artifacts holds the key in separating true variants from false positives. Here, we develop Mapinsights, a toolkit that performs quality control (QC) analysis of sequence alignment files, capable of detecting outliers based on sequencing artifacts of HTS data at a deeper resolution compared with existing methods. Mapinsights performs a cluster analysis based on novel and existing QC features derived from the sequence alignment for outlier detection. We applied Mapinsights on community standard open-source datasets and identified various quality issues including technical errors related to sequencing cycles, sequencing chemistry, sequencing libraries and across various orthogonal sequencing platforms. Mapinsights also enables identification of anomalies related to sequencing depth. A logistic regression-based model built on the features of Mapinsights shows high accuracy in detecting 'low-confidence' variant sites. Quantitative estimates and probabilistic arguments provided by Mapinsights can be utilized in identifying errors, bias and outlier samples, and also aid in improving the authenticity of variant calls.
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Affiliation(s)
- Subrata Das
- National Institute of Biomedical Genomics, Kalyani, 741251, West Bengal, India
| | - Nidhan K Biswas
- National Institute of Biomedical Genomics, Kalyani, 741251, West Bengal, India
| | - Analabha Basu
- National Institute of Biomedical Genomics, Kalyani, 741251, West Bengal, India
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46
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Bang S, Won D, Shin S, Cho KS, Park JW, Lee J, Choi YD, Kang S, Lee ST, Choi JR, Han H. Circulating Tumor DNA Analysis on Metastatic Prostate Cancer with Disease Progression. Cancers (Basel) 2023; 15:3998. [PMID: 37568814 PMCID: PMC10416850 DOI: 10.3390/cancers15153998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/26/2023] [Accepted: 08/02/2023] [Indexed: 08/13/2023] Open
Abstract
The positivity rate of circulating tumor DNA (ctDNA) next-generation sequencing (NGS) varies among patients with metastatic prostate cancer (mPC), complicating its incorporation into regular practice. This retrospective study analyzed the ctDNA sequencing results of 100 mPC patients from May 2021 to March 2023 to identify the factors associated with positive ctDNA. Three custom gene panels were used for sequencing. Overall, 63% of the patients exhibited tier I/II somatic alterations, while 12% had pathogenic/likely pathogenic germline alterations. The key genes that were altered included AR, TP53, RB1, PTEN, and APC. Mutations in BRCA1/2, either germline or somatic, were observed in 21% of the patients. Among the metastatic castration-resistant prostate cancer (mCRPC) patients, the ctDNA-positive samples generally showed higher median prostate-specific antigen (PSA) levels and were more likely to be at the radiographic and clinical progressive disease stages, although they were not significantly associated with PSA progression. Our results suggest that ctDNA analysis could detect meaningful genetic changes in mPC patients, especially during disease progression.
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Affiliation(s)
- Sungun Bang
- Department of Urology, Severance Hospital, Urological Science Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.B.); (J.L.); (Y.D.C.)
- Department of Urology, National Health Insurance Service Ilsan Hospital, Goyang 10444, Republic of Korea;
| | - Dongju Won
- Department of Laboratory Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.S.); (S.-T.L.); (J.R.C.)
| | - Saeam Shin
- Department of Laboratory Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.S.); (S.-T.L.); (J.R.C.)
| | - Kang Su Cho
- Department of Urology, Prostate Cancer Center, Gangnam Severance Hospital, Urological Science Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea;
| | - Jae Won Park
- Department of Urology, National Health Insurance Service Ilsan Hospital, Goyang 10444, Republic of Korea;
| | - Jongsoo Lee
- Department of Urology, Severance Hospital, Urological Science Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.B.); (J.L.); (Y.D.C.)
| | - Young Deuk Choi
- Department of Urology, Severance Hospital, Urological Science Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.B.); (J.L.); (Y.D.C.)
| | - Suwan Kang
- Department of Urology, Severance Hospital, Urological Science Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.B.); (J.L.); (Y.D.C.)
| | - Seung-Tae Lee
- Department of Laboratory Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.S.); (S.-T.L.); (J.R.C.)
| | - Jong Rak Choi
- Department of Laboratory Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.S.); (S.-T.L.); (J.R.C.)
| | - Hyunho Han
- Department of Urology, Severance Hospital, Urological Science Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.B.); (J.L.); (Y.D.C.)
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47
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McCaw ZR, O'Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. An allelic-series rare-variant association test for candidate-gene discovery. Am J Hum Genet 2023; 110:1330-1342. [PMID: 37494930 PMCID: PMC10432147 DOI: 10.1016/j.ajhg.2023.07.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 06/30/2023] [Accepted: 07/01/2023] [Indexed: 07/28/2023] Open
Abstract
Allelic series are of candidate therapeutic interest because of the existence of a dose-response relationship between the functionality of a gene and the degree or severity of a phenotype. We define an allelic series as a collection of variants in which increasingly deleterious mutations lead to increasingly large phenotypic effects, and we have developed a gene-based rare-variant association test specifically targeted to identifying genes containing allelic series. Building on the well-known burden test and sequence kernel association test (SKAT), we specify a variety of association models covering different genetic architectures and integrate these into a Coding-Variant Allelic-Series Test (COAST). Through extensive simulations, we confirm that COAST maintains the type I error and improves the power when the pattern of coding-variant effect sizes increases monotonically with mutational severity. We applied COAST to identify allelic-series genes for four circulating-lipid traits and five cell-count traits among 145,735 subjects with available whole-exome sequencing data from the UK Biobank. Compared with optimal SKAT (SKAT-O), COAST identified 29% more Bonferroni-significant associations with circulating-lipid traits, on average, and 82% more with cell-count traits. All of the gene-trait associations identified by COAST have corroborating evidence either from rare-variant associations in the full cohort (Genebass, n = 400,000) or from common-variant associations in the GWAS Catalog. In addition to detecting many gene-trait associations present in Genebass by using only a fraction (36.9%) of the sample, COAST detects associations, such as that between ANGPTL4 and triglycerides, that are absent from Genebass but that have clear common-variant support.
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Affiliation(s)
| | | | | | | | | | | | - Francesco Paolo Casale
- Institute of AI for Health, Helmholtz Munich, Neuherberg, Germany; Helmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
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48
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Wilton R, Szalay AS. Short-read aligner performance in germline variant identification. Bioinformatics 2023; 39:btad480. [PMID: 37527006 PMCID: PMC10421969 DOI: 10.1093/bioinformatics/btad480] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 06/01/2023] [Accepted: 07/31/2023] [Indexed: 08/03/2023] Open
Abstract
MOTIVATION Read alignment is an essential first step in the characterization of DNA sequence variation. The accuracy of variant-calling results depends not only on the quality of read alignment and variant-calling software but also on the interaction between these complex software tools. RESULTS In this review, we evaluate short-read aligner performance with the goal of optimizing germline variant-calling accuracy. We examine the performance of three general-purpose short-read aligners-BWA-MEM, Bowtie 2, and Arioc-in conjunction with three germline variant callers: DeepVariant, FreeBayes, and GATK HaplotypeCaller. We discuss the behavior of the read aligners with regard to the data elements on which the variant callers rely, and illustrate how the runtime configurations of these software tools combine to affect variant-calling performance. AVAILABILITY AND IMPLEMENTATION The quick brown fox jumps over the lazy dog.
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Affiliation(s)
- Richard Wilton
- Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Alexander S Szalay
- Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218, United States
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, United States
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49
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Yuan K, Longchamps RJ, Pardiñas AF, Yu M, Chen TT, Lin SC, Chen Y, Lam M, Liu R, Xia Y, Guo Z, Shi W, Shen C, Daly MJ, Neale BM, Feng YCA, Lin YF, Chen CY, O'Donovan M, Ge T, Huang H. Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.07.23284293. [PMID: 36711496 PMCID: PMC9882563 DOI: 10.1101/2023.01.07.23284293] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestries has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping, which builds on the single-population fine-mapping framework, Sum of Single Effects (SuSiE). SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and LD patterns, accounts for multiple causal variants in a genomic region, and can be applied to GWAS summary statistics. We comprehensively evaluated SuSiEx using simulations, a range of quantitative traits measured in both UK Biobank and Taiwan Biobank, and schizophrenia GWAS across East Asian and European ancestries. In all evaluations, SuSiEx fine-mapped more association signals, produced smaller credible sets and higher posterior inclusion probability (PIP) for putative causal variants, and captured population-specific causal variants.
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Affiliation(s)
- Kai Yuan
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ryan J Longchamps
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | - Mingrui Yu
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Tzu-Ting Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Shu-Chin Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Yu Chen
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Max Lam
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Human Genetics, Genome Institute of Singapore, Singapore, Singapore
- Division of Psychiatry Research, the Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Research Division Institute of Mental Health Singapore, Singapore, Singapore
| | - Ruize Liu
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Yan Xia
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Zhenglin Guo
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wenzhao Shi
- Digital Health China Technologies Corp. Ltd., Beijing, China
| | - Chengguo Shen
- Digital Health China Technologies Corp. Ltd., Beijing, China
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Yen-Chen A Feng
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
- Department of Public Health & Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University
| | | | - Michael O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | - Tian Ge
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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50
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Lassen FH, Venkatesh SS, Baya N, Zhou W, Bloemendal A, Neale BM, Kessler BM, Whiffin N, Lindgren CM, Palmer DS. Exome-wide evidence of compound heterozygous effects across common phenotypes in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.29.23291992. [PMID: 37461573 PMCID: PMC10350147 DOI: 10.1101/2023.06.29.23291992] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Exome-sequencing association studies have successfully linked rare protein-coding variation to risk of thousands of diseases. However, the relationship between rare deleterious compound heterozygous (CH) variation and their phenotypic impact has not been fully investigated. Here, we leverage advances in statistical phasing to accurately phase rare variants (MAF ~ 0.001%) in exome sequencing data from 175,587 UK Biobank (UKBB) participants, which we then systematically annotate to identify putatively deleterious CH coding variation. We show that 6.5% of individuals carry such damaging variants in the CH state, with 90% of variants occurring at MAF < 0.34%. Using a logistic mixed model framework, systematically accounting for relatedness, polygenic risk, nearby common variants, and rare variant burden, we investigate recessive effects in common complex diseases. We find six exome-wide significant (P < 1.68 × 10 - 7 ) and 17 nominally significant (P < 5.25 × 10 - 5 ) gene-trait associations. Among these, only four would have been identified without accounting for CH variation in the gene. We further incorporate age-at-diagnosis information from primary care electronic health records, to show that genetic phase influences lifetime risk of disease across 20 gene-trait combinations (FDR < 5%). Using a permutation approach, we find evidence for genetic phase contributing to disease susceptibility for a collection of gene-trait pairs, including FLG-asthma (P = 0.00205 ) and USH2A-visual impairment (P = 0.0084 ). Taken together, we demonstrate the utility of phasing large-scale genetic sequencing cohorts for robust identification of the phenome-wide consequences of compound heterozygosity.
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Affiliation(s)
- Frederik H. Lassen
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Samvida S. Venkatesh
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Nikolas Baya
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Wei Zhou
- Program in Medical and Population Genetics Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytical and Translational Genetics Unit, Department of Medicine Massachusetts General Hospital, Boston, MA, USA
| | - Alex Bloemendal
- Program in Medical and Population Genetics Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Novo Nordisk Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin M. Neale
- Program in Medical and Population Genetics Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytical and Translational Genetics Unit, Department of Medicine Massachusetts General Hospital, Boston, MA, USA
| | - Benedikt M. Kessler
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Nicola Whiffin
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
- Program in Medical and Population Genetics Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Cecilia M. Lindgren
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Population Health Health, Medical Sciences Division University of Oxford, Oxford, United Kingdom
| | - Duncan S. Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
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