1
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Zhang S, Zhang H, Forrest MP, Zhou Y, Sun X, Bagchi VA, Kozlova A, Santos MD, Piguel NH, Dionisio LE, Sanders AR, Pang ZP, He X, Penzes P, Duan J. Multiple genes in a single GWAS risk locus synergistically mediate aberrant synaptic development and function in human neurons. Cell Genom 2023; 3:100399. [PMID: 37719141 PMCID: PMC10504676 DOI: 10.1016/j.xgen.2023.100399] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/22/2023] [Accepted: 08/07/2023] [Indexed: 09/19/2023]
Abstract
The mechanistic tie between genome-wide association study (GWAS)-implicated risk variants and disease-relevant cellular phenotypes remains largely unknown. Here, using human induced pluripotent stem cell (hiPSC)-derived neurons as a neurodevelopmental model, we identify multiple schizophrenia (SZ) risk variants that display allele-specific open chromatin (ASoC) and are likely to be functional. Editing the strongest ASoC SNP, rs2027349, near vacuolar protein sorting 45 homolog (VPS45) alters the expression of VPS45, lncRNA AC244033.2, and a distal gene, C1orf54. Notably, the transcriptomic changes in neurons are associated with SZ and other neuropsychiatric disorders. Neurons carrying the risk allele exhibit increased dendritic complexity and hyperactivity. Interestingly, individual/combinatorial gene knockdown shows that these genes alter cellular phenotypes in a non-additive synergistic manner. Our study reveals that multiple genes at a single GWAS risk locus mediate a compound effect on neural function, providing a mechanistic link between a non-coding risk variant and disease-related cellular phenotypes.
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Affiliation(s)
- Siwei Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA
| | - Hanwen Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Marc P. Forrest
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Yifan Zhou
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Xiaotong Sun
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Vikram A. Bagchi
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Alena Kozlova
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Marc Dos Santos
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Nicolas H. Piguel
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Leonardo E. Dionisio
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Alan R. Sanders
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA
| | - Zhiping P. Pang
- Department of Neuroscience and Cell Biology, Child Health Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Xin He
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Peter Penzes
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA
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2
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Abstract
Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.
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Affiliation(s)
- Joyce B Kang
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | | | - Aparna Nathan
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | - Nicole Soranzo
- Human Technopole, Milan, Italy; ,
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
- British Heart Foundation Centre of Research Excellence and Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Soumya Raychaudhuri
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, United Kingdom
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3
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Jeong R, Bulyk ML. Blood cell traits' GWAS loci colocalization with variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants. Cell Genom 2023; 3:100327. [PMID: 37492098 PMCID: PMC10363807 DOI: 10.1016/j.xgen.2023.100327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/10/2023] [Accepted: 04/25/2023] [Indexed: 07/27/2023]
Abstract
Genome-wide association studies (GWASs) have uncovered numerous trait-associated loci across the human genome, most of which are located in noncoding regions, making interpretation difficult. Moreover, causal variants are hard to statistically fine-map at many loci because of widespread linkage disequilibrium. To address this challenge, we present a strategy utilizing transcription factor (TF) binding quantitative trait loci (bQTLs) for colocalization analysis to identify trait associations likely mediated by TF occupancy variation and to pinpoint likely causal variants using motif scores. We applied this approach to PU.1 bQTLs in lymphoblastoid cell lines and blood cell trait GWAS data. Colocalization analysis revealed 69 blood cell trait GWAS loci putatively driven by PU.1 occupancy variation. We nominate PU.1 motif-altering variants as the likely shared causal variants at 51 loci. Such integration of TF bQTL data with other GWAS data may reveal transcriptional regulatory mechanisms and causal noncoding variants underlying additional complex traits.
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Affiliation(s)
- Raehoon Jeong
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
| | - Martha L. Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
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4
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Htet M, Lei S, Bajpayi S, Zoitou A, Chamakioti M, Tampakakis E. The role of noncoding genetic variants in cardiomyopathy. Front Cardiovasc Med 2023; 10:1116925. [PMID: 37283586 PMCID: PMC10239966 DOI: 10.3389/fcvm.2023.1116925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 05/04/2023] [Indexed: 06/08/2023] Open
Abstract
Cardiomyopathies remain one of the leading causes of morbidity and mortality worldwide. Environmental risk factors and genetic predisposition account for most cardiomyopathy cases. As with all complex diseases, there are significant challenges in the interpretation of the molecular mechanisms underlying cardiomyopathy-associated genetic variants. Given the technical improvements and reduced costs of DNA sequence technologies, an increasing number of patients are now undergoing genetic testing, resulting in a continuously expanding list of novel mutations. However, many patients carry noncoding genetic variants, and although emerging evidence supports their contribution to cardiac disease, their role in cardiomyopathies remains largely understudied. In this review, we summarize published studies reporting on the association of different types of noncoding variants with various types of cardiomyopathies. We focus on variants within transcriptional enhancers, promoters, intronic sites, and untranslated regions that are likely associated with cardiac disease. Given the broad nature of this topic, we provide an overview of studies that are relatively recent and have sufficient evidence to support a significant degree of causality. We believe that more research with additional validation of noncoding genetic variants will provide further mechanistic insights on the development of cardiac disease, and noncoding variants will be increasingly incorporated in future genetic screening tests.
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Affiliation(s)
- Myo Htet
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD, United States
| | - Shunyao Lei
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Sheetal Bajpayi
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD, United States
| | - Asimina Zoitou
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | | | - Emmanouil Tampakakis
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, United States
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5
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Douben HCW, Nellist M, van Unen L, Elfferich P, Kasteleijn E, Hoogeveen-Westerveld M, Louwen J, van Veghel-Plandsoen M, de Valk W, Saris JJ, Hendriks F, Korpershoek E, Hoefsloot LH, van Vliet M, van Bever Y, van de Laar I, Aten E, Lachmeijer AMA, Taal W, van den Bersselaar L, Schuurmans J, Oostenbrink R, van Minkelen R, van Ierland Y, van Ham TJ. High-yield identification of pathogenic NF1 variants by skin fibroblast transcriptome screening after apparently normal diagnostic DNA testing. Hum Mutat 2022; 43:2130-2140. [PMID: 36251260 PMCID: PMC10099955 DOI: 10.1002/humu.24487] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 08/29/2022] [Accepted: 09/26/2022] [Indexed: 01/25/2023]
Abstract
Neurofibromatosis type 1 (NF1) is caused by inactivating mutations in NF1. Due to the size, complexity, and high mutation rate at the NF1 locus, the identification of causative variants can be challenging. To obtain a molecular diagnosis in 15 individuals meeting diagnostic criteria for NF1, we performed transcriptome analysis (RNA-seq) on RNA obtained from cultured skin fibroblasts. In each case, routine molecular DNA diagnostics had failed to identify a disease-causing variant in NF1. A pathogenic variant or abnormal mRNA splicing was identified in 13 cases: 6 deep intronic variants and 2 transposon insertions causing noncanonical splicing, 3 postzygotic changes, 1 branch point mutation and, in 1 case, abnormal splicing for which the responsible DNA change remains to be identified. These findings helped resolve the molecular findings for an additional 17 individuals in multiple families with NF1, demonstrating the utility of skin-fibroblast-based transcriptome analysis for molecular diagnostics. RNA-seq improves mutation detection in NF1 and provides a powerful complementary approach to DNA-based methods. Importantly, our approach is applicable to other genetic disorders, particularly those caused by a wide variety of variants in a limited number of genes and specifically for individuals in whom routine molecular DNA diagnostics did not identify the causative variant.
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Affiliation(s)
- Hannie C W Douben
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Mark Nellist
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Leontine van Unen
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Peter Elfferich
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Esmee Kasteleijn
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Jesse Louwen
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Walter de Valk
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jasper J Saris
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Femke Hendriks
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Esther Korpershoek
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Pathology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Lies H Hoefsloot
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Margreethe van Vliet
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.,ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus MC, Rotterdam, The Netherlands
| | - Yolande van Bever
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ingrid van de Laar
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Emmelien Aten
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Augusta M A Lachmeijer
- Department of Genetics, Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Walter Taal
- ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus MC, Rotterdam, The Netherlands.,Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Lisa van den Bersselaar
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Juliette Schuurmans
- Department of Genetics, Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Rianne Oostenbrink
- ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus MC, Rotterdam, The Netherlands.,Department of General Pediatrics, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Rick van Minkelen
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.,ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus MC, Rotterdam, The Netherlands
| | - Yvette van Ierland
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.,ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus MC, Rotterdam, The Netherlands
| | - Tjakko J van Ham
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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6
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Borja N, Bivona S, Peart LS, Johnson B, Gonzalez J, Barbouth D, Moore H, Guo S, Bademci G, Tekin M. Genome sequencing reveals novel noncoding variants in PLA2G6 and LMNB1 causing progressive neurologic disease. Mol Genet Genomic Med 2022; 10:e1892. [PMID: 35247231 PMCID: PMC9000935 DOI: 10.1002/mgg3.1892] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 01/20/2022] [Accepted: 01/24/2022] [Indexed: 12/18/2022] Open
Abstract
Neurodegenerative disorders and leukodystrophies are progressive neurologic conditions that can occur following the disruption of intricately coordinated patterns of gene expression. Exome sequencing has been adopted as an effective diagnostic tool for determining the underlying genetic etiology of Mendelian neurologic disorders, however genome sequencing offer advantages in its ability to identify and characterize copy number, structural, and sequence variants in noncoding regions. Genome sequencing from peripheral leukocytes was performed on two patients with progressive neurologic disease of unknown etiology following negative genetic investigations including exome sequencing. RNA sequencing from peripheral blood was performed to determine gene expression patterns in one of the patients. Potential causative variants were matched to the patients' clinical presentation. The first proband was found to be heterozygous for a likely pathogenic missense variant in PLA2G6 (c.386T>C; p.Leu129Pro) and have an additional deep intronic variant in PLA2G6 (c.2035-926G>A). RNA sequencing indicated this latter variant created a splice acceptor site leading to the incorporation of a pseudo-exon introducing a premature termination codon. The second proband was heterozygous for a 261 kb deletion upstream of LMNB1 that included an enhancer region. Previous reports of copy number variants spanning this region of cis-acting regulatory elements corroborated its pathogenicity. When combined with clinical presentations, these findings led to a definitive diagnosis of autosomal recessive infantile neuroaxonal dystrophy and autosomal dominant adult-onset demyelinating leukodystrophy, respectively. In patients with progressive neurologic disease of unknown etiology, genome sequencing with the addition of RNA analysis where appropriate should be considered for the identification of causative noncoding pathogenic variants.
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Affiliation(s)
- Nicholas Borja
- John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Stephanie Bivona
- John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Lé Shon Peart
- John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Brittany Johnson
- John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Joanna Gonzalez
- John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Deborah Barbouth
- John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Henry Moore
- Department of Neurology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Shengru Guo
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Guney Bademci
- John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Mustafa Tekin
- John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, Florida, USA
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, USA
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7
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Zhang K, Hocker JD, Miller M, Hou X, Chiou J, Poirion OB, Qiu Y, Li YE, Gaulton KJ, Wang A, Preissl S, Ren B. A single-cell atlas of chromatin accessibility in the human genome. Cell 2021; 184:5985-6001.e19. [PMID: 34774128 PMCID: PMC8664161 DOI: 10.1016/j.cell.2021.10.024] [Citation(s) in RCA: 141] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 07/30/2021] [Accepted: 10/21/2021] [Indexed: 12/12/2022]
Abstract
Current catalogs of regulatory sequences in the human genome are still incomplete and lack cell type resolution. To profile the activity of gene regulatory elements in diverse cell types and tissues in the human body, we applied single-cell chromatin accessibility assays to 30 adult human tissue types from multiple donors. We integrated these datasets with previous single-cell chromatin accessibility data from 15 fetal tissue types to reveal the status of open chromatin for ∼1.2 million candidate cis-regulatory elements (cCREs) in 222 distinct cell types comprised of >1.3 million nuclei. We used these chromatin accessibility maps to delineate cell-type-specificity of fetal and adult human cCREs and to systematically interpret the noncoding variants associated with complex human traits and diseases. This rich resource provides a foundation for the analysis of gene regulatory programs in human cell types across tissues, life stages, and organ systems.
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Affiliation(s)
- Kai Zhang
- Ludwig Institute for Cancer Research, La Jolla, CA, USA; Department of Cellular and Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - James D Hocker
- Ludwig Institute for Cancer Research, La Jolla, CA, USA; Medical Scientist Training Program, University of California San Diego, La Jolla, CA, USA; Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA
| | - Michael Miller
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Xiaomeng Hou
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Joshua Chiou
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA; Department of Pediatrics, Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Olivier B Poirion
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Yunjiang Qiu
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Yang E Li
- Ludwig Institute for Cancer Research, La Jolla, CA, USA; Department of Cellular and Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Kyle J Gaulton
- Department of Pediatrics, Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA; Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Allen Wang
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, La Jolla, CA, USA; Center for Epigenomics, University of California San Diego, La Jolla, CA, USA; Department of Cellular and Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA; Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
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8
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Wang Y, Jiang Y, Yao B, Huang K, Liu Y, Wang Y, Qin X, Saykin AJ, Chen L. WEVar: a novel statistical learning framework for predicting noncoding regulatory variants. Brief Bioinform 2021; 22:6279833. [PMID: 34021560 DOI: 10.1093/bib/bbab189] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/05/2021] [Accepted: 04/23/2021] [Indexed: 11/15/2022] Open
Abstract
Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies or quantitative trait locus analyses have identified variants associated with traits or molecular phenotypes, most of them are located in the noncoding regions, making the identification of causal variants a particular challenge. Existing computational approaches developed for prioritizing noncoding variants produce inconsistent and even conflicting results. To address these challenges, we propose a novel statistical learning framework, which directly integrates the precomputed functional scores from representative scoring methods. It will maximize the usage of integrated methods by automatically learning the relative contribution of each method and produce an ensemble score as the final prediction. The framework consists of two modes. The first 'context-free' mode is trained using curated causal regulatory variants from a wide range of context and is applicable to predict regulatory variants of unknown and diverse context. The second 'context-dependent' mode further improves the prediction when the training and testing variants are from the same context. By evaluating the framework via both simulation and empirical studies, we demonstrate that it outperforms integrated scoring methods and the ensemble score successfully prioritizes experimentally validated regulatory variants in multiple risk loci.
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Affiliation(s)
- Ye Wang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Yuchao Jiang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Bing Yao
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, 36849, USA
| | - Kun Huang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Yunlong Liu
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yue Wang
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
| | - Xiao Qin
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Li Chen
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
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9
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Li W, Duren Z, Jiang R, Wong WH. A method for scoring the cell type-specific impacts of noncoding variants in personal genomes. Proc Natl Acad Sci U S A 2020; 117:21364-72. [PMID: 32817564 DOI: 10.1073/pnas.1922703117] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Here we use the expression and accessibility data from a diverse set of cell types to learn a model for the dependence of the accessibility of a regulatory element on its DNA sequence and TF expression. Using GTEx samples with WGS data, we show that the noncoding variants predicted to affect accessibility are more strongly associated with the expression of nearby genes. To interpret a personal genome, we combine the sequence information with context-specific TF expression to prioritize variants and regulatory elements in any genomic region of interest. This approach should be helpful in the study of risk loci previously identified by GWAS. Results from analysis of height and WGS data from the GTEx project support this hypothesis. A person’s genome typically contains millions of variants which represent the differences between this personal genome and the reference human genome. The interpretation of these variants, i.e., the assessment of their potential impact on a person’s phenotype, is currently of great interest in human genetics and medicine. We have developed a prioritization tool called OpenCausal which takes as inputs 1) a personal genome and 2) a reference context-specific TF expression profile and returns a list of noncoding variants prioritized according to their impact on chromatin accessibility for any given genomic region of interest. We applied OpenCausal to 6,430 samples across 18 tissues derived from the GTEx project and found that the variants prioritized by OpenCausal are highly enriched for eQTLs and caQTLs. We further propose a strategy to integrate the predicted open scores with genome-wide association studies (GWAS) data to prioritize putative causal variants and regulatory elements for a given risk locus (i.e., fine-mapping analysis). As an initial example, we applied this method to a GWAS dataset of human height and found that the prioritized putative variants and elements are correlated with the phenotype (i.e., heights of individuals) better than others.
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10
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French JD, Edwards SL. The Role of Noncoding Variants in Heritable Disease. Trends Genet 2020; 36:880-91. [PMID: 32741549 DOI: 10.1016/j.tig.2020.07.004] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 06/30/2020] [Accepted: 07/02/2020] [Indexed: 12/26/2022]
Abstract
The genetic basis of disease has largely focused on coding regions. However, it has become clear that a large proportion of the noncoding genome is functional and harbors genetic variants that contribute to disease etiology. Here, we review recent examples of inherited noncoding alterations that are responsible for Mendelian disorders or act to influence complex traits. We explore both rare and common genetic variants and discuss the wide range of mechanisms by which they affect gene regulation to promote disease. We also debate the challenges and progress associated with identifying and interpreting the functional and clinical significance of genetic variation in the context of the noncoding regulatory landscape.
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Burdick KJ, Cogan JD, Rives LC, Robertson AK, Koziura ME, Brokamp E, Duncan L, Hannig V, Pfotenhauer J, Vanzo R, Paul MS, Bican A, Morgan T, Duis J, Newman JH, Hamid R, Phillips JA. Limitations of exome sequencing in detecting rare and undiagnosed diseases. Am J Med Genet A 2020; 182:1400-1406. [PMID: 32190976 DOI: 10.1002/ajmg.a.61558] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 01/28/2020] [Accepted: 03/03/2020] [Indexed: 12/21/2022]
Abstract
While exome sequencing (ES) is commonly the final diagnostic step in clinical genetics, it may miss diagnoses. To clarify the limitations of ES, we investigated the diagnostic yield of genetic tests beyond ES in our Undiagnosed Diseases Network (UDN) participants. We reviewed the yield of additional genetic testing including genome sequencing (GS), copy number variant (CNV), noncoding variant (NCV), repeat expansion (RE), or methylation testing in UDN cases with nondiagnostic ES results. Overall, 36/54 (67%) of total diagnoses were based on clinical findings and coding variants found by ES and 3/54 (6%) were based on clinical findings only. The remaining 15/54 (28%) required testing beyond ES. Of these, 7/15 (47%) had NCV, 6/15 (40%) CNV, and 2/15 (13%) had a RE or a DNA methylation disorder. Thus 18/54 (33%) of diagnoses were not solved exclusively by ES. Several methods were needed to detect and/or confirm the functional effects of the variants missed by ES, and in some cases by GS. These results indicate that tests to detect elusive variants should be considered after nondiagnostic preliminary steps. Further studies are needed to determine the cost-effectiveness of tests beyond ES that provide diagnoses and insights to possible treatment.
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Affiliation(s)
- Kendall J Burdick
- University of Massachusetts of Medical School, Worcester, Massachusetts, USA
| | - Joy D Cogan
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lynette C Rives
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Amy K Robertson
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Mary E Koziura
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Elly Brokamp
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Laura Duncan
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Vickie Hannig
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jean Pfotenhauer
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Rena Vanzo
- Lineagen Inc., Salt Lake City, Utah, USA
| | | | - Anna Bican
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Thomas Morgan
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jessica Duis
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - John H Newman
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Rizwan Hamid
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - John A Phillips
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Blue EE, Yu CE, Thornton TA, Chapman NH, Kernfeld E, Jiang N, Shively KM, Buckingham KJ, Marvin CT, Bamshad MJ, Bird TD, Wijsman EM. Variants regulating ZBTB4 are associated with age-at-onset of Alzheimer's disease. Genes Brain Behav 2017; 17:e12429. [PMID: 29045054 DOI: 10.1111/gbb.12429] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 10/11/2017] [Accepted: 10/12/2017] [Indexed: 01/01/2023]
Abstract
The identification of novel genetic modifiers of age-at-onset (AAO) of Alzheimer's disease (AD) could advance our understanding of AD and provide novel therapeutic targets. A previous genome scan for modifiers of AAO among families affected by early-onset AD caused by the PSEN2 N141I variant identified 2 loci with significant evidence for linkage: 1q23.3 and 17p13.2. Here, we describe the fine-mapping of these 2 linkage regions, and test for replication in 6 independent datasets. By fine-mapping these linkage signals in a single large family, we reduced the linkage regions to 11% their original size and nominated 54 candidate variants. Among the 11 variants associated with AAO of AD in a larger sample of Germans from Russia, the strongest evidence implicated promoter variants influencing NCSTN on 1q23.3 and ZBTB4 on 17p13.2. The association between ZBTB4 and AAO of AD was replicated by multiple variants in independent, trans-ethnic datasets. Our results show association between AAO of AD and both ZBTB4 and NCSTN. ZBTB4 is a transcriptional repressor that regulates the cell cycle, including the apoptotic response to amyloid beta, while NCSTN is part of the gamma secretase complex, known to influence amyloid beta production. These genes therefore suggest important roles for amyloid beta and cell cycle pathways in AAO of AD.
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Affiliation(s)
- E E Blue
- Division of Medical Genetics, University of Washington, Seattle, Washington
| | - C-E Yu
- Division of Gerontology, University of Washington, Seattle, Washington.,Geriatric Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, Washington
| | - T A Thornton
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - N H Chapman
- Division of Medical Genetics, University of Washington, Seattle, Washington
| | - E Kernfeld
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - N Jiang
- Department of Biology, University of Washington, Seattle, Washington
| | - K M Shively
- Department of Pediatrics, University of Washington, Seattle, Washington
| | - K J Buckingham
- Department of Pediatrics, University of Washington, Seattle, Washington
| | - C T Marvin
- Department of Pediatrics, University of Washington, Seattle, Washington
| | - M J Bamshad
- Department of Pediatrics, University of Washington, Seattle, Washington.,Department of Genome Sciences, University of Washington, Seattle, Washington.,Division of Genetic Medicine, Seattle Children's Hospital, Seattle, Washington
| | - T D Bird
- Division of Medical Genetics, University of Washington, Seattle, Washington.,Geriatric Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, Washington.,Department of Neurology, University of Washington, Seattle, Washington
| | - E M Wijsman
- Division of Medical Genetics, University of Washington, Seattle, Washington.,Department of Biostatistics, University of Washington, Seattle, Washington.,Department of Genome Sciences, University of Washington, Seattle, Washington
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