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Strober BJ, Zhang MJ, Amariuta T, Rossen J, Price AL. Fine-mapping causal tissues and genes at disease-associated loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.01.23297909. [PMID: 37961337 PMCID: PMC10635248 DOI: 10.1101/2023.11.01.23297909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of CD52 in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.
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Affiliation(s)
- Benjamin J. Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Martin Jinye Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Tiffany Amariuta
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Jordan Rossen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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2
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Shen S, Sobczyk MK, Paternoster L, Brown SJ. From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics. J Invest Dermatol 2024; 144:1189-1199.e8. [PMID: 38782533 DOI: 10.1016/j.jid.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/13/2024] [Accepted: 03/06/2024] [Indexed: 05/25/2024]
Abstract
Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.
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Affiliation(s)
- Silvia Shen
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Institute for Evolution and Ecology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
| | - Maria K Sobczyk
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Sara J Brown
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Department of Dermatology, NHS Lothian, Edinburgh, United Kingdom
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3
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Boetto C, Frouin A, Henches L, Auvergne A, Suzuki Y, Patin E, Bredon M, Chiu A, Consortium MI, Sankararaman S, Zaitlen N, Kennedy SP, Quintana-Murci L, Duffy D, Sokol H, Aschard H. MANOCCA: a robust and computationally efficient test of covariance in high-dimension multivariate omics data. Brief Bioinform 2024; 25:bbae272. [PMID: 38856173 PMCID: PMC11163461 DOI: 10.1093/bib/bbae272] [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: 11/16/2023] [Revised: 04/16/2024] [Accepted: 05/28/2024] [Indexed: 06/11/2024] Open
Abstract
Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix of a multivariate outcome. The proposed test is by construction orthogonal to tests based on the mean and variance and is able to capture effects that are missed by both approaches. We first compare the performances of MANOCCA with existing correlation-based methods and show that MANOCCA is the only test correctly calibrated in simulation mimicking omics data. We then investigate the impact of reducing the dimensionality of the data using principal component analysis when the sample size is smaller than the number of pairwise covariance terms analysed. We show that, in many realistic scenarios, the maximum power can be achieved with a limited number of components. Finally, we apply MANOCCA to 1000 healthy individuals from the Milieu Interieur cohort, to assess the effect of health, lifestyle and genetic factors on the covariance of two sets of phenotypes, blood biomarkers and flow cytometry-based immune phenotypes. Our analyses identify significant associations between multiple factors and the covariance of both omics data.
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Affiliation(s)
- Christophe Boetto
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Arthur Frouin
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Léo Henches
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Antoine Auvergne
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Yuka Suzuki
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Etienne Patin
- Human Evolutionary Genetics Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, 25-28 rue Dr Roux, 75015 Paris, France
| | - Marius Bredon
- Sorbonne Université, INSERM, Centre de recherche Saint-Antoine, CRSA, Microbiota, Gut and Inflammation Laboratory, Hôpital Saint-Antoine (UMR S938) Sorbonne Université, 27 rue Chaligny, 75012 Paris, France
| | - Alec Chiu
- Department of Human Genetics, University California Los Angeles, 695 Charles E. Young Drive South, Box 708822, Los Angeles, CA 90095-7088, United States
| | | | - Sriram Sankararaman
- Department of Human Genetics, University California Los Angeles, 695 Charles E. Young Drive South, Box 708822, Los Angeles, CA 90095-7088, United States
| | - Noah Zaitlen
- Department of Human Genetics, University California Los Angeles, 695 Charles E. Young Drive South, Box 708822, Los Angeles, CA 90095-7088, United States
| | - Sean P Kennedy
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Lluis Quintana-Murci
- Human Evolutionary Genetics Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, 25-28 rue Dr Roux, 75015 Paris, France
- Chair of Human Genomics and Evolution, Collège de France, 11 Pl. Marcelin Berthelot, 75005 Paris, France
| | - Darragh Duffy
- Translational Immunology Unit, Institut Pasteur, Université de Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Harry Sokol
- Sorbonne Université, INSERM, Centre de recherche Saint-Antoine, CRSA, Microbiota, Gut and Inflammation Laboratory, Hôpital Saint-Antoine (UMR S938) Sorbonne Université, 27 rue Chaligny, 75012 Paris, France
- Paris Center for Microbiome Medicine, Fédération Hospitalo-Universitaire, 184 rue du Faubourg Saint-Antoine, 75571 PARIS Cedex 12, France
- Gastroenterology Department, AP-HP, Saint Antoine Hospital, 184 rue du faubourg Saint-Antoine, 75012 Paris, France
- INRAE Micalis & AgroParisTech, UMR1319, Micalis & AgroParisTech, 4 avenue Jean Jaurès, 78352 Jouy en Josas, France
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
- Department of Epidemiology, Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, United States
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Kim A, Zhang Z, Legros C, Lu Z, de Smith A, Moore JE, Mancuso N, Gazal S. Inferring causal cell types of human diseases and risk variants from candidate regulatory elements. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.17.24307556. [PMID: 38798383 PMCID: PMC11118635 DOI: 10.1101/2024.05.17.24307556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The heritability of human diseases is extremely enriched in candidate regulatory elements (cRE) from disease-relevant cell types. Critical next steps are to infer which and how many cell types are truly causal for a disease (after accounting for co-regulation across cell types), and to understand how individual variants impact disease risk through single or multiple causal cell types. Here, we propose CT-FM and CT-FM-SNP, two methods that leverage cell-type-specific cREs to fine-map causal cell types for a trait and for its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using nearly one thousand cRE annotations, primarily coming from ENCODE4. CT-FM inferred 81 causal cell types with corresponding SNP-annotations explaining a high fraction of trait SNP-heritability (~2/3 of the SNP-heritability explained by existing cREs), identified 16 traits with multiple causal cell types, highlighted cell-disease relationships consistent with known biology, and uncovered previously unexplored cellular mechanisms in psychiatric and immune-related diseases. Finally, we applied CT-FM-SNP to 39 UK Biobank traits and predicted high confidence causal cell types for 2,798 candidate causal non-coding SNPs. Our results suggest that most SNPs impact a phenotype through a single cell type, and that pleiotropic SNPs target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to impact disease risk.
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Affiliation(s)
- Artem Kim
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zixuan Zhang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Come Legros
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zeyun Lu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Adam de Smith
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jill E Moore
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
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Conery M, Pippin JA, Wagley Y, Trang K, Pahl MC, Villani DA, Favazzo LJ, Ackert-Bicknell CL, Zuscik MJ, Katsevich E, Wells AD, Zemel BS, Voight BF, Hankenson KD, Chesi A, Grant SF. GWAS-informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral density. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585778. [PMID: 38562830 PMCID: PMC10983984 DOI: 10.1101/2024.03.19.585778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Over 1,100 independent signals have been identified with genome-wide association studies (GWAS) for bone mineral density (BMD), a key risk factor for mortality-increasing fragility fractures; however, the effector gene(s) for most remain unknown. Informed by a variant-to-gene mapping strategy implicating 89 non-coding elements predicted to regulate osteoblast gene expression at BMD GWAS loci, we executed a single-cell CRISPRi screen in human fetal osteoblast 1.19 cells (hFOBs). The BMD relevance of hFOBs was supported by heritability enrichment from cross-cell type stratified LD-score regression involving 98 cell types grouped into 15 tissues. 24 genes showed perturbation in the screen, with four (ARID5B, CC2D1B, EIF4G2, and NCOA3) exhibiting consistent effects upon siRNA knockdown on three measures of osteoblast maturation and mineralization. Lastly, additional heritability enrichments, genetic correlations, and multi-trait fine-mapping revealed that many BMD GWAS signals are pleiotropic and likely mediate their effects via non-bone tissues that warrant attention in future screens.
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Affiliation(s)
- Mitchell Conery
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - James A. Pippin
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Yadav Wagley
- Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, MI 48109
| | - Khanh Trang
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Matthew C. Pahl
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - David A. Villani
- Colorado Program for Musculoskeletal Research, University of Colorado Anschutz Medical Campus, Aurora, CO
- Cell Biology, Stems Cells and Development Ph.D. Program, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Lacey J. Favazzo
- Colorado Program for Musculoskeletal Research, University of Colorado Anschutz Medical Campus, Aurora, CO
- Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- University of Colorado Interdisciplinary Joint Biology Program, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Cheryl L. Ackert-Bicknell
- Colorado Program for Musculoskeletal Research, University of Colorado Anschutz Medical Campus, Aurora, CO
- Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- University of Colorado Interdisciplinary Joint Biology Program, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Michael J. Zuscik
- Colorado Program for Musculoskeletal Research, University of Colorado Anschutz Medical Campus, Aurora, CO
- Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- University of Colorado Interdisciplinary Joint Biology Program, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Eugene Katsevich
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew D. Wells
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Babette S. Zemel
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Benjamin F. Voight
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute of Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kurt D. Hankenson
- Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, MI 48109
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Struan F.A. Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute of Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Endocrinology and Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
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Ding R, Zou X, Qin Y, Gong L, Chen H, Ma X, Guang S, Yu C, Wang G, Li L. xQTLbiolinks: a comprehensive and scalable tool for integrative analysis of molecular QTLs. Brief Bioinform 2023; 25:bbad440. [PMID: 38058186 PMCID: PMC10701093 DOI: 10.1093/bib/bbad440] [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: 06/27/2023] [Revised: 10/23/2023] [Accepted: 11/11/2023] [Indexed: 12/08/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of disease-associated non-coding variants, posing urgent needs for functional interpretation. Molecular Quantitative Trait Loci (xQTLs) such as eQTLs serve as an essential intermediate link between these non-coding variants and disease phenotypes and have been widely used to discover disease-risk genes from many population-scale studies. However, mining and analyzing the xQTLs data presents several significant bioinformatics challenges, particularly when it comes to integration with GWAS data. Here, we developed xQTLbiolinks as the first comprehensive and scalable tool for bulk and single-cell xQTLs data retrieval, quality control and pre-processing from public repositories and our integrated resource. In addition, xQTLbiolinks provided a robust colocalization module through integration with GWAS summary statistics. The result generated by xQTLbiolinks can be flexibly visualized or stored in standard R objects that can easily be integrated with other R packages and custom pipelines. We applied xQTLbiolinks to cancer GWAS summary statistics as case studies and demonstrated its robust utility and reproducibility. xQTLbiolinks will profoundly accelerate the interpretation of disease-associated variants, thus promoting a better understanding of disease etiologies. xQTLbiolinks is available at https://github.com/lilab-bioinfo/xQTLbiolinks.
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Affiliation(s)
- Ruofan Ding
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Xudong Zou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Yangmei Qin
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Lihai Gong
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Hui Chen
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Xuelian Ma
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Shouhong Guang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of USTC, The USTC RNA Institute, Ministry of Education Key Laboratory for Membraneless Organelles & Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Chen Yu
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Gao Wang
- The Gertrude H. Sergievsky Center and the Department of Neurology, Columbia University, NY 10032, USA
| | - Lei Li
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
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