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da Silva CC, Trevino CM, Mitchell J, Murali H, Tsimbal C, Dalessandro E, Carroll SH, Kochhar S, Curtis SW, Cheng CHE, Wang F, Kutschera E, Carstens RP, Xing Y, Wang K, Leslie EJ, Liao EC. Functional analysis of ESRP1/2 gene variants and CTNND1 isoforms in orofacial cleft pathogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.02.601574. [PMID: 39005284 PMCID: PMC11245018 DOI: 10.1101/2024.07.02.601574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Orofacial cleft (OFC) is a common human congenital anomaly. Epithelial-specific RNA splicing regulators ESRP1 and ESRP2 regulate craniofacial morphogenesis and their disruption result in OFC in zebrafish, mouse and humans. Using esrp1/2 mutant zebrafish and murine Py2T cell line models, we functionally tested the pathogenicity of human ESRP1/2 gene variants. We found that many variants predicted by in silico methods to be pathogenic were functionally benign. Esrp1 also regulates the alternative splicing of Ctnnd1 and these genes are co-expressed in the embryonic and oral epithelium. In fact, over-expression of ctnnd1 is sufficient to rescue morphogenesis of epithelial-derived structures in esrp1/2 zebrafish mutants. Additionally, we identified 13 CTNND1 variants from genome sequencing of OFC cohorts, confirming CTNND1 as a key gene in human OFC. This work highlights the importance of functional assessment of human gene variants and demonstrates the critical requirement of Esrp-Ctnnd1 acting in the embryonic epithelium to regulate palatogenesis.
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Affiliation(s)
- Caroline Caetano da Silva
- Center for Craniofacial Innovation, Division of Plastic and Reconstructive Surgery, Department of Surgery, Children’s Hospital of Philadelphia, PA, USA
| | | | | | - Hemma Murali
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Casey Tsimbal
- Center for Craniofacial Innovation, Division of Plastic and Reconstructive Surgery, Department of Surgery, Children’s Hospital of Philadelphia, PA, USA
- Shriners Hospital for Children, Tampa, FL, USA
| | - Eileen Dalessandro
- Center for Craniofacial Innovation, Division of Plastic and Reconstructive Surgery, Department of Surgery, Children’s Hospital of Philadelphia, PA, USA
| | - Shannon H. Carroll
- Center for Craniofacial Innovation, Division of Plastic and Reconstructive Surgery, Department of Surgery, Children’s Hospital of Philadelphia, PA, USA
- Shriners Hospital for Children, Tampa, FL, USA
| | - Simren Kochhar
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Sarah W. Curtis
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Ching Hsun Eric Cheng
- Center for Craniofacial Innovation, Division of Plastic and Reconstructive Surgery, Department of Surgery, Children’s Hospital of Philadelphia, PA, USA
| | - Feng Wang
- Center for Genomic Medicine, Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, PA, USA
| | - Eric Kutschera
- Center for Genomic Medicine, Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, PA, USA
| | - Russ P. Carstens
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Yi Xing
- Center for Genomic Medicine, Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kai Wang
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Elizabeth J. Leslie
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Eric C. Liao
- Center for Craniofacial Innovation, Division of Plastic and Reconstructive Surgery, Department of Surgery, Children’s Hospital of Philadelphia, PA, USA
- Harvard Medical School, Boston, MA, USA
- Shriners Hospital for Children, Tampa, FL, USA
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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Dorans E, Jagadeesh K, Dey K, Price AL. Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.24.24307813. [PMID: 38826240 PMCID: PMC11142273 DOI: 10.1101/2024.05.24.24307813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Methods that analyze single-cell paired RNA-seq and ATAC-seq multiome data have shown great promise in linking regulatory elements to genes. However, existing methods differ in their modeling assumptions and approaches to account for biological and technical noise-leading to low concordance in their linking scores-and do not capture the effects of genomic distance. We propose pgBoost, an integrative modeling framework that trains a non-linear combination of existing linking strategies (including genomic distance) on fine-mapped eQTL data to assign a probabilistic score to each candidate SNP-gene link. We applied pgBoost to single-cell multiome data from 85k cells representing 6 major immune/blood cell types. pgBoost attained higher enrichment for fine-mapped eSNP-eGene pairs (e.g. 21x at distance >10kb) than existing methods (1.2-10x; p-value for difference = 5e-13 vs. distance-based method and < 4e-35 for each other method), with larger improvements at larger distances (e.g. 35x vs. 0.89-6.6x at distance >100kb; p-value for difference < 0.002 vs. each other method). pgBoost also outperformed existing methods in enrichment for CRISPR-validated links (e.g. 4.8x vs. 1.6-4.1x at distance >10kb; p-value for difference = 0.25 vs. distance-based method and < 2e-5 for each other method), with larger improvements at larger distances (e.g. 15x vs. 1.6-2.5x at distance >100kb; p-value for difference < 0.009 for each other method). Similar improvements in enrichment were observed for links derived from Activity-By-Contact (ABC) scores and GWAS data. We further determined that restricting pgBoost to features from a focal cell type improved the identification of SNP-gene links relevant to that cell type. We highlight several examples where pgBoost linked fine-mapped GWAS variants to experimentally validated or biologically plausible target genes that were not implicated by other methods. In conclusion, a non-linear combination of linking strategies, including genomic distance, improves power to identify target genes underlying GWAS associations.
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Saeidian AH, Youssefian L, Naji M, Mahmoudi H, Barnada SM, Huang C, Naghipoor K, Hozhabrpour A, Park JS, Manzo Margiotta F, Vahidnezhad F, Saffarian Z, Kamyab-Hesari K, Tolouei M, Faraji N, Azimi SZ, Namdari G, Mansouri P, Casanova JL, Béziat V, Jouanguy E, Uitto J, Vahidnezhad H. Whole transcriptome-based skin virome profiling in typical epidermodysplasia verruciformis reveals α-, β-, and γ-HPV infections. JCI Insight 2023; 8:e162558. [PMID: 36602881 PMCID: PMC10077487 DOI: 10.1172/jci.insight.162558] [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: 06/10/2022] [Accepted: 12/27/2022] [Indexed: 01/06/2023] Open
Abstract
HPVs are DNA viruses include approximately 450 types that are classified into 5 genera (α-, β-, γ-, μ-, and ν-HPV). The γ- and β-HPVs are present in low copy numbers in healthy individuals; however, in patients with an inborn error of immunity, certain species of β-HPVs can cause epidermodysplasia verruciformis (EV), manifesting as recalcitrant cutaneous warts and skin cancer. EV presents as either typical or atypical. Manifestations of typical EV are limited to the skin and are caused by abnormal keratinocyte-intrinsic immunity to β-HPVs due to pathogenic sequence variants in TMC6, TMC8, or CIB1. We applied a transcriptome-based computational pipeline, VirPy, to RNA extracted from normal-appearing skin and wart samples of patients with typical EV to explore the viral and human genetic determinants. In 26 patients, 9 distinct biallelic mutations were detected in TMC6, TMC8, and CIB1, 7 of which are previously unreported to our knowledge. Additionally, 20 different HPV species, including 3 α-HPVs, 16 β-HPVs, and 1 γ-HPV, were detected, 8 of which are reported here for the first time to our knowledge in patients with EV (β-HPV-37, -47, -80, -151, and -159; α-HPV-2 and -57; and γ-HPV-128). This study expands the TMC6, TMC8, and CIB1 sequence variant spectrum and implicates new HPV subtypes in the pathogenesis of typical EV.
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Affiliation(s)
- Amir Hossein Saeidian
- Department of Dermatology and Cutaneous Biology, Sidney Kimmel Medical College, and
- Jefferson Institute of Molecular Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Leila Youssefian
- Department of Dermatology and Cutaneous Biology, Sidney Kimmel Medical College, and
- Jefferson Institute of Molecular Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
- Department of Pathology and Laboratory Medicine, UCLA Clinical Genomics Center, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Mahtab Naji
- University of California, Riverside, School of Medicine, California, USA
| | - Hamidreza Mahmoudi
- Department of Dermatology, Razi Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Samantha M. Barnada
- Genetics, Genomics and Cancer Biology PhD Program, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Charles Huang
- Department of Dermatology and Cutaneous Biology, Sidney Kimmel Medical College, and
- Jefferson Institute of Molecular Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Karim Naghipoor
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amir Hozhabrpour
- Department of Medical Genetics and Molecular Biology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Jason S. Park
- Department of Dermatology and Cutaneous Biology, Sidney Kimmel Medical College, and
- Jefferson Institute of Molecular Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
- Geisinger Commonwealth School of Medicine, Scranton, Pennsylvania, USA
| | | | - Fatemeh Vahidnezhad
- UCSC Silicon Valley Extension, University of California, Santa Cruz, California, USA
| | - Zahra Saffarian
- Department of Dermatology, Razi Hospital, Tehran University of Medical Sciences, Tehran, Iran
- Imam Khomeini Hospital, Tehran University of Medical Science, Tehran, Iran
| | - Kambiz Kamyab-Hesari
- Department of Dermatology, Razi Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Niloofar Faraji
- Razi Clinical Research Development Unit, Razi Hospital, Guilan University of Medical Science, Rasht, Iran
| | - Seyyede Zeinab Azimi
- Center for Research and Training in Skin Diseases and Leprosy, Tehran University of Medical Sciences, Tehran, Iran
| | - Ghazal Namdari
- Department of Dermatology, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Parvin Mansouri
- Department of Research, Skin and Stem Cell Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Jean-Laurent Casanova
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, New York, USA
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, Paris, France
- Imagine Institute, Paris University, Paris, France
- Department of Pediatrics, Necker Hospital for Sick Children, Paris, France
- Howard Hughes Medical Institute, New York, New York, USA
| | - Vivien Béziat
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, New York, USA
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, Paris, France
- Imagine Institute, Paris University, Paris, France
| | - Emmanuelle Jouanguy
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, New York, USA
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, Paris, France
- Imagine Institute, Paris University, Paris, France
| | - Jouni Uitto
- Department of Dermatology and Cutaneous Biology, Sidney Kimmel Medical College, and
- Jefferson Institute of Molecular Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Hassan Vahidnezhad
- Department of Dermatology and Cutaneous Biology, Sidney Kimmel Medical College, and
- Jefferson Institute of Molecular Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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Cai X, Teng J, Ren D, Zhang H, Li J, Zhang Z. Model Comparison of Heritability Enrichment Analysis in Livestock Population. Genes (Basel) 2022; 13:genes13091644. [PMID: 36140810 PMCID: PMC9498849 DOI: 10.3390/genes13091644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/03/2022] [Accepted: 09/10/2022] [Indexed: 11/16/2022] Open
Abstract
Heritability enrichment analysis is an important means of exploring the genetic architecture of complex traits in human genetics. Heritability enrichment is typically defined as the proportion of an SNP subset explained heritability, divided by the proportion of SNPs. Heritability enrichment enables better study of underlying complex traits, such as functional variant/gene subsets, biological networks and metabolic pathways detected through integrating explosively increased omics data. This would be beneficial for genomic prediction of disease risk in humans and genetic values estimation of important economical traits in livestock and plant species. However, in livestock, factors affecting the heritability enrichment estimation of complex traits have not been examined. Previous studies on humans reported that the frequencies, effect sizes, and levels of linkage disequilibrium (LD) of underlying causal variants (CVs) would affect the heritability enrichment estimation. Therefore, the distribution of heritability across the genome should be fully considered to obtain the unbiased estimation of heritability enrichment. To explore the performance of different heritability enrichment models in livestock populations, we used the VanRaden, GCTA and α models, assuming different α values, and the LDAK model, considering LD weight. We simulated three types of phenotypes, with CVs from various minor allele frequency (MAF) ranges: genome-wide (0.005 ≤ MAF ≤ 0.5), common (0.05 ≤ MAF ≤ 0.5), and uncommon (0.01 ≤ MAF < 0.05). The performances of the models with two different subsets (one of which contained known CVs and the other consisting of randomly selected markers) were compared to verify the accuracy of heritability enrichment estimation of functional variant sets. Our results showed that models with known CV subsets provided more robust enrichment estimation. Models with different α values tended to provide stable and accurate estimates for common and genome-wide CVs (relative deviation 0.5−2.2%), while tending to underestimate the enrichment of uncommon CVs. As the α value increased, enrichments from 15.73% higher than true value (i.e., 3.00) to 48.93% lower than true value for uncommon CVs were observed. In addition, the long-range LD windows (e.g., 5000 kb) led to large bias of the enrichment estimations for both common and uncommon CVs. Overall, heritability enrichment estimations were sensitive for the α value assumption and LD weight consideration of different models. Accuracy would be greatly improved by using a suitable model. This study would be helpful in understanding the genetic architecture of complex traits and provides a reference for genetic analysis in the livestock population.
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Dey KK, Gazal S, van de Geijn B, Kim SS, Nasser J, Engreitz JM, Price AL. SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease. CELL GENOMICS 2022; 2:100145. [PMID: 35873673 PMCID: PMC9306342 DOI: 10.1016/j.xgen.2022.100145] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.
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Affiliation(s)
- Kushal K. Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Corresponding author
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Bryce van de Geijn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Genentech, South San Francisco, CA 94080, USA
| | - Samuel Sungil Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jesse M. Engreitz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- BASE Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford University School of Medicine, Stanford, CA 94304, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Abstract
Interpreting the effects of genetic variants is key to understanding individual susceptibility to disease and designing personalized therapeutic approaches. Modern experimental technologies are enabling the generation of massive compendia of human genome sequence data and associated molecular and phenotypic traits, together with genome-scale expression, epigenomics and other functional genomic data. Integrative computational models can leverage these data to understand variant impact, elucidate the effect of dysregulated genes on biological pathways in specific disease and tissue contexts, and interpret disease risk beyond what is feasible with experiments alone. In this Review, we discuss recent developments in machine learning algorithms for genome interpretation and for integrative molecular-level modelling of cells, tissues and organs relevant to disease. More specifically, we highlight existing methods and key challenges and opportunities in identifying specific disease-causing genetic variants and linking them to molecular pathways and, ultimately, to disease phenotypes.
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Miyazawa K, Ito K. The Evolving Story in the Genetic Analysis for Heart Failure. Front Cardiovasc Med 2021; 8:646816. [PMID: 33928132 PMCID: PMC8076510 DOI: 10.3389/fcvm.2021.646816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 03/19/2021] [Indexed: 11/23/2022] Open
Abstract
Genomic studies of cardiovascular diseases have achieved great success, not only in Mendelian genetic diseases such as hereditary arrhythmias and cardiomyopathies, but also in common diseases such as ischemic heart disease and atrial fibrillation. However, only limited success has been achieved in heart failure due to the complexity of its disease background. In this paper, we will review the genetic research for heart failure to date and discuss how we can discover new aspects of heart failure from the viewpoint of genomic perspective.
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Affiliation(s)
- Kazuo Miyazawa
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kaoru Ito
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
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