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Chen YJ, Wang MW, Qiu YS, Yuan RY, Wang N, Lin X, Chen WJ. Alu Retrotransposition Event in SPAST Gene as a Novel Cause of Hereditary Spastic Paraplegia. Mov Disord 2023; 38:1750-1755. [PMID: 37394769 DOI: 10.1002/mds.29522] [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: 01/31/2023] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 07/04/2023] Open
Abstract
OBJECTIVES To diagnose the molecular cause of hereditary spastic paraplegia (HSP) observed in a four-generation family with autosomal dominant inheritance. METHODS Multiplex ligation-dependent probe amplification (MLPA), whole-exome sequencing (WES), and RNA sequencing (RNA-seq) of peripheral blood leukocytes were performed. Reverse transcription polymerase chain reaction (RT-PCR) and Sanger sequencing were used to characterize target regions of SPAST. RESULTS A 121-bp AluYb9 insertion with a 30-bp poly-A tail flanked by 15-bp direct repeats on both sides was identified in the edge of intron 16 in SPAST that segregated with the disease phenotype. CONCLUSIONS We identified an intronic AluYb9 insertion inducing splicing alteration in SPAST causing pure HSP phenotype that was not detected by routine WES analysis. Our findings suggest RNA-seq is a recommended implementation for undiagnosed cases by first-line diagnostic approaches. © 2023 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Yi-Jun Chen
- Department of Neurology and Institute of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- Department of Geriatrics, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- Department of Geriatrics, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Meng-Wen Wang
- Department of Neurology and Institute of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yu-Sen Qiu
- Department of Neurology and Institute of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Ru-Ying Yuan
- Department of Neurology and Institute of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Ning Wang
- Department of Neurology and Institute of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, China
| | - Xiang Lin
- Department of Neurology and Institute of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, China
| | - Wan-Jin Chen
- Department of Neurology and Institute of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, China
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2
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Kang JE, Jun JH, Kwon JH, Lee JH, Hwang K, Kim S, Jeong N. Arabidopsis Transcription Regulatory Factor Domain/Domain Interaction Analysis Tool-Liquid/Liquid Phase Separation, Oligomerization, GO Analysis: A Toolkit for Interaction Data-Based Domain Analysis. Genes (Basel) 2023; 14:1476. [PMID: 37510380 PMCID: PMC10379056 DOI: 10.3390/genes14071476] [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: 06/05/2023] [Revised: 07/04/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023] Open
Abstract
Although a large number of databases are available for regulatory elements, a bottleneck has been created by the lack of bioinformatics tools to predict the interaction modes of regulatory elements. To reduce this gap, we developed the Arabidopsis Transcription Regulatory Factor Domain/Domain Interaction Analysis Tool-liquid/liquid phase separation (LLPS), oligomerization, GO analysis (ART FOUNDATION-LOG), a useful toolkit for protein-nucleic acid interaction (PNI) and protein-protein interaction (PPI) analysis based on domain-domain interactions (DDIs). LLPS, protein oligomerization, the structural properties of protein domains, and protein modifications are major components in the orchestration of the spatiotemporal dynamics of PPIs and PNIs. Our goal is to integrate PPI/PNI information into the development of a prediction model for identifying important genetic variants in peaches. Our program unified interdatabase relational keys based on protein domains to facilitate inference from the model species. A key advantage of this program lies in the integrated information of related features, such as protein oligomerization, LOG analysis, structural characterizations of domains (e.g., domain linkers, intrinsically disordered regions, DDIs, domain-motif (peptide) interactions, beta sheets, and transmembrane helices), and post-translational modification. We provided simple tests to demonstrate how to use this program, which can be applied to other eukaryotic organisms.
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Affiliation(s)
- Jee Eun Kang
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Republic of Korea
| | - Ji Hae Jun
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Republic of Korea
| | - Jung Hyun Kwon
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Republic of Korea
| | - Ju-Hyun Lee
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Republic of Korea
| | - Kidong Hwang
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Republic of Korea
| | - Sungjong Kim
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Republic of Korea
| | - Namhee Jeong
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Republic of Korea
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3
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Sakura F, Noma K, Asano T, Tanita K, Toyofuku E, Kato K, Tsumura M, Nihira H, Izawa K, Mitsui-Sekinaka K, Konno R, Kawashima Y, Mizoguchi Y, Karakawa S, Hayakawa S, Kawaguchi H, Imai K, Nonoyama S, Yasumi T, Ohnishi H, Kanegane H, Ohara O, Okada S. A complementary approach for genetic diagnosis of inborn errors of immunity using proteogenomic analysis. PNAS NEXUS 2023; 2:pgad104. [PMID: 37077884 PMCID: PMC10109033 DOI: 10.1093/pnasnexus/pgad104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 03/06/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023]
Abstract
Advances in next-generation sequencing technology have identified many genes responsible for inborn errors of immunity (IEI). However, there is still room for improvement in the efficiency of genetic diagnosis. Recently, RNA sequencing and proteomics using peripheral blood mononuclear cells (PBMCs) have gained attention, but only some studies have integrated these analyses in IEI. Moreover, previous proteomic studies for PBMCs have achieved limited coverage (approximately 3000 proteins). More comprehensive data are needed to gain valuable insights into the molecular mechanisms underlying IEI. Here, we propose a state-of-the-art method for diagnosing IEI using PBMCs proteomics integrated with targeted RNA sequencing (T-RNA-seq), providing unique insights into the pathogenesis of IEI. This study analyzed 70 IEI patients whose genetic etiology had not been identified by genetic analysis. In-depth proteomics identified 6498 proteins, which covered 63% of 527 genes identified in T-RNA-seq, allowing us to examine the molecular cause of IEI and immune cell defects. This integrated analysis identified the disease-causing genes in four cases undiagnosed in previous genetic studies. Three of them could be diagnosed by T-RNA-seq, while the other could only be diagnosed by proteomics. Moreover, this integrated analysis showed high protein-mRNA correlations in B- and T-cell-specific genes, and their expression profiles identified patients with immune cell dysfunction. These results indicate that integrated analysis improves the efficiency of genetic diagnosis and provides a deep understanding of the immune cell dysfunction underlying the etiology of IEI. Our novel approach demonstrates the complementary role of proteogenomic analysis in the genetic diagnosis and characterization of IEI.
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Affiliation(s)
- Fumiaki Sakura
- Department of Pediatrics, Hiroshima University Graduate School of Biomedical and Health Sciences, 1-2-3 Kasumi, Minami Ward, Hiroshima 734-8551, Japan
| | - Kosuke Noma
- Department of Pediatrics, Hiroshima University Graduate School of Biomedical and Health Sciences, 1-2-3 Kasumi, Minami Ward, Hiroshima 734-8551, Japan
| | - Takaki Asano
- Department of Pediatrics, Hiroshima University Graduate School of Biomedical and Health Sciences, 1-2-3 Kasumi, Minami Ward, Hiroshima 734-8551, Japan
| | - Kay Tanita
- Department of Pediatrics and Developmental Biology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo City, Tokyo 113-0034, Japan
| | - Etsushi Toyofuku
- Department of Pediatrics and Developmental Biology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo City, Tokyo 113-0034, Japan
| | - Kentaro Kato
- Department of Pediatrics, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyo Ward, Kyoto City 606-8507, Japan
| | - Miyuki Tsumura
- Department of Pediatrics, Hiroshima University Graduate School of Biomedical and Health Sciences, 1-2-3 Kasumi, Minami Ward, Hiroshima 734-8551, Japan
| | - Hiroshi Nihira
- Department of Pediatrics, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyo Ward, Kyoto City 606-8507, Japan
| | - Kazushi Izawa
- Department of Pediatrics, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyo Ward, Kyoto City 606-8507, Japan
| | - Kanako Mitsui-Sekinaka
- Department of Pediatrics, National Defense Medical College, 3-2 Namiki, Tokorozawa City, Saitama 359-8513, Japan
| | - Ryo Konno
- Kazusa DNA Research Institute, 2-6-7 Kazusakamatari, Kisarazu City, Chiba 292-0818, Japan
| | - Yusuke Kawashima
- Kazusa DNA Research Institute, 2-6-7 Kazusakamatari, Kisarazu City, Chiba 292-0818, Japan
| | - Yoko Mizoguchi
- Department of Pediatrics, Hiroshima University Graduate School of Biomedical and Health Sciences, 1-2-3 Kasumi, Minami Ward, Hiroshima 734-8551, Japan
| | - Shuhei Karakawa
- Department of Pediatrics, Hiroshima University Graduate School of Biomedical and Health Sciences, 1-2-3 Kasumi, Minami Ward, Hiroshima 734-8551, Japan
| | - Seiichi Hayakawa
- Department of Pediatrics, Hiroshima University Graduate School of Biomedical and Health Sciences, 1-2-3 Kasumi, Minami Ward, Hiroshima 734-8551, Japan
| | - Hiroshi Kawaguchi
- Department of Pediatrics, Hiroshima University Graduate School of Biomedical and Health Sciences, 1-2-3 Kasumi, Minami Ward, Hiroshima 734-8551, Japan
| | - Kohsuke Imai
- Department of Pediatrics, National Defense Medical College, 3-2 Namiki, Tokorozawa City, Saitama 359-8513, Japan
| | - Shigeaki Nonoyama
- Department of Pediatrics, National Defense Medical College, 3-2 Namiki, Tokorozawa City, Saitama 359-8513, Japan
| | - Takahiro Yasumi
- Department of Pediatrics, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyo Ward, Kyoto City 606-8507, Japan
| | - Hidenori Ohnishi
- Department of Pediatrics, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City 501-1112, Japan
| | - Hirokazu Kanegane
- Department of Pediatrics and Developmental Biology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo City, Tokyo 113-0034, Japan
| | - Osamu Ohara
- Kazusa DNA Research Institute, 2-6-7 Kazusakamatari, Kisarazu City, Chiba 292-0818, Japan
| | - Satoshi Okada
- Department of Pediatrics, Hiroshima University Graduate School of Biomedical and Health Sciences, 1-2-3 Kasumi, Minami Ward, Hiroshima 734-8551, Japan
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Dapas M, Thompson EE, Wentworth-Sheilds W, Clay S, Visness CM, Calatroni A, Sordillo JE, Gold DR, Wood RA, Makhija M, Khurana Hershey GK, Sherenian MG, Gruchalla RS, Gill MA, Liu AH, Kim H, Kattan M, Bacharier LB, Rastogi D, Altman MC, Busse WW, Becker PM, Nicolae D, O’Connor GT, Gern JE, Jackson DJ, Ober C. Multi-omic association study identifies DNA methylation-mediated genotype and smoking exposure effects on lung function in children living in urban settings. PLoS Genet 2023; 19:e1010594. [PMID: 36638096 PMCID: PMC9879483 DOI: 10.1371/journal.pgen.1010594] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 01/26/2023] [Accepted: 12/23/2022] [Indexed: 01/14/2023] Open
Abstract
Impaired lung function in early life is associated with the subsequent development of chronic respiratory disease. Most genetic associations with lung function have been identified in adults of European descent and therefore may not represent those most relevant to pediatric populations and populations of different ancestries. In this study, we performed genome-wide association analyses of lung function in a multiethnic cohort of children (n = 1,035) living in low-income urban neighborhoods. We identified one novel locus at the TDRD9 gene in chromosome 14q32.33 associated with percent predicted forced expiratory volume in one second (FEV1) (p = 2.4x10-9; βz = -0.31, 95% CI = -0.41- -0.21). Mendelian randomization and mediation analyses revealed that this genetic effect on FEV1 was partially mediated by DNA methylation levels at this locus in airway epithelial cells, which were also associated with environmental tobacco smoke exposure (p = 0.015). Promoter-enhancer interactions in airway epithelial cells revealed chromatin interaction loops between FEV1-associated variants in TDRD9 and the promoter region of the PPP1R13B gene, a stimulator of p53-mediated apoptosis. Expression of PPP1R13B in airway epithelial cells was significantly associated the FEV1 risk alleles (p = 1.3x10-5; β = 0.12, 95% CI = 0.06-0.17). These combined results highlight a potential novel mechanism for reduced lung function in urban youth resulting from both genetics and smoking exposure.
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Affiliation(s)
- Matthew Dapas
- Department of Human Genetics, University of Chicago, Chicago Illinois, United States of America
| | - Emma E. Thompson
- Department of Human Genetics, University of Chicago, Chicago Illinois, United States of America
| | | | - Selene Clay
- Department of Human Genetics, University of Chicago, Chicago Illinois, United States of America
| | | | | | - Joanne E. Sordillo
- Department of Population Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Diane R. Gold
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Robert A. Wood
- Department of Pediatrics, Johns Hopkins University Medical Center, Baltimore, Maryland, United States of America
| | - Melanie Makhija
- Division of Allergy and Immunology, Ann & Robert H. Lurie Children’s Hospital, Chicago, Illinois, United States of America
| | - Gurjit K. Khurana Hershey
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Asthma Research, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Michael G. Sherenian
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Asthma Research, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Rebecca S. Gruchalla
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Michelle A. Gill
- Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Andrew H. Liu
- Department of Allergy and Immunology, Children’s Hospital Colorado, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Haejin Kim
- Department of Medicine, Henry Ford Health System, Detroit, Michigan, United States of America
| | - Meyer Kattan
- Columbia University College of Physicians and Surgeons, New York, New York, United States of America
| | - Leonard B. Bacharier
- Monroe Carell Jr. Children’s Hospital at Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Deepa Rastogi
- Children’s National Health System, Washington, District of Columbia, United States of America
| | - Matthew C. Altman
- Department of Allergy and Infectious Diseases, University of Washington, Seattle, Washington, United States of America
| | - William W. Busse
- Department of Pediatrics and Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America
| | - Patrice M. Becker
- National Institute of Allergy and Infectious Diseases, Bethesda, Maryland, United States of America
| | - Dan Nicolae
- Department of Statistics, University of Chicago, Chicago, Illinois, United States of America
| | - George T. O’Connor
- Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - James E. Gern
- Department of Pediatrics and Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America
| | - Daniel J. Jackson
- Department of Pediatrics and Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago Illinois, United States of America
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5
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Prowse-Wilkins CP, Lopdell TJ, Xiang R, Vander Jagt CJ, Littlejohn MD, Chamberlain AJ, Goddard ME. Genetic variation in histone modifications and gene expression identifies regulatory variants in the mammary gland of cattle. BMC Genomics 2022; 23:815. [PMID: 36482302 PMCID: PMC9733386 DOI: 10.1186/s12864-022-09002-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 11/10/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Causal variants for complex traits, such as eQTL are often found in non-coding regions of the genome, where they are hypothesised to influence phenotypes by regulating gene expression. Many regulatory regions are marked by histone modifications, which can be assayed by chromatin immunoprecipitation followed by sequencing (ChIP-seq). Sequence reads from ChIP-seq form peaks at putative regulatory regions, which may reflect the amount of regulatory activity at this region. Therefore, eQTL which are also associated with differences in histone modifications are excellent candidate causal variants. RESULTS We assayed the histone modifications H3K4Me3, H3K4Me1 and H3K27ac and mRNA in the mammary gland of up to 400 animals. We identified QTL for peak height (histone QTL), exon expression (eeQTL), allele specific expression (aseQTL) and allele specific binding (asbQTL). By intersecting these results, we identify variants which may influence gene expression by altering regulatory regions of the genome, and may be causal variants for other traits. Lastly, we find that these variants are found in putative transcription factor binding sites, identifying a mechanism for the effect of many eQTL. CONCLUSIONS We find that allele specific and traditional QTL analysis often identify the same genetic variants and provide evidence that many eQTL are regulatory variants which alter activity at regulatory regions of the bovine genome. Our work provides methodological and biological updates on how regulatory mechanisms interplay at multi-omics levels.
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Affiliation(s)
- Claire P Prowse-Wilkins
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3082, Australia.
- Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, Victoria, 3010, Australia.
| | - Thomas J Lopdell
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240, New Zealand
| | - Ruidong Xiang
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3082, Australia
- Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Christy J Vander Jagt
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3082, Australia
| | - Mathew D Littlejohn
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240, New Zealand
| | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3082, Australia
| | - Michael E Goddard
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3082, Australia
- Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, Victoria, 3010, Australia
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6
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Yépez VA, Gusic M, Kopajtich R, Mertes C, Smith NH, Alston CL, Ban R, Beblo S, Berutti R, Blessing H, Ciara E, Distelmaier F, Freisinger P, Häberle J, Hayflick SJ, Hempel M, Itkis YS, Kishita Y, Klopstock T, Krylova TD, Lamperti C, Lenz D, Makowski C, Mosegaard S, Müller MF, Muñoz-Pujol G, Nadel A, Ohtake A, Okazaki Y, Procopio E, Schwarzmayr T, Smet J, Staufner C, Stenton SL, Strom TM, Terrile C, Tort F, Van Coster R, Vanlander A, Wagner M, Xu M, Fang F, Ghezzi D, Mayr JA, Piekutowska-Abramczuk D, Ribes A, Rötig A, Taylor RW, Wortmann SB, Murayama K, Meitinger T, Gagneur J, Prokisch H. Clinical implementation of RNA sequencing for Mendelian disease diagnostics. Genome Med 2022; 14:38. [PMID: 35379322 PMCID: PMC8981716 DOI: 10.1186/s13073-022-01019-9] [Citation(s) in RCA: 80] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 02/03/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Lack of functional evidence hampers variant interpretation, leaving a large proportion of individuals with a suspected Mendelian disorder without genetic diagnosis after whole genome or whole exome sequencing (WES). Research studies advocate to further sequence transcriptomes to directly and systematically probe gene expression defects. However, collection of additional biopsies and establishment of lab workflows, analytical pipelines, and defined concepts in clinical interpretation of aberrant gene expression are still needed for adopting RNA sequencing (RNA-seq) in routine diagnostics. METHODS We implemented an automated RNA-seq protocol and a computational workflow with which we analyzed skin fibroblasts of 303 individuals with a suspected mitochondrial disease that previously underwent WES. We also assessed through simulations how aberrant expression and mono-allelic expression tests depend on RNA-seq coverage. RESULTS We detected on average 12,500 genes per sample including around 60% of all disease genes-a coverage substantially higher than with whole blood, supporting the use of skin biopsies. We prioritized genes demonstrating aberrant expression, aberrant splicing, or mono-allelic expression. The pipeline required less than 1 week from sample preparation to result reporting and provided a median of eight disease-associated genes per patient for inspection. A genetic diagnosis was established for 16% of the 205 WES-inconclusive cases. Detection of aberrant expression was a major contributor to diagnosis including instances of 50% reduction, which, together with mono-allelic expression, allowed for the diagnosis of dominant disorders caused by haploinsufficiency. Moreover, calling aberrant splicing and variants from RNA-seq data enabled detecting and validating splice-disrupting variants, of which the majority fell outside WES-covered regions. CONCLUSION Together, these results show that streamlined experimental and computational processes can accelerate the implementation of RNA-seq in routine diagnostics.
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Affiliation(s)
- Vicente A. Yépez
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Garching, Germany
- Quantitative Biosciences Munich, Department of Biochemistry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Mirjana Gusic
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Robert Kopajtich
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Christian Mertes
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Nicholas H. Smith
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Charlotte L. Alston
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH UK
- NHS Highly Specialised Services for Rare Mitochondrial Disorders, Royal Victoria Infirmary, Newcastle upon Tyne Hospitals NHS Foundation Trust, Queen Victoria Road, Newcastle upon Tyne, NE1 4LP UK
| | - Rui Ban
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Pediatric Neurology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Skadi Beblo
- Department of Women and Child Health, Hospital for Children and Adolescents, Center for Pediatric Research Leipzig (CPL), Center for Rare Diseases, University Hospitals, University of Leipzig, Leipzig, Germany
| | - Riccardo Berutti
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Holger Blessing
- Department for Inborn Metabolic Diseases, Children’s and Adolescents’ Hospital, University of Erlangen-Nürnberg, Erlangen, Germany
| | - Elżbieta Ciara
- Department of Medical Genetics, Children’s Memorial Health Institute, Warsaw, Poland
| | - Felix Distelmaier
- Department of General Pediatrics, Neonatology and Pediatric Cardiology, Heinrich-Heine-University, Düsseldorf, Germany
| | - Peter Freisinger
- Department of Pediatrics, Klinikum Reutlingen, Reutlingen, Germany
| | - Johannes Häberle
- University Children’s Hospital Zurich and Children’s Research Centre, Zürich, Switzerland
| | - Susan J. Hayflick
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, USA
| | - Maja Hempel
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Yoshihito Kishita
- Diagnostics and Therapeutics of Intractable Diseases, Intractable Disease Research Center, Juntendo University, Graduate School of Medicine, Tokyo, Japan
- Department of Life Science, Faculty of Science and Engineering, Kindai University, Osaka, Japan
| | - Thomas Klopstock
- Department of Neurology, Friedrich-Baur-Institute, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | | | - Costanza Lamperti
- Unit of Medical Genetics and Neurogenetics, Fondazione IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico) Istituto Neurologico Carlo Besta, Milan, Italy
| | - Dominic Lenz
- Division of Neuropediatrics and Pediatric Metabolic Medicine, Center for Pediatric and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Christine Makowski
- Department of Pediatrics, Technical University of Munich, Munich, Germany
| | - Signe Mosegaard
- Research Unit for Molecular Medicine, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Michaela F. Müller
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Gerard Muñoz-Pujol
- Section of Inborn Errors of Metabolism-IBC, Department of Biochemistry and Molecular Genetics, Hospital Clínic, IDIBAPS, CIBERER, Barcelona, Spain
| | - Agnieszka Nadel
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Akira Ohtake
- Department of Pediatrics & Clinical Genomics, Faculty of Medicine, Saitama Medical University, Saitama, Japan
- Center for Intractable Diseases, Saitama Medical University Hospital, Saitama, Japan
| | - Yasushi Okazaki
- Diagnostics and Therapeutics of Intractable Diseases, Intractable Disease Research Center, Juntendo University, Graduate School of Medicine, Tokyo, Japan
| | - Elena Procopio
- Inborn Metabolic and Muscular Disorders Unit, Anna Meyer Children Hospital, Florence, Italy
| | - Thomas Schwarzmayr
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Joél Smet
- Department of Pediatric Neurology and Metabolism, Ghent University Hospital, Ghent, Belgium
| | - Christian Staufner
- Division of Neuropediatrics and Pediatric Metabolic Medicine, Center for Pediatric and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Sarah L. Stenton
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Tim M. Strom
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Caterina Terrile
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Frederic Tort
- Section of Inborn Errors of Metabolism-IBC, Department of Biochemistry and Molecular Genetics, Hospital Clínic, IDIBAPS, CIBERER, Barcelona, Spain
| | - Rudy Van Coster
- Department of Pediatric Neurology and Metabolism, Ghent University Hospital, Ghent, Belgium
| | - Arnaud Vanlander
- Department of Pediatric Neurology and Metabolism, Ghent University Hospital, Ghent, Belgium
| | - Matias Wagner
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Manting Xu
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Pediatric Neurology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Fang Fang
- Department of Pediatric Neurology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Daniele Ghezzi
- Unit of Medical Genetics and Neurogenetics, Fondazione IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico) Istituto Neurologico Carlo Besta, Milan, Italy
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Johannes A. Mayr
- University Children’s Hospital, Paracelsus Medical University Salzburg, Salzburg, Austria
| | | | - Antonia Ribes
- Section of Inborn Errors of Metabolism-IBC, Department of Biochemistry and Molecular Genetics, Hospital Clínic, IDIBAPS, CIBERER, Barcelona, Spain
| | - Agnès Rötig
- Université de Paris, Institut Imagine, INSERM UMR 1163, Paris, France
| | - Robert W. Taylor
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH UK
- NHS Highly Specialised Services for Rare Mitochondrial Disorders, Royal Victoria Infirmary, Newcastle upon Tyne Hospitals NHS Foundation Trust, Queen Victoria Road, Newcastle upon Tyne, NE1 4LP UK
| | - Saskia B. Wortmann
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- University Children’s Hospital, Paracelsus Medical University Salzburg, Salzburg, Austria
- Amalia Children’s Hospital, Radboudumc Nijmegen, Nijmegen, The Netherlands
| | - Kei Murayama
- Department of Metabolism, Chiba Children’s Hospital, Chiba, Japan
| | - Thomas Meitinger
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julien Gagneur
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Garching, Germany
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Holger Prokisch
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Pediatric Neurology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
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7
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Use of Next-Generation Sequencing for Identifying Mitochondrial Disorders. Curr Issues Mol Biol 2022; 44:1127-1148. [PMID: 35723297 PMCID: PMC8947152 DOI: 10.3390/cimb44030074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 02/18/2022] [Accepted: 02/24/2022] [Indexed: 12/06/2022] Open
Abstract
Mitochondria are major contributors to ATP synthesis, generating more than 90% of the total cellular energy production through oxidative phosphorylation (OXPHOS): metabolite oxidation, such as the β-oxidation of fatty acids, and the Krebs’s cycle. OXPHOS inadequacy due to large genetic lesions in mitochondrial as well as nuclear genes and homo- or heteroplasmic point mutations in mitochondrially encoded genes is a characteristic of heterogeneous, maternally inherited genetic disorders known as mitochondrial disorders that affect multisystemic tissues and organs with high energy requirements, resulting in various signs and symptoms. Several traditional diagnostic approaches, including magnetic resonance imaging of the brain, cardiac testing, biochemical screening, variable heteroplasmy genetic testing, identifying clinical features, and skeletal muscle biopsies, are associated with increased risks, high costs, a high degree of false-positive or false-negative results, or a lack of precision, which limits their diagnostic abilities for mitochondrial disorders. Variable heteroplasmy levels, mtDNA depletion, and the identification of pathogenic variants can be detected through genetic sequencing, including the gold standard Sanger sequencing. However, sequencing can be time consuming, and Sanger sequencing can result in the missed recognition of larger structural variations such as CNVs or copy-number variations. Although each sequencing method has its own limitations, genetic sequencing can be an alternative to traditional diagnostic methods. The ever-growing roster of possible mutations has led to the development of next-generation sequencing (NGS). The enhancement of NGS methods can offer a precise diagnosis of the mitochondrial disorder within a short period at a reasonable expense for both research and clinical applications.
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8
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Technological Improvements in the Genetic Diagnosis of Rett Syndrome Spectrum Disorders. Int J Mol Sci 2021; 22:ijms221910375. [PMID: 34638716 PMCID: PMC8508637 DOI: 10.3390/ijms221910375] [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: 07/30/2021] [Revised: 09/17/2021] [Accepted: 09/22/2021] [Indexed: 11/17/2022] Open
Abstract
Rett syndrome (RTT) is a severe neurodevelopmental disorder that constitutes the second most common cause of intellectual disability in females worldwide. In the past few years, the advancements in genetic diagnosis brought by next generation sequencing (NGS), have made it possible to identify more than 90 causative genes for RTT and significantly overlapping phenotypes (RTT spectrum disorders). Therefore, the clinical entity known as RTT is evolving towards a spectrum of overlapping phenotypes with great genetic heterogeneity. Hence, simultaneous multiple gene testing and thorough phenotypic characterization are mandatory to achieve a fast and accurate genetic diagnosis. In this review, we revise the evolution of the diagnostic process of RTT spectrum disorders in the past decades, and we discuss the effectiveness of state-of-the-art genetic testing options, such as clinical exome sequencing and whole exome sequencing. Moreover, we introduce recent technological advancements that will very soon contribute to the increase in diagnostic yield in patients with RTT spectrum disorders. Techniques such as whole genome sequencing, integration of data from several “omics”, and mosaicism assessment will provide the tools for the detection and interpretation of genomic variants that will not only increase the diagnostic yield but also widen knowledge about the pathophysiology of these disorders.
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9
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Gu C, Wang H, Shu J, Zheng J, Li D, Cai C, Zhang P. RNA sequencing combining with whole exome sequencing reveals a compound heterozygous variant in ATM in a girl with atypical ataxia-telangiectasia. Clin Chim Acta 2021; 523:6-9. [PMID: 34453918 DOI: 10.1016/j.cca.2021.08.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/21/2021] [Accepted: 08/24/2021] [Indexed: 10/20/2022]
Abstract
Ataxia-telangiectasia (A-T) is an infrequent autosomal recessive multisystem disorder characterized by progressive cerebellar ataxia, oculo-cutaneous telangiectasia, a tendency to malignancies and variable immunodeficiency. Here we described a 5-year-old girl with atypical A-T symptoms. And 2 different ATM variants c.5939_5948del in exon 40 and c.2639-384A > G in intron 17 were detected by whole exome sequencing (WES) combined with RNA sequencing (RNA-seq). The variant spectrum of ATM was expanded. RNA-seq makes up for deficiencies of WES. We proposed a new approach, a dual-omics that combines RNA-seq with WES, for the diagnosis of genetic diseases. Moreover, our study discussed the phenotypic heterogeneity of A-T among family members as well as individuals. For children with recurrent infections and immunodeficiency, we suggested focusing on A-T after the exclusion of other potential diseases.
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Affiliation(s)
- Chunyu Gu
- Graduate College of Tianjin Medical University, No. 22 Qixiangtai Road, Heping District, Tianjin 300070, China; Tianjin Children's Hospital (Children's Hospital of Tianjin University), No. 238 Longyan Road, Beichen District, Tianjin 300134, China
| | - Hong Wang
- Tianjin Children's Hospital (Children's Hospital of Tianjin University), No. 238 Longyan Road, Beichen District, Tianjin 300134, China; The Medical Department of Neurology, Tianjin Children's Hospital, No. 238 Longyan Road, Beichen District, Tianjin 300134, China
| | - Jianbo Shu
- Tianjin Children's Hospital (Children's Hospital of Tianjin University), No. 238 Longyan Road, Beichen District, Tianjin 300134, China; Tianjin Pediatric Research Institute, No. 238 Longyan Road, Beichen District, Tianjin 300134, China; Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, No. 238 Longyan Road, Beichen District, Tianjin 300134, China
| | - Jie Zheng
- Tianjin Children's Hospital (Children's Hospital of Tianjin University), No. 238 Longyan Road, Beichen District, Tianjin 300134, China
| | - Dong Li
- Tianjin Children's Hospital (Children's Hospital of Tianjin University), No. 238 Longyan Road, Beichen District, Tianjin 300134, China; The Medical Department of Neurology, Tianjin Children's Hospital, No. 238 Longyan Road, Beichen District, Tianjin 300134, China
| | - Chunquan Cai
- Tianjin Children's Hospital (Children's Hospital of Tianjin University), No. 238 Longyan Road, Beichen District, Tianjin 300134, China; Tianjin Pediatric Research Institute, No. 238 Longyan Road, Beichen District, Tianjin 300134, China; Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, No. 238 Longyan Road, Beichen District, Tianjin 300134, China; Department of Neurosurgery, Tianjin Children's Hospital, No. 238 Longyan Road, Beichen District, Tianjin 300134, China.
| | - Peiyuan Zhang
- Tianjin Children's Hospital (Children's Hospital of Tianjin University), No. 238 Longyan Road, Beichen District, Tianjin 300134, China; The Medical Department of Neurology, Tianjin Children's Hospital, No. 238 Longyan Road, Beichen District, Tianjin 300134, China.
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10
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Prowse-Wilkins CP, Wang J, Xiang R, Garner JB, Goddard ME, Chamberlain AJ. Putative Causal Variants Are Enriched in Annotated Functional Regions From Six Bovine Tissues. Front Genet 2021; 12:664379. [PMID: 34249087 PMCID: PMC8260860 DOI: 10.3389/fgene.2021.664379] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 05/24/2021] [Indexed: 12/13/2022] Open
Abstract
Genetic variants which affect complex traits (causal variants) are thought to be found in functional regions of the genome. Identifying causal variants would be useful for predicting complex trait phenotypes in dairy cows, however, functional regions are poorly annotated in the bovine genome. Functional regions can be identified on a genome-wide scale by assaying for post-translational modifications to histone proteins (histone modifications) and proteins interacting with the genome (e.g., transcription factors) using a method called Chromatin immunoprecipitation followed by sequencing (ChIP-seq). In this study ChIP-seq was performed to find functional regions in the bovine genome by assaying for four histone modifications (H3K4Me1, H3K4Me3, H3K27ac, and H3K27Me3) and one transcription factor (CTCF) in 6 tissues (heart, kidney, liver, lung, mammary and spleen) from 2 to 3 lactating dairy cows. Eighty-six ChIP-seq samples were generated in this study, identifying millions of functional regions in the bovine genome. Combinations of histone modifications and CTCF were found using ChromHMM and annotated by comparing with active and inactive genes across the genome. Functional marks differed between tissues highlighting areas which might be particularly important to tissue-specific regulation. Supporting the cis-regulatory role of functional regions, the read counts in some ChIP peaks correlated with nearby gene expression. The functional regions identified in this study were enriched for putative causal variants as seen in other species. Interestingly, regions which correlated with gene expression were particularly enriched for potential causal variants. This supports the hypothesis that complex traits are regulated by variants that alter gene expression. This study provides one of the largest ChIP-seq annotation resources in cattle including, for the first time, in the mammary gland of lactating cows. By linking regulatory regions to expression QTL and trait QTL we demonstrate a new strategy for identifying causal variants in cattle.
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Affiliation(s)
- Claire P Prowse-Wilkins
- Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, VIC, Australia.,Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
| | - Jianghui Wang
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
| | - Ruidong Xiang
- Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, VIC, Australia.,Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
| | - Josie B Garner
- Agriculture Victoria, Ellinbank Dairy Centre, Ellinbank, VIC, Australia
| | - Michael E Goddard
- Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, VIC, Australia.,Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
| | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
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11
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Costantini A, Muurinen MH, Mäkitie O. New gene discoveries in skeletal diseases with short stature. Endocr Connect 2021; 10:R160-R174. [PMID: 33830070 PMCID: PMC8183621 DOI: 10.1530/ec-21-0083] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 04/07/2021] [Indexed: 12/19/2022]
Abstract
In the last decade, the widespread use of massively parallel sequencing has considerably boosted the number of novel gene discoveries in monogenic skeletal diseases with short stature. Defects in genes playing a role in the maintenance and function of the growth plate, the site of longitudinal bone growth, are a well-known cause of skeletal diseases with short stature. However, several genes involved in extracellular matrix composition or maintenance as well as genes partaking in various biological processes have also been characterized. This review aims to describe the latest genetic findings in spondyloepiphyseal dysplasias, spondyloepimetaphyseal dysplasias, and some monogenic forms of isolated short stature. Some examples of novel genetic mechanisms leading to skeletal conditions with short stature will be described. Strategies on how to successfully characterize novel skeletal phenotypes with short stature and genetic approaches to detect and validate novel gene-disease correlations will be discussed in detail. In summary, we review the latest gene discoveries underlying skeletal diseases with short stature and emphasize the importance of characterizing novel molecular mechanisms for genetic counseling, for an optimal management of the disease, and for therapeutic innovations.
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Affiliation(s)
- Alice Costantini
- Department of Molecular Medicine and Surgery and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Mari H Muurinen
- Folkhälsan Institute of Genetics, University of Helsinki, Helsinki, Finland
- Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
| | - Outi Mäkitie
- Department of Molecular Medicine and Surgery and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Folkhälsan Institute of Genetics, University of Helsinki, Helsinki, Finland
- Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
- Correspondence should be addressed to O Mäkitie:
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12
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Gusic M, Prokisch H. Genetic basis of mitochondrial diseases. FEBS Lett 2021; 595:1132-1158. [PMID: 33655490 DOI: 10.1002/1873-3468.14068] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/17/2021] [Accepted: 02/18/2021] [Indexed: 12/13/2022]
Abstract
Mitochondrial disorders are monogenic disorders characterized by a defect in oxidative phosphorylation and caused by pathogenic variants in one of over 340 different genes. The implementation of whole-exome sequencing has led to a revolution in their diagnosis, duplicated the number of associated disease genes, and significantly increased the diagnosed fraction. However, the genetic etiology of a substantial fraction of patients exhibiting mitochondrial disorders remains unknown, highlighting limitations in variant detection and interpretation, which calls for improved computational and DNA sequencing methods, as well as the addition of OMICS tools. More intriguingly, this also suggests that some pathogenic variants lie outside of the protein-coding genes and that the mechanisms beyond the Mendelian inheritance and the mtDNA are of relevance. This review covers the current status of the genetic basis of mitochondrial diseases, discusses current challenges and perspectives, and explores the contribution of factors beyond the protein-coding regions and monogenic inheritance in the expansion of the genetic spectrum of disease.
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Affiliation(s)
- Mirjana Gusic
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany.,Institute of Human Genetics, Technical University of Munich, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Germany
| | - Holger Prokisch
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany.,Institute of Human Genetics, Technical University of Munich, Germany
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13
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Dharshini SAP, Jemimah S, Taguchi YH, Gromiha MM. Exploring Common Therapeutic Targets for Neurodegenerative Disorders Using Transcriptome Study. Front Genet 2021; 12:639160. [PMID: 33815473 PMCID: PMC8017312 DOI: 10.3389/fgene.2021.639160] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 02/22/2021] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) and Parkinson's disease (PD) are well-known neuronal degenerative disorders that share common pathological events. Approved medications alleviate symptoms but do not address the root cause of the disease. Energy dysfunction in the neuronal population leads to various pathological events and ultimately results in neuronal death. Identifying common therapeutic targets for these disorders may help in the drug discovery process. The Brodmann area 9 (BA9) region is affected in both the disease conditions and plays an essential role in cognitive, motor, and memory-related functions. Analyzing transcriptome data of BA9 provides deep insights related to common pathological pathways involved in AD and PD. In this work, we map the preprocessed BA9 fastq files generated by RNA-seq for disease and control samples with reference hg38 genomic assembly and identify common variants and differentially expressed genes (DEG). These variants are predominantly located in the 3' UTR (non-promoter) region, affecting the conserved transcription factor (TF) binding motifs involved in the methylation and acetylation process. We have constructed BA9-specific functional interaction networks, which show the relationship between TFs and DEGs. Based on expression signature analysis, we propose that MAPK1, VEGFR1/FLT1, and FGFR1 are promising drug targets to restore blood-brain barrier functionality by reducing neuroinflammation and may save neurons.
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Affiliation(s)
- S Akila Parvathy Dharshini
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
| | - Sherlyn Jemimah
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
| | - Y H Taguchi
- Department of Physics, Chuo University, Hachioji, Japan
| | - M Michael Gromiha
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
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14
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Sarkar A, Yang Y, Vihinen M. Variation benchmark datasets: update, criteria, quality and applications. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5710862. [PMID: 32016318 PMCID: PMC6997940 DOI: 10.1093/database/baz117] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 06/03/2019] [Accepted: 07/01/2019] [Indexed: 02/07/2023]
Abstract
Development of new computational methods and testing their performance has to be carried out using experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed. High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing variation benchmark datasets used mainly for variation interpretation. They have been used for training and benchmarking predictors for various types of variations and their effects. VariBench was updated with 419 new datasets from 109 papers containing altogether 329 014 152 variants; however, there is plenty of redundancy between the datasets. VariBench is freely available at http://structure.bmc.lu.se/VariBench/. The contents of the datasets vary depending on information in the original source. The available datasets have been categorized into 20 groups and subgroups. There are datasets for insertions and deletions, substitutions in coding and non-coding region, structure mapped, synonymous and benign variants. Effect-specific datasets include DNA regulatory elements, RNA splicing, and protein property for aggregation, binding free energy, disorder and stability. Then there are several datasets for molecule-specific and disease-specific applications, as well as one dataset for variation phenotype effects. Variants are often described at three molecular levels (DNA, RNA and protein) and sometimes also at the protein structural level including relevant cross references and variant descriptions. The updated VariBench facilitates development and testing of new methods and comparison of obtained performances to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies, and show that such comparisons are feasible and useful, however, there may be limitations due to lack of provided details and shared data. Database URL: http://structure.bmc.lu.se/VariBench
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Affiliation(s)
- Anasua Sarkar
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22 184 Lund, Sweden
| | - Yang Yang
- School of Computer Science and Technology, Soochow University, No1. Shizi Street, Suzhou, 215006 Jiangsu, China.,Provincial Key Laboratory for Computer Information Processing Technology, No1. Shizi Street, Soochow University, Suzhou, 215006 Jiangsu, China
| | - Mauno Vihinen
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22 184 Lund, Sweden
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15
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Zheng C, Liu M, Fan H. Targeting complexes of super-enhancers is a promising strategy for cancer therapy. Oncol Lett 2020; 20:2557-2566. [PMID: 32782573 PMCID: PMC7400756 DOI: 10.3892/ol.2020.11855] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Accepted: 05/27/2020] [Indexed: 12/16/2022] Open
Abstract
The hyperactivation and overexpression of critical oncogenes is a common occurrence in multiple types of malignant tumors. Recently, the abnormal activation mechanism of an oncogene by a super-enhancer (SE) has attracted significant attention. A series of changes (insertion, deletion, translocation and rearrangement) in the genome occurring in cancer cells may generate new SEs, leading to the overexpression of SE-driven oncogenes. SEs are composed of typical enhancers densely loaded with mediator complexes, transcription factors, and chromatin regulators, and drive the overexpression of oncogenes associated with cellular identity and disease. Cyclin-dependent kinase 7 (CDK7) and bromodomain protein 4 (BRD4) are critical mediator complexes associated with SE-mediated transcription. Clinical trials have shown that emerging small-molecule inhibitors (CDK7 and BRD4 inhibitor), targeting the SE exert a notable effect on cancer treatment. Increasing evidences has illustrated that the SE and its associated complexes play a critical role in the development of various types of cancer. The present review discusses the composition, function and regulation of SEs and their contribution to oncogenic transcription. In addition, creative therapeutic approaches that target SE, their advantages and disadvantages, as well as the problems with their clinical application are discussed. It was found that targeting SE may be used in conventional treatment and establish more access for patients with cancer.
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Affiliation(s)
- Chuqian Zheng
- Department of Medical Genetics and Developmental Biology, School of Medicine, The Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, Southeast University, Nanjing, Jiangsu 210009, P.R. China
| | - Min Liu
- Department of Medical Genetics and Developmental Biology, School of Medicine, The Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, Southeast University, Nanjing, Jiangsu 210009, P.R. China.,School of Life Science and Technology, Southeast University, Nanjing, Jiangsu 210018, P.R. China
| | - Hong Fan
- Department of Medical Genetics and Developmental Biology, School of Medicine, The Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, Southeast University, Nanjing, Jiangsu 210009, P.R. China
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16
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Bocher O, Génin E. Rare variant association testing in the non-coding genome. Hum Genet 2020; 139:1345-1362. [PMID: 32500240 DOI: 10.1007/s00439-020-02190-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 05/29/2020] [Indexed: 12/25/2022]
Abstract
The development of next-generation sequencing technologies has opened-up some new possibilities to explore the contribution of genetic variants to human diseases and in particular that of rare variants. Statistical methods have been developed to test for association with rare variants that require the definition of testing units and, in these testing units, the selection of qualifying variants to include in the test. In the coding regions of the genome, testing units are usually the different genes and qualifying variants are selected based on their functional effects on the encoded proteins. Extending these tests to the non-coding regions of the genome is challenging. Testing units are difficult to define as the non-coding genome organisation is still rather unknown. Qualifying variants are difficult to select as the functional impact of non-coding variants on gene expression is hard to predict. These difficulties could explain why very few investigators so far have analysed the non-coding parts of their whole genome sequencing data. These non-coding parts yet represent the vast majority of the genome and some studies suggest that they could play a major role in disease susceptibility. In this review, we discuss recent experimental and statistical developments to gain knowledge on the non-coding genome and how this knowledge could be used to include rare non-coding variants in association tests. We describe the few studies that have considered variants from the non-coding genome in association tests and how they managed to define testing units and select qualifying variants.
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Affiliation(s)
- Ozvan Bocher
- Génétique, Génomique Fonctionnelle Et Biotechnologies, Faculté de Médecine, Univ Brest, Inserm, Inserm UMR1078, Bâtiment E-IBRBS 2ieme étage, 22 avenue Camille Desmoulins, 29238, Brest Cedex 3, France.
| | - Emmanuelle Génin
- Génétique, Génomique Fonctionnelle Et Biotechnologies, Faculté de Médecine, Univ Brest, Inserm, Inserm UMR1078, Bâtiment E-IBRBS 2ieme étage, 22 avenue Camille Desmoulins, 29238, Brest Cedex 3, France.
- CHU Brest, Brest, France.
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17
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Stenton SL, Prokisch H. Genetics of mitochondrial diseases: Identifying mutations to help diagnosis. EBioMedicine 2020; 56:102784. [PMID: 32454403 PMCID: PMC7248429 DOI: 10.1016/j.ebiom.2020.102784] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 04/21/2020] [Accepted: 04/22/2020] [Indexed: 12/15/2022] Open
Abstract
Mitochondrial diseases are amongst the most genetically and phenotypically diverse groups of inherited diseases. The vast phenotypic overlap with other disease entities together with the absence of reliable biomarkers act as driving forces for the integration of unbiased methodologies early in the diagnostic algorithm, such as whole exome sequencing (WES) and whole genome sequencing (WGS). Such approaches are used in variant discovery and in combination with high-throughput functional assays such as transcriptomics in simultaneous variant discovery and validation. By capturing all genes, they not only increase the diagnostic rate in heterogenous mitochondrial disease patients, but accelerate novel disease gene discovery, and are valuable in side-stepping the risk of overlooking unexpected or even treatable genetic disease diagnoses.
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Affiliation(s)
- Sarah L Stenton
- Institut für Humangenetik, Klinikum rechts der Isar, Technische Universität München, Trogerstraße 32, 81675 München, Germany; Institute of Neurogenomics, Helmholtz Zentrum München, Ingolstaedter Landstraße 1, D-85764 Neuherberg, Germany
| | - Holger Prokisch
- Institut für Humangenetik, Klinikum rechts der Isar, Technische Universität München, Trogerstraße 32, 81675 München, Germany; Institute of Neurogenomics, Helmholtz Zentrum München, Ingolstaedter Landstraße 1, D-85764 Neuherberg, Germany.
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18
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Dapas M, Dunaif A. The contribution of rare genetic variants to the pathogenesis of polycystic ovary syndrome. ACTA ACUST UNITED AC 2020; 12:26-32. [PMID: 32440573 DOI: 10.1016/j.coemr.2020.02.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Polycystic ovary syndrome (PCOS) is a highly heritable disorder, but only a small proportion of the heritability can be accounted for by common genetic risk variants identified to date. It is possible that variants with lower allele frequencies that cannot be detected using genome-wide association study arrays contribute to PCOS. Here, we discuss the challenges inherent to studying rare genetic variants in complex disease and review several recent studies that have used DNA sequencing techniques to investigate whether rare variants play a role in PCOS pathogenesis. We evaluate these findings in the context of the latest literature in PCOS and complex disease genetics.
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19
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Li Y, McGrail DJ, Latysheva N, Yi S, Babu MM, Sahni N. Pathway perturbations in signaling networks: Linking genotype to phenotype. Semin Cell Dev Biol 2020; 99:3-11. [PMID: 29738884 PMCID: PMC6230320 DOI: 10.1016/j.semcdb.2018.05.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 03/29/2018] [Accepted: 05/04/2018] [Indexed: 02/07/2023]
Abstract
Genes and gene products interact with each other to form signal transduction networks in the cell. The interactome networks are under intricate regulation in physiological conditions, but could go awry upon genome instability caused by genetic mutations. In the past decade with next-generation sequencing technologies, an increasing number of genomic mutations have been identified in a variety of disease patients and healthy individuals. As functional and systematic studies on these mutations leap forward, they begin to reveal insights into cellular homeostasis and disease mechanisms. In this review, we discuss recent advances in the field of network biology and signaling pathway perturbations upon genomic changes, and highlight the success of various omics datasets in unraveling genotype-to-phenotype relationships.
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Affiliation(s)
- Yongsheng Li
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Daniel J McGrail
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Natasha Latysheva
- Medical Research Council Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK
| | - Song Yi
- Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX, 78712, USA.
| | - M Madan Babu
- Medical Research Council Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK.
| | - Nidhi Sahni
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA; Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, 77030, USA; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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20
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Meta-Analysis of Polymyositis and Dermatomyositis Microarray Data Reveals Novel Genetic Biomarkers. Genes (Basel) 2019; 10:genes10110864. [PMID: 31671645 PMCID: PMC6895911 DOI: 10.3390/genes10110864] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/07/2019] [Accepted: 10/25/2019] [Indexed: 02/07/2023] Open
Abstract
Polymyositis (PM) and dermatomyositis (DM) are both classified as idiopathic inflammatory myopathies. They share a few common characteristics such as inflammation and muscle weakness. Previous studies have indicated that these diseases present aspects of an auto-immune disorder; however, their exact pathogenesis is still unclear. In this study, three gene expression datasets (PM: 7, DM: 50, Control: 13) available in public databases were used to conduct meta-analysis. We then conducted expression quantitative trait loci analysis to detect the variant sites that may contribute to the pathogenesis of PM and DM. Six-hundred differentially expressed genes were identified in the meta-analysis (false discovery rate (FDR) < 0.01), among which 317 genes were up-regulated and 283 were down-regulated in the disease group compared with those in the healthy control group. The up-regulated genes were significantly enriched in interferon-signaling pathways in protein secretion, and/or in unfolded-protein response. We detected 10 single nucleotide polymorphisms (SNPs) which could potentially play key roles in driving the PM and DM. Along with previously reported genes, we identified 4 novel genes and 10 SNP-variant regions which could be used as candidates for potential drug targets or biomarkers for PM and DM.
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21
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Martínez-Jaramillo C, Gutierrez-Hincapie S, Arango JCO, Vásquez-Duque GM, Erazo-Garnica RM, Franco JL, Trujillo-Vargas CM. Clinical, immunological and genetic characteristic of patients with clinical phenotype associated to LRBA-deficiency in Colombia. Colomb Med (Cali) 2019; 50:176-191. [PMID: 32284663 PMCID: PMC7141146 DOI: 10.25100/cm.v50i3.3969] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Background LPS-responsive beige -like anchor protein (LRBA) deficiency is a primary immunodeficiency disease caused by loss of LRBA protein expression, due to biallelic mutations in LRBA gene. LRBA deficiency patients exhibit a clinically heterogeneous syndrome. The main clinical complication of LRBA deficiency is immune dysregulation. Furthermore, hypogammaglobulinemia is found in more than half of patients with LRBA-deficiency. To date, no patients with this condition have been reported in Colombia. Objective To evaluate the expression of the LRBA protein in patients from Colombia with clinical phenotype associated to LRBA-deficiency. Methods In the present study the LRBA-expression in patients from Colombia with clinical phenotype associated to LRBA-deficiency was evaluated. After then, the clinical, the immunological characteristics and the possible genetic variants in LRBA or other genes associated with the immune system in patients that exhibit decrease protein expression was evaluated. Results In total, 112 patients with different clinical manifestations associated to the clinical LRBA phenotype were evaluated. The LRBA expression varies greatly between different healthy donors and patients. Despite the great variability in the LRBA expression, six patients with a decrease in LRBA protein expression were observed. However, no pathogenic or possible pathogenic biallelic variants in LRBA, or in genes related with the immune system were found. Conclusion LRBA expression varies greatly between different healthy donors and patients. Reduction LRBA-expression in 6 patients without homozygous mutations in LRBA or in associated genes with the immune system was observed. These results suggest the other genetic, epigenetic or environmental mechanisms, that might be regulated the LRBA-expression.
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Affiliation(s)
- Catalina Martínez-Jaramillo
- Universidad de Antioquia UdeA, Facultad de Medicina, Grupo de Inmunodeficiencias Primarias, Medellin, Colombia
| | | | | | | | | | - Jose Luis Franco
- Universidad de Antioquia UdeA, Facultad de Medicina, Grupo de Inmunodeficiencias Primarias, Medellin, Colombia
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22
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Rojano E, Seoane P, Ranea JAG, Perkins JR. Regulatory variants: from detection to predicting impact. Brief Bioinform 2019; 20:1639-1654. [PMID: 29893792 PMCID: PMC6917219 DOI: 10.1093/bib/bby039] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 04/18/2018] [Indexed: 02/01/2023] Open
Abstract
Variants within non-coding genomic regions can greatly affect disease. In recent years, increasing focus has been given to these variants, and how they can alter regulatory elements, such as enhancers, transcription factor binding sites and DNA methylation regions. Such variants can be considered regulatory variants. Concurrently, much effort has been put into establishing international consortia to undertake large projects aimed at discovering regulatory elements in different tissues, cell lines and organisms, and probing the effects of genetic variants on regulation by measuring gene expression. Here, we describe methods and techniques for discovering disease-associated non-coding variants using sequencing technologies. We then explain the computational procedures that can be used for annotating these variants using the information from the aforementioned projects, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches. We provide the details of techniques to validate these predictions, by mapping chromatin-chromatin and chromatin-protein interactions, and introduce Clustered Regularly Interspaced Short Palindromic Repeats-Associated Protein 9 (CRISPR-Cas9) technology, which has already been used in this field and is likely to have a big impact on its future evolution. We also give examples of regulatory variants associated with multiple complex diseases. This review is aimed at bioinformaticians interested in the characterization of regulatory variants, molecular biologists and geneticists interested in understanding more about the nature and potential role of such variants from a functional point of views, and clinicians who may wish to learn about variants in non-coding genomic regions associated with a given disease and find out what to do next to uncover how they impact on the underlying mechanisms.
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Affiliation(s)
- Elena Rojano
- Department of Molecular Biology and Biochemistry, University of Malaga (UMA), 29010 Malaga, Spain
| | - Pedro Seoane
- Department of Molecular Biology and Biochemistry, University of Malaga (UMA), 29010 Malaga, Spain
| | - Juan A G Ranea
- CIBER de Enfermedades Raras, ISCIII, Madrid, Spain and Department of Molecular Biology and Biochemistry, University of Malaga (UMA), 29010 Malaga, Spain
| | - James R Perkins
- Research laboratory, IBIMA-Regional University Hospital of Malaga, UMA, Malaga 29009, Spain
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23
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Dapas M, Sisk R, Legro RS, Urbanek M, Dunaif A, Hayes MG. Family-Based Quantitative Trait Meta-Analysis Implicates Rare Noncoding Variants in DENND1A in Polycystic Ovary Syndrome. J Clin Endocrinol Metab 2019; 104:3835-3850. [PMID: 31038695 PMCID: PMC6660913 DOI: 10.1210/jc.2018-02496] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 04/17/2019] [Indexed: 02/07/2023]
Abstract
CONTEXT Polycystic ovary syndrome (PCOS) is among the most common endocrine disorders of premenopausal women, affecting 5% to15% of this population depending on the diagnostic criteria applied. It is characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology. PCOS is highly heritable, but only a small proportion of this heritability can be accounted for by the common genetic susceptibility variants identified to date. OBJECTIVE The objective of this study was to test whether rare genetic variants contribute to PCOS pathogenesis. DESIGN, PATIENTS, AND METHODS We performed whole-genome sequencing on DNA from 261 individuals from 62 families with one or more daughters with PCOS. We tested for associations of rare variants with PCOS and its concomitant hormonal traits using a quantitative trait meta-analysis. RESULTS We found rare variants in DENND1A (P = 5.31 × 10-5, adjusted P = 0.039) that were significantly associated with reproductive and metabolic traits in PCOS families. CONCLUSIONS Common variants in DENND1A have previously been associated with PCOS diagnosis in genome-wide association studies. Subsequent studies indicated that DENND1A is an important regulator of human ovarian androgen biosynthesis. Our findings provide additional evidence that DENND1A plays a central role in PCOS and suggest that rare noncoding variants contribute to disease pathogenesis.
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Affiliation(s)
- Matthew Dapas
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Ryan Sisk
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Richard S Legro
- Department of Obstetrics and Gynecology, Penn State College of Medicine, Hershey, Pennsylvania
| | - Margrit Urbanek
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Reproductive Science, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Andrea Dunaif
- Division of Endocrinology, Diabetes, and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, New York
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Department of Anthropology, Northwestern University, Evanston, Illinois
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24
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Donlin LT, Park SH, Giannopoulou E, Ivovic A, Park-Min KH, Siegel RM, Ivashkiv LB. Insights into rheumatic diseases from next-generation sequencing. Nat Rev Rheumatol 2019; 15:327-339. [PMID: 31000790 PMCID: PMC6673602 DOI: 10.1038/s41584-019-0217-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Rheumatic diseases have complex aetiologies that are not fully understood, which makes the study of pathogenic mechanisms in these diseases a challenge for researchers. Next-generation sequencing (NGS) and related omics technologies, such as transcriptomics, epigenomics and genomics, provide an unprecedented genome-wide view of gene expression, environmentally responsive epigenetic changes and genetic variation. The integrated application of NGS technologies to samples from carefully phenotyped clinical cohorts of patients has the potential to solve remaining mysteries in the pathogenesis of several rheumatic diseases, to identify new therapeutic targets and to underpin a precision medicine approach to the diagnosis and treatment of rheumatic diseases. This Review provides an overview of the NGS technologies available, showcases important advances in rheumatic disease research already powered by these technologies and highlights NGS approaches that hold particular promise for generating new insights and advancing the field.
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Affiliation(s)
- Laura T Donlin
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY, USA
- David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Sung-Ho Park
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY, USA
- David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA
| | - Eugenia Giannopoulou
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY, USA
- David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA
- Biological Sciences Department, New York City College of Technology, City University of New York, New York, NY, USA
| | - Aleksandra Ivovic
- Immunoregulation Section, Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Kyung-Hyun Park-Min
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY, USA
- David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Richard M Siegel
- Immunoregulation Section, Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Lionel B Ivashkiv
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY, USA.
- David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA.
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
- Immunology and Microbial Pathogenesis Program, Weill Cornell Graduate School of Medical Sciences, New York, NY, USA.
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25
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Sun Q, Wang Y, Fan J, Li Z, Zhang J, Wang L, Fan X, Ji M, Zhu M, Dai J, Ma H, Jin G, Hu Z, Shen H. Association of expression quantitative trait loci for long noncoding RNAs with lung cancer risk in Asians. Mol Carcinog 2019; 58:1303-1313. [DOI: 10.1002/mc.23013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 03/09/2019] [Accepted: 03/20/2019] [Indexed: 02/06/2023]
Affiliation(s)
- Qi Sun
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
| | - Yuzhuo Wang
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
| | - Jingyi Fan
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
| | - Zhihua Li
- Department of Thoracic SurgeryFirst Affiliated Hospital of Nanjing Medical UniversityNanjing China
| | - Jiahui Zhang
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
| | - Lijuan Wang
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
| | - Xikang Fan
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
| | - Mengmeng Ji
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
| | - Juncheng Dai
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjing China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjing China
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26
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Gonorazky HD, Naumenko S, Ramani AK, Nelakuditi V, Mashouri P, Wang P, Kao D, Ohri K, Viththiyapaskaran S, Tarnopolsky MA, Mathews KD, Moore SA, Osorio AN, Villanova D, Kemaladewi DU, Cohn RD, Brudno M, Dowling JJ. Expanding the Boundaries of RNA Sequencing as a Diagnostic Tool for Rare Mendelian Disease. Am J Hum Genet 2019; 104:466-483. [PMID: 30827497 PMCID: PMC6407525 DOI: 10.1016/j.ajhg.2019.01.012] [Citation(s) in RCA: 138] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 01/22/2019] [Indexed: 02/06/2023] Open
Abstract
Gene-panel and whole-exome analyses are now standard methodologies for mutation detection in Mendelian disease. However, the diagnostic yield achieved is at best 50%, leaving the genetic basis for disease unsolved in many individuals. New approaches are thus needed to narrow the diagnostic gap. Whole-genome sequencing is one potential strategy, but it currently has variant-interpretation challenges, particularly for non-coding changes. In this study we focus on transcriptome analysis, specifically total RNA sequencing (RNA-seq), by using monogenetic neuromuscular disorders as proof of principle. We examined a cohort of 25 exome and/or panel "negative" cases and provided genetic resolution in 36% (9/25). Causative mutations were identified in coding and non-coding exons, as well as in intronic regions, and the mutational pathomechanisms included transcriptional repression, exon skipping, and intron inclusion. We address a key barrier of transcriptome-based diagnostics: the need for source material with disease-representative expression patterns. We establish that blood-based RNA-seq is not adequate for neuromuscular diagnostics, whereas myotubes generated by transdifferentiation from an individual's fibroblasts accurately reflect the muscle transcriptome and faithfully reveal disease-causing mutations. Our work confirms that RNA-seq can greatly improve diagnostic yield in genetically unresolved cases of Mendelian disease, defines strengths and challenges of the technology, and demonstrates the suitability of cell models for RNA-based diagnostics. Our data set the stage for development of RNA-seq as a powerful clinical diagnostic tool that can be applied to the large population of individuals with undiagnosed, rare diseases and provide a framework for establishing minimally invasive strategies for doing so.
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Affiliation(s)
- Hernan D Gonorazky
- Division of Neurology, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; Program in Genetics and Genome Biology, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Sergey Naumenko
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Arun K Ramani
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Viswateja Nelakuditi
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Pouria Mashouri
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Peiqui Wang
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Dennis Kao
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Krish Ohri
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1X8, Canada
| | | | - Mark A Tarnopolsky
- Department of Pediatrics, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Katherine D Mathews
- Departments of Pediatrics and Neurology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA
| | - Steven A Moore
- Department of Pathology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA
| | - Andres N Osorio
- Neuromuscular Unit, Neuropaediatrics Department, Institut de Recerca Hospital Universitari Sant Joan de Deu, Barcelona 08950, Spain; Center for the Biomedical Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III (ISCIII), Barcelona 08950, Spain
| | - David Villanova
- GenomicTales Parc de la Mola, 10, AD700 Escaldes-Engordany, Andorra
| | - Dwi U Kemaladewi
- Program in Genetics and Genome Biology, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Ronald D Cohn
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1X8, Canada; Program in Genetics and Genome Biology, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Michael Brudno
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; Program in Genetics and Genome Biology, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5G 0A4, Canada.
| | - James J Dowling
- Division of Neurology, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1X8, Canada; Program in Genetics and Genome Biology, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 0A4, Canada.
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27
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Behlmann AM, Goyal NA, Yang X, Chen PH, Ankala A. A Hemizygous Deletion Within the PGK1 Gene in Males with PGK1 Deficiency. JIMD Rep 2018; 45:105-110. [PMID: 30570712 PMCID: PMC6336546 DOI: 10.1007/8904_2018_147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 08/24/2018] [Accepted: 09/25/2018] [Indexed: 02/05/2023] Open
Abstract
Phosphoglycerate kinase-1 (PGK1) deficiency is a rare X-linked disorder caused by pathogenic variants in the PGK1 gene. Complete loss-of-function variants have not been reported in this gene, indicating that residual enzyme function is critical for viability in males. Therefore, copy number variants (CNVs) that include single exon or multiple exon deletions or duplications are generally not expected in individuals with PGK1 deficiency. Here we describe a 64-year-old male presenting with a family history (three additional affected males) and a personal history of childhood-onset metabolic myopathy that involves episodes of muscle pain, stiffness after activity, exercise intolerance, and myoglobinuria after exertion. Biochemical analysis on a muscle biopsy indicated significantly reduced activity (15% compared to normal) for phosphoglycerate kinase (PGK1), a glycolytic enzyme encoded by PGK1. A diagnosis of PGK1 deficiency was established by molecular analysis which detected an approximately 886 kb deletion involving the polyadenylation site in the 3'UTR of the PGK1 gene. RNA analysis showed significantly reduced PGK1 transcript levels (30% compared to normal). This is the first deletion reported in the PGK1 gene and is the first pathogenic variant involving the 3'UTR polyadenylation site of this gene. Our report emphasizes the role of 3'UTR variants in human disorders and underscores the need for exploring noncoding regions of disease-associated genes when seeking a molecular diagnosis.
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Affiliation(s)
- Andrea Medrano Behlmann
- grid.189967.80000 0001 0941 6502Department of Human Genetics, Emory University School of Medicine, Atlanta, GA USA
| | - Namita A. Goyal
- grid.266093.80000 0001 0668 7243Department of Neurology, University of California, Irvine, CA USA
| | - Xiaoyu Yang
- grid.189967.80000 0001 0941 6502Department of Cell Biology, Emory University School of Medicine, Atlanta, GA USA
| | - Ping H. Chen
- grid.189967.80000 0001 0941 6502Department of Cell Biology, Emory University School of Medicine, Atlanta, GA USA
| | - Arunkanth Ankala
- grid.189967.80000 0001 0941 6502Department of Human Genetics, Emory University School of Medicine, Atlanta, GA USA ,EGL Genetic Diagnostics LLC, Tucker, GA USA
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28
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Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants. Sci Rep 2017; 7:2959. [PMID: 28592878 PMCID: PMC5462751 DOI: 10.1038/s41598-017-03011-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 04/21/2017] [Indexed: 12/15/2022] Open
Abstract
Disease and trait-associated variants represent a tiny minority of all known genetic variation, and therefore there is necessarily an imbalance between the small set of available disease-associated and the much larger set of non-deleterious genomic variation, especially in non-coding regulatory regions of human genome. Machine Learning (ML) methods for predicting disease-associated non-coding variants are faced with a chicken and egg problem - such variants cannot be easily found without ML, but ML cannot begin to be effective until a sufficient number of instances have been found. Most of state-of-the-art ML-based methods do not adopt specific imbalance-aware learning techniques to deal with imbalanced data that naturally arise in several genome-wide variant scoring problems, thus resulting in a significant reduction of sensitivity and precision. We present a novel method that adopts imbalance-aware learning strategies based on resampling techniques and a hyper-ensemble approach that outperforms state-of-the-art methods in two different contexts: the prediction of non-coding variants associated with Mendelian and with complex diseases. We show that imbalance-aware ML is a key issue for the design of robust and accurate prediction algorithms and we provide a method and an easy-to-use software tool that can be effectively applied to this challenging prediction task.
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Sewell JA, Fuxman Bass JI. Cellular network perturbations by disease-associated variants. ACTA ACUST UNITED AC 2017; 3:60-66. [PMID: 29057377 DOI: 10.1016/j.coisb.2017.04.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Genetic and genome-wide association studies (GWAS) have identified a myriad of human disease-associated genomic variants. However, these studies do not reveal the mechanisms by which these variants perturb cellular networks, a necessary step to intervene and improve disease outcomes. This has been challenging because multiple variants are present in haplotype blocks, thereby confounding the identification of causal variants, and because most reside in noncoding regions. Here, we review recent advances in the identification of functional variants and gene-variant associations. In addition, we examine approaches used to study perturbations in protein-protein and protein-DNA interactions associated with disease, and discuss how these perturbations affect cellular networks.
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Affiliation(s)
- Jared A Sewell
- Department of Biology, Boston University, Boston, MA 02215, USA
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Liang S, Tippens ND, Zhou Y, Mort M, Stenson PD, Cooper DN, Yu H. iRegNet3D: three-dimensional integrated regulatory network for the genomic analysis of coding and non-coding disease mutations. Genome Biol 2017; 18:10. [PMID: 28100260 PMCID: PMC5241969 DOI: 10.1186/s13059-016-1138-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 12/16/2016] [Indexed: 01/05/2023] Open
Abstract
The mechanistic details of most disease-causing mutations remain poorly explored within the context of regulatory networks. We present a high-resolution three-dimensional integrated regulatory network (iRegNet3D) in the form of a web tool, where we resolve the interfaces of all known transcription factor (TF)-TF, TF-DNA and chromatin-chromatin interactions for the analysis of both coding and non-coding disease-associated mutations to obtain mechanistic insights into their functional impact. Using iRegNet3D, we find that disease-associated mutations may perturb the regulatory network through diverse mechanisms including chromatin looping. iRegNet3D promises to be an indispensable tool in large-scale sequencing and disease association studies.
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Affiliation(s)
- Siqi Liang
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853, USA.,Weill Institute for Cell and Molecular Biology, Ithaca, NY, 14853, USA
| | - Nathaniel D Tippens
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853, USA.,Weill Institute for Cell and Molecular Biology, Ithaca, NY, 14853, USA
| | - Yaoda Zhou
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853, USA.,Weill Institute for Cell and Molecular Biology, Ithaca, NY, 14853, USA
| | - Matthew Mort
- Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Peter D Stenson
- Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - David N Cooper
- Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Haiyuan Yu
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853, USA. .,Weill Institute for Cell and Molecular Biology, Ithaca, NY, 14853, USA.
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Abstract
Whole-genome and exome sequencing in human populations has revealed the tolerance of each gene for loss-of-function variation. By understanding this tolerance, it has become increasingly possible to identify genes that would make safe therapeutic targets and to identify rare genetic risk factors and phenotypes at the scale of individual genomes. To date, the vast majority of surveyed loss-of-function variants are in protein-coding regions of the genome mainly due to the focus on these regions by exome-based sequencing projects and their relative ease of interpretability. As whole-genome sequencing becomes more prevalent, new strategies will be required to uncover impactful variation in non-coding regions of the genome where the architecture of genome function is more complex. In this review, we investigate recent studies of loss-of-function variation and emerging approaches for interpreting whole-genome sequencing data to identify rare and impactful non-coding loss-of-function variants.
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Affiliation(s)
- Zachary Zappala
- Department of Genetics, Stanford University, California, USA
| | - Stephen B. Montgomery
- Department of Genetics, Stanford University, California, USA
- Department of Pathology, Stanford University, California, USA
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Smedley D, Schubach M, Jacobsen J, Köhler S, Zemojtel T, Spielmann M, Jäger M, Hochheiser H, Washington N, McMurry J, Haendel M, Mungall C, Lewis S, Groza T, Valentini G, Robinson P. A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease. Am J Hum Genet 2016; 99:595-606. [PMID: 27569544 DOI: 10.1016/j.ajhg.2016.07.005] [Citation(s) in RCA: 171] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Accepted: 07/01/2016] [Indexed: 12/17/2022] Open
Abstract
The interpretation of non-coding variants still constitutes a major challenge in the application of whole-genome sequencing in Mendelian disease, especially for single-nucleotide and other small non-coding variants. Here we present Genomiser, an analysis framework that is able not only to score the relevance of variation in the non-coding genome, but also to associate regulatory variants to specific Mendelian diseases. Genomiser scores variants through either existing methods such as CADD or a bespoke machine learning method and combines these with allele frequency, regulatory sequences, chromosomal topological domains, and phenotypic relevance to discover variants associated to specific Mendelian disorders. Overall, Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes, allowing effective detection and discovery of regulatory variants in Mendelian disease.
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Uzilov AV, Ding W, Fink MY, Antipin Y, Brohl AS, Davis C, Lau CY, Pandya C, Shah H, Kasai Y, Powell J, Micchelli M, Castellanos R, Zhang Z, Linderman M, Kinoshita Y, Zweig M, Raustad K, Cheung K, Castillo D, Wooten M, Bourzgui I, Newman LC, Deikus G, Mathew B, Zhu J, Glicksberg BS, Moe AS, Liao J, Edelmann L, Dudley JT, Maki RG, Kasarskis A, Holcombe RF, Mahajan M, Hao K, Reva B, Longtine J, Starcevic D, Sebra R, Donovan MJ, Li S, Schadt EE, Chen R. Development and clinical application of an integrative genomic approach to personalized cancer therapy. Genome Med 2016; 8:62. [PMID: 27245685 PMCID: PMC4888213 DOI: 10.1186/s13073-016-0313-0] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2016] [Accepted: 05/04/2016] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Personalized therapy provides the best outcome of cancer care and its implementation in the clinic has been greatly facilitated by recent convergence of enormous progress in basic cancer research, rapid advancement of new tumor profiling technologies, and an expanding compendium of targeted cancer therapeutics. METHODS We developed a personalized cancer therapy (PCT) program in a clinical setting, using an integrative genomics approach to fully characterize the complexity of each tumor. We carried out whole exome sequencing (WES) and single-nucleotide polymorphism (SNP) microarray genotyping on DNA from tumor and patient-matched normal specimens, as well as RNA sequencing (RNA-Seq) on available frozen specimens, to identify somatic (tumor-specific) mutations, copy number alterations (CNAs), gene expression changes, gene fusions, and also germline variants. To provide high sensitivity in known cancer mutation hotspots, Ion AmpliSeq Cancer Hotspot Panel v2 (CHPv2) was also employed. We integrated the resulting data with cancer knowledge bases and developed a specific workflow for each cancer type to improve interpretation of genomic data. RESULTS We returned genomics findings to 46 patients and their physicians describing somatic alterations and predicting drug response, toxicity, and prognosis. Mean 17.3 cancer-relevant somatic mutations per patient were identified, 13.3-fold, 6.9-fold, and 4.7-fold more than could have been detected using CHPv2, Oncomine Cancer Panel (OCP), and FoundationOne, respectively. Our approach delineated the underlying genetic drivers at the pathway level and provided meaningful predictions of therapeutic efficacy and toxicity. Actionable alterations were found in 91 % of patients (mean 4.9 per patient, including somatic mutations, copy number alterations, gene expression alterations, and germline variants), a 7.5-fold, 2.0-fold, and 1.9-fold increase over what could have been uncovered by CHPv2, OCP, and FoundationOne, respectively. The findings altered the course of treatment in four cases. CONCLUSIONS These results show that a comprehensive, integrative genomic approach as outlined above significantly enhanced genomics-based PCT strategies.
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Affiliation(s)
- Andrew V Uzilov
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Wei Ding
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Marc Y Fink
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Biomedical Sciences, Long Island University Post, Brookville, NY, 11548, USA
| | - Yevgeniy Antipin
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Andrew S Brohl
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Sarcoma Department, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Claire Davis
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Chun Yee Lau
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Chetanya Pandya
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Hardik Shah
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Yumi Kasai
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - James Powell
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Mark Micchelli
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Rafael Castellanos
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Zhongyang Zhang
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Michael Linderman
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Yayoi Kinoshita
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Micol Zweig
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Katie Raustad
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Kakit Cheung
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Diane Castillo
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Melissa Wooten
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Imane Bourzgui
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Leah C Newman
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Gintaras Deikus
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Bino Mathew
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jun Zhu
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Aye S Moe
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jun Liao
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lisa Edelmann
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Joel T Dudley
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Robert G Maki
- Division of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Andrew Kasarskis
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Randall F Holcombe
- Division of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Milind Mahajan
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Boris Reva
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Janina Longtine
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniela Starcevic
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Michael J Donovan
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Shuyu Li
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| | - Rong Chen
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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Bendl J, Musil M, Štourač J, Zendulka J, Damborský J, Brezovský J. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions. PLoS Comput Biol 2016; 12:e1004962. [PMID: 27224906 PMCID: PMC4880439 DOI: 10.1371/journal.pcbi.1004962] [Citation(s) in RCA: 133] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Accepted: 05/05/2016] [Indexed: 12/20/2022] Open
Abstract
An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools’ predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To enable comprehensive evaluation of variants, the predictions are complemented with annotations from eight databases. The web server is freely available to the community at http://loschmidt.chemi.muni.cz/predictsnp2.
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Affiliation(s)
- Jaroslav Bendl
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, Brno, Czech Republic
- Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech Republic
| | - Miloš Musil
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, Brno, Czech Republic
- Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
| | - Jan Štourač
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech Republic
| | - Jaroslav Zendulka
- Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
| | - Jiří Damborský
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech Republic
- * E-mail: (JD); (JBr)
| | - Jan Brezovský
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech Republic
- * E-mail: (JD); (JBr)
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Bromberg Y, Capriotti E. VarI-SIG 2014--From SNPs to variants: interpreting different types of genetic variants. BMC Genomics 2015; 16 Suppl 8:I1. [PMID: 26110281 PMCID: PMC4480323 DOI: 10.1186/1471-2164-16-s8-i1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
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