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Huang C, Huang Y, Pan L, Li L, Ling X, Wang C, Xiao Q, Zhai N, Long Y, Mo W, Lin F, Huang Y. A novel duplication mutation of SLC2A1 gene causing glucose transporter 1 deficiency syndrome. Gene 2024; 928:148762. [PMID: 39009233 DOI: 10.1016/j.gene.2024.148762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 07/05/2024] [Accepted: 07/10/2024] [Indexed: 07/17/2024]
Affiliation(s)
- Chaoyu Huang
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, Guangxi, China
| | - Yunhua Huang
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, Guangxi, China
| | - Liqiu Pan
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, Guangxi, China
| | - Linlin Li
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, Guangxi, China
| | - Xiaoting Ling
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, Guangxi, China
| | - Chenghan Wang
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, Guangxi, China
| | - Qingxing Xiao
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, Guangxi, China
| | - Ningneng Zhai
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, Guangxi, China
| | - Yan Long
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, Guangxi, China
| | - Wuning Mo
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, Guangxi, China.
| | - Faquan Lin
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, Guangxi, China.
| | - Yifang Huang
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, Guangxi, China.
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Zhang R, Cui D, Song C, Ma X, Cai N, Zhang Y, Feng M, Cao Y, Chen L, Qiang R. Evaluating the efficacy of a long-read sequencing-based approach in the clinical diagnosis of neonatal congenital adrenocortical hyperplasia. Clin Chim Acta 2024; 555:117820. [PMID: 38307397 DOI: 10.1016/j.cca.2024.117820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/22/2024] [Accepted: 01/29/2024] [Indexed: 02/04/2024]
Abstract
Congenital adrenal hyperplasia (CAH) is a group of autosomal recessive disorders predominantly characterized by impaired corticosteroid synthesis. Clinical phenotypes include hypoadrenocorticism, electrolyte disturbances, abnormal gonadal development, and short stature, of which severe hyponadrenocorticism and salt wasting can be life-threatening. Genetic analysis can help in the clinical diagnosis of CAH. However, the 21-OHD-causing gene CYP21A2 is arranged in tandem with the highly homologous CYP21A1P pseudogene, making it difficult to determine the exact genotypes using the traditional method of multiplex ligation-dependent probe amplification (MLPA) plus Sanger sequencing or next-generation sequencing (NGS). We applied a long-read sequencing-based approach termed comprehensive analysis of CAH (CACAH) to 48 newborns with CAH that were diagnosed by clinical features and the traditional MLPA plus Sanger sequencing method for retrospective analysis, to evaluate its efficacy in the clinical diagnosis of neonatal CAH. Compared with the MLPA plus Sanger sequencing method, CACAH showed 100 % consistency in detecting SNV/indel variants located in exons and exon-intron boundary regions of CAH-related genes. It can directly determine the cis-trans relationship without the need to analyze parental genotypes, which reduces the time to diagnosis. Moreover, CACAH was able to distinguish different CYP21A1P/CYP21A2 and TNXA/TNXB chimeras, and detect additional variants (CYP21A2 variants c.-121C > T, c.*13G > A, c.*52C > T, c.*440C > T, c.*443 T > C, and TNXB variants c.12463 + 2 T > C, c.12204 + 5G > A). We also identified the TNXB variant c.11435_11524 + 30del alone instead of as a part of the TNXA/TNXB-CH-1 chimera in two newborns, which might be introduced by gene conversion. All of these characteristics enabled clinicians to better explain the phenotype of subjects and manage them more effectively. CACAH has a great advantage over the traditional MLPA and Sanger sequencing methods, showing substantial potential in the genetic diagnosis and screening of neonatal CAH.
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Affiliation(s)
- Ruixue Zhang
- Center of Neonatal Disease Screening, Department of Clinical Genetics, Northwest Women's and Children's Hospital, China
| | - Di Cui
- Berry Genomics Corporation, Beijing 102200, China
| | - Chengrong Song
- Center of Neonatal Disease Screening, Department of Clinical Genetics, Northwest Women's and Children's Hospital, China
| | - Xiaoping Ma
- Center of Neonatal Disease Screening, Department of Clinical Genetics, Northwest Women's and Children's Hospital, China
| | - Na Cai
- Center of Neonatal Disease Screening, Department of Clinical Genetics, Northwest Women's and Children's Hospital, China
| | - Yan Zhang
- Center of Neonatal Disease Screening, Department of Clinical Genetics, Northwest Women's and Children's Hospital, China
| | - Mei Feng
- Center of Neonatal Disease Screening, Department of Clinical Genetics, Northwest Women's and Children's Hospital, China
| | - Yanlin Cao
- Berry Genomics Corporation, Beijing 102200, China
| | - Libao Chen
- Berry Genomics Corporation, Beijing 102200, China
| | - Rong Qiang
- Center of Neonatal Disease Screening, Department of Clinical Genetics, Northwest Women's and Children's Hospital, China.
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Wang Z, Zhao G, Zhu Z, Wang Y, Xiang X, Zhang S, Luo T, Zhou Q, Qiu J, Tang B, Xia K, Li B, Li J. VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome. Nucleic Acids Res 2024; 52:D1478-D1489. [PMID: 37956311 PMCID: PMC10767961 DOI: 10.1093/nar/gkad1061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/21/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.
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Affiliation(s)
- Zheng Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Guihu Zhao
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Zhaopo Zhu
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Yijing Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Xudong Xiang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Shiyu Zhang
- Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, China
| | - Tengfei Luo
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Qiao Zhou
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jian Qiu
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Beisha Tang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, & Multi-Omics Research Center for Brain Disorders, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China
| | - Kun Xia
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Bin Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jinchen Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
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Liu R, Guo Y, Han L, Feng S, Cao J, Sun Y, Cao Z, Cui X. Somatic ARAF mutations in pediatric Langerhans cell histiocytosis: clinicopathologic, genetic and functional profiling. Clin Exp Med 2023; 23:5269-5279. [PMID: 37572153 DOI: 10.1007/s10238-023-01134-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 06/29/2023] [Indexed: 08/14/2023]
Abstract
ARAF mutations have been identified in a limited subset of patients with Langerhans cell histiocytosis (LCH), a rare disorder characterized by abnormal proliferation of Langerhans cells. LCH is primarily instigated by mutations in the mitogen-activated protein kinase (MAPK) signaling pathway, with BRAFV600E and MAP2K1 mutations constituting most cases. ARAF mutations in LCH highlight the heterogeneity of the disease and provide insights into its underlying molecular mechanisms. However, the occurrence of ARAF-positive LCH cases is extremely rare, with only two reported globally. Although they may be linked to a more aggressive form of LCH and a more severe clinical progression, the clinical significance and functional consequences of these mutations remain uncertain. We performed next-generation sequencing (NGS) to explore driver mutations in 148 pediatric LCH patients and recognized a series of mutations, including an identical novel somatic ARAF mutation, c.1046_1051delAGGCTT (p.Q349_F351delinsL), in four pediatric LCH patients. It was considered an ARAF hotspot mutation. All reported ARAF-positive patients worldwide exhibited characteristic pathological features of LCH, albeit with involvement across multiple systems. In vitro functional studies showed that this mutation could trigger the MAPKinase pathway and phosphorylate its downstream effectors MEK1/2 and ERK1/2 (relatively weaker than BRAFV600E). Over-activation of mutant A-Raf kinase could be inhibited by the BRAF inhibitor vemurafenib. LCH is uncommon, and ARAF mutation is even rarer. In our study, we have identified a novel hotspot somatic ARAF mutation, which has been verified through functional analysis to be an activating mutation. LCH patients with ARAF mutation typically have an unfavorable prognosis due to limited treatment experiences, although they do not exhibit a high relapse rate. To aid in the development of personalized treatment approaches and prognostic markers for LCH patients, it is recommended to conduct typical pathological and immunohistochemical examinations, as well as genetic tests utilizing a targeted gene panel or whole exome sequencing (WES), for LCH diagnosis, thereby promoting the use of inhibitor treatment strategies.
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Affiliation(s)
- Rong Liu
- Department of Hematology, Capital Institute of Pediatrics, Chaoyang District, Beijing, 100020, China
| | - Yibing Guo
- GrandOmics Inc, Haidian District, Beijing, 100081, China
| | - Lin Han
- GrandOmics Inc, Haidian District, Beijing, 100081, China
| | - Shunqiao Feng
- Department of Hematology, Capital Institute of Pediatrics, Chaoyang District, Beijing, 100020, China
| | - Jing Cao
- Department of Hematology, Capital Institute of Pediatrics, Chaoyang District, Beijing, 100020, China
| | - Yanling Sun
- GrandOmics Inc, Haidian District, Beijing, 100081, China
| | - Zhenhua Cao
- GrandOmics Inc, Haidian District, Beijing, 100081, China.
| | - Xiaodai Cui
- Clinical Central Laboratory, Capital Institute of Pediatrics, Beijing, 100020, China.
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Sharapov SZ, Timoshchuk AN, Aulchenko YS. Genetic control of N-glycosylation of human blood plasma proteins. Vavilovskii Zhurnal Genet Selektsii 2023; 27:224-239. [PMID: 37293449 PMCID: PMC10244589 DOI: 10.18699/vjgb-23-29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/20/2023] [Accepted: 01/23/2022] [Indexed: 06/10/2023] Open
Abstract
Glycosylation is an important protein modification, which influences the physical and chemical properties as well as biological function of these proteins. Large-scale population studies have shown that the levels of various plasma protein N-glycans are associated with many multifactorial human diseases. Observed associations between protein glycosylation levels and human diseases have led to the conclusion that N-glycans can be considered a potential source of biomarkers and therapeutic targets. Although biochemical pathways of glycosylation are well studied, the understanding of the mechanisms underlying general and tissue-specific regulation of these biochemical reactions in vivo is limited. This complicates both the interpretation of the observed associations between protein glycosylation levels and human diseases, and the development of glycan-based biomarkers and therapeutics. By the beginning of the 2010s, high-throughput methods of N-glycome profiling had become available, allowing research into the genetic control of N-glycosylation using quantitative genetics methods, including genome-wide association studies (GWAS). Application of these methods has made it possible to find previously unknown regulators of N-glycosylation and expanded the understanding of the role of N-glycans in the control of multifactorial diseases and human complex traits. The present review considers the current knowledge of the genetic control of variability in the levels of N-glycosylation of plasma proteins in human populations. It briefly describes the most popular physical-chemical methods of N-glycome profiling and the databases that contain genes involved in the biosynthesis of N-glycans. It also reviews the results of studies of environmental and genetic factors contributing to the variability of N-glycans as well as the mapping results of the genomic loci of N-glycans by GWAS. The results of functional in vitro and in silico studies are described. The review summarizes the current progress in human glycogenomics and suggests possible directions for further research.
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Affiliation(s)
- S Zh Sharapov
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russia
| | - A N Timoshchuk
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russia
| | - Y S Aulchenko
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russia Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
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Cannon S, Williams M, Gunning AC, Wright CF. Evaluation of in silico pathogenicity prediction tools for the classification of small in-frame indels. BMC Med Genomics 2023; 16:36. [PMID: 36855133 PMCID: PMC9972633 DOI: 10.1186/s12920-023-01454-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 02/09/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND The use of in silico pathogenicity predictions as evidence when interpreting genetic variants is widely accepted as part of standard variant classification guidelines. Although numerous algorithms have been developed and evaluated for classifying missense variants, in-frame insertions/deletions (indels) have been much less well studied. METHODS We created a dataset of 3964 small (< 100 bp) indels predicted to result in in-frame amino acid insertions or deletions using data from gnomAD v3.1 (minor allele frequency of 1-5%), ClinVar and the Deciphering Developmental Disorders (DDD) study. We used this dataset to evaluate the performance of nine pathogenicity predictor tools: CADD, CAPICE, FATHMM-indel, MutPred-Indel, MutationTaster2021, PROVEAN, SIFT-indel, VEST-indel and VVP. RESULTS Our dataset consisted of 2224 benign/likely benign and 1740 pathogenic/likely pathogenic variants from gnomAD (n = 809), ClinVar (n = 2882) and, DDD (n = 273). We were able to generate scores across all tools for 91% of the variants, with areas under the ROC curve (AUC) of 0.81-0.96 based on the published recommended thresholds. To avoid biases caused by inclusion of our dataset in the tools' training data, we also evaluated just DDD variants not present in either gnomAD or ClinVar (70 pathogenic and 81 benign). Using this subset, the AUC of all tools decreased substantially to 0.64-0.87. Several of the tools performed similarly however, VEST-indel had the highest AUCs of 0.93 (full dataset) and 0.87 (DDD subset). CONCLUSIONS Algorithms designed for predicting the pathogenicity of in-frame indels perform well enough to aid clinical variant classification in a similar manner to missense prediction tools.
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Affiliation(s)
- S Cannon
- Department of Clinical and Biomedical Sciences (Medical School), Faculty of Health and Life Sciences, University of Exeter, Research, Innovation, Learning and Development Building, Royal Devon and Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - M Williams
- Department of Clinical and Biomedical Sciences (Medical School), Faculty of Health and Life Sciences, University of Exeter, Research, Innovation, Learning and Development Building, Royal Devon and Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - A C Gunning
- Department of Clinical and Biomedical Sciences (Medical School), Faculty of Health and Life Sciences, University of Exeter, Research, Innovation, Learning and Development Building, Royal Devon and Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - C F Wright
- Department of Clinical and Biomedical Sciences (Medical School), Faculty of Health and Life Sciences, University of Exeter, Research, Innovation, Learning and Development Building, Royal Devon and Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK.
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Agarwal A, Zhao F, Jiang Y, Chen L. TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions. Bioinformatics 2023; 39:btad060. [PMID: 36707993 PMCID: PMC9900211 DOI: 10.1093/bioinformatics/btad060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 01/20/2023] [Accepted: 01/25/2023] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION Small insertion and deletion (sindel) of human genome has an important implication for human disease. One important mechanism for non-coding sindel (nc-sindel) to have an impact on human diseases and phenotypes is through the regulation of gene expression. Nevertheless, current sequencing experiments may lack statistical power and resolution to pinpoint the functional sindel due to lower minor allele frequency or small effect size. As an alternative strategy, a supervised machine learning method can identify the otherwise masked functional sindels by predicting their regulatory potential directly. However, computational methods for annotating and predicting the regulatory sindels, especially in the non-coding regions, are underdeveloped. RESULTS By leveraging labeled nc-sindels identified by cis-expression quantitative trait loci analyses across 44 tissues in Genotype-Tissue Expression (GTEx), and a compilation of both generic functional annotations and large-scale epigenomic profiles, we develop TIssue-specific Variant Annotation for Non-coding indel (TIVAN-indel), which is a supervised computational framework for predicting non-coding regulatory sindels. As a result, we demonstrate that TIVAN-indel achieves the best prediction performance in both with-tissue prediction and cross-tissue prediction. As an independent evaluation, we train TIVAN-indel from the 'Whole Blood' tissue in GTEx and test the model using 15 immune cell types from an independent study named Database of Immune Cell Expression. Lastly, we perform an enrichment analysis for both true and predicted sindels in key regulatory regions such as chromatin interactions, open chromatin regions and histone modification sites, and find biologically meaningful enrichment patterns. AVAILABILITY AND IMPLEMENTATION https://github.com/lichen-lab/TIVAN-indel. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Aman Agarwal
- Department of Computer Science, Indiana University, Bloomington, IN 47405, USA
| | - Fengdi Zhao
- Department of Biostatistics, University of Florida, Gainesville, FL 32603, USA
| | - Yuchao Jiang
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27516, USA
| | - Li Chen
- Department of Biostatistics, University of Florida, Gainesville, FL 32603, USA
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Campbell C, Francis A, Gaunt TR. Predicting pathogenicity from non-coding mutations. Nat Biomed Eng 2022:10.1038/s41551-022-00996-x. [DOI: 10.1038/s41551-022-00996-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Deletions in GSN gene associated with growth traits of four Chinese cattle breeds. Mol Genet Genomics 2022; 297:1269-1275. [PMID: 35780194 DOI: 10.1007/s00438-022-01915-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 06/01/2022] [Indexed: 10/17/2022]
Abstract
The aim of this study was to assess the potential of 21 bp mutation in the second intron of the GSN gene as a molecular marker-assisted by exploring the effect of 21 bp mutation on growth traits in four beef cattle breeds. Gelsolin (GSN), a member of the superfamily of gel proteins, is involved in the regulation of a variety of cellular activities in the organism and plays an important function in cell motility, apoptosis, signal transduction and inflammatory responses. Gelatin can not only negatively regulate the pro-apoptotic effect of P53 protein, but also promote apoptosis by blocking the interaction between actin and deoxyribonuclease, so, the GSN gene was selected as a candidate gene in this study. In this study, a 21 bp mutation on the second intron to the GSN gene was verified in 573 individuals of Yunling (YL, n = 220), Jiaxian (JX, n = 140), Xianan (XN, n = 114) and Qinchuan (QC, n = 97) cattle breeds using Once PCR and agarose gel electrophoresis. The association analysis of polymorphisms in the GSN gene with growth traits in four breeds was revealed: in YL cattle, the heart girth and forehead size of heterozygous ID genotype were significantly higher than II genotype (P < 0.05). In JX cattle, the withers height, body length and heart girth of II and ID genotype were significantly highest than DD genotype (P < 0.01); the height at hip cross and height at sacrum of II genotype was significantly highest than DD genotype (P < 0.01), but ID genotype was significantly higher than DD genotype. In XN cattle, the abdominal girth and circumference of the cannon bone of II genotype were significantly higher than ID genotype (P < 0.05). In QC cattle, the hucklebone width of ID genotype was significantly the highest than II genotype (P < 0.01). The results suggest that GSN may be an important candidate gene and that a 21 bp mutation on the second intron to the GSN gene can be used for molecular marker-assisted selection of four beef cattle breeds.
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Ma LY, Han L, Niu M, Chen L, Yu YZ, Feng T. Screening of the TMEM151A Gene in Patients With Paroxysmal Kinesigenic Dyskinesia and Other Movement Disorders. Front Neurol 2022; 13:865690. [PMID: 35707035 PMCID: PMC9189402 DOI: 10.3389/fneur.2022.865690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 04/22/2022] [Indexed: 11/16/2022] Open
Abstract
Background Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder characterized by recurrent involuntary movements usually triggered by sudden movements. Mutations in the TMEM151A gene were found to be the causative factor of PKD in recent studies. It has also been revealed that loss-of-function is the mechanism by which TMEM151A mutations cause PKD. Methods To investigate the genetic basis of PKD and broaden the clinical spectrum of the TMEM151A mutations, we recruited 181 patients of Chinese origin with movement disorders (MDs), including 39 PRRT2-negative PKD, 3 paroxysmal exercise-induced dyskinesia (PED), 2 paroxysmal non-kinesigenic dyskinesia (PNKD), 127 isolated dystonia, 8 choreas, and 2 myoclonus-dystonia syndromes. Whole-exome sequencing was applied to identify their possible disease-causing mutations. Then, Sanger sequencing was performed for validation and co-segregation analysis. Genetic analysis was also performed on additional family members of patients with TMEM151A mutations. Clinical manifestations of all PKD cases with mutations in TMEM151A reported, so far, were reviewed. Results Two novel variants of the TMEM151A gene (NM_153266.4, NP_694998.1), c.627_643dup (p.A215Gfs*53) and c.627delG (p.L210Wfs*52), were identified in 2 patients with PKD by whole-exome sequencing and further Sanger sequencing. Both variants were inherited by the patients from their respective mothers. No mutation of the TMEM151A gene was found in the other type of movement disorders. In reviewing the clinical presentation of TMEM151A-related PKD, no statistically significant difference in the age of onset, family history, duration of attacks, laterality, and phenotype was found between genders. More male patients received treatment and had a good response. A higher proportion of female patients did not receive any treatment, possibly because they had a milder condition of the disease. Conclusions This study further validated the role of TMEM151A in PKD. Future studies on protein function will be needed to ascertain the pathogenesis of TMEM151A in PKD.
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Affiliation(s)
- Ling-Yan Ma
- Department of Neurology, Center for Movement Disorders, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Lin Han
- Running Gene Inc., Beijing, China
| | - Meng Niu
- Department of Neurology, Hengshui Eighth People's Hospital, Hebei, China
| | - Lu Chen
- Department of Encephalopathy, Dong Fang Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Ya-Zhen Yu
- Department of Pediatrics, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- *Correspondence: Ya-Zhen Yu
| | - Tao Feng
- Department of Neurology, Center for Movement Disorders, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Parkinson's Disease Center, Beijing Institute for Brain Disorders, Beijing, China
- Tao Feng
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11
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Novel Loss-of-Function Variants in CHD2 Cause Childhood-Onset Epileptic Encephalopathy in Chinese Patients. Genes (Basel) 2022; 13:genes13050908. [PMID: 35627293 PMCID: PMC9140428 DOI: 10.3390/genes13050908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 11/16/2022] Open
Abstract
Developmental and epileptic encephalopathy-94 (DEE94) is a severe form of epilepsy characterized by a broad spectrum of neurodevelopmental disorders. It is caused by pathogenic CHD2 variants. While only a few pathogenic CHD2 variants have been reported with detailed clinical phenotypes, most of which lack molecular analysis. In this study, next-generation sequencing (NGS) was performed to identify likely pathogenic CHD2 variants in patients with epilepsy. Three likely pathogenic variants were finally identified in different patients. The seizure onset ages were from two years to six years. Patients 1 and 2 had developmental delays before epilepsy, while patient 3 had intellectual regression after the first seizure onset. The observed seizures were myoclonic, febrile, and generalized tonic-clonic, which had been controlled by different combinations of antiepileptic drugs. Two de novo (c.1809_1809+1delGGinsTT, p.? and c.3455+2_3455+3insTG, p.?) and one maternal (c.3783G>A, p.W1261*) variant were identified, which were all predicted to be pathogenic/likely pathogenic. Molecular analysis was performed in patient 1, and we detected aberrantly spliced products, proving the pathogenicity of this CHD2 variant. New cases with novel variants, along with a detailed clinical and molecular analysis, are important for a better understanding of CHD2-related epileptic encephalopathy.
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12
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de Schaetzen van Brienen L, Miclotte G, Larmuseau M, Van den Eynden J, Marchal K. Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic. Cancers (Basel) 2021; 13:5291. [PMID: 34771455 PMCID: PMC8582433 DOI: 10.3390/cancers13215291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/19/2021] [Accepted: 10/19/2021] [Indexed: 11/16/2022] Open
Abstract
Most known driver genes of metastatic prostate cancer are frequently mutated. To dig into the long tail of rarely mutated drivers, we performed network-based driver identification on the Hartwig Medical Foundation metastatic prostate cancer data set (HMF cohort). Hereto, we developed GoNetic, a method based on probabilistic pathfinding, to identify recurrently mutated subnetworks. In contrast to most state-of-the-art network-based methods, GoNetic can leverage sample-specific mutational information and the weights of the underlying prior network. When applied to the HMF cohort, GoNetic successfully recovered known primary and metastatic drivers of prostate cancer that are frequently mutated in the HMF cohort (TP53, RB1, and CTNNB1). In addition, the identified subnetworks contain frequently mutated genes, reflect processes related to metastatic prostate cancer, and contain rarely mutated driver candidates. To further validate these rarely mutated genes, we assessed whether the identified genes were more mutated in metastatic than in primary samples using an independent cohort. Then we evaluated their association with tumor evolution and with the lymph node status of the patients. This resulted in forwarding several novel putative driver genes for metastatic prostate cancer, some of which might be prognostic for disease evolution.
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Affiliation(s)
- Louise de Schaetzen van Brienen
- Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, 9052 Ghent, Belgium; (L.d.S.v.B.); (G.M.); (M.L.)
- Department of Information Technology, Faculty of Engineering and Architecture, Ghent University-IMEC, 9052 Ghent, Belgium
| | - Giles Miclotte
- Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, 9052 Ghent, Belgium; (L.d.S.v.B.); (G.M.); (M.L.)
- Department of Information Technology, Faculty of Engineering and Architecture, Ghent University-IMEC, 9052 Ghent, Belgium
| | - Maarten Larmuseau
- Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, 9052 Ghent, Belgium; (L.d.S.v.B.); (G.M.); (M.L.)
- Department of Information Technology, Faculty of Engineering and Architecture, Ghent University-IMEC, 9052 Ghent, Belgium
| | - Jimmy Van den Eynden
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium;
| | - Kathleen Marchal
- Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, 9052 Ghent, Belgium; (L.d.S.v.B.); (G.M.); (M.L.)
- Department of Information Technology, Faculty of Engineering and Architecture, Ghent University-IMEC, 9052 Ghent, Belgium
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13
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Zheng J, Deng T, Jiang E, Li J, Wijayanti D, Wang Y, Ding X, Lan X. Genetic variations of bovine PCOS-related DENND1A gene identified in GWAS significantly affect female reproductive traits. Gene 2021; 802:145867. [PMID: 34352299 DOI: 10.1016/j.gene.2021.145867] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 07/13/2021] [Accepted: 07/30/2021] [Indexed: 12/20/2022]
Abstract
Genome-wide association studies (GWAS) have identified DENND1A as a potential candidate gene linked to the fertility-related phenotypes in dairy cows. However, to date, no studies have examined the association of the DENND1A insertion/deletions (indels) to bovine fertility on a large scale. Herein, two indel sites, including P4-del-26-bp and P8-ins-15-bp were identified in 1064 Holstein cows. The values of the minor allelic frequency (MAF) ranged between 0.471 (deletion) and 0.230 (deletion), respectively, and combined four different haplotypes by analyzing the haplotype combination. It is noteworthy that P4-del-26-bp is associated with the ovarian width (P = 0.0004) and corpus luteum diameter (P = 0.004). Meanwhile, P8-ins-15-bp was found to have a significant association with the ovarian width (P = 0.020), ovarian weight (P = 0.004), the number of mature follicles (P = 0.020), and diameter of the mature follicles (P = 0.016). Furthermore, the combinatorial analysis showed that the two indel combined-genotypes were significantly related to several reproductive traits (ovarian width, ovarian weight, etc.). Collectively, our findings indicated that these two novel indels and their combinations are correlated with the reproductive traits, and hence, they can serve in the marker-assisted selection (MAS) in cattle breeding. Nevertheless, further functional experiments are needed for understanding the mechanisms of these indels in cattle reproduction in a better way.
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Affiliation(s)
- Juanshan Zheng
- Key Laboratory of Yak Breeding Engineering, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Laboratory of Animal Genome and Gene Function, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Tianyu Deng
- Laboratory of Animal Genome and Gene Function, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Enhui Jiang
- Laboratory of Animal Genome and Gene Function, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jie Li
- Laboratory of Animal Genome and Gene Function, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Dwi Wijayanti
- Laboratory of Animal Genome and Gene Function, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yongsheng Wang
- College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xuezhi Ding
- Key Laboratory of Yak Breeding Engineering, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
| | - Xianyong Lan
- Laboratory of Animal Genome and Gene Function, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China.
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14
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Rogers MF, Gaunt TR, Campbell C. Prediction of driver variants in the cancer genome via machine learning methodologies. Brief Bioinform 2021; 22:bbaa250. [PMID: 33094325 PMCID: PMC8293831 DOI: 10.1093/bib/bbaa250] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/04/2020] [Accepted: 09/06/2020] [Indexed: 01/18/2023] Open
Abstract
Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a variety of bioinformatics tools have been proposed for predicting which variants may drive disease, and which may be causatively neutral. After briefly reviewing generic tools, we focus on a subset of these methods specifically geared toward predicting which variants in the human cancer genome may act as enablers of unregulated cell proliferation. We consider the resultant view of the cancer genome indicated by these predictors and discuss ways in which these types of prediction tools may be progressed by further research.
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Affiliation(s)
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol
| | - Colin Campbell
- University of Bristol with interests in machine learning and medical bioinformatics
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15
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Gu W, Zhou A, Wang L, Sun S, Cui X, Zhu D. SVLR: Genome Structural Variant Detection Using Long-Read Sequencing Data. J Comput Biol 2021; 28:774-788. [PMID: 33973820 DOI: 10.1089/cmb.2021.0048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Genome structural variants (SVs) have great impacts on human phenotype and diversity, and have been linked to numerous diseases. Long-read sequencing technologies arise to make it possible to find SVs of as long as 10,000 nucleotides. Thus, long read-based SV detection has been drawing attention of many recent research projects, and many tools have been developed for long reads to detect SVs recently. In this article, we present a new method, called SVLR, to detect SVs based on long-read sequencing data. Comparing with existing methods, SVLR can detect three new kinds of SVs: block replacements, block interchanges, and translocations. Although these new SVs are structurally more complicated, SVLR achieves accuracies that are comparable with those of the classic SVs. Moreover, for the classic SVs that can be detected by state-of-the-art methods (e.g., SVIM and Sniffles), our experiments demonstrate recall improvements of up to 38% without harming the precisions (i.e., >78%). We also point out three directions to further improve SV detection in the future. Source codes: https://github.com/GWYSDU/SVLR.
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Affiliation(s)
- Wenyan Gu
- School of Computer Science and Technology, Shandong University, Qindao, China
| | - Aizhong Zhou
- School of Computer Science and Technology, Shandong University, Qindao, China
| | - Lusheng Wang
- Department of Computer Science, City University of Hong Kong, Hong Kong, China
| | - Shiwei Sun
- Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Xuefeng Cui
- School of Computer Science and Technology, Shandong University, Qindao, China
| | - Daming Zhu
- School of Computer Science and Technology, Shandong University, Qindao, China
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16
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Ing A, Wlodaver A, Kirschmann D, Toledo E, McCabe C, Kadri S, McIntyre MK, Salazar J, Firestein K, Charrow J, Sanders V, Laguna T, Yap KL. Transcript analysis for variant classification resolution in a child with primary ciliary dyskinesia. Cold Spring Harb Mol Case Stud 2021; 7:mcs.a005363. [PMID: 33608380 PMCID: PMC7903884 DOI: 10.1101/mcs.a005363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 12/29/2020] [Indexed: 12/17/2022] Open
Abstract
Transcriptional analysis can be utilized to reconcile variants of uncertain significance, particularly those predicted to impact splicing. Laboratory analysis of the predicted mRNA transcript may allow inference of the in vivo impact of the variant and aid prediction of its clinical significance. We present a patient with classical features of primary ciliary dyskinesia (PCD) who was identified to have compound heterozygous variants in the DNAH11 gene (c.10691 + 2T > C, c.13523_13543dup21) via trio whole-exome sequencing in 2013. These variants were originally classified as Mutation and Likely Mutation. However, these variants were downgraded to variants of uncertain significance (VUSs) during reanalysis in 2016 because of uncertainty that they caused a loss of function of the gene. c.10691 + 2T > C is predicted to abrogate the canonical splice site and lead to the skipping of exon 65, but the adjoining of exon 64 and exon 66 in the DNAH11 transcript preserves the reading frame of the resultant protein. c.13523_13543dup21 is located in the last exon of the DNAH11 coding sequence, upstream of the canonical stop codon, which suggests a reduced likelihood to trigger nonsense-mediated decay (NMD). Transcriptional analysis was performed to characterize the impact of the variants, resulting in reclassification of c.10691 + 2T > C to Likely Pathogenic by providing evidence that it results in a deleterious effect and subsequent downstream reclassification of c.13523_13543dup21 to Likely Pathogenic as well. Our case illustrates the potential impact of transcriptional analysis on variant resolution, supporting its usage on variants that exert an unpredictable effect on splicing.
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Affiliation(s)
- Alexander Ing
- Center for Genomics, Department of Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA.,Division of Pulmonary and Sleep Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA.,Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA.,Division of Genetics, Birth Defects and Metabolism, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Alissa Wlodaver
- Center for Genomics, Department of Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Dawn Kirschmann
- Center for Genomics, Department of Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Erica Toledo
- Center for Genomics, Department of Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Christopher McCabe
- Center for Genomics, Department of Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Sabah Kadri
- Center for Genomics, Department of Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA.,Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - Mary Kate McIntyre
- Division of Pulmonary and Sleep Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Joanne Salazar
- Division of Pulmonary and Sleep Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Kristina Firestein
- Division of Pulmonary and Sleep Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Joel Charrow
- Center for Genomics, Department of Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA.,Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA.,Division of Genetics, Birth Defects and Metabolism, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Victoria Sanders
- Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA.,Division of Genetics, Birth Defects and Metabolism, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Theresa Laguna
- Division of Pulmonary and Sleep Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA.,Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - Kai Lee Yap
- Center for Genomics, Department of Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA.,Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
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17
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Susak H, Serra-Saurina L, Demidov G, Rabionet R, Domènech L, Bosio M, Muyas F, Estivill X, Escaramís G, Ossowski S. Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation. PLoS Comput Biol 2021; 17:e1007784. [PMID: 33606672 PMCID: PMC7928502 DOI: 10.1371/journal.pcbi.1007784] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 03/03/2021] [Accepted: 01/04/2021] [Indexed: 12/02/2022] Open
Abstract
Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the 'Rare Variant Genome Wide Association Study' (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.
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Affiliation(s)
- Hana Susak
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Laura Serra-Saurina
- Biomedical Research Networking Centre consortium of Public Health and Epidemiology (CIBERESP), Madrid, Spain
- Center for research in occupational Health (CiSAL), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Research Group on Statistics, Econometrics and Health (GRECS), Universitat de Girona (UdG), Girona, Spain
| | - German Demidov
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Raquel Rabionet
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, IBUB, Universitat de Barcelona; CIBERER, IRSJD, Barcelona, Spain
| | - Laura Domènech
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Biomedical Research Networking Centre consortium of Public Health and Epidemiology (CIBERESP), Madrid, Spain
| | - Mattia Bosio
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Francesc Muyas
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Xavier Estivill
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Women’s Health Dexeus, Barcelona, Spain
| | - Geòrgia Escaramís
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Biomedical Research Networking Centre consortium of Public Health and Epidemiology (CIBERESP), Madrid, Spain
- Departament de Biomedicina, Facultat de Medicina i Ciències de la Salut, Institut de Neurociències, Universitat de Barcelona, Spain
| | - Stephan Ossowski
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
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18
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Chen J, Guo JT. Comparative assessments of indel annotations in healthy and cancer genomes with next-generation sequencing data. BMC Med Genomics 2020; 13:170. [PMID: 33167946 PMCID: PMC7653722 DOI: 10.1186/s12920-020-00818-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 10/29/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Insertion and deletion (indel) is one of the major variation types in human genomes. Accurate annotation of indels is of paramount importance in genetic variation analysis and investigation of their roles in human diseases. Previous studies revealed a high number of false positives from existing indel calling methods, which limits downstream analyses of the effects of indels on both healthy and disease genomes. In this study, we evaluated seven commonly used general indel calling programs for germline indels and four somatic indel calling programs through comparative analysis to investigate their common features and differences and to explore ways to improve indel annotation accuracy. METHODS In our comparative analysis, we adopted a more stringent evaluation approach by considering both the indel positions and the indel types (insertion or deletion sequences) between the samples and the reference set. In addition, we applied an efficient way to use a benchmark for improved performance comparisons for the general indel calling programs RESULTS: We found that germline indels in healthy genomes derived by combining several indel calling tools could help remove a large number of false positive indels from individual programs without compromising the number of true positives. The performance comparisons of somatic indel calling programs are more complicated due to the lack of a reliable and comprehensive benchmark. Nevertheless our results revealed large variations among the programs and among cancer types. CONCLUSIONS While more accurate indel calling programs are needed, we found that the performance for germline indel annotations can be improved by combining the results from several programs. In addition, well-designed benchmarks for both germline and somatic indels are key in program development and evaluations.
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Affiliation(s)
- Jing Chen
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
| | - Jun-Tao Guo
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
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19
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Akhatayeva Z, Li H, Mao C, Cheng H, Zhang G, Jiang F, Meng X, Yao Y, Lan X, Song E, Zhang D. Detecting novel Indel variants within the GHR gene and their associations with growth traits in Luxi Blackhead sheep. Anim Biotechnol 2020; 33:214-222. [PMID: 32615865 DOI: 10.1080/10495398.2020.1784184] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The growth hormone is important in the regulation of metabolism and energy homeostasis and acts through a growth hormone receptor (GHR). In this work, genetic variations within the ovine GHR gene were identified and tested for associations with body morphometric traits in Chinese Luxi Blackhead (LXBH) sheep. Novel deletion loci in the LXBH GHR gene included P2-del-23 bp and P8-del-23 bp indel variants. The polymorphic information content (PIC) was 0.329 in P2-del-23 bp and 0.257 in P8-del-23 bp. Moreover, both indel polymorphisms were not at Hardy-Weinberg equilibrium (p < 0.05) in the LXBH population. Statistical analyses revealed that the P2-del-23 bp and P8-del-23 bp indels were significantly associated (p < 0.05) with several growth traits in rams and ewes, including body weight, body height, chest depth, chest width, chest circumference, cannon circumference, paunch girth and hip width. Among the tested sheep, the body traits of those with genotype DD were superior to those with II and ID genotypes, suggesting that the 'D' allele was responsible for the positive effects on growth traits. Thus, these results indicate that the P2-del-23 bp and P8-del-23 bp indel sites and the DD genotype can be useful in marker-assisted selection in sheep.
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Affiliation(s)
- Zhanerke Akhatayeva
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Haixia Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Cui Mao
- Shandong Key Lab of Animal Disease Control and Breeding, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Haijian Cheng
- Shandong Key Lab of Animal Disease Control and Breeding, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Guoping Zhang
- Shandong Key Lab of Animal Disease Control and Breeding, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Fugui Jiang
- Shandong Key Lab of Animal Disease Control and Breeding, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, China.,College of Life Sciences, Shandong Normal University, Jinan, China
| | - Xianfeng Meng
- Shandong Key Lab of Animal Disease Control and Breeding, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Yuni Yao
- Shandong Key Lab of Animal Disease Control and Breeding, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Xianyong Lan
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Enliang Song
- Shandong Key Lab of Animal Disease Control and Breeding, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, China.,College of Life Sciences, Shandong Normal University, Jinan, China
| | - Dongfu Zhang
- Shandong Liaocheng Luxi Blackhead Sheep Farm, Liaocheng, Shandong, China
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20
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Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions. Commun Biol 2020; 3:329. [PMID: 32587327 PMCID: PMC7316754 DOI: 10.1038/s42003-020-1051-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 06/03/2020] [Indexed: 12/04/2022] Open
Abstract
Chronic musculoskeletal pain affects all aspects of human life. However, mechanisms of its genetic control remain poorly understood. Genetic studies of pain are complicated by the high complexity and heterogeneity of pain phenotypes. Here, we apply principal component analysis to reduce phenotype heterogeneity of chronic musculoskeletal pain at four locations: the back, neck/shoulder, hip, and knee. Using matrices of genetic covariances, we constructed four genetically independent phenotypes (GIPs) with the leading GIP (GIP1) explaining 78.4% of the genetic variance of the analyzed conditions, and GIP2–4 explain progressively less. We identified and replicated five GIP1-associated loci and one GIP2-associated locus and prioritized the most likely causal genes. For GIP1, we showed enrichment with multiple nervous system-related terms and genetic correlations with anthropometric, sociodemographic, psychiatric/personality traits and osteoarthritis. We suggest that GIP1 represents a biopsychological component of chronic musculoskeletal pain, related to physiological and psychological aspects and reflecting pain perception and processing. Yakov Tsepilov, Maxim Freidin et al. find that chronic musculoskeletal pain conditions at four distinct anatomical sites share a common genetic background. The authors constructed genetically independent phenotypes (GIP) from principal components analysis of the different pain phenotypes and used the GIPs to perform genome-wide association studies to identify underlying genetic factors.
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21
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Banfi P, Coll M, Oliva A, Alcalde M, Striano P, Mauri M, Princiotta L, Campuzano O, Versino M, Brugada R. Lamotrigine induced Brugada-pattern in a patient with genetic epilepsy associated with a novel variant in SCN9A. Gene 2020; 754:144847. [PMID: 32531456 DOI: 10.1016/j.gene.2020.144847] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/22/2020] [Accepted: 06/02/2020] [Indexed: 01/28/2023]
Abstract
BACKGROUND A 30-year-old man presented with intellectual disability associated with epilepsy. The epilepsy was initially treated with sodium valproate and since he was 28 years-old with lamotrigine. With the addition of lamotrigine, a pattern of Brugada syndrome appeared on the electrocardiogram. The family history was positive for epilepsy from the motheŕs side, who had never been treated with lamotrigine. OBJECTIVE Determine the genetic cause of the intellectual disability, epilepsy and Brugada syndrome of the patient and try to establish a possible correlation between the genetic background and the Brugada syndrome pattern under lamotrigine treatment. METHODS A standard karyotype, array comparative genomic hybridization and two different NGS panels have done to the index case to identify the genetic causes of the intellectual disability, epilepsy and Brugada syndrome pattern. RESULTS Genetic analyses in the family identified a de novo duplication of 1.3 Mb in 8p21.3 as well as two novel heterozygous rare variants in SCN9A and AKAP9 genes, both inherited from the mother. CONCLUSION We hypothesize that in this family the SCN9A variant was responsible for the epileptic syndrome. In addition, given that SCN9A is lightly expressed in the heart tissue, we postulate that this SCN9A variant, alone or in combination with AKAP9 variant, might be responsible for the Brugada pattern when challenged by lamotrigine.
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Affiliation(s)
- P Banfi
- Neurology and Stroke Unit Divison, Circolo Hospital ASST Settelaghi University of Insubria Varese, Italy
| | - M Coll
- Cardiovascular Genetics Center, University of Girona-IDIBGI, Girona, Spain
| | - A Oliva
- Institute of Public Health, Section of Legal Medicine, Catholic University, Rome, Italy
| | - M Alcalde
- Cardiovascular Genetics Center, University of Girona-IDIBGI, Girona, Spain
| | - P Striano
- Pediatric Neurology and Muscular Diseases Unit, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, "G. Gaslini" Institute, Genova, Italy
| | - M Mauri
- Neurology and Stroke Unit Divison, Circolo Hospital ASST Settelaghi University of Insubria Varese, Italy
| | - L Princiotta
- Neurology and Stroke Unit Divison, Circolo Hospital ASST Settelaghi University of Insubria Varese, Italy
| | - O Campuzano
- Cardiovascular Genetics Center, University of Girona-IDIBGI, Girona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain; Medical Science Department, School of Medicine, University of Girona, Girona, Spain
| | - M Versino
- Neurology and Stroke Unit Divison, Circolo Hospital ASST Settelaghi University of Insubria Varese, Italy
| | - R Brugada
- Cardiovascular Genetics Center, University of Girona-IDIBGI, Girona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain; Medical Science Department, School of Medicine, University of Girona, Girona, Spain; Cardiology Service, Hospital JosepTrueta, University of Girona, Girona, Spain.
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22
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Darbyshire M, du Toit Z, Rogers MF, Gaunt TR, Campbell C. Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours. Sci Rep 2019; 9:13452. [PMID: 31530827 PMCID: PMC6748970 DOI: 10.1038/s41598-019-48765-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 08/05/2019] [Indexed: 02/08/2023] Open
Abstract
For cancers, such as common solid tumours, variants in the genome give a selective growth advantage to certain cells. It has recently been argued that the mean count of coding single nucleotide variants acting as disease-drivers in common solid tumours is frequently small in size, but significantly variable by cancer type (hypermutation is excluded from this study). In this paper we investigate this proposal through the use of integrative machine-learning-based classifiers we have proposed recently for predicting the disease-driver status of single nucleotide variants (SNVs) in the human cancer genome. We find that predicted driver counts are compatible with this proposal, have similar variabilities by cancer type and, to a certain extent, the drivers are identifiable by these machine learning methods. We further discuss predicted driver counts stratified by stage of disease and driver counts in non-coding regions of the cancer genome, in addition to driver-genes.
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Affiliation(s)
- Madeleine Darbyshire
- Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, United Kingdom
| | - Zachary du Toit
- Bristol Medical School, University of Bristol, Bristol, BS8 1UD, United Kingdom
| | - Mark F Rogers
- Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, United Kingdom
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, BS8 2BN, United Kingdom
| | - Colin Campbell
- Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, United Kingdom.
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23
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Sharapov SZ, Tsepilov YA, Klaric L, Mangino M, Thareja G, Shadrina AS, Simurina M, Dagostino C, Dmitrieva J, Vilaj M, Vuckovic F, Pavic T, Stambuk J, Trbojevic-Akmacic I, Kristic J, Simunovic J, Momcilovic A, Campbell H, Doherty M, Dunlop MG, Farrington SM, Pucic-Bakovic M, Gieger C, Allegri M, Louis E, Georges M, Suhre K, Spector T, Williams FMK, Lauc G, Aulchenko YS. Defining the genetic control of human blood plasma N-glycome using genome-wide association study. Hum Mol Genet 2019; 28:2062-2077. [PMID: 31163085 PMCID: PMC6664388 DOI: 10.1093/hmg/ddz054] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 03/01/2019] [Accepted: 03/06/2019] [Indexed: 01/10/2023] Open
Abstract
Glycosylation is a common post-translational modification of proteins. Glycosylation is associated with a number of human diseases. Defining genetic factors altering glycosylation may provide a basis for novel approaches to diagnostic and pharmaceutical applications. Here we report a genome-wide association study of the human blood plasma N-glycome composition in up to 3811 people measured by Ultra Performance Liquid Chromatography (UPLC) technology. Starting with the 36 original traits measured by UPLC, we computed an additional 77 derived traits leading to a total of 113 glycan traits. We studied associations between these traits and genetic polymorphisms located on human autosomes. We discovered and replicated 12 loci. This allowed us to demonstrate an overlap in genetic control between total plasma protein and IgG glycosylation. The majority of revealed loci contained genes that encode enzymes directly involved in glycosylation (FUT3/FUT6, FUT8, B3GAT1, ST6GAL1, B4GALT1, ST3GAL4, MGAT3 and MGAT5) and a known regulator of plasma protein fucosylation (HNF1A). However, we also found loci that could possibly reflect other more complex aspects of glycosylation process. Functional genomic annotation suggested the role of several genes including DERL3, CHCHD10, TMEM121, IGH and IKZF1. The hypotheses we generated may serve as a starting point for further functional studies in this research area.
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Affiliation(s)
- Sodbo Zh Sharapov
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentyeva 10, Novosibirsk, Russia
- Novosibirsk State University, 1, Pirogova str., Novosibirsk, Russia
| | - Yakov A Tsepilov
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentyeva 10, Novosibirsk, Russia
- Novosibirsk State University, 1, Pirogova str., Novosibirsk, Russia
| | - Lucija Klaric
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh, UK
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, St Thomas’ Campus, London, UK
- NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK
| | - Gaurav Thareja
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | | | - Mirna Simurina
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovacica 1, Zagreb, Croatia
| | - Concetta Dagostino
- Department of Medicine and Surgery, University of Parma, Via Gramsci 14, Parma, Italy
| | - Julia Dmitrieva
- Unit of Animal Genomics, WELBIO, GIGA-R and Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Marija Vilaj
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Frano Vuckovic
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Tamara Pavic
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovacica 1, Zagreb, Croatia
| | - Jerko Stambuk
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | | | - Jasminka Kristic
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Jelena Simunovic
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Ana Momcilovic
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
- Colon Cancer Genetics Group, MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, Western General Hospital, The University of Edinburgh, Edinburgh, UK
| | - Margaret Doherty
- Institute of Technology Sligo, Department of Life Sciences, Sligo, Ireland
- National Institute for Bioprocessing Research & Training, Dublin, Ireland
| | - Malcolm G Dunlop
- Colon Cancer Genetics Group, MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, Western General Hospital, The University of Edinburgh, Edinburgh, UK
| | - Susan M Farrington
- Colon Cancer Genetics Group, MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, Western General Hospital, The University of Edinburgh, Edinburgh, UK
| | - Maja Pucic-Bakovic
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Christian Gieger
- Institute of Epidemiology II, Research Unit of Molecular Epidemiology, Helmholtz Centre Munich, German Research Center for Environmental Health, Ingolstädter Landstr. 1, Neuherberg, Germany
| | - Massimo Allegri
- Pain Therapy Department, Policlinico Monza Hospital, Monza, Italy
| | - Edouard Louis
- CHU-Liège and Unit of Gastroenterology, GIGA-R and Faculty of Medicine, University of Liège, 1 Avenue de l’Hôpital, Liège, Belgium
| | - Michel Georges
- Unit of Animal Genomics, WELBIO, GIGA-R and Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, St Thomas’ Campus, London, UK
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, St Thomas’ Campus, London, UK
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovacica 1, Zagreb, Croatia
| | - Yurii S Aulchenko
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentyeva 10, Novosibirsk, Russia
- Novosibirsk State University, 1, Pirogova str., Novosibirsk, Russia
- PolyOmica, Het Vlaggeschip 61, PA 's-Hertogenbosch, The Netherlands
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24
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D’Angelo F, Ceccarelli M, Tala, Garofano L, Zhang J, Frattini V, Caruso FP, Lewis G, Alfaro KD, Bauchet L, Berzero G, Cachia D, Cangiano M, Capelle L, de Groot J, DiMeco F, Ducray F, Farah W, Finocchiaro G, Goutagny S, Kamiya-Matsuoka C, Lavarino C, Loiseau H, Lorgis V, Marras CE, McCutcheon I, Nam DH, Ronchi S, Saletti V, Seizeur R, Slopis J, Suñol M, Vandenbos F, Varlet P, Vidaud D, Watts C, Tabar V, Reuss DE, Kim SK, Meyronet D, Mokhtari K, Salvador H, Bhat KP, Eoli M, Sanson M, Lasorella A, lavarone A. The molecular landscape of glioma in patients with Neurofibromatosis 1. Nat Med 2019; 25:176-187. [PMID: 30531922 PMCID: PMC6857804 DOI: 10.1038/s41591-018-0263-8] [Citation(s) in RCA: 127] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 10/17/2018] [Indexed: 12/30/2022]
Abstract
Neurofibromatosis type 1 (NF1) is a common tumor predisposition syndrome in which glioma is one of the prevalent tumors. Gliomagenesis in NF1 results in a heterogeneous spectrum of low- to high-grade neoplasms occurring during the entire lifespan of patients. The pattern of genetic and epigenetic alterations of glioma that develops in NF1 patients and the similarities with sporadic glioma remain unknown. Here, we present the molecular landscape of low- and high-grade gliomas in patients affected by NF1 (NF1-glioma). We found that the predisposing germline mutation of the NF1 gene was frequently converted to homozygosity and the somatic mutational load of NF1-glioma was influenced by age and grade. High-grade tumors harbored genetic alterations of TP53 and CDKN2A, frequent mutations of ATRX associated with Alternative Lengthening of Telomere, and were enriched in genetic alterations of transcription/chromatin regulation and PI3 kinase pathways. Low-grade tumors exhibited fewer mutations that were over-represented in genes of the MAP kinase pathway. Approximately 50% of low-grade NF1-gliomas displayed an immune signature, T lymphocyte infiltrates, and increased neo-antigen load. DNA methylation assigned NF1-glioma to LGm6, a poorly defined Isocitrate Dehydrogenase 1 wild-type subgroup enriched with ATRX mutations. Thus, the profiling of NF1-glioma defined a distinct landscape that recapitulates a subset of sporadic tumors.
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Affiliation(s)
- Fulvio D’Angelo
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY, USA.,BIOGEM Istituto di Ricerche Genetiche ‘G. Salvatore’, Ariano Irpino, Italy.,These authors contributed equally: F. D’Angelo, M. Ceccarelli
| | - Michele Ceccarelli
- BIOGEM Istituto di Ricerche Genetiche ‘G. Salvatore’, Ariano Irpino, Italy.,Department of Science and Technology, Università degli Studi del Sannio, Benevento, Italy.,These authors contributed equally: F. D’Angelo, M. Ceccarelli
| | - Tala
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY, USA
| | - Luciano Garofano
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY, USA.,BIOGEM Istituto di Ricerche Genetiche ‘G. Salvatore’, Ariano Irpino, Italy
| | - Jing Zhang
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY, USA
| | - Véronique Frattini
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY, USA
| | - Francesca P. Caruso
- BIOGEM Istituto di Ricerche Genetiche ‘G. Salvatore’, Ariano Irpino, Italy.,Department of Science and Technology, Università degli Studi del Sannio, Benevento, Italy
| | - Genevieve Lewis
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY, USA
| | - Kristin D. Alfaro
- The University of Texas M.D. Anderson Cancer Center John Mendelsohn Faculty Center (FC7.3025) – Neuro-Oncology – Unit 0431, Houston, TX, USA
| | - Luc Bauchet
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, Montpellier, France
| | - Giulia Berzero
- Sorbonne Universités UPMC Université Paris 06, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, APHP, Paris, France
| | - David Cachia
- Department of Neuro-Oncology, Medical University of South Carolina, Charleston, SC, USA.,Department of Neurosurgery, Medical University of South Carolina, Charleston, SC, USA
| | - Mario Cangiano
- BIOGEM Istituto di Ricerche Genetiche ‘G. Salvatore’, Ariano Irpino, Italy
| | - Laurent Capelle
- AP-HP, Hôpital de la Pitié-Salpêtrière, Service de Neurochirurgie, Paris, France
| | - John de Groot
- The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Francesco DiMeco
- Department of Neurological Surgery, Carlo Besta Neurological Institute, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.,Hunterian Brain Tumor Research Laboratory CRB2 2M41, Baltimore, MD, USA
| | - François Ducray
- Service de Neuro-Oncologie, Hospices Civils de Lyon, Université Claude Bernard Lyon 1, Department of Cancer Cell Plasticity, Cancer Research Center of Lyon, INSERM U1052, CNRS UMR5286, Lyon, France
| | - Walid Farah
- Department of Neurosurgery, CHU, Dijon, France
| | - Gaetano Finocchiaro
- Unit of Molecular Neuro-Oncology, IRCCS Foundation, Carlo Besta Neurological Institute, Milan, Italy
| | - Stéphane Goutagny
- Service de Neurochirurgie, Hôpital Beaujon, Assistance PubliqueHôpitaux de Paris, Clichy, France
| | | | - Cinzia Lavarino
- Developmental Tumor Laboratory, Fundación Sant Joan de Déu, Barcelona, Spain
| | - Hugues Loiseau
- Department of Neurosurgery, Bordeaux University Hospital. Labex TRAIL (ANR-10-LABX-57). EA 7435 – IMOTION Bordeaux University, Bordeaux, France
| | - Véronique Lorgis
- Department of Medical Oncology, Centre GF Leclerc, Dijon, France
| | - Carlo E. Marras
- Pediatric Neurosurgery Unit, Department of Neuroscience and Neurorehabilitation, Bambino Gesù Children’s Hospital, Rome, Italy
| | - Ian McCutcheon
- The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Do-Hyun Nam
- Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Susanna Ronchi
- Sorbonne Universités UPMC Université Paris 06, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, APHP, Paris, France
| | - Veronica Saletti
- Developmental Neurology Unit, IRCCS Foundation, Carlo Besta Neurological Institute, Milan, Italy
| | - Romuald Seizeur
- Service de Neurochirurgie, Hôpital de la Cavale Blanche, CHRU de Brest, Université de Brest, Brest, France
| | - John Slopis
- The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Mariona Suñol
- Department of Pathology, Hospital Sant Joan de Déu, Barcelona, Spain
| | - Fanny Vandenbos
- Central Laboratory of Pathology, Pasteur I University Hospital, Nice, France
| | - Pascale Varlet
- Department of Neuropathology, Sainte-Anne Hospital, Paris, France.,IMA-Brain, Inserm U894, Institute of Psychiatry and Neuroscience of Paris, Paris, France
| | - Dominique Vidaud
- EA7331, Université Paris Descartes, France; Service de Génétique et Biologie Moléculaires, Hôpital Cochin, AP-HP, Paris, France
| | - Colin Watts
- Institute of Cancer and Genomic Sciences University of Birmingham Edgbaston, Birmingham, United Kingdom
| | - Viviane Tabar
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - David E. Reuss
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Seung-Ki Kim
- Division of Pediatric Neurosurgery, Seoul National University Children’s Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - David Meyronet
- Centre de Pathologie Et Neuropathologie Est Hospices Civils de Lyon, Lyon, France
| | - Karima Mokhtari
- Sorbonne Universités UPMC Université Paris 06, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, APHP, Paris, France
| | - Hector Salvador
- Pediatric Oncology Unit, Hospital Sant Joan de Déu, Esplugues, Barcelona, Spain
| | - Krishna P. Bhat
- The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Marica Eoli
- Unit of Molecular Neuro-Oncology, IRCCS Foundation, Carlo Besta Neurological Institute, Milan, Italy
| | - Marc Sanson
- Sorbonne Universités UPMC Université Paris 06, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, APHP, Paris, France
| | - Anna Lasorella
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY, USA. .,Department of Pediatrics, Columbia University Medical Center, New York, NY, USA. .,Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA.
| | - Antonio lavarone
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY, USA.,Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA.,Department of Neurology, Columbia University Medical Center, New York, NY, USA.,These authors jointly supervised this work: A. Lasorella, A. Iavarone.,Correspondence and requests for materials should be addressed to A.L. or A.I. ;
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25
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Lee PH, Lee C, Li X, Wee B, Dwivedi T, Daly M. Principles and methods of in-silico prioritization of non-coding regulatory variants. Hum Genet 2018; 137:15-30. [PMID: 29288389 PMCID: PMC5892192 DOI: 10.1007/s00439-017-1861-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 12/14/2017] [Indexed: 12/13/2022]
Abstract
Over a decade of genome-wide association, studies have made great strides toward the detection of genes and genetic mechanisms underlying complex traits. However, the majority of associated loci reside in non-coding regions that are functionally uncharacterized in general. Now, the availability of large-scale tissue and cell type-specific transcriptome and epigenome data enables us to elucidate how non-coding genetic variants can affect gene expressions and are associated with phenotypic changes. Here, we provide an overview of this emerging field in human genomics, summarizing available data resources and state-of-the-art analytic methods to facilitate in-silico prioritization of non-coding regulatory mutations. We also highlight the limitations of current approaches and discuss the direction of much-needed future research.
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Affiliation(s)
- Phil H Lee
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA.
- Quantitative Genomics Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Christian Lee
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA
- Department of Life Sciences, Harvard University, Cambridge, MA, USA
| | - Xihao Li
- Quantitative Genomics Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brian Wee
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA
| | - Tushar Dwivedi
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Mark Daly
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
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