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      Expanding the genotypes and phenotypes for 19 rare diseases by exome sequencing performed in pediatric intensive care unit


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          Phenotypes of some rare genetic diseases are atypical and it is a challenge for pediatric intensive care units (PICUs) to diagnose and manage such patients in an emergency. In this study, we investigated 58 PICU patients (39 deceased and 19 surviving) in critical ill status or died shortly without a clear etiology. Whole exome sequencing was performed of 103 DNA samples from their families. Disease‐causing single‐nucleotide variants (SNVs) and copy number variants (CNVs) were identified to do genotype‐phenotypes analysis. In total, 27 (46.6%) patients received a genetic diagnosis. We identified 34 pathogenic or likely pathogenic SNVs from 26 genes, which are related to at least 19 rare diseases. Each rare disease involved an isolated patient except two patients caused by the same gene ACAT1. The genotypic spectrum was expanded by 23 novel SNVs from gene MARS1, PRRT2, TBCK, TOR1A, ECE1, ARX, ZEB2, ACAT1, CPS1, VWF, NBAS, COG4, and INVS. We also identified two novel pathogenic CNVs. Phenotypes associated with respiratory, multiple congenital anomalies, neuromuscular, or metabolic disorders were the most common. Twenty patients (74.1%) accompanied severe infection, 19 patients (70.1%) died. In summary, our findings expanded the genotypes and phenotypes of 19 rare diseases from PICU with complex characteristics.


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          Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

          The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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            ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

            High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
              • Record: found
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              Fast and accurate long-read alignment with Burrows–Wheeler transform

              Motivation: Many programs for aligning short sequencing reads to a reference genome have been developed in the last 2 years. Most of them are very efficient for short reads but inefficient or not applicable for reads >200 bp because the algorithms are heavily and specifically tuned for short queries with low sequencing error rate. However, some sequencing platforms already produce longer reads and others are expected to become available soon. For longer reads, hashing-based software such as BLAT and SSAHA2 remain the only choices. Nonetheless, these methods are substantially slower than short-read aligners in terms of aligned bases per unit time. Results: We designed and implemented a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), to align long sequences up to 1 Mb against a large sequence database (e.g. the human genome) with a few gigabytes of memory. The algorithm is as accurate as SSAHA2, more accurate than BLAT, and is several to tens of times faster than both. Availability: http://bio-bwa.sourceforge.net Contact: rd@sanger.ac.uk

                Author and article information

                Hum Mutat
                Hum Mutat
                Human Mutation
                John Wiley and Sons Inc. (Hoboken )
                15 August 2021
                November 2021
                : 42
                : 11 ( doiID: 10.1002/humu.v42.11 )
                : 1443-1460
                [ 1 ] Pediatric Intensive Care Unit, Hunan Childrens Hospital University of South China Changsha Hunan China
                [ 2 ] Pediatrics Research Institute of Hunan Province Hunan Children's Hospital Changsha Hunan China
                [ 3 ] Center for Medical Genetics, School of Life Sciences Central South University Changsha Hunan China
                [ 4 ] Emergency Medicine Institute of Hunan Province Changsha Hunan China
                Author notes
                [*] [* ] Correspondence Zhenghui Xiao and Xiulan Lu, #86 Ziyuan Rd, Changsha, 410007 Hunan, China.

                Email: xiaozh888@ 123456126.com and 391118947@ 123456qq.com

                Yimin Zhu, #61 Jiefang Rd, Changsha, 410005 Hunan, China.

                Email: cszhuyimin@ 123456163.com

                Author information
                © 2021 The Authors. Human Mutation published by Wiley Periodicals LLC

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                : 30 June 2021
                : 05 May 2021
                : 21 July 2021
                Page count
                Figures: 2, Tables: 2, Pages: 18, Words: 9978
                Funded by: the Key Laboratory of Emergency Medicine for Children
                Award ID: 2018TP1028
                Funded by: the Ministry of Science and Technology in China
                Award ID: 2012BAI04B02
                Research Article
                Research Articles
                Custom metadata
                November 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:18.07.2022

                Human biology
                exome sequencing (es),genetic disease,pediatric intensive care unit (picu),phenotype,variant


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