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Kanzi AM, San JE, Chimukangara B, Wilkinson E, Fish M, Ramsuran V, de Oliveira T. Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance. Front Genet 2020; 11:544162. [PMID: 33193618 PMCID: PMC7649788 DOI: 10.3389/fgene.2020.544162] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 09/21/2020] [Indexed: 12/29/2022] Open
Abstract
Mendelian and complex genetic trait diseases continue to burden and affect society both socially and economically. The lack of effective tests has hampered diagnosis thus, the affected lack proper prognosis. Mendelian diseases are caused by genetic mutations in a singular gene while complex trait diseases are caused by the accumulation of mutations in either linked or unlinked genomic regions. Significant advances have been made in identifying novel diseases associated mutations especially with the introduction of next generation and third generation sequencing. Regardless, some diseases are still without diagnosis as most tests rely on SNP genotyping panels developed from population based genetic analyses. Analysis of family genetic inheritance using whole genomes, whole exomes or a panel of genes has been shown to be effective in identifying disease-causing mutations. In this review, we discuss next generation and third generation sequencing platforms, bioinformatic tools and genetic resources commonly used to analyze family based genomic data with a focus on identifying inherited or novel disease-causing mutations. Additionally, we also highlight the analytical, ethical and regulatory challenges associated with analyzing personal genomes which constitute the data used for family genetic inheritance.
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Affiliation(s)
- Aquillah M. Kanzi
- Kwazulu-Natal Research and Innovation Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
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Rhoades R, Jackson F, Teng S. Discovery of rare variants implicated in schizophrenia using next-generation sequencing. JOURNAL OF TRANSLATIONAL GENETICS AND GENOMICS 2019; 3:1-20. [PMID: 33981965 PMCID: PMC8112455 DOI: 10.20517/jtgg.2018.26] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
Schizophrenia is a highly heritable psychiatric disorder that affects 1% of the population. Genome-wide association studies have identified common variants in candidate genes associated with schizophrenia, but the genetics mechanisms of this disorder have not yet been elucidated. The discovery of rare genetic variants that contribute to schizophrenia symptoms promises to help explain the missing heritability of the disease. Next generation sequencing techniques are revolutionizing the field of psychiatric genetics. Various statistical approaches have been developed for rare variant association testing in case-control and family studies. Targeted resequencing, whole exome sequencing and whole genome sequencing combined with these computational tools are used for the discovery of rare genetic variations in schizophrenia. The findings provide useful information for characterizing the rare mutations and elucidating the genetic mechanisms by which the variants cause schizophrenia.
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Affiliation(s)
- Raina Rhoades
- Department of Biology, Howard University, Washington, DC 20059, USA
| | - Fatimah Jackson
- Department of Biology, Howard University, Washington, DC 20059, USA
| | - Shaolei Teng
- Department of Biology, Howard University, Washington, DC 20059, USA
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Doig KD, Fellowes A, Bell AH, Seleznev A, Ma D, Ellul J, Li J, Doyle MA, Thompson ER, Kumar A, Lara L, Vedururu R, Reid G, Conway T, Papenfuss AT, Fox SB. PathOS: a decision support system for reporting high throughput sequencing of cancers in clinical diagnostic laboratories. Genome Med 2017; 9:38. [PMID: 28438193 PMCID: PMC5404673 DOI: 10.1186/s13073-017-0427-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 04/07/2017] [Indexed: 01/08/2023] Open
Abstract
Background The increasing affordability of DNA sequencing has allowed it to be widely deployed in pathology laboratories. However, this has exposed many issues with the analysis and reporting of variants for clinical diagnostic use. Implementing a high-throughput sequencing (NGS) clinical reporting system requires a diverse combination of capabilities, statistical methods to identify variants, global variant databases, a validated bioinformatics pipeline, an auditable laboratory workflow, reproducible clinical assays and quality control monitoring throughout. These capabilities must be packaged in software that integrates the disparate components into a useable system. Results To meet these needs, we developed a web-based application, PathOS, which takes variant data from a patient sample through to a clinical report. PathOS has been used operationally in the Peter MacCallum Cancer Centre for two years for the analysis, curation and reporting of genetic tests for cancer patients, as well as the curation of large-scale research studies. PathOS has also been deployed in cloud environments allowing multiple institutions to use separate, secure and customisable instances of the system. Increasingly, the bottleneck of variant curation is limiting the adoption of clinical sequencing for molecular diagnostics. PathOS is focused on providing clinical variant curators and pathology laboratories with a decision support system needed for personalised medicine. While the genesis of PathOS has been within cancer molecular diagnostics, the system is applicable to NGS clinical reporting generally. Conclusions The widespread availability of genomic sequencers has highlighted the limited availability of software to support clinical decision-making in molecular pathology. PathOS is a system that has been developed and refined in a hospital laboratory context to meet the needs of clinical diagnostics. The software is available as a set of Docker images and source code at https://github.com/PapenfussLab/PathOS. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0427-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kenneth D Doig
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia. .,Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia. .,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. .,Peter MacCallum Cancer Centre, 305 Grattan Street, Parkville, VIC, 3000, Australia.
| | - Andrew Fellowes
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Anthony H Bell
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Andrei Seleznev
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - David Ma
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Jason Ellul
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Jason Li
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Maria A Doyle
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Ella R Thompson
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Amit Kumar
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Children's Cancer Institute, University of New South Wales, Sydney, NSW, Australia
| | - Luis Lara
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Ravikiran Vedururu
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Gareth Reid
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Thomas Conway
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Anthony T Papenfuss
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia.,Department of Medical Biology, University of Melbourne, Melbourne, VIC, Australia.,Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Stephen B Fox
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia.,Department of Pathology, University of Melbourne, Melbourne, VIC, Australia
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Försti A, Kumar A, Paramasivam N, Schlesner M, Catalano C, Dymerska D, Lubinski J, Eils R, Hemminki K. Pedigree based DNA sequencing pipeline for germline genomes of cancer families. Hered Cancer Clin Pract 2016; 14:16. [PMID: 27508007 PMCID: PMC4977614 DOI: 10.1186/s13053-016-0058-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 07/04/2016] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND In the course of our whole-genome sequencing efforts, we have developed a pipeline for analyzing germline genomes from Mendelian types of cancer pedigrees (familial cancer variant prioritization pipeline, FCVPP). RESULTS The variant calling step distinguishes two types of genomic variants: single nucleotide variants (SNVs) and indels, which undergo technical quality control. Mendelian types of variants are assumed to be rare and variants with frequencies higher that 0.1 % are screened out using human 1000 Genomes (Phase 3) and non-TCGA ExAC population data. Segregation in the pedigree allows variants to be present in affected family members and not in old, unaffected ones. The effectiveness of variant segregation depends on the number and relatedness of the family members: if over 5 third-degree (or more distant) relatives are available, the experience has shown that the number of likely variants is reduced from many hundreds to a few tens. These are then subjected to bioinformatics analysis, starting with the combined annotation dependent depletion (CADD) tool, which predicts the likelihood of the variant being deleterious. Different sets of individual tools are used for further evaluation of the deleteriousness of coding variants, 5' and 3' untranslated regions (UTRs), and intergenic variants. CONLUSIONS The likelihood of success of the present genomic pipeline in finding novel high- or medium-penetrant genes depends on many steps but first and foremost, the pedigree needs to be reasonably large and the assignments and diagnoses among the members need to be correct.
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Affiliation(s)
- Asta Försti
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), D69120 Heidelberg, Germany
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Abhishek Kumar
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), D69120 Heidelberg, Germany
| | - Nagarajan Paramasivam
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), D69120 Heidelberg, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Matthias Schlesner
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), D69120 Heidelberg, Germany
| | - Calogerina Catalano
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), D69120 Heidelberg, Germany
| | - Dagmara Dymerska
- Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland
| | - Jan Lubinski
- Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland
| | - Roland Eils
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), D69120 Heidelberg, Germany
- Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany
| | - Kari Hemminki
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), D69120 Heidelberg, Germany
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
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