Over the past decade, developments in next-generation sequencing (NGS) technologies have dramatically reshaped the field of clinical genetic testing, improving diagnostic rates and shortening turnaround times. Contemporary genetic testing methods include targeted NGS gene panel, whole-exome sequencing (WES), chromosomal microarray (CMA) to detect genetic variations such as single nucleotide variants (SNVs), small indels, copy number variation (CNV), and structural variation (SV). Further genetic analysis links genetic variations to disease outcomes and provides molecular diagnostics to predict prognosis and guide physicians to optimize individualized treatment. However, these widespread genetic testing methods have workflow and test content limitations that may limit their overall efficacy, resulting in an uncertain and unpredictable journey for undiagnosed patients, referred to as a diagnostic odyssey. As the sequencing price continually decreases, emerging evidence posits WGS as the potential first-tier method for clinical genetic testing. Compared to WES that only covers 1-2% of the human genome coding region, WGS can cover 98% of the genome, increase CNV and SV detection efficacy, and identify non-coding variants that may interrupt regulatory regions, non-coding RNAs, and mRNA splicing. The utility of WGS also includes HLA genotyping, pharmacogenetic testing, and the generation of polygenic risk scores for complex diseases. Most importantly, WGS facilitates periodic reanalysis with updated annotation and analysis pipelines to improve diagnostic performance without the need to test again.
Although WGS has shown diagnostic superiority, several critical scientific and logistical challenges must be addressed before rolling out for clinical testing. Among those challenges are establishing standard quality control of sequencing data, efficiency improvement in storage and analysis, developing analysis frameworks for identifying genomic variations and genes in a comprehensive and unbiased manner, Implementing data warehouses for coding and non-coding variation annotation, new disease gene discovery, interpretation of pathogenic variations (especially in non-coding genomic regions), standardization of phenotypic terminology, and the advancement of evidence model in genetic analysis. Besides, ethical considerations and financial issues are also critical to the use of WGS in clinical genetic testing. This topic aims to tackle these issues and inform future clinical WGS practice.
We welcome submissions of Original Research, Review, Mini-review, and Systematic Review articles dealing with the following themes, including but not limited to:
? The deployment of consensus recommendations, standards, and best practices for clinical WGS test
? WGS data management systems, compressing algorithms, and accelerating approaches to improve analysis efficiency
? New bioinformatics tools, annotation databases, evidence models, and analysis pipelines to improve variant interpretation, especially for non-coding or significant genetic variations
? Explore novel genetic etiology and disease mechanisms in case- or cohort-based studies
? Secondary finding reporting, genetic counselling, and ethical studies for clinical utility of WGS
Over the past decade, developments in next-generation sequencing (NGS) technologies have dramatically reshaped the field of clinical genetic testing, improving diagnostic rates and shortening turnaround times. Contemporary genetic testing methods include targeted NGS gene panel, whole-exome sequencing (WES), chromosomal microarray (CMA) to detect genetic variations such as single nucleotide variants (SNVs), small indels, copy number variation (CNV), and structural variation (SV). Further genetic analysis links genetic variations to disease outcomes and provides molecular diagnostics to predict prognosis and guide physicians to optimize individualized treatment. However, these widespread genetic testing methods have workflow and test content limitations that may limit their overall efficacy, resulting in an uncertain and unpredictable journey for undiagnosed patients, referred to as a diagnostic odyssey. As the sequencing price continually decreases, emerging evidence posits WGS as the potential first-tier method for clinical genetic testing. Compared to WES that only covers 1-2% of the human genome coding region, WGS can cover 98% of the genome, increase CNV and SV detection efficacy, and identify non-coding variants that may interrupt regulatory regions, non-coding RNAs, and mRNA splicing. The utility of WGS also includes HLA genotyping, pharmacogenetic testing, and the generation of polygenic risk scores for complex diseases. Most importantly, WGS facilitates periodic reanalysis with updated annotation and analysis pipelines to improve diagnostic performance without the need to test again.
Although WGS has shown diagnostic superiority, several critical scientific and logistical challenges must be addressed before rolling out for clinical testing. Among those challenges are establishing standard quality control of sequencing data, efficiency improvement in storage and analysis, developing analysis frameworks for identifying genomic variations and genes in a comprehensive and unbiased manner, Implementing data warehouses for coding and non-coding variation annotation, new disease gene discovery, interpretation of pathogenic variations (especially in non-coding genomic regions), standardization of phenotypic terminology, and the advancement of evidence model in genetic analysis. Besides, ethical considerations and financial issues are also critical to the use of WGS in clinical genetic testing. This topic aims to tackle these issues and inform future clinical WGS practice.
We welcome submissions of Original Research, Review, Mini-review, and Systematic Review articles dealing with the following themes, including but not limited to:
? The deployment of consensus recommendations, standards, and best practices for clinical WGS test
? WGS data management systems, compressing algorithms, and accelerating approaches to improve analysis efficiency
? New bioinformatics tools, annotation databases, evidence models, and analysis pipelines to improve variant interpretation, especially for non-coding or significant genetic variations
? Explore novel genetic etiology and disease mechanisms in case- or cohort-based studies
? Secondary finding reporting, genetic counselling, and ethical studies for clinical utility of WGS