Genome sequencing data analysis for rare disease gene discovery

Clinical Exome Sequencing in Pediatric Patients

INTRODUCTION

The development of genomic sequencing techniques has led to the effective diagnosis of genetic diseases. In this study, clinical exome sequencing (CES) results applied to genetic disorders are reported.

METHODS

The CES results of pediatric patients with different system involvements and whose complaints were thought to be of genetic origin were evaluated retrospectively.

RESULTS

Significant variants associated with complaints were detected in 41 (60%) of 68 patients. Copy number variations were detected in two patients, and single nucleotide variants (SNVs) were detected in the other 39 patients. A total of 46 SNVs were detected in these 39 patients. Sixteen of the detected SNVs were previously reported in the literature, but 30 were novel.

CONCLUSIONS

This study shows that CES can provide a high diagnosis rate (60%) in childhood genetic diseases. Novel mutations (30) have contributed to the mutation profiles of genetic disorders.

A next-generation dynamic programming language Julia: Its features and applications in biological science

BACKGROUND

The advent of Julia as a sophisticated and dynamic programming language in 2012 represented a significant milestone in computational programming, mathematical analysis, and statistical modeling. Having reached its stable release in version 1.9.0 on May 7, 2023, Julia has developed into a powerful and versatile instrument. Despite its potential and widespread adoption across various scientific and technical domains, there exists a noticeable knowledge gap in comprehending its utilization within biological sciences.

THE AIM OF REVIEW

This comprehensive review aims to address this particular knowledge gap and offer a thorough examination of Julia's fundamental characteristics and its applications in biology.

KEY SCIENTIFIC CONCEPTS OF THE REVIEW

The review focuses on a research gap in the biological science. The review aims to equip researchers with knowledge and tools to utilize Julia's capabilities in biological science effectively and to demonstrate the gap. It paves the way for innovative solutions and discoveries in this rapidly evolving field. It encompasses an analysis of Julia's characteristics, packages, and performance compared to the other programming languages in this field. The initial part of this review discusses the key features of Julia, such as its dynamic and interactive nature, fast processing speed, ease of expression manipulation, user-friendly syntax, code readability, strong support for multiple dispatch, and advanced type system. It also explores Julia's capabilities in data analysis, visualization, machine learning, and algorithms, making it suitable for scientific applications. The next section emphasizes the importance of using Julia in biological research, highlighting its seamless integration with biological studies for data analysis, and computational biology. It also compares Julia with other programming languages commonly used in biological research through benchmarking and performance analysis. Additionally, it provides insights into future directions and potential challenges in Julia's applications in biology.

Leaving no patient behind! Expert recommendation in the use of innovative technologies for diagnosing rare diseases

Genetic diagnosis plays a crucial role in rare diseases, particularly with the increasing availability of emerging and accessible treatments. The International Rare Diseases Research Consortium (IRDiRC) has set its primary goal as: "Ensuring that all patients who present with a suspected rare disease receive a diagnosis within one year if their disorder is documented in the medical literature". Despite significant advances in genomic sequencing technologies, more than half of the patients with suspected Mendelian disorders remain undiagnosed. In response, IRDiRC proposes the establishment of "a globally coordinated diagnostic and research pipeline". To help facilitate this, IRDiRC formed the Task Force on Integrating New Technologies for Rare Disease Diagnosis. This multi-stakeholder Task Force aims to provide an overview of the current state of innovative diagnostic technologies for clinicians and researchers, focusing on the patient's diagnostic journey. Herein, we provide an overview of a broad spectrum of emerging diagnostic technologies involving genomics, epigenomics and multi-omics, functional testing and model systems, data sharing, bioinformatics, and Artificial Intelligence (AI), highlighting their advantages, limitations, and the current state of clinical adaption. We provide expert recommendations outlining the stepwise application of these innovative technologies in the diagnostic pathways while considering global differences in accessibility. The importance of FAIR (Findability, Accessibility, Interoperability, and Reusability) and CARE (Collective benefit, Authority to control, Responsibility, and Ethics) data management is emphasized, along with the need for enhanced and continuing education in medical genomics. We provide a perspective on future technological developments in genome diagnostics and their integration into clinical practice. Lastly, we summarize the challenges related to genomic diversity and accessibility, highlighting the significance of innovative diagnostic technologies, global collaboration, and equitable access to diagnosis and treatment for people living with rare disease.

Rare and undiagnosed diseases: From disease-causing gene identification to mechanism elucidation

Rare and undiagnosed diseases substantially decrease patient quality of life and have increasingly become a heavy burden on healthcare systems. Because of the challenges in disease-causing gene identification and mechanism elucidation, patients are often confronted with difficulty obtaining a precise diagnosis and treatment. Due to advances in sequencing and multiomics analysis approaches combined with patient-derived iPSC models and gene-editing platforms, substantial progress has been made in the diagnosis and treatment of rare and undiagnosed diseases. The aforementioned techniques also provide an operational basis for future precision medicine studies. In this review, we summarize recent progress in identifying disease-causing genes based on GWAS/WES/WGS-guided multiomics analysis approaches. In addition, we discuss recent advances in the elucidation of pathogenic mechanisms and treatment of diseases with state-of-the-art iPSC and organoid models, which are improved by cell maturation level and gene editing technology. The comprehensive strategies described above will generate a new paradigm of disease classification that will significantly promote the precision and efficiency of diagnosis and treatment for rare and undiagnosed diseases.

Need for revision of the ACMG/AMP guidelines for interpretation of X-linked variants

The guidelines provided by American College of Medical Genetics and Genomics (ACMG) and the Association of Molecular Pathology (AMP) (ACMG/AMP guidelines) suggest a framework for the classification of clinical variants. However, the interpretations can be inconsistent, with each definition sometimes proving to be ambiguous. In particular, there can be difficulty with interpretation of variants related to the X-linked recessive trait. To confirm whether there are biases in the interpretation of inherited traits, we reanalyzed variants reported prior to the release of the ACMG/AMP guidelines. As expected, the interpretation ratio as pathogenic or likely pathogenic was significantly lower for variants related to the X-linked recessive trait. Evaluation of variants related to the X-linked recessive trait, hence, need to consider whether the variant is identified only in males in accordance with the X-linked recessive trait. The ACMG/AMP guidelines should be revised to eliminate the bias revealed in this study.

Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases

It’s challenging work to identify disease-causing genes from the next-generation sequencing (NGS) data of patients with Mendelian disorders. To improve this situation, researchers have developed many phenotype-driven gene prioritization methods using a patient’s genotype and phenotype information, or phenotype information only as input to rank the candidate’s pathogenic genes. Evaluations of these ranking methods provide practitioners with convenience for choosing an appropriate tool for their workflows, but retrospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate. In this research, the performance of ten recognized causal-gene prioritization methods was benchmarked using 305 cases from the Deciphering Developmental Disorders (DDD) project and 209 in-house cases via a relatively unbiased methodology. The evaluation results show that methods using Human Phenotype Ontology (HPO) terms and Variant Call Format (VCF) files as input achieved better overall performance than those using phenotypic data alone. Besides, LIRICAL and AMELIE, two of the best methods in our benchmark experiments, complement each other in cases with the causal genes ranked highly, suggesting a possible integrative approach to further enhance the diagnostic efficiency. Our benchmarking provides valuable reference information to the computer-assisted rapid diagnosis in Mendelian diseases and sheds some light on the potential direction of future improvement on disease-causing gene prioritization methods.