Rare diseases are defined by their severity, but each single disease affects a small number of people; but overall, rare diseases are estimated to affect at least 3.5-5.9% of the world population.1 80% of rare diseases have a genetic cause, systemic reactions are often used to aid in diagnosis.2 But even with advances in genetic engineering, many people with rare diseases are not diagnosed and the molecular causative agent is not yet known. This disease and its manifestations
Solve-RD integration aims to improve rare disease diagnosis by combining skills to analyze existing system data and integrate new data from other systems.3
Advanced procedures for the diagnosis of rare diseases
Sophisticated disease systems, such as the whole exome system (WES) and the genetic system (WGS), improve the diagnosis of rare diseases and are becoming important tools for diagnosing the genes that cause rare diseases. while WES includes only the secret area of the genome. System data is often analyzed against reference systems (such as genes or mutations of known diseases). Correspondence between the patient’s genetic system and the reference system was reported by conducting a clinical trial
Being a rare disease that can cause mutations in uncoded DNA, WGS offers wider coverage than WES. However, this additional information means that the WGS study is associated with higher costs.6 New ways of obtaining this genetic information need to be found in order to improve study results and improve patient outcomes.
Solve-RD: Strategies for Advancing Rare Disease Diagnosis
New disease types and diseases are defined each year, and continually reflecting patient data against an updated database can improve test results. But re-analysis poses challenges: the process is expensive and time-consuming, and the dataset grows exponentially, although clinical practice does not have the bioinformatics procedures to perform the analysis effectively.7 Strategy that seeks to combine the information obtained from WGS and WES and win more items. The challenge of his analysis is the Solve-RD combination.
The Solve-RD Consortium was formed to diagnose rare (or untreated) diseases, connecting researchers, doctors, and patients in 15 countries. Data from the existing system were not altered and supplemented with new data from other systems (epigenomics, metabolomics, deep-WES, RNA sequencing, and deep molecular phenotypic analysis). The data is collected by the European Reference Networks (ERN), or by companies associated with these networks. Rare diseases are divided into four groups, which are determined by the diagnostic plan:
• Part 1: The case has already received WGS or WES data from ERN or related companies, where the system data will be reviewed.
• Group 2: Patients with a disease from each ERN will receive additional tests.
• Group 3: cases with unique phenotypes identified by ERN experts.
• Team 4: Expert cases identified quickly but for no reason. The team will carry out further research using all appropriate integration methods.
The information is shared and accessible from centralized databases, such as the European Genome-Phenomena Archive (EGA) and the RD-Connect Genome-Phenomena Analysis Platform (GPAP). For effective data analysis and analysis, Solve-RD is designed with management in a single project. The Data Analysis Task Force (data scientists and genomics experts) focuses on analyzing data and developing research tools, including those working on specific research. The Task Force for Data Interpretation (Physicians and Geneticists) focuses on diagnostic criteria, such as case diagnosis and data analysis requirements at ERNs. Both partners work together for the analysis, resulting in an effective sharing of expertise. 3
So far, the Solve-RD consortium has identified 255 previously unresolved issues.3 In this statement, Matalonga and colleagues describe how Solve-RD can identify unresolved cases by renaming WES and WGS records, as well as working documentation for review. . 7 simple inputs (detection and filtering) are required in the Python development package before processing system data quickly and automatically.
After analyzing genomic data from 4,703 people, the researchers were able to identify 120 undiagnosed cases of the disease. The authors report that 13% of the most common pathological forms were diagnosed two years ago, highlighting the rapid advances in knowledge about rare diseases. The methodology used in this study can simplify the diagnostic process by providing useful information and production files for clinical research. Therefore, this study demonstrates an effective method for routine and rapid genome reassessment of rare disease patients using Solve-RD.7
Mosaic transformation detection
Furthermore, several other studies have demonstrated the ability of the Solve-RD consortium to determine the genetic causes of rare diseases. For example, researchers at Radboud University Medical Center renamed the WES data to patients with hereditary gastric cancer, who didn’t know what caused the changes in many of the people affected. A new analysis of WES data revealed mosaic activity in PIK3CA and patients.8
Although the investigators noted that PIK3CA was not associated with hereditary gastric cancer, this gene difference has been associated with disease syndromes that have severe tissue damage, injury, or deformity. Significantly, using low-altitude alveolar alloys in their analysis, Hot Paske and his colleagues were able to detect mosaic variation, which can be destructive, and repeated inspections and interruptions. be a small change for the mosaic type. .8.9
Mitochondrial DNA mutation detection
From the Solve-RD consortium, a new analysis of WES data by de Boer and colleagues demonstrates alterations in the mitochondrial transfer RNA (MT-TL1) gene in patients with unresolved severe intellectual disability.10 Variants included. from MT-TL1. and other conditions such as myopathy, encephalopathy, lactic acidosis and stroke, and disease manifestations may vary. The patients in this study were not in any other neuromuscular and neurodevelopmental condition as well as some of the clinical features of other species (in combination with MT-TL1). However, this study demonstrates the ability to modify WES data to study mitochondrial DNA variation
Next-generation systems, such as WES and WGS, are powerful tools that can unlock diagnostic information to patients with rare diseases. Although the molecular mechanisms of many rare diseases are unknown and new genetic factors are also revealed every year, test results can be improved by changing the names of existing patients. . The Solve-RD connection provides a robust plan for using this system data, providing an updated bioinformatics channel that can detect rare diseases.