Review of autism spectrum disorder databases for the identification of candidate genes.

Researchers looked at databases that collect information about genes linked to autism. They found 13 databases but focused on the best 4 ones. However, these databases often disagreed about which genes are most important for autism - they only agreed 1.5% of the time. This makes it confusing for researchers and doctors trying to understand autism genetics. The study shows we need better ways to organize and share genetic information about autism.

This systematic mapping study evaluated 13 autism spectrum disorder (ASD) genetic databases to assess their quality and reliability for identifying candidate genes. Four key databases were selected: AutDB, SFARI Gene, GeisingerDBD, and SysNDD. The analysis revealed significant inconsistencies between databases, with only 1.5% agreement in classification of high-confidence ASD candidate genes. SFARI Gene showed highest completeness at schema level (89%) while AutDB demonstrated highest data-level completeness (90%).

The disparities stem from different scoring criteria and scientific evidence standards across databases. These findings highlight challenges in reliable ASD gene identification and emphasize the need for standardized approaches to support consistent research interpretation and clinical decision-making.

Clinicians should be aware that genetic database recommendations may vary significantly depending on the source consulted. Multiple database consultation recommended for comprehensive genetic counseling. Standardized criteria needed for reliable clinical genetic interpretation.

Study focuses on database quality assessment rather than direct genetic research. No validation of gene classifications against clinical outcomes. Limited to English-language databases and may not capture all available resources.

Research into the genetics of autism spectrum disorder (ASD) seeks to unravel its complex genetic background by identifying genes associated with the condition at varying levels of confidence. While these findings hold significant potential for clinical applications, the dispersed nature of scientific evidence presents a challenge for the reliable identification of ASD candidate genes. Although ASD candidate genes are gathered in genetic databases, these vary widely in the gene sets, biological information, and confidence level classification methods, leading to inconsistencies and complicating research efforts. This study aims to identify and assess the quality and reliability of ASD genetic databases to support more robust identification of ASD candidate genes.

Using a Systematic Mapping Study, we identified 13 specialized databases. We then followed a Data Quality Approach in two stages, first assessing Accessibility, Currency, and Relevance dimensions to select the potentially relevant databases to be used as ASD candidate gene sources. The selected databases were analysed, assessing Completeness-at schema and data level-, and Consistency between high-confidence ASD genes. The four selected databases are: AutDB, SFARI Gene, GeisingerDBD, and SysNDD.

SFARI Gene demonstrated the highest completeness at schema level (89%), while AutDB showed the highest completeness at data level (90%). However, only 1.5% consistency was observed across the four databases in their classification of high-confidence ASD candidate genes. Our findings highlight the unique contributions of each database and reveal substantial inconsistencies in gene classification, driven by differences in scoring criteria and the scientific evidence considered. These inconsistencies have important implications for both clinical users and researchers, as conclusions may vary depending on the database used.

This study supports researchers when using ASD genetic databases, promoting consistent interpretation and improved clinical decisions.