Analyzing the large and complex SFARI autism cohort data using the Genotypes and Phenotypes in Families (GPF)...

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Investigating genotypic variants that influence phenotypes is crucial in genetics.
• Large datasets of genotyped and phenotyped families enable the search for variants associated with complex diseases.
• Managing these extensive datasets requires specialized computational tools.

Purpose of the Study:

• To introduce Genotypes and Phenotypes in Families (GPF), an open-source platform for managing family-based genetic and phenotypic data.
• To highlight the features of GPF, particularly within the GPF-SFARI instance, for autism research.
• To showcase GPF's versatility in handling diverse family collection data.

Main Methods:

• Development of an open-source platform (GPF) for genotype and phenotype data management.
• Implementation of interactive exploration, de novo mutation analysis, and association tools.
• Dissemination of the Simons Simplex Collection (SSC) and SPARK family datasets via GPF-SFARI.
• Provision of protected and public access to autism-related genetic and phenotypic data.

Main Results:

• GPF effectively manages large-scale genotypic and phenotypic data from family collections.
• GPF-SFARI provides secure access to SSC and SPARK datasets for autism research.
• Public access to de novo mutation collections and gene-level autism association statistics is available.
• The platform demonstrates versatility for various family-based genetic studies.

Conclusions:

• GPF is a valuable computational tool for genetic research involving family data.
• GPF-SFARI significantly supports autism research by providing access to comprehensive data.
• The platform's features enable efficient analysis and secure sharing of genetic and phenotypic information.