An automatic and efficient pipeline for disease gene identification through utilizing family-based sequencing data

Area of Science:

• Genomics
• Medical Genetics
• Bioinformatics

Background:

• Whole-exome sequencing (WES) generates vast genomic data, offering opportunities for identifying disease genes in Mendelian disorders.
• Analyzing thousands of variants per exome presents challenges in filtering pathogenic mutations and minimizing false negatives.

Purpose of the Study:

• To develop an automatic and efficient bioinformatics pipeline for identifying disease-causing variants in Mendelian disorders.
• To enhance the analysis of family-based exome sequencing data for improved candidate gene identification.

Main Methods:

• Implementation of a multi-step variant filtering strategy within a bioinformatics pipeline.
• Application of the pipeline to analyze family-based whole-exome sequencing data.

Main Results:

• The developed pipeline effectively filters pathogenic variants from exome sequencing data.
• The method demonstrated superior performance in identifying candidate genes compared to existing approaches, as shown in studies of Freeman-Sheldon disease.

Conclusions:

• The proposed pipeline offers an efficient and automated solution for variant filtering in Mendelian disorder gene discovery.
• This approach significantly aids in identifying disease genes using family-based exome sequencing, outperforming previous methods.