Evaluation of SNP calling using single and multiple-sample calling algorithms by validation against array base...

Area of Science:

• Genomics
• Bioinformatics
• Medical Genetics

Background:

• Next-generation sequencing (NGS) costs are decreasing, making whole genome analysis a standard for identifying genetic causes of inherited diseases.
• Commercial NGS providers offer raw reads and SNP calls, raising questions about optimal data analysis strategies.
• Users must decide whether to use provided SNP data or reprocess raw sequencing data with advanced pipelines.

Purpose of the Study:

• To compare the performance of SNP calling between the GATK multi-sample protocol and Illumina's CASAVA pipeline.
• To evaluate the utility of variants identified by each pipeline in detecting causative mutations for inherited diseases.
• To assess the robustness and statistical parameters of variants called by different pipelines.

Main Methods:

• Comparison of Single Nucleotide Polymorphism (SNP) calls from GATK multi-sample calling and Illumina CASAVA pipeline.
• Analysis of whole genome sequencing data from 171 human genomes of Arab descent (40x coverage).
• Evaluation of variant robustness for Mendelian consistency and statistical parameters (e.g., TsTv ratio).

Main Results:

• GATK multi-sample calling identified a greater number of variants compared to the CASAVA pipeline.
• Additional variants found by GATK demonstrated robustness for Mendelian consistency.
• Despite differences in variant numbers and statistical parameters, both pipelines showed similar effectiveness in identifying causative variants for the studied phenotypes.

Conclusions:

• Both GATK multi-sample calling and Illumina CASAVA single-sample calling exhibit comparable performance in identifying putatively causative variants.
• The choice between pipelines may depend on specific research needs regarding variant detection and statistical rigor.
• For identifying causative variants in inherited diseases, both methods provide similar outcomes.