A practical method to detect SNVs and indels from whole genome and exome sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Massively parallel sequencing enables comprehensive genetic variation catalogs.
• Short-read sequencing data presents challenges due to high error rates, necessitating advanced analysis for accurate variant calling.

Purpose of the Study:

• To develop a probabilistic multinomial method for single-sample variant detection.
• To accurately identify single nucleotide variants (SNVs) and short insertions/deletions (indels) in whole genome sequencing (WGS) and whole exome sequencing (WES) data.

Main Methods:

• Development of a probabilistic multinomial model for variant detection.
• Application of the method to single-sample WGS and WES data.
• Evaluation using DNA genotyping arrays and Sanger sequencing for accuracy assessment.

Main Results:

• High concordance rates observed: 99.98% for WGS and 99.99% for WES compared to genotyping arrays.
• Low false positive and false negative rates for SNVs in WGS (0.0068%, 0.17%) and WES (0.0036%, 0.0084%).
• Accurate identification of short indels with 94.7% accuracy in WGS and 97.3% in WES.

Conclusions:

• The developed probabilistic method provides high-quality variant calls for SNVs and indels.
• This approach enhances the reliability of genetic variation analysis in WGS and WES data.
• The method has the potential to advance the understanding of human diseases through improved genetic insights.