Using Mendelian inheritance errors as quality control criteria in whole genome sequencing data set

Area of Science:

• Genomics
• Bioinformatics
• Genetic Data Analysis

Background:

• Whole genome sequencing (WGS) complexity is high, but data cleaning and quality control (QC) best practices are undefined.
• Family-based data can standardize QC metrics for non-family-based WGS data.
• Mendelian inheritance errors often result from inaccurate genotype calls due to low mutation rates.

Purpose of the Study:

• Identify characteristics of Mendelian inheritance errors in WGS data.
• Standardize QC metrics using family-based data.
• Improve accuracy of WGS data analysis.

Main Methods:

• Utilized chromosome 3 WGS family-based data from the Genetic Analysis Workshop 18 (GAW18).
• Analyzed provided Mendelian inheritance errors (MIEs) from GAW18 dataset.
• Calculated MIEs for binary variants using PLINK.

Main Results:

• Nonbinary single-nucleotide variants exhibit a high number of MIEs.
• MIEs in binary variants are nonrandomly distributed, with 3 peaks enriched in repetitive elements.
• Applying a single filter from sequencing files can reduce these MIE peaks.

Conclusions:

• Erroneous sequencing calls are nonrandomly distributed across the genome.
• QC metrics can substantially decrease MIEs in WGS data.
• Appropriate QC is crucial for optimal utilization of WGS data and realizing its full potential.