Abstract
The success of next-generation sequencing depends on the accuracy of variant calls. Few objective protocols exist for QC following variant calling from whole genome sequencing (WGS) data. After applying QC filtering based on Genome Analysis Tool Kit (GATK) best practices, we used genotype discordance of eight samples that were sequenced twice each to evaluate the proportion of potentially inaccurate variant calls. We designed a QC pipeline involving hard filters to improve replicate genotype concordance, which indicates improved accuracy of genotype calls. Our pipeline analyzes the efficacy of each filtering step. We initially applied this strategy to well-characterized variants from the ClinVar database, and subsequently to the full WGS dataset. The genome-wide biallelic pipeline removed 82.11% of discordant and 14.89% of concordant genotypes, and improved the concordance rate from 98.53% to 99.69%. The variant-level read depth filter most improved the genome-wide biallelic concordance rate. We also adapted this pipeline for triallelic sites, given the increasing proportion of multiallelic sites as sample sizes increase. For triallelic sites containing only SNVs, the concordance rate improved from 97.68% to 99.80%. Our QC pipeline removes many potentially false positive calls that pass in GATK, and may inform future WGS studies prior to variant effect analysis.
Highlights
The success of next-generation sequencing depends on the accuracy of variant calls
The three empirical variant-level quality control (QC) thresholds—variant quality score log-odds (VQSLOD), mapping quality (MQ), and overall read depth (DP)—were derived from plots comparing the density curves of each parameter for discordant versus concordant ClinVar-indexed sites (Fig. 1)
The VQSLOD for a given variant is a calibrated quality score estimated through the Genome Analysis Tool Kit (GATK) Variant Quality Score Recalibration (VQSR) process that attempts to balance sensitivity and specificity, through a machine learning approach[21]
Summary
The success of next-generation sequencing depends on the accuracy of variant calls. Few objective protocols exist for QC following variant calling from whole genome sequencing (WGS) data. After applying QC filtering based on Genome Analysis Tool Kit (GATK) best practices, we used genotype discordance of eight samples that were sequenced twice each to evaluate the proportion of potentially inaccurate variant calls. The variant-level read depth filter most improved the genome-wide biallelic concordance rate. We adapted this pipeline for triallelic sites, given the increasing proportion of multiallelic sites as sample sizes increase. Several WES QC pipelines have been described[9,10], which use the Genome Analysis Tool Kit (GATK) Variant Quality Score Recalibration (VQSR) approach as their backbones while enhancing GATK’s output by utilizing various hard filters to further screen data based on specific QC metrics. Our pipeline, which includes variant-level, genotype-level, and sample-level filters, quantifies the efficacy of each filtering step
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