Empirical evaluation of variant calling accuracy using ultra-deep whole-genome sequencing data

Abstract

In the design of whole-genome sequencing (WGS) studies, sequencing depth is a crucial parameter to define variant calling accuracy and study cost, with no standard recommendations having been established. We empirically evaluated the variant calling accuracy of the WGS pipeline using ultra-deep WGS data (approximately 410×). We randomly sampled sequence reads and constructed a series of simulation WGS datasets with a variety of gradual depths (n = 54; from 0.05× to 410×). Next, we evaluated the genotype concordances of the WGS data with those in the SNP microarray data or the WGS data using all the sequence reads. In addition, we assessed the accuracy of HLA allele genotyping using the WGS data with multiple software tools (PHLAT, HLA-VBseq, HLA-HD, and SNP2HLA). The WGS data with higher depths showed higher concordance rates, and >13.7× depth achieved as high as >99% of concordance. Comparisons with the WGS data using all the sequence reads showed that SNVs achieved >95% of concordance at 17.6× depth, whereas indels showed only 60% concordance. For the accuracy of HLA allele genotyping using the WGS data, 13.7× depth showed sufficient accuracy while performance heterogeneity among the software tools was observed (the highest concordance of 96.9% was observed with HLA-HD). Improvement in HLA genotyping accuracy by further increasing the depths was limited. These results suggest a medium degree of the WGS depth setting (approximately 15×) to achieve both accurate SNV calling and cost-effectiveness, whereas relatively higher depths are required for accurate indel calling.

A Practical Guide for Structural Variation Detection in the Human Genome

“…The undetected SVs were mostly due to lack of supporting reads. A similar trend was observed in SNV calling by down-sampling a 410× WGS dataset (Kishikawa et al, 2019). The simulation reflects the best-case scenario.…”

The Value of Reference Genomes in the Conservation of Threatened Species

“…Higher-coverage sequence data enables variants and heterozygosity to be called much more accurately than low-coverage sequence data and hence allows for SNPs to be called more confidently without additional targeted sequencing (e.g., SNP panels) [108]. High-coverage (~45×) WGR of 25 Tasmanian devils has allowed for reliable estimates of genome-wide heterozygosity, which are being used to assess the accuracy of estimates from other techniques including microsatellites, SNP panels and RRS data.…”

A customized scaffolds approach for the detection and phasing of complex variants by next-generation sequencing

“…The success of NGS in high-throughput sequence variant detection with human genomic DNA has revolutionized the field of clinical genetic testing in the past few years. NGS performance for the detection of single nucleotide variants and small insertion or deletion variants is highly accurate 33 , 34 using the current standard human reference genome build for read alignment. However, the single reference genome approach frequently falls short for variant detection in regions with duplications, pseudogenes, paralogous genes or complex variants because reads from different haplotypes are forced to align to the same genomic region, leading to false negative and false positive calls.…”