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Sci Rep. 2019 Feb 11;9(1):1784. doi: 10.1038/s41598-018-38346-0.

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

Author information

1
Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.
2
Department of Otorhinolaryngology - Head and Neck Surgery, Osaka University Graduate School of Medicine, Osaka, 565-0871, Japan.
3
Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
4
Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
5
Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
6
Kyoto-McGill International Collaborative School in Genomic Medicine, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, 606-8507, Japan.
7
RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
8
Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan. yokada@sg.med.osaka-u.ac.jp.
9
Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan. yokada@sg.med.osaka-u.ac.jp.
10
Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Japan. yokada@sg.med.osaka-u.ac.jp.

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.

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