Estimating genotype error rates from high-coverage next-generation sequence data

Abstract

Exome and whole-genome sequencing studies are becoming increasingly common, but little is known about the accuracy of the genotype calls made by the commonly used platforms. Here we use replicate high-coverage sequencing of blood and saliva DNA samples from four European-American individuals to estimate lower bounds on the error rates of Complete Genomics and Illumina HiSeq whole-genome and whole-exome sequencing. Error rates for nonreference genotype calls range from 0.1% to 0.6%, depending on the platform and the depth of coverage. Additionally, we found (1) no difference in the error profiles or rates between blood and saliva samples; (2) Complete Genomics sequences had substantially higher error rates than Illumina sequences had; (3) error rates were higher (up to 6%) for rare or unique variants; (4) error rates generally declined with genotype quality (GQ) score, but in a nonlinear fashion for the Illumina data, likely due to loss of specificity of GQ scores greater than 60; and (5) error rates increased with increasing depth of coverage for the Illumina data. These findings, especially (3)- (5), suggest that caution should be taken in interpreting the results of next-generation sequencing-based association studies, and even more so in clinical application of this technology in the absence of validation by other more robust sequencing or genotyping methods.

Genotype-Frequency Estimation from High-Throughput Sequencing Data

“…Although the error rate estimates in this region were not very different from those in the other regions ( Figure S5C), depths of coverage in this region were somewhat higher compared to those in the others (Figure 7, B and D), raising the possibility of misassembly and subsequent mismapping. In addition, the overall error rate estimates were positively correlated with depths of coverage outside this region, which is consistent with recent findings that error rates in genotype calling increased with higher depths of coverage with Illumina sequencing data (Wall et al 2014).…”

Review of Clinical Next-Generation Sequencing

“…44,92 Studies have shown that systematic errors lead to a 4% to 6% error rate; counterintuitively, the rate is higher with increasing coverage. 93 Systematic errors may be sequencespecific errors, errors at a particular location of the read (eg, the ends for Illumina sequencers), or related to the base pair content (GC rich for Illumina). [93][94][95] As neither PCR nor fixation causes insertions/deletions (indels), outside of repeat regions there is better sensitivity for detecting small indels than SNVs.…”

“…93 Systematic errors may be sequencespecific errors, errors at a particular location of the read (eg, the ends for Illumina sequencers), or related to the base pair content (GC rich for Illumina). [93][94][95] As neither PCR nor fixation causes insertions/deletions (indels), outside of repeat regions there is better sensitivity for detecting small indels than SNVs. 81 There are 2 main methods for improving sensitivity; however, both of these methods decrease the number of useable reads and therefore will increase the sequencing cost to obtain a comparable coverage.…”

Self-nonself recognition: genomic and transcriptomic insights from the sponge aggregation factors

“…Between ~197,000 (Sponge B) and ~398,000 (Sponge D) total potential variant sites were detected in each dataset; this disparity is a direct consequence of the differences in sequencing depth between individuals (Table 4. significantly increase genomic coverage and, therefore, the number of detected polymorphisms. The sequencing error rate is expected to be 0.1% based on the Illumina HiSeq 2000 specifications in the year 2012 (Glenn 2011), although this value is likely to be an underestimate of the actual error rate (Wall et al 2014). Filtering of low-frequency nucleotide differences was performed prior to analysis, reducing the expected number of false positive nucleotide variants.…”