TY  - JOUR
A1  - Wall, Jeffrey D.
A1  - Tang, Ling Fung
A1  - Zerbe, Brandon
A1  - Kvale, Mark N.
A1  - Kwok, Pui-Yan
A1  - Schaefer, Catherine
A1  - Risch, Neil
T1  - Estimating genotype error rates from high-coverage next-generation sequence data
Y1  - 2014/11/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1734 
EP  - 1739 
DO  - 10.1101/gr.168393.113 
VL  - 24 
IS  - 11 
UR  - http://genome.cshlp.org/content/24/11/1734.abstract 
N2  - 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. 
ER  -