title: 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.
authors.affiliation: Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California 94143, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, California 94143, USA; wallj@humgen.ucsf.edu.
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Wall
authors.affiliation: Cardiovascular Research Institute, University of California San Francisco, San Francisco, California 94143, USA;
authors.forename: Ling Fung
authors.initials: LF
authors.lastname: Tang
authors.affiliation: Institute for Human Genetics, University of California San Francisco, San Francisco, California 94143, USA;
authors.forename: Brandon
authors.initials: B
authors.lastname: Zerbe
authors.affiliation: Institute for Human Genetics, University of California San Francisco, San Francisco, California 94143, USA;
authors.forename: Mark N
authors.initials: MN
authors.lastname: Kvale
authors.affiliation: Institute for Human Genetics, University of California San Francisco, San Francisco, California 94143, USA; Cardiovascular Research Institute, University of California San Francisco, San Francisco, California 94143, USA;
authors.forename: Pui-Yan
authors.initials: PY
authors.lastname: Kwok
authors.affiliation: Kaiser Permanente Northern California Division of Research, Oakland, California 94612, USA.
authors.forename: Catherine
authors.initials: C
authors.lastname: Schaefer
authors.affiliation: Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California 94143, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, California 94143, USA; Kaiser Permanente Northern California Division of Research, Oakland, California 94612, USA.
authors.forename: Neil
authors.initials: N
authors.lastname: Risch
mesh_terms: D059472:Exome / Q000235:genetics*; D005787:Gene Frequency; D015894:Genome, Human / Q000235:genetics; D023281:Genomics / Q000379:methods*; D005838:Genotype; D060005:Genotyping Techniques / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D044465:White People / Q000235:genetics