Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencing

Reumers, Joke; Rijk, Peter De; Zhao, Hui; Liekens, Anthony; Smeets, Dominiek; Cleary, John G.; Loo, Peter Van; Bossche, Maarten Van Den; Catthoor, Kirsten; Sabbe, Bernard; Despierre, Evelyn; Vergote, Ignace; Hilbush, Brian S.; Lambrechts, Diether; Del-Favero, Jurgen

doi:10.1038/nbt.2053

Cited by 206 publications

(189 citation statements)

References 49 publications

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“…Our finding of a higher error rate in the CG data compared with the IL data is different from the results of Lam et al (2012), but similar to what was reported by Ross et al (2013). We suspect that the difference between our results and those of Lam et al (2012) is primarily due to (1) the filters used by each study (see, e.g., Reumers et al 2012), (2) the improvements in GATK over the past 2 yr, and (3) our use of genotype quality (GQ) scores rather than simple quality (QUAL) scores. (QUAL scores reflect the SNP caller's estimate of how likely there is to be a polymorphism at a given site, while GQ scores are an estimate of how likely the called genotype is to be correct.)…”

Section: Discussioncontrasting

confidence: 57%

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

et al. 2014

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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.

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Section: Discussioncontrasting

confidence: 57%

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

et al. 2014

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“…Recent publications have demonstrated hundreds of thousands of differences between variant calls from different whole human genome sequencing methods or different bioinformatics methods [5][6][7][8][9][10][11] . To understand these differences, we describe a high-confidence set of genome-wide genotype calls that can be used as a benchmark.…”

Section: A N a Ly S I Smentioning

confidence: 99%

Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls

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Chapman

Wang³

et al. 2014

Nat Biotechnol

743

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“…MZ twins are, in fact, genetically identical at conception, but can accumulate mutations after the zygote splits, making MZ twins informative for the study of somatic mutations. Post-twinning point mutations have been reported (Kondo et al, 2002;Reumers et al, 2012;Sakuntabhai et al, 1999;Vadlamudi et al, 2010;Ye et al, 2013), but are expected to be scarcer than post-twinning de novo CNVs. CNVs, the most studied type of structural variant, are segments of DNA ranging from 1 kb to several Mb that differ in copy number (CN) across different members of the species.…”

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confidence: 99%

CNV Concordance in 1,097 MZ Twin Pairs

et al. 2015

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Monozygotic (MZ) twins are genetically identical at conception, making them informative subjects for studies on somatic mutations. Copy number variants (CNVs) are responsible for a substantial part of genetic variation, have relatively high mutation rates, and are likely to be involved in phenotypic variation. We conducted a genome-wide survey for post-twinning de novo CNVs in 1,097 MZ twin pairs. Comparisons between MZ twins were made by CNVs measured in DNA from blood or buccal epithelium with the Affymetrix 6.0 microarray and two calling algorithms. In addition, CNV concordance rates were compared between the different sources of DNA, and gene-enrichment association analyses were conducted for thought problems (TP) and attention problems (AP) using CNVs concordant within MZ pairs. We found a total of 153 putative post-twinning de novo CNVs >100 kb, of which the majority resided in 15q11.2. Based on the discordance of raw intensity signals a selection was made of 20 de novo CNVs for a qPCR validation experiments. Two out of 20 post-twinning de novo CNVs were validated with qPCR in the same twin pair. The 13-year-old MZ twin pair that showed two discordances in CN in 15q11.2 in their buccal DNA did not show large phenotypic differences. From the remaining 18 putative de novo CNVs, 17 were deletions or duplications that were concordant within MZ twin pairs. Concordance rates within twin pairs of CNV calls with CN ࣔ 2 were ß80%. Buccal epithelium-derived DNA showed a slightly but significantly higher concordance rate, and blood-derived DNA showed significantly more concordant CNVs per twin pair. The gene-enrichment analyses on concordant CNVs showed no significant associations between CNVs overlapping with genes involved in neuronal processes and TP or AP after accounting for the source of DNA.

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Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencing

Cited by 206 publications

References 49 publications

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

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

Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls

CNV Concordance in 1,097 MZ Twin Pairs

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