Effective filtering strategies to improve data quality from population-based whole exome sequencing studies
- Equal contributors
1 Department of Pediatrics and Rady Children’s Hospital, University of California San Diego, San Diego, USA
2 Department of Clinical Medicine, Hematological Research Group, University of Tromsø, Tromsø, Norway
3 Division of Internal Medicine, University Hospital of North Norway, Tromsø, Norway
4 Clinical and Translational Research Institute, University of California, San Diego, USA
5 Department of Clinical Medicine, University of Tromsø, Tromsø, Norway
6 Moores UCSD Cancer Center, University of California San Diego, La Jolla, CA, USA
BMC Bioinformatics 2014, 15:125 doi:10.1186/1471-2105-15-125Published: 2 May 2014
Genotypes generated in next generation sequencing studies contain errors which can significantly impact the power to detect signals in common and rare variant association tests. These genotyping errors are not explicitly filtered by the standard GATK Variant Quality Score Recalibration (VQSR) tool and thus remain a source of errors in whole exome sequencing (WES) projects that follow GATK’s recommended best practices. Therefore, additional data filtering methods are required to effectively remove these errors before performing association analyses with complex phenotypes. Here we empirically derive thresholds for genotype and variant filters that, when used in conjunction with the VQSR tool, achieve higher data quality than when using VQSR alone.
The detailed filtering strategies improve the concordance of sequenced genotypes with array genotypes from 99.33% to 99.77%; improve the percent of discordant genotypes removed from 10.5% to 69.5%; and improve the Ti/Tv ratio from 2.63 to 2.75. We also demonstrate that managing batch effects by separating samples based on different target capture and sequencing chemistry protocols results in a final data set containing 40.9% more high-quality variants. In addition, imputation is an important component of WES studies and is used to estimate common variant genotypes to generate additional markers for association analyses. As such, we demonstrate filtering methods for imputed data that improve genotype concordance from 79.3% to 99.8% while removing 99.5% of discordant genotypes.
The described filtering methods are advantageous for large population-based WES studies designed to identify common and rare variation associated with complex diseases. Compared to data processed through standard practices, these strategies result in substantially higher quality data for common and rare association analyses.