ArticleBMC bioinformatics2014
Effective filtering strategies to improve data quality from population-based whole exome sequencing studies.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 1 of them a synthesis that pooled it.
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Who cites it
88 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association of Rare Variants in ARSA with Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2023Pooled it
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- Development of an Integrated Multifunctional Column for Rapid Pretreatment and Determination of Trichothecenes in Cereals and Feeds with HPLC-MS/MS.Foods (Basel, Switzerland) · 2025Article
- Missing genotype imputation in non-model species using self-organizing maps.Molecular ecology resources · 2025Article
- A Transformers-based framework for refinement of genetic variants.Frontiers in bioinformatics · 2025Article
- Are rare heterozygous SYNJ1 variants associated with Parkinson's disease?NPJ Parkinson's disease · 2024Article
- Resilience of genetic diversity in forest trees over the Quaternary.Nature communications · 2024Article
- Genomic Insights into Idiopathic Granulomatous Mastitis through Whole-Exome Sequencing: A Case Report of Eight Patients.International journal of molecular sciences · 2024Article
- Lack of genetic evidence for NLRP3 inflammasome involvement in Parkinson's disease pathogenesis.NPJ Parkinson's disease · 2024Article
- Clinical Validation of the Somatic FANCD2 Mutation (c.2022-5C>T) as a Novel Molecular Biomarker for Early Disease Progression in Chronic Myeloid Leukemia: A Case-Control Study.Hematology reports · 2024Article
- Are rare heterozygousmedRxiv : the preprint server for health sciences · 2024Article
- Exome-wide analysis implicates rare protein-altering variants in human handedness.Nature communications · 2024Article
28 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
backgroundGenotypes 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.
resultsThe 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.
conclusionsThe 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.
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