Effective filtering strategies to improve data quality from population-based whole exome sequencing studies View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12

AUTHORS

Andrew R Carson, Erin N Smith, Hiroko Matsui, Sigrid K Brækkan, Kristen Jepsen, John-Bjarne Hansen, Kelly A Frazer

ABSTRACT

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. More... »

PAGES

125

References to SciGraph publications

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1471-2105-15-125

    DOI

    http://dx.doi.org/10.1186/1471-2105-15-125

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1040883851

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24884706


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    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-15-125'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-15-125'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-15-125'

    RDF/XML is a standard XML format for linked data.

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    291 schema:name Department of Clinical Medicine, Hematological Research Group, University of Tromsø, Tromsø, Norway
    292 Division of Internal Medicine, University Hospital of North Norway, Tromsø, Norway
    293 rdf:type schema:Organization
     




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