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Quality Control of Common and Rare Variants

  • Kalliope PanoutsopoulouEmail author
  • Klaudia Walter
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1793)

Abstract

Thorough data quality control (QC) is a key step to the success of high-throughput genotyping approaches. Following extensive research several criteria and thresholds have been established for data QC at the sample and variant level. Sample QC is aimed at the identification and removal (when appropriate) of individuals with (1) low call rate, (2) discrepant sex or other identity-related information, (3) excess genome-wide heterozygosity and homozygosity, (4) relations to other samples, (5) ethnicity differences, (6) batch effects, and (7) contamination. Variant QC is aimed at identification and removal or refinement of variants with (1) low call rate, (2) call rate differences by phenotypic status, (3) gross deviation from Hardy-Weinberg Equilibrium (HWE), (4) bad genotype intensity plots, (5) batch effects, (6) differences in allele frequencies with published data sets, (7) very low minor allele counts (MAC), (8) low imputation quality score, (9) low variant quality score log-odds, and (10) few or low quality reads.

Key words

Genome-wide association study Whole genome sequencing Sample quality control Variant quality control 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Wellcome Sanger InstituteWellcome Genome CampusHinxtonUnited Kingdom

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