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.
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