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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References
The Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447(7145):661–678. https://doi.org/10.1038/nature05911
Anderson CA, Pettersson FH, Clarke GM et al (2010) Data quality control in genetic case-control association studies. Nat Protoc 5(9):1564–1573. https://doi.org/10.1038/nprot.2010.116
Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575. https://doi.org/10.1086/519795
Chang CC, Chow CC, Tellier LC et al (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4:7. https://doi.org/10.1186/s13742-015-0047-8
Reich D, Price AL, Patterson N (2008) Principal component analysis of genetic data. Nat Genet 40(5):491–492. https://doi.org/10.1038/ng0508-491
1000 Genomes Project Consortium, Auton A, Brooks LD et al (2015) A global reference for human genetic variation. Nature 526(7571):68–74. https://doi.org/10.1038/nature15393
Jun G, Flickinger M, Hetrick KN et al (2012) Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am J Hum Genet 91(5):839–848. https://doi.org/10.1016/j.ajhg.2012.09.004
Clayton DG, Walker NM, Smyth DJ et al (2005) Population structure, differential bias and genomic control in a large-scale, case-control association study. Nat Genet 37(11):1243–1246. https://doi.org/10.1038/ng1653
Gurdasani D, Carstensen T, Tekola-Ayele F et al (2015) The African genome variation project shapes medical genetics in Africa. Nature 517(7534):327–332. https://doi.org/10.1038/nature13997
Walter K, Min JL, Huang J et al (2015) The UK10K project identifies rare variants in health and disease. Nature 526(7571):82–90. https://doi.org/10.1038/nature14962
Ma C, Blackwell T, Boehnke M et al (2013) Recommended joint and meta-analysis strategies for case-control association testing of single low-count variants. Genet Epidemiol 37(6):539–550. https://doi.org/10.1002/gepi.21742
Browning BL, Browning SR (2009) A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet 84(2):210–223. https://doi.org/10.1016/j.ajhg.2009.01.005
Fuchsberger C, Abecasis GR, Hinds DA (2015) minimac2: faster genotype imputation. Bioinformatics 31(5):782–784. https://doi.org/10.1093/bioinformatics/btu704
Howie B, Marchini J, Stephens M (2011) Genotype imputation with thousands of genomes. G3 (Bethesda) 1(6):457–470. https://doi.org/10.1534/g3.111.001198
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Panoutsopoulou, K., Walter, K. (2018). Quality Control of Common and Rare Variants. In: Evangelou, E. (eds) Genetic Epidemiology. Methods in Molecular Biology, vol 1793. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7868-7_3
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DOI: https://doi.org/10.1007/978-1-4939-7868-7_3
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