期刊
GENETIC EPIDEMIOLOGY
卷 34, 期 6, 页码 591-602出版社
WILEY
DOI: 10.1002/gepi.20516
关键词
GWAS; DNA sample quality; genotyping artifact; Hardy-Weinberg equilibrium; chromosome aberration
资金
- NIH [U01HG004422, HHSN268200782096C]
- NIAAA [U10AA008401]
- NIDA [P01CA089392, R01DA013423, T2D, U01HG004399]
- NIH GEI [HG-06-033-NCI-01, U01HG04424]
- National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics
- Johns Hopkins University Center for Inherited Disease Research [U01HG004438]
- [U01 HG 004446]
- NATIONAL CANCER INSTITUTE [P01CA089392] Funding Source: NIH RePORTER
- NATIONAL HUMAN GENOME RESEARCH INSTITUTE [U01HG004399, U01HG004438, U01HG004422, U01HG004424, U01HG004446] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON ALCOHOL ABUSE AND ALCOHOLISM [U10AA008401] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON DRUG ABUSE [R01DA019963, R01DA013423] Funding Source: NIH RePORTER
Genome-wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of systematic or random error can cause spurious results or obscure real effects. The need for careful attention to data quality has been appreciated for some time in this field, and a number of strategies for quality control and quality assurance (QC/QA) have been developed. Here we extend these methods and describe a system of QC/QA for genotypic data in genome-wide association studies (GWAS). This system includes some new approaches that (1) combine analysis of allelic probe intensities and called genotypes to distinguish gender misidentification from sex chromosome aberrations, (2) detect autosomal chromosome aberrations that may affect genotype calling accuracy, (3) infer DNA sample quality from relatedness and allelic intensities, (4) use duplicate concordance to infer SNP quality, (5) detect genotyping artifacts from dependence of Hardy-Weinberg equilibrium test P-values on allelic frequency, and (6) demonstrate sensitivity of principal components analysis to SNP selection. The methods are illustrated with examples from the Gene Environment Association Studies (GENEVA) program. The results suggest several recommendations for QC/QA in the design and execution of GWAS. Genet. Epidemiol. 34 :591-602, 2010. (C) 2010 Wiley-Liss, Inc.
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