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Quality control and quality assurance in genotypic data for genome‐wide association studies
Author(s) -
Laurie Cathy C.,
Doheny Kimberly F.,
Mirel Daniel B.,
Pugh Elizabeth W.,
Bierut Laura J.,
Bhangale Tushar,
Boehm Frederick,
Caporaso Neil E.,
Cornelis Marilyn C.,
Edenberg Howard J.,
Gabriel Stacy B.,
Harris Emily L.,
Hu Frank B.,
Jacobs Kevin B.,
Kraft Peter,
Landi Maria Teresa,
Lumley Thomas,
Manolio Teri A.,
McHugh Caitlin,
Painter Ian,
Paschall Justin,
Rice John P.,
Rice Kenneth M.,
Zheng Xiuwen,
Weir Bruce S.
Publication year - 2010
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20516
Subject(s) - genome wide association study , genotyping , genetics , genetic association , biology , spurious relationship , single nucleotide polymorphism , sample size determination , quality assurance , snp , computational biology , genotype , statistics , mathematics , gene , external quality assessment , medicine , pathology
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. © 2010 Wiley‐Liss, Inc.