Abstract
Genome-wide association studies (GWAS) using a large number of single nucleotide polymorphisms (SNPs) have successfully been applied to identify genetic variants of common diseases. However, genotyping using the new array technologies is often associated with spurious results that could unfavorably affect analyses of GWAS. Consequently, data cleaning is of paramount importance in excluding spurious genotyping results. In this study, we investigated the criteria required for the appropriate cleaning of 389 unrelated healthy Japanese samples analyzed using the GeneChip Human Mapping 500K Array Set for GWAS. The samples were randomly subdivided into two groups, and the allele frequencies in the groups were compared for individual SNPs as a quasi-case-control study. Then, observed results were filtered by four parameters (SNP call rate, confidence score obtained using the Bayesian Robust Linear Model with Mahalanobis genotype-calling algorithm, Hardy–Weinberg equilibrium, and minor allele frequency) and assessed for deviation from the null hypothesis. We found that appropriate data cleaning could be achieved using these four parameters. Our findings offer an avenue for obtaining appropriate data from GWAS.
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References
Arking DE, Pfeufer A, Post W, Kao WH, Newton-Cheh C, Ikeda M, West K, Kashuk C, Akyol M, Perz S, Jalilzadeh S, Illig T, Gieger C, Guo CY, Larson MG, Wichmann HE, Marban E, O’Donnell CJ, Hirschhorn JN, Kaab S, Spooner PM, Meitinger T, Chakravarti A (2006) A common genetic variant in the NOS1 regulator NOS1AP modulates cardiac repolarization. Nat Genet 38:644–651
Bailey JA, Eichler EE (2006) Primate segmental duplications: crucibles of evolution, diversity and disease. Nat Rev Genet 7:552–564
Balding DJ (2006) A tutorial on statistical methods for population association studies. Nat Rev Genet 7:781–791
Buch S, Schafmayer C, Volzke H, Becker C, Franke A, von Eller-Eberstein H, Kluck C, Bassmann I, Brosch M, Lammert F, Miquel JF, Nervi F, Wittig M, Rosskopf D, Timm B, Holl C, Seeger M, ElSharawy A, Lu T, Egberts J, Fandrich F, Folsch UR, Krawczak M, Schreiber S, Nurnberg P, Tepel J, Hampe J (2007) A genome-wide association scan identifies the hepatic cholesterol transporter ABCG8 as a susceptibility factor for human gallstone disease. Nat Genet 39:995–999
Clayton DG, Walker NM, Smyth DJ, Pask R, Cooper JD, Maier LM, Smink LJ, Lam AC, Ovington NR, Stevens HE, Nutland S, Howson JM, Faham M, Moorhead M, Jones HB, Falkowski M, Hardenbol P, Willis TD, Todd JA (2005) Population structure, differential bias and genomic control in a large-scale, case-control association study. Nat Genet 37:1243–1246
Conrad DF, Andrews TD, Carter NP, Hurles ME, Pritchard JK (2006) A high-resolution survey of deletion polymorphism in the human genome. Nat Genet 38:75–81
Dewan A, Liu M, Hartman S, Zhang SS, Liu DT, Zhao C, Tam PO, Chan WM, Lam DS, Snyder M, Barnstable C, Pang CP, Hoh J (2006) HTRA1 promoter polymorphism in wet age-related macular degeneration. Science 314:989–992
Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C, Hoh J (2005) Complement factor H polymorphism in age-related macular degeneration. Science 308:385–389
Kubo M, Hata J, Ninomiya T, Matsuda K, Yonemoto K, Nakano T, Matsushita T, Yamazaki K, Ohnishi Y, Saito S, Kitazono T, Ibayashi S, Sueishi K, Iida M, Nakamura Y, Kiyohara Y (2007) A nonsynonymous SNP in PRKCH (protein kinase C eta) increases the risk of cerebral infarction. Nat Genet 39:212–217
Matsuzaki H, Loi H, Dong S, Tsai YY, Fang J, Law J, Di X, Liu WM, Yang G, Liu G, Huang J, Kennedy GC, Ryder TB, Marcus GA, Walsh PS, Shriver MD, Puck JM, Jones KW, Mei R (2004) Parallel genotyping of over 10, 000 SNPs using a one-primer assay on a high-density oligonucleotide array. Genome Res 14:414–425
Nielsen DM, Ehm MG, Weir BS (1998) Detecting marker-disease association by testing for Hardy–Weinberg disequilibrium at a marker locus. Am J Hum Genet 63:1531–1540
Ohashi J, Tokunaga K (2001) The power of genome-wide association studies of complex disease genes: statistical limitations of indirect approaches using SNP markers. J Hum Genet 46:478–482
Ohashi J, Tokunaga K (2002) The expected power of genome-wide linkage disequilibrium testing using single nucleotide polymorphism markers for detecting a low-frequency disease variant. Ann Hum Genet 66:297–306
Ohashi J, Yamamoto S, Tsuchiya N, Hatta Y, Komata T, Matsushita M, Tokunaga K (2001) Comparison of statistical power between 2 × 2 allele frequency and allele positivity tables in case-control studies of complex disease genes. Ann Hum Genet 65:197–206
Oliphant A, Barker DL, Stuelpnagel JR, Chee MS (2002) BeadArray technology: enabling an accurate, cost-effective approach to high-throughput genotyping. Biotechniques Suppl:56–58, 60–61
Rabbee N, Speed TP (2006) A genotype calling algorithm for affymetrix SNP arrays. Bioinformatics 22:7–12
Rioux JD, Xavier RJ, Taylor KD, Silverberg MS, Goyette P, Huett A, Green T, Kuballa P, Barmada MM, Datta LW, Shugart YY, Griffiths AM, Targan SR, Ippoliti AF, Bernard EJ, Mei L, Nicolae DL, Regueiro M, Schumm LP, Steinhart AH, Rotter JI, Duerr RH, Cho JH, Daly MJ, Brant SR (2007) Genome-wide association study identifies new susceptibility loci for Crohn disease and implicates autophagy in disease pathogenesis. Nat Genet 39:596–604
Risch NJ (2000) Searching for genetic determinants in the new millennium. Nature 405:847–856
Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hudson TJ, Montpetit A, Pshezhetsky AV, Prentki M, Posner BI, Balding DJ, Meyre D, Polychronakos C, Froguel P (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445:881–885
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 447:661–678
Weir BS, Hill WG, Cardon LR (2004) Allelic association patterns for a dense SNP map. Genet Epidemiol 27:442–450
Zondervan KT, Cardon LR (2004) The complex interplay among factors that influence allelic association. Nat Rev Genet 5:89–100
Acknowledgments
We are deeply grateful to the people participating in this study. We thank JAMSAC (Japan Multiple System Atrophy Research Consortium) for a part of the samples. This study is supported by a grant-in-aid for Scientific Research on Priority Areas “Comprehensive Genomics” and “Applied Genomics” from the Ministry of Education, Culture, Sports, Science and Technology of Japan and a grant-in-aid for JSPS fellows.
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Miyagawa, T., Nishida, N., Ohashi, J. et al. Appropriate data cleaning methods for genome-wide association study. J Hum Genet 53, 886–893 (2008). https://doi.org/10.1007/s10038-008-0322-y
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DOI: https://doi.org/10.1007/s10038-008-0322-y
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