OBJECTIVES: It is well known that genotyping error adversely affects the power of genetic case-control association studies but there is little research on its effects on type I error, and none that has addressed possible differences in genotype error rates between cases and controls. METHODS: We used simulations to examine the influence of genotyping error on the type I error probability given by case-control studies. The effect of genotyping error on the magnitude of type I error was explored for a single marker of varying minor allele frequency (MAF), and for haplotypic tests based on two markers with varying MAF and linkage disequilibrium (LD) measure r(2). RESULTS: We show that even with low genotyping error rates (<0.01), systematic differences in the error rate between samples can result in type I error rates substantially above 0.05. The effect was maximal for markers with small MAF, markers in strong LD, and where a common allele is more frequently misclassified as a rare allele than vice versa. The problem was also exacerbated by the use of large samples. CONCLUSIONS: Our results show that small differential genotyping error rates between cases and controls pose significant problems for association analyses. Differential genotyping error rates are particularly likely to arise where genotype data are combined from multiple sites, or where case genotypes are examined against archived reference population cohort genotypes that are being generated in several countries. Although these strategies may be necessary to obtain adequately powered samples, our data show the importance of stringent quality control. Furthermore, associations based on rare haplotypes should be treated with caution.
OBJECTIVES: It is well known that genotyping error adversely affects the power of genetic case-control association studies but there is little research on its effects on type I error, and none that has addressed possible differences in genotype error rates between cases and controls. METHODS: We used simulations to examine the influence of genotyping error on the type I error probability given by case-control studies. The effect of genotyping error on the magnitude of type I error was explored for a single marker of varying minor allele frequency (MAF), and for haplotypic tests based on two markers with varying MAF and linkage disequilibrium (LD) measure r(2). RESULTS: We show that even with low genotyping error rates (<0.01), systematic differences in the error rate between samples can result in type I error rates substantially above 0.05. The effect was maximal for markers with small MAF, markers in strong LD, and where a common allele is more frequently misclassified as a rare allele than vice versa. The problem was also exacerbated by the use of large samples. CONCLUSIONS: Our results show that small differential genotyping error rates between cases and controls pose significant problems for association analyses. Differential genotyping error rates are particularly likely to arise where genotype data are combined from multiple sites, or where case genotypes are examined against archived reference population cohort genotypes that are being generated in several countries. Although these strategies may be necessary to obtain adequately powered samples, our data show the importance of stringent quality control. Furthermore, associations based on rare haplotypes should be treated with caution.
Authors: Anna Pluzhnikov; Jennifer E Below; Anuar Konkashbaev; Anna Tikhomirov; Emily Kistner-Griffin; Cheryl A Roe; Dan L Nicolae; Nancy J Cox Journal: Am J Hum Genet Date: 2010-07-09 Impact factor: 11.025
Authors: Yan Lin; George C Tseng; Soo Yeon Cheong; Lora J H Bean; Stephanie L Sherman; Eleanor Feingold Journal: Bioinformatics Date: 2008-09-29 Impact factor: 6.937
Authors: Denise Harold; Richard Abraham; Paul Hollingworth; Rebecca Sims; Amy Gerrish; Marian L Hamshere; Jaspreet Singh Pahwa; Valentina Moskvina; Kimberley Dowzell; Amy Williams; Nicola Jones; Charlene Thomas; Alexandra Stretton; Angharad R Morgan; Simon Lovestone; John Powell; Petroula Proitsi; Michelle K Lupton; Carol Brayne; David C Rubinsztein; Michael Gill; Brian Lawlor; Aoibhinn Lynch; Kevin Morgan; Kristelle S Brown; Peter A Passmore; David Craig; Bernadette McGuinness; Stephen Todd; Clive Holmes; David Mann; A David Smith; Seth Love; Patrick G Kehoe; John Hardy; Simon Mead; Nick Fox; Martin Rossor; John Collinge; Wolfgang Maier; Frank Jessen; Britta Schürmann; Reinhard Heun; Hendrik van den Bussche; Isabella Heuser; Johannes Kornhuber; Jens Wiltfang; Martin Dichgans; Lutz Frölich; Harald Hampel; Michael Hüll; Dan Rujescu; Alison M Goate; John S K Kauwe; Carlos Cruchaga; Petra Nowotny; John C Morris; Kevin Mayo; Kristel Sleegers; Karolien Bettens; Sebastiaan Engelborghs; Peter P De Deyn; Christine Van Broeckhoven; Gill Livingston; Nicholas J Bass; Hugh Gurling; Andrew McQuillin; Rhian Gwilliam; Panagiotis Deloukas; Ammar Al-Chalabi; Christopher E Shaw; Magda Tsolaki; Andrew B Singleton; Rita Guerreiro; Thomas W Mühleisen; Markus M Nöthen; Susanne Moebus; Karl-Heinz Jöckel; Norman Klopp; H-Erich Wichmann; Minerva M Carrasquillo; V Shane Pankratz; Steven G Younkin; Peter A Holmans; Michael O'Donovan; Michael J Owen; Julie Williams Journal: Nat Genet Date: 2009-09-06 Impact factor: 38.330
Authors: Kristine L Bucasas; Gagan A Pandya; Sonal Pradhan; Robert D Fleischmann; Scott N Peterson; John W Belmont Journal: BMC Genet Date: 2009-12-18 Impact factor: 2.797