1. ¹Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany
2. ²Institute for Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany
3. ³University Hospital Schleswig-Holstein Campus Kiel, Schittenhelmstr. 12, Kiel, Germany
4. ⁴Institute for Medical Informatics and Statistics, Christian-Alb rechts- University, Kiel, Germany
5. ⁵University Hospital Schleswig-Holstein Campus Kiel, Brunswikerstr. 10, Kiel, Germany
6. ⁶Institute for Medical Biometry and Epidemiology, Philipps-University Marburg, Marburg, Germany

Correspondence: Dr M Knapp, Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Sigmund-Freud-Str. 25, D-53105 Bonn, Germany. Tel: +49 228 287 5810; Fax: +49 228 287 5854; E-mail: knapp@uni-bonn.de

Received 7 July 2005; Revised 6 October 2005; Accepted 24 November 2005; Published online 25 January 2006.

Abstract

Undetected genotyping errors pose a problem in genetic epidemiological studies, as they may invalidate statistical analysis or reduce its power. Haplotype analysis requires an improved standard of the data, because a haplotype can be inferred correctly only if the genotypes of all its markers are correct. Here, we present a method that identifies probable genotyping errors in trio samples with the help of the estimated haplotype frequency distribution of the sample. If the likelihood of the most likely haplotype explanation depends strongly on just one genotype, in the sense that setting the genotype to be missing leads to a much more likely haplotype explanation, this genotype is considered as a potential genotyping error. We describe a method that systematically searches the whole data set for such potential errors. Based on the haplotype distribution of a real data set, we carry out a simulation study to estimate the sensitivity and specifity of the method. In addition, we apply our approach to the real data set itself. Potentially erroneous genotypes are re-determined via sequencing. The results of both the simulation study and of the application to the real data set show that a considerable proportion of true genotyping errors is detected and that the number of false-positive signals is acceptable. We conclude that it is indeed possible to identify probable genotyping errors by considering haplotypes. The method described here will be part of the next release of our FAMHAP software.