Please use this identifier to cite or link to this item: https://doi.org/10.1016/B978-0-12-375142-3.10005-7
Title: Genotype Calling
Authors: Inouye, M.
Teo, Y.Y. 
Issue Date: 2011
Citation: Inouye, M.,Teo, Y.Y. (2011). Genotype Calling. Analysis of Complex Disease Association Studies : 69-86. ScholarBank@NUS Repository. https://doi.org/10.1016/B978-0-12-375142-3.10005-7
Abstract: This chapter begins with the bias and error in genotype calling that are obvious for large database. Any systematic bias in calling can reduce both the power and coverage of a study. This is especially true for case-control studies where informative missingness, differential patterns of missing genotype calls between cases and controls can lead to an artificial inflation in the number of "significant associations." To identify potential informative missingness or other areas of bias, quantile-quantile plots of observed and expected test statistics are typically generated and the degree of inflation is determined and the genotype calling algorithm directly targets this inflation. There are different strategies one can take to reduce bias. When there are cluster shifts between cases and controls (or indeed for any kind of batch processing), calling the cases and controls separately can alleviate test statistic inflation. Genotype imputation can offer a powerful solution to informative missingness as well as increase the power and coverage of a study. The chapter further concerns the genotyping platforms, normalization algorithms, genotype calling from a single array, the illuminus algorithm, and various other genotype calling algorithms. © 2011 Elsevier Inc. All rights reserved..
Source Title: Analysis of Complex Disease Association Studies
URI: http://scholarbank.nus.edu.sg/handle/10635/105487
ISBN: 9780123751423
DOI: 10.1016/B978-0-12-375142-3.10005-7
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