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An Evaluation of the MiDCoP Method
for Imputing Allele Frequency in Genome Wide
Association Studies *
Yadu Gautam, Carl Lee, Chin-I Cheng, and Carl Langefeld
Abstract. A genome wide association studies require genotyping DNA sequence
of a large sample of individuals with and without the specific disease of interest.
The current technologies of genotyping individual DNA sequence only genotype a
limited DNA sequence of each individual in the study. As a result, a large fraction
of Single Nucleotide Polymorphisms (SNPs) are not genotyped. Existing
imputation methods are based on individual level data, which are often time
consuming and costly. A new method, the Minimum Deviation of Conditional
Probability (MiDCoP), was recently developed that aims at imputing the allele
frequencies of the missing SNPs using the allele frequencies of neighboring SNPs
without using the individual level SNP information. This article studies the
performance of the MiDCoP approach using association analysis based on the
imputed allele frequency by analyzing the GAIN Schizophrenia data. The results
indicate that the choice of reference sets has strong impact on the performance.
The imputation accuracy improves if the case and control data sets are imputed
using a separate but better matched reference set, respectively.
Keywords: Association Tests, Conditional Probability, Imputation, Minimum
Deviation, Multilocus Information Measure, Single Nucleotide Polymorphisms.
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