Haplotype analysis in the presence of informatively missing genotype data

Genetic Epidemiology
Nianjun LiuHongyu Zhao

Abstract

It is common to have missing genotypes in practical genetic studies, but the exact underlying missing data mechanism is generally unknown to the investigators. Although some statistical methods can handle missing data, they usually assume that genotypes are missing at random, that is, at a given marker, different genotypes and different alleles are missing with the same probability. These include those methods on haplotype frequency estimation and haplotype association analysis. However, it is likely that this simple assumption does not hold in practice, yet few studies to date have examined the magnitude of the effects when this simplifying assumption is violated. In this study, we demonstrate that the violation of this assumption may lead to serious bias in haplotype frequency estimates, and haplotype association analysis based on this assumption can induce both false-positive and false-negative evidence of association. To address this limitation in the current methods, we propose a general missing data model to characterize missing data patterns across a set of two or more markers simultaneously. We prove that haplotype frequencies and missing data probabilities are identifiable if and only if there is linkage disequilibrium...Continue Reading

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Citations

Sep 14, 2007·Human Genetics·Zhaoxia Yu, Daniel J Schaid
Apr 14, 2012·Annals of Human Genetics·Wan-Yu LinNianjun Liu
Jan 7, 2010·Communications in Statistics: Theory and Methods·Nianjun LiuHongyu Zhao
Oct 13, 2006·Human Molecular Genetics·Xiangning ChenKenneth S Kendler
Feb 27, 2007·Genetic Epidemiology·F K MensahG R Law

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