Genetic association studies with bivariate mixed responses subject to measurement error and misclassification.

Abstract:

:In genetic association studies, mixed effects models have been widely used in detecting the pleiotropy effects which occur when one gene affects multiple phenotype traits. In particular, bivariate mixed effects models are useful for describing the association of a gene with a continuous trait and a binary trait. However, such models are inadequate to feature the data with response mismeasurement, a characteristic that is often overlooked. It has been well studied that in univariate settings, ignorance of mismeasurement in variables usually results in biased estimation. In this paper, we consider the setting with a bivariate outcome vector which contains a continuous component and a binary component both subject to mismeasurement. We propose an induced likelihood approach and an EM algorithm method to handle measurement error in continuous response and misclassification in binary response simultaneously. Simulation studies confirm that the proposed methods successfully remove the bias induced from the response mismeasurement.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Zhang Q,Yi GY

doi

10.1002/sim.8688

subject

Has Abstract

pub_date

2020-11-20 00:00:00

pages

3700-3719

issue

26

eissn

0277-6715

issn

1097-0258

journal_volume

39

pub_type

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