Efficient inference of parental origin effects using case-control mother-child genotype data

TL;DR

This paper introduces a robust logistic regression framework with an EM algorithm for efficiently inferring parental origin effects from case-control mother-child genotype data, incorporating covariates and linked markers.

Contribution

It develops a novel statistical inference procedure using a modified profile likelihood and an EM algorithm tailored for parental origin effect analysis with covariates and incomplete data.

Findings

The method achieves consistent and efficient parameter estimation.

Simulation studies validate the finite sample performance.

Application to real data demonstrates practical utility.

Abstract

Parental origin effects play an important role in mammal development and disorder. Case-control mother-child pair genotype data can be used to detect parental origin effects and is often convenient to collect in practice. Most existing methods for assessing parental origin effects do not incorporate any covariates, which may be required to control for confounding factors. We propose to model the parental origin effects through a logistic regression model, with predictors including maternal and child genotypes, parental origins, and covariates. The parental origins may not be fully inferred from genotypes of a target genetic marker, so we propose to use genotypes of markers tightly linked to the target marker to increase inference efficiency. A computationally robust statistical inference procedure is developed based on a modified profile likelihood in a retrospective way. A computationally feasible expectation-maximization algorithm is devised to estimate all unknown parameters involved in the modified profile likelihood. This algorithm differs from the conventional expectation-maximization algorithm in the sense that it is based on a modified instead of the original profile likelihood function. The convergence of the algorithm is established under some mild regularity conditions. This expectation-maximization algorithm also allows convenient handling of missing child genotypes. Large sample properties, including weak consistency, asymptotic normality, and asymptotic efficiency, are established for the proposed estimator under some mild regularity conditions. Finite sample properties are evaluated through extensive simulation studies and the application to a real dataset.