TY - JOUR
T1 - Efficient inference of parent-of-origin effect using case-control mother–child genotype data
AU - Tian, Yuang
AU - Zhang, Hong
AU - Bureau, Alexandre
AU - Hochner, Hagit
AU - Chen, Jinbo
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/12
Y1 - 2024/12
N2 - Parent-of-origin effect plays an important role in mammal development and disorder. Case-control mother–child pair genotype data can be used to detect parent-of-origin effect and is often convenient to collect in practice. Most existing methods for assessing parent-of-origin effect do not incorporate any covariates, which may be required to control for confounding factors. We propose to model the parent-of-origin effect 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 robust statistical inference procedure is developed based on a modified profile log-likelihood in a retrospective way. A computationally feasible expectation–maximization algorithm is devised to estimate all unknown parameters involved in the modified profile log-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 log-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.
AB - Parent-of-origin effect plays an important role in mammal development and disorder. Case-control mother–child pair genotype data can be used to detect parent-of-origin effect and is often convenient to collect in practice. Most existing methods for assessing parent-of-origin effect do not incorporate any covariates, which may be required to control for confounding factors. We propose to model the parent-of-origin effect 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 robust statistical inference procedure is developed based on a modified profile log-likelihood in a retrospective way. A computationally feasible expectation–maximization algorithm is devised to estimate all unknown parameters involved in the modified profile log-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 log-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.
KW - Modified profile log-likelihood
KW - Mother–child pair
KW - Parent-of-origin effect
UR - http://www.scopus.com/inward/record.url?scp=85192701537&partnerID=8YFLogxK
U2 - 10.1016/j.jspi.2024.106190
DO - 10.1016/j.jspi.2024.106190
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C2 - 38818512
AN - SCOPUS:85192701537
SN - 0378-3758
VL - 233
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
M1 - 106190
ER -