The Quantitative-MFG Test: A Linear Mixed Effect Model to Detect Maternal-Offspring Gene Interactions.

Area of Science:

• Genetics
• Quantitative Trait Analysis
• Complex Disease Etiology

Background:

• Maternal-offspring gene interactions, or maternal-fetal genotype (MFG) incompatibilities, are understudied in complex diseases.
• Existing methods have limitations in investigating MFG influence on quantitative traits.
• MFG incompatibilities are implicated in diseases from birth to adult onset.

Purpose of the Study:

• To introduce the quantitative-MFG (QMFG) test, a novel statistical method for analyzing maternal-offspring genotype interactions.
• To provide tools for investigating the role of MFG incompatibilities in quantitative traits and complex diseases.
• To demonstrate the limitations of standard genetic association models in capturing MFG effects.

Main Methods:

• Developed the quantitative-MFG (QMFG) test using a linear mixed model framework.
• Incorporated maternal and offspring genotypes as fixed effects and family correlations as random effects.
• Developed likelihood ratio tests (LRTs) and rapid score tests for inference and parameter estimation.

Main Results:

• The QMFG test correctly handles various family structures and MFG incompatibility scenarios.
• Demonstrated that standard models (offspring genotypes only) have low power or yield incorrect conclusions for MFG effects.
• Showcased that overly restrictive MFG models can miss important offspring genetic effects.
• Validated the QMFG score test's effectiveness and speed using genome-wide association study data.

Conclusions:

• The QMFG test offers a robust method for analyzing maternal-offspring gene interactions in complex trait studies.
• This approach enhances the power to detect genetic associations influenced by maternal-fetal genotype incompatibilities.
• The QMFG test has significant potential for discovering novel genetic pathways underlying complex diseases.