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Updated: Apr 28, 2026
Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy
Published on: August 25, 2019
Entropy-based selection for maternal-fetal genotype incompatibility with application to preterm prelabor rupture of
Shaoyu Li1, Yuehua Cui, Roberto Romero
1Department of Biostatistics, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, USA. shaoyu.li@stjude.org.
Background:
Maternal-fetal genotype incompatibility (MFGI) is increasingly reported to influence human diseases, especially pregnancy-related complications. In practice, it is challenging to identify the ideal incompatibility model for analysis, since the true MFGI mechanism is generally unknown. The underlying MFGI mechanism for different genetic variants can vary, and to use a single incompatibility model for all circumstances would cause power loss in testing MFGI.
Results:
In this article, we propose a practical 2-step procedure that incorporates a model selection strategy based on an entropy measurement to select the most appropriate MFGI model represented by data and test the significance of the MFGI effect using the chosen model within the generalized linear regression framework.
Conclusions:
Our simulation studies show that the proposed two-step procedure controls the type I error rate and increase the testing power under various scenarios. In a real data application, our analysis reveals genes having an MFGI effect, which may not be detected with a non-model selection counterpart.
Insights
This study introduces a two-step method to accurately analyze maternal-fetal genotype incompatibility (MFGI) effects. It improves disease association studies by selecting the best model, increasing detection power for MFGI.
Area of Science:
- Genetics
- Reproductive Health
- Biostatistics
Background:
- Maternal-fetal genotype incompatibility (MFGI) impacts human diseases, particularly pregnancy complications.
- Identifying the correct MFGI model for analysis is difficult due to unknown mechanisms.
- Using a single model for all MFGI scenarios leads to reduced statistical power.
Purpose of the Study:
- To develop a practical two-step procedure for analyzing MFGI.
- To incorporate an entropy-based model selection strategy for MFGI analysis.
- To test MFGI significance using a data-driven model within generalized linear regression.
Main Methods:
- A two-step procedure combining model selection and significance testing.
- Entropy measurement for selecting the most appropriate MFGI model.
- Generalized linear regression framework for MFGI effect testing.
Main Results:
- The proposed method effectively controls type I error rates.
- Increased statistical power for detecting MFGI effects across various scenarios.
- Identified genes with MFGI effects in real data that were missed by non-model selection methods.
Conclusions:
- The two-step procedure offers a robust approach to analyzing MFGI.
- This method enhances the detection of MFGI effects in genetic studies.
- The approach is valuable for understanding MFGI's role in disease and pregnancy outcomes.
