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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

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Mediation analysis in longitudinal study with high-dimensional methylation mediators.

Yidan Cui¹, Qingmin Lin², Xin Yuan¹

¹Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Rd, 200240 Shanghai, China.

Briefings in Bioinformatics

|October 15, 2024

Summary

This summary is machine-generated.

We developed a new method for high-dimensional longitudinal mediation analysis, improving accuracy and reducing errors. This approach identified novel DNA methylation sites linking paternal BMI to offspring growth.

Keywords:

DNA methylation high-dimensional mediation analysis longitudinal data sure independence screening

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Area of Science:

Biostatistics
Genetics
Epidemiology

Background:

Mediation analysis is crucial for understanding exposure-outcome pathways.
Existing methods struggle with high-dimensional longitudinal data.
Identifying mediators in complex biological systems remains challenging.

Purpose of the Study:

To develop and validate a novel high-dimensional mediation analysis method for longitudinal data.
To enable robust variable selection and indirect effect assessment.
To identify novel mediating factors in the paternal BMI-offspring BMI relationship.

Main Methods:

Proposed a novel approach combining linear mixed-effect models and generalized estimating equations.
Utilized sure independence screening for dimension reduction of candidate mediators.
Employed the Sobel test with Bonferroni correction for indirect effect hypothesis testing.

Main Results:

The proposed method demonstrated superior performance with higher F1 scores compared to linear methods.
Achieved more accurate parameter estimation and a significantly lower false discovery rate.
Identified two novel CpG sites mediating the effect of paternal BMI on offspring BMI in a large cohort.

Conclusions:

The novel method effectively addresses high-dimensional mediation analysis in longitudinal studies.
This approach enhances the identification of biological pathways and potential therapeutic targets.
The findings provide new insights into the intergenerational effects of BMI.