Statistical Methods for Multi-Omics Analysis in Neurodevelopmental Disorders: From High Dimensionality to Mechanistic...

Area of Science:

• Genetics
• Neuroscience
• Bioinformatics

Background:

• Neurodevelopmental disorders (NDDs) like autism and ADHD are complex and affect millions globally.
• High-throughput omics technologies provide insights into genetic and molecular underpinnings of NDDs.
• Analyzing high-dimensional omics data presents significant statistical challenges.

Purpose of the Study:

• To provide a comprehensive overview of statistical frameworks for analyzing high-dimensional omics datasets in NDDs.
• To illustrate how these methods reveal convergent molecular signatures across different omics layers.
• To discuss emerging strategies for advancing mechanistic understanding and precision medicine in NDDs.

Main Methods:

• Review of statistical frameworks including univariate/multivariate models, penalized regression, sparse canonical correlation analysis, partial least squares.
• Overview of integrative multi-omics methods like DIABLO, similarity network fusion, and MOFA.
• Illustration of methods using transcriptomic, proteomic, and metabolomic data from human cohorts and experimental models.

Main Results:

• Statistical approaches have identified convergent molecular signatures in NDDs, including synaptic, mitochondrial, and immune dysregulation.
• These signatures are consistently observed across transcriptomic, proteomic, and metabolomic data layers.
• Emerging strategies like single-cell omics and machine learning integration show promise.

Conclusions:

• Integrative multi-omics analysis, supported by robust statistical methods, is essential for understanding NDD mechanisms.
• These approaches can translate complex molecular patterns into valuable biomarkers and therapeutic targets.
• The findings pave the way for advancing precision medicine in the field of neurodevelopmental disorders.