Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.

Area of Science:

• Neurogenetics
• Computational Biology
• Genomic Medicine

Background:

• Brain disorders possess complex genetic architectures that are challenging to decode using traditional methods.
• Machine learning (ML) offers advanced computational approaches for analyzing large-scale genetic and phenotypic data.

Purpose of the Study:

• To review the strengths and limitations of ML methods in the context of brain disorder genetics.
• To highlight ML applications in genetic prediction, patient stratification, and modeling genetic interactions.
• To provide a roadmap for leveraging ML to unravel genomic complexity in brain disorders.

Main Methods:

• Review of ML techniques including their application in augmenting polygenic risk scores (PRS).
• Integration of functional genomics and multimodal data.
• Incorporation of biological knowledge into ML models for enhanced interpretability.

Main Results:

• ML methods show promise in improving genetic prediction and patient stratification for brain disorders.
• Advanced ML techniques can address challenges posed by rare variants and weak genetic effects.
• ML is expected to surpass classical statistical methods in disease risk prediction and subgroup identification.

Conclusions:

• ML is a powerful tool for decoding the genetic complexity of brain disorders.
• Integrating diverse data types and biological knowledge enhances ML model performance and interpretability.
• ML advancements, including federated learning, are poised to drive significant discoveries in neurogenetics.