M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits

Area of Science:

• Genetics
• Genomics
• Computational Biology

Background:

• Shared genetic risk factors are increasingly identified for early-onset diseases using de novo mutations (DNMs).
• Leveraging multi-trait data can enhance statistical power for gene discovery.
• Existing methods for jointly analyzing DNMs across multiple traits are limited.

Purpose of the Study:

• To develop a novel framework, M-DATA (Multi-trait framework for De novo mutation Association Test with Annotations), for integrated analysis of DNMs from multiple correlated traits.
• To increase statistical power in identifying disease-associated genes by incorporating functional annotations and multi-trait data.
• To infer disease associations and gene-specific probabilities through a joint analysis approach.

Main Methods:

• Developed a framework (M-DATA) integrating data from multiple correlated traits and functional annotations.
• Employed an Expectation-Maximization algorithm to estimate disease association degrees and gene association probabilities.
• Applied the framework to jointly analyze de novo mutation data from congenital heart disease (CHD) and autism.

Main Results:

• The M-DATA framework successfully identified 23 genes associated with congenital heart disease (CHD) through joint analysis.
• Included in the identified genes were 12 novel genes, significantly increasing discovery compared to single-trait analyses.
• Demonstrated enhanced statistical power for gene discovery by integrating multi-trait DNM data.

Conclusions:

• Jointly analyzing de novo mutations from multiple correlated traits using M-DATA significantly enhances the identification of disease-associated genes.
• The framework provides novel insights into the genetic etiology of complex diseases like CHD.
• M-DATA represents a powerful new approach for multi-trait genetic association studies.