Pathway-based factor analysis of gene expression data produces highly heritable phenotypes that associate with age

Area of Science:

• Genomics
• Systems Biology
• Statistical Genetics

Background:

• Factor analysis is used to reduce noise in high-dimensional data for genetic association studies.
• Derived factors can summarize biologically relevant variation.
• Understanding relationships between gene expression, heritability, and aging is crucial.

Purpose of the Study:

• To demonstrate how pathway expression factors can analyze relationships between expression, heritability, and aging.
• To create more reliable phenotypes from gene expression data using factor analysis.
• To increase the power of discovering biologically relevant associations.

Main Methods:

• Applied statistical factor analysis to skin gene expression data from 647 twins (MuTHER Consortium).
• Summarized gene expression patterns into 930 pathway phenotypes across 186 KEGG pathways.
• Identified associations between age and pathway phenotypes using a stringent Bonferroni threshold.

Main Results:

• Identified 69 significant age-phenotype associations across 57 KEGG pathways.
• Pathway phenotypes exhibited higher heritability compared to individual gene expression levels.
• Significant pathways involved sugar/fatty acid metabolism and insulin signaling.

Conclusions:

• Factor analysis combined with biological knowledge generates reliable, low-noise phenotypes from gene expression data.
• This approach enhances the power to discover biologically relevant associations, including those with aging.
• Pathway phenotypes offer a promising tool for association studies with other environmental factors.