Abstract
Distance correlation is a measure that can detect both linear and nonlinear associations. However, applying distance correlation to imaging genetic studies often needs multiple testing correction due to the large number of multiple inferences. As a result, the sensitivity of its detection may be low. We propose a new model, distance canonical correlation analysis (DCCA), which overcomes this problem by searching a combination of features with the highest distance correlation. This is achieved by constructing a distance kernel function followed by solving a subsequent optimization problem. The ability to detect both linear and nonlinear associations makes DCCA suitable for analyzing complex multimodal and imaging-genetic associations. When applied to a brain imaging-genetic study from the Philadelphia Neurodevelopmental Cohort (PNC), DCCA detected several mental disorder-related gene pathways and brain networks. Experiments on brain connectivity found that the default mode network had strong nonlinear connections with other brain networks. When applied to the study of age effects, DCCA revealed that the connections of brain networks were relatively weak in younger groups but became stronger at older age stages. It indicates that adolescence is a vital stage for brain development. DCCA thus reveals a number of interesting findings and demonstrates a powerful new approach for analyzing multimodal brain imaging data.
Highlights
The brain is a complex organ and investigating its development and relationship with genomics is of great interest
We proposed a new model, distance canonical correlation analysis (DCCA), which overcomes the limitation of distance correlation in detecting significant associations when feature size is large
The ability to detect nonlinear group–group associations makes DCCA more suitable for analyzing complex multi-omics and imaging-genetic associations, in which both genetic factors and brain regions of interest (ROI) may work as groups when regulating a phenotype or performing a specific brain function
Summary
The brain is a complex organ and investigating its development and relationship with genomics is of great interest. E.g., functional magnetic resonance imaging (fMRI), and sequencing of genetic variations, e.g., singular nucleotide polymorphism (SNP), have facilitated the analysis of the relationship between brain regions and genetic variations. FMRI detects changes in functional brain activity at each voxel, which can be clustered into regions of interest (ROI). SNPs are important genetic factors underlying differences in phenotypes among human beings. Association analyses, e.g., canonical correlation analysis (CCA),[1] have been conducted to study brain connectivity and how genetic factors and endophenotypes interact.[2] these methods typically use Pearson correlation which only captures linear relationships while nonlinear correlations may exist among brain regions.[3]
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