Two-dimensional enrichment analysis for mining high-level imaging genetic associations

Abstract

Enrichment analysis has been widely applied in the genome-wide association studies, where gene sets corresponding to biological pathways are examined for significant associations with a phenotype to help increase statistical power and improve biological interpretation. In this work, we expand the scope of enrichment analysis into brain imaging genetics, an emerging field that studies how genetic variation influences brain structure and function measured by neuroimaging quantitative traits (QT). Given the high dimensionality of both imaging and genetic data, we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly considers meaningful gene sets (GS) and brain circuits (BC) and examines whether any given GS-BC pair is enriched in a list of gene-QT findings. Using gene expression data from Allen Human Brain Atlas and imaging genetics data from Alzheimer's Disease Neuroimaging Initiative as test beds, we present an IGEA framework and conduct a proof-of-concept study. This empirical study identifies 25 significant high-level two-dimensional imaging genetics modules. Many of these modules are relevant to a variety of neurobiological pathways or neurodegenerative diseases, showing the promise of the proposal framework for providing insight into the mechanism of complex diseases.

Keywords: Enrichment analysis; Genome-wide association study; Imaging genetics; Quantitative trait.

Fig. 1

Overview of the proposed Imaging Genetic Enrichment Analysis (IGEA) framework. A Perform SNP-level GWAS of brain wide imaging measures. B Map SNP-level GWAS findings to gene-based. C Construct gene-ROI expression matrix from AHBA data. D Construct GS–BC modules by performing 2D hierarchical clustering, and then filter out 2D clusters with an average correlation below a user-given threshold. E Perform IGEA by mapping gene-based GWAS findings to the identified GS–BC modules. F For each enriched GS–BC module, examine the GS using GO terms, KEGG pathways, and OMIM disease databases, and map the BC to the brain

Fig. 2

Manhattan plot of imaging quantitative genome-wide association for Alzheimer’s Disease individuals based on Precuneus (right) measurement from amyloid imaging data. The x axis represents the chromosomes and the y axis represents $-{log}_{10}(P)$, where P is the gene-based significance

Fig. 3

Eight unique brain circuits (BCs) identified from IGEA. ROIs belonging to each BC are colored in red

Fig. 4

Brain maps of four brain circuits (BCs) identified from IGEA

Fig. 5

Results of KEGG pathway enrichment for identified GSs. The x axis represents unique GS ID, and y axis represents −log p value of enrichment significance of KEGG pathways. Marked cell represents significant enrichment (p value $<0.05$)