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

Abstract

Enrichment analysis has been widely applied in the genome-wide association studies (GWAS), 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.

Overcoming bias in gene-set enrichment analyses of brain-wide transcriptomic data

“…The most reported GO category in our survey was 'chemical synaptic transmission' (GO:000726), which has been reported in 15 human analyses from 10 different studies (21,26,32,(69)(70)(71)(72)(73)(74)(75) and four different mouse analyses from three different studies ( organization of human resting-state functional connectivity (21), human adolescent cortical shrinkage and myelination (26), and tract-traced structural connectivity in the mouse brain (17). Some other commonly reported categories include 'potassium ion transmembrane transport' (22,26,32,74,77,78), 'learning or memory' (15,74,75,79,80), and 'electron transport chain' (18,19,23,26,74)…”

Understanding disease progression and improving Alzheimer's disease clinical trials: Recent highlights from the Alzheimer's Disease Neuroimaging Initiative

“…5). Similarly, a novel method that explored higher level associations between sets of genes in the pathway and brain circuits from Αβ PET imaging identified 12 gene set–brain circuit modules relevant to calcium signaling, oxidative stress, other neurodegenerative disorders, and cancers [21]. The results from these studies attest to a biological complexity of AD involving multiple contributing factors.…”

Brain Imaging Genomics: Integrated Analysis and Machine Learning

“…Yao et al [90] expanded the scope of enrichment analysis from GWAS to brain imaging genomics studies. They proposed a new 2-D enrichment analysis paradigm, called imaging genetic enrichment analysis (IGEA).…”

“…For example, the enrichment analysis can be transferred from the genomic domain to the imaging domain to perform an ROI enrichment analysis based on voxelwise findings [95]. It can also be extended to 2-D IGEA to mine high-level imaging genetic associations based on massive BWGW SNP-QT results [90]. In addition, given the recent availability of tissue-specific networks, the imaging GWAS-based module identification can be extended to use the functional interaction network specific to the studied imaging QT (i.e., tissue from the corresponding brain region) [99].…”

“…1) To help biological interpretation, we can incorporate prior knowledge and structure into the learning methods and try to identify associations between meaningful biological entities, such as genes, pathways, ROIs, and genetic and brain networks. One strategy is to perform GWAS enrichment analysis (see [90] and [99]) to measure collective effects at the set level. This can reduce the number of tests and increase the detection power.…”