Correspondence between fMRI and SNP data by group sparse canonical correlation analysis

Abstract

Both genetic variants and brain region abnormalities are recognized as important factors for complex diseases (e.g., schizophrenia). In this paper, we investigated the correspondence between single nucleotide polymorphism (SNP) and brain activity measured by functional magnetic resonance imaging (fMRI) to understand how genetic variation influences the brain activity. A group sparse canonical correlation analysis method (group sparse CCA) was developed to explore the correlation between these two datasets which are high dimensional-the number of SNPs/voxels is far greater than the number of samples. Different from the existing sparse CCA methods (sCCA), our approach can exploit structural information in the correlation analysis by introducing group constraints. A simulation study demonstrates that it outperforms the existing sCCA. We applied this method to the real data analysis and identified two pairs of significant canonical variates with average correlations of 0.4527 and 0.4292 respectively, which were used to identify genes and voxels associated with schizophrenia. The selected genes are mostly from 5 schizophrenia (SZ)-related signalling pathways. The brain mappings of the selected voxles also indicate the abnormal brain regions susceptible to schizophrenia. A gene and brain region of interest (ROI) correlation analysis was further performed to confirm the significant correlations between genes and ROIs.

Multimodal image analysis of clinical influences on preterm brain development

“…Several advances have been made recently in the joint analysis of imaging and genetic information within joint, or linked, ICA frameworks 50. Using similar methods in this cohort could reveal a genetic underpinning to susceptibility for some imaging phenotypes or environmental impacts and represents an important future avenue for investigation.…”

Joint sparse canonical correlation analysis for detecting differential imaging genetics modules

“…In the realm of imaging genetics, CCA is regarded as an efficient algorithm for multivariate analysis of correlations with low computational complexity, which has been used in our previous studies (Lin et al, 2014). Our main results in this paper presented an extension of sparse CCA to discover differential association modules from different disease statuses.…”

“…To simulate the correlation between fMRI and SNPs, a latent variable model similar to (Lin et al, 2014;Parkhomenko et al, 2009) was used. We first generated one imaging canonical vector a with l non-zero entries and three genomic canonical vectors b k with m non-zero entries.…”

“…Examples include sparse CCA (Parkhomenko et al, 2009;Witten and Tibshirani, 2009), sparse PLS (Chun and Keles¸, 2010) and sparse reduced rank regression (Vounou et al, 2010), which have been demonstrated to be effective in detecting multivariate genomic and brain imaging associations (Grellmann et al, 2015;Liu and Calhoun, 2014). To incorporate biological prior knowledge and data structures to guide the search of associations, group SCCA (Lin et al, 2014) and network-guided sparse reduced rank regression (Wang et al, 2014) were proposed, which can further improve variable selection.…”

Sparse models for correlative and integrative analysis of imaging and genetic data

“…Both are multivariate models to account for pleiotropic effects [43, 44] (i.e., genetic variants associated with multiple imaging quantitative traits) and the covariance structure among imaging endophenotypes [45, 46]. Two block methods include sparse CCA [47, 48], regularized kernel CCA [49] and sparse PLS correlation (PLSC) [50, 51], which are usually used for correspondence analysis between two modalities. Floch et al [50] compared the performance of detecting the correlation between fMRI imaging and SNP data with different methods such as univariate approach, PLSC, sparse PLSC, regularized kernel CCA, and their combinations with PCA and pre-filters.…”