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. Author manuscript; available in PMC: 2020 May 15.
Published in final edited form as: J Neurosci Methods. 2019 Mar 19;320:64–71. doi: 10.1016/j.jneumeth.2019.03.011

A method for building a genome-connectome bipartite graph model

Qingbao Yu 1,#, Jiayu Chen 1,#,*, Yuhui Du 1,2, Jing Sui 1,3,4, Eswar Damaraju 1, Jessica A Turner 5, Theo GM van Erp 6, Fabio Macciardi 6, Aysenil Belger 7, Judith M Ford 8,9, Sarah McEwen 10, Daniel H Mathalon 8,9, Bryon A Mueller 11, Adrian Preda 6, Jatin Vaidya 12, Godfrey D Pearlson 13,14,15, Vince D Calhoun 1,15,16,*
PMCID: PMC6504548  NIHMSID: NIHMS1525826  PMID: 30902651

Abstract

It has been widely shown that genomic factors influence both risk for schizophrenia and variation in functional brain connectivity. Moreover, schizophrenia is characterized by disrupted brain connectivity. In this work, we proposed a genome-connectome bipartite graph model to perform imaging genomic analysis. Functional network connectivity (FNC) was estimated after decomposing resting state functional magnetic resonance imaging data from both healthy controls (HC) and patients with schizophrenia (SZ) into spatial brain components using group independent component analysis (G-ICA). Then 83 FNC connections showing a group difference (HC vs SZ) were selected as fMRI nodes, and eighty-one schizophrenia-related single nucleotide polymorphisms (SNPs) were selected as genetic nodes respectively in the bipartite graph. Edges connecting pairs of genetic and fMRI nodes were defined based on the SNP-FNC associations across subjects evaluated by a general linear model. Results show that some SNP nodes in the bipartite graph have a high degree implying they are influential in modulating brain connectivity and may be more strongly associated with the risk of schizophrenia than other SNPs. A bi-clustering analysis detected a cluster with 15 SNPs interacting with 38 FNC connections, most of which were within or between somato-motor and visual brain areas. This suggests that the activity of these brain regions may be related to common SNPs and provides insights into the pathology of schizophrenia. The findings suggest that the SNP-FNC bipartite graph approach is a novel model to investigate genetic influences on functional brain connectivity in mental illness.

Keywords: fMRI, FNC, SNPs, bipartite graph

1. Introduction:

Resting state functional magnetic resonance imaging (R-fMRI) is a popular non-invasive technique to investigate the characteristics of the human brain (Fox and Raichle, 2007; van den Heuvel and Hulshoff Pol, 2010). Functional connectivity (FC) analysis is a widely used and informative approach to analyze R-fMRI data (Biswal et al., 1995; Biswal, 2012; Friston, 2011; Friston et al., 1996). Functional network connectivity (FNC) analysis is a data driven method to compute the inter-network FC in R-fMRI data (Jafri et al., 2008). In this approach, group spatial independent component analysis (G-ICA) is firstly performed on R-fMRI data, and then brain components of interest are determined. Finally, an FNC matrix is constructed by computing the correlation values of each pair of ICA time courses of the brain components of interest (He et al., 2016). Using this method, disrupted brain connectivity between spatial brain components has been consistently shown in individuals with brain disorders such as schizophrenia (Arbabshirani and Calhoun, 2011; Arbabshirani et al., 2013; Arbabshirani et al., 2017; Du et al., 2018; Du et al., 2015). Graph measures of FNC in both healthy controls (HCs) and patients with schizophrenia (SZs) are also widely studied (Betzel and Bassett, 2016; van den Heuvel and Fornito, 2014; Yu et al., 2018; Yu et al., 2015; Yu et al., 2011a; Yu et al., 2013a; Yu et al., 2013b; Yu et al., 2011b). In addition, it has been demonstrated that, in some cases, data-driven ICA derived brain nodes outperform brain atlas based-ROI nodes for building a brain connectivity brain graph (Yu et al., 2017). However, the genetic basis of brain FNC is largely unknown, though resting brain functional patterns are strongly heritable (Adhikari et al., 2018; Glahn et al., 2010), which motivated us to explore the associations of FNC with genetic variants.

It is well known that genetic factors influence both risk for schizophrenia and variation in functional brain connectivity. For example, heritability (the proportion of total variance in a trait due to genetic variation) has been estimated from family and twin studies at 81% for schizophrenia (Sullivan et al., 2003). Genome-wide association studies (GWAS) are increasingly used to characterize genetic risk profiles for schizophrenia (Potkin et al., 2009). In addition, numerous twin and family studies have provided strong evidence for heritability of diverse aspects of functional brain connectivity (Anokhin, 2014; Fu et al., 2015; Ge et al., 2017; Yang et al., 2016). Heritability of effective and functional connectivity in default mode network has been reported to be 0.54 (Xu et al., 2017), and 0.424 (Glahn et al., 2010) respectively. Heritability of graph measures in brain connectivity has also been investigated. A study with monozygotic and dizygotic twins has estimated the heritability of five graph metrics (clustering coefficient, modularity, rich-club coefficient, global efficiency, and small-worldness), and results show that the graph metrics are moderately influenced by genetic factors suggesting that these metrics may be potential endophenotypes for psychiatric diseases and suitable for genetic association studies (Sinclair et al., 2015). In a twin fMRI study involving young children, global efficiency of functional connectivity brain graphs have been found to be under genetic control (42%) implying that a set of genes is shaping the underlying architecture of functional brain communication during development (van den Heuvel et al., 2013). This also implies that neuroimaging parameters such as functional brain connectivity may be useful as endophenotypes.

The combination of neuroimaging and genetic analyses, or imaging genetics (Petrella et al., 2008), has been increasingly used in the past decade. By incorporating neuroimaging endophenotypes with the genetic data from healthy and/or disease subjects, the effects of genetic variations (such as SNPs) on brain structure and function can be assessed. Imaging genetics is a powerful way to link genetic factors to structural and functional variation in brain systems related to cognition and emotion (Meyer-Lindenberg and Weinberger, 2006). One strategy is to utilize endophenotypes derived from neuroimaging data to test the genetic association of a relatively well validated candidate gene of specific mental illness (Arslan, 2018). The other strategy is blind genome wide assessment, i.e. genome-wide association studies with imaging phenotypes serving as the trait. The common assumption of using intermediate imaging phenotype is that genetic variants present more direct effects at the level of the brain than complex behavior, and carriers of risk alleles may present brain abnormalities even if they show no clinical diagnostic characteristics. Imaging genetics therefore becomes a guide to the discovery of neural circuitry that translates genetic effects into behavior, and endophenotypes implicate endo-mechanisms (Meyer-Lindenberg and Weinberger, 2006).

In this work, we propose a genome-connectome multimodal bipartite graph model to perform imaging genetic analysis. A bipartite graph, also called a bigraph, is a set of graph nodes decomposed into two disjoint sets such that no two graph nodes within the same set are adjacent (Brualdi et al., 1980). Here we build a bipartite graph in which one group nodes are schizophrenia-related SNPs and the other group nodes represent schizophrenia-related functional brain connectivity. Associations between nodes from these two sets are evaluated using a general linear model. This pilot study provides a novel framework by combining group ICA, bipartite graph analysis, and biclustering analysis to investigate possible genetic basis of the variations in functional brain connectivity and the risk of schizophrenia.

2. Materials and Methods:

2.1. Participants

A total of 97 HCs (30 female; 5 left handed; age: range 19 – 60; mean ± SD 37 ± 11) and 70 SZs (11 female; 1 left handed; age: range 18 – 60; mean ± SD 38 ± 11) from 7 research sites participated in this study. All participants provided written, informed consent in accordance with internal review boards of their corresponding institutions.

2.2. fMRI data acquisition

Brain imaging data were collected from 3T Siemens Tim Trio MRI Systems (6 of the 7 sites) and a 3T General Electric Discovery MR750 scanner (the other site). Resting state fMRI scans were acquired using a standard gradient-echo planar imaging paradigm: FOV of 220 × 220 mm (64 × 64 matrix), TR = 2 s, TE = 30 ms, flip angle = 70°, 32 sequential ascending axial slices of 4 mm thickness and 1 mm skip. A total of 162 brain volumes were acquired for each subject with eyes closed over 5 minutes and 24 seconds.

2.3. fMRI data preprocessing

FMRI data preprocessing was performed using a combination of toolboxes (AFNI: https://afni.nimh.nih.gov/; SPM: http://www.fil.ion.ucl.ac.uk/spm/; GIFT: http://mialab.mrn.org/software/gift) and custom Matlab code (https://www.mathworks.com/products/matlab.html). We performed rigid body motion correction using the INRIAlign (Freire et al., 2002) toolbox in SPM to correct for subject head motion followed by slice-timing correction to account for timing differences in slice acquisition. Then the fMRI data were de-spiked using AFNI’s 3dDespike algorithm to mitigate the impact of outliers. The fMRI data were subsequently warped to a Montreal Neurological Institute (MNI) template and resampled to 3 × 3 × 3 mm isotropic voxels. Instead of Gaussian smoothing, we smoothed the data to 6 mm full width at half maximum (FWHM) using AFNI’s BlurToFWHM algorithm which performs smoothing by a conservative finite difference approximation to the diffusion equation. This approach had been shown to reduce scanner specific variability in smoothness providing “smoothness equivalence” to data across sites (Friedman et al., 2006a; Friedman et al., 2006b). Each voxel time course was variance normalized prior to performing group ICA as this has shown to better decompose subcortical sources in addition to cortical networks (Allen et al., 2011; Damaraju et al., 2014).

2.4. Group ICA of fMRI

After preprocessing, fMRI of both controls and patients were analyzed using a spatial group ICA as implemented in GIFT software (Calhoun et al., 2001; Erhardt et al., 2011). Spatial ICA decomposes the subject data into linear mixtures of spatially independent components (ICs) that exhibit unique time course profiles. A subject-specific data reduction step was first used to reduce 162-time-point data into 100 directions of maximal variability using principal component analysis (PCA). Then subject-reduced data were concatenated along the time dimension and a second PCA step reduced this grouped data matrix further into 100 components along directions of maximal group variability. One hundred ICs were obtained from the group PCA reduced matrix using the Infomax algorithm (Bell and Sejnowski, 1995). To ensure stability of estimation, we repeated the ICA algorithm 20 times in ICASSO (http://research.ics.aalto.fi/ica/icasso/), and the most central run was selected and further analyzed (Ma et al., 2011). Subject specific spatial maps (SMs) and time courses (TCs) were obtained using the spatiotemporal regression back reconstruction approach implemented in GIFT (Calhoun et al., 2001; Erhardt et al., 2011).

2.5. Post-ICA processing

Subject-specific SMs and TCs underwent post-processing as described in our earlier work (Allen et al., 2014). Briefly, we obtained one sample t-test maps for each SM across all subjects and thresholded these maps to obtain regions of peak activation clusters for that component. We also computed mean power spectra of the corresponding TCs. We identified a set of components as intrinsic connectivity networks (ICNs) if their peak activation clusters fell on gray matter and showed less overlap with known vascular, susceptibility, ventricular, and edge regions corresponding to head motion. We also ensured that the mean power spectra of the selected ICN time courses showed higher low frequency spectral power. This selection procedure resulted in 50 ICNs out of the 100 ICs obtained. The subject specific TCs corresponding to the 50 ICNs selected were detrended, despiked, and then band pass filtered (0.01 − 0.10 Hz) before the subsequent FNC analysis (Allen et al., 2014; Damaraju et al., 2014; Yu et al., 2015).

2.6. FNC analysis and the definition of connectome nodes

We constructed the FNC which is defined as pairwise correlations between ICN time courses, as a measure of connectivity among different ICNs during the scan duration for each individual. In this work, the FNC computed using the whole ICN time courses is referred to as stationary or static FNC (sFNC). The mean sFNC matrix across subjects was also computed in each group.

FMRI nodes in the gene-fMRI bipartite graph represent FNC connections in the sFNC matrix. Eighty-three fMRI nodes were selected by below criteria: 1. Correlation value of the group mean FNC connection of HCs is higher than 0.3268 (r > 0.3268). This criterion was determined based on the significant correlation between ICNs (q < 0.05, Bonferroni correction). 2. Group difference (HCs vs SZs) on the correlation value is significant (q < 0.05, false discovery rate [FDR] correction).

2.7. SNP data acquisition and processing

DNA of each subject was extracted from blood or saliva samples. Illumina Human Omni1-Quad, Illumina Human Omni5, and Illumina Infinium MEGA + Psych were used for genotyping. No significant difference was noted in genotyping call rates between blood and saliva samples. The data then went through quality control (QC), imputation and post-imputation QC as described in (Chen et al., 2018). In brief, a standard QC (Chen et al., 2013) was firstly performed using PLINK (Purcell et al., 2007). Then imputation was conducted with SHAPEIT used for pre-phasing (Delaneau et al., 2011), IMPUTE2 for imputation (Marchini and Howie, 2010), and the 1000 Genomes data as the reference panel (Altshuler et al., 2012). Only markers with high imputation qualities (INFO score > 0.95) were retained. The imputed data were then aggregated and went through the post-imputation QC. Finally, linkage disequilibrium (LD) pruning (r2 > 0.9) was applied to yield 977,242 SNPs for which population structure was corrected using PCA (Price et al., 2006).

2.8. Definition of genome nodes

In this study, we used schizophrenia-related SNPs as genetic nodes. Firstly, the SNP with the lowest p value in each of the 108 schizophrenia-associated genetic loci reported by the largest psychiatric genomic consortium (PGC) study (Schizophrenia Working Group of the Psychiatric Genomics, 2014) was identified. SNPs with 0 risk alleles occurring in less than 17 subjects were excluded. This resulted in a set of 81 SNPs selected as gene nodes in the genome-connectome bipartite graph.

2.9. Building the genome-connectome bipartite graph

To build edges in the bipartite graph, we used a general linear model (a MANCOVA framework: http://mialab.mrn.org/software/mancovan) to evaluate the association between each pair of genome-connectome nodes. In this model, for each pair of SNP-FNC, correlation values of all subjects for the FNC connection were input as the dependent variable, and the SNP data coded based on number of risk allele (0, 1, or 2) of all subjects were input as the independent variable. To control sites, groups, and ethnicity effects, they were input as covariates. A significant (p < 0.05, uncorrected) association was used to determine whether there is an edge between those two nodes. Finally the degree of each SNP node in the bipartite graph was computed.

2.10. Biclustering analysis

Biclustering is a data mining technique which allows simultaneous clustering of the rows and columns of a matrix (Govaert and Nadif, 2008; Van Mechelen et al., 2004). As a result, submatrices exhibiting unique patterns can be revealed helping us to better understand the relationship between row and column variables (Gupta et al., 2013). To investigate if any subset of SNPs are densely associated with any subset of FNC connections, we performed a biclustering analysis on the SNP-FNC bipartite graph using MTBA (a Matlab toolbox for biclustering analysis) (Gupta et al., 2013).

3. Results:

3.1. Functional brain connectivity

Figure 1A displays the spatial maps of the 50 ICNs identified using group ICA on the fMRI data. Based on their anatomical and presumed functional properties, 50 ICN are arranged into groups of subcortical (SC), auditory (AUD), somato-motor (SM), visual (VIS), cognitive control (CC), default mode (DM), and cerebellum (CB) components. Figure 1B displays the structure of group mean FNC in HCs and SZs. Patterns of FNC are consistent with prior literature, showing modular organization within sensory-cognitive systems and default mode networks, as well as anti-correlation between these domains (Allen et al., 2014; Fox et al., 2005; Yu et al., 2015; Yu et al., 2011a). In addition, SZs show lower brain connectivity, in line with previous work (Liu et al., 2008; Lynall et al., 2010; Yu et al., 2015; Yu et al., 2011a; Yu et al., 2013b). Figure 1C shows the 83 FNC connections that are selected as fMRI nodes to build the gene-fMRI bipartite graph.

Figure 1.

Figure 1.

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Spatial maps (A) of 50 brain components selected to compute functional network connectivity (FNC). Brain components are divided into groups and arranged based on their anatomical and functional properties. Structure of group mean FNC (B. HC: healthy controls; SZ: patients with schizophrenia) shows that brain connectivity is lower in SZ. Eighty three connections (C) in the FNC are selected to be fMRI nodes for constructing the gene-fMRI bipartite graph. (SC: subcortical; AUD: auditory; SM: somatomotor; VIS: visual; CC: cognitive control; DM: default mode; CB: cerebellum)

3.2. SNP nodes

Eighty-one schizophrenia-related SNPs were selected as genetic nodes in the genetic-fMRI multimodal bipartite graph. Chromosome 2 had the maximum (10) such number across all 22 chromosomes. For the p values of schizophrenia association of the 81 SNPs and the distribution of them across chromosomes see Figure 2. SNP names are listed in Table 1.

Figure 2.

Figure 2.

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Scatter plot showing schizophrenia-related associations of the selected SNPs as genetic nodes and their distribution across chromosomes. The x axis is chromosomal position and the y axis is the significance (-log10(p)) of association as reported by the PGC study (Schizophrenia Working Group of the Psychiatric Genomics, 2014).

Table 1.

Eighty-one SNP nodes and their degree in the gene-fMRI bipartite graph (chromosomal positions based on hg19 genome assembly).

SNP rsID chromosome position degree SNP rsID chromosome position degree SNP rsID chromosome position degree
rs10503253 8 4180844 31 rs778341 2 233728801 5 rs9906500 17 2129210 2
rs4674917 2 225387522 24 rs2068012 14 30190316 5 rs12903146 15 61854663 2
rs7201930 16 9958655 21 rs12129573 1 73768366 4 rs4648845 1 2387101 2
rs79235996 14 72419030 15 rs9611520 22 41613303 4 rs67401222 1 8490320 2
rs604362 4 103147494 14 rs4481150 3 52837793 4 rs1209749 9 84802861 2
rs3784399 15 40598294 14 rs5995756 22 40000313 4 rs12446487 16 58671815 2
rs12623667 2 149405178 14 rs1501362 5 45378207 4 rs7722581 5 153676440 2
rs7267348 20 48131036 13 rs10206411 2 193933194 4 rs10860955 12 103581985 2
rs4908939 3 17793850 12 rs7197756 16 68275516 4 rs6867549 5 140153730 2
rs11693094 2 185601420 11 rs12939020 17 17950163 4 rs2007044 12 2344960 1
rs1009080 1 30431560 9 rs2789605 6 73155289 4 rs7108770 11 46648432 1
rs34026011 14 104050883 8 rs215411 4 23423603 4 rs2973155 5 152608619 1
rs11210892 1 44100084 8 rs4129585 8 143312933 3 rs34538000 19 19481379 1
rs12132780 1 207999605 8 rs11688767 2 57988194 3 rs217326 6 84349538 1
rs7896519 10 104866863 7 rs16880919 8 111620054 3 rs7438 4 170642246 1
rs7240986 18 53195249 7 rs2053079 19 30987423 3 rs134900 22 42683343 1
rs67733815 3 2549591 7 rs11956240 5 137840293 3 rs2693698 14 99719219 1
rs4788190 16 29948401 7 rs6846161 4 176866459 3 rs17149781 7 24695495 1
rs6670165 1 177280121 7 rs7838490 8 89585048 3 rs211792 7 110072128 1
rs2851447 12 123665113 6 rs1355585 15 70586617 3 rs4546329 5 60589739 0
rs9876421 3 36848316 6 rs2300990 5 109033393 3 rs12705304 7 104929633 0
rs1076884 16 13747803 6 rs10745572 12 92252357 3 rs68002929 2 146419989 0
rs4728408 7 137072096 6 rs8039305 15 91422543 2 rs11229116 11 57534542 0
rs7018304 8 60717504 6 rs13240464 7 110898915 2 rs1470276 11 24397676 0
rs7927176 11 123395864 6 rs787983 2 198345797 2 rs832190 3 63842629 0
rs13227554 7 2048220 5 rs2514218 11 113392994 2 rs302321 12 29928388 0
rs11693528 2 200736507 5 rs66691851 3 136154828 2 rs2909456 2 162836954 0
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3.3. Bipartite graph

Edges in the bipartite graph between SNP nodes and FNC nodes were determined based on the associations yielded by the general linear model. There are 401 total edges. SNP rs10503253 has the maximum degree of 31 across all 81 SNP nodes. Ten SNPs have a degree value above 10. For the degree of each SNP node see Table 1. Figure 3 shows the structure of this genome-connectome multimodal bipartite graph.

Figure 3.

Figure 3.

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Structure of the gene-fMRI bipartite graph. Some SNP nodes are showing high degree (> 10, see Table 1).

3.4. Biclustering

A biclustering analysis revealed one cluster with 15 SNP nodes and 38 FNC nodes (Figure 4). Most (27) of those 38 FNC nodes are connectivity within or between somato-motor and visual brain components. For spatial brain maps of the 38 FNC pairs see supplemental Figures.

Figure 4.

Figure 4.

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Cluster with 15 SNP nodes and 38 FNC nodes detected by biclustering analysis.

4. Discussion:

In this genome-connectome multimodal study, we proposed a SNP-FNC bipartite graph model. First, fMRI data of HC and SZ were decomposed into 100 spatial components. Then FNC was estimated using 50 components selected as ICNs. Eighty-three FNC connections showing group differences (HCs vs SZs) were chosen as fMRI nodes in the bipartite graph. Eighty-one SNPs previously associated with schizophrenia based on psychiatric genomics consortium (PGC) studies were chosen as genetic nodes. Edges between SNP and FNC were defined by computing the association between SNP node (numbers of risk alleles) and FNC node (correlation values) across subjects using a general linear model. N=401 edges were built in the bipartite graph. Ten SNP nodes are with a degree higher than 10 (see Table 1). A bi-clustering analysis revealed that 15 SNP nodes and 38 FNC nodes are densely associated as a cluster. Most (27) of the 38 FNC nodes are brain connectivity within or between somato-motor and visual components.

To our knowledge, this is the first study which used SNPs and FNC connections as genetic and fMRI nodes respectively to build a genome-connectome multimodal bipartite graph. This model may be widely adopted in a variety of imaging genetic studies. One advantage of the bipartite graph model is that associations between a group of SNPs and a group of connections of brain connectivity may be evaluated in one analysis, rather than most previous studies that focus only on one or a few candidate SNPs and brain connectivity (Arslan, 2018; Petrella et al., 2008). In addition, both SNP and FNC nodes are schizophrenia-related, and the associations are evaluated across both HCs and SZs, thus the edges in the bipartite graph indicate a consistent relationship in controls and patients.

After computing the degree of each SNP node in the bipartite graph, a total of 10 SNPs (rs10503253, rs4674917, rs7201930, rs79235996, rs604362, rs3784399, rs12623667, rs7267348, rs4908939, rs11693094) had a degree higher than 10 which suggests that they are modulating more functional brain connectivity and may be more strongly associated with the risk of schizophrenia than other SNPs (see Table 1). Future studies may use other methods to validate the possibility of these 10 SNPs carrying more risk for disrupted functional brain connectivity in schizophrenia. A bi-clustering analysis revealed a cluster consisting of 15 SNPs (rs604362, rs9876421, rs11693094, rs7240986, rs67733815, rs1009080, rs3784399, rs4908939, rs79235996, rs10503253, rs4674917, rs7201930, rs12623667, rs12132780, rs7267348) and 38 FNC connections, most (27) of which are within or between somato-motor and visual brain components. These results provide evidence about the genetic basis of disrupted functional brain connectivity in schizophrenia.

However, the findings of this study should be interpreted in light of several methodological limitations. Functional brain connectivity was computed using FNC method in which the brain was firstly decomposed into spatial components. Though a previous simulation study exhibited the benefits of estimating the functional brain network using data driven ICA rather than a fixed atlas based ROI method (Yu et al., 2017), future work may use different approaches (Chong et al., 2017; Cohen et al., 2008; Craddock et al., 2012; Eickhoff et al., 2015; Power et al., 2011; Shen et al., 2010; Wang et al., 2015; Wig et al., 2014; Yeo et al., 2011) to perform brain parcellation for defining brain nodes to compute the functional connectivity. To reduce the number of multiple comparisons when building the edges using general linear model in the gene-fMRI bipartite graph, we just selected a small number of SNPs (81) and FNC connections (83) as nodes. Nevertheless, the edges were still defined via an uncorrected p values (p < 0.05). Future studies may include more SNPs and FNC connections, and many more samples (typically thousands of subjects in GWAS studies) to enhance the power of the statistical analysis. To evaluate the statistical probability of the bipartite graph relative to a random one, we built 50000 random bipartite graphs using the same 81 SNP nodes and 83 FNC nodes by randomly re-order the subjects. Results show that the mean degree of each node is 4, and the degree of 11 for any SNP node occurs at about the 10th percentile of the 50000 permutated bipartite graphs.

Conclusions:

This work proposed a new framework for performing image genetics analysis by combining group ICA, bipartite graph analysis, and bi-clustering analysis. We built a genetic-fMRI bipartite graph in which genetic nodes were 81 schizophrenia-related SNPs and fMRI nodes were 83 schizophrenia-related connections of functional brain connectivity. Edges between SNP-FNC nodes which indicate the association between them were assessed using a general linear model across both HCs and SZs. Results showed that 10 SNPs had a degree higher than 10 implying that they modulate functional brain connectivity and may be the most strongly associated with the risk for schizophrenia. A bi-clustering analysis revealed a cluster with 15 SNPs and 38 connections of the FNC most of which were within or between somato-motor and visual brain components suggesting that brain connectivity in these sensory systems is modulated by a group of SNPs which provides insight for the pathology of schizophrenia. The results of this pilot study suggest that the SNP-FNC bipartite graph may potentially be a powerful model to access the genetic basis of functional brain connectivity as well as that of mental illnesses.

Supplementary Material

1

Highlights.

  • A genome-connectome bipartite graph model for imaging genomic analysis.

  • We used this model to explore associations of schizophrenia-related genetic variants with group-discriminative functional connectivity features.

  • Genetic nodes with high degree in the bipartite graph were identified to indicate their role in modulating brain connectivity.

  • A bi-clustering analysis detected a cluster where 15 genetic variants interact with 38 functional connectivity features.

Acknowledgements:

This work is supported by the National Institutes of Health (NIH) grants including a COBRE grant (P20GM103472/5P20RR021938), R01 grants (R01EB005846, 1R01EB006841, 1R01DA040487, R01REB020407, R01EB000840, and R37MH43775) and the National Science Foundation (NSF) grants #1539067, #1618551 and #1631838. This work is also partly supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDBS03040100), Brain Initiative of Beijing City (Z181100001518005) Chinese NSF (81471367, 61773380, PI: Sui J; 61703253, PI: YHD) and Natural Science Foundation of Shanxi (2016021077, PI: YHD).”

Footnotes

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