A review of multivariate analyses in imaging genetics

Multitrait analysis to decipher the intertwined genetic architecture of neuroanatomical phenotypes and psychiatric disorders

BACKGROUND

There is increasing evidence of shared genetic factors between psychiatric disorders and brain magnetic resonance imaging (MRI) phenotypes. However, deciphering the joint genetic architecture of these outcomes has proven challenging, and new approaches are needed to infer potential genetic structure underlying those phenotypes. Multivariate analyses is arising as a meaningful approach to reveal links between MRI phenotypes and psychiatric disorders missed by univariate approaches.

METHODS

We first conducted univariate and multivariate genome-wide association studies (GWAS) for nine MRI-derived brain volume phenotypes in 20K UK Biobank participants. We next performed various complementary enrichment analyses to assess whether and how univariate and multitrait approaches can distinguish disorder-associated and non-disorder-associated variants from six psychiatric disorders: bipolarity, attention-deficit/hyperactivity disorder (ADHD), autism, schizophrenia, obsessive-compulsive disorder, and major depressive disorder. Finally, we conducted a clustering analysis of top associated variants based on their MRI multitrait association using an optimized k-medoids approach.

RESULTS

Univariate MRI GWAS displayed only negligible genetic correlation with psychiatric disorders, while multitrait GWAS identified multiple new associations and showed significant enrichment for variants related to both ADHD and schizophrenia. Clustering analyses further detected two clusters displaying not only enrichment for association with ADHD and schizophrenia, but also consistent direction of effects. Functional annotation analyses of those clusters pointed to multiple potential mechanisms, suggesting in particular a role of neurotrophins pathways on both MRI and schizophrenia.

CONCLUSIONS

Our results show that multitrait association signature can be used to infer genetically-driven latent MRI variables associated with psychiatric disorders, opening paths for future biomarker development.

FPLS-DC: functional partial least squares through distance covariance for imaging genetics

MOTIVATION

Imaging genetics integrates imaging and genetic techniques to examine how genetic variations influence the function and structure of organs like the brain or heart, providing insights into their impact on behavior and disease phenotypes. The use of organ-wide imaging endophenotypes has increasingly been used to identify potential genes associated with complex disorders. However, analyzing organ-wide imaging data alongside genetic data presents two significant challenges: high dimensionality and complex relationships. To address these challenges, we propose a novel, nonlinear inference framework designed to partially mitigate these issues.

RESULTS

We propose a functional partial least squares through distance covariance (FPLS-DC) framework for efficient genome wide analyses of imaging phenotypes. It consists of two components. The first component utilizes the FPLS-derived base functions to reduce image dimensionality while screening genetic markers. The second component maximizes the distance correlation between genetic markers and projected imaging data, which is a linear combination of the FPLS-basis functions, using simulated annealing algorithm. In addition, we proposed an iterative FPLS-DC method based on FPLS-DC framework, which effectively overcomes the influence of inter-gene correlation on inference analysis. We efficiently approximate the null distribution of test statistics using a gamma approximation. Compared to existing methods, FPLS-DC offers computational and statistical efficiency for handling large-scale imaging genetics. In real-world applications, our method successfully detected genetic variants associated with the hippocampus, demonstrating its value as a statistical toolbox for imaging genetic studies.

AVAILABILITY AND IMPLEMENTATION

The FPLS-DC method we propose opens up new research avenues and offers valuable insights for analyzing functional and high-dimensional data. In addition, it serves as a useful tool for scientific analysis in practical applications within the field of imaging genetics research. The R package FPLS-DC is available in Github: https://github.com/BIG-S2/FPLSDC.

Dynamic fusion of genomics and functional network connectivity in UK biobank reveals static and time-varying SNP manifolds

Many psychiatric and neurological disorders show significant heritability, indicating strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) measures functional temporal coupling between brain networks in a time-varying manner and has proven to identify disease-related changes in the brain. However, it remains largely unclear how genetic risk contributes to brain dysconnectivity that further manifests into clinical symptoms. The current work aimed to address this gap by proposing a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned SNP manifolds by linking static SNPs to dynamic functional information of the brain. The sliding window approach was utilized to estimate four dFNC states and compute subject-level state-specific dFNC features. Each state of dFNC features were then combined with 12946 SZ risk SNPs for jICA decomposition, resulting in four parallel fusions in 32861 European ancestry individuals within the UK Biobank cohort. The identified joint SNP-dFNC components were further validated for SZ relevance in an aggregated SZ cohort, and compared for across-state similarity to indicate level of dynamism. The results supported that dynamic fusion yielded "static" and "dynamic" components (i.e., high and low across-state similarity, respectively) for SNP and dFNC modalities. As expected, the SNP components presented a mixture of static and dynamic manifolds, with the latter largely driven by fusion with dFNC. We also showed that some of the dynamic SNP manifolds uniquely elicited by fusion with state-specific dFNC features complemented each other in terms of biological interpretation. This dynamic fusion framework thus allows expanding the SNP modality to manifolds in the time dimension, which provides a unique lens to elicit unique SNP correlates of dFNC otherwise unseen, promising additional insights on how genetic risk links to disease-related dysconnectivity.

A Glucocorticoid-Sensitive Hippocampal Gene Network Moderates the Impact of Early-Life Adversity on Mental Health Outcomes

BACKGROUND

Early stress increases the risk for psychiatric disorders. Glucocorticoids are stress mediators that regulate transcriptional activity and morphology in the hippocampus, which is implicated in the pathophysiology of multiple psychiatric conditions. We aimed to establish the relevance of hippocampal glucocorticoid-induced transcriptional activity as a mediator of the effects of early life on later psychopathology in humans.

METHODS

RNA sequencing was performed with anterior and posterior hippocampal dentate gyrus from adult female macaques (n = 12/group) that were chronically treated with betamethasone (glucocorticoid receptor agonist) or vehicle. Coexpression network analysis identified a preserved gene network in the posterior hippocampal dentate gyrus that was strongly associated with glucocorticoid exposure. The single nucleotide polymorphisms in the genes in this network were used to create an expression-based polygenic score in humans.

RESULTS

The expression-based polygenic score significantly moderated the association between early adversity and psychotic disorders in adulthood (UK Biobank, women, n = 44,519) and on child peer relations (ALSPAC [Avon Longitudinal Study of Parents and Children], girls, n = 1666 for 9-year-olds and n = 1594 for 11-year-olds), an endophenotype for later psychosis. Analyses revealed that this network was enriched for glucocorticoid-induced epigenetic remodeling in human hippocampal cells. We also found a significant association between single nucleotide polymorphisms from the expression-based polygenic score and adult brain gray matter density.

CONCLUSIONS

We provide an approach for the use of transcriptomic data from animal models together with human data to study the impact of environmental influences on mental health. The results are consistent with the hypothesis that hippocampal glucocorticoid-related transcriptional activity mediates the effects of early adversity on neural mechanisms implicated in psychiatric disorders.

Identifying the joint signature of brain atrophy and gene variant scores in Alzheimer's Disease

The joint modeling of genetic data and brain imaging information allows for determining the pathophysiological pathways of neurodegenerative diseases such as Alzheimer's disease (AD). This task has typically been approached using mass-univariate methods that rely on a complete set of Single Nucleotide Polymorphisms (SNPs) to assess their association with selected image-derived phenotypes (IDPs). However, such methods are prone to multiple comparisons bias and, most importantly, fail to account for potential cross-feature interactions, resulting in insufficient detection of significant associations. Ways to overcome these limitations while reducing the number of traits aim at conveying genetic information at the gene level and capturing the integrated genetic effects of a set of genetic variants, rather than looking at each SNP individually. Their associations with brain IDPs are still largely unexplored in the current literature, though they can uncover new potential genetic determinants for brain modulations in the AD continuum. In this work, we explored an explainable multivariate model to analyze the genetic basis of the grey matter modulations, relying on the AD Neuroimaging Initiative (ADNI) phase 3 dataset. Cortical thicknesses and subcortical volumes derived from T1-weighted Magnetic Resonance were considered to describe the imaging phenotypes. At the same time the genetic counterpart was represented by gene variant scores extracted by the Sequence Kernel Association Test (SKAT) filtering model. Moreover, transcriptomic analysis was carried on to assess the expression of the resulting genes in the main brain structures as a form of validation. Results highlighted meaningful genotype-phenotype interactionsas defined by three latent components showing a significant difference in the projection scores between patients and controls. Among the significant associations, the model highlighted EPHX1 and BCAS1 gene variant scores involved in neurodegenerative and myelination processes, hence relevant for AD. In particular, the first was associated with decreased subcortical volumes and the second with decreasedtemporal lobe thickness. Noteworthy, BCAS1 is particularly expressed in the dentate gyrus. Overall, the proposed approach allowed capturing genotype-phenotype interactions in a restricted study cohort that was confirmed by transcriptomic analysis, offering insights into the underlying mechanisms of neurodegeneration in AD in line with previous findings and suggesting new potential disease biomarkers.

Decentralized Parallel Independent Component Analysis for Multimodal, Multisite Data

Large amounts of neuroimaging and omics data have been generated for studies of mental health. Collaborations among research groups that share data have shown increased power for new discoveries of brain abnormalities, genetic mutations, and associations among genetics, neuroimaging and behavior. However, sharing raw data can be challenging for various reasons. A federated data analysis allowing for collaboration without exposing the raw dataset of each site becomes ideal. Following this strategy, a decentralized parallel independent component analysis (dpICA) is proposed in this study which is an extension of the state-of-art Parallel ICA (pICA). pICA is an effective method to analyze two data modalities simultaneously by jointly extracting independent components of each modality and maximizing connections between modalities. We evaluated the dpICA algorithm using neuroimage and genetic data from patients with schizophrenia and health controls, and compared its performances under various conditions with the centralized pICA. The results showed dpICA is robust to sample distribution across sites as long as numbers of samples in each site are sufficient. It can produce the same imaging and genetic components and the same connections between those components as the centralized pICA. Thus our study supports dpICA is an accurate and effective decentralized algorithm to extract connections from two data modalities.

Joint Sparse Collaborative Regression on Imaging Genetics Study of Schizophrenia

The imaging genetics approach generates large amount of high dimensional and multi-modal data, providing complementary information for comprehensive study of Schizophrenia, a complex mental disease. However, at the same time, the variety of these data in structures, resolutions, and formats makes their integrative study a forbidding task. In this paper, we propose a novel model called Joint Sparse Collaborative Regression (JSCoReg), which can extract class-specific features from different health conditions/disease classes. We first evaluate the performance of feature selection in terms of Receiver operating characteristic curve and the area under the ROC curve in the simulation experiment. We demonstrate that the JSCoReg model can achieve higher accuracy compared with similar models including Joint Sparse Canonical Correlation Analysis and Sparse Collaborative Regression. We then applied the JSCoReg model to the analysis of schizophrenia dataset collected from the Mind Clinical Imaging Consortium. The JSCoReg enables us to better identify biomarkers associated with schizophrenia, which are verified to be both biologically and statistically significant.

Neuroimaging Genetics and Network Analysis in Alzheimer's Disease

The issue of the genetics in brain imaging phenotypes serves as a crucial link between two distinct scientific fields: neuroimaging genetics (NG). The articles included here provide solid proof that this NG link has considerable synergy. There is a suitable collection of articles that offer a wide range of viewpoints on how genetic variations affect brain structure and function. They serve as illustrations of several study approaches used in contemporary genetics and neuroscience. Genome-wide association studies and candidate-gene association are two examples of genetic techniques. Cortical gray matter structural/volumetric measures from magnetic resonance imaging (MRI) are sources of information on brain phenotypes. Together, they show how various scientific disciplines have benefited from significant technological advances, such as the single-nucleotide polymorphism array in genetics and the development of increasingly higher-resolution MRI imaging. Moreover, we discuss NG's contribution to expanding our knowledge about the heterogeneity within Alzheimer's disease as well as the benefits of different network analyses.

Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study

Recent studies show that multi-modal data fusion techniques combine information from diverse sources for comprehensive diagnosis and prognosis of complex brain disorder, often resulting in improved accuracy compared to single-modality approaches. However, many existing data fusion methods extract features from homogeneous networs, ignoring heterogeneous structural information among multiple modalities. To this end, we propose a Hypergraph-based Multi-modal data Fusion algorithm, namely HMF. Specifically, we first generate a hypergraph similarity matrix to represent the high-order relationships among subjects, and then enforce the regularization term based upon both the inter- and intra-modality relationships of the subjects. Finally, we apply HMF to integrate imaging and genetics datasets. Validation of the proposed method is performed on both synthetic data and real samples from schizophrenia study. Results show that our algorithm outperforms several competing methods, and reveals significant interactions among risk genes, environmental factors and abnormal brain regions.

A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures

The advent of simultaneously collected imaging-genetics data in large study cohorts provides an unprecedented opportunity to assess the causal effect of brain imaging traits on externally measured experimental results (e.g., cognitive tests) by treating genetic variants as instrumental variables. However, classic Mendelian Randomization methods are limited when handling high-throughput imaging traits as exposures to identify causal effects. We propose a new Mendelian Randomization framework to jointly select instrumental variables and imaging exposures, and then estimate the causal effect of multivariable imaging data on the outcome. We validate the proposed method with extensive data analyses and compare it with existing methods. We further apply our method to evaluate the causal effect of white matter microstructure integrity (WM) on cognitive function. The findings suggest that our method achieved better performance regarding sensitivity, bias, and false discovery rate compared to individually assessing the causal effect of a single exposure and jointly assessing the causal effect of multiple exposures without dimension reduction. Our application results indicated that WM measures across different tracts have a joint causal effect that significantly impacts the cognitive function among the participants from the UK Biobank.

Cognitive Development and Brain Gray Matter Susceptibility to Prenatal Adversities: Moderation by the Prefrontal Cortex Brain-Derived Neurotrophic Factor Gene Co-expression Network

Background: Previous studies focused on the relationship between prenatal conditions and neurodevelopmental outcomes later in life, but few have explored the interplay between gene co-expression networks and prenatal adversity conditions on cognitive development trajectories and gray matter density. Methods: We analyzed the moderation effects of an expression polygenic score (ePRS) for the Brain-derived Neurotrophic Factor gene network (BDNF ePRS) on the association between prenatal adversity and child cognitive development. A score based on genes co-expressed with the prefrontal cortex (PFC) BDNF was created, using the effect size of the association between the individual single nucleotide polymorphisms (SNP) and the BDNF expression in the PFC. Cognitive development trajectories of 157 young children from the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) cohort were assessed longitudinally in 4-time points (6, 12, 18, and 36 months) using the Bayley-II mental scales. Results: Linear mixed-effects modeling indicated that BDNF ePRS moderates the effects of prenatal adversity on cognitive growth. In children with high BDNF ePRS, higher prenatal adversity was associated with slower cognitive development in comparison with those exposed to lower prenatal adversity. Parallel-Independent Component Analysis (pICA) suggested that associations of expression-based SNPs and gray matter density significantly differed between low and high prenatal adversity groups. The brain IC included areas involved in visual association processes (Brodmann area 19 and 18), reallocation of attention, and integration of information across the supramodal cortex (Brodmann area 10). Conclusion: Cognitive development trajectories and brain gray matter seem to be influenced by the interplay of prenatal environmental conditions and the expression of an important BDNF gene network that guides the growth and plasticity of neurons and synapses.

Prefrontal cortex VAMP1 gene network moderates the effect of the early environment on cognitive flexibility in children

During development, genetic and environmental factors interact to modify specific phenotypes. Both in humans and in animal models, early adversities influence cognitive flexibility, an important brain function related to behavioral adaptation to variations in the environment. Abnormalities in cognitive functions are related to changes in synaptic connectivity in the prefrontal cortex (PFC), and altered levels of synaptic proteins. We investigated if individual variations in the expression of a network of genes co-expressed with the synaptic protein VAMP1 in the prefrontal cortex moderate the effect of early environmental quality on the performance of children in cognitive flexibility tasks. Genes overexpressed in early childhood and co-expressed with the VAMP1 gene in the PFC were selected for study. SNPs from these genes (post-clumping) were compiled in an expression-based polygenic score (PFC-ePRS-VAMP1). We evaluated cognitive performance of the 4 years-old children in two cohorts using similar cognitive flexibility tasks. In the first cohort (MAVAN) we utilized two CANTAB tasks: (a) the Intra-/Extra-dimensional Set Shift (IED) task, and (b) the Spatial Working Memory (SWM) task. In the second cohort, GUSTO, we used the Dimensional Change Card Sort (DCCS) task. The results show that in 4 years-old children, the PFC-ePRS-VAMP1 network moderates responsiveness to the effects of early adversities on the performance in attentional flexibility tests. The same result was observed for a spatial working memory task. Compared to attentional flexibility, reversal learning showed opposite effects of the environment, as moderated by the ePRS. A parallel ICA analysis was performed to identify relationships between whole-brain voxel based gray matter density and SNPs that comprise the PFC-ePRS-VAMP1. The early environment predicts differences in gray matter content in regions such as prefrontal and temporal cortices, significantly associated with a genetic component related to Wnt signaling pathways. Our data suggest that a network of genes co-expressed with VAMP1 in the PFC moderates the influence of early environment on cognitive function in children.

Omics sciences for systems biology in Alzheimer's disease: State-of-the-art of the evidence

Alzheimer's disease (AD) is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in biological alterations and disease spatial-temporal progression. Human in-vivo and post-mortem studies point out a failure of multi-level biological networks underlying AD pathophysiology, including proteostasis (amyloid-β and tau), synaptic homeostasis, inflammatory and immune responses, lipid and energy metabolism, oxidative stress. Therefore, a holistic, systems-level approach is needed to fully capture AD multi-faceted pathophysiology. Omics sciences - genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics - embedded in the systems biology (SB) theoretical and computational framework can generate explainable readouts describing the entire biological continuum of a disease. Such path in Neurology is encouraged by the promising results of omics sciences and SB approaches in Oncology, where stage-driven pathway-based therapies have been developed in line with the precision medicine paradigm. Multi-omics data integrated in SB network approaches will help detect and chart AD upstream pathomechanistic alterations and downstream molecular effects occurring in preclinical stages. Finally, integrating omics and neuroimaging data - i.e., neuroimaging-omics - will identify multi-dimensional biological signatures essential to track the clinical-biological trajectories, at the subpopulation or even individual level.

Multilevel Survival Modeling with Structured Penalties for Disease Prediction from Imaging Genetics data

This paper introduces a framework for disease prediction from multimodal genetic and imaging data. We propose a multilevel survival model which allows predicting the time of occurrence of a future disease state in patients initially exhibiting mild symptoms. This new multilevel setting allows modeling the interactions between genetic and imaging variables. This is in contrast with classical additive models which treat all modalities in the same manner and can result in undesirable elimination of specific modalities when their contributions are unbalanced. Moreover, the use of a survival model allows overcoming the limitations of previous approaches based on classification which consider a fixed time frame. Furthermore, we introduce specific penalties taking into account the structure of the different types of data, such as a group lasso penalty over the genetic modality and a L2-penalty over the imaging modality. Finally, we propose a fast optimization algorithm, based on a proximal gradient method. The approach was applied to the prediction of Alzheimer's disease (AD) among patients with mild cognitive impairment (MCI) based on genetic (single nucleotide polymorphisms - SNP) and imaging (anatomical MRI measures) data from the ADNI database. The experiments demonstrate the effectiveness of the method for predicting the time of conversion to AD. It revealed how genetic variants and brain imaging alterations interact in the prediction of future disease status. The approach is generic and could potentially be useful for the prediction of other diseases.

A Latent Gaussian Copula Model for Mixed Data Analysis in Brain Imaging Genetics

Recent advances in imaging genetics make it possible to combine different types of data including medical images like functional magnetic resonance imaging (fMRI) and genetic data like single nucleotide polymorphisms (SNPs) for comprehensive diagnosis of mental disorders. Understanding complex interactions among these heterogeneous data may give rise to a new perspective, while at the same time demand statistical models for their integration. Various graphical models have been proposed for the study of interaction or association networks with continuous, binary, and count data as well as the mixture of them. However, limited efforts have been made for the multinomial case, for instance, SNP data. Our goal is therefore to fill the void by developing a graphical model for the integration of fMRI image and SNP data, which can provide deeper understanding of the unknown neurogenetic mechanism. In this article, we propose a latent Gaussian copula model for mixed data containing multinomial components. We assume that the discrete variable is obtained by discretizing a latent (unobserved) continuous variable and then create a semi-rank based estimator of the graph structure. The simulation results demonstrate that the proposed latent correlation has more steady and accurate performance than several existing methods in detecting graph structure. When applying to a real schizophrenia data consisting of SNP array and fMRI image collected by the Mind Clinical Imaging Consortium (MCIC), the proposed method reveals a set of distinct SNP-brain associations, which are verified to be biologically significant. The proposed model is statistically promising in handling mixed types of data including multinomial components, which can find widespread applications. To promote reproducible research, the R code is available at https://github.com/Aiying0512/LGCM.

Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage

BACKGROUND

Brain image genetics provides enormous opportunities for examining the effects of genetic variations on the brain. Many studies have shown that the structure, function, and abnormality (e.g., those related to Alzheimer's disease) of the brain are heritable. However, which genetic variations contribute to these phenotypic changes is not completely clear. Advances in neuroimaging and genetics have led us to obtain detailed brain anatomy and genome-wide information. These data offer us new opportunities to identify genetic variations such as single nucleotide polymorphisms (SNPs) that affect brain structure. In this paper, we perform a genome-wide variant-based study, and aim to identify top SNPs or SNP sets which have genetic effects with the largest neuroanotomic coverage at both voxel and region-of-interest (ROI) levels. Based on the voxelwise genome-wide association study (GWAS) results, we used the exhaustive search to find the top SNPs or SNP sets that have the largest voxel-based or ROI-based neuroanatomic coverage. For SNP sets with >2 SNPs, we proposed an efficient genetic algorithm to identify top SNP sets that can cover all ROIs or a specific ROI.

RESULTS

We identified an ensemble of top SNPs, SNP-pairs and SNP-sets, whose effects have the largest neuroanatomic coverage. Experimental results on real imaging genetics data show that the proposed genetic algorithm is superior to the exhaustive search in terms of computational time for identifying top SNP-sets.

CONCLUSIONS

We proposed and applied an informatics strategy to identify top SNPs, SNP-pairs and SNP-sets that have genetic effects with the largest neuroanatomic coverage. The proposed genetic algorithm offers an efficient solution to accomplish the task, especially for identifying top SNP-sets.

A Bayesian spatial model for imaging genetics

We develop a Bayesian bivariate spatial model for multivariate regression analysis applicable to studies examining the influence of genetic variation on brain structure. Our model is motivated by an imaging genetics study of the Alzheimer's Disease Neuroimaging Initiative (ADNI), where the objective is to examine the association between images of volumetric and cortical thickness values summarizing the structure of the brain as measured by magnetic resonance imaging (MRI) and a set of 486 single nucleotide polymorphism (SNPs) from 33 Alzheimer's disease (AD) candidate genes obtained from 632 subjects. A bivariate spatial process model is developed to accommodate the correlation structures typically seen in structural brain imaging data. First, we allow for spatial correlation on a graph structure in the imaging phenotypes obtained from a neighborhood matrix for measures on the same hemisphere of the brain. Second, we allow for correlation in the same measures obtained from different hemispheres (left/right) of the brain. We develop a mean-field variational Bayes algorithm and a Gibbs sampling algorithm to fit the model. We also incorporate Bayesian false discovery rate (FDR) procedures to select SNPs. We implement the methodology in a new release of the R package bgsmtr. We show that the new spatial model demonstrates superior performance over a standard model in our application. Data used in the preparation of this article were obtained from the ADNI database (https://adni.loni.usc.edu).

A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis

With advances in neuroimaging and genetics, imaging genetics is a naturally emerging field that combines genetic and neuroimaging data with behavioral or cognitive outcomes to examine genetic influence on altered brain functions associated with behavioral or cognitive variation. We propose a statistical approach, termed imaging genetics generalized structured component analysis (IG-GSCA), which allows researchers to investigate such gene-brain-behavior/cognitive associations, taking into account well-documented biological characteristics (e.g., genetic pathways, gene-environment interactions, etc.) and methodological complexities (e.g., multicollinearity) in imaging genetic studies. We begin by describing the conceptual and technical underpinnings of IG-GSCA. We then apply the approach for investigating how nine depression-related genes and their interactions with an environmental variable (experience of potentially traumatic events) influence the thickness variations of 53 brain regions, which in turn affect depression severity in a sample of Korean participants. Our analysis shows that a dopamine receptor gene and an interaction between a serotonin transporter gene and the environment variable have statistically significant effects on a few brain regions' variations that have statistically significant negative impacts on depression severity. These relationships are largely supported by previous studies. We also conduct a simulation study to safeguard whether IG-GSCA can recover parameters as expected in a similar situation.

A technical review of canonical correlation analysis for neuroscience applications

Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA-variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well-known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA-related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA-related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

Brain imaging genomics: influences of genomic variability on the structure and function of the human brain

Brain imaging genomics is an emerging discipline in which genomic and brain imaging data are integrated in order to elucidate the molecular mechanisms that underly brain phenotypes and diseases, including neuropsychiatric disorders. As with all genetic analyses of complex traits and diseases, brain imaging genomics has evolved from small, individual candidate gene investigations towards large, collaborative genome-wide association studies. Recent investigations, mostly population-based, have studied well-powered cohorts comprising tens of thousands of individuals and identified multiple robust associations of single-nucleotide polymorphisms and copy number variants with structural and functional brain phenotypes. Such systematic genomic screens of millions of genetic variants have generated initial insights into the genetic architecture of brain phenotypes and demonstrated that their etiology is polygenic in nature, involving multiple common variants with small effect sizes and rare variants with larger effect sizes. Ongoing international collaborative initiatives are now working to obtain a more complete picture of the underlying biology. As in other complex phenotypes, novel approaches – such as gene–gene interaction, gene–environment interaction, and epigenetic analyses – are being implemented in order to investigate their contribution to the observed phenotypic variability. An important consideration for future research will be the translation of brain imaging genomics findings into clinical practice.

Independent Multiple Factor Association Analysis for Multiblock Data in Imaging Genetics

Multivariate methods have the potential to better capture complex relationships that may exist between different biological levels. Multiple Factor Analysis (MFA) is one of the most popular methods to obtain factor scores and measures of discrepancy between data sets. However, singular value decomposition in MFA is based on PCA, which is adequate only if the data is normally distributed, linear or stationary. In addition, including strongly correlated variables can overemphasize the contribution of the estimated components. In this work, we introduced a novel method referred as Independent Multifactorial Analysis (ICA-MFA) to derive relevant features from multiscale data. This method is an extended implementation of MFA, where the component value decomposition is based on Independent Component Analysis. In addition, ICA-MFA incorporates a predictive step based on an Independent Component Regression. We evaluated and compared the performance of ICA-MFA with both, the MFA method and traditional univariate analyses, in a simulation study. We showed how ICA-MFA explained up to 10-fold more variance than MFA and univariate methods. We applied the proposed algorithm in a study of 4057 individuals belonging to the population-based Rotterdam Study with available genetic and neuroimaging data, as well as information about executive cognitive functioning. Specifically, we used ICA-MFA to detect relevant genetic features related to structural brain regions, which in turn were involved, in the mechanisms of executive cognitive function. The proposed strategy makes it possible to determine the degree to which the whole set of genetic and/or neuroimaging markers contribute to the variability of the symptomatology jointly, rather than individually. While univariate results and MFA combinations only explained a limited proportion of variance (less than 2%), our method increased the explained variance (10%) and allowed the identification of significant components that maximize the variance explained in the model. The potential application of the ICA-MFA algorithm constitutes an important aspect of integrating multivariate multiscale data, specifically in the field of Neurogenetics.

L2RM: Low-rank Linear Regression Models for High-dimensional Matrix Responses

The aim of this paper is to develop a low-rank linear regression model (L2RM) to correlate a high-dimensional response matrix with a high dimensional vector of covariates when coefficient matrices have low-rank structures. We propose a fast and efficient screening procedure based on the spectral norm of each coefficient matrix in order to deal with the case when the number of covariates is extremely large. We develop an efficient estimation procedure based on the trace norm regularization, which explicitly imposes the low rank structure of coefficient matrices. When both the dimension of response matrix and that of covariate vector diverge at the exponential order of the sample size, we investigate the sure independence screening property under some mild conditions. We also systematically investigate some theoretical properties of our estimation procedure including estimation consistency, rank consistency and non-asymptotic error bound under some mild conditions. We further establish a theoretical guarantee for the overall solution of our two-step screening and estimation procedure. We examine the finite-sample performance of our screening and estimation methods using simulations and a large-scale imaging genetic dataset collected by the Philadelphia Neurodevelopmental Cohort (PNC) study.

Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists

The 21st century marks the emergence of "big data" with a rapid increase in the availability of datasets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or hundreds of phenotypic subject descriptors on the behavioral, neural, and genomic level. The complexity of such "big data" repositories offer new opportunities and pose new challenges for systems neuroscience. Canonical correlation analysis (CCA) is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities. Importantly, CCA is well suited to describing relationships across multiple sets of data, such as in recently available big biomedical datasets. Our primer discusses the rationale, promises, and pitfalls of CCA.

Amygdala 5-HTT Gene Network Moderates the Effects of Postnatal Adversity on Attention Problems: Anatomo-Functional Correlation and Epigenetic Changes

Variations in serotoninergic signaling have been related to behavioral outcomes. Alterations in the genome, such as DNA methylation and histone modifications, are affected by serotonin neurotransmission. The amygdala is an important brain region involved in emotional responses and impulsivity, which receives serotoninergic input. In addition, studies suggest that the serotonin transporter gene network may interact with the environment and influence the risk for psychiatric disorders. We propose to investigate whether/how interactions between the exposure to early life adversity and serotonin transporter gene network in the amygdala associate with behavioral disorders. We constructed a co-expression-based polygenic risk score (ePRS) reflecting variations in the function of the serotonin transporter gene network in the amygdala and investigated its interaction with postnatal adversity on attention problems in two independent cohorts from Canada and Singapore. We also described how interactions between ePRS-5-HTT and postnatal adversity exposure predict brain gray matter density and variation in DNA methylation across the genome. We observed that the expression-based polygenic risk score, reflecting the function of the amygdala 5-HTT gene network, interacts with postnatal adversity, to predict attention and hyperactivity problems across both cohorts. Also, both postnatal adversity score and amygdala ePRS-5-HTT score, as well as their interaction, were observed to be associated with variation in DNA methylation across the genome. Variations in gray matter density in brain regions linked to attentional processes were also correlated to our ePRS score. These results confirm that the amygdala 5-HTT gene network is strongly associated with ADHD-related behaviors, brain cortical density, and epigenetic changes in the context of adversity in young children.

Fusion analysis of gray matter and white matter in bipolar disorder by multimodal CCA-joint ICA

BACKGROUND

Bipolar disorder (BD) patients show morphological abnormalities in gray matter (GM) and white matter (WM), which can be revealed by structure MRI (sMRI) and diffusion tensor imaging (DTI) respectively. However, previous studies on BD mainly relied on separated analysis of single neuroimaging modality, and it remains unclear how GM and WM covary to the abnormal brain structures of BD patients.

METHODS

We recorded multimodal sMRI-DTI data of 35 BD patients and 30 healthy controls (HC) and used multimodal canonical component analysis and joint independent component analysis (mCCA-jICA) to identify altered covariant structures in GM and WM of BD. Group-discriminative and joint group-discriminative independent components (ICs) were identified between BD and HC. Correlation analysis was performed between the mixing coefficients and behavioral index.

RESULTS

For BD patients, experiments results revealed that the GM atrophy in inferior frontal gyrus, right anterior cingulate gyrus and left superior frontal gyrus are associated with the WM integrity reduction in corticospinal tract and superior longitudinal fasciculus. Further, compared with HC, different correlation between mixing coefficients of ICs and age was observed for BD patients.

LIMITATIONS

The number of participants needs to be increased to more rigorously validate the results of this study, ideally from multiple sites. Functional imaging data could be utilized to explore structural-functional covariant pattern in BD. Possible confounding effect of medication.

CONCLUSIONS

We performed fusion analysis of sMRI and DTI and revealed covariant (GM-WM) structural patterns of BD patients. This study could be useful for developing more reliable neural biomarkers of BD.

Brain Imaging Genomics: Integrated Analysis and Machine Learning

Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.

Distinct structural brain circuits indicate mood and apathy profiles in bipolar disorder

Bipolar disorder (BD) is a severe manic-depressive illness. Patients with BD have been shown to have gray matter (GM) deficits in prefrontal, frontal, parietal, and temporal regions; however, the relationship between structural effects and clinical profiles has proved elusive when considered on a region by region or voxel by voxel basis. In this study, we applied parallel independent component analysis (pICA) to structural neuroimaging measures and the positive and negative syndrome scale (PANSS) in 110 patients (mean age 34.9 ± 11.65) with bipolar disorder, to examine networks of brain regions that relate to symptom profiles. The pICA revealed two distinct symptom profiles and associated GM concentration alteration circuits. The first PANSS pICA profile mainly involved anxiety, depression and guilty feelings, reflecting mood symptoms. Reduced GM concentration in right temporal regions predicted worse mood symptoms in this profile. The second PANSS pICA profile generally covered blunted affect, emotional withdrawal, passive/apathetic social withdrawal, depression and active social avoidance, exhibiting a withdrawal or apathy dominating component. Lower GM concentration in bilateral parietal and frontal regions showed worse symptom severity in this profile. In summary, a pICA decomposition suggested BD patients showed distinct mood and apathy profiles differing from the original PANSS subscales, relating to distinct brain structural networks.

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Structural relationships with symptoms in bipolar disorder are complex.

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A parallel ICA analysis of PANSS questions and structural images finds two correlated profiles.

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The first pair links mood symptoms with right temporal regions.

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The second pair highlights social withdrawal and apathy symptoms linked to bilateral frontal and parietal regions.

Association between the oral microbiome and brain resting state connectivity in smokers

Recent studies have shown a critical role of the gastrointestinal microbiome in brain and behavior via the complex gut-microbiome-brain axis. However, the influence of the oral microbiome in neurological processes is much less studied, especially in response to the stimuli, such as smoking, within the oral microenvironment. Additionally, given the complex structural and functional networks in brain, our knowledge about the relationship between microbiome and brain function through specific brain circuits is still very limited. In this pilot study, we leveraged next generation sequencing for microbiome and functional neuroimaging technique to enable the delineation of microbiome-brain network links as well as their relationship to cigarette smoking. Thirty smokers and 30 age- and sex-matched nonsmokers were recruited for 16S sequencing of their oral microbial community. Among them, 56 subjects were scanned by resting-state functional magnetic resonance imaging to derive brain functional networks. Statistical analyses were performed to demonstrate the influence of smoking on the oral microbial composition, functional network connectivity, and the associations between microbial shifts and functional network connectivity alternations. Compared to nonsmokers, we found a significant decrease of beta diversity (P = 6 × 10^-3) in smokers and identified several classes (Betaproteobacteria, Spirochaetia, Synergistia, and Mollicutes) with significant alterations in microbial abundance. Pathway analysis on the predicted KEGG pathways shows that the microbiota with altered abundance are mainly involved in pathways related to cell processes, DNA repair, immune system, and neurotransmitters signaling. One brain functional network connectivity component was identified to have a significant difference between smokers and nonsmokers (P = 0.032), mainly including connectivity between brain default network and other task-positive networks. This brain functional component was also significantly associated with smoking related microbiota, suggesting a correlated cross-individual pattern between smoking-induced oral microbiome dysbiosis and brain functional connectivity alternation, possibly involving immunological and neurotransmitter signaling pathways. This work is the first attempt to link oral microbiome and brain functional networks, and provides support for future work in characterizing the role of oral microbiome in mediating smoking effects on brain activity.

Brain function, structure and genomic data are linked but show different sensitivity to duration of illness and disease stage in schizophrenia

The progress of schizophrenia at various stages is an intriguing question, which has been explored to some degree using single-modality brain imaging data, e.g. gray matter (GM) or functional connectivity (FC). However it remains unclear how those changes from different modalities are correlated with each other and if the sensitivity to duration of illness and disease stages across modalities is different. In this work, we jointly analyzed FC, GM volume and single nucleotide polymorphisms (SNPs) data of 159 individuals including healthy controls (HC), drug-naïve first-episode schizophrenia (FESZ) and chronic schizophrenia patients (CSZ), aiming to evaluate the links among SNP, FC and GM patterns, and their sensitivity to duration of illness and disease stages in schizophrenia. Our results suggested: 1) both GM and FC highlighted impairments in hippocampal, temporal gyrus and cerebellum in schizophrenia, which were significantly correlated with genes like SATB2, GABBR2, PDE4B, CACNA1C etc. 2) GM and FC presented gradually decrease trend (HC > FESZ>CSZ), while SNP indicated a non-gradual variation trend with un-significant group difference observed between FESZ and CSZ; 3) Group difference between HC and FESZ of FC was more remarkable than GM, and FC presented a stronger negative correlation with duration of illness than GM (p = 0.0006). Collectively, these results highlight the benefit of leveraging multimodal data and provide additional clues regarding the impact of mental illness at various disease stages.

The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders

Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.

A Selective Review of Multi-Level Omics Data Integration Using Variable Selection

High-throughput technologies have been used to generate a large amount of omics data. In the past, single-level analysis has been extensively conducted where the omics measurements at different levels, including mRNA, microRNA, CNV and DNA methylation, are analyzed separately. As the molecular complexity of disease etiology exists at all different levels, integrative analysis offers an effective way to borrow strength across multi-level omics data and can be more powerful than single level analysis. In this article, we focus on reviewing existing multi-omics integration studies by paying special attention to variable selection methods. We first summarize published reviews on integrating multi-level omics data. Next, after a brief overview on variable selection methods, we review existing supervised, semi-supervised and unsupervised integrative analyses within parallel and hierarchical integration studies, respectively. The strength and limitations of the methods are discussed in detail. No existing integration method can dominate the rest. The computation aspects are also investigated. The review concludes with possible limitations and future directions for multi-level omics data integration.

Shared Genetic Risk of Schizophrenia and Gray Matter Reduction in 6p22.1

Genetic factors are known to influence both risk for schizophrenia (SZ) and variation in brain structure. A pressing question is whether the genetic underpinnings of brain phenotype and the disorder overlap. Using multivariate analytic methods and focusing on 1,402 common single-nucleotide polymorphisms (SNPs) mapped from the Psychiatric Genomics Consortium (PGC) 108 regions, in 777 discovery samples, we identified 39 SNPs to be significantly associated with SZ-discriminating gray matter volume (GMV) reduction in inferior parietal and superior temporal regions. The findings were replicated in 609 independent samples. These 39 SNPs in chr6:28308034-28684183 (6p22.1), the most significant SZ-risk region reported by PGC, showed regulatory effects on both DNA methylation and gene expression of postmortem brain tissue and saliva. Furthermore, the regulated methylation site and gene showed significantly different levels of methylation and expression in the prefrontal cortex between cases and controls. In addition, for one regulated methylation site we observed a significant in vivo methylation-GMV association in saliva, suggesting a potential SNP-methylation-GMV pathway. Notably, the risk alleles inferred for GMV reduction from in vivo imaging are all consistent with the risk alleles for SZ inferred from postmortem data. Collectively, we provide evidence for shared genetic risk of SZ and regional GMV reduction in 6p22.1 and demonstrate potential molecular mechanisms that may drive the observed in vivo associations. This study motivates dissecting SZ-risk variants to better understand their associations with focal brain phenotypes and the complex pathophysiology of the illness.

A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study

Multimodal, imaging-genomics techniques offer a platform for understanding genetic influences on brain abnormalities in psychiatric disorders. Such approaches utilize the information available from both imaging and genomics data and identify their association. Particularly for complex disorders such as schizophrenia, the relationship between imaging and genomic features may be better understood by incorporating additional information provided by advanced multimodal modeling. In this study, we propose a novel framework to combine features corresponding to functional magnetic resonance imaging (functional) and single nucleotide polymorphism (SNP) data from 61 schizophrenia (SZ) patients and 87 healthy controls (HC). In particular, the features for the functional and genetic modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) features and the SNP data, respectively. The dFNC features are estimated from component time-courses, obtained using group independent component analysis (ICA), by computing sliding-window functional network connectivity, and then estimating subject specific states from this dFNC data using a k-means clustering approach. For each subject, both the functional (dFNC states) and SNP data are selected as features for a parallel ICA (pICA) based imaging-genomic framework. This analysis identified a significant association between a SNP component (defined by large clusters of functionally related SNPs statistically correlated with phenotype components) and time-varying or dFNC component (defined by clusters of related connectivity links among distant brain regions distributed across discrete dynamic states, and statistically correlated with genomic components) in schizophrenia. Importantly, the polygenetic risk score (PRS) for SZ (computed as a linearly weighted sum of the genotype profiles with weights derived from the odds ratios of the psychiatric genomics consortium (PGC)) was negatively correlated with the significant dFNC component, which were mostly present within a state that exhibited a lower occupancy rate in individuals with SZ compared with HC, hence identifying a potential dFNC imaging biomarker for schizophrenia. Taken together, the current findings provide preliminary evidence for a link between dFNC measures and genetic risk, suggesting the application of dFNC patterns as biomarkers in imaging genetic association study.

Enforcing Co-Expression Within a Brain-Imaging Genomics Regression Framework

Among the challenges arising in brain imaging genetic studies, estimating the potential links between neurological and genetic variability within a population is key. In this paper, we propose a multivariate, multimodal formulation for variable selection that leverages co-expression patterns across various data modalities. Our approach is based on an intuitive combination of two widely used statistical models: sparse regression and canonical correlation analysis (CCA). While the former seeks multivariate linear relationships between a given phenotype and associated observations, the latter searches to extract co-expression patterns between sets of variables belonging to different modalities. In the following, we propose to rely on a "CCA-type" formulation in order to regularize the classical multimodal sparse regression problem (essentially incorporating both CCA and regression models within a unified formulation). The underlying motivation is to extract discriminative variables that are also co-expressed across modalities. We first show that the simplest formulation of such model can be expressed as a special case of collaborative learning methods. After discussing its limitation, we propose an extended, more flexible formulation, and introduce a simple and efficient alternating minimization algorithm to solve the associated optimization problem. We explore the parameter space and provide some guidelines regarding parameter selection. Both the original and extended versions are then compared on a simple toy data set and a more advanced simulated imaging genomics data set in order to illustrate the benefits of the latter. Finally, we validate the proposed formulation using single nucleotide polymorphisms data and functional magnetic resonance imaging data from a population of adolescents ( subjects, age 16.9 ± 1.9 years from the Philadelphia Neurodevelopmental Cohort) for the study of learning ability. Furthermore, we carry out a significance analysis of the resulting features that allow us to carefully extract brain regions and genes linked to learning and cognitive ability.

A structural equation model for imaging genetics using spatial transcriptomics

Imaging genetics deals with relationships between genetic variation and imaging variables, often in a disease context. The complex relationships between brain volumes and genetic variants have been explored with both dimension reduction methods and model-based approaches. However, these models usually do not make use of the extensive knowledge of the spatio-anatomical patterns of gene activity. We present a method for integrating genetic markers (single nucleotide polymorphisms) and imaging features, which is based on a causal model and, at the same time, uses the power of dimension reduction. We use structural equation models to find latent variables that explain brain volume changes in a disease context, and which are in turn affected by genetic variants. We make use of publicly available spatial transcriptome data from the Allen Human Brain Atlas to specify the model structure, which reduces noise and improves interpretability. The model is tested in a simulation setting and applied on a case study of the Alzheimer’s Disease Neuroimaging Initiative.

A Perspective of the Cross-Tissue Interplay of Genetics, Epigenetics, and Transcriptomics, and Their Relation to Brain Based Phenotypes in Schizophrenia

Genetic association studies of psychiatric disorders have provided unprecedented insight into disease risk profiles with high confidence. Yet, the next research challenge is how to translate this rich information into mechanisms of disease, and further help interventions and treatments. Given other comprehensive reviews elsewhere, here we want to discuss the research approaches that integrate information across various tissue types. Taking schizophrenia as an example, the tissues, cells, or organisms being investigated include postmortem brain tissues or neurons, peripheral blood and saliva, in vivo brain imaging, and in vitro cell lines, particularly human induced pluripotent stem cells (iPSC) and iPSC derived neurons. There is a wealth of information on the molecular signatures including genetics, epigenetics, and transcriptomics of various tissues, along with neuronal phenotypic measurements including neuronal morphometry and function, together with brain imaging and other techniques that provide data from various spatial temporal points of disease development. Through consistent or complementary processes across tissues, such as cross-tissue methylation quantitative trait loci (QTL) and expression QTL effects, systemic integration of such information holds the promise to put the pieces of puzzle together for a more complete view of schizophrenia disease pathogenesis.

Strategies for integrated analysis in imaging genetics studies

Imaging Genetics (IG) integrates neuroimaging and genomic data from the same individual, deepening our knowledge of the biological mechanisms behind neurodevelopmental domains and neurological disorders. Although the literature on IG has exponentially grown over the past years, the majority of studies have mainly analyzed associations between candidate brain regions and individual genetic variants. However, this strategy is not designed to deal with the complexity of neurobiological mechanisms underlying behavioral and neurodevelopmental domains. Moreover, larger sample sizes and increased multidimensionality of this type of data represents a challenge for standardizing modeling procedures in IG research. This review provides a systematic update of the methods and strategies currently used in IG studies, and serves as an analytical framework for researchers working in this field. To complement the functionalities of the Neuroconductor framework, we also describe existing R packages that implement these methodologies. In addition, we present an overview of how these methodological approaches are applied in integrating neuroimaging and genetic data.

Supervised multiblock sparse multivariable analysis with application to multimodal brain imaging genetics

This article proposes a procedure for describing the relationship between high-dimensional data sets, such as multimodal brain images and genetic data. We propose a supervised technique to incorporate the clinical outcome to determine a score, which is a linear combination of variables with hieratical structures to multimodalities. This approach is expected to obtain interpretable and predictive scores. The proposed method was applied to a study of Alzheimer's disease (AD). We propose a diagnostic method for AD that involves using whole-brain magnetic resonance imaging (MRI) and positron emission tomography (PET), and we select effective brain regions for the diagnostic probability and investigate the genome-wide association with the regions using single nucleotide polymorphisms (SNPs). The two-step dimension reduction method, which we previously introduced, was considered applicable to such a study and allows us to partially incorporate the proposed method. We show that the proposed method offers classification functions with feasibility and reasonable prediction accuracy based on the receiver operating characteristic (ROC) analysis and reasonable regions of the brain and genomes. Our simulation study based on the synthetic structured data set showed that the proposed method outperformed the original method and provided the characteristic for the supervised feature.

Whole genome association study of brain-wide imaging phenotypes: A study of the ping cohort

Neuropsychological disorders have a biological basis rooted in brain function, and neuroimaging data are expected to better illuminate the complex genetic basis of neuropsychological disorders. Because they are biological measures, neuroimaging data avoid biases arising from clinical diagnostic criteria that are subject to human understanding and interpretation. A challenge with analyzing neuroimaging data is their high dimensionality and complex spatial relationships. To tackle this challenge, we introduced a novel distance covariance tests that can assess the association between genetic markers and multivariate diffusion tensor imaging measurements, and analyzed a genome-wide association study (GWAS) dataset collected by the Pediatric Imaging, Neurocognition, and Genetics (PING) study. We also considered existing approaches as comparisons. Our results showed that, after correcting for multiplicity, distance covariance tests of the multivariate phenotype yield significantly greater power at detecting genetic markers affecting brain structure than standard mass univariate GWAS of individual neuroimaging biomarkers. Our results underscore the usefulness of utilizing the distance covariance to incorporate neuroimaging data in GWAS.

Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation

Recent advances in imaging genetics produce large amounts of data including functional MRI images, single nucleotide polymorphisms (SNPs), and cognitive assessments. Understanding the complex interactions among these heterogeneous and complementary data has the potential to help with diagnosis and prevention of mental disorders. However, limited efforts have been made due to the high dimensionality, group structure, and mixed type of these data. In this paper we present a novel method to detect conditional associations between imaging genetics data. We use projected distance correlation to build a conditional dependency graph among high-dimensional mixed data, then use multiple testing to detect significant group level associations (e.g., ROI-gene). In addition, we introduce a scalable algorithm based on orthogonal greedy algorithm, yielding the greedy projected distance correlation (G-PDC). This can reduce the computational cost, which is critical for analyzing large-volume of imaging genomics data. The results from our simulations demonstrate a higher degree of accuracy with GPDC than distance correlation, Pearson's correlation and partial correlation, especially when the correlation is nonlinear. Finally, we apply our method to the Philadelphia Neurodevelopmental data cohort with 866 samples including fMRI images and SNP profiles. The results uncover several statistically significant and biologically interesting interactions, which are further validated with many existing studies. The Matlab code is available at https://sites.google.com/site/jianfang86/gPDC.

Susceptibility of brain atrophy to TRIB3 in Alzheimer's disease, evidence from functional prioritization in imaging genetics

The joint modeling of brain imaging information and genetic data is a promising research avenue to highlight the functional role of genes in determining the pathophysiological mechanisms of Alzheimer's disease (AD). However, since genome-wide association (GWA) studies are essentially limited to the exploration of statistical correlations between genetic variants and phenotype, the validation and interpretation of the findings are usually nontrivial and prone to false positives. To address this issue, in this work, we investigate the functional genetic mechanisms underlying brain atrophy in AD by studying the involvement of candidate variants in known genetic regulatory functions. This approach, here termed functional prioritization, aims at testing the sets of gene variants identified by high-dimensional multivariate statistical modeling with respect to known biological processes to introduce a biology-driven validation scheme. When applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, the functional prioritization allowed for identifying a link between tribbles pseudokinase 3 (TRIB3) and the stereotypical pattern of gray matter loss in AD, which was confirmed in an independent validation sample, and that provides evidence about the relation between this gene and known mechanisms of neurodegeneration.

Variability in Resting State Network and Functional Network Connectivity Associated With Schizophrenia Genetic Risk: A Pilot Study

Imaging genetics posits a valuable strategy for elucidating genetic influences on brain abnormalities in psychiatric disorders. However, association analysis between 2D genetic data (subject × genetic variable) and 3D first-level functional magnetic resonance imaging (fMRI) data (subject × voxel × time) has been challenging given the asymmetry in data dimension. A summary feature needs to be derived for the imaging modality to compute inter-modality association at subject level. In this work, we propose to use variability in resting state networks (RSNs) and functional network connectivity (FNC) as potential features for purpose of association analysis. We conducted a pilot study to investigate the proposed features in a dataset of 171 healthy controls and 134 patients with schizophrenia (SZ). We computed variability in RSN and FNC in a group independent component analysis framework and tested three types of variability metrics, namely Euclidean distance, Pearson correlation and Kullback-Leibler (KL) divergence. Euclidean distance and Pearson correlation metrics more effectively discriminated controls from patients than KL divergence. The group differences observed with variability in RSN and FNC were highly consistent, indicating patients presenting increased deviation from the cohort-common pattern of RSN and FNC than controls. The variability in RSN and FNC showed significant associations with network global efficiency, the more the deviation, the lower the efficiency. Furthermore, the RSN and FNC variability were found to associate with individual SZ risk SNPs as well as cumulative polygenic risk score for SZ. Collectively the current findings provide preliminary evidence for variability in RSN and FNC being promising imaging features that may find applications as biomarkers and in imaging genetic association analysis.

Cross-Tissue Exploration of Genetic and Epigenetic Effects on Brain Gray Matter in Schizophrenia

Closely linking genetics and environment factors, epigenetics has been of increasing interest in psychiatric disease studies. In this work, we integrated single nucleotide polymorphisms (SNPs), DNA methylation of blood and saliva, and brain gray matter (GM) measures to explore the role of genetic and epigenetic variation to the brain structure changes in schizophrenia (SZ). By focusing on the reported SZ genetic risk regions, we applied a multi-stage multivariate analysis to a discovery dataset (92 SZ patients and 110 controls, blood) and an independent replication dataset (93 SZ patients and 99 controls, saliva). Two pairs of SNP-methylation components were significantly correlated (r = .48 and .35) in blood DNA, and replicated (r = .46 and .29) in saliva DNA, reflecting cross-tissue SNP cis-effects. In the discovery data, both SNP-related methylation components were also associated with one GM component primarily located in cerebellum, caudate, and thalamus. Additionally, another methylation component in NOSIP gene with significant SZ patient differences (P = .009), was associated with 8 GM components (7 with patient differences) including superior, middle, and inferior frontal gyri, superior, middle, and inferior temporal gyri, cerebellum, insula, cuneus, and lingual gyrus. Of these, 5 methylation-GM associations were replicated (P < .05). In contrast, no pairwise significant associations were observed between SNP and GM components. This study strongly supports that compared to genetic variation, epigenetics show broader and more significant associations with brain structure as well as diagnosis, which can be cross-tissue, and the potential in explaining the mechanism of genetic risks in SZ.

Three-dimensional cardiovascular imaging-genetics: a mass univariate framework

Motivation

Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for high-throughput mapping of genotype-phenotype associations in three dimensions (3D).

Results

High-resolution cardiac magnetic resonance images were automatically segmented in 1124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts.

Availability and implementation

The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work.

Contact

declan.oregan@imperial.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Neuroimaging genomics in psychiatry-a translational approach

Neuroimaging genomics is a relatively new field focused on integrating genomic and imaging data in order to investigate the mechanisms underlying brain phenotypes and neuropsychiatric disorders. While early work in neuroimaging genomics focused on mapping the associations of candidate gene variants with neuroimaging measures in small cohorts, the lack of reproducible results inspired better-powered and unbiased large-scale approaches. Notably, genome-wide association studies (GWAS) of brain imaging in thousands of individuals around the world have led to a range of promising findings. Extensions of such approaches are now addressing epigenetics, gene–gene epistasis, and gene–environment interactions, not only in brain structure, but also in brain function. Complementary developments in systems biology might facilitate the translation of findings from basic neuroscience and neuroimaging genomics to clinical practice. Here, we review recent approaches in neuroimaging genomics—we highlight the latest discoveries, discuss advantages and limitations of current approaches, and consider directions by which the field can move forward to shed light on brain disorders.

Partition-based ultrahigh-dimensional variable screening

Traditional variable selection methods are compromised by overlooking useful information on covariates with similar functionality or spatial proximity, and by treating each covariate independently. Leveraging prior grouping information on covariates, we propose partition-based screening methods for ultrahigh-dimensional variables in the framework of generalized linear models. We show that partition-based screening exhibits the sure screening property with a vanishing false selection rate, and we propose a data-driven partition screening framework with unavailable or unreliable prior knowledge on covariate grouping and investigate its theoretical properties. We consider two special cases: correlation-guided partitioning and spatial location- guided partitioning. In the absence of a single partition, we propose a theoretically justified strategy for combining statistics from various partitioning methods. The utility of the proposed methods is demonstrated via simulation and analysis of functional neuroimaging data.

FGWAS: Functional genome wide association analysis

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.