Multifaceted genomic risk for brain function in schizophrenia

A joint study of whole exome sequencing and structural MRI analysis in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide and influenced by both environmental and genetic factors. Genetic studies of MDD have focused on common variants and have been constrained by the heterogeneity of clinical symptoms.

METHODS

We sequenced the exome of 77 cases and 245 controls of Han Chinese ancestry and scanned their brain. Burden tests of rare variants were performed first to explore the association between genes/pathways and MDD. Secondly, parallel Independent Component Analysis was conducted to investigate genetic underpinnings of gray matter volume (GMV) changes of MDD.

RESULTS

Two genes (CSMD1, p = 5.32×10-6; CNTNAP5, p = 1.32×10-6) and one pathway (Neuroactive Ligand Receptor Interactive, p = 1.29×10-5) achieved significance in burden test. In addition, we identified one pair of imaging-genetic components of significant correlation (r = 0.38, p = 9.92×10-6). The imaging component reflected decreased GMV in cases and correlated with intelligence quotient (IQ). IQ mediated the effects of GMV on MDD. The genetic component enriched in two gene sets, namely Singling by G-protein coupled receptors [false discovery rate (FDR) q = 3.23×10-4) and Alzheimer Disease Up (FDR q = 6.12×10-4).

CONCLUSIONS

Both rare variants analysis and imaging-genetic analysis found evidence corresponding with the neuroinflammation and synaptic plasticity hypotheses of MDD. The mediation of IQ indicates that genetic component may act on MDD through GMV alteration and cognitive impairment.

Exploring different impaired speed of genetic-related brain function and structures in schizophrenic progress using multimodal analysis

Schizophrenia (SZ) is a highly heritable disease exhibiting substantial structural and functional brain impairments. The duration of illness and medication use may cause different presentations of impairments in patients. To understand the progressive variations of the disease, most recent studies have reported brain functional or structural abnormalities associated with illness duration, but a comprehensive study of pathology underlying brain structure, function and illness duration is still limited. In this work, we employed a three-way parallel independent component analysis (pICA) algorithm to jointly analyze grey matter volume(GM), functional connectivity (FC) and single nucleotide polymorphisms (SNPs) from drug-naïve first-episode [FESZ], chronic schizophrenia [CSZ]) and healthy controls[HC], aiming to identify the linked alterations in SNP-GM-FC components, and evaluate the impairment speed of imaging measures associated with SZ-susceptible genetic variants in different disease stages (FESZ and CSZ). Results demonstrated significant group differences on GM and FC in hippocampus, temporal gyrus and cerebellum between SZ and HC, which are also significantly correlated with SNPs residing in genes like GABBR2, SATB2, CACNA1C, PDE4B, involved in pathways of cell junction, synapse and neuron projection. Moreover, two-sample t-tests showed that GM volume and FC strength presented similar trends of progressive decrease with the increase of the illness duration (HC>FESZ>CSZ). Besides that genetic-related GM and FC components both showed significant associations with illness duration, FC indicates the higher impairment speed than GM, suggesting that functional connectivity may serve as a more sensitive measure to detect the disruptions in SZ at the very early stage.

Creation of a myosin Va-TAP-tagged mouse and identification of potential myosin Va-interacting proteins in the cerebellum

The actin-based motor myosin Va transports numerous cargos, including the smooth endoplasmic reticulum (SER) in cerebellar Purkinje neurons (PNs) and melanosomes in melanocytes. Identifying proteins that interact with this myosin is key to understanding its cellular functions. Toward that end, we used recombineering to insert via homologous recombination a tandem affinity purification (TAP) tag composed of the immunoglobulin G-binding domain of protein A, a tobacco etch virus cleavage site, and a FLAG tag into the mouse MYO5A locus immediately after the initiation codon. Importantly, we provide evidence that the TAP-tagged version of myosin Va (TAP-MyoVa) functions normally in terms of SER transport in PNs and melanosome positioning in melanocytes. Given this and other evidence that TAP-MyoVa is fully functional, we purified it together with associated proteins directly from juvenile mouse cerebella and subjected the samples to mass spectroscopic analyses. As expected, known myosin Va-binding partners like dynein light chain were identified. Importantly, numerous novel interacting proteins were also tentatively identified, including guanine nucleotide-binding protein G(o) subunit alpha (Gnao1), a biomarker for schizophrenia. Consistently, an antibody to Gnao1 immunoprecipitates myosin Va, and Gnao1's localization to PN dendritic spines depends on myosin Va. The mouse model created here should facilitate the identification of novel myosin Va-binding partners, which in turn should advance our understanding of the roles played by this important myosin in vivo.

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.

Multivariate Analyses Reveal Biological Components Related to Neuronal Signaling and Immunity Mediating Electroencephalograms Abnormalities in Alcohol-Dependent Individuals from the Collaborative Study on the Genetics of Alcoholism Cohort

BACKGROUND

The underlying molecular mechanisms associated with alcohol use disorder (AUD) risk have only been partially revealed using traditional approaches such as univariate genomewide association and linkage-based analyses. We therefore aimed to identify gene clusters related to Electroencephalograms (EEG) neurobiological phenotypes distinctive to individuals with AUD using a multivariate approach.

METHODS

The current project adopted a bimultivariate data-driven approach, parallel independent component analysis (para-ICA), to derive and explore significant genotype-phenotype associations in a case-control subset of the Collaborative Study on the Genetics of Alcoholism (COGA) dataset. Para-ICA subjects comprised N = 799 self-reported European Americans (367 controls and 432 AUD cases), recruited from COGA, who had undergone resting EEG and genotyping. Both EEG and genomewide single nucleotide polymorphism (SNP) data were preprocessed prior to being subjected to para-ICA in order to derive genotype-phenotype relationships.

RESULTS

From the data, 4 EEG frequency and 4 SNP components were estimated, with 2 significantly correlated EEG-genetic relationship pairs. The first such pair primarily represented theta activity, negatively correlated with a genetic cluster enriched for (but not limited to) ontologies/disease processes representing cell signaling, neurogenesis, transmembrane drug transportation, alcoholism, and lipid/cholesterol metabolism. The second component pair represented mainly alpha activity, positively correlated with a genetic cluster with ontologies similarly enriched as the first component. Disease-related enrichments for this component revealed heart and autoimmune disorders as top hits. Loading coefficients for both the alpha and theta components were significantly reduced in cases compared to controls.

CONCLUSIONS

Our data suggest plausible multifactorial genetic components, primarily enriched for neuronal/synaptic signaling/transmission, immunity, and neurogenesis, mediating low-frequency alpha and theta abnormalities in alcohol addiction.

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.

A Schizophrenia-Related Genetic-Brain-Cognition Pathway Revealed in a Large Chinese Population

BACKGROUND

In the past decades, substantial effort has been made to explore the genetic influence on brain structural/functional abnormalities in schizophrenia, as well as cognitive impairments. In this work, we aimed to extend previous studies to explore the internal mediation pathway among genetic factor, brain features and cognitive scores in a large Chinese dataset.

METHODS

Gray matter (GM) volume, fractional amplitude of low-frequency fluctuations (fALFF), and 4522 schizophrenia-susceptible single nucleotide polymorphisms (SNP) from 905 Chinese subjects were jointly analyzed, to investigate the multimodal association. Based on the identified imaging-genetic pattern, correlations with cognition and mediation analysis were then conducted to reveal the potential mediation pathways.

FINDINGS

One linked imaging-genetic pattern was identified to be group discriminative, which was also associated with working memory performance. Particularly, GM reduction in thalamus, putamen and bilateral temporal gyrus in schizophrenia was associated with fALFF decrease in medial prefrontal cortex, both were also associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including CSMD1, CNTNAP2, DCC, GABBR2 etc. This linked pattern was also replicated in an independent cohort (166 subjects), which although showed certain age and clinical differences with the discovery cohort. A further mediation analysis suggested that GM alterations significantly mediated the association from SNP to fALFF, while fALFF mediated the association from SNP and GM to working memory performance.

INTERPRETATION

This study has not only verified the impaired imaging-genetic association in schizophrenia, but also initially revealed a potential genetic-brain-cognition mediation pathway, indicating that polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus could further affect cognition in schizophrenia.

A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia

BACKGROUND

Technological advances are enabling us to collect multimodal datasets at an increasing depth and resolution while with decreasing labors. Understanding complex interactions among multimodal datasets, however, is challenging.

NEW METHOD

In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel machine for detecting higher order interactions among biologically relevant multimodal data. Using a semiparametric method on a reproducing kernel Hilbert space, we formulated the proposed method as a standard mixed-effects linear model and derived a score-based variance component statistic to test higher order interactions between multimodal datasets.

RESULTS

The method was evaluated using extensive numerical simulation and real data from the Mind Clinical Imaging Consortium with both schizophrenia patients and healthy controls. Our method identified 13-triplets that included 6 gene-derived SNPs, 10 ROIs, and 6 gene-specific DNA methylations that are correlated with the changes in hippocampal volume, suggesting that these triplets may be important for explaining schizophrenia-related neurodegeneration.

COMPARISON WITH EXISTING METHOD(S)

The performance of the proposed method is compared with the following methods: test based on only first and first few principal components followed by multiple regression, and full principal component analysis regression, and the sequence kernel association test.

CONCLUSIONS

With strong evidence (p-value ≤0.000001), the triplet (MAGI2, CRBLCrus1.L, FBXO28) is a significant biomarker for schizophrenia patients. This novel method can be applicable to the study of other disease processes, where multimodal data analysis is a common task.

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.

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.

Novel gene-brain structure relationships in psychotic disorder revealed using parallel independent component analyses

BACKGROUND

Schizophrenia, schizoaffective disorder, and psychotic bipolar disorder overlap with regard to symptoms, structural and functional brain abnormalities, and genetic risk factors. Neurobiological pathways connecting genes to clinical phenotypes across the spectrum from schizophrenia to psychotic bipolar disorder remain largely unknown.

METHODS

We examined the relationship between structural brain changes and risk alleles across the psychosis spectrum in the multi-site Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) cohort. Regional MRI brain volumes were examined in 389 subjects with a psychotic disorder (139 schizophrenia, 90 schizoaffective disorder, and 160 psychotic bipolar disorder) and 123 healthy controls. 451,701 single-nucleotide polymorphisms were screened and processed using parallel independent component analysis (para-ICA) to assess associations between genes and structural brain abnormalities in probands.

RESULTS

482 subjects were included after quality control (364 individuals with psychotic disorder and 118 healthy controls). Para-ICA identified four genetic components including several risk genes already known to contribute to schizophrenia and bipolar disorder and revealed three structural components that showed overlapping relationships with the disease risk genes across the three psychotic disorders. Functional ontologies representing these gene clusters included physiological pathways involved in brain development, synaptic transmission, and ion channel activity.

CONCLUSIONS

Heritable brain structural findings such as reduced cortical thickness and surface area in probands across the psychosis spectrum were associated with somewhat distinct genes related to putative disease pathways implicated in psychotic disorders. This suggests that brain structural alterations might represent discrete psychosis intermediate phenotypes along common neurobiological pathways underlying disease expression across the psychosis spectrum.

An image resolution perspective on functional activity mapping

In this paper we apply techniques for numerical estimation of system resolution from imaging, to the regression problem of relating biological data to phenotypes. Our approach can be viewed as an extension of Backus-Gilbert theory, which attempts to find the most concentrated estimator that may be reliably computed in an inverse problem. Applied to a regression model, we estimate a minimal combination of collinear variables that may be found in a predictor, which gives a robust multivariable estimate of the network relationships in the data. Our extension of this approach incorporates a sparsity prior in order to adapt the concept to the high noise and small sample regime. The result is a compromise between the Backus-Gilbert and sparse regularized estimates, which may be adjusted to trade-off benefits of both and provide a result which we demonstrate to be more robust. This is applied to a dataset containing fMRI activity maps and SNP's for subjects with schizophrenia and related disorders. We find the resolution estimate identifies plausible modular behavior among neighboring variables and between regions. We further demonstrate the ability to find differences in these relationships using different response variables or additional data, providing a means to extract more specialized information.

A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI

BACKGROUND

Our goal is to identify the brain regions most relevant to mental illness using neuroimaging. State of the art machine learning methods commonly suffer from repeatability difficulties in this application, particularly when using large and heterogeneous populations for samples.

NEW METHOD

We revisit both dimensionality reduction and sparse modeling, and recast them in a common optimization-based framework. This allows us to combine the benefits of both types of methods in an approach which we call unambiguous components. We use this to estimate the image component with a constrained variability, which is best correlated with the unknown disease mechanism.

RESULTS

We apply the method to the estimation of neuroimaging biomarkers for schizophrenia, using task fMRI data from a large multi-site study. The proposed approach yields an improvement in both robustness of the estimate and classification accuracy.

COMPARISON WITH EXISTING METHODS

We find that unambiguous components incorporate roughly two thirds of the same brain regions as sparsity-based methods LASSO and elastic net, while roughly one third of the selected regions differ. Further, unambiguous components achieve superior classification accuracy in differentiating cases from controls.

CONCLUSIONS

Unambiguous components provide a robust way to estimate important regions of imaging data.

Multivariate Genetic Correlates of the Auditory Paired Stimuli-Based P2 Event-Related Potential in the Psychosis Dimension From the BSNIP Study

OBJECTIVE

The complex molecular etiology of psychosis in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is not well defined, presumably due to their multifactorial genetic architecture. Neurobiological correlates of psychosis can be identified through genetic associations of intermediate phenotypes such as event-related potential (ERP) from auditory paired stimulus processing (APSP). Various ERP components of APSP are heritable and aberrant in SZ, PBP and their relatives, but their multivariate genetic factors are less explored.

METHODS

We investigated the multivariate polygenic association of ERP from 64-sensor auditory paired stimulus data in 149 SZ, 209 PBP probands, and 99 healthy individuals from the multisite Bipolar-Schizophrenia Network on Intermediate Phenotypes study. Multivariate association of 64-channel APSP waveforms with a subset of 16 999 single nucleotide polymorphisms (SNPs) (reduced from 1 million SNP array) was examined using parallel independent component analysis (Para-ICA). Biological pathways associated with the genes were assessed using enrichment-based analysis tools.

RESULTS

Para-ICA identified 2 ERP components, of which one was significantly correlated with a genetic network comprising multiple linearly coupled gene variants that explained ~4% of the ERP phenotype variance. Enrichment analysis revealed epidermal growth factor, endocannabinoid signaling, glutamatergic synapse and maltohexaose transport associated with P2 component of the N1-P2 ERP waveform. This ERP component also showed deficits in SZ and PBP.

CONCLUSIONS

Aberrant P2 component in psychosis was associated with gene networks regulating several fundamental biologic functions, either general or specific to nervous system development. The pathways and processes underlying the gene clusters play a crucial role in brain function, plausibly implicated in psychosis.

DNA Repair Gene (XRCC1) Polymorphism (Arg399Gln) Associated with Schizophrenia in South Indian Population: A Genotypic and Molecular Dynamics Study

This paper depicts the first report from an Indian population on the association between the variant Arg399Gln of XRCC1 locus in the DNA repair system and schizophrenia, the debilitating disease that affects 1% of the world population. Genotypic analysis of a total of 523 subjects (260 patients and 263 controls) revealed an overwhelming presence of Gln399Gln in the case subjects against the controls (P < 0.0068), indicating significant level of association of this nsSNP with schizophrenia; the Gln399 allele frequency was also perceptibly more in cases than in controls (p < 0.003; OR = 1.448). The results of the genotypic studies were further validated using pathogenicity and stability prediction analysis employing computational tools [I-Mutant Suite, iStable, PolyPhen2, SNAP, and PROVEAN], with a view toassess the magnitude of deleteriousness of the mutation. The pathogenicity analysis reveals that the nsSNP could be deleterious inasmuch as it could affect the functionality of the gene, and interfere with protein function. Molecular dynamics simulation of 60ns was performed using GROMACS to analyse structural change due to a mutation (Arg399Gln) that was never examined before. RMSD, RMSF, hydrogen bonds, radius of gyration and SASA analysis showedthe existence of asignificant difference between the native and the mutant protein. The present study gives astrong indication that the XRCC1 locus deserves serious attention, as it could be a potential candidatecontributing to the etio-pathogenesis of the disease.

SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration

SchizConnect (www.schizconnect.org) is built to address the issues of multiple data repositories in schizophrenia neuroimaging studies. It includes a level of mediation--translating across data sources--so that the user can place one query, e.g. for diffusion images from male individuals with schizophrenia, and find out from across participating data sources how many datasets there are, as well as downloading the imaging and related data. The current version handles the Data Usage Agreements across different studies, as well as interpreting database-specific terminologies into a common framework. New data repositories can also be mediated to bring immediate access to existing datasets. Compared with centralized, upload data sharing models, SchizConnect is a unique, virtual database with a focus on schizophrenia and related disorders that can mediate live data as information is being updated at each data source. It is our hope that SchizConnect can facilitate testing new hypotheses through aggregated datasets, promoting discovery related to the mechanisms underlying schizophrenic dysfunction.

An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders

Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010). Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g., genome wide association. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e., simultaneous combination of SNP and neuroimage information) independent component analysis (p-ICA), which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.

Interaction effect between handedness and CNTNAP2 polymorphism (rs7794745 genotype) on voice-specific frontotemporal activity in healthy individuals: an fMRI study

Recent neuroimaging studies have demonstrated that Contactin-associated protein-like2 (CNTNAP2) polymorphisms affect left-hemispheric function of language processing in healthy individuals, but no study has investigated the influence of these polymorphisms on right-hemispheric function involved in human voice perception. Further, although recent reports suggest that determination of handedness is influenced by genetic effect, the interaction effect between handedness and CNTNAP2 polymorphisms for brain activity in human voice perception and language processing has not been revealed. We aimed to investigate the interaction effect of handedness and CNTNAP2 polymorphisms in respect to brain function for human voice perception and language processing in healthy individuals. Brain function of 108 healthy volunteers (74 right-handed and 34 non-right-handed) was examined while they were passively listening to reverse sentences (rSEN), identifiable non-vocal sounds (SND), and sentences (SEN). Full factorial design analysis was calculated by using three factors: (1) rs7794745 (A/A or A/T), (2) rs2710102 [G/G or A carrier (A/G and A/A)], and (3) voice-specific response (rSEN or SND). The main effect of rs7794745 (A/A or A/T) was significantly revealed at the right middle frontal gyrus (MFG) and bilateral superior temporal gyrus (STG). This result suggests that rs7794745 genotype affects voice-specific brain function. Furthermore, interaction effect was significantly observed among MFG-STG activations by human voice perception, rs7794745 (A/A or A/T), and handedness. These results suggest that CNTNAP2 polymorphisms could be one of the important factors in the neural development related to vocal communication and language processing in both right-handed and non-right-handed healthy individuals.

Genetic markers of white matter integrity in schizophrenia revealed by parallel ICA

It is becoming a consensus that white matter integrity is compromised in schizophrenia (SZ), however the underlying genetics remains elusive. Evidence suggests a polygenic basis of the disorder, which involves various genetic variants with modest individual effect sizes. In this work, we used a multivariate approach, parallel independent component analysis (P-ICA), to explore the genetic underpinnings of white matter abnormalities in SZ. A pre-filtering step was first applied to locate 6527 single nucleotide polymorphisms (SNPs) discriminating patients from controls with a nominal uncorrected p-value of 0.01. These potential susceptibility loci were then investigated for associations with fractional anisotropy (FA) images in a cohort consisting of 73 SZ patients and 87 healthy controls (HC). A significant correlation (r = −0.37, p = 1.25 × 10⁻⁶) was identified between one genetic factor and one FA component after controlling for scanning site, ethnicity, age, and sex. The identified FA-SNP association remained stable in a 10-fold validation. A 5000-run permutation test yielded a p-value of 2.00 × 10⁻⁴. The FA component reflected decreased white matter integrity in the forceps major for SZ patients. The SNP component was overrepresented in genes whose products are involved in corpus callosum morphology (e.g., CNTNAP2, NPAS3, and NFIB) as well as canonical pathways of synaptic long term depression and protein kinase A signaling. Taken together, our finding delineates a part of genetic architecture underlying SZ-related FA reduction, emphasizing the important role of genetic variants involved in neural development.

Three-way parallel independent component analysis for imaging genetics using multi-objective optimization

In the biomedical field, current technology allows for the collection of multiple data modalities from the same subject. In consequence, there is an increasing interest for methods to analyze multi-modal data sets. Methods based on independent component analysis have proven to be effective in jointly analyzing multiple modalities, including brain imaging and genetic data. This paper describes a new algorithm, three-way parallel independent component analysis (3pICA), for jointly identifying genomic loci associated with brain function and structure. The proposed algorithm relies on the use of multi-objective optimization methods to identify correlations among the modalities and maximally independent sources within modality. We test the robustness of the proposed approach by varying the effect size, cross-modality correlation, noise level, and dimensionality of the data. Simulation results suggest that 3p-ICA is robust to data with SNR levels from 0 to 10 dB and effect-sizes from 0 to 3, while presenting its best performance with high cross-modality correlations, and more than one subject per 1,000 variables. In an experimental study with 112 human subjects, the method identified links between a genetic component (pointing to brain function and mental disorder associated genes, including PPP3CC, KCNQ5, and CYP7B1), a functional component related to signal decreases in the default mode network during the task, and a brain structure component indicating increases of gray matter in brain regions of the default mode region. Although such findings need further replication, the simulation and in-vivo results validate the three-way parallel ICA algorithm presented here as a useful tool in biomedical data decomposition applications.

Bayesian Models for fMRI Data Analysis

Functional magnetic resonance imaging (fMRI), a noninvasive neuroimaging method that provides an indirect measure of neuronal activity by detecting blood flow changes, has experienced an explosive growth in the past years. Statistical methods play a crucial role in understanding and analyzing fMRI data. Bayesian approaches, in particular, have shown great promise in applications. A remarkable feature of fully Bayesian approaches is that they allow a flexible modeling of spatial and temporal correlations in the data. This paper provides a review of the most relevant models developed in recent years. We divide methods according to the objective of the analysis. We start from spatio-temporal models for fMRI data that detect task-related activation patterns. We then address the very important problem of estimating brain connectivity. We also touch upon methods that focus on making predictions of an individual's brain activity or a clinical or behavioral response. We conclude with a discussion of recent integrative models that aim at combining fMRI data with other imaging modalities, such as EEG/MEG and DTI data, measured on the same subjects. We also briefly discuss the emerging field of imaging genetics.

The rise of large-scale imaging studies in psychiatry

From the initial arguments over whether 12 to 20 subjects were sufficient for an fMRI study, sample sizes in psychiatric neuroimaging studies have expanded into the tens of thousands. These large-scale imaging studies fall into several categories, each of which has specific advantages and challenges. The different study types can be grouped based on their level of control: meta-analyses, at one extreme of the spectrum, control nothing about the imaging protocol or subject selection criteria in the datasets they include, On the other hand, planned multi-site mega studies pour intense efforts into strictly having the same protocols. However, there are several other combinations possible, each of which is best used to address certain questions. The growing investment of all these studies is delivering on the promises of neuroimaging for psychiatry, and holds incredible potential for impact at the level of the individual patient. However, to realize this potential requires both standardized data-sharing efforts, so that there is more staying power in the datasets for re-use and new applications, as well as training the next generation of neuropsychiatric researchers in "Big Data" techniques in addition to traditional experimental methods. The increased access to thousands of datasets along with the needed informatics demands a new emphasis on integrative scientific methods.

The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

Association of GRM3 polymorphism with white matter integrity in schizophrenia

BACKGROUND

While the functional disconnectivity hypothesis of schizophrenia has received considerable attention, fewer studies have investigated the contribution of genotype to structural connectivity between brain regions either in schizophrenia patients or in healthy controls. In this study, we obtained diffusion tensor imaging (DTI) data and genome-wide single nucleotide polymorphism (SNP) data from 74 cases and 87 age- and gender-matched controls.

METHODS

We used independent component analysis (ICA) to analyze fractional anisotropy (FA) values and correlated FA values with 121 SNPs in genes associated with myelination and/or schizophrenia risk.

RESULTS

Using ICA, we identified 6 maximally independent components in which the majority of the voxels corresponded to known white matter (WM) tracts. Among these WM-enriched components, two had FA values that were significantly decreased in patients. In addition, we examined the relationship between FA values and genotype and found that a SNP located in the intronic region of the metabotropic glutamate receptor 3 gene, GRM3, shows a significant correlation with FA values in a component containing tracts from the cortico-cerebellar-thalamic-cortical circuit of patients but not controls.

CONCLUSIONS

Our findings strengthen the evidence for an association between GRM3 genotype and schizophrenia and suggest a role for glutamate neurotransmission in the establishment and maintenance of myelinated fibers.

Recognition of facial emotion and affective prosody in children with ASD (+ADHD) and their unaffected siblings

Autism is a highly heritable and clinically heterogeneous neuropsychiatric disorder that frequently co-occurs with other psychopathologies, such as attention-deficit/hyperactivity disorder (ADHD). An approach to parse heterogeneity is by forming more homogeneous subgroups of autism spectrum disorder (ASD) patients based on their underlying, heritable cognitive vulnerabilities (endophenotypes). Emotion recognition is a likely endophenotypic candidate for ASD and possibly for ADHD. Therefore, this study aimed to examine whether emotion recognition is a viable endophenotypic candidate for ASD and to assess the impact of comorbid ADHD in this context. A total of 90 children with ASD (43 with and 47 without ADHD), 79 ASD unaffected siblings, and 139 controls aged 6-13 years, were included to test recognition of facial emotion and affective prosody. Our results revealed that the recognition of both facial emotion and affective prosody was impaired in children with ASD and aggravated by the presence of ADHD. The latter could only be partly explained by typical ADHD cognitive deficits, such as inhibitory and attentional problems. The performance of unaffected siblings could overall be considered at an intermediate level, performing somewhat worse than the controls and better than the ASD probands. Our findings suggest that emotion recognition might be a viable endophenotype in ASD and a fruitful target in future family studies of the genetic contribution to ASD and comorbid ADHD. Furthermore, our results suggest that children with comorbid ASD and ADHD are at highest risk for emotion recognition problems.

Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia

The brain's default mode network (DMN) is highly heritable and is compromised in a variety of psychiatric disorders. However, genetic control over the DMN in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is largely unknown. Study subjects (n = 1,305) underwent a resting-state functional MRI scan and were analyzed by a two-stage approach. The initial analysis used independent component analysis (ICA) in 324 healthy controls, 296 SZ probands, 300 PBP probands, 179 unaffected first-degree relatives of SZ probands (SZREL), and 206 unaffected first-degree relatives of PBP probands to identify DMNs and to test their biomarker and/or endophenotype status. A subset of controls and probands (n = 549) then was subjected to a parallel ICA (para-ICA) to identify imaging-genetic relationships. ICA identified three DMNs. Hypo-connectivity was observed in both patient groups in all DMNs. Similar patterns observed in SZREL were restricted to only one network. DMN connectivity also correlated with several symptom measures. Para-ICA identified five sub-DMNs that were significantly associated with five different genetic networks. Several top-ranking SNPs across these networks belonged to previously identified, well-known psychosis/mood disorder genes. Global enrichment analyses revealed processes including NMDA-related long-term potentiation, PKA, immune response signaling, axon guidance, and synaptogenesis that significantly influenced DMN modulation in psychoses. In summary, we observed both unique and shared impairments in functional connectivity across the SZ and PBP cohorts; these impairments were selectively familial only for SZREL. Genes regulating specific neurodevelopment/transmission processes primarily mediated DMN disconnectivity. The study thus identifies biological pathways related to a widely researched quantitative trait that might suggest novel, targeted drug treatments for these diseases.

A review of multivariate analyses in imaging genetics

Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA), and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype-associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and limitations are discussed.

The MCIC collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia

Expertly collected, well-curated data sets consisting of comprehensive clinical characterization and raw structural, functional and diffusion-weighted DICOM images in schizophrenia patients and sex and age-matched controls are now accessible to the scientific community through an on-line data repository (coins.mrn.org). The Mental Illness and Neuroscience Discovery Institute, now the Mind Research Network (MRN, http://www.mrn.org/ ), comprised of investigators at the University of New Mexico, the University of Minnesota, Massachusetts General Hospital, and the University of Iowa, conducted a cross-sectional study to identify quantitative neuroimaging biomarkers of schizophrenia. Data acquisition across multiple sites permitted the integration and cross-validation of clinical, cognitive, morphometric, and functional neuroimaging results gathered from unique samples of schizophrenia patients and controls using a common protocol across sites. Particular effort was made to recruit patients early in the course of their illness, at the onset of their symptoms. There is a relatively even sampling of illness duration in chronic patients. This data repository will be useful to 1) scientists who can study schizophrenia by further analysis of this cohort and/or by pooling with other data; 2) computer scientists and software algorithm developers for testing and validating novel registration, segmentation, and other analysis software; and 3) educators in the fields of neuroimaging, medical image analysis and medical imaging informatics who need exemplar data sets for courses and workshops. Sharing provides the opportunity for independent replication of already published results from this data set and novel exploration. This manuscript describes the inclusion/exclusion criteria, imaging parameters and other information that will assist those wishing to use this data repository.

Integrating fMRI and SNP data for biomarker identification for schizophrenia with a sparse representation based variable selection method

Background

In recent years, both single-nucleotide polymorphism (SNP) array and functional magnetic resonance imaging (fMRI) have been widely used for the study of schizophrenia (SCZ). In addition, a few studies have been reported integrating both SNPs data and fMRI data for comprehensive analysis.

Methods

In this study, a novel sparse representation based variable selection (SRVS) method has been proposed and tested on a simulation data set to demonstrate its multi-resolution properties. Then the SRVS method was applied to an integrative analysis of two different SCZ data sets, a Single-nucleotide polymorphism (SNP) data set and a functional resonance imaging (fMRI) data set, including 92 cases and 116 controls. Biomarkers for the disease were identified and validated with a multivariate classification approach followed by a leave one out (LOO) cross-validation. Then we compared the results with that of a previously reported sparse representation based feature selection method.

Results

Results showed that biomarkers from our proposed SRVS method gave significantly higher classification accuracy in discriminating SCZ patients from healthy controls than that of the previous reported sparse representation method. Furthermore, using biomarkers from both data sets led to better classification accuracy than using single type of biomarkers, which suggests the advantage of integrative analysis of different types of data.

Conclusions

The proposed SRVS algorithm is effective in identifying significant biomarkers for complicated disease as SCZ. Integrating different types of data (e.g. SNP and fMRI data) may identify complementary biomarkers benefitting the diagnosis accuracy of the disease.

An Integrative Bayesian Modeling Approach to Imaging Genetics

In this paper we present a Bayesian hierarchical modeling approach for imaging genetics, where the interest lies in linking brain connectivity across multiple individuals to their genetic information. We have available data from a functional magnetic resonance (fMRI) study on schizophrenia. Our goals are to identify brain regions of interest (ROIs) with discriminating activation patterns between schizophrenic patients and healthy controls, and to relate the ROIs' activations with available genetic information from single nucleotide polymorphisms (SNPs) on the subjects. For this task we develop a hierarchical mixture model that includes several innovative characteristics: it incorporates the selection of ROIs that discriminate the subjects into separate groups; it allows the mixture components to depend on selected covariates; it includes prior models that capture structural dependencies among the ROIs. Applied to the schizophrenia data set, the model leads to the simultaneous selection of a set of discriminatory ROIs and the relevant SNPs, together with the reconstruction of the correlation structure of the selected regions. To the best of our knowledge, our work represents the first attempt at a rigorous modeling strategy for imaging genetics data that incorporates all such features.

Investigating the genetic basis of theory of mind (ToM): the role of catechol-O-methyltransferase (COMT) gene polymorphisms

The ability to deduce other persons' mental states and emotions which has been termed ‘theory of mind (ToM)’ is highly heritable. First molecular genetic studies focused on some dopamine-related genes, while the genetic basis underlying different components of ToM (affective ToM and cognitive ToM) remain unknown. The current study tested 7 candidate polymorphisms (rs4680, rs4633, rs2020917, rs2239393, rs737865, rs174699 and rs59938883) on the catechol-O-methyltransferase (COMT) gene. We investigated how these polymorphisms relate to different components of ToM. 101 adults participated in our study; all were genetically unrelated, non-clinical and healthy Chinese subjects. Different ToM tasks were applied to detect their theory of mind ability. The results showed that the COMT gene rs2020917 and rs737865 SNPs were associated with cognitive ToM performance, while the COMT gene rs5993883 SNP was related to affective ToM, in which a significant gender-genotype interaction was found (p = 0.039). Our results highlighted the contribution of DA-related COMT gene on ToM performance. Moreover, we found out that the different SNP at the same gene relates to the discriminative aspect of ToM. Our research provides some preliminary evidence to the genetic basis of theory of mind which still awaits further studies.

IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis

Background

Next-Generation Sequencing (NGS) technologies and Genome-Wide Association Studies (GWAS) generate millions of reads and hundreds of datasets, and there is an urgent need for a better way to accurately interpret and distill such large amounts of data. Extensive pathway and network analysis allow for the discovery of highly significant pathways from a set of disease vs. healthy samples in the NGS and GWAS. Knowledge of activation of these processes will lead to elucidation of the complex biological pathways affected by drug treatment, to patient stratification studies of new and existing drug treatments, and to understanding the underlying anti-cancer drug effects. There are approximately 141 biological human pathway resources as of Jan 2012 according to the Pathguide database. However, most currently available resources do not contain disease, drug or organ specificity information such as disease-pathway, drug-pathway, and organ-pathway associations. Systematically integrating pathway, disease, drug and organ specificity together becomes increasingly crucial for understanding the interrelationships between signaling, metabolic and regulatory pathway, drug action, disease susceptibility, and organ specificity from high-throughput omics data (genomics, transcriptomics, proteomics and metabolomics).

Results

We designed the Integrated Pathway Analysis Database for Systematic Enrichment Analysis (IPAD, http://bioinfo.hsc.unt.edu/ipad), defining inter-association between pathway, disease, drug and organ specificity, based on six criteria: 1) comprehensive pathway coverage; 2) gene/protein to pathway/disease/drug/organ association; 3) inter-association between pathway, disease, drug, and organ; 4) multiple and quantitative measurement of enrichment and inter-association; 5) assessment of enrichment and inter-association analysis with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources; and 6) cross-linking of multiple available data sources.

IPAD is a comprehensive database covering about 22,498 genes, 25,469 proteins, 1956 pathways, 6704 diseases, 5615 drugs, and 52 organs integrated from databases including the BioCarta, KEGG, NCI-Nature curated, Reactome, CTD, PharmGKB, DrugBank, PharmGKB, and HOMER. The database has a web-based user interface that allows users to perform enrichment analysis from genes/proteins/molecules and inter-association analysis from a pathway, disease, drug, and organ.

Moreover, the quality of the database was validated with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources. Two case studies were also presented to demonstrate: 1) self-validation of enrichment analysis and inter-association analysis on brain-specific markers, and 2) identification of previously undiscovered components by the enrichment analysis from a prostate cancer study.

Conclusions

IPAD is a new resource for analyzing, identifying, and validating pathway, disease, drug, organ specificity and their inter-associations. The statistical method we developed for enrichment and similarity measurement and the two criteria we described for setting the threshold parameters can be extended to other enrichment applications. Enriched pathways, diseases, drugs, organs and their inter-associations can be searched, displayed, and downloaded from our online user interface. The current IPAD database can help users address a wide range of biological pathway related, disease susceptibility related, drug target related and organ specificity related questions in human disease studies.

ICA order selection based on consistency: application to genotype data

Independent component analysis (ICA), a blind source separation method, has been shown to be a useful approach to identify genetic components representing combined effects from multiple mutations. However, the ICA order selection for genotype data has been a challenge, since a genetic component usually accounts for a small amount of variance of the data, and makes it difficult to distinguish true signals from background. To address this issue, we propose to select ICA order based on consistency and implement three strategies in this study. Simulations demonstrate robust performances of all three strategies where the selected orders lead to optimal results regardless of ICA performances.