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

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

[1]  Daniela M Witten,et al.  Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data , 2009, Statistical applications in genetics and molecular biology.

[2]  E. Gershon,et al.  Meta-analysis of whole-genome linkage scans of bipolar disorder and schizophrenia , 2002, Molecular Psychiatry.

[3]  T. Arinami,et al.  Mutation analysis of the NMDAR2B (GRIN2B) gene in schizophrenia , 2001, Molecular Psychiatry.

[4]  Marcella Bellani,et al.  The potential role of the parietal lobe in schizophrenia , 2010, Epidemiologia e Psichiatria Sociale.

[5]  M. Lidow,et al.  Calcium signaling dysfunction in schizophrenia: a unifying approach , 2003, Brain Research Reviews.

[6]  D. Dietrich,et al.  Audiovisual integration of speech is disturbed in schizophrenia: An fMRI study , 2009, Schizophrenia Research.

[7]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

[8]  N. Ramsey,et al.  Working memory capacity in schizophrenia: a parametric fMRI study , 2004, Schizophrenia Research.

[9]  E. Bullmore,et al.  Functional dysconnectivity in schizophrenia associated with attentional modulation of motor function. , 2005, Brain : a journal of neurology.

[10]  J. Meador-Woodruff,et al.  Thalamic dysfunction in schizophrenia: neurochemical, neuropathological, and in vivo imaging abnormalities , 2004, Schizophrenia Research.

[11]  Babak A. Ardekani,et al.  fMRI study of language activation in schizophrenia, schizoaffective disorder and in individuals genetically at high risk , 2007, Schizophrenia Research.

[12]  H Steven Wiley,et al.  Integrating Multiple Types of Data for Signaling Research: Challenges and Opportunities , 2011, Science Signaling.

[13]  R. McCarley,et al.  A review of MRI findings in schizophrenia , 2001, Schizophrenia Research.

[14]  D. Pinault,et al.  Dysfunctional thalamus-related networks in schizophrenia. , 2011, Schizophrenia bulletin.

[15]  Philippe Besse,et al.  Statistical Applications in Genetics and Molecular Biology A Sparse PLS for Variable Selection when Integrating Omics Data , 2011 .

[16]  N. Andreasen,et al.  The Role of the Cerebellum in Schizophrenia , 2008, Biological Psychiatry.

[17]  F. Bushman,et al.  Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis. , 2013, Biostatistics.

[18]  A. Meyer-Lindenberg,et al.  Variation in DISC1 affects hippocampal structure and function and increases risk for schizophrenia. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Jingyu Liu,et al.  Sparse canonical correlation analysis applied to fMRI and genetic data fusion , 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[20]  C. Greenwood,et al.  Data Integration in Genetics and Genomics: Methods and Challenges , 2009, Human genomics and proteomics : HGP.

[21]  David J. Porteous,et al.  The Genetics and Biology of Disc1—An Emerging Role in Psychosis and Cognition , 2006, Biological Psychiatry.

[22]  A. Buonanno,et al.  The neuregulin signaling pathway and schizophrenia: From genes to synapses and neural circuits , 2010, Brain Research Bulletin.

[23]  R. Tibshirani,et al.  A note on the group lasso and a sparse group lasso , 2010, 1001.0736.

[24]  E. Torrey,et al.  Schizophrenia and the inferior parietal lobule , 2007, Schizophrenia Research.

[25]  Kent A. Kiehl,et al.  Abnormal hemodynamics in schizophrenia during an auditory oddball task , 2005, Biological Psychiatry.

[26]  R. Gibbs,et al.  Variation in GRM3 affects cognition, prefrontal glutamate, and risk for schizophrenia. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Robert S. Astur,et al.  An fMRI study of working memory in first-degree unaffected relatives of schizophrenia patients , 2008, Schizophrenia Research.

[28]  E. Bullmore,et al.  Procedural learning in schizophrenia: a functional magnetic resonance imaging investigation , 2002, Schizophrenia Research.

[29]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[30]  K. Kiehl,et al.  An event-related functional magnetic resonance imaging study of an auditory oddball task in schizophrenia , 2001, Schizophrenia Research.

[31]  V D Calhoun,et al.  Auditory oddball deficits in schizophrenia: an independent component analysis of the fMRI multisite function BIRN study. , 2009, Schizophrenia bulletin.

[32]  Trevor J. Hastie,et al.  Genome-wide association analysis by lasso penalized logistic regression , 2009, Bioinform..

[33]  R. Kikinis,et al.  Middle and inferior temporal gyrus gray matter volume abnormalities in first-episode schizophrenia: an MRI study. , 2006, The American journal of psychiatry.

[34]  Peter Fransson,et al.  The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis , 2008, NeuroImage.

[35]  Lin He,et al.  An association study of the N-methyl-D-aspartate receptor NR1 subunit gene (GRIN1) and NR2B subunit gene (GRIN2B) in schizophrenia with universal DNA microarray , 2005, European Journal of Human Genetics.

[36]  R. Kikinis,et al.  Middle and inferior temporal gyrus gray matter volume abnormalities in chronic schizophrenia: an MRI study. , 2004, The American journal of psychiatry.

[37]  Philippe Besse,et al.  Sparse canonical methods for biological data integration: application to a cross-platform study , 2009, BMC Bioinformatics.

[38]  Robert Karlsson,et al.  MAGI1 Copy Number Variation in Bipolar Affective Disorder and Schizophrenia , 2012, Biological Psychiatry.

[39]  Vicki L. Ellingrod,et al.  Association between the polymorphic GRM3 gene and negative symptom improvement during olanzapine treatment , 2005, Schizophrenia Research.

[40]  Gonçalo R Abecasis,et al.  Genomewide scan in families with schizophrenia from the founder population of Afrikaners reveals evidence for linkage and uniparental disomy on chromosome 1. , 2004, American journal of human genetics.

[41]  R. Tibshirani,et al.  A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. , 2009, Biostatistics.

[42]  Xi Chen,et al.  An Efficient Optimization Algorithm for Structured Sparse CCA, with Applications to eQTL Mapping , 2011, Statistics in Biosciences.

[43]  W. Honer,et al.  DNA copy-number analysis in bipolar disorder and schizophrenia reveals aberrations in genes involved in glutamate signaling. , 2006, Human molecular genetics.

[44]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[45]  Bernard Ng,et al.  Generalized Sparse Regularization with Application to fMRI Brain Decoding , 2011, IPMI.

[46]  Aeilko H. Zwinderman,et al.  Correlating multiple SNPs and multiple disease phenotypes: penalized non-linear canonical correlation analysis , 2009, Bioinform..

[47]  Vince D. Calhoun,et al.  Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model , 2011, NeuroImage.

[48]  A. Zwinderman,et al.  Statistical Applications in Genetics and Molecular Biology Quantifying the Association between Gene Expressions and DNA-Markers by Penalized Canonical Correlation Analysis , 2011 .

[49]  N. Williams,et al.  Gene copy number variation in schizophrenia , 2008, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[50]  Vince D. Calhoun,et al.  Group sparse canonical correlation analysis for genomic data integration , 2013, BMC Bioinformatics.

[51]  D. Posada,et al.  Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests. , 2004, Systematic biology.

[52]  Jian Huang,et al.  Incorporating group correlations in genome-wide association studies using smoothed group Lasso. , 2013, Biostatistics.

[53]  Robert Tibshirani,et al.  STANDARDIZATION AND THE GROUP LASSO PENALTY. , 2012, Statistica Sinica.

[54]  D. Tritchler,et al.  Sparse Canonical Correlation Analysis with Application to Genomic Data Integration , 2009, Statistical applications in genetics and molecular biology.

[55]  Nicholas Ayache,et al.  Medical Image Analysis: Progress over Two Decades and the Challenges Ahead , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[56]  Giovanni Parmigiani,et al.  Integrating diverse genomic data using gene sets , 2011, Genome Biology.