Genetics of structural connectivity and information processing in the brain

Understanding the genetic factors underlying brain structural connectivity is a major challenge in imaging genetics. Here, we present results from genome-wide association studies (GWASs) of whole-brain white matter (WM) fractional anisotropy (FA), an index of microstructural coherence measured using diffusion tensor imaging. Data from independent GWASs of 355 Swedish and 250 Norwegian healthy adults were integrated by meta-analysis to enhance power. Complementary GWASs on behavioral data reflecting processing speed, which is related to microstructural properties of WM pathways, were performed and integrated with WM FA results via multimodal analysis to identify shared genetic associations. One locus on chromosome 17 (rs145994492) showed genome-wide significant association with WM FA (meta P value = 1.87 × 10−08). Suggestive associations (Meta P value <1 × 10−06) were observed for 12 loci, including one containing ZFPM2 (lowest meta P value = 7.44 × 10−08). This locus was also implicated in multimodal analysis of WM FA and processing speed (lowest Fisher P value = 8.56 × 10−07). ZFPM2 is relevant in specification of corticothalamic neurons during brain development. Analysis of SNPs associated with processing speed revealed association with a locus that included SSPO (lowest meta P value = 4.37 × 10−08), which has been linked to commissural axon growth. An intergenic SNP (rs183854424) 14 kb downstream of CSMD1, which is implicated in schizophrenia, showed suggestive evidence of association in the WM FA meta-analysis (meta P value = 1.43 × 10−07) and the multimodal analysis (Fisher P value = 1 × 10−07). These findings provide novel data on the genetics of WM pathways and processing speed, and highlight a role of ZFPM2 and CSMD1 in information processing in the brain.

[1]  J. Marchini,et al.  Fast and accurate genotype imputation in genome-wide association studies through pre-phasing , 2012, Nature Genetics.

[2]  N. Woodward,et al.  Processing speed impairment in schizophrenia is mediated by white matter integrity , 2014, Psychological Medicine.

[3]  M. Daly,et al.  PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes , 2003, Nature Genetics.

[4]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[5]  Thomas E. Nichols,et al.  Common genetic variants influence human subcortical brain structures , 2015, Nature.

[6]  Lars-Göran Nilsson,et al.  Age-related white matter microstructural differences partly mediate age-related decline in processing speed but not cognition. , 2012, Biochimica et biophysica acta.

[7]  V. Calhoun,et al.  Association of genetic copy number variations at 11 q14.2 with brain regional volume differences in an alcohol use disorder population. , 2012, Alcohol.

[8]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[9]  Da-Zhi Wang,et al.  FOG-2, a Heart- and Brain-Enriched Cofactor for GATA Transcription Factors , 1999, Molecular and Cellular Biology.

[10]  Andrew J. Saykin,et al.  Hippocampal Atrophy as a Quantitative Trait in a Genome-Wide Association Study Identifying Novel Susceptibility Genes for Alzheimer's Disease , 2009, PloS one.

[11]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. , 1996, Journal of magnetic resonance. Series B.

[12]  A. Lundervold,et al.  Imaging and Cognitive Genetics: The Norwegian Cognitive NeuroGenetics Sample , 2012, Twin Research and Human Genetics.

[13]  Norbert Schuff,et al.  Association of common genetic variants in GPCPD1 with scaling of visual cortical surface area in humans , 2012, Proceedings of the National Academy of Sciences.

[14]  Jason P Lerch,et al.  The ZNF804A Gene: Characterization of a Novel Neural Risk Mechanism for the Major Psychoses , 2011, Neuropsychopharmacology.

[15]  P. Majerus,et al.  Characterization of myotubularin-related protein 7 and its binding partner, myotubularin-related protein 9 , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[16]  B. Carter,et al.  Jedi-1 and MEGF10 Signal Engulfment of Apoptotic Neurons through the Tyrosine Kinase Syk , 2012, The Journal of Neuroscience.

[17]  Michael A. Black,et al.  Microarray-based gene set analysis: a comparison of current methods , 2008, BMC Bioinformatics.

[18]  Michael Boehnke,et al.  LocusZoom: regional visualization of genome-wide association scan results , 2010, Bioinform..

[19]  Klaus P. Ebmeier,et al.  The APOE ɛ4 allele modulates brain white matter integrity in healthy adults , 2011, Molecular Psychiatry.

[20]  B Johansson,et al.  Substantial genetic influence on cognitive abilities in twins 80 or more years old. , 1997, Science.

[21]  J. Spencer Flavonoids: modulators of brain function? , 2008, British Journal of Nutrition.

[22]  Lars T Westlye,et al.  Becoming Consistent: Developmental Reductions in Intraindividual Variability in Reaction Time Are Related to White Matter Integrity , 2012, The Journal of Neuroscience.

[23]  Thomas E. Nichols,et al.  The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data , 2014, Brain Imaging and Behavior.

[24]  Yu-ping Wang,et al.  On individual genome-wide association studies and their meta-analysis , 2014, Human Genetics.

[25]  Inge Jonassen,et al.  Linkage-disequilibrium-based binning affects the interpretation of GWASs. , 2012, American journal of human genetics.

[26]  Lars-Göran Nilsson,et al.  High Prevalence of White Matter Hyperintensities in Normal Aging: Relation to Blood Pressure and Cognition , 2003, Cortex.

[27]  Ole A. Andreassen,et al.  Gene-Based Analysis of Regionally Enriched Cortical Genes in GWAS Data Sets of Cognitive Traits and Psychiatric Disorders , 2012, PloS one.

[28]  G. Abecasis,et al.  MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes , 2010, Genetic epidemiology.

[29]  Y. Bae,et al.  MEGF10 functions as a receptor for the uptake of amyloid‐β , 2010, FEBS letters.

[30]  Rita Holdhus,et al.  Neuropsychological Deficits in Mice Depleted of the Schizophrenia Susceptibility Gene CSMD1 , 2013, PloS one.

[31]  Peter Kochunov,et al.  Perfusion shift from white to gray matter may account for processing speed deficits in schizophrenia , 2015, Human brain mapping.

[32]  Roland Bammer,et al.  Cognitive processing speed and the structure of white matter pathways: Convergent evidence from normal variation and lesion studies , 2008, NeuroImage.

[33]  Chunlei Liu,et al.  No association of ZNF804A rs1344706 with white matter integrity in schizophrenia: A tract-based spatial statistics study , 2013, Neuroscience Letters.

[34]  P. Thompson,et al.  Diffusion imaging, white matter, and psychopathology. , 2011, Annual review of clinical psychology.

[35]  Kristine B. Walhovd,et al.  Reduced White Matter Integrity Is Related to Cognitive Instability , 2011, The Journal of Neuroscience.

[36]  C. Jack,et al.  Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity , 2013, Proceedings of the National Academy of Sciences.

[37]  Joanna M. Wardlaw,et al.  White Matter Integrity in the Splenium of the Corpus Callosum is Related to Successful Cognitive Aging and Partly Mediates the Protective Effect of an Ancestral Polymorphism in ADRB2 , 2010, Behavior genetics.

[38]  B. Sakmann,et al.  Cortex Is Driven by Weak but Synchronously Active Thalamocortical Synapses , 2006, Science.

[39]  G. Abecasis,et al.  Genotype imputation. , 2009, Annual review of genomics and human genetics.

[40]  Joshua M. Korn,et al.  Accurately Assessing the Risk of Schizophrenia Conferred by Rare Copy-Number Variation Affecting Genes with Brain Function , 2010, PLoS genetics.

[41]  Lars Nyberg,et al.  The APOE ε4 allele in relation to brain white-matter microstructure in adulthood and aging. , 2014, Scandinavian journal of psychology.

[42]  Blair H. Smith,et al.  GWAS for executive function and processing speed suggests involvement of the CADM2 gene , 2016, Molecular Psychiatry.

[43]  T. Salthouse,et al.  Processing speed as a mental capacity. , 1994, Acta psychologica.

[44]  Paul M. Thompson,et al.  Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group , 2013, NeuroImage.

[45]  Peilin Jia,et al.  Gene set analysis of genome-wide association studies: methodological issues and perspectives. , 2011, Genomics.

[46]  W. Usrey,et al.  Emerging views of corticothalamic function , 2008, Current Opinion in Neurobiology.

[47]  A. Dale,et al.  Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. , 2010, Cerebral cortex.

[48]  Elizabeth A. Grove,et al.  Pathfinding of Corticothalamic Axons Relies on a Rendezvous with Thalamic Projections , 2013, Neuron.

[49]  Brian T. Gold,et al.  Speed of lexical decision correlates with diffusion anisotropy in left parietal and frontal white matter: Evidence from diffusion tensor imaging , 2007, Neuropsychologia.

[50]  A. Hofman,et al.  A Genetic Deconstruction of Neurocognitive Traits in Schizophrenia and Bipolar Disorder , 2013, PloS one.

[51]  Arvid Lundervold,et al.  General fluid-type intelligence is related to indices of white matter structure in middle-aged and old adults , 2013, NeuroImage.

[52]  P. Basser,et al.  Toward a quantitative assessment of diffusion anisotropy , 1996, Magnetic resonance in medicine.

[53]  N. Raz,et al.  Aging white matter and cognition: Differential effects of regional variations in diffusion properties on memory, executive functions, and speed , 2009, Neuropsychologia.

[54]  David C. Glahn,et al.  Common genetic variants and gene expression associated with white matter microstructure in the human brain , 2014, NeuroImage.

[55]  J. Grondona,et al.  The subcommissural organ and the development of the posterior commissure. , 2012, International review of cell and molecular biology.

[56]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[57]  Tyrone D. Cannon,et al.  Genetic influences on brain structure , 2001, Nature Neuroscience.

[58]  Pasko Rakic,et al.  Not(ch) just development: Notch signalling in the adult brain , 2011, Nature Reviews Neuroscience.

[59]  Korbinian Strimmer,et al.  A general modular framework for gene set enrichment analysis , 2009, BMC Bioinformatics.

[60]  Paul M. Thompson,et al.  The common genetic influence over processing speed and white matter microstructure: Evidence from the Old Order Amish and Human Connectome Projects , 2016, NeuroImage.

[61]  Hong-Wen Deng,et al.  Is Replication the Gold Standard for Validating Genome-Wide Association Findings? , 2008, PloS one.

[62]  D. Wechsler WAIS-R manual : Wechsler adult intelligence scale-revised , 1981 .

[63]  S. Soimakallio,et al.  To exclude or not to exclude: White matter hyperintensities in diffusion tensor imaging research , 2011, Brain injury.

[64]  A. Saykin,et al.  Polymorphisms in the brain-derived neurotrophic factor gene influence memory and processing speed one month after brain injury. , 2012, Journal of neurotrauma.

[65]  Caroline Hayward,et al.  Cognitive ability at age 11 and 70 years, information processing speed, and APOE variation: the Lothian Birth Cohort 1936 study. , 2009, Psychology and aging.

[66]  Henry J. Alitto,et al.  Corticothalamic feedback and sensory processing , 2003, Current Opinion in Neurobiology.

[67]  O. Almkvist,et al.  Callosal atrophy in multiple sclerosis is related to cognitive speed , 2013, Acta neurologica Scandinavica.

[68]  L. Westlye,et al.  Effects of APOE on brain white matter microstructure in healthy adults , 2012, Neurology.

[69]  V. Kim,et al.  Conserved MicroRNA miR-8/miR-200 and Its Target USH/FOG2 Control Growth by Regulating PI3K , 2009, Cell.

[70]  Yurii S. Aulchenko,et al.  BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btm108 Genetics and population analysis GenABEL: an R library for genome-wide association analysis , 2022 .

[71]  M. Jenkinson Non-linear registration aka Spatial normalisation , 2007 .

[72]  Agnieszka Z. Burzynska,et al.  Diffusion tensor imaging of cerebral white matter integrity in cognitive aging. , 2012, Biochimica et biophysica acta.

[73]  Paul M. Thompson,et al.  Lifespan trajectory of myelin integrity and maximum motor speed , 2010, Neurobiology of Aging.

[74]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[75]  V. Lefebvre,et al.  SOX5 postmitotically regulates migration, postmigratory differentiation, and projections of subplate and deep-layer neocortical neurons , 2008, Proceedings of the National Academy of Sciences.

[76]  Yun Li,et al.  METAL: fast and efficient meta-analysis of genomewide association scans , 2010, Bioinform..

[77]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[78]  L. Nyberg,et al.  Betula: A Prospective Cohort Study on Memory, Health and Aging , 2004 .

[79]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[80]  Marisa O. Hollinshead,et al.  Identification of common variants associated with human hippocampal and intracranial volumes , 2012, Nature Genetics.

[81]  Nicholas G Martin,et al.  Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs. , 2007, Human molecular genetics.

[82]  Jack L. Lancaster,et al.  Processing speed is correlated with cerebral health markers in the frontal lobes as quantified by neuroimaging , 2010, NeuroImage.

[83]  Paul M. Thompson,et al.  Genetics of microstructure of cerebral white matter using diffusion tensor imaging , 2010, NeuroImage.

[84]  Stephen M. Smith,et al.  Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.

[85]  D. Salat,et al.  Choice reaction time performance correlates with diffusion anisotropy in white matter pathways supporting visuospatial attention. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[86]  J. Noraberg,et al.  Zbtb20 defines a hippocampal neuronal identity through direct repression of genes that control projection neuron development in the isocortex. , 2014, Cerebral cortex.

[87]  Steen Moeller,et al.  Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data , 2015, NeuroImage.

[88]  V. Calhoun,et al.  Association of GRM3 polymorphism with white matter integrity in schizophrenia , 2014, Schizophrenia Research.

[89]  L. Nyberg,et al.  Lack of association of the rs1344706 ZNF804A variant with cognitive functions and DTI indices of white matter microstructure in two independent healthy populations , 2014, Psychiatry Research: Neuroimaging.

[90]  Joanna M. Wardlaw,et al.  A genome-wide search for genetic influences and biological pathways related to the brain's white matter integrity , 2012, Neurobiology of Aging.

[91]  Paul M. Thompson,et al.  Genetics of white matter development: A DTI study of 705 twins and their siblings aged 12 to 29 , 2011, NeuroImage.

[92]  Vidar M. Steen,et al.  The Complement Control-Related Genes CSMD1 and CSMD2 Associate to Schizophrenia , 2011, Biological Psychiatry.

[93]  A. Hofman,et al.  Cognitive Test Battery of Cascade: Tasks and Data , 2005 .

[94]  G. Abecasis,et al.  Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies , 2006, Nature Genetics.

[95]  Agatha D. Lee,et al.  Genetics of Brain Fiber Architecture and Intellectual Performance , 2009, The Journal of Neuroscience.

[96]  C. Spencer,et al.  Biological Insights From 108 Schizophrenia-Associated Genetic Loci , 2014, Nature.

[97]  R. Ophoff,et al.  ITPR2 as a susceptibility gene in sporadic amyotrophic lateral sclerosis: a genome-wide association study , 2007, The Lancet Neurology.

[98]  Mark E. Bastin,et al.  White matter integrity as an intermediate phenotype: Exploratory genome-wide association analysis in individuals at high risk of bipolar disorder , 2013, Psychiatry Research.

[99]  Maria J Perez,et al.  Oligodendrocyte differentiation and signaling after transferrin internalization: A mechanism of action , 2013, Experimental Neurology.

[100]  Anders M. Dale,et al.  Cortical Thickness Is Influenced by Regionally Specific Genetic Factors , 2010, Biological Psychiatry.