Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes

[1]  Thomas E. Nichols,et al.  Reliability of multi-site UK Biobank MRI brain phenotypes for the assessment of neuropsychiatric complications of SARS-CoV-2 infection: The COVID-CNS travelling heads study , 2022, PloS one.

[2]  Ryan L. Collins,et al.  Rare coding variation provides insight into the genetic architecture and phenotypic context of autism , 2022, Nature Genetics.

[3]  Iyad Ba Gari,et al.  Multisite test–retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3 , 2022, bioRxiv.

[4]  T. Ge,et al.  Multivariate genomic architecture of cortical thickness and surface area at multiple levels of analysis , 2022, Nature Communications.

[5]  Jeffrey S. Anderson,et al.  Test-retest reliability of FreeSurfer-derived volume, area and cortical thickness from MPRAGE and MP2RAGE brain MRI images , 2022, Neuroimage. Reports.

[6]  M. Glymour,et al.  Mendelian randomization , 2022, Nature Reviews Methods Primers.

[7]  P. Visscher,et al.  Discovery of genomic loci of the human cerebral cortex using genetically informed brain atlases , 2022, Science.

[8]  Dan J Stein,et al.  Genetic variants associated with longitudinal changes in brain structure across the lifespan , 2021, Nature Neuroscience.

[9]  Barbara Brito Vega,et al.  Reliability of structural MRI measurements: The effects of scan session, head tilt, inter-scan interval, acquisition sequence, FreeSurfer version and processing stream , 2021, NeuroImage.

[10]  A. Kriegstein,et al.  An atlas of cortical arealization identifies dynamic molecular signatures , 2021, Nature.

[11]  Pawel F. Przytycki,et al.  Single-cell epigenomics reveals mechanisms of human cortical development , 2021, Nature.

[12]  Michael J. Purcaro,et al.  Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders , 2021, Nature Communications.

[13]  Dan J Stein,et al.  Brain charts for the human lifespan , 2021, Nature.

[14]  G. Douaud,et al.  An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank , 2021, Nature Neuroscience.

[15]  Ryan J. Eller,et al.  Shared heritability of human face and brain shape , 2021, Nature Genetics.

[16]  D. Lawlor,et al.  Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations , 2021, International journal of epidemiology.

[17]  Luke R. Lloyd-Jones,et al.  Widespread signatures of natural selection across human complex traits and functional genomic categories , 2021, Nature Communications.

[18]  Jian Yang,et al.  Genetic control of RNA splicing and its distinct role in complex trait variation , 2021, Nature Genetics.

[19]  D. Belsky,et al.  Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction , 2021, Nature Genetics.

[20]  M. P. van den Heuvel,et al.  Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence , 2020, Nature Communications.

[21]  Xiaoqun Wang,et al.  Cellular and molecular properties of neural progenitors in the developing mammalian hypothalamus , 2020, Nature Communications.

[22]  M. Hurles,et al.  Genetic correlates of phenotypic heterogeneity in autism , 2020, Nature Genetics.

[23]  C. Francks,et al.  The genetic architecture of structural left–right asymmetry of the human brain , 2020, Nature Human Behaviour.

[24]  Hongtu Zhu,et al.  Common genetic variation influencing human white matter microstructure , 2020, Science.

[25]  Soroosh Afyouni,et al.  Confound modelling in UK Biobank brain imaging , 2020, NeuroImage.

[26]  A. Kriegstein,et al.  Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia , 2020, Nature Neuroscience.

[27]  Harper B. Fauni,et al.  A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles , 2020, Nature Neuroscience.

[28]  S. Aoki,et al.  Scan–rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics , 2019, Neuroradiology.

[29]  James B. Brewer,et al.  Brain cell type–specific enhancer–promoter interactome maps and disease-risk association , 2019, Science.

[30]  Matti Pirinen,et al.  Functionally-informed fine-mapping and polygenic localization of complex trait heritability , 2019, Nature Genetics.

[31]  M. Gerstein,et al.  A Single-Cell Transcriptomic Atlas of Human Neocortical Development during Mid-gestation , 2019, Neuron.

[32]  Tamar Sofer,et al.  Genetic association testing using the GENESIS R/Bioconductor package , 2019, Bioinform..

[33]  Rebecca C. Knickmeyer,et al.  ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries , 2019, Biological Psychiatry.

[34]  Joseph H. Marcus,et al.  Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics , 2019, bioRxiv.

[35]  A. Evans,et al.  Influence of Processing Pipeline on Cortical Thickness Measurement , 2019, Cerebral cortex.

[36]  P. Visscher,et al.  A resource-efficient tool for mixed model association analysis of large-scale data , 2019, Nature Genetics.

[37]  Stephen Burgess,et al.  A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits , 2019, Nature Communications.

[38]  Ryan L. Collins,et al.  The mutational constraint spectrum quantified from variation in 141,456 humans , 2020, Nature.

[39]  Hunna J. Watson,et al.  Genetic Identification of Cell Types Underlying Brain Complex Traits Yields Novel Insights Into the Etiology of Parkinson’s Disease , 2019, bioRxiv.

[40]  Matthew Stephens,et al.  A simple new approach to variable selection in regression, with application to genetic fine-mapping , 2018, bioRxiv.

[41]  Stephan J Sanders,et al.  Integrative functional genomic analysis of human brain development and neuropsychiatric risks , 2018, Science.

[42]  Daniel J. Miller,et al.  Spatiotemporal transcriptomic divergence across human and macaque brain development , 2018, Science.

[43]  Arthur S. Lee,et al.  MACF1 Mutations Encoding Highly Conserved Zinc-Binding Residues of the GAR Domain Cause Defects in Neuronal Migration and Axon Guidance. , 2018, American journal of human genetics.

[44]  M. Owen,et al.  Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders , 2018, Genome Biology.

[45]  Anders M. Dale,et al.  Image processing and analysis methods for the Adolescent Brain Cognitive Development Study , 2018, NeuroImage.

[46]  Hui Zhang,et al.  Neurite imaging reveals microstructural variations in human cerebral cortical gray matter , 2018, NeuroImage.

[47]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.

[48]  J. Marchini,et al.  Genome-wide association studies of brain imaging phenotypes in UK Biobank , 2018, Nature.

[49]  C. Kroenke,et al.  Mechanics of cortical folding: stress, growth and stability , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[50]  Yang Ni,et al.  Polygenic prediction via Bayesian regression and continuous shrinkage priors , 2018, Nature Communications.

[51]  Jeffrey A. Dahlke,et al.  psychmeta: An R Package for Psychometric Meta-Analysis , 2018, Applied psychological measurement.

[52]  Anders M. Dale,et al.  The genetic architecture of the human cerebral cortex , 2020, Science.

[53]  Haochang Shou,et al.  On testing for spatial correspondence between maps of human brain structure and function , 2018, NeuroImage.

[54]  C. Walsh,et al.  The Genetics of Primary Microcephaly. , 2018, Annual review of genomics and human genetics.

[55]  B. Neale,et al.  Publisher Correction: Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases , 2018, Nature Genetics.

[56]  David C Van Essen,et al.  The impact of traditional neuroimaging methods on the spatial localization of cortical areas , 2018, Proceedings of the National Academy of Sciences.

[57]  Po-Ru Loh,et al.  Mixed-model association for biobank-scale datasets , 2018, Nature Genetics.

[58]  Caroline F. Wright,et al.  Common genetic variants contribute to risk of rare severe neurodevelopmental disorders , 2018, Nature.

[59]  Stuart J. Ritchie,et al.  Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits , 2019, Nature Human Behaviour.

[60]  M. Dylan Tisdall,et al.  Quantitative assessment of structural image quality , 2018, NeuroImage.

[61]  Kathryn E Kemper,et al.  Comparison of Genotypic and Phenotypic Correlations: Cheverud’s Conjecture in Humans , 2018, Genetics.

[62]  Anders M. Dale,et al.  The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites , 2018, Developmental Cognitive Neuroscience.

[63]  Sina A. Gharib,et al.  Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood , 2018, Nature Communications.

[64]  John H. Gilmore,et al.  Imaging structural and functional brain development in early childhood , 2018, Nature Reviews Neuroscience.

[65]  Anibal Gutierrez,et al.  SPARK: A US Cohort of 50,000 Families to Accelerate Autism Research , 2018, Neuron.

[66]  L. Zou,et al.  A mitosis-specific and R loop–driven ATR pathway promotes faithful chromosome segregation , 2018, Science.

[67]  Terry L. Jernigan,et al.  Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description , 2017, Developmental Cognitive Neuroscience.

[68]  G. Davey Smith,et al.  Orienting the causal relationship between imprecisely measured traits using GWAS summary data , 2017, PLoS genetics.

[69]  Paul M. Thompson,et al.  Submission PDF Mapping Cortical Brain Asymmetry in 17 , 141 Healthy Individuals Worldwide via the ENIGMA Consortium , 2018 .

[70]  P. Visscher,et al.  Causal associations between risk factors and common diseases inferred from GWAS summary data , 2017, bioRxiv.

[71]  Tom R. Gaunt,et al.  PhenoSpD: an integrated toolkit for phenotypic correlation estimation and multiple testing correction using GWAS summary statistics , 2017, bioRxiv.

[72]  Ludovica Griffanti,et al.  Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank , 2017, NeuroImage.

[73]  Evan Z. Macosko,et al.  Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types , 2017, Nature Genetics.

[74]  Deciphering Developmental Disorders Study,et al.  Prevalence and architecture of de novo mutations in developmental disorders , 2017, Nature.

[75]  Christopher R. Madan,et al.  Test–retest reliability of brain morphology estimates , 2017, Brain Informatics.

[76]  Mark E Bastin,et al.  Ageing and brain white matter structure in 3,513 UK Biobank participants , 2016, Nature Communications.

[77]  Daning Lu,et al.  Chromosome conformation elucidates regulatory relationships in developing human brain , 2016, Nature.

[78]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[79]  Daniel C. Alexander,et al.  Bingham–NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI , 2016, NeuroImage.

[80]  G. Davey Smith,et al.  Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator , 2016, Genetic epidemiology.

[81]  P. Visscher,et al.  Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets , 2016, Nature Genetics.

[82]  F. Cunningham,et al.  The Ensembl Variant Effect Predictor , 2016, bioRxiv.

[83]  Po-Ru Loh,et al.  A Robust Example of Collider Bias in a Genetic Association Study. , 2016, American journal of human genetics.

[84]  Michael J. Purcaro,et al.  The PsychENCODE project , 2015, Nature Neuroscience.

[85]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[86]  Yakir A Reshef,et al.  Partitioning heritability by functional annotation using genome-wide association summary statistics , 2015, Nature Genetics.

[87]  Bradley P. Coe,et al.  The Koolen-de Vries syndrome: a phenotypic comparison of patients with a 17q21.31 microdeletion versus a KANSL1 sequence variant , 2015, European Journal of Human Genetics.

[88]  Joseph K. Pickrell,et al.  Approximately independent linkage disequilibrium blocks in human populations , 2015, bioRxiv.

[89]  M. Daly,et al.  An Atlas of Genetic Correlations across Human Diseases and Traits , 2015, Nature Genetics.

[90]  Joris M. Mooij,et al.  MAGMA: Generalized Gene-Set Analysis of GWAS Data , 2015, PLoS Comput. Biol..

[91]  G. Davey Smith,et al.  Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression , 2015, International journal of epidemiology.

[92]  M. Carella,et al.  A splicing mutation of the HMGA2 gene is associated with Silver–Russell syndrome phenotype , 2015, Journal of Human Genetics.

[93]  Peter Kraft,et al.  Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. , 2015, American journal of human genetics.

[94]  S. Thompson,et al.  Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects , 2015, American journal of epidemiology.

[95]  Jean-Philippe Thiran,et al.  Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data , 2015, NeuroImage.

[96]  Eileen Luders,et al.  A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM) , 2015, Nature Protocols.

[97]  Marc Brysbaert,et al.  New human-specific brain landmark: The depth asymmetry of superior temporal sulcus , 2015, Proceedings of the National Academy of Sciences.

[98]  K. Sullivan,et al.  CENP-W Plays a Role in Maintaining Bipolar Spindle Structure , 2014, PloS one.

[99]  T. Tallinen,et al.  Gyrification from constrained cortical expansion , 2014, Proceedings of the National Academy of Sciences.

[100]  Wieland B Huttner,et al.  Neural progenitors, neurogenesis and the evolution of the neocortex , 2014, Development.

[101]  S. Burgess Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome , 2014, International journal of epidemiology.

[102]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[103]  Mingfeng Li,et al.  Temporal Specification and Bilaterality of Human Neocortical Topographic Gene Expression , 2014, Neuron.

[104]  D. Geschwind,et al.  Cortical Evolution: Judge the Brain by Its Cover , 2013, Neuron.

[105]  Jay N. Giedd,et al.  Differential Tangential Expansion as a Mechanism for Cortical Gyrification , 2013, Cerebral cortex.

[106]  Timothy S. Coalson,et al.  Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. , 2012, Cerebral cortex.

[107]  R. Kahn,et al.  Human brain changes across the life span: A review of 56 longitudinal magnetic resonance imaging studies , 2012, Human brain mapping.

[108]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

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

[110]  M. A. García-Cabezas,et al.  A role for intermediate radial glia in the tangential expansion of the mammalian cerebral cortex. , 2011, Cerebral cortex.

[111]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[112]  C. Westin,et al.  An introduction to diffusion tensor image analysis. , 2011, Neurosurgery clinics of North America.

[113]  Josyf Mychaleckyj,et al.  Robust relationship inference in genome-wide association studies , 2010, Bioinform..

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

[115]  P. Visscher,et al.  Common SNPs explain a large proportion of heritability for human height , 2011 .

[116]  T. Paus,et al.  Why do many psychiatric disorders emerge during adolescence? , 2008, Nature Reviews Neuroscience.

[117]  B. Yoon,et al.  Region-specific changes of cerebral white matter during normal aging: a diffusion-tensor analysis. , 2008, Archives of gerontology and geriatrics.

[118]  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.

[119]  A. Kriegstein,et al.  Patterns of neural stem and progenitor cell division may underlie evolutionary cortical expansion , 2006, Nature Reviews Neuroscience.

[120]  Anjen Chenn,et al.  Regulation of Cerebral Cortical Size by Control of Cell Cycle Exit in Neural Precursors , 2002, Science.

[121]  P. Rakić,et al.  Genetic control of cortical development. , 1999, Cerebral cortex.

[122]  M. Mesulam,et al.  From sensation to cognition. , 1998, Brain : a journal of neurology.

[123]  P. Rakic Specification of cerebral cortical areas. , 1988, Science.

[124]  V. Caviness,et al.  Mechanical model of brain convolutional development. , 1975, Science.

[125]  Hongtu Zhu,et al.  Diffusion tensor imaging-based characterization of brain neurodevelopment in primates. , 2013, Cerebral cortex.

[126]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.