Population-level variation in enhancer expression identifies disease mechanisms in the human brain

[1]  Michael F. Green,et al.  Mapping genomic loci implicates genes and synaptic biology in schizophrenia , 2022, Nature.

[2]  Haiyuan Yu,et al.  A comparison of experimental assays and analytical methods for genome-wide identification of active enhancers , 2022, Nature Biotechnology.

[3]  P. Roussos,et al.  Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits , 2022, Nature Genetics.

[4]  Dan J Stein,et al.  Genome-wide association study of over 40,000 bipolar disorder cases provides new insights into the underlying biology , 2021, Nature Genetics.

[5]  Y. Hurd,et al.  Common schizophrenia risk variants are enriched in open chromatin regions of human glutamatergic neurons , 2020, Nature Communications.

[6]  Han Liang,et al.  A High-Resolution Map of Human Enhancer RNA Loci Characterizes Super-enhancer Activities in Cancer. , 2020, Cancer cell.

[7]  Y. Gilad,et al.  Where Are the Disease-Associated eQTLs? , 2020, Trends in genetics : TIG.

[8]  W. Kraus,et al.  Spirits in the Material World: Enhancer RNAs in Transcriptional Regulation. , 2020, Trends in biochemical sciences.

[9]  P. Roussos,et al.  dream: Powerful differential expression analysis for repeated measures designs. , 2020, Bioinformatics.

[10]  V. Sartorelli,et al.  Enhancer RNAs are an important regulatory layer of the epigenome , 2020, Nature Structural & Molecular Biology.

[11]  David B. Goldstein,et al.  Enhancer Domains Predict Gene Pathogenicity and Inform Gene Discovery in Complex Disease. , 2020, American journal of human genetics.

[12]  Sonja W. Scholz,et al.  Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies , 2019, The Lancet Neurology.

[13]  Shayne D. Wierbowski,et al.  Transcription imparts architecture, function, and logic to enhancer units , 2019, Nature Genetics.

[14]  S. Goldman,et al.  Glial cells in schizophrenia: a unified hypothesis. , 2019, The lancet. Psychiatry.

[15]  Christopher D. Brown,et al.  The GTEx Consortium atlas of genetic regulatory effects across human tissues , 2019, Science.

[16]  Simon C. Potter,et al.  Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility , 2019, Science.

[17]  Kelsey S. Montgomery,et al.  CommonMind Consortium provides transcriptomic and epigenomic data for Schizophrenia and Bipolar Disorder , 2019, Scientific Data.

[18]  K. Brennand,et al.  Synergistic effects of common schizophrenia risk variants , 2019, Nature Genetics.

[19]  Alkes L. Price,et al.  Quantifying genetic effects on disease mediated by assayed gene expression levels , 2019, Nature Genetics.

[20]  P. Fraser,et al.  Long-range enhancer–promoter contacts in gene expression control , 2019, Nature Reviews Genetics.

[21]  Hagen U. Tilgner,et al.  SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse , 2019, Neuron.

[22]  A. Kundaje,et al.  The ENCODE Blacklist: Identification of Problematic Regions of the Genome , 2019, Scientific Reports.

[23]  M. Owen,et al.  Publisher Correction: Gene expression imputation across multiple brain regions provides insights into schizophrenia risk , 2019, Nature Genetics.

[24]  David A. Knowles,et al.  Opportunities and challenges for transcriptome-wide association studies , 2019, Nature Genetics.

[25]  J. Sutcliffe,et al.  A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data , 2019, Nature Neuroscience.

[26]  D. Geschwind,et al.  Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders , 2019, Cell.

[27]  John P. Rice,et al.  Identification of common genetic risk variants for autism spectrum disorder , 2019, Nature Genetics.

[28]  V. Vacic,et al.  Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways , 2019, Nature Genetics.

[29]  Marc S. Halfon,et al.  Studying Transcriptional Enhancers: The Founder Fallacy, Validation Creep, and Other Biases. , 2019, Trends in genetics : TIG.

[30]  E. Schadt,et al.  Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits , 2019, Nature Communications.

[31]  Jonathan P. Beauchamp,et al.  Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences , 2019, Nature Genetics.

[32]  Timothy J. Hohman,et al.  Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk , 2019, Nature Genetics.

[33]  Annie W Shieh,et al.  Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder , 2018, Science.

[34]  Prashant S. Emani,et al.  Comprehensive functional genomic resource and integrative model for the human brain , 2018, Science.

[35]  David Haussler,et al.  The UCSC Genome Browser database: 2019 update , 2018, Nucleic Acids Res..

[36]  Anthony J. Payne,et al.  Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps , 2018, Nature Genetics.

[37]  Alicia R. Martin,et al.  Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder , 2018, Nature Genetics.

[38]  Michael E. Greenberg,et al.  Activity-Regulated Transcription: Bridging the Gap between Neural Activity and Behavior , 2018, Neuron.

[39]  Benjamin A. Logsdon,et al.  The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer's disease , 2018, Scientific Data.

[40]  Annie W Shieh,et al.  Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia , 2018, Nature Communications.

[41]  M. Maccarrone,et al.  2-Arachidonoylglycerol: A signaling lipid with manifold actions in the brain. , 2018, Progress in lipid research.

[42]  Y. Hurd,et al.  An atlas of chromatin accessibility in the adult human brain , 2018, Genome research.

[43]  Z. Weng,et al.  Cell-specific histone modification maps link schizophrenia risk to the neuronal epigenome , 2018, Nature Neuroscience.

[44]  J. Weinstein,et al.  A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples. , 2018, Cell.

[45]  Timothy A. Miller,et al.  Genome-wide Analyses Identify KIF5A as a Novel ALS Gene in and for Therapeutic , 2018 .

[46]  D. Posthuma,et al.  Item-level analyses reveal genetic heterogeneity in neuroticism , 2018, Nature Communications.

[47]  T. Meyer,et al.  Transcription-coupled changes in nuclear mobility of mammalian cis-regulatory elements , 2018, Science.

[48]  Kathryn Roeder,et al.  Differential Activity of Transcribed Enhancers in the Prefrontal Cortex of 537 cases with Schizophrenia and Controls , 2018, Molecular Psychiatry.

[49]  Warren W. Kretzschmar,et al.  Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression , 2017, Nature Genetics.

[50]  A. Stark,et al.  Assessing sufficiency and necessity of enhancer activities for gene expression and the mechanisms of transcription activation , 2018, Genes & development.

[51]  D. Geschwind,et al.  The Dynamic Landscape of Open Chromatin during Human Cortical Neurogenesis , 2018, Cell.

[52]  I. Schor,et al.  The degree of enhancer or promoter activity is reflected by the levels and directionality of eRNA transcription , 2018, Genes & development.

[53]  A. Gusev,et al.  Probabilistic fine-mapping of transcriptome-wide association studies , 2017, bioRxiv.

[54]  J. Rapoport,et al.  Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with post-mortem adult brains , 2017, Nature Communications.

[55]  D. Weinberger,et al.  Genetic insights into the neurodevelopmental origins of schizophrenia , 2017, Nature Reviews Neuroscience.

[56]  Mary Goldman,et al.  Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics , 2016, Nature Communications.

[57]  Wen Zhang,et al.  A Bayesian Framework for Multiple Trait Colocalization from Summary Association Statistics , 2017, bioRxiv.

[58]  Jakob Grove,et al.  Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder , 2017, bioRxiv.

[59]  P. Visscher,et al.  10 Years of GWAS Discovery: Biology, Function, and Translation. , 2017, American journal of human genetics.

[60]  Gerome Breen,et al.  Genetic identification of brain cell types underlying schizophrenia , 2017, Nature Genetics.

[61]  P. Kharchenko,et al.  Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain , 2017, Nature Biotechnology.

[62]  Alexander Gusev,et al.  Functional Architectures of Local and Distal Regulation of Gene Expression in Multiple Human Tissues. , 2017, American journal of human genetics.

[63]  Tanner Koomar,et al.  cerebroViz: an R package for anatomical visualization of spatiotemporal brain data , 2016, Bioinform..

[64]  J. Wysocka,et al.  Ever-Changing Landscapes: Transcriptional Enhancers in Development and Evolution , 2016, Cell.

[65]  D. Geschwind,et al.  Histone Acetylome-wide Association Study of Autism Spectrum Disorder , 2016, Cell.

[66]  Alexander Gusev,et al.  Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights , 2016, Nature Genetics.

[67]  Benjamin A. Logsdon,et al.  Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia , 2016, Nature Neuroscience.

[68]  C. Spencer,et al.  A contribution of novel CNVs to schizophrenia from a genome-wide study of 41,321 subjects: CNV Analysis Group and the Schizophrenia Working Group of the Psychiatric Genomics Consortium , 2016, bioRxiv.

[69]  E. Schadt,et al.  variancePartition: interpreting drivers of variation in complex gene expression studies , 2016, bioRxiv.

[70]  Yoshihide Hayashizaki,et al.  Enhanced Identification of Transcriptional Enhancers Provides Mechanistic Insights into Diseases. , 2016, Trends in genetics : TIG.

[71]  H. Overkleeft,et al.  Rapid and profound rewiring of brain lipid signaling networks by acute diacylglycerol lipase inhibition , 2015, Proceedings of the National Academy of Sciences.

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

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

[74]  Judith B. Zaugg,et al.  Genetic Control of Chromatin States in Humans Involves Local and Distal Chromosomal Interactions , 2015, Cell.

[75]  J. Danesh,et al.  A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease , 2016 .

[76]  Ji Zhang,et al.  GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach , 2015, Bioinform..

[77]  T. Lehtimäki,et al.  Integrative approaches for large-scale transcriptome-wide association studies , 2015, Nature Genetics.

[78]  E. Anton,et al.  Developmental disruptions underlying brain abnormalities in ciliopathies , 2015, Nature Communications.

[79]  Judy H. Cho,et al.  Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations , 2015, Nature Genetics.

[80]  Irina Voineagu,et al.  Coexpression networks identify brain region–specific enhancer RNAs in the human brain , 2015, Nature Neuroscience.

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

[82]  Ross M. Fraser,et al.  Genetic studies of body mass index yield new insights for obesity biology , 2015, Nature.

[83]  C. Glass,et al.  The selection and function of cell type-specific enhancers , 2015, Nature Reviews Molecular Cell Biology.

[84]  Michael Q. Zhang,et al.  Integrative analysis of 111 reference human epigenomes , 2015, Nature.

[85]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[86]  André L. Martins,et al.  Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers , 2014, Nature Genetics.

[87]  Ross M. Fraser,et al.  Defining the role of common variation in the genomic and biological architecture of adult human height , 2014, Nature Genetics.

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

[89]  T. Meehan,et al.  An atlas of active enhancers across human cell types and tissues , 2014, Nature.

[90]  Jun S. Liu,et al.  Genetics of rheumatoid arthritis contributes to biology and drug discovery , 2013 .

[91]  Wei Wang,et al.  Predicting enhancer transcription and activity from chromatin modifications , 2013, Nucleic acids research.

[92]  C. Wallace,et al.  Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics , 2013, PLoS genetics.

[93]  Wei Shi,et al.  featureCounts: an efficient general purpose program for assigning sequence reads to genomic features , 2013, Bioinform..

[94]  D. Camerino,et al.  Major channels involved in neuropsychiatric disorders and therapeutic perspectives , 2013, Front. Genet..

[95]  G. Bejerano,et al.  Enhancers: five essential questions , 2013, Nature Reviews Genetics.

[96]  C. Sudlow,et al.  Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE Collaboration): a meta-analysis of genome-wide association studies , 2012, The Lancet Neurology.

[97]  Bronwen L. Aken,et al.  GENCODE: The reference human genome annotation for The ENCODE Project , 2012, Genome research.

[98]  Wei Li,et al.  RSeQC: quality control of RNA-seq experiments , 2012, Bioinform..

[99]  R. Durbin,et al.  Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses , 2012, Nature Protocols.

[100]  Helga Thorvaldsdóttir,et al.  Molecular signatures database (MSigDB) 3.0 , 2011, Bioinform..

[101]  P. Visscher,et al.  GCTA: a tool for genome-wide complex trait analysis. , 2011, American journal of human genetics.

[102]  G. Kreiman,et al.  Widespread transcription at neuronal activity-regulated enhancers , 2010, Nature.

[103]  M. Robinson,et al.  A scaling normalization method for differential expression analysis of RNA-seq data , 2010, Genome Biology.

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

[105]  K. Anderson,et al.  Mouse Kif7/Costal2 is a cilia-associated protein that regulates Sonic hedgehog signaling , 2009, Proceedings of the National Academy of Sciences.

[106]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[107]  Thomas Lengauer,et al.  ROCR: visualizing classifier performance in R , 2005, Bioinform..

[108]  Toshihiro Tanaka.  The International HapMap Project , 2003, Nature.

[109]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[110]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.