Informing disease modelling with brain-relevant functional genomic annotations

How can we best translate the success of genome-wide association studies for neurological and neuropsychiatric diseases into therapeutic targets? Reynolds et al. critically assess existing brain-relevant functional genomic annotations and the tools available for integrating such annotations with summary-level genetic association data.

[1]  Sarah A. Gagliano,et al.  It’s All in the Brain: A Review of Available Functional Genomic Annotations , 2017, Biological Psychiatry.

[2]  Allan R. Jones,et al.  An anatomically comprehensive atlas of the adult human brain transcriptome , 2012, Nature.

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

[4]  R. Silva,et al.  Genetic Variants in SNCA and the Risk of Sporadic Parkinson's Disease and Clinical Outcomes: A Review , 2017, Parkinson's disease.

[5]  David G. Knowles,et al.  Predicting Splicing from Primary Sequence with Deep Learning , 2019, Cell.

[6]  Robert Andrews,et al.  Inter-individual variability contrasts with regional homogeneity in the human brain DNA methylome , 2015, Nucleic acids research.

[7]  Sashwati Roy,et al.  Laser capture microdissection: Big data from small samples. , 2015, Histology and histopathology.

[8]  Sandy L. Klemm,et al.  Chromatin accessibility and the regulatory epigenome , 2019, Nature Reviews Genetics.

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

[10]  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.

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

[12]  Nick C Fox,et al.  Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease , 2013, Nature Genetics.

[13]  Jun S. Liu,et al.  The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans , 2015, Science.

[14]  Nina S. Hsu,et al.  The State of the NIH BRAIN Initiative , 2018, The Journal of Neuroscience.

[15]  C. Allis,et al.  The molecular hallmarks of epigenetic control , 2016, Nature Reviews Genetics.

[16]  Z. Knight,et al.  Molecular Profiling of Neurons Based on Connectivity , 2014, Cell.

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

[18]  Andrew D. Johnson,et al.  GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes , 2018, Nature Communications.

[19]  Ling Lin,et al.  Cell type-specific gene expression of midbrain dopaminergic neurons reveals molecules involved in their vulnerability and protection. , 2005, Human molecular genetics.

[20]  Richard H. Scheuermann,et al.  Equivalent high-resolution identification of neuronal cell types with single-nucleus and single-cell RNA-sequencing , 2017, bioRxiv.

[21]  D. Schübeler Function and information content of DNA methylation , 2015, Nature.

[22]  Sarah A. Gagliano,et al.  Moving beyond neurons: the role of cell type-specific gene regulation in Parkinson’s disease heritability , 2018, bioRxiv.

[23]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[24]  O. Stegle,et al.  Single-cell epigenomics: Recording the past and predicting the future , 2017, Science.

[25]  Cynthia C. Hession,et al.  Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons , 2016, Science.

[26]  Peter Donnelly,et al.  Progress and promise in understanding the genetic basis of common diseases , 2015, Proceedings of the Royal Society B: Biological Sciences.

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

[28]  Evan Z. Macosko,et al.  Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain , 2018, Cell.

[29]  Valentine Svensson,et al.  Power Analysis of Single Cell RNA-Sequencing Experiments , 2016, Nature Methods.

[30]  Ryan P. Adams,et al.  Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk , 2017, bioRxiv.

[31]  O. Troyanskaya,et al.  Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.

[32]  P. Greengard,et al.  Identification of the Cortical Neurons that Mediate Antidepressant Responses , 2012, Cell.

[33]  Alexander van Oudenaarden,et al.  Spatially resolved transcriptomics and beyond , 2014, Nature Reviews Genetics.

[34]  Tao Wang,et al.  Enhancers active in dopamine neurons are a primary link between genetic variation and neuropsychiatric disease , 2018, Nature Neuroscience.

[35]  Erik Sundström,et al.  RNA velocity of single cells , 2018, Nature.

[36]  Justin P Sandoval,et al.  Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex , 2017, Science.

[37]  Sarah A. Gagliano,et al.  Genomics implicates adaptive and innate immunity in Alzheimer's and Parkinson's diseases , 2016, bioRxiv.

[38]  J. Shendure,et al.  A general framework for estimating the relative pathogenicity of human genetic variants , 2014, Nature Genetics.

[39]  Towfique Raj,et al.  Prioritizing Parkinson’s disease genes using population-scale transcriptomic data , 2017, Nature Communications.

[40]  A. Singleton,et al.  Genetic variability in the regulation of gene expression in ten regions of the human brain , 2014, Nature Neuroscience.

[41]  Fabian J Theis,et al.  The Human Cell Atlas , 2017, bioRxiv.

[42]  C. Óvilo,et al.  Modulatory Effects of Breed, Feeding Status, and Diet on Adipogenic, Lipogenic, and Lipolytic Gene Expression in Growing Iberian and Duroc Pigs , 2017, International journal of molecular sciences.

[43]  J. Morris,et al.  A single-nuclei RNA sequencing study of Mendelian and sporadic AD in the human brain , 2019, Alzheimer's Research & Therapy.

[44]  Madhav Thambisetty,et al.  A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer's Disease. , 2017, Cell systems.

[45]  Shane J. Neph,et al.  Systematic Localization of Common Disease-Associated Variation in Regulatory DNA , 2012, Science.

[46]  Alkes L Price,et al.  Reconciling S-LDSC and LDAK functional enrichment estimates , 2019, Nature Genetics.

[47]  Ash A. Alizadeh,et al.  Robust enumeration of cell subsets from tissue expression profiles , 2015, Nature Methods.

[48]  Michael Brudno,et al.  Identification of deleterious synonymous variants in human genomes , 2013, Bioinform..

[49]  Athanasia G. Palasantza,et al.  Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq , 2015, Nature Biotechnology.

[50]  Aviv Regev,et al.  Massively-parallel single nucleus RNA-seq with DroNc-seq , 2017, Nature Methods.

[51]  Buhm Han,et al.  Disentangling effects of colocalizing genomic annotations to functionally prioritize non-coding variants within complex trait loci , 2014 .

[52]  E. Chang,et al.  Purification and Characterization of Progenitor and Mature Human Astrocytes Reveals Transcriptional and Functional Differences with Mouse , 2016, Neuron.

[53]  Manolis Kellis,et al.  Single-cell transcriptomic analysis of Alzheimer’s disease , 2019, Nature.

[54]  D. Weinberger,et al.  Regional heterogeneity in gene expression, regulation and coherence in hippocampus and dorsolateral prefrontal cortex across development and in schizophrenia , 2018, bioRxiv.

[55]  R. Wysocki,et al.  Molecular Profiling of Activated Neurons by Phosphorylated Ribosome Capture , 2012, Cell.

[56]  Yupu Liang,et al.  Rapid Molecular Profiling of Defined Cell Types Using Viral TRAP. , 2017, Cell reports.

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

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

[59]  Ellis Patrick,et al.  An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome , 2017, Nature Neuroscience.

[60]  Brian J. Eastwood,et al.  BrainSeq: Neurogenomics to Drive Novel Target Discovery for Neuropsychiatric Disorders , 2015, Neuron.

[61]  Sarah A. Teichmann,et al.  Computational approaches for interpreting scRNA‐seq data , 2017, FEBS letters.

[62]  S. Balasubramanian,et al.  Quantitative Sequencing of 5-Methylcytosine and 5-Hydroxymethylcytosine at Single-Base Resolution , 2012, Science.

[63]  D. Ucar,et al.  Type 2 Diabetes–Associated Genetic Variants Regulate Chromatin Accessibility in Human Islets , 2018, Diabetes.

[64]  Sean M. Grimmond,et al.  SnapShot-Seq: A Method for Extracting Genome-Wide, In Vivo mRNA Dynamics from a Single Total RNA Sample , 2014, PloS one.

[65]  Joshua T. Burdick,et al.  Mapping determinants of human gene expression by regional and genome-wide association , 2005, Nature.

[66]  Eric T. Wang,et al.  Alternative Isoform Regulation in Human Tissue Transcriptomes , 2008, Nature.

[67]  David A. Knowles,et al.  Annotation-free quantification of RNA splicing using LeafCutter , 2017, Nature Genetics.

[68]  C. Cañestro,et al.  Wnt evolution and function shuffling in liberal and conservative chordate genomes , 2018, Genome Biology.

[69]  Pawel Zajac,et al.  Topographical transcriptome mapping of the mouse medial ganglionic eminence by spatially resolved RNA-seq , 2014, Genome Biology.

[70]  Jonathan Pevsner,et al.  Bioinformatics and functional genomics , 2003 .

[71]  E. Petricoin,et al.  Laser Capture Microdissection , 1996, Science.

[72]  S. Quake,et al.  A survey of human brain transcriptome diversity at the single cell level , 2015, Proceedings of the National Academy of Sciences.

[73]  D. Choi Alzheimer's disease research , 1994, Neurobiology of Aging.

[74]  John F. Ouyang,et al.  A single cell brain atlas in human Alzheimer’s disease , 2019, bioRxiv.

[75]  A. Price,et al.  Dissecting the genetics of complex traits using summary association statistics , 2016, Nature Reviews Genetics.

[76]  Aleksandra A. Kolodziejczyk,et al.  The technology and biology of single-cell RNA sequencing. , 2015, Molecular cell.

[77]  L. Liotta,et al.  Laser Capture Microdissection , 1996, Science.

[78]  Jiacheng Yao,et al.  Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems , 2018, bioRxiv.

[79]  Daphne Koller,et al.  Polarization of the Effects of Autoimmune and Neurodegenerative Risk Alleles in Leukocytes , 2014, Science.

[80]  Benjamin A. Logsdon,et al.  Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS , 2018, American journal of human genetics.

[81]  A single-nuclei RNA sequencing study of Mendelian and sporadic AD in the human brain , 2019 .

[82]  A. Munnich,et al.  Stabilization of RNA during laser capture microdissection by performing experiments under argon atmosphere or using ethanol as a solvent in staining solutions. , 2008, RNA.

[83]  Mary Kay Lobo,et al.  FACS-array profiling of striatal projection neuron subtypes in juvenile and adult mouse brains , 2006, Nature Neuroscience.

[84]  G. Kirov,et al.  CNVs in neuropsychiatric disorders. , 2015, Human molecular genetics.

[85]  E. Zeggini,et al.  Functional annotation of non-coding sequence variants , 2014, Nature Methods.

[86]  J. Nap,et al.  Genetical genomics: the added value from segregation. , 2001, Trends in genetics : TIG.

[87]  R. Axelrod,et al.  Evolutionary Dynamics , 2004 .

[88]  S Newcomb THE PHILOSOPHY OF HYPER-SPACE. , 1898, Science.

[89]  Hidetoshi Kotera,et al.  SINC-seq: correlation of transient gene expressions between nucleus and cytoplasm reflects single-cell physiology , 2018, Genome Biology.

[90]  Gene W. Yeo,et al.  Variation in alternative splicing across human tissues , 2004, Genome Biology.

[91]  Trygve E Bakken,et al.  Single-nucleus and single-cell transcriptomes compared in matched cortical cell types , 2018, PloS one.

[92]  Simon C. Potter,et al.  Mapping cis- and trans-regulatory effects across multiple tissues in twins , 2012, Nature Genetics.

[93]  L. Kruglyak,et al.  The role of regulatory variation in complex traits and disease , 2015, Nature Reviews Genetics.

[94]  S. Horvath,et al.  Functional organization of the transcriptome in human brain , 2008, Nature Neuroscience.

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

[96]  Eleazar Eskin,et al.  Improved methods for multi-trait fine mapping of pleiotropic risk loci , 2016, bioRxiv.

[97]  N. Cox,et al.  Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS , 2010, PLoS genetics.

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

[99]  Cory C. Funk,et al.  Conserved brain myelination networks are altered in Alzheimer's and other neurodegenerative diseases , 2018, Alzheimer's & Dementia.

[100]  B. Ren,et al.  Mapping Human Epigenomes , 2013, Cell.

[101]  M. Mahajan,et al.  RNA-Seq Profiling of Spinal Cord Motor Neurons from a Presymptomatic SOD1 ALS Mouse , 2013, PloS one.

[102]  T. Owen-Hughes,et al.  Structure of the chromatin remodelling enzyme Chd1 bound to a ubiquitinylated nucleosome , 2018, bioRxiv.

[103]  Yuchio Yanagawa,et al.  Integration of electrophysiological recordings with single-cell RNA-seq data identifies novel neuronal subtypes , 2015, Nature Biotechnology.

[104]  Michael B. Stadler,et al.  Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation , 2015, Nature Biotechnology.

[105]  P. Greengard,et al.  Resource Application of a Translational Profiling Approach for the Comparative Analysis of CNS Cell Types , 2009 .

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

[107]  Xiaofeng Zhu,et al.  Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations , 2017, PLoS genetics.

[108]  D. Reich,et al.  The contribution of rare variation to prostate cancer heritability , 2015, Nature Genetics.

[109]  Cole Trapnell,et al.  Defining cell types and states with single-cell genomics , 2015, Genome research.

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

[111]  Jakob Grove,et al.  Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection , 2018, Nature Genetics.

[112]  E. Birney,et al.  GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals , 2019, Nature Genetics.

[113]  William S. DeWitt,et al.  A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility , 2018, Cell.

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

[115]  M. Ronaghi,et al.  Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain , 2016, Science.

[116]  Kaanan P. Shah,et al.  A gene-based association method for mapping traits using reference transcriptome data , 2015, Nature Genetics.

[117]  Paola Arlotta,et al.  Neuronal Subtype-Specific Genes that Control Corticospinal Motor Neuron Development In Vivo , 2005, Neuron.

[118]  Bo Wang,et al.  Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities , 2018, Inf. Fusion.

[119]  Xiaohui Xie,et al.  DANN: a deep learning approach for annotating the pathogenicity of genetic variants , 2015, Bioinform..

[120]  Yvan Saeys,et al.  A comparison of single-cell trajectory inference methods , 2019, Nature Biotechnology.

[121]  Robert W. Williams,et al.  The nature and identification of quantitative trait loci: a community's view , 2003, Nature Reviews Genetics.

[122]  Yara T. E. Lechanteur,et al.  Nature Genetics Advance Online Publication , 2022 .

[123]  E. Salido,et al.  CRISPR/Cas9-mediated glycolate oxidase disruption is an efficacious and safe treatment for primary hyperoxaluria type I , 2018, Nature Communications.

[124]  Hans-Ulrich Klein,et al.  A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research , 2018, Scientific Data.

[125]  Staci A. Sorensen,et al.  Adult Mouse Cortical Cell Taxonomy Revealed by Single Cell Transcriptomics , 2016 .

[126]  Tanya M. Teslovich,et al.  Biobank-driven genomic discovery yields new insight into atrial fibrillation biology , 2018, Nature Genetics.

[127]  A. Feinberg,et al.  Neuronal brain region-specific DNA methylation and chromatin accessibility are associated with neuropsychiatric trait heritability , 2018, Nature Neuroscience.

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

[129]  Stefan Posch,et al.  Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding , 2017, bioRxiv.

[130]  M. Kubista,et al.  Platforms for Single-Cell Collection and Analysis , 2018, International journal of molecular sciences.

[131]  Andreas S Tolias,et al.  Multimodal profiling of single-cell morphology, electrophysiology, and gene expression using Patch-seq , 2017, Nature Protocols.

[132]  S. Horvath,et al.  Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways , 2010, Proceedings of the National Academy of Sciences.

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

[134]  Hunna J. Watson,et al.  Genetic Identification of Cell Types Underlying Brain Complex Traits Yields Insights Into the Etiology of Parkinson’s Disease , 2020, Nature Genetics.

[135]  F. Collins,et al.  Potential etiologic and functional implications of genome-wide association loci for human diseases and traits , 2009, Proceedings of the National Academy of Sciences.

[136]  Lars E. Borm,et al.  Molecular Architecture of the Mouse Nervous System , 2018, Cell.

[137]  I. Hellmann,et al.  Comparative Analysis of Single-Cell RNA Sequencing Methods , 2016, bioRxiv.

[138]  Anthony D. Schmitt,et al.  Genome-wide mapping and analysis of chromosome architecture , 2016, Nature Reviews Molecular Cell Biology.

[139]  Rachel Shanahan,et al.  The Bacteroidales produce an N-acylated derivative of glycine with both cholesterol-solubilising and hemolytic activity , 2017, Scientific Reports.

[140]  P. Greengard,et al.  A Translational Profiling Approach for the Molecular Characterization of CNS Cell Types , 2008, Cell.

[141]  P. van der Harst,et al.  Dissecting the genetics of complex traits: lessons from hypertension. , 2010, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[142]  A. Singleton,et al.  Cell population-specific expression analysis of human cerebellum , 2012, BMC Genomics.

[143]  N. Heintz,et al.  Layer 2/3 pyramidal cells in the medial prefrontal cortex moderate stress induced depressive behaviors , 2015, eLife.

[144]  Lars E. Borm,et al.  The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing , 2017, Science.

[145]  Sonja W. Scholz,et al.  Moving beyond neurons: the role of cell type-specific gene regulation in Parkinson’s disease heritability , 2019, npj Parkinson's Disease.

[146]  Shijie C. Zheng,et al.  Cell-type deconvolution in epigenome-wide association studies: a review and recommendations. , 2017, Epigenomics.

[147]  Helen E. Parkinson,et al.  The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019 , 2018, Nucleic Acids Res..

[148]  T. Maniatis,et al.  An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex , 2014, The Journal of Neuroscience.

[149]  D. Koller,et al.  Population genomics of human gene expression , 2007, Nature Genetics.

[150]  B. Williams,et al.  Mapping and quantifying mammalian transcriptomes by RNA-Seq , 2008, Nature Methods.

[151]  Y. Xing,et al.  A Transcriptome Database for Astrocytes, Neurons, and Oligodendrocytes: A New Resource for Understanding Brain Development and Function , 2008, The Journal of Neuroscience.

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

[153]  Cole Trapnell,et al.  Single-Cell Multi-omics: An Engine for New Quantitative Models of Gene Regulation. , 2018, Trends in genetics : TIG.

[154]  Kun Zhang,et al.  A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA , 2017, Scientific Reports.

[155]  Dianna Gellar single cell rna sequencing , 2019 .

[156]  C. Burge,et al.  Evolutionary Dynamics of Gene and Isoform Regulation in Mammalian Tissues , 2012, Science.

[157]  Winfried Denk,et al.  EM connectomics reveals axonal target variation in a sequence-generating network , 2017, eLife.

[158]  Michael J. Purcaro,et al.  Revealing the brain's molecular architecture. , 2018, Science.

[159]  Kenneth D Harris,et al.  Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics , 2017, bioRxiv.

[160]  Sara B. Linker,et al.  Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons , 2016, Nature Protocols.

[161]  X. Wen,et al.  Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization , 2016, bioRxiv.

[162]  Joseph K. Pickrell Joint analysis of functional genomic data and genome-wide association studies of 18 human traits , 2013, bioRxiv.

[163]  Allan R. Jones,et al.  Canonical Genetic Signatures of the Adult Human Brain , 2015, Nature Neuroscience.

[164]  A. Bittner,et al.  Comparison of RNA-Seq and Microarray in Transcriptome Profiling of Activated T Cells , 2014, PloS one.

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

[166]  Doug Speed,et al.  SumHer better estimates the SNP heritability of complex traits from summary statistics , 2018, Nature Genetics.

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

[168]  Valeriia Haberland,et al.  The MR-Base platform supports systematic causal inference across the human phenome , 2018, eLife.

[169]  D. Hernandez,et al.  Mitochondria function associated genes contribute to Parkinson’s Disease risk and later age at onset , 2019, npj Parkinson's Disease.

[170]  S. Linnarsson,et al.  Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq , 2015, Science.

[171]  Gene W. Yeo,et al.  RNA-binding proteins in neurodegeneration: Seq and you shall receive , 2015, Trends in Neurosciences.

[172]  Jia Qian Wu,et al.  Single-cell RNA-sequencing of the brain , 2017, Clinical and Translational Medicine.

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

[174]  F. Benes,et al.  Gene expression profiling of substantia nigra dopamine neurons: further insights into Parkinson's disease pathology. , 2009, Brain : a journal of neurology.

[175]  Benjamin J. Strober,et al.  A method to predict the impact of regulatory variants from DNA sequence , 2015, Nature Genetics.

[176]  Ayellet V. Segrè,et al.  Colocalization of GWAS and eQTL Signals Detects Target Genes , 2016, bioRxiv.

[177]  Conor Fitzpatrick,et al.  Nuclear RNA-seq of single neurons reveals molecular signatures of activation , 2016, Nature communications.

[178]  E. Eskin,et al.  Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies , 2014, PLoS genetics.

[179]  S. Nelson,et al.  Molecular taxonomy of major neuronal classes in the adult mouse forebrain , 2006, Nature Neuroscience.

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

[181]  Todd L Edwards,et al.  Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics , 2018, Nature Communications.

[182]  C. Ponting,et al.  Single-Cell Multiomics: Multiple Measurements from Single Cells , 2017, Trends in genetics : TIG.

[183]  R. Xavier,et al.  The kinase DYRK1A reciprocally regulates the differentiation of Th17 and regulatory T cells , 2015, eLife.

[184]  Sonja W. Scholz,et al.  Expanding Parkinson’s disease genetics: novel risk loci, genomic context, causal insights and heritable risk , 2018 .

[185]  D. Bennett,et al.  Deconstructing and targeting the genomic architecture of human neurodegeneration , 2018, Nature Neuroscience.

[186]  Dmitri D. Pervouchine,et al.  The human transcriptome across tissues and individuals , 2015, Science.