Functional genomics links genetic origins to pathophysiology in neurodegenerative and neuropsychiatric disease

James, 2018, Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 2017: a systematic analysis for the Global Burden of Disease Study 2017, Lancet, 392, 1789, 10.1016/S0140-6736(18)32279-7 la Torre Ubieta de, 2016, Advancing the understanding of autism disease mechanisms through genetics, Nat Med, 22, 345, 10.1038/nm.4071 Sullivan, 2012, Genetic architectures of psychiatric disorders: the emerging picture and its implications, Nat Rev Genet, 13, 537, 10.1038/nrg3240 Sullivan, 2019, Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders, Cell, 177, 162, 10.1016/j.cell.2019.01.015 Sanders, 2019, A framework for the investigation of rare genetic disorders in neuropsychiatry, Nat Med, 25, 1477, 10.1038/s41591-019-0581-5 Gandal, 2016, The road to precision psychiatry: translating genetics into disease mechanisms, Nat Neurosci, 19, 1397, 10.1038/nn.4409 Geschwind, 2015, Genetics and genomics of psychiatric disease, Science, 349, 1489, 10.1126/science.aaa8954 Wray, 2018, Common disease is more complex than implied by the core gene omnigenic model, Cell, 173, 1573, 10.1016/j.cell.2018.05.051 Brainstorm Consortium, 2018, Analysis of shared heritability in common disorders of the brain, Science, 360, 10.1126/science.aap8757 Lappalainen, 2019, Genomic analysis in the age of human genome sequencing, Cell, 177, 70, 10.1016/j.cell.2019.02.032 Gaugler, 2014, Most genetic risk for autism resides with common variation, Nat Genet, 46, 881, 10.1038/ng.3039 Iossifov, 2014, The contribution of de novo coding mutations to autism spectrum disorder, Nature, 515, 216, 10.1038/nature13908 The Schizophrenia Psychiatric Genome-Wide Association Study Consortium (PGC-SCZ), 2012, Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs, Nat Genet, 44, 247, 10.1038/ng.1108 Howrigan, 2020, Exome sequencing in schizophrenia-affected parent–offspring trios reveals risk conferred by protein-coding de novo mutations, Nat Neurosci, 23, 185, 10.1038/s41593-019-0564-3 Rees, 2020, De novo mutations identified by exome sequencing implicate rare missense variants in SLC6A1 in schizophrenia, Nat Neurosci, 23, 179, 10.1038/s41593-019-0565-2 Pardiñas, 2018, Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection, Nat Genet, 50, 381, 10.1038/s41588-018-0059-2 Stahl, 2019, Genome-wide association study identifies 30 loci associated with bipolar disorder, Nat Genet, 51, 793, 10.1038/s41588-019-0397-8 Grove, 2017, Common risk variants identified in autism spectrum disorder, Nat Genet Chow, 1999, Inheritance of frontotemporal dementia, Arch Neurol, 56, 817, 10.1001/archneur.56.7.817 Rohrer, 2009, The heritability and genetics of frontotemporal lobar degeneration, Neurology, 73, 1451, 10.1212/WNL.0b013e3181bf997a Campion, 1999, Early-onset autosomal dominant Alzheimer disease: prevalence, genetic heterogeneity, and mutation spectrum, Am J Hum Genet, 65, 664, 10.1086/302553 Hernandez, 2016, Genetics in Parkinson disease: Mendelian versus non-Mendelian inheritance, J Neurochem, 139, 59, 10.1111/jnc.13593 Lesage, 2009, Parkinson’s disease: from monogenic forms to genetic susceptibility factors, Hum Mol Genet, 18, R48, 10.1093/hmg/ddp012 Li, 2002, Age at onset in two common neurodegenerative diseases is genetically controlled, Am J Hum Genet, 70, 985, 10.1086/339815 van Duijn, 1993, Genetic transmission of Alzheimer’s disease among families in a Dutch population based study, J Med Genet, 30, 640, 10.1136/jmg.30.8.640 Zweier, 2009, CNTNAP2 and NRXN1 are mutated in autosomal-recessive Pitt-Hopkins-like mental retardation and determine the level of a common synaptic protein in drosophila, Am J Hum Genet, 85, 655, 10.1016/j.ajhg.2009.10.004 Kim, 2008, Disruption of neurexin 1 associated with autism spectrum disorder, Am J Hum Genet, 82, 199, 10.1016/j.ajhg.2007.09.011 McCarthy, 2019, De novo mutations in schizophrenia implicate chromatin remodeling and support a genetic overlap with autism and intellectual disability, Mol Psychiatry, 19, 652, 10.1038/mp.2014.29 Rapoport, 2009, Autism spectrum disorders and childhood-onset schizophrenia: clinical and biological contributions to a relation revisited, J Am Acad Child Adolesc Psychiatry, 48, 10, 10.1097/CHI.0b013e31818b1c63 Chen, 2015, A multiancestral genome-wide exome array study of Alzheimer disease, frontotemporal dementia, and progressive supranuclear palsy, JAMA Neurol, 72, 414, 10.1001/jamaneurol.2014.4040 Ruzzo, 2019, Inherited and de novo genetic risk for autism impacts shared networks, Cell, 178, 850, 10.1016/j.cell.2019.07.015 International Parkinson’s Disease Genomics Consortium, 2017, A meta-analysis of genome-wide association studies identifies 17 new Parkinson's disease risk loci, Nat Genet, 49, 1511, 10.1038/ng.3955 Nalls, 2019, Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies, Lancet Neurol, 18, 1091, 10.1016/S1474-4422(19)30320-5 Satake, 2009, Genome-wide association study identifies common variants at four loci as genetic risk factors for Parkinson's disease, Nat Genet, 41, 1303, 10.1038/ng.485 Simón-Sánchez, 2009, Genome-wide association study reveals genetic risk underlying Parkinson's disease, Nat Genet, 41, 1308, 10.1038/ng.487 European Alzheimer’s Disease Initiative (EADI), 2013, Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease, Nat Genet, 45, 1452, 10.1038/ng.2802 Bis, 2019, Whole exome sequencing study identifies novel rare and common Alzheimer’s-associated variants involved in immune response and transcriptional regulation, Mol Psychiatry, 9 Kunkle, 2019, Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing, Nat Genet, 51, 414, 10.1038/s41588-019-0358-2 Ferrari, 2014, Frontotemporal dementia and its subtypes: a genome-wide association study, Lancet Neurol, 13, 686, 10.1016/S1474-4422(14)70065-1 Van Deerlin, 2010, Common variants at 7p21 are associated with frontotemporal lobar degeneration with TDP-43 inclusions, Nat Genet, 42, 234, 10.1038/ng.536 Chen, 2018, Joint genome-wide association study of progressive supranuclear palsy identifies novel susceptibility loci and genetic correlation to neurodegenerative diseases, Mol Neurodegener, 13, 11, 10.1186/s40035-018-0116-x Höglinger, 2011, Identification of common variants influencing risk of the tauopathy progressive supranuclear palsy, Nat Genet, 43, 699, 10.1038/ng.859 Lamb, 2016, Progressive supranuclear palsy and corticobasal degeneration: pathophysiology and treatment options, Curr Treat Options Neurol, 9, 18 Yokoyama, 2016, Association between genetic traits for immune-mediated diseases and Alzheimer disease, JAMA Neurol, 73, 691, 10.1001/jamaneurol.2016.0150 Broce, 2018, Immune-related genetic enrichment in frontotemporal dementia: an analysis of genome-wide association studies, PLoS Med, 15 Hetz, 2017, ER stress and the unfolded protein response in neurodegeneration, Nat Rev Neurol, 13, 477, 10.1038/nrneurol.2017.99 Heneka, 2019, Microglia take centre stage in neurodegenerative disease, Nat Rev Immunol, 19, 79, 10.1038/s41577-018-0112-5 Perry, 2010, Microglia in neurodegenerative disease, Nat Rev Neurol, 6, 193, 10.1038/nrneurol.2010.17 Scrivo, 2018, Selective autophagy as a potential therapeutic target for neurodegenerative disorders, Lancet Neurol, 17, 802, 10.1016/S1474-4422(18)30238-2 Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014, Biological insights from 108 schizophrenia-associated genetic loci, Nature, 511, 421, 10.1038/nature13595 Demontis, 2018, Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder, Nat Genet, 51, 63, 10.1038/s41588-018-0269-7 Autism Spectrum Disorder Working Group of the Psychiatric Genomics Consortium, 2019, Identification of common genetic risk variants for autism spectrum disorder, Nat Genet, 51, 431, 10.1038/s41588-019-0344-8 Chen, 2015, The emerging picture of autism spectrum disorder: genetics and pathology, Annu Rev Pathol Mech Dis, 10, 111, 10.1146/annurev-pathol-012414-040405 Cheng, 2017, Co-aggregation of major psychiatric disorders in individuals with first-degree relatives with schizophrenia: a nationwide population-based study, Mol Psychiatry, 23, 1756, 10.1038/mp.2017.217 Sullivan, 2012, Family history of schizophrenia and bipolar disorder as risk factors for autism, Arch Gen Psychiatry, 69, 1, 10.1001/archgenpsychiatry.2012.730 Larsson, 2018, Risk of bipolar disorder and schizophrenia in relatives of people with attention-deficit hyperactivity disorder, Br J Psychiatry, 203, 103, 10.1192/bjp.bp.112.120808 Faraone, 2012, Examining the comorbidity between attention deficit hyperactivity disorder and bipolar I disorder: a meta-analysis of family genetic studies, Am J Psychiatry, 169, 1256, 10.1176/appi.ajp.2012.12010087 Ghirardi, 2017, The familial co-aggregation of ASD and ADHD: a register-based cohort study, Mol Psychiatry, 23, 257, 10.1038/mp.2017.17 Consortium C-DGOTPG, 2013, Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis, Lancet, 381, 1371, 10.1016/S0140-6736(12)62129-1 Pettersson, 2018, Genetic influences on eight psychiatric disorders based on family data of 4 408 646 full and half-siblings, and genetic data of 333 748 cases and controls, Psychol Med, 49, 1166, 10.1017/S0033291718002039 Consortium C-DGOTPG, 2019, Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders, Cell, 179, 1469, 10.1016/j.cell.2019.11.020 Parikshak, 2015, Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders, Nat Rev Genet, 16, 441, 10.1038/nrg3934 Maurano, 2012, Systematic localization of common disease-associated variation in regulatory DNA, Science, 337, 1190, 10.1126/science.1222794 Chatterjee, 2017, Gene regulatory elements, major drivers of human disease, Annu Rev Genomics Hum Genet, 18, 45, 10.1146/annurev-genom-091416-035537 Visscher, 2017, Challenges in understanding common disease, Genome Med, 9, 112, 10.1186/s13073-017-0506-1 Edwards, 2013, Beyond GWASs: illuminating the dark road from association to function, Am J Hum Genet, 93, 779, 10.1016/j.ajhg.2013.10.012 Zarrei, 2019, A large data resource of genomic copy number variation across neurodevelopmental disorders, NPJ Genomic Med, 4, 26, 10.1038/s41525-019-0098-3 Cardoso, 2019, Essential genetic findings in neurodevelopmental disorders, Hum Genomics, 13, 31, 10.1186/s40246-019-0216-4 Malhotra, 2011, High frequencies of de novo CNVs in bipolar disorder and schizophrenia, Neuron, 72, 951, 10.1016/j.neuron.2011.11.007 Geschwind, 2009, Neuroscience in the era of functional genomics and systems biology, Nature, 461, 908, 10.1038/nature08537 Reich, 2001, Linkage disequilibrium in the human genome, Nature, 411, 199, 10.1038/35075590 Hormozdiari, 2014, Identifying causal variants at loci with multiple signals of association, Genetics, 10.1534/genetics.114.167908 Kichaev, 2014, Integrating functional data to prioritize causal variants in statistical fine-mapping studies, PLoS Genet, 10, 10.1371/journal.pgen.1004722 Ernst, 2016, Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions, Nat Biotechnol, 34, 1180, 10.1038/nbt.3678 Won, 2016, Chromosome conformation elucidates regulatory relationships in developing human brain, Nature, 538, 523, 10.1038/nature19847 The GTEx Consortium, 2015, The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans, Science, 348, 648, 10.1126/science.1262110 Whalen, 2019, Most chromatin interactions are not in linkage disequilibrium, Genome Res, 29, 334, 10.1101/gr.238022.118 Wang, 2018, Comprehensive functional genomic resource and integrative model for the human brain, Science, 362, 10.1126/science.aat8464 Hauberg, 2017, Large-scale identification of common trait and disease variants affecting gene expression, Am J Hum Genet, 100, 885, 10.1016/j.ajhg.2017.04.016 Walker, 2019, Genetic control of expression and splicing in developing human brain informs disease mechanisms, Cell, 179, 750, 10.1016/j.cell.2019.09.021 Li, 2016, RNA splicing is a primary link between genetic variation and disease, Science, 352, 600, 10.1126/science.aad9417 O’Brien, 2018, Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders, Genome Biol, 19, 194, 10.1186/s13059-018-1567-1 la Torre Ubieta de, 2018, The dynamic landscape of open chromatin during human cortical neurogenesis, Cell, 172, 289, 10.1016/j.cell.2017.12.014 Short, 2018, De novo mutations in regulatory elements in neurodevelopmental disorders, Nature, 555, 611, 10.1038/nature25983 Gamazon, 2018, Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation, Nat Genet, 10.1038/s41588-018-0154-4 The PsychENCODE Consortium, 2018, Revealing the brain’s molecular architecture, Science, 362, 1262, 10.1126/science.362.6420.1262 Akbarian, 2015, The PsychENCODE project, Nat Neurosci, 18, 1707, 10.1038/nn.4156 Ecker, 2012, ENCODE explained, Nature, 489, 52, 10.1038/489052a Battle, 2017, Genetic effects on gene expression across human tissues, Nature, 550, 204, 10.1038/nature24277 Dekker, 2017, The 4D nucleome project, Nature, 549, 219, 10.1038/nature23884 Hodes, 2016, Accelerating medicines partnership: Alzheimer’s disease (AMP-AD) knowledge portal aids Alzheimer’s drug discovery through open data sharing, Expert Opin Ther Targets, 20, 389, 10.1517/14728222.2016.1135132 Seyfried, 2017, A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer’s disease, Cell Syst, 4, 60, 10.1016/j.cels.2016.11.006 Canchi, 2019, Integrating gene and protein expression reveals perturbed functional networks in Alzheimer's disease, Cell Rep, 28, 1103, 10.1016/j.celrep.2019.06.073 Matharu, 2019, CRISPR-mediated activation of a promoter or enhancer rescues obesity caused by haploinsufficiency, Science, 363, 10.1126/science.aau0629 Gasperini, 2019, A genome-wide framework for mapping gene regulation via cellular genetic screens, Cell, 176, 1516, 10.1016/j.cell.2019.02.027 Cirillo, 2017, A review of pathway-based analysis tools that visualize genetic variants, Front Genet, 8, 174, 10.3389/fgene.2017.00174 Koopmans, 2019, SynGO: an evidence-based, expert-curated knowledge base for the synapse, Neuron, 103, 217, 10.1016/j.neuron.2019.05.002 Schadt, 2009, Molecular networks as sensors and drivers of common human diseases, Nature, 461, 218, 10.1038/nature08454 Carter, 2013, Genotype to phenotype via network analysis, Curr Opin Genet Dev, 23, 611, 10.1016/j.gde.2013.10.003 Zhang, 2011, A general framework for weighted gene co-expression network analysis, Stat Appl Genet Mol Biol, 4, 1 Mitra, 2013, Integrative approaches for finding modular structure in biological networks, Nat Rev Genet, 14, 719, 10.1038/nrg3552 Parikshak, 2013, Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism, Cell, 155, 1008, 10.1016/j.cell.2013.10.031 Hawrylycz, 2012, An anatomically comprehensive atlas of the adult human brain transcriptome, Nature, 489, 391, 10.1038/nature11405 Miller, 2014, Transcriptional landscape of the prenatal human brain, Nature, 508, 199, 10.1038/nature13185 BrainSpan Consortium, 2013 Parikshak, 2016, Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism, Nature, 540, 423, 10.1038/nature20612 Velmeshev, 2019, Single-cell genomics identifies cell type–specific molecular changes in autism, Science, 364, 685, 10.1126/science.aav8130 Polioudakis, 2019, A single-cell transcriptomic atlas of human neocortical development during mid-gestation, Neuron, 103, 785, 10.1016/j.neuron.2019.06.011 Satterstrom, 2020, Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism, Cell, 180, 568, 10.1016/j.cell.2019.12.036 Wang, 2016, Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer’s disease, Genome Med, 10.1186/s13073-016-0355-3 Mathys, 2019, Single-cell transcriptomic analysis of Alzheimer’s disease, Nature, 570, 332, 10.1038/s41586-019-1195-2 Johnson, 2020, Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation, Nat Med, 10.1038/s41591-020-0815-6 Swarup, 2020, Identification of conserved protemoic networks in neurodegenerative dementia, Cell Reports, 31, 107, 10.1016/j.celrep.2020.107807 Nott, 2019, Brain cell type-specific enhancer-promoter interactome maps and disease-risk association, Science, 366, 1134, 10.1126/science.aay0793