Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder

Science

Volumn 362, Issue 6420, 2018, Pages

  Gandal, Michael J ^a,b   Zhang, Pan ^c   Hadjimichael, Evi ^d   Walker, Rebecca L ^b   Chen, Chao ^e   Liu, Shuang ^f   Won, Hyejung ^b,g   Van Bakel, Harm ^d   Varghese, Merina ^d   Wang, Yongjun ^h   Shieh, Annie W ⁱ   Haney, Jillian ^a,b   Parhami, Sepideh ^a,b   Belmont, Judson ^d   Kim, Minsoo ^a,b   Losada, Patricia Moran ^c   Khan, Zenab ^d   Mleczko, Justyna ^d   Xia, Yan ^e,i   Dai, Rujia ^e,i   Wang, Daifeng ^j   Yang, Yucheng T ^f   Xu, Min ^f   Fish, Kenneth ^d   Hof, Patrick R ^d   Warrell, Jonathan ^f   Fitzgerald, Dominic ^k   White, Kevin ^k,l,m   Jaffe, Andrew E ^n,o   Peters, Mette A ^p   Gerstein, Mark ^f   Liu, Chunyu ^e,i,q   Iakoucheva, Lilia M ^c   Pinto, Dalila ^d   Geschwind, Daniel H ^a,b   more..

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^e CENTRAL SOUTH UNIVERSITY   (China)

^f Yale University   (United States)

^g UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^h CENTRAL SOUTH UNIVERSITY   (China)

ⁱ State University of New York Upstate Medical University: College of Medicine   (United States)

^j Stony Brook University   (United States)

^k UNIVERSITY OF CHICAGO   (United States)

^l UNIVERSITY OF CHICAGO   (United States)

^m Tempus Labs Inc   (United States)

ⁿ LIEBER INSTITUTE FOR BRAIN DEVELOPMENT   (United States)

^o JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^p SAGE BIONETWORKS   (United States)

^q SHAANXI NORMAL UNIVERSITY   (China)

more..

Author keywords

[No Author keywords available]

Indexed keywords

RNA ISOFORM; TRANSCRIPTOME; UNTRANSLATED RNA; ISOPROTEIN;

ASSESSMENT METHOD; AUTISM; BRAIN; DISABILITY; GENE EXPRESSION; GENETIC ANALYSIS; GENOTYPE; IMMUNE RESPONSE; PATHOLOGY; RISK FACTOR;

ARTICLE; ASTROCYTE; AUTISM; BIPOLAR DISORDER; BRAIN DYSFUNCTION; COHORT ANALYSIS; CONTROLLED STUDY; DISEASE CLASSIFICATION; GENE EXPRESSION; GENE MUTATION; GENE SWITCHING; GENETIC ASSOCIATION; GENETIC RISK; GENETIC VARIABILITY; GENOME-WIDE ASSOCIATION STUDY; HUMAN; ILLNESS TRAJECTORY; INTERNEURON; MENDELIAN RANDOMIZATION ANALYSIS; MICROGLIA; MOLECULAR PATHOLOGY; NEUROPATHOLOGY; PHENOTYPE; PRIORITY JOURNAL; RNA SEQUENCE; RNA SPLICING; SCHIZOPHRENIA; TRANSCRIPTOMICS; BRAIN; GENETIC PREDISPOSITION; GENETICS; METABOLISM; SEQUENCE ANALYSIS;

AUTISM SPECTRUM DISORDER; BIPOLAR DISORDER; BRAIN; GENETIC PREDISPOSITION TO DISEASE; HUMANS; PROTEIN ISOFORMS; RNA SPLICING; SCHIZOPHRENIA; SEQUENCE ANALYSIS, RNA; TRANSCRIPTOME;

EID: 85058422390     PISSN: 00368075     EISSN: 10959203     Source Type: Journal    
DOI: 10.1126/science.aat8127     Document Type: Article

References (106)

1. H. A. Whiteford, A. J. Ferrari, L. Degenhardt, V. Feigin, T. Vos, The global burden of mental, neurological and substance use disorders: An analysis from the Global Burden of Disease Study 2010. PLOS ONE 10, e0116820 (2015). doi: 10.1371/ journal.pone.0116820; pmid: 25658103
2. M. J. Gandal, V. Leppa, H. Won, N. N. Parikshak, D. H. Geschwind, The road to precision psychiatry: Translating genetics into disease mechanisms. Nat. Neurosci. 19, 1397-1407 (2016). doi: 10.1038/nn.4409; pmid: 27786179
3. A. Sekar et al., Schizophrenia risk from complex variation of complement component 4. Nature 530, 177-183 (2016). doi: 10.1038/nature16549; pmid: 26814963
4. L. de la Torre-Ubieta, H. Won, J. L. Stein, D. H. Geschwind, Advancing the understanding of autism disease mechanisms through genetics. Nat. Med. 22, 345-361 (2016). doi: 10.1038/nm.4071; pmid: 27050589
5. M. T. Maurano et al., Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190-1195 (2012). doi: 10.1126/science.1222794; pmid: 22955828
6. L. D. Ward, M. Kellis, Interpreting noncoding genetic variation in complex traits and human disease. Nat. Biotechnol. 30, 1095-1106 (2012). doi: 10.1038/nbt.2422; pmid: 23138309
7. A. Visel, E. M. Rubin, L. A. Pennacchio, Genomic views of distant-acting enhancers. Nature 461, 199-205 (2009). doi: 10.1038/nature08451; pmid: 19741700
8. K. G. Ardlie et al., Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science 348, 648-660 (2015). doi: 10.1126/ science.1262110; pmid: 25954001
9. S. K. Reilly et al., Evolutionary genomics. Evolutionary changes in promoter and enhancer activity during human corticogenesis. Science 347, 1155-1159 (2015). doi: 10.1126/ science.1260943; pmid: 25745175
10. The ENCODE Project Consortium, Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799-816 (2007). doi: 10.1038/nature05874; pmid: 17571346
11. A. Kundaje et al., Integrative analysis of 111 reference human epigenomes. Nature 518, 317-330 (2015). doi: 10.1038/ nature14248; pmid: 25693563
12. R. Andersson et al., An atlas of active enhancers across human cell types and tissues. Nature 507, 455-461 (2014). doi: 10.1038/nature12787; pmid: 24670763
13. M. J. Gandal et al., Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693-697 (2018). doi: 10.1126/science.aad6469; pmid: 29439242
14. N. N. Parikshak, M. J. Gandal, D. H. Geschwind, Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat. Rev. Genet. 16, 441-458 (2015). doi: 10.1038/nrg3934; pmid: 26149713
15. I. Voineagu et al., Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380-384 (2011). doi: 10.1038/nature10110; pmid: 21614001
16. S. Akbarian et al., The PsychENCODE project. Nat. Neurosci. 18, 1707-1712 (2015). doi: 10.1038/nn.4156; pmid: 26605881
17. D. Wang et al., Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).
18. M. Fromer et al., Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442-1453 (2016). doi: 10.1038/nn.4399; pmid: 27668389
19. N. N. Parikshak et al., Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423-427 (2016). doi: 10.1038/nature20612; pmid: 27919067
20. H. J. Kang et al., Spatio-temporal transcriptome of the human brain. Nature 478, 483-489 (2011). doi: 10.1038/ nature10523; pmid: 22031440
21. See supplementary materials and methods.
22. A. E. Jaffe et al., qSVA framework for RNA quality correction in differential expression analysis. Proc. Natl. Acad. Sci. U.S.A. 114, 7130-7135 (2017). doi: 10.1073/pnas.1617384114; pmid: 28634288
23. B. Li, C. N. Dewey, RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011). doi: 10.1186/ 1471-2105-12-323; pmid: 21816040
24. A. E. Jaffe et al., Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nat. Neurosci. 21, 1117-1125 (2018). doi: 10.1038/s41593-018-0197-y; pmid: 30050107
25. Y. I. Li et al., RNA splicing is a primary link between genetic variation and disease. Science 352, 600-604 (2016). doi: 10.1126/science.aad9417; pmid: 27126046
26. P. J. Batista, H. Y. Chang, Long noncoding RNAs: Cellular address codes in development and disease. Cell 152, 1298-1307 (2013). doi: 10.1016/j.cell.2013.02.012; pmid: 23498938
27. J. di Iulio et al., The human noncoding genome defined by genetic diversity. Nat. Genet. 50, 333-337 (2018). doi: 10.1038/s41588-018-0062-7; pmid: 29483654
28. C. K. Vuong, D. L. Black, S. Zheng, The neurogenetics of alternative splicing. Nat. Rev. Neurosci. 17, 265-281 (2016). doi: 10.1038/nrn.2016.27; pmid: 27094079
29. Y. I. Li et al., Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50, 151-158 (2018). doi: 10.1038/s41588-017-0004-9; pmid: 29229983
30. M. Irimia et al., A highly conserved program of neuronal microexons is misregulated in autistic brains. Cell 159, 1511-1523 (2014). doi: 10.1016/j.cell.2014.11.035; pmid: 25525873
31. N. Akula et al., RNA-sequencing of the brain transcriptome implicates dysregulation of neuroplasticity, circadian rhythms and GTPase binding in bipolar disorder. Mol. Psychiatry 19, 1179-1185 (2014). doi: 10.1038/mp.2013.170; pmid: 24393808
32. J.-A. Lee et al., Cytoplasmic Rbfox1 Regulates the Expression of Synaptic and Autism-Related Genes. Neuron 89, 113-128 (2016). doi: 10.1016/ j.neuron.2015.11.025; pmid: 26687839
33. J. C. Darnell et al., FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247-261 (2011). doi: 10.1016/j.cell.2011.06.013; pmid: 21784246
34. E. Sebestyén et al., Large-scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks. Genome Res. 26, 732-744 (2016). doi: 10.1101/gr.199935.115; pmid: 27197215
35. J. Gauthier et al., Truncating mutations in NRXN2 and NRXN1 in autism spectrum disorders and schizophrenia. Hum. Genet. 130, 563-573 (2011). doi: 10.1007/ s00439-011-0975-z; pmid: 21424692
36. F. F. Hamdan et al., Excess of de novo deleterious mutations in genes associated with glutamatergic systems in nonsyndromic intellectual disability. Am. J. Hum. Genet. 88, 306-316 (2011). doi: 10.1016/j.ajhg.2011.02.001; pmid: 21376300
37. B. Treutlein, O. Gokce, S. R. Quake, T. C. Südhof, Cartography of neurexin alternative splicing mapped by single-molecule long-read mRNA sequencing. Proc. Natl. Acad. Sci. U.S.A. 111, E1291-E1299 (2014). doi: 10.1073/pnas.1403244111; pmid: 24639501
38. J. Grove et al., Common risk variants identified in autism spectrum disorder. bioRxiv 224774 [Preprint]. 27 November 2017.doi: 10.1101/224774
39. H. K. Finucane et al., Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228-1235 (2015). doi: 10.1038/ ng.3404; pmid: 26414678
40. A. E. West, M. E. Greenberg, Neuronal activity-regulated gene transcription in synapse development and cognitive function. Cold Spring Harb. Perspect. Biol. 3, a005744 (2011). doi: 10.1101/cshperspect.a005744; pmid: 21555405
41. Y. Zhu, L. Wang, Y. Yin, E. Yang, Systematic analysis of gene expression patterns associated with postmortem interval in human tissues. Sci. Rep. 7, 5435 (2017). doi: 10.1038/ s41598-017-05882-0; pmid: 28710439
42. J. Z. Li et al., Systematic changes in gene expression in postmortem human brains associated with tissue pH and terminal medical conditions. Hum. Mol. Genet. 13, 609-616 (2004). doi: 10.1093/hmg/ddh065; pmid: 14734628
43. J. T. Leek, J. D. Storey, Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLOS Genet. 3, e161 (2007). doi: 10.1371/journal.pgen.0030161; pmid: 17907809
44. GTEx Consortium, Genetic effects on gene expression across human tissues. Nature 550, 204-213 (2017). doi: 10.1038/ nature24277; pmid: 29022597
45. M. C. Zody et al., Evolutionary toggling of the MAPT 17q21.31 inversion region. Nat. Genet. 40, 1076-1083 (2008). doi: 10.1038/ng.193; pmid: 19165922
46. A. Gusev et al., Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245-252 (2016). doi: 10.1038/ng.3506; pmid: 26854917
47. E. R. Gamazon et al., A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091-1098 (2015). doi: 10.1038/ng.3367; pmid: 26258848
48. Z. Zhu et al., Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481-487 (2016). doi: 10.1038/ng.3538; pmid: 27019110
49. M. C. Oldham et al., Functional organization of the transcriptome in human brain. Nat. Neurosci. 11, 1271-1282 (2008). doi: 10.1038/nn.2207; pmid: 18849986
50. B. Zhang, S. Horvath, A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, e17 (2005). doi: 10.2202/1544-6115.1128; pmid: 16646834
51. J. A. Miller, M. C. Oldham, D. H. Geschwind, A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J. Neurosci. 28, 1410-1420 (2008). doi: 10.1523/JNEUROSCI.4098-07.2008; pmid: 18256261
52. C. Chen et al., Two gene co-expression modules differentiate psychotics and controls. Mol. Psychiatry 18, 1308-1314 (2013). doi: 10.1038/mp.2012.146; pmid: 23147385
53. R. Daneman et al., The mouse blood-brain barrier transcriptome: A new resource for understanding the development and function of brain endothelial cells. PLOS ONE 5, e13741 (2010). doi: 10.1371/journal.pone.0013741; pmid: 21060791
54. B. Obermeier, R. Daneman, R. M. Ransohoff, Development, maintenance and disruption of the blood-brain barrier. Nat. Med. 19, 1584-1596 (2013). doi: 10.1038/nm.3407; pmid: 24309662
55. G. Genovese et al., Increased burden of ultra-rare proteinaltering variants among 4, 877 individuals with schizophrenia. Nat. Neurosci. 19, 1433-1441 (2016). doi: 10.1038/nn.4402; pmid: 27694994
56. S. E. Ellis, R. Panitch, A. B. West, D. E. Arking, Transcriptome analysis of cortical tissue reveals shared sets of downregulated genes in autism and schizophrenia. Transl. Psychiatry 6, e817 (2016). doi: 10.1038/tp.2016.87; pmid: 27219343
57. R. C. Ramaker et al., Post-mortem molecular profiling of three psychiatric disorders. Genome Med. 9, 72 (2017). doi: 10.1186/s13073-017-0458-5; pmid: 28754123
58. C. K. Vuong et al., Rbfox1 Regulates Synaptic Transmission through the Inhibitory Neuron-Specific vSNARE Vamp1. Neuron 98, 127-141.e7 (2018). doi: 10.1016/ j.neuron.2018.03.008; pmid: 29621484
59. A. F. Pardiñas et al., Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381-389 (2018). doi: 10.1038/s41588-018-0059-2; pmid: 29483656
60. L. T. Gehman et al., The splicing regulator Rbfox1 (A2BP1) controls neuronal excitation in the mammalian brain. Nat. Genet. 43, 706-711 (2011). doi: 10.1038/ng.841; pmid: 21623373
61. B. L. Fogel et al., RBFOX1 regulates both splicing and transcriptional networks in human neuronal development. Hum. Mol. Genet. 21, 4171-4186 (2012). doi: 10.1093/hmg/ dds240; pmid: 22730494
62. A. M. Bond et al., Balanced gene regulation by an embryonic brain ncRNA is critical for adult hippocampal GABA circuitry. Nat. Neurosci. 12, 1020-1027 (2009). doi: 10.1038/nn.2371; pmid: 19620975
63. N. G. Skene et al., Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825-833 (2018). doi: 10.1038/s41588-018-0129-5; pmid: 29785013
64. B. Labonté et al., Sex-specific transcriptional signatures in human depression. Nat. Med. 23, 1102-1111 (2017). doi: 10.1038/nm.4386; pmid: 28825715
65. A. de Bartolomeis et al., Immediate-Early Genes Modulation by Antipsychotics: Translational Implications for a Putative Gateway to Drug-Induced Long-Term Brain Changes. Front. Behav. Neurosci. 11, 240 (2017). doi: 10.3389/ fnbeh.2017.00240; pmid: 29321734
66. R. Birnbaum et al., Investigating the neuroimmunogenic architecture of schizophrenia. Mol. Psychiatry 23, 1251-1260 (2018). doi: 10.1038/mp.2017.89; pmid: 28485405
67. S. G. Fillman et al., Increased inflammatory markers identified in the dorsolateral prefrontal cortex of individuals with schizophrenia. Mol. Psychiatry 18, 206-214 (2013). doi: 10.1038/mp.2012.110; pmid: 22869038
68. R. Pacifico, R. L. Davis, Transcriptome sequencing implicates dorsal striatum-specific gene network, immune response and energy metabolism pathways in bipolar disorder. Mol. Psychiatry 22, 441-449 (2017). doi: 10.1038/mp.2016.94; pmid: 27350034
69. J. S. Rao, G. J. Harry, S. I. Rapoport, H. W. Kim, Increased excitotoxicity and neuroinflammatory markers in postmortem frontal cortex from bipolar disorder patients. Mol. Psychiatry 15, 384-392 (2010). doi: 10.1038/mp.2009.47; pmid: 19488045
70. S. Gupta et al., Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activitydependent genes in autism. Nat. Commun. 5, 5748 (2014). doi: 10.1038/ncomms6748; pmid: 25494366
71. I. Rusinova et al., Interferome v2.0: An updated database of annotated interferon-regulated genes. Nucleic Acids Res. 41, D1040-D1046 (2013). doi: 10.1093/nar/gks1215; pmid: 23203888
72. A. Necsulea et al., The evolution of lncRNA repertoires and expression patterns in tetrapods. Nature 505, 635-640 (2014). doi: 10.1038/nature12943; pmid: 24463510
73. R. Dai, Y. Xia, C. Liu, C. Chen, csuWGCNA: A combination of signed and unsigned WGCNA to capture negative correlations. bioRxiv 288225 [Preprint]. 27 September 2018. doi: 10.1101/288225
74. P. Pruunsild, C. P. Bengtson, H. Bading, Networks of Cultured iPSC-Derived Neurons Reveal the Human Synaptic Activity-Regulated Adaptive Gene Program. Cell Reports 18, 122-135 (2017). doi: 10.1016/ j.celrep.2016.12.018; pmid: 28052243
75. P. P. Amaral et al., Complex architecture and regulated expression of the Sox2ot locus during vertebrate development. RNA 15, 2013-2027 (2009). doi: 10.1261/ rna.1705309; pmid: 19767420
76. T. R. Mercer, M. E. Dinger, S. M. Sunkin, M. F. Mehler, J. S. Mattick, Specific expression of long noncoding RNAs in the mouse brain. Proc. Natl. Acad. Sci. U.S.A. 105, 716-721 (2008). doi: 10.1073/pnas.0706729105; pmid: 18184812
77. K. Aberg, P. Saetre, N. Jareborg, E. Jazin, Human QKI, a potential regulator of mRNA expression of human oligodendrocyte-related genes involved in schizophrenia. Proc. Natl. Acad. Sci. U.S.A. 103, 7482-7487 (2006). doi: 10.1073/pnas.0601213103; pmid: 16641098
78. G. Barry et al., The long non-coding RNA Gomafu is acutely regulated in response to neuronal activation and involved in schizophrenia-associated alternative splicing. Mol. Psychiatry 19, 486-494 (2014). doi: 10.1038/mp.2013.45; pmid: 23628989
79. M. Koga et al., Involvement of SMARCA2/BRM in the SWI/ SNF chromatin-remodeling complex in schizophrenia. Hum. Mol. Genet. 18, 2483-2494 (2009). doi: 10.1093/hmg/ ddp166; pmid: 19363039
80. I. D. Krantz et al., Cornelia de Lange syndrome is caused by mutations in NIPBL, the human homolog of Drosophila melanogaster Nipped-B. Nat. Genet. 36, 631-635 (2004). doi: 10.1038/ng1364; pmid: 15146186
81. S. J. Sanders et al., Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci. Neuron 87, 1215-1233 (2015). doi: 10.1016/ j.neuron.2015.09.016; pmid: 26402605
82. M. Melé et al., The human transcriptome across tissues and individuals. Science 348, 660-665 (2015). doi: 10.1126/ science.aaa0355; pmid: 25954002
83. M. Quesnel-Vallières et al., Misregulation of an Activity-Dependent Splicing Network as a Common Mechanism Underlying Autism Spectrum Disorders. Mol. Cell 64, 1023-1034 (2016). doi: 10.1016/j.molcel.2016.11.033; pmid: 27984743
84. T. Steijger et al., Assessment of transcript reconstruction methods for RNA-seq. Nat. Methods 10, 1177-1184 (2013). doi: 10.1038/nmeth.2714; pmid: 24185837
85. M. I. Love, J. B. Hogenesch, R. A. Irizarry, Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation. Nat. Biotechnol. 34, 1287-1291 (2016). doi: 10.1038/nbt.3682; pmid: 27669167
86. M. L. Estes, A. K. McAllister, Immune mediators in the brain and peripheral tissues in autism spectrum disorder. Nat. Rev. Neurosci. 16, 469-486 (2015). doi: 10.1038/ nrn3978; pmid: 26189694
87. U. Meyer, J. Feldon, O. Dammann, Schizophrenia and autism: Both shared and disorder-specific pathogenesis via perinatal inflammation? Pediatr. Res. 69, 26R-33R (2011). doi: 10.1203/PDR.0b013e318212c196; pmid: 21289540
88. J. D. Rosenblat et al., Inflammation as a neurobiological substrate of cognitive impairment in bipolar disorder: Evidence, pathophysiology and treatment implications. J. Affect. Disord. 188, 149-159 (2015). doi: 10.1016/ j.jad.2015.08.058; pmid: 26363613
89. T. Lappalainen, Functional genomics bridges the gap between quantitative genetics and molecular biology. Genome Res. 25, 1427-1431 (2015). doi: 10.1101/gr.190983.115; pmid: 26430152
90. PsychENCODE Capstone Data Collection (2018); www.doi.org/ 10.7303/syn12080241.
91. M. C. Oldham, P. Langfelder, S. Horvath, Network methods for describing sample relationships in genomic datasets: Application to Huntington's disease. BMC Syst. Biol. 6, 63 (2012). doi: 10.1186/1752-0509-6-63; pmid: 22691535
92. K. S. Pollard, M. J. Hubisz, K. R. Rosenbloom, A. Siepel, Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110-121 (2010). doi: 10.1101/ gr.097857.109; pmid: 19858363
93. A. Siepel et al., Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034-1050 (2005). doi: 10.1101/gr.3715005; pmid: 16024819
94. N. N. Parikshak et al., Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008-1021 (2013). doi: 10.1016/j.cell.2013.10.031; pmid: 24267887
95. B. J. Vilhjálmsson et al., Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. Am. J. Hum. Genet. 97, 576-592 (2015). doi: 10.1016/j.ajhg.2015.09.001; pmid: 26430803
96. J. Yang, S. H. Lee, M. E. Goddard, P. M. Visscher, GCTA: A tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76-82 (2011). doi: 10.1016/j.ajhg.2010.11.011; pmid: 21167468
97. D. M. Ruderfer et al., Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes. Cell 173, 1705-1715.e16 (2018). doi: 10.1016/j.cell.2018.05.046; pmid: 29906448
98. B. B. Lake et al., Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70-80 (2018). doi: 10.1038/ nbt.4038; pmid: 29227469
99. T. Goldmann et al., Origin, fate and dynamics of macrophages at central nervous system interfaces. Nat. Immunol. 17, 797-805 (2016). doi: 10.1038/ni.3423; pmid: 27135602
100. H. Keren-Shaul et al., A Unique Microglia Type Associated with Restricting Development of Alzheimer's Disease. Cell 169, 1276-1290.e17 (2017). doi: 10.1016/j.cell.2017.05.018; pmid: 28602351
101. A. Zeisel et al., Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138-1142 (2015). doi: 10.1126/science. aaa1934; pmid: 25700174
102. Y. Zhang et al., Purification and Characterization of Progenitor and Mature Human Astrocytes Reveals Transcriptional and Functional Differences with Mouse. Neuron 89, 37-53 (2016). doi: 10.1016/j.neuron.2015.11.013; pmid: 26687838
103. B. Wilkinson et al., The autism-associated gene chromodomain helicase DNA-binding protein 8 (CHD8) regulates noncoding RNAs and autism-related genes. Transl. Psychiatry 5, e568 (2015). doi: 10.1038/tp.2015.62; pmid: 25989142
104. K. E. Samocha et al., A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944-950 (2014). doi: 10.1038/ng.3050; pmid: 25086666
105. I. Iossifov et al., Low load for disruptive mutations in autism genes and their biased transmission. Proc. Natl. Acad. Sci. U.S.A. 112, E5600-E5607 (2015). doi: 10.1073/ pnas.1516376112; pmid: 26401017
106. K. J. Karczewski et al., The ExAC browser: Displaying reference data information from over 60 000 exomes. Nucleic Acids Res. 45 (D1), D840-D845 (2017). doi: 10.1093/ nar/gkw971; pmid: 27899611