Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.
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The summary statistics are available for download the iPSYCH and at the PGC download sites (see URLs). For access to genotype data from the PGC samples and the iPSYCH sample, researchers should contact the lead principal investigators M.J.D. and A.D.B. for PGC-ASD and iPSYCH-ASD, respectively.
De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).
Gaugler, T. et al. Most genetic risk for autism resides with common variation. Nat. Genet. 46, 881–885 (2014).
Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).
Krumm, N. et al. Excess of rare, inherited truncating mutations in autism. Nat. Genet. 47, 582–588 (2015).
Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol. Autism 8, 21 (2017).
Ma, D. et al. A genome-wide association study of autism reveals a common novel risk locus at 5p14.1. Ann. Hum. Genet. 73, 263–273 (2009).
Devlin, B., Melhem, N. & Roeder, K. Do common variants play a role in risk for autism? Evidence and theoretical musings. Brain Res. 1380, 78–84 (2011).
Anney, R. et al. Individual common variants exert weak effects on the risk for autism spectrum disorders. Hum. Mol. Genet. 21, 4781–4792 (2012).
Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).
Pedersen, C. B. et al. The iPSYCH2012 case-cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders. Mol. Psychiatry 23, 6–14 (2018).
Lauritsen, M. B. et al. Validity of childhood autism in the Danish Psychiatric Central Register: findings from a cohort sample born 1990–1999. J. Autism Dev. Disord. 40, 139–148 (2010).
Mors, O., Perto, G. P. & Mortensen, P. B. The Danish Psychiatric Central Research Register. Scand. J. Public Health 39 (Suppl.), 54–57 (2011).
Hollegaard, M. V. et al. Robustness of genome-wide scanning using archived dried blood spot samples as a DNA source. BMC Genet. 12, 58 (2011).
Hollegaard, M. V. et al. Genome-wide scans using archived neonatal dried blood spot samples. BMC Genomics 10, 297 (2009).
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Cross-Disorder Group of the Psychiatric Genomics Consortium. et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).
Gratten, J., Wray, N. R., Keller, M. C. & Visscher, P. M. Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nat. Neurosci. 17, 782–790 (2014).
Hansen, S. N., Overgaard, M., Andersen, P. K. & Parner, E. T. Estimating a population cumulative incidence under calendar time trends. BMC Med. Res. Methodol. 17, 7 (2017).
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).
Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).
Clarke, T.-K. et al. Common polygenic risk for autism spectrum disorder (ASD) is associated with cognitive ability in the general population. Mol. Psychiatry 21, 419–425 (2016).
Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).
Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).
St Pourcain, B. et al. ASD and schizophrenia show distinct developmental profiles in common genetic overlap with population-based social communication difficulties. Mol. Psychiatry 23, 263–270 (2018).
Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).
Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013).
Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).
Sanders, S. J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).
SPARK Consortium. SPARK: a US cohort of 50,000 families to accelerate autism research. Neuron 97, 488–493 (2018).
Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
Sniekers, S. et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat. Genet. 49, 1107–1112 (2017).
Jones, S. E. et al. Genome-wide association analyses in 128,266 individuals identifies new morningness and sleep duration loci. PLoS Genet. 12, e1006125 (2016).
Robinson, E. B. et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat. Genet. 48, 552–555 (2016).
Weiner, D. J. et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat. Genet. 49, 978–985 (2017).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Shlyueva, D., Stampfel, G. & Stark, A. Transcriptional enhancers: from properties to genome-wide predictions. Nat. Rev. Genet. 15, 272–286 (2014).
Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).
Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B. & Eskin, E. Identifying causal variants at loci with multiple signals of association. Genetics 198, 497–508 (2014).
Kosmicki, J. A. et al. Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples. Nat. Genet. 49, 504–510 (2017).
Robinson, E. B. et al. Autism spectrum disorder severity reflects the average contribution of de novo and familial influences. Proc. Natl Acad. Sci. USA 111, 15161–15165 (2014).
Reichenberg, A. et al. Discontinuity in the genetic and environmental causes of the intellectual disability spectrum. Proc. Natl Acad. Sci. USA 113, 1098–1103 (2016).
Polderman, T. J. C. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).
Sadakata, T. et al. Autistic-like phenotypes in Cadps2-knockout mice and aberrant CADPS2 splicing in autistic patients. J. Clin. Invest. 117, 931–943 (2007).
Parikshak, N. N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016).
Davies, G. et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112 151). Mol. Psychiatry 21, 758–767 (2016).
Deary, V. et al. Genetic contributions to self-reported tiredness. Mol. Psychiatry 23, 609–620 (2017).
Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).
Thorleifsson, G. et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41, 18–24 (2009).
Willer, C. J. et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat. Genet. 41, 25–34 (2009).
Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).
Berndt, S. I. et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat. Genet. 45, 501–512 (2013).
Hashimoto, T., Yamada, M., Maekawa, S., Nakashima, T. & Miyata, S. IgLON cell adhesion molecule Kilon is a crucial modulator for synapse number in hippocampal neurons. Brain Res. 1224, 1–11 (2008).
Hashimoto, T., Maekawa, S. & Miyata, S. IgLON cell adhesion molecules regulate synaptogenesis in hippocampal neurons. Cell Biochem. Funct. 27, 496–498 (2009).
Pischedda, F. et al. A cell surface biotinylation assay to reveal membrane-associated neuronal cues: Negr1 regulates dendritic arborization. Mol. Cell. Proteomics 13, 733–748 (2014).
Pischedda, F. & Piccoli, G. The IgLON family member Negr1 promotes neuronal arborization acting as soluble factor via FGFR2. Front. Mol. Neurosci. 8, 89 (2016).
Marg, A. et al. Neurotractin, a novel neurite outgrowth-promoting Ig-like protein that interacts with CEPU-1 and LAMP. J. Cell Biol. 145, 865–876 (1999).
Funatsu, N. et al. Characterization of a novel rat brain glycosylphosphatidylinositol-anchored protein (Kilon), a member of the IgLON cell adhesion molecule family. J. Biol. Chem. 274, 8224–8230 (1999).
Sanz, R., Ferraro, G. B. & Fournier, A. E. IgLON cell adhesion molecules are shed from the cell surface of cortical neurons to promote neuronal growth. J. Biol. Chem. 290, 4330–4342 (2015).
Schäfer, M., Bräuer, A. U., Savaskan, N. E., Rathjen, F. G. & Brümmendorf, T. Neurotractin/kilon promotes neurite outgrowth and is expressed on reactive astrocytes after entorhinal cortex lesion. Mol. Cell. Neurosci. 29, 580–590 (2005).
Lee, A. W. S. et al. Functional inactivation of the genome-wide association study obesity gene neuronal growth regulator 1 in mice causes a body mass phenotype. PLoS One 7, e41537 (2012).
Doan, R. N. et al. Mutations in human accelerated regions disrupt cognition and social behavior. Cell 167, 341–354.e12 (2016).
Vuong, J. K. et al. PTBP1 and PTBP2 serve both specific and redundant functions in neuronal pre-mRNA splicing. Cell Rep. 17, 2766–2775 (2016).
Boutz, P. L. et al. A post-transcriptional regulatory switch in polypyrimidine tract-binding proteins reprograms alternative splicing in developing neurons. Genes Dev. 21, 1636–1652 (2007).
Makeyev, E. V., Zhang, J., Carrasco, M. A. & Maniatis, T. The microRNA miR-124 promotes neuronal differentiation by triggering brain-specific alternative pre-mRNA splicing. Mol. Cell 27, 435–448 (2007).
Spellman, R., Llorian, M. & Smith, C. W. J. Crossregulation and functional redundancy between the splicing regulator PTB and its paralogs nPTB and ROD1. Mol. Cell 27, 420–434 (2007).
Zheng, S. et al. Psd-95 is post-transcriptionally repressed during early neural development by PTBP1 and PTBP2. Nat. Neurosci. 15, 381–388 (2012).
Li, Q. S., Parrado, A. R., Samtani, M. N. & Narayan, V. A. & Alzheimer’s Disease Neuroimaging Initiative. Variations in the fra10ac1 fragile site and 15q21 are associated with cerebrospinal fluid aβ1–42 level. PLoS One 10, e0134000 (2015).
Wassenberg, J. J. & Martin, T. F. J. Role of CAPS in dense-core vesicle exocytosis. Ann. NY Acad. Sci. 971, 201–209 (2002).
Shinoda, Y. et al. CAPS1 stabilizes the state of readily releasable synaptic vesicles to fusion competence at CA3-CA1 synapses in adult hippocampus. Sci. Rep. 6, 31540 (2016).
Farina, M. et al. Caps-1 promotes fusion competence of stationary dense-core vesicles in presynaptic terminals of mammalian neurons. eLife 4, e05438 (2015).
Rietveld, C. A. et al. Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proc. Natl Acad. Sci. USA 111, 13790–13794 (2014).
Sun, J. et al. Ube3a regulates synaptic plasticity and learning and memory by controlling sk2 channel endocytosis. Cell Rep. 12, 449–461 (2015).
Cook, E. H. Jr. et al. Autism or atypical autism in maternally but not paternally derived proximal 15q duplication. Am. J. Hum. Genet. 60, 928–934 (1997).
Lin, M. T., Luján, R., Watanabe, M., Adelman, J. P. & Maylie, J. SK2 channel plasticity contributes to LTP at Schaffer collateral-CA1 synapses. Nat. Neurosci. 11, 170–177 (2008).
Hammond, R. S. et al. Small-conductance Ca2+-activated K+ channel type 2 (SK2) modulates hippocampal learning, memory, and synaptic plasticity. J. Neurosci. 26, 1844–1853 (2006).
Murthy, S. R. K. et al. Small-conductance Ca2+-activated potassium type 2 channels regulate the formation of contextual fear memory. PLoS One 10, e0127264 (2015).
Fakira, A. K., Portugal, G. S., Carusillo, B., Melyan, Z. & Morón, J. A. Increased small conductance calcium-activated potassium type 2 channel-mediated negative feedback on N-methyl-d-aspartate receptors impairs synaptic plasticity following context-dependent sensitization to morphine. Biol. Psychiatry 75, 105–114 (2014).
Goes, F. S. et al. Genome-wide association study of schizophrenia in Ashkenazi Jews. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 168, 649–659 (2015).
Mas-Y-Mas, S. et al. The human mixed lineage leukemia 5 (mll5), a sequentially and structurally divergent set domain-containing protein with no intrinsic catalytic activity. PLoS One 11, e0165139 (2016).
Sun, X.-J. et al. Genome-wide survey and developmental expression mapping of zebrafish SET domain-containing genes. PLoS One 3, e1499 (2008).
Ali, M. et al. Molecular basis for chromatin binding and regulation of MLL5. Proc. Natl Acad. Sci. USA 110, 11296–11301 (2013).
Lemak, A. et al. Solution NMR structure and histone binding of the PHD domain of human MLL5. PLoS One 8, e77020 (2013).
Zhang, X., Novera, W., Zhang, Y. & Deng, L.-W. MLL5 (KMT2E): structure, function, and clinical relevance. Cell. Mol. Life Sci. 74, 2333–2344 (2017).
Anney, R. et al. A genome-wide scan for common alleles affecting risk for autism. Hum. Mol. Genet. 19, 4072–4082 (2010).
Torrico, B. et al. Lack of replication of previous autism spectrum disorder GWAS hits in European populations. Autism Res. 10, 202–211 (2017).
Feijs, K. L. H., Forst, A. H., Verheugd, P. & Lüscher, B. Macrodomain-containing proteins: regulating new intracellular functions of mono(ADP-ribosyl)ation. Nat. Rev. Mol. Cell Biol. 14, 443–451 (2013).
Børglum, A. D. et al. Genome-wide study of association and interaction with maternal cytomegalovirus infection suggests new schizophrenia loci. Mol. Psychiatry 19, 325–333 (2014).
Illumina, Inc. Illumina Gencall Data Analysis Software. (Illumina, Inc., San Diego, 2005).
Korn, J. M. et al. Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nat. Genet. 40, 1253–1260 (2008).
Goldstein, J. I. et al. zCall: a rare variant caller for array-based genotyping: genetics and population analysis. Bioinformatics 28, 2543–2545 (2012).
Lajonchere, C. M., AGRE Consortium. Changing the landscape of autism research: the autism genetic resource exchange. Neuron 68, 187–191 (2010).
Geschwind, D. H. et al. The autism genetic resource exchange: a resource for the study of autism and related neuropsychiatric conditions. Am. J. Hum. Genet. 69, 463–466 (2001).
Gauthier, J. et al. Autism spectrum disorders associated with X chromosome markers in French-Canadian males. Mol. Psychiatry 11, 206–213 (2006).
Fischbach, G. D. & Lord, C. The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron 68, 192–195 (2010).
Chaste, P. et al. A genome-wide association study of autism using the Simons Simplex Collection: does reducing phenotypic heterogeneity in autism increase genetic homogeneity? Biol. Psychiatry 77, 775–784 (2015).
Delaneau, O., Marchini, J. & Zagury, J.-F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2011).
Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).
Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).
1000 Genomes Project Consortium. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Price, A. L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–135 (2008). author reply 135–139.
Chang, C. C. et al. Second-generation plink: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).
Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Begum, F., Ghosh, D., Tseng, G. C. & Feingold, E. Comprehensive literature review and statistical considerations for GWAS meta-analysis. Nucleic Acids Res. 40, 3777–3784 (2012).
Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294–305 (2011).
Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).
1000 Genomes Project Consortium. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Altshuler, D. M. et al. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).
Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).
The iPSYCH project is funded by the Lundbeck Foundation (R102-A9118 and R155-2014-1724) and the universities and university hospitals of Aarhus and Copenhagen. Genotyping of iPSYCH and PGC samples was supported by grants from the Lundbeck Foundation, the Stanley Foundation, the Simons Foundation (SFARI 311789 to M.J.D.), and NIMH (5U01MH094432-02 to M.J.D.). The Danish National Biobank resource was supported by the Novo Nordisk Foundation. Data handling and analysis on the GenomeDK HPC facility was supported by NIMH (1U01MH109514-01 to M.C.O.D and A.D.B.). High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the Centre for Integrative Sequencing, iSEQ, Aarhus University, Denmark (grant to A.D.B.). S.D.R. and J.D.B. were supported by NIH grants MH097849 (to J.D.B.) and MH111661 (to J.D.B.), and by the Seaver Foundation (to S.D.R. and J.D.B.). J. Martine was supported by the Wellcome Trust (grant 106047). O.A.A. received funding from the Research Council of Norway (213694, 223273, 248980, and 248778), Stiftelsen KG Jebsen, and South-East Norway Health Authority. We thank the research participants and employees of 23andMe for making this work possible.
H.S., K.S., S. Steinberg, and G.B.W. are employees of deCODE genetics/Amgen. The 23andMe Research Team members are employed by 23andMe. D.H.G. is a scientific advisor for Ovid Therapeutic, Falcon Computing, and Axial Biotherapeutics. T.W. has acted as scientific advisor and lecturer for H. Lundbeck A/S.
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Grove, J., Ripke, S., Als, T.D. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet 51, 431–444 (2019). https://doi.org/10.1038/s41588-019-0344-8
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