Mutational Landscape of Autism Spectrum Disorder Brain Tissue
Abstract
:1. Introduction
2. Methods
3. Results
3.1. Post-Zygotic Mutations
3.2. Germline Mutations
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Woodbury-Smith, M.; Scherer, S.W. Progress in the genetics of autism spectrum disorder. Dev. Med. Child Neurol. 2018, 60, 445–451. [Google Scholar] [CrossRef] [PubMed]
- Yuen, R.K.C.; Merico, D.; Bookman, M.; Howe, J.L.; Thiruvahindrapuram, B.; Patel, R.V.; Whitney, J.; Deflaux, N.; Bingham, J.; Wang, Z.; et al. Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat. Neurosci. 2017, 20, 602–611. [Google Scholar] [CrossRef] [PubMed]
- Zarrei, M.; Burton, C.L.; Engchuan, W.; Young, E.J.; Higginbotham, E.J.; MacDonald, J.R.; Trost, B.; Chan, A.J.S.; Walker, S.; Lamoureux, S.; et al. A large data resource of genomic copy number variation across neurodevelopmental disorders. NPJ Genom. Med. 2019, 4, 26. [Google Scholar] [CrossRef] [Green Version]
- Trost, B.; Engchuan, W.; Nguyen, C.M.; Thiruvahindrapuram, B.; Dolzhenko, E.; Backstrom, I.; Mirceta, M.; Mojarad, B.A.; Yin, Y.; Dov, A.; et al. Genome-wide detection of tandem DNA repeats that are expanded in autism. Nature 2020, 586, 80–86. [Google Scholar] [CrossRef]
- 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 2017, 8, 21. [Google Scholar] [CrossRef]
- Saskin, A.; Fulginiti, V.; Birch, A.H.; Trakadis, Y. Prevalence of four Mendelian disorders associated with autism in 2392 affected families. J. Hum. Genet. 2017, 62, 657–659. [Google Scholar] [CrossRef]
- Sanders, S.J.; He, X.; Willsey, A.J.; Ercan-Sencicek, A.G.; Samocha, K.E.; Cicek, A.E.; Murtha, M.T.; Bal, V.H.; Bishop, S.L.; Dong, S.; et al. Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci. Neuron 2015, 87, 1215–1233. [Google Scholar] [CrossRef] [Green Version]
- Dou, Y.; Yang, X.; Li, Z.; Wang, S.; Zhang, Z.; Ye, A.Y.; Yan, L.; Yang, C.; Wu, Q.; Li, J.; et al. Postzygotic single-nucleotide mosaicisms contribute to the etiology of autism spectrum disorder and autistic traits and the origin of mutations. Hum. Mutat. 2017, 38, 1002–1013. [Google Scholar] [CrossRef] [Green Version]
- Lim, E.T.; Uddin, M.; De Rubeis, S.; Chan, Y.; Kamumbu, A.S.; Zhang, X.; D’Gama, A.M.; Kim, S.N.; Hill, R.S.; Goldberg, A.P.; et al. Rates, distribution and implications of postzygotic mosaic mutations in autism spectrum disorder. Nat. Neurosci. 2017, 20, 1217–1224. [Google Scholar] [CrossRef] [Green Version]
- Keogh, M.J.; Wei, W.; Wilson, I.; Coxhead, J.; Ryan, S.; Rollinson, S.; Griffin, H.; Kurzawa-Akanbi, M.; Santibanez-Koref, M.; Talbot, K.; et al. Genetic compendium of 1511 human brains available through the UK Medical Research Council Brain Banks Network Resource. Genome Res. 2017, 27, 165–173. [Google Scholar] [CrossRef] [Green Version]
- D’Gama, A.M.; Pochareddy, S.; Li, M.; Jamuar, S.S.; Reiff, R.E.; Lam, A.N.; Sestan, N.; Walsh, C.A. Targeted DNA Sequencing from Autism Spectrum Disorder Brains Implicates Multiple Genetic Mechanisms. Neuron 2015, 88, 910–917. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Alonso-Gonzalez, A.; Calaza, M.; Amigo, J.; Gonzalez-Penas, J.; Martinez-Regueiro, R.; Fernandez-Prieto, M.; Parellada, M.; Arango, C.; Rodriguez-Fontenla, C.; Carracedo, A.; et al. Exploring the biological role of postzygotic and germinal de novo mutations in ASD. Sci. Rep. 2021, 11, 319. [Google Scholar] [CrossRef] [PubMed]
- Stosser, M.B.; Lindy, A.S.; Butler, E.; Retterer, K.; Piccirillo-Stosser, C.M.; Richard, G.; McKnight, D.A. High frequency of mosaic pathogenic variants in genes causing epilepsy-related neurodevelopmental disorders. Genet. Med. 2018, 20, 403–410. [Google Scholar] [CrossRef] [PubMed]
- Rodin, R.E.; Dou, Y.; Kwon, M.; Sherman, M.A.; D’Gama, A.M.; Doan, R.N.; Rento, L.M.; Girskis, K.M.; Bohrson, C.L.; Kim, S.N.; et al. The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing. Nat. Neurosci. 2021, 24, 176–185. [Google Scholar] [CrossRef]
- Wintle, R.F.; Lionel, A.C.; Hu, P.; Ginsberg, S.D.; Pinto, D.; Thiruvahindrapduram, B.; Wei, J.; Marshall, C.R.; Pickett, J.; Cook, E.H.; et al. A genotype resource for postmortem brain samples from the Autism Tissue Program. Autism Res. 2011, 4, 89–97. [Google Scholar] [CrossRef] [Green Version]
- Uddin, M.; Woodbury-Smith, M.; Chan, A.; Brunga, L.; Lamoureux, S.; Pellecchia, G.; Yuen, R.K.C.; Faheem, M.; Stavropoulos, D.J.; Drake, J.; et al. Germline and somatic mutations in STXBP1 with diverse neurodevelopmental phenotypes. Neurol. Genet. 2017, 3, e199. [Google Scholar] [CrossRef] [Green Version]
- Lionel, A.C.; Costain, G.; Monfared, N.; Walker, S.; Reuter, M.S.; Hosseini, S.M.; Thiruvahindrapuram, B.; Merico, D.; Jobling, R.; Nalpathamkalam, T.; et al. Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test. Genet. Med. 2018, 20, 435–443. [Google Scholar] [CrossRef] [Green Version]
- Li, H.; Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009, 25, 1754–1760. [Google Scholar] [CrossRef] [Green Version]
- Wang, K.; Li, M.; Hakonarson, H. ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010, 38, e164. [Google Scholar] [CrossRef]
- Cibulskis, K.; Lawrence, M.S.; Carter, S.L.; Sivachenko, A.; Jaffe, D.; Sougnez, C.; Gabriel, S.; Meyerson, M.; Lander, E.S.; Getz, G.; et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 2013, 31, 213–219. [Google Scholar] [CrossRef]
- Adzhubei, I.A.; Schmidt, S.; Peshkin, L.; Ramensky, V.E.; Gerasimova, A.; Bork, P.; Kondrashov, A.S.; Sunyaev, S.R. A method and server for predicting damaging missense mutations. Nat. Methods 2010, 7, 248–249. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Vaser, R.; Adusumalli, S.; Leng, S.N.; Sikic, M.; Ng, P.C. SIFT missense predictions for genomes. Nat. Protoc. 2016, 11, 1–9. [Google Scholar] [CrossRef] [PubMed]
- Rentzsch, P.; Witten, D.; Cooper, G.M.; Shendure, J.; Kircher, M. CADD: Predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019, 47, D886–D894. [Google Scholar] [CrossRef] [PubMed]
- Raudvere, U.; Kolberg, L.; Kuzmin, I.; Arak, T.; Adler, P.; Peterson, H.; Vilo, J. g:Profiler: A web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019, 47, W191–W198. [Google Scholar] [CrossRef] [Green Version]
- Mi, H.; Muruganujan, A.; Huang, X.; Ebert, D.; Mills, C.; Guo, X.; Thomas, P.D. Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0). Nat. Protoc. 2019, 14, 703–721. [Google Scholar] [CrossRef]
- Richards, S.; Aziz, N.; Bale, S.; Bick, D.; Das, S.; Gastier-Foster, J.; Grody, W.W.; Hegde, M.; Lyon, E.; Spector, E.; et al. Standards and guidelines for the interpretation of sequence variants: A joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 2015, 17, 405–424. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schaaf, C.P.; Betancur, C.; Yuen, R.K.C.; Parr, J.R.; Skuse, D.H.; Gallagher, L.; Bernier, R.A.; Buchanan, J.A.; Buxbaum, J.D.; Chen, C.A.; et al. A framework for an evidence-based gene list relevant to autism spectrum disorder. Nat. Rev. Genet. 2020, 21, 367–376. [Google Scholar] [CrossRef]
- Chandran, J.S.; Kazanis, I.; Clapcote, S.J.; Ogawa, F.; Millar, J.K.; Porteous, D.J.; Ffrench-Constant, C. Disc1 variation leads to specific alterations in adult neurogenesis. PLoS ONE 2014, 9, e108088. [Google Scholar]
- Ogawa, F.; Malavasi, E.L.; Crummie, D.K.; Eykelenboom, J.E.; Soares, D.C.; Mackie, S.; Porteous, D.J.; Millar, J.K. DISC1 complexes with TRAK1 and Miro1 to modulate anterograde axonal mitochondrial trafficking. Hum. Mol. Genet. 2014, 23, 906–919. [Google Scholar] [CrossRef] [Green Version]
- Um, J.W.; Pramanik, G.; Ko, J.S.; Song, M.Y.; Lee, D.; Kim, H.; Park, K.S.; Sudhof, T.C.; Tabuchi, K.; Ko, J. Calsyntenins function as synaptogenic adhesion molecules in concert with neurexins. Cell Rep. 2014, 6, 1096–1109. [Google Scholar] [CrossRef] [Green Version]
- Busch, R.M.; Srivastava, S.; Hogue, O.; Frazier, T.W.; Klaas, P.; Hardan, A.; Martinez-Agosto, J.A.; Sahin, M.; Eng, C.; Developmental Synaptopathies Consortium. Neurobehavioral phenotype of autism spectrum disorder associated with germline heterozygous mutations in PTEN. Transl. Psychiatry 2019, 9, 253. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Harkin, L.A.; McMahon, J.M.; Iona, X.; Dibbens, L.; Pelekanos, J.T.; Zuberi, S.M.; Sadleir, L.G.; Andermann, E.; Gill, D.; Farrell, K.; et al. The spectrum of SCN1A-related infantile epileptic encephalopathies. Brain 2007, 130, 843–852. [Google Scholar] [CrossRef] [PubMed]
- Scheffer, I.E.; Nabbout, R. SCN1A-related phenotypes: Epilepsy and beyond. Epilepsia 2019, 60 (Suppl. S3), S17–S24. [Google Scholar] [CrossRef] [PubMed]
- Hawi, Z.; Tong, J.; Dark, C.; Yates, H.; Johnson, B.; Bellgrove, M.A. The role of cadherin genes in five major psychiatric disorders: A literature update. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2018, 177, 168–180. [Google Scholar] [CrossRef] [Green Version]
- Moon, A.L.; Haan, N.; Wilkinson, L.S.; Thomas, K.L.; Hall, J. CACNA1C: Association with Psychiatric Disorders, Behavior, and Neurogenesis. Schizophr. Bull. 2018, 44, 958–965. [Google Scholar] [CrossRef]
- Sykes, L.; Haddon, J.; Lancaster, T.M.; Sykes, A.; Azzouni, K.; Ihssen, N.; Moon, A.L.; Lin, T.E.; Linden, D.E.; Owen, M.J.; et al. Genetic Variation in the Psychiatric Risk Gene CACNA1C Modulates Reversal Learning Across Species. Schizophr. Bull. 2019, 45, 1024–1032. [Google Scholar] [CrossRef]
- Li, J.; Zhao, L.; You, Y.; Lu, T.; Jia, M.; Yu, H.; Ruan, Y.; Yue, W.; Liu, J.; Lu, L.; et al. Schizophrenia Related Variants in CACNA1C also Confer Risk of Autism. PLoS ONE 2015, 10, e0133247. [Google Scholar] [CrossRef]
- Malavasi, E.L.V.; Economides, K.D.; Grunewald, E.; Makedonopoulou, P.; Gautier, P.; Mackie, S.; Murphy, L.C.; Murdoch, H.; Crummie, D.; Ogawa, F.; et al. DISC1 regulates N-methyl-D-aspartate receptor dynamics: Abnormalities induced by a Disc1 mutation modelling a translocation linked to major mental illness. Transl. Psychiatry 2018, 8, 184. [Google Scholar] [CrossRef] [Green Version]
- Xu, B.; Roos, J.L.; Dexheimer, P.; Boone, B.; Plummer, B.; Levy, S.; Gogos, J.A.; Karayiorgou, M. Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat. Genet. 2011, 43, 864–868. [Google Scholar] [CrossRef] [Green Version]
- Vissers, L.E.; Gilissen, C.; Veltman, J.A. Genetic studies in intellectual disability and related disorders. Nat. Rev. Genet. 2016, 17, 9–18. [Google Scholar] [CrossRef]
- Pettem, K.L.; Yokomaku, D.; Luo, L.; Linhoff, M.W.; Prasad, T.; Connor, S.A.; Siddiqui, T.J.; Kawabe, H.; Chen, F.; Zhang, L.; et al. The specific α-neurexin interactor calsyntenin-3 promotes excitatory and inhibitory synapse development. Neuron 2013, 80, 113–128. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Guo, H.; Duyzend, M.H.; Coe, B.P.; Baker, C.; Hoekzema, K.; Gerdts, J.; Turner, T.N.; Zody, M.C.; Beighley, J.S.; Murali, S.C.; et al. Genome sequencing identifies multiple deleterious variants in autism patients with more severe phenotypes. Genet. Med. 2019, 21, 1611–1620. [Google Scholar] [CrossRef] [PubMed]
- Lipina, T.V.; Prasad, T.; Yokomaku, D.; Luo, L.; Connor, S.A.; Kawabe, H.; Wang, Y.T.; Brose, N.; Roder, J.C.; Craig, A.M. Cognitive Deficits in Calsyntenin-2-deficient Mice Associated with Reduced GABAergic Transmission. Neuropsychopharmacology 2016, 41, 802–810. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tan, M.H.; Mester, J.; Peterson, C.; Yang, Y.; Chen, J.L.; Rybicki, L.A.; Milas, K.; Pederson, H.; Remzi, B.; Orloff, M.S.; et al. A clinical scoring system for selection of patients for PTEN mutation testing is proposed on the basis of a prospective study of 3042 probands. Am. J. Hum. Genet. 2011, 88, 42–56. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ouss, L.; Leunen, D.; Laschet, J.; Chemaly, N.; Barcia, G.; Losito, E.M.; Aouidad, A.; Barrault, Z.; Desguerre, I.; Breuillard, D.; et al. Autism spectrum disorder and cognitive profile in children with Dravet syndrome: Delineation of a specific phenotype. Epilepsia Open 2019, 4, 40–53. [Google Scholar] [CrossRef] [Green Version]
- Shinnick-Gallagher, P.; McKernan, M.G.; Xie, J.; Zinebi, F. L-type voltage-gated calcium channels are involved in the in vivo and in vitro expression of fear conditioning. Ann. N. Y. Acad. Sci. 2003, 985, 135–149. [Google Scholar] [CrossRef]
Sample | SNV | CNV | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Type | LocusID | Ref | Alt | GnomAD 1 | Gene | Classification | Band | Pos | Size | Type | Genes | |
AN00090 | - | - | - | - | - | - | - | 15q11.1-15q13.1 | 21,192,955–26,500,067 | 5307.10 | Loss | Many |
AN01093 | - | - | - | - | - | - | - | 7q34 | 142,538,076–142,561,946 | 23.9 | Loss | PIP |
AN01570 | Germline | chr7:1510817:1510817 | C | T | 8.4 × 10−6 | INTS1 | Pathogenic | - | - | - | - | - |
Germline | chr10:89685300:89685300 | C | A | NO | PTEN | Pathogenic | ||||||
AN03345 | Germline | chr6:43008300:43008300 | G | C | NO | CUL7 | Pathogenic | - | - | - | - | - |
AN03935 | Germline | chr12:23687394:23687394 | G | T | NO | SOX5 | Pathogenic | 15q11.1-q13.2 | 18,278,739–28,280,653 | 10,001.90 | Gain | Many |
AN06420 | Germline | chr13:23904448:23904448 | C | T | NO | SACS | Likely pathogenic | 6q26 | 162,863,051–162,917,072 | 54 | Loss | PARK2 |
Germline | chr6:146708134:146708134 | C | T | NO | GRM1 | Pathogenic | ||||||
AN00764 | Germline | chr8:133187845:133187845 | G | A | NO | KCNQ3 | Pathogenic | 19p13.42 | 60,601,790–60,943,899 | 342.1 | Gain | Many |
AN08043 | Germline | chr16:83711952:83711952 | C | T | NO | CDH13 | Pathogenic | - | - | - | - | - |
AN08166 | Germline | chr2:8871659:8871659 | A | G | 4.9 × 10−5 | KIDINS220 | Likely pathogenic | - | - | - | - | - |
AN08873 | PZM | chr12:7310284:7310284 | G | A | NO | CLSTN3 | Likely pathogenic | - | - | - | - | |
AN09402 | Germline | chr3:158383151:158383151 | G | A | 8.1 × 10−6 | GFM1 | Likely pathogenic | 15q11.1-q13.1 | 18,276,341–26,752,537 | 8476.20 | Gain | Many |
20p12.1 | 15,760,493–15,769,465 | 9 | Loss | MACROD2 | ||||||||
AN10949 | - | - | - | - | - | - | - | 8q22.1 | 95,265,603–95,304,988 | 39.4 | Gain | CDH17 |
AN13872 | - | - | - | - | - | - | - | 16p13.2 | 6,992,775–7,021,963 | 29.2 | Loss | A2BP1 |
AN14613 | - | - | - | - | - | - | - | 3p14.2 | 60,464,015–60,502,990 | 39 | Loss | FHIT |
AN14762 | - | - | - | - | - | - | - | 2p16.3 | 51,075,080–51,087,539 | 12.5 | Loss | NRXN1 |
AN14829 | Germline | chr19:42857121:42857121 | C | T | 8.2 × 10−6 | MEGF8 | Likely pathogenic | Xp22.33 | 2,281,299–2,493,943 | 212.6 | Gain | ZBED1, DHRSX |
15q11.1-q13.2 | 18,276,341–28,289,587 | 10,013.20 | Gain | Many | ||||||||
AN16115 | Germline | chr2:166856252:166856252 | G | A | NO | SCN1A | Pathogenic | - | - | - | - | - |
AN16641 | Germline | chr12:2717736:2717736 | G | A | NO | CACNA1C | Pathogenic | - | - | - | - | - |
AN17138 | - | - | - | - | - | - | - | 7p21.1 | 16,315,368–16,370,511 | 55.1 | Loss | hCG_1745121/ISPD |
15q11.2 | 20,302,458–21,937,715 | 1635.30 | Gain | Many | ||||||||
15q11.2 | 21,985,041–22,943,182 | 958.1 | Gain | Many | ||||||||
15q11.2-q12 | 22,989,278–23,915,837 | 926.6 | Gain | Many | ||||||||
15q12-q13.1 | 23,925,463–26,500,067 | 2574.60 | Gain | Many | ||||||||
AN19511 | - | - | - | - | - | - | - | 4p15.31 | 22,351,359–22,429,602 | 78.2 | Loss | GBA3 |
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Woodbury-Smith, M.; Lamoureux, S.; Begum, G.; Nassir, N.; Akter, H.; O’Rielly, D.D.; Rahman, P.; Wintle, R.F.; Scherer, S.W.; Uddin, M. Mutational Landscape of Autism Spectrum Disorder Brain Tissue. Genes 2022, 13, 207. https://doi.org/10.3390/genes13020207
Woodbury-Smith M, Lamoureux S, Begum G, Nassir N, Akter H, O’Rielly DD, Rahman P, Wintle RF, Scherer SW, Uddin M. Mutational Landscape of Autism Spectrum Disorder Brain Tissue. Genes. 2022; 13(2):207. https://doi.org/10.3390/genes13020207
Chicago/Turabian StyleWoodbury-Smith, Marc, Sylvia Lamoureux, Ghausia Begum, Nasna Nassir, Hosneara Akter, Darren D. O’Rielly, Proton Rahman, Richard F. Wintle, Stephen W. Scherer, and Mohammed Uddin. 2022. "Mutational Landscape of Autism Spectrum Disorder Brain Tissue" Genes 13, no. 2: 207. https://doi.org/10.3390/genes13020207
APA StyleWoodbury-Smith, M., Lamoureux, S., Begum, G., Nassir, N., Akter, H., O’Rielly, D. D., Rahman, P., Wintle, R. F., Scherer, S. W., & Uddin, M. (2022). Mutational Landscape of Autism Spectrum Disorder Brain Tissue. Genes, 13(2), 207. https://doi.org/10.3390/genes13020207