75 results on '"Constantino JN"'
Search Results
2. The female protective effect in autism spectrum disorder is not mediated by a single genetic locus
- Author
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Sanders, Stephan, Gockley, J, Willsey, AJ, Dong, S, Dougherty, JD, Constantino, JN, and Sanders, SJ
- Abstract
© 2015 Gockley et al.; licensee BioMed Central.Background: A 4:1 male to female sex bias has consistently been observed in autism spectrum disorder (ASD). Epidemiological and genetic studies suggest a female protective effect (FPE) may account for part of
- Published
- 2015
3. Autism in neurofibromatosis type 1: misuse of covariance to dismiss autistic trait burden
- Author
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Morris, SM, Acosta, MT, Garg, S, Green, J, Legius, E, North, K, Payne, JM, Weiss, LA, Constantino, JN, Gutmann, DH, Morris, SM, Acosta, MT, Garg, S, Green, J, Legius, E, North, K, Payne, JM, Weiss, LA, Constantino, JN, and Gutmann, DH
- Published
- 2021
4. AMPA receptor GluA2 subunit defects are a cause of neurodevelopmental disorders
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Salpietro, V, Dixon, CL, Guo, H, Bello, OD, Vandrovcova, J, Efthymiou, S, Maroofian, R, Heimer, G, Burglen, L, Valence, S, Torti, E, Hacke, M, Rankin, J, Tariq, H, Colin, E, Procaccio, V, Striano, P, Mankad, K, Lieb, A, Chen, S, Pisani, L, Bettencourt, C, Mannikko, R, Manole, A, Brusco, A, Grosso, E, Ferrero, GB, Armstrong-Moron, J, Gueden, S, Bar-Yosef, O, Tzadok, M, Monaghan, KG, Santiago-Sim, T, Person, RE, Cho, MT, Willaert, R, Yoo, Y, Chae, J-H, Quan, Y, Wu, H, Wang, T, Bernier, RA, Xia, K, Blesson, A, Jain, M, Motazacker, MM, Jaeger, B, Schneider, AL, Boysen, K, Muir, AM, Myers, CT, Gavrilova, RH, Gunderson, L, Schultz-Rogers, L, Klee, EW, Dyment, D, Osmond, M, Parellada, M, Llorente, C, Gonzalez-Penas, J, Carracedo, A, Van Haeringen, A, Ruivenkamp, C, Nava, C, Heron, D, Nardello, R, Iacomino, M, Minetti, C, Skabar, A, Fabretto, A, Chez, M, Tsai, A, Fassi, E, Shinawi, M, Constantino, JN, De Zorzi, R, Fortuna, S, Kok, F, Keren, B, Bonneau, D, Choi, M, Benzeev, B, Zara, F, Mefford, HC, Scheffer, IE, Clayton-Smith, J, Macaya, A, Rothman, JE, Eichler, EE, Kullmann, DM, Houlden, H, Raspall-Chaure, M, Hanna, MG, Bugiardini, E, Hostettler, I, O'Callaghan, B, Khan, A, Cortese, A, O'Connor, E, Yau, WY, Bourinaris, T, Kaiyrzhanov, R, Chelban, V, Madej, M, Diana, MC, Vari, MS, Pedemonte, M, Bruno, C, Balagura, G, Scala, M, Fiorillo, C, Nobili, L, Malintan, NT, Zanetti, MN, Krishnakumar, SS, Lignani, G, Jepson, JEC, Broda, P, Baldassari, S, Rossi, P, Fruscione, F, Madia, F, Traverso, M, De-Marco, P, Perez-Duenas, B, Munell, F, Kriouile, Y, El-Khorassani, M, Karashova, B, Avdjieva, D, Kathom, H, Tincheva, R, Van-Maldergem, L, Nachbauer, W, Boesch, S, Gagliano, A, Amadori, E, Goraya, JS, Sultan, T, Kirmani, S, Ibrahim, S, Jan, F, Mine, J, Banu, S, Veggiotti, P, Zuccotti, G, Ferrari, MD, Van Den Maagdenberg, AMJ, Verrotti, A, Marseglia, GL, Savasta, S, Soler, MA, Scuderi, C, Borgione, E, Chimenz, R, Gitto, E, Dipasquale, V, Sallemi, A, Fusco, M, Cuppari, C, Cutrupi, MC, Ruggieri, M, Cama, A, Capra, V, Mencacci, NE, Boles, R, Gupta, N, Kabra, M, Papacostas, S, Zamba-Papanicolaou, E, Dardiotis, E, Maqbool, S, Rana, N, Atawneh, O, Lim, SY, Shaikh, F, Koutsis, G, Breza, M, Coviello, DA, Dauvilliers, YA, AlKhawaja, I, AlKhawaja, M, Al-Mutairi, F, Stojkovic, T, Ferrucci, V, Zollo, M, Alkuraya, FS, Kinali, M, Sherifa, H, Benrhouma, H, Turki, IBY, Tazir, M, Obeid, M, Bakhtadze, S, Saadi, NW, Zaki, MS, Triki, CC, Benfenati, F, Gustincich, S, Kara, M, Belcastro, V, Specchio, N, Capovilla, G, Karimiani, EG, Salih, AM, Okubadejo, NU, Ojo, OO, Oshinaike, OO, Oguntunde, O, Wahab, K, Bello, AH, Abubakar, S, Obiabo, Y, Nwazor, E, Ekenze, O, Williams, U, Iyagba, A, Taiwo, L, Komolafe, M, Senkevich, K, Shashkin, C, Zharkynbekova, N, Koneyev, K, Manizha, G, Isrofilov, M, Guliyeva, U, Salayev, K, Khachatryan, S, Rossi, S, Silvestri, G, Haridy, N, Ramenghi, LA, Xiromerisiou, G, David, E, Aguennouz, M, Fidani, L, Spanaki, C, Tucci, A, Salpietro, V, Dixon, CL, Guo, H, Bello, OD, Vandrovcova, J, Efthymiou, S, Maroofian, R, Heimer, G, Burglen, L, Valence, S, Torti, E, Hacke, M, Rankin, J, Tariq, H, Colin, E, Procaccio, V, Striano, P, Mankad, K, Lieb, A, Chen, S, Pisani, L, Bettencourt, C, Mannikko, R, Manole, A, Brusco, A, Grosso, E, Ferrero, GB, Armstrong-Moron, J, Gueden, S, Bar-Yosef, O, Tzadok, M, Monaghan, KG, Santiago-Sim, T, Person, RE, Cho, MT, Willaert, R, Yoo, Y, Chae, J-H, Quan, Y, Wu, H, Wang, T, Bernier, RA, Xia, K, Blesson, A, Jain, M, Motazacker, MM, Jaeger, B, Schneider, AL, Boysen, K, Muir, AM, Myers, CT, Gavrilova, RH, Gunderson, L, Schultz-Rogers, L, Klee, EW, Dyment, D, Osmond, M, Parellada, M, Llorente, C, Gonzalez-Penas, J, Carracedo, A, Van Haeringen, A, Ruivenkamp, C, Nava, C, Heron, D, Nardello, R, Iacomino, M, Minetti, C, Skabar, A, Fabretto, A, Chez, M, Tsai, A, Fassi, E, Shinawi, M, Constantino, JN, De Zorzi, R, Fortuna, S, Kok, F, Keren, B, Bonneau, D, Choi, M, Benzeev, B, Zara, F, Mefford, HC, Scheffer, IE, Clayton-Smith, J, Macaya, A, Rothman, JE, Eichler, EE, Kullmann, DM, Houlden, H, Raspall-Chaure, M, Hanna, MG, Bugiardini, E, Hostettler, I, O'Callaghan, B, Khan, A, Cortese, A, O'Connor, E, Yau, WY, Bourinaris, T, Kaiyrzhanov, R, Chelban, V, Madej, M, Diana, MC, Vari, MS, Pedemonte, M, Bruno, C, Balagura, G, Scala, M, Fiorillo, C, Nobili, L, Malintan, NT, Zanetti, MN, Krishnakumar, SS, Lignani, G, Jepson, JEC, Broda, P, Baldassari, S, Rossi, P, Fruscione, F, Madia, F, Traverso, M, De-Marco, P, Perez-Duenas, B, Munell, F, Kriouile, Y, El-Khorassani, M, Karashova, B, Avdjieva, D, Kathom, H, Tincheva, R, Van-Maldergem, L, Nachbauer, W, Boesch, S, Gagliano, A, Amadori, E, Goraya, JS, Sultan, T, Kirmani, S, Ibrahim, S, Jan, F, Mine, J, Banu, S, Veggiotti, P, Zuccotti, G, Ferrari, MD, Van Den Maagdenberg, AMJ, Verrotti, A, Marseglia, GL, Savasta, S, Soler, MA, Scuderi, C, Borgione, E, Chimenz, R, Gitto, E, Dipasquale, V, Sallemi, A, Fusco, M, Cuppari, C, Cutrupi, MC, Ruggieri, M, Cama, A, Capra, V, Mencacci, NE, Boles, R, Gupta, N, Kabra, M, Papacostas, S, Zamba-Papanicolaou, E, Dardiotis, E, Maqbool, S, Rana, N, Atawneh, O, Lim, SY, Shaikh, F, Koutsis, G, Breza, M, Coviello, DA, Dauvilliers, YA, AlKhawaja, I, AlKhawaja, M, Al-Mutairi, F, Stojkovic, T, Ferrucci, V, Zollo, M, Alkuraya, FS, Kinali, M, Sherifa, H, Benrhouma, H, Turki, IBY, Tazir, M, Obeid, M, Bakhtadze, S, Saadi, NW, Zaki, MS, Triki, CC, Benfenati, F, Gustincich, S, Kara, M, Belcastro, V, Specchio, N, Capovilla, G, Karimiani, EG, Salih, AM, Okubadejo, NU, Ojo, OO, Oshinaike, OO, Oguntunde, O, Wahab, K, Bello, AH, Abubakar, S, Obiabo, Y, Nwazor, E, Ekenze, O, Williams, U, Iyagba, A, Taiwo, L, Komolafe, M, Senkevich, K, Shashkin, C, Zharkynbekova, N, Koneyev, K, Manizha, G, Isrofilov, M, Guliyeva, U, Salayev, K, Khachatryan, S, Rossi, S, Silvestri, G, Haridy, N, Ramenghi, LA, Xiromerisiou, G, David, E, Aguennouz, M, Fidani, L, Spanaki, C, and Tucci, A
- Abstract
AMPA receptors (AMPARs) are tetrameric ligand-gated channels made up of combinations of GluA1-4 subunits encoded by GRIA1-4 genes. GluA2 has an especially important role because, following post-transcriptional editing at the Q607 site, it renders heteromultimeric AMPARs Ca2+-impermeable, with a linear relationship between current and trans-membrane voltage. Here, we report heterozygous de novo GRIA2 mutations in 28 unrelated patients with intellectual disability (ID) and neurodevelopmental abnormalities including autism spectrum disorder (ASD), Rett syndrome-like features, and seizures or developmental epileptic encephalopathy (DEE). In functional expression studies, mutations lead to a decrease in agonist-evoked current mediated by mutant subunits compared to wild-type channels. When GluA2 subunits are co-expressed with GluA1, most GRIA2 mutations cause a decreased current amplitude and some also affect voltage rectification. Our results show that de-novo variants in GRIA2 can cause neurodevelopmental disorders, complementing evidence that other genetic causes of ID, ASD and DEE also disrupt glutamatergic synaptic transmission.
- Published
- 2019
5. Mapping autism risk loci using genetic linkage and chromosomal rearrangements
- Author
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Szatmari, P, Paterson, AD, Zwaigenbaum, L, Roberts, W, Brian, J, Liu, XQ, Vincent, JB, Skaug, JL, Thompson, AP, Senman, L, Feuk, L, Qian, C, Bryson, SE, Jones, MB, Marshall, CR, Scherer, SW, Vieland, VJ, Bartlett, C, Mangin, LV, Goedken, R, Segre, A, Pericak-Vance, MA, Cuccaro, ML, Gilbert, JR, Wright, HH, Abramson, RK, Betancur, C, Bourgeron, T, Gillberg, C, Leboyer, M, Buxbaum, JD, Davis, KL, Hollander, E, Silverman, JM, Hallmayer, J, Lotspeich, L, Sutcliffe, JS, Haines, JL, Folstein, SE, Piven, J, Wassink, TH, Sheffield, V, Geschwind, DH, Bucan, M, Brown, WT, Cantor, RM, Constantino, JN, Gilliam, TC, Herbert, M, LaJonchere, C, Ledbetter, DH, Lese-Martin, C, Miller, J, Nelson, S, Samango-Sprouse, CA, Spence, S, State, M, Tanzi, RE, Coon, H, Dawson, G, Devlin, B, Estes, A, Flodman, P, Klei, L, McMahon, WM, Minshew, N, Munson, J, Korvatska, E, Rodier, PM, Schellenberg, GD, Smith, M, Spence, MA, Stodgell, C, Tepper, PG, Wijsman, EM, Yu, CE, Rogé, B, Mantoulan, C, Wittemeyer, K, Poustka, A, Felder, B, Klauck, SM, Schuster, C, Poustka, F, Bölte, S, Feineis-Matthews, S, Herbrecht, E, Schmötzer, G, Tsiantis, J, Papanikolaou, K, Maestrini, E, and Bacchelli, E
- Subjects
mental disorders - Abstract
Autism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,168 families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs. © 2007 Nature Publishing Group.
- Published
- 2007
6. AMPA receptor GluA2 subunit defects are a cause of neurodevelopmental disorders
- Author
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Vincenzo Salpietro1, 2 3, 140, Christine L. Dixon4, Hui Guo5, 6 140, Oscar D. Bello Stephanie Efthymiou 1, 4, Reza Maroofian1, Gali Heimer7, Lydie Burglen 8, Stephanie Valence 9, Erin Torti 10, Moritz Hacke11, Julia Rankin12, Huma Tariq1, Estelle Colin13, Vincent Procaccio13, Pasquale Striano2, 3, Kshitij Mankad15, Andreas Lieb 4, Sharon Chen16, Laura Pisani16, Conceicao Bettencourt 17, Roope Männikkö 1, Andreea Manole1, Alfredo Brusco 18, Enrico Grosso18, Giovanni Battista Ferrero19, Judith Armstrong-Moron20, Sophie Gueden21, Omer Bar-Yosef7, Michal Tzadok7, Kristin G. Monaghan10, Teresa Santiago-Sim10, Richard E. Person10, Megan T. Cho10, Rebecca Willaert10, Yongjin Yoo22, Jong-Hee Chae23, Yingting Quan6, Huidan Wu6, Tianyun Wang5, 6, Raphael A. Bernier24, Kun Xia6, Alyssa Blesson25, Mahim Jain25, Mohammad M. Motazacker26, Bregje Jaeger27, Amy L. Schneider 28, Katja Boysen28, Alison M. Muir 29, Candace T. Myers30, Ralitza H. Gavrilova31, Lauren Gunderson31, Laura Schultz-Rogers 31, Eric W. Klee31, David Dyment32, Matthew Osmond32, 33 34, Mara Parellada35, Cloe Llorente36, Javier Gonzalez-Peñas37, Angel Carracedo38, Arie Van Haeringen40, Claudia Ruivenkamp40, Caroline Nava41, Delphine Heron41, Rosaria Nardello42, Michele Iacomino43, Carlo Minetti2, Aldo Skabar44, Antonella Fabretto44, SYNAPS Study GroupMiquel Raspall-Chaure45, Michael Chez46, Anne Tsai47, Emily Fassi48, Marwan Shinawi48, John N. Constantino49, Rita De Zorzi50, Sara Fortuna 50, Fernando Kok51, Boris Keren41, Dominique Bonneau13, Murim Choi 22, Bruria Benzeev7, Federico Zara43, Heather C. Mefford29, Ingrid E. Scheffer28, Jill Clayton-Smith53, Alfons Macaya45, James E. Rothman4, Evan E. Eichler 5, Dimitri M. Kullmann 4, Henry Houlden 1, SYNAPS Study Group Michael G. Hanna1, Enrico Bugiardini1, Isabel Hostettler1, Benjamin O’Callaghan1, Alaa Khan1, Andrea Cortese1, Emer O’Connor1, Wai Y. Yau1, Thomas Bourinaris1, Rauan Kaiyrzhanov1, Viorica Chelban1, Monika Madej1, Maria C. Diana2, Maria S. Vari2, Marina Pedemonte2, Claudio Bruno2, Ganna Balagura3, Marcello Scala3, Chiara Fiorillo3, Lino Nobili3, Nancy T. Malintan4, Maria N. Zanetti4, Shyam S. Krishnakumar4, Gabriele Lignani4, James E. C. Jepson4, Paolo Broda43, Simona Baldassari43, Pia Rossi43, Floriana Fruscione43, Francesca Madia43, Monica Traverso43, Patrizia De-Marco43, Belen Pérez-Dueñas45, Francina Munell45, Yamna Kriouile57, Mohamed El-Khorassani57, Blagovesta Karashova58, Daniela Avdjieva58, Hadil Kathom58, Radka Tincheva58, Lionel Van-Maldergem59, Wolfgang Nachbauer60, Sylvia Boesch60, Antonella Gagliano61, Elisabetta Amadori62, Jatinder S. Goraya63, Tipu Sultan64, Salman Kirmani65, Shahnaz Ibrahim66, Farida Jan66, Jun Mine67, Selina Banu68, Pierangelo Veggiotti69, Gian V. Zuccotti69, Michel D. Ferrari70, Arn M. J. Van Den Maagdenberg70, Alberto Verrotti71, Gian L. Marseglia72, Salvatore Savasta72, Miguel A. Soler73, Carmela Scuderi74, Eugenia Borgione74, Roberto Chimenz75, Eloisa Gitto75, Valeria Dipasquale75, Alessia Sallemi75, Monica Fusco75, Caterina Cuppari75, Maria C. Cutrupi75, Martino Ruggieri76, Armando Cama77, Valeria Capra77, Niccolò E. Mencacci78, Richard Boles79, Neerja Gupta80, Madhulika Kabra80, Savvas Papacostas81, Eleni Zamba-Papanicolaou81, Efthymios Dardiotis82, Shazia Maqbool83, Nuzhat Rana84, Osama Atawneh85, Shen Y. Lim86, Farooq Shaikh87, George Koutsis88, Marianthi Breza88, Domenico A. Coviello89, Yves A. Dauvilliers90, Issam AlKhawaja91, Mariam AlKhawaja92, Fuad Al-Mutairi93, Tanya Stojkovic94, Veronica Ferrucci, Massimo Zollo, Fowzan S. Alkuraya96, Maria Kinali97, Hamed Sherifa98, Hanene Benrhouma99, Ilhem B. Y. Turki99, Meriem Tazir100, Makram Obeid101, Sophia Bakhtadze102, Nebal W. Saadi103, Maha S. Zaki104, Chahnez C. Triki105, Fabio Benfenati106, Stefano Gustincich106, Majdi Kara107, Vincenzo Belcastro108, Nicola Specchio109, Giuseppe Capovilla110, Ehsan G. Karimiani111, Ahmed M. Salih112, Njideka U. Okubadejo113, Oluwadamilola O. Ojo113, Olajumoke O. Oshinaike113, Olapeju Oguntunde113, Kolawole Wahab114, Abiodun H. Bello114, Sanni Abubakar115, Yahaya Obiabo116, Ernest Nwazor117, Oluchi Ekenze118, Uduak Williams119, Alagoma Iyagba120, Lolade Taiwo121, Morenikeji Komolafe122, Konstantin Senkevich123, Chingiz Shashkin124, Nazira Zharkynbekova125, Kairgali Koneyev126, Ganieva Manizha127, Maksud Isrofilov127, Ulviyya Guliyeva128, Kamran Salayev129, Samson Khachatryan130, Salvatore Rossi131, Gabriella Silvestri131, Nourelhoda Haridy132, Luca A. Ramenghi133, Georgia Xiromerisiou134, Emanuele David135, Mhammed Aguennouz136, Liana Fidani137, Cleanthe Spanaki138, Arianna Tucci139, University College of London [London] (UCL), Instituto Giannina Gaslini, Genoa, University of Genoa (UNIGE), University of Washington [Seattle], Institute of Neurology, Queen Square, London, King‘s College London, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, Queen Square, London, Molecular and Clinical Sciences Institute - St George’s [London, UK] (Genetics Research Centre), University of London [London], Tel Aviv University Sackler School of Medicine [Tel Aviv, Israël], Service de génétique et embryologie médicales [CHU Trousseau], CHU Trousseau [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Service de Neuropédiatrie [CHU Trousseau], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Trousseau [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), GeneDx [Gaithersburg, MD, USA], Heidelberg University Hospital [Heidelberg], Royal Devon and Exeter NHS Foundation Trust [UK], Biologie Neurovasculaire et Mitochondriale Intégrée (BNMI), Université d'Angers (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Universitaire d'Angers (CHU Angers), PRES Université Nantes Angers Le Mans (UNAM), Universita degli studi di Genova, Great Ormond Street Hospital for Children [London] (GOSH), The University of Sydney, Hofstra University [Hempstead], Università degli studi di Torino (UNITO), Hospital Sant Joan de Déu [Barcelona], Safra Children's Hospital, Seoul National University Hospital, Central South University [Changsha], Kennedy Krieger Institute [Baltimore], University of Amsterdam [Amsterdam] (UvA), University of Melbourne, Mayo Clinic [Rochester], Department of Health Sciences Research [Mayo Clinic] (HSR), Mayo Clinic, University of Ottawa [Ottawa], University of British Columbia (UBC), Universidad Complutense de Madrid = Complutense University of Madrid [Madrid] (UCM), Universidade de Santiago de Compostela [Spain] (USC ), Universiteit Leiden [Leiden], Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Università degli studi di Palermo - University of Palermo, University of Trieste, Universitat Autònoma de Barcelona (UAB), Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, University of California [Davis] (UC Davis), University of California-University of California, Children’s Hospital Colorado, University of Colorado Anschutz [Aurora], Washington University in Saint Louis (WUSTL), Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Baylor University-Baylor University, Department of Psychiatry, Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, University of Oxford [Oxford], University of São Paulo (USP), Service de Génétique Cytogénétique et Embryologie [CHU Pitié-Salpêtrière], Service de Pédiatrie, CHUR Poitiers, Seoul National University [Seoul] (SNU), Pediatric Neurology and Neuromuscular Diseases Unit, University of Manchester [Manchester], Yale University School of Medicine, Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Salvy-Córdoba, Nathalie, Università degli studi di Genova = University of Genoa (UniGe), Tel Aviv University (TAU), Università degli studi di Torino = University of Turin (UNITO), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Università degli studi di Trieste = University of Trieste, University of California (UC)-University of California (UC), University of Oxford, Universidade de São Paulo = University of São Paulo (USP), Yale School of Medicine [New Haven, Connecticut] (YSM), Salpietro V, Dixon CL, Guo H, Bello OD, Vandrovcova J, Efthymiou S, Maroofian R, Heimer G, Burglen L, Valence S, Torti E, Hacke M, Rankin J, Tariq H, Colin E, Procaccio V, Striano P, Mankad K, Lieb A, Chen S, Pisani L, Bettencourt C, Männikkö R, Manole A, Brusco A, Grosso E, Ferrero GB, Armstrong-Moron J, Gueden S, Bar-Yosef O, Tzadok M, Monaghan KG, Santiago-Sim T, Person RE, Cho MT, Willaert R, Yoo Y, Chae JH, Quan Y, Wu H, Wang T, Bernier RA, Xia K, Blesson A, Jain M, Motazacker MM, Jaeger B, Schneider AL, Boysen K, Muir AM, Myers CT, Gavrilova RH, Gunderson L, Schultz-Rogers L, Klee EW, Dyment D, Osmond M, Parellada M, Llorente C, Gonzalez-Peñas J, Carracedo A, Van Haeringen A, Ruivenkamp C, Nava C, Heron D, Nardello R, Iacomino M, Minetti C, Skabar A, Fabretto A, SYNAPS Study Group, Raspall-Chaure M, Chez M, Tsai A, Fassi E, Shinawi M, Constantino JN, De Zorzi R, Fortuna S, Kok F, Keren B, Bonneau D, Choi M, Benzeev B, Zara F, Mefford HC, Scheffer IE, Clayton-Smith J, Macaya A, Rothman JE, Eichler EE, Kullmann DM, Houlden H, Salpietro, Vincenzo, Dixon, Christine L, Guo, Hui, Bello, Oscar D, Vandrovcova, Jana, Efthymiou, Stephanie, Maroofian, Reza, Heimer, Gali, Burglen, Lydie, Valence, Stephanie, Torti, Erin, Hacke, Moritz, Rankin, Julia, Tariq, Huma, Colin, Estelle, Procaccio, Vincent, Striano, Pasquale, Mankad, Kshitij, Lieb, Andrea, Chen, Sharon, Pisani, Laura, Bettencourt, Conceicao, Männikkö, Roope, Manole, Andreea, Brusco, Alfredo, Grosso, Enrico, Ferrero, Giovanni Battista, Armstrong-Moron, Judith, Gueden, Sophie, Bar-Yosef, Omer, Tzadok, Michal, Monaghan, Kristin G, Santiago-Sim, Teresa, Person, Richard E, Cho, Megan T, Willaert, Rebecca, Yoo, Yongjin, Chae, Jong-Hee, Quan, Yingting, Wu, Huidan, Wang, Tianyun, Bernier, Raphael A, Xia, Kun, Blesson, Alyssa, Jain, Mahim, Motazacker, Mohammad M, Jaeger, Bregje, Schneider, Amy L, Boysen, Katja, Muir, Alison M, Myers, Candace T, Gavrilova, Ralitza H, Gunderson, Lauren, Schultz-Rogers, Laura, Klee, Eric W, Dyment, David, Osmond, Matthew, Parellada, Mara, Llorente, Cloe, Gonzalez-Peñas, Javier, Carracedo, Angel, Van Haeringen, Arie, Ruivenkamp, Claudia, Nava, Caroline, Heron, Delphine, Nardello, Rosaria, Iacomino, Michele, Minetti, Carlo, Skabar, Aldo, Fabretto, Antonella, Raspall-Chaure, Miquel, Chez, Michael, Tsai, Anne, Fassi, Emily, Shinawi, Marwan, Constantino, John N, De Zorzi, Rita, Fortuna, Sara, Kok, Fernando, Keren, Bori, Bonneau, Dominique, Choi, Murim, Benzeev, Bruria, Zara, Federico, Mefford, Heather C, Scheffer, Ingrid E, Clayton-Smith, Jill, Macaya, Alfon, Rothman, James E, Eichler, Evan E, Kullmann, Dimitri M, Houlden, Henry, Salpietro1, Vincenzo, 3, 2, Dixon4, Christine L., Guo5, Hui, 140, 6, Bello Stephanie Efthymiou 1, Oscar D., Maroofian1, Reza, Heimer7, Gali, 8, Lydie Burglen, 9, Stephanie Valence, Torti 10, Erin, Hacke11, Moritz, Rankin12, Julia, Tariq1, Huma, Colin13, Estelle, Procaccio13, Vincent, Striano2, Pasquale, Mankad15, Kshitij, 4, Andreas Lieb, Chen16, Sharon, Pisani16, Laura, Bettencourt 17, Conceicao, 1, Roope Männikkö, Manole1, Andreea, Brusco 18, Alfredo, Grosso18, Enrico, Battista Ferrero19, Giovanni, Armstrong-Moron20, Judith, Gueden21, Sophie, Bar-Yosef7, Omer, Tzadok7, Michal, Monaghan10, Kristin G., Santiago-Sim10, Teresa, Person10, Richard E., Cho10, Megan T., Willaert10, Rebecca, Yoo22, Yongjin, Chae23, Jong-Hee, Quan6, Yingting, Wu6, Huidan, Wang5, Tianyun, Bernier24, Raphael A., Xia6, Kun, Blesson25, Alyssa, Jain25, Mahim, Motazacker26, Mohammad M., Jaeger27, Bregje, Schneider 28, Amy L., Boysen28, Katja, Muir 29, Alison M., Myers30, Candace T., Gavrilova31, Ralitza H., Gunderson31, Lauren, Schultz-Rogers 31, Laura, Klee31, Eric W., Dyment32, David, Osmond32, Matthew, 34, 33, Parellada35, Mara, Llorente36, Cloe, Gonzalez-Peñas37, Javier, Carracedo38, Angel, Van Haeringen40, Arie, Ruivenkamp40, Claudia, Nava41, Caroline, Heron41, Delphine, Nardello42, Rosaria, Iacomino43, Michele, Minetti2, Carlo, Skabar44, Aldo, Fabretto44, Antonella, Study GroupMiquel Raspall-Chaure45, Synap, Chez46, Michael, Tsai47, Anne, Fassi48, Emily, Shinawi48, Marwan, Constantino49, John N., De Zorzi50, Rita, Fortuna 50, Sara, Kok51, Fernando, Keren41, Bori, Bonneau13, Dominique, Choi 22, Murim, Benzeev7, Bruria, Zara43, Federico, Mefford29, Heather C., Scheffer28, Ingrid E., Clayton-Smith53, Jill, Macaya45, Alfon, Rothman4, James E., Eichler 5, Evan E., Kullmann 4 &, Dimitri M., 1, Henry Houlden, Hanna1, SYNAPS Study Group Michael G., Bugiardini1, Enrico, Hostettler1, Isabel, O’Callaghan1, Benjamin, Khan1, Alaa, Cortese1, Andrea, O’Connor1, Emer, Yau1, Wai Y., Bourinaris1, Thoma, Kaiyrzhanov1, Rauan, Chelban1, Viorica, Madej1, Monika, Diana2, Maria C., Vari2, Maria S., Pedemonte2, Marina, Bruno2, Claudio, Balagura3, Ganna, Scala3, Marcello, Fiorillo3, Chiara, Nobili3, Lino, Malintan4, Nancy T., Zanetti4, Maria N., Krishnakumar4, Shyam S., Lignani4, Gabriele, Jepson4, James E. C., Broda43, Paolo, Baldassari43, Simona, Rossi43, Pia, Fruscione43, Floriana, Madia43, Francesca, Traverso43, Monica, De-Marco43, Patrizia, Pérez-Dueñas45, Belen, Munell45, Francina, Kriouile57, Yamna, El-Khorassani57, Mohamed, Karashova58, Blagovesta, Avdjieva58, Daniela, Kathom58, Hadil, Tincheva58, Radka, Van-Maldergem59, Lionel, Nachbauer60, Wolfgang, Boesch60, Sylvia, Gagliano61, Antonella, Amadori62, Elisabetta, Goraya63, Jatinder S., Sultan64, Tipu, Kirmani65, Salman, Ibrahim66, Shahnaz, Jan66, Farida, Mine67, Jun, Banu68, Selina, Veggiotti69, Pierangelo, Zuccotti69, Gian V., Ferrari70, Michel D., Van Den Maagdenberg70, Arn M. J., Verrotti71, Alberto, Marseglia72, Gian L., Savasta72, Salvatore, Soler73, Miguel A., Scuderi74, Carmela, Borgione74, Eugenia, Chimenz75, Roberto, Gitto75, Eloisa, Dipasquale75, Valeria, Sallemi75, Alessia, Fusco75, Monica, Cuppari75, Caterina, Cutrupi75, Maria C., Ruggieri76, Martino, Cama77, Armando, Capra77, Valeria, Mencacci78, Niccolò E., Boles79, Richard, Gupta80, Neerja, Kabra80, Madhulika, Papacostas81, Savva, Zamba-Papanicolaou81, Eleni, Dardiotis82, Efthymio, Maqbool83, Shazia, Rana84, Nuzhat, Atawneh85, Osama, Lim86, Shen Y., Shaikh87, Farooq, Koutsis88, George, Breza88, Marianthi, Coviello89, Domenico A., Dauvilliers90, Yves A., Alkhawaja91, Issam, Alkhawaja92, Mariam, Al-Mutairi93, Fuad, Stojkovic94, Tanya, Ferrucci, Veronica, Zollo, Massimo, Alkuraya96, Fowzan S., Kinali97, Maria, Sherifa98, Hamed, Benrhouma99, Hanene, Turki99, Ilhem B. Y., Tazir100, Meriem, Obeid101, Makram, Bakhtadze102, Sophia, Saadi103, Nebal W., Zaki104, Maha S., Triki105, Chahnez C., Benfenati106, Fabio, Gustincich106, Stefano, Kara107, Majdi, Belcastro108, Vincenzo, Specchio109, Nicola, Capovilla110, Giuseppe, Karimiani111, Ehsan G., Salih112, Ahmed M., Okubadejo113, Njideka U., Ojo113, Oluwadamilola O., Oshinaike113, Olajumoke O., Oguntunde113, Olapeju, Wahab114, Kolawole, Bello114, Abiodun H., Abubakar115, Sanni, Obiabo116, Yahaya, Nwazor117, Ernest, Ekenze118, Oluchi, Williams119, Uduak, Iyagba120, Alagoma, Taiwo121, Lolade, Komolafe122, Morenikeji, Senkevich123, Konstantin, Shashkin124, Chingiz, Zharkynbekova125, Nazira, Koneyev126, Kairgali, Manizha127, Ganieva, Isrofilov127, Maksud, Guliyeva128, Ulviyya, Salayev129, Kamran, Khachatryan130, Samson, Rossi131, Salvatore, Silvestri131, Gabriella, Haridy132, Nourelhoda, Ramenghi133, Luca A., Xiromerisiou134, Georgia, David135, Emanuele, Aguennouz136, Mhammed, Fidani137, Liana, Spanaki138 &, Cleanthe, and Tucci139, Arianna
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Male ,[SDV.GEN] Life Sciences [q-bio]/Genetics ,Ion channels in the nervous system ,Cohort Studies ,fluids and secretions ,Loss of Function Mutation ,Receptors ,AMPA ,AMPA receptor ,lcsh:Science ,Child ,reproductive and urinary physiology ,AMPA receptor, GluA2, neurodevelopmental disorders, autism spectrum disorder, glutamatergic synaptic transmission, GRIA2 ,neurodevelopmental disorders ,Developmental disorders ,Neurodevelopmental disorders ,Brain ,Magnetic Resonance Imaging ,Settore MED/26 - NEUROLOGIA ,GluA2 ,Child, Preschool ,Female ,Adult ,Heterozygote ,Adolescent ,Science ,autism spectrum disorder ,Article ,Young Adult ,[SDV.MHEP.PED] Life Sciences [q-bio]/Human health and pathology/Pediatrics ,MESH: Intellectual Disability/genetics ,Neurodevelopmental Disorders/genetics ,Receptors AMPA/genetics ,Intellectual Disability ,mental disorders ,Humans ,Infant ,Neurodevelopmental Disorders ,Receptors, AMPA ,GRIA2 ,Preschool ,Ion channel in the nervous system, Developmental disorders, Synaptic development, NG sequencing ,[SDV.GEN]Life Sciences [q-bio]/Genetics ,[SDV.MHEP.PED]Life Sciences [q-bio]/Human health and pathology/Pediatrics ,glutamatergic synaptic transmission ,[SCCO.NEUR]Cognitive science/Neuroscience ,[SCCO.NEUR] Cognitive science/Neuroscience ,NG sequencing ,Synaptic development ,Ion channel in the nervous system ,Next-generation sequencing ,lcsh:Q - Abstract
AMPA receptors (AMPARs) are tetrameric ligand-gated channels made up of combinations of GluA1-4 subunits encoded by GRIA1-4 genes. GluA2 has an especially important role because, following post-transcriptional editing at the Q607 site, it renders heteromultimeric AMPARs Ca2+-impermeable, with a linear relationship between current and trans-membrane voltage. Here, we report heterozygous de novo GRIA2 mutations in 28 unrelated patients with intellectual disability (ID) and neurodevelopmental abnormalities including autism spectrum disorder (ASD), Rett syndrome-like features, and seizures or developmental epileptic encephalopathy (DEE). In functional expression studies, mutations lead to a decrease in agonist-evoked current mediated by mutant subunits compared to wild-type channels. When GluA2 subunits are co-expressed with GluA1, most GRIA2 mutations cause a decreased current amplitude and some also affect voltage rectification. Our results show that de-novo variants in GRIA2 can cause neurodevelopmental disorders, complementing evidence that other genetic causes of ID, ASD and DEE also disrupt glutamatergic synaptic transmission., Genetic variants in ionotropic glutamate receptors have been implicated in neurodevelopmental disorders. Here, the authors report heterozygous de novo mutations in the GRIA2 gene in 28 individuals with intellectual disability and neurodevelopmental abnormalities associated with reduced Ca2+ transport and AMPAR currents.”
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- 2019
7. Mapping autism risk loci using genetic linkage and chromosomal rearrangements
- Author
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Peter, Szatmari, Andrew D, Paterson, Lonnie, Zwaigenbaum, Wendy, Roberts, Jessica, Brian, Xiao-Qing, Liu, John B, Vincent, Jennifer L, Skaug, Ann P, Thompson, Lili, Senman, Lars, Feuk, Cheng, Qian, Susan E, Bryson, Marshall B, Jones, Christian R, Marshall, Stephen W, Scherer, Veronica J, Vieland, Christopher, Bartlett, La Vonne, Mangin, Rhinda, Goedken, Alberto, Segre, Margaret A, Pericak-Vance, Michael L, Cuccaro, John R, Gilbert, Harry H, Wright, Ruth K, Abramson, Catalina, Betancur, Thomas, Bourgeron, Christopher, Gillberg, Marion, Leboyer, Joseph D, Buxbaum, Kenneth L, Davis, Eric, Hollander, Jeremy M, Silverman, Joachim, Hallmayer, Linda, Lotspeich, James S, Sutcliffe, Jonathan L, Haines, Susan E, Folstein, Joseph, Piven, Thomas H, Wassink, Val, Sheffield, Daniel H, Geschwind, Maja, Bucan, W Ted, Brown, Rita M, Cantor, John N, Constantino, T Conrad, Gilliam, Martha, Herbert, Clara, Lajonchere, David H, Ledbetter, Christa, Lese-Martin, Janet, Miller, Stan, Nelson, Carol A, Samango-Sprouse, Sarah, Spence, Matthew, State, Rudolph E, Tanzi, Hilary, Coon, Geraldine, Dawson, Bernie, Devlin, Annette, Estes, Pamela, Flodman, Lambertus, Klei, William M, McMahon, Nancy, Minshew, Jeff, Munson, Elena, Korvatska, Patricia M, Rodier, Gerard D, Schellenberg, Moyra, Smith, M Anne, Spence, Chris, Stodgell, Ping Guo, Tepper, Ellen M, Wijsman, Chang-En, Yu, Bernadette, Rogé, Carine, Mantoulan, Kerstin, Wittemeyer, Annemarie, Poustka, Bärbel, Felder, Sabine M, Klauck, Claudia, Schuster, Fritz, Poustka, Sven, Bölte, Sabine, Feineis-Matthews, Evelyn, Herbrecht, Gabi, Schmötzer, John, Tsiantis, Katerina, Papanikolaou, Elena, Maestrini, Elena, Bacchelli, Francesca, Blasi, Simona, Carone, Claudio, Toma, Herman, Van Engeland, Maretha, de Jonge, Chantal, Kemner, Frederieke, Koop, Frederike, Koop, Marjolein, Langemeijer, Marjolijn, Langemeijer, Channa, Hijmans, Channa, Hijimans, Wouter G, Staal, Gillian, Baird, Patrick F, Bolton, Michael L, Rutter, Emma, Weisblatt, Jonathan, Green, Catherine, Aldred, Julie-Anne, Wilkinson, Andrew, Pickles, Ann, Le Couteur, Tom, Berney, Helen, McConachie, Anthony J, Bailey, Kostas, Francis, Gemma, Honeyman, Aislinn, Hutchinson, Jeremy R, Parr, Simon, Wallace, Anthony P, Monaco, Gabrielle, Barnby, Kazuhiro, Kobayashi, Janine A, Lamb, Ines, Sousa, Nuala, Sykes, Edwin H, Cook, Stephen J, Guter, Bennett L, Leventhal, Jeff, Salt, Catherine, Lord, Christina, Corsello, Vanessa, Hus, Daniel E, Weeks, Fred, Volkmar, Maïté, Tauber, Eric, Fombonne, Andy, Shih, Kacie J, Meyer, Department of Psychiatry and Behavioural Neurosciences, McMaster University [Hamilton, Ontario]-Offord Centre for Child Studies, The Centre for Applied Genomics, Toronto, University of Toronto-The Hospital for sick children [Toronto] (SickKids)-Department of Molecular Genetics-McLaughlin Centre, Department of Pediatrics, University of Alberta, Autism Research Unit, The Hospital for sick children [Toronto] (SickKids)-University of Toronto, Department of Psychiatry, University of Toronto, Departments of Pediatrics and Psychology, Dalhousie University [Halifax], Department of Neural and Behavioral Sciences, Pennsylvania State University (Penn State), Penn State System-Penn State System, Department of Molecular Genetics [Toronto], Battelle Center for Mathematical Medicine, Ohio State University [Columbus] (OSU)-Nationwide Children's Hospital, Department of Pathology and Laboratory Medicine, University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC), Department of Computer Science, University of Iowa [Iowa City], John P. Hussman Institute for Human Genomics, University of Miami [Coral Gables], W.S. Hall Psychiatric Institute, University of South Carolina [Columbia], Physiopathologie des Maladies du Système Nerveux Central, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Génétique Humaine et Fonctions Cognitives, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Gillberg Neuropsychiatry Centre [Göteborg, Sueden], Institute of Neuroscience and Physiology [Göteborg]-University of Gothenburg (GU), Institute of Child Health, University College of London [London] (UCL), Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Friedman Brain Institute, Mount Sinai, Icahn School of Medicine at Mount Sinai [New York] (MSSM), Department of Neuroscience, PennState Meteorology Department, Department of Psychiatry [Pittsburgh], University of Pittsburgh School of Medicine, Pennsylvania Commonwealth System of Higher Education (PCSHE)-Pennsylvania Commonwealth System of Higher Education (PCSHE), Stanford School of Medicine [Stanford], Stanford Medicine, Stanford University-Stanford University, Department of Psychiatry and Behavioral Sciences [Stanford], Vanderbilt Brain Institute, Vanderbilt University School of Medicine [Nashville], Department of Molecular Physiology & Biophysics and Psychiatry, Vanderbilt University [Nashville]-Centers for Human Genetics Research and Molecular Neuroscience, Johns Hopkins University (JHU), Carolina Institute for Developmental Disabilities, Carver College of Medicine [Iowa City], University of Iowa [Iowa City]-University of Iowa [Iowa City], University of Iowa [Iowa City]-Howard Hughes Medical-Institute Carver College of Medicine, Department of Neurology, UCLA School of Medicine, Department of Genetics, University of Pennsylvania [Philadelphia]-School of Medicine, N.Y.S. Institute for Basic Research in Developmental Disabilities, Department of Human Genetics, UCLA, University of California [Los Angeles] (UCLA), University of California-University of California-Semel Institute, Washington University in Saint Louis (WUSTL), University of Chicago, Harvard Medical School [Boston] (HMS), Autism Genetic Resource Exchange, Autism Speaks, Emory University [Atlanta, GA], Developmental Brain and Behaviour Unit, University of Southampton, Cure Autism Now, Institute of Human Genetics, Rheinische Friedrich-Wilhelms-Universität Bonn, Children's National Medical Center, The George Washington University (GW), Massachusetts General Hospital, Massachusetts General Hospital [Boston], Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris [Pisa], Autism Speaks and the Department of Psychiatry, Department of Speech and Hearing Sciences [Washington], University of Washington [Seattle], University of California [Irvine] (UCI), University of California-University of California, Department of Psychiatry and Behavioral Sciences, Department of OB/GYN, University of Rochester Medical Center, Pathology and Laboratory Medicine, University of Pennsylvania [Philadelphia], Department of Epidemiology, University of Pittsburgh (PITT), Departments of Biostatistics and Medicine, Department of Medicine, Octogone Unité de Recherche Interdisciplinaire (Octogone), Université Toulouse - Jean Jaurès (UT2J), Centre de Référence du Syndrome de Prader-Willi, CHU Toulouse [Toulouse], University of Oxford [Oxford]-Warneford Hospital, Division of Molecular Genome Analysis, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe-Universität Frankfurt am Main, University Department of Child Psychiatry, National and Kapodistrian University of Athens (NKUA), Department of Pharmacy and Biotechnology, Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), Medical Genetics Laboratory, Policlinico S. Orsola-Malpighi, University Medical Center [Utrecht]-Brain Center Rudolf Magnus, Department of Neurocognition, Maastricht University [Maastricht], Newcomen Centre, Guy's Hospital [London], Department of Child and Adolescent Psychiatry, Institute of psychiatry, MRC Social, Genetic and Developmental Psychiatry Centre (SGDP), The Institute of Psychiatry-King‘s College London, University of Cambridge Clinical School, University of Cambridge [UK] (CAM), Manchester Academic Health Sciences Centre, Department of Medicine, Manchester, University of Manchester [Manchester]-School of Epidemiology and Health Science, Newcastle University [Newcastle]-Institute of Health & Society (Child & Adolescent Psychiatry), Child and Adolescent Mental Health, Newcastle University [Newcastle], Institutes of Neuroscience and Health and Society, The Wellcome Trust Centre for Human Genetics [Oxford], University of Oxford [Oxford], Centre for Integrated Genomic Medical Research, Manchester, University of Manchester [Manchester], Institute for Juvenile Research-University of Illinois [Chicago] (UIC), University of Illinois System-University of Illinois System, Institute for Juvenile Research, University of Illinois [Chicago] (UIC), Department of Disability and Human Development, New York University [New York] (NYU), NYU System (NYU)-NYU System (NYU), Autism and Communicative Disorders Centre, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Human Genetics Department, SFU Discrete Mathematics Group (SFU-DMG), Simon Fraser University (SFU.ca), Child Study Centre, Yale University School of Medicine, Centre d'Endocrinologie, Maladies Osseuses, Génétique et Gynécologie Médicale, Hôpital des Enfants, CHU Toulouse [Toulouse]-CHU Toulouse [Toulouse], Department of Child Psychiatry, McGill University = Université McGill [Montréal, Canada]-Montreal Children's Hospital, McGill University Health Center [Montreal] (MUHC)-McGill University Health Center [Montreal] (MUHC), Scientific Affairs, Autism Genome Project Consortium, RS: FPN CN II, Cognitive Neuroscience, MUMC+: HZC Klinische Neurofysiologie (5), The Hospital for sick children [Toronto] (SickKids)-University of Toronto-Department of Molecular Genetics-McLaughlin Centre, University of California (UC)-University of California (UC)-Semel Institute, University of California [Irvine] (UC Irvine), University of California (UC)-University of California (UC), King‘s College London-The Institute of Psychiatry, Yale School of Medicine [New Haven, Connecticut] (YSM), Betancur, Catalina, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), University of Pennsylvania-School of Medicine, University of Pennsylvania, Pôle Enfants [CHU Toulouse], Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), University of Oxford-Warneford Hospital, University of Oxford, Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Université de Toulouse (UT)-Université de Toulouse (UT), Szatmari P, Paterson AD, Zwaigenbaum L, Roberts W, Brian J, Liu XQ, Vincent JB, Skaug JL, Thompson AP, Senman L, Feuk L, Qian C, Bryson SE, Jones MB, Marshall CR, Scherer SW, Vieland VJ, Bartlett C, Mangin LV, Goedken R, Segre A, Pericak-Vance MA, Cuccaro ML, Gilbert JR, Wright HH, Abramson RK, Betancur C, Bourgeron T, Gillberg C, Leboyer M, Buxbaum JD, Davis KL, Hollander E, Silverman JM, Hallmayer J, Lotspeich L, Sutcliffe JS, Haines JL, Folstein SE, Piven J, Wassink TH, Sheffield V, Geschwind DH, Bucan M, Brown WT, Cantor RM, Constantino JN, Gilliam TC, Herbert M, Lajonchere C, Ledbetter DH, Lese-Martin C, Miller J, Nelson S, Samango-Sprouse CA, Spence S, State M, Tanzi RE, Coon H, Dawson G, Devlin B, Estes A, Flodman P, Klei L, McMahon WM, Minshew N, Munson J, Korvatska E, Rodier PM, Schellenberg GD, Smith M, Spence MA, Stodgell C, Tepper PG, Wijsman EM, Yu CE, Roge B, Mantoulan C, Wittemeyer K, Poustka A, Felder B, Klauck SM, Schuster C, Poustka F, Bolte S, Feineis-Matthews S, Herbrecht E, Schmotzer G, Tsiantis J, Papanikolaou K, Maestrini E, Bacchelli E, Blasi F, Carone S, Toma C, Van Engeland H, de Jonge M, Kemner C, Koop F, Langemeijer M, Hijimans C, Staal WG, Baird G, Bolton PF, Rutter ML, Weisblatt E, Green J, Aldred C, Wilkinson JA, Pickles A, Le Couteur A, Berney T, McConachie H, Bailey AJ, Francis K, Honeyman G, Hutchinson A, Parr JR, Wallace S, Monaco AP, Barnby G, Kobayashi K, Lamb JA, Sousa I, Sykes N, Cook EH, Guter SJ, Leventhal BL, Salt J, Lord C, Corsello C, Hus V, Weeks DE, Volkmar F, Tauber M, Fombonne E, and Shih A.
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Male ,genetic structures ,Genetic Linkage ,Neurexin ,[SDV.GEN] Life Sciences [q-bio]/Genetics ,0302 clinical medicine ,Risk Factors ,MESH: Risk Factors ,Heritability of autism ,Copy-number variation ,MESH: Genetic Variation ,Genetics ,0303 health sciences ,medicine.diagnostic_test ,MESH: Genetic Testing ,MESH: Genetic Predisposition to Disease ,Chromosome Mapping ,3. Good health ,Female ,MESH: Genetic Linkage ,MESH: Autistic Disorder ,Epigenetics of autism ,Biology ,Article ,03 medical and health sciences ,Genetic linkage ,mental disorders ,medicine ,Humans ,MESH: Chromosome Aberrations ,Family ,Genetic Predisposition to Disease ,Genetic Testing ,Autistic Disorder ,MESH: Family ,030304 developmental biology ,Genetic testing ,Chromosome Aberrations ,[SDV.GEN]Life Sciences [q-bio]/Genetics ,MESH: Humans ,Genetic Variation ,medicine.disease ,Genetic architecture ,MESH: Male ,MESH: Lod Score ,Autism ,Lod Score ,MESH: Chromosome Mapping ,MESH: Female ,030217 neurology & neurosurgery - Abstract
International audience; Autism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,181 [corrected] families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs.
- Published
- 2007
8. Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography.
- Author
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Yang D, Svoboda AM, George TG, Mansfield PK, Wheelock MD, Schroeder ML, Rafferty SM, Sherafati A, Tripathy K, Burns-Yocum T, Forsen E, Pruett JR, Marrus NM, Culver JP, Constantino JN, and Eggebrecht AT
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- Humans, Male, Child, Female, Brain diagnostic imaging, Brain physiopathology, Autistic Disorder physiopathology, Autistic Disorder diagnostic imaging, Magnetic Resonance Imaging methods, Adolescent, Tomography, Optical methods, Motion Perception physiology, Brain Mapping methods, Autism Spectrum Disorder diagnostic imaging, Autism Spectrum Disorder physiopathology
- Abstract
Background: Autism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits., Methods: We used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models., Results: We found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits., Limitations: Optical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism., Conclusions: This study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits., (© 2024. The Author(s).)
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- 2024
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9. Mate selection and current trends in the prevalence of autism.
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Forsen E, Marrus N, Joyce J, Zhang Y, and Constantino JN
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- Adult, Child, Female, Humans, Male, California epidemiology, Missouri epidemiology, Prevalence, White, Autistic Disorder epidemiology, Autistic Disorder genetics, Hispanic or Latino
- Abstract
Background: According to the most recent U.S. CDC surveillance data, the rise in prevalence of childhood autism spectrum disorder among minority children has begun to outpace that of non-Hispanic white children. Since prior research has identified possible differences in the extent of mate selection for autistic traits across families of different ethnicity, this study examined variation in autism related traits in contemporaneous, epidemiologically ascertained samples of spousal pairs representing Hispanic and non-Hispanic white populations. The purpose was to determine whether discrepancies by ethnicity could contribute to differential increases in prevalence in the current generation of young children., Methods: Birth records were used to identify all twin pairs born between 2011 and 2013 in California and Missouri. Families were selected at random from pools of English-speaking Hispanic families in California and Non-Hispanic White families in Missouri. Autistic trait data of parents was obtained using the Adult Report Form of the Social Responsiveness Scale (SRS-2)., Results: We did not identify a statistically significant difference in the degree of mate selection for autism related traits between Hispanic and non-Hispanic white spousal pairs. However, the degree of spousal correlation observed in this recent cohort was pronounced (on the order of ICC 0.45) and exceeded that typically reported in prior research (on the order of 0.30), surpassing also widely reported estimates for sibling correlation (also on the order of 0.30)., Limitations: The sample did not allow for a direct appraisal of change in the magnitude of spousal correlation over time and the ascertainments of trait burden were derived from spouse report., Conclusion: Across two epidemiologically ascertained samples of spousal pairs representing Hispanic and non-Hispanic white families across two U.S. states (respectively, California and Missouri), the extent of autism-related trait co-variation for parents of the current generation of young children is substantial and exceeds correlations typically observed for siblings. Given the heritability of these traits and their relation to autism risk, societal trends in the degree of mate selection for these traits should be considered as possible contributors to subtle increases in the incidence of autism over time and across generations., (© 2024. The Author(s).)
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- 2024
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10. The Brain Gene Registry: a data snapshot.
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Baldridge D, Kaster L, Sancimino C, Srivastava S, Molholm S, Gupta A, Oh I, Lanzotti V, Grewal D, Riggs ER, Savatt JM, Hauck R, Sveden A, Constantino JN, Piven J, Gurnett CA, Chopra M, Hazlett H, and Payne PRO
- Subjects
- Humans, Male, Female, Brain, Registries, Methyltransferases, Autism Spectrum Disorder genetics, Autistic Disorder, Neurodevelopmental Disorders, Intellectual Disability
- Abstract
Monogenic disorders account for a large proportion of population-attributable risk for neurodevelopmental disabilities. However, the data necessary to infer a causal relationship between a given genetic variant and a particular neurodevelopmental disorder is often lacking. Recognizing this scientific roadblock, 13 Intellectual and Developmental Disabilities Research Centers (IDDRCs) formed a consortium to create the Brain Gene Registry (BGR), a repository pairing clinical genetic data with phenotypic data from participants with variants in putative brain genes. Phenotypic profiles are assembled from the electronic health record (EHR) and a battery of remotely administered standardized assessments collectively referred to as the Rapid Neurobehavioral Assessment Protocol (RNAP), which include cognitive, neurologic, and neuropsychiatric assessments, as well as assessments for attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Co-enrollment of BGR participants in the Clinical Genome Resource's (ClinGen's) GenomeConnect enables display of variant information in ClinVar. The BGR currently contains data on 479 participants who are 55% male, 6% Asian, 6% Black or African American, 76% white, and 12% Hispanic/Latine. Over 200 genes are represented in the BGR, with 12 or more participants harboring variants in each of these genes: CACNA1A, DNMT3A, SLC6A1, SETD5, and MYT1L. More than 30% of variants are de novo and 43% are classified as variants of uncertain significance (VUSs). Mean standard scores on cognitive or developmental screens are below average for the BGR cohort. EHR data reveal developmental delay as the earliest and most common diagnosis in this sample, followed by speech and language disorders, ASD, and ADHD. BGR data has already been used to accelerate gene-disease validity curation of 36 genes evaluated by ClinGen's BGR Intellectual Disability (ID)-Autism (ASD) Gene Curation Expert Panel. In summary, the BGR is a resource for use by stakeholders interested in advancing translational research for brain genes and continues to recruit participants with clinically reported variants to establish a rich and well-characterized national resource to promote research on neurodevelopmental disorders., (© 2024. The Author(s).)
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- 2024
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11. Development and Replication of Objective Measurements of Social Visual Engagement to Aid in Early Diagnosis and Assessment of Autism.
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Jones W, Klaiman C, Richardson S, Lambha M, Reid M, Hamner T, Beacham C, Lewis P, Paredes J, Edwards L, Marrus N, Constantino JN, Shultz S, and Klin A
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- Female, Humans, Male, Cognition, Early Diagnosis, Prospective Studies, Infant, Child, Preschool, Double-Blind Method, Autism Spectrum Disorder diagnosis, Autistic Disorder diagnosis
- Abstract
Importance: Autism spectrum disorder is a common and early-emerging neurodevelopmental condition. While 80% of parents report having had concerns for their child's development before age 2 years, many children are not diagnosed until ages 4 to 5 years or later., Objective: To develop an objective performance-based tool to aid in early diagnosis and assessment of autism in children younger than 3 years., Design, Setting, and Participants: In 2 prospective, consecutively enrolled, broad-spectrum, double-blind studies, we developed an objective eye-tracking-based index test for children aged 16 to 30 months, compared its performance with best-practice reference standard diagnosis of autism (discovery study), and then replicated findings in an independent sample (replication study). Discovery and replication studies were conducted in specialty centers for autism diagnosis and treatment. Reference standard diagnoses were made using best-practice standardized protocols by specialists blind to eye-tracking results. Eye-tracking tests were administered by staff blind to clinical results. Children were enrolled from April 27, 2013, until September 26, 2017. Data were analyzed from March 28, 2018, to January 3, 2019., Main Outcomes and Measures: Prespecified primary end points were the sensitivity and specificity of the eye-tracking-based index test compared with the reference standard. Prespecified secondary end points measured convergent validity between eye-tracking-based indices and reference standard assessments of social disability, verbal ability, and nonverbal ability., Results: Data were collected from 1089 children: 719 children (mean [SD] age, 22.4 [3.6] months) in the discovery study, and 370 children (mean [SD] age, 25.4 [6.0] months) in the replication study. In discovery, 224 (31.2%) were female and 495 (68.8%) male; in replication, 120 (32.4%) were female and 250 (67.6%) male. Based on reference standard expert clinical diagnosis, there were 386 participants (53.7%) with nonautism diagnoses and 333 (46.3%) with autism diagnoses in discovery, and 184 participants (49.7%) with nonautism diagnoses and 186 (50.3%) with autism diagnoses in replication. In the discovery study, the area under the receiver operating characteristic curve was 0.90 (95% CI, 0.88-0.92), sensitivity was 81.9% (95% CI, 77.3%-85.7%), and specificity was 89.9% (95% CI, 86.4%-92.5%). In the replication study, the area under the receiver operating characteristic curve was 0.89 (95% CI, 0.86-0.93), sensitivity was 80.6% (95% CI, 74.1%-85.7%), and specificity was 82.3% (95% CI, 76.1%-87.2%). Eye-tracking test results correlated with expert clinical assessments of children's individual levels of ability, explaining 68.6% (95% CI, 58.3%-78.6%), 63.4% (95% CI, 47.9%-79.2%), and 49.0% (95% CI, 33.8%-65.4%) of variance in reference standard assessments of social disability, verbal ability, and nonverbal cognitive ability, respectively., Conclusions and Relevance: In two diagnostic studies of children younger than 3 years, objective eye-tracking-based measurements of social visual engagement quantified diagnostic status as well as individual levels of social disability, verbal ability, and nonverbal ability in autism. These findings suggest that objective measurements of social visual engagement can be used to aid in autism diagnosis and assessment.
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- 2023
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12. Social attention during object engagement: toward a cross-species measure of preferential social orienting.
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Weichselbaum C, Hendrix N, Albright J, Dougherty JD, Botteron KN, Constantino JN, and Marrus N
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- Infant, Humans, Animals, Dogs, Social Behavior, Prospective Studies, Attention, Cognition, Autism Spectrum Disorder psychology
- Abstract
Background: A central challenge in preclinical research investigating the biology of autism spectrum disorder (ASD) is the translation of ASD-related social phenotypes across humans and animal models. Social orienting, an observable, evolutionarily conserved behavior, represents a promising cross-species ASD phenotype given that disrupted social orienting is an early-emerging ASD feature with evidence for predicting familial recurrence. Here, we adapt a competing-stimulus social orienting task from domesticated dogs to naturalistic play behavior in human toddlers and test whether this approach indexes decreased social orienting in ASD., Methods: Play behavior was coded from the Autism Diagnostic Observation Schedule (ADOS) in two samples of toddlers, each with and without ASD. Sample 1 (n = 16) consisted of community-ascertained research participants, while Sample 2 involved a prospective study of infants at a high or low familial liability for ASD (n = 67). Coding quantified the child's looks towards the experimenter and caregiver, a social stimulus, while playing with high-interest toys, a non-social stimulus. A competing-stimulus measure of "Social Attention During Object Engagement" (SADOE) was calculated by dividing the number of social looks by total time spent playing with toys. SADOE was compared based on ASD diagnosis and differing familial liability for ASD., Results: In both samples, toddlers with ASD exhibited significantly lower SADOE compared to toddlers without ASD, with large effect sizes (Hedges' g ≥ 0.92) driven by a lower frequency of child-initiated spontaneous looks. Among toddlers at high familial likelihood of ASD, toddlers with ASD showed lower SADOE than toddlers without ASD, while SADOE did not differ based on presence or absence of familial ASD risk alone. SADOE correlated negatively with ADOS social affect calibrated severity scores and positively with the Communication and Symbolic Behavior Scales social subscale. In a binary logistic regression model, SADOE alone correctly classified 74.1% of cases, which rose to 85.2% when combined with cognitive development., Conclusions: This work suggests that a brief behavioral measure pitting a high-interest nonsocial stimulus against the innate draw of social partners can serve as a feasible cross-species measure of social orienting, with implications for genetically informative behavioral phenotyping of social deficits in ASD and other neurodevelopmental disorders., (© 2022. The Author(s).)
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- 2022
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13. Can the "female protective effect" liability threshold model explain sex differences in autism spectrum disorder?
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Dougherty JD, Marrus N, Maloney SE, Yip B, Sandin S, Turner TN, Selmanovic D, Kroll KL, Gutmann DH, Constantino JN, and Weiss LA
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- Female, Male, Humans, Sex Characteristics, Phenotype, Penetrance, Autism Spectrum Disorder diagnosis
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Male sex is a strong risk factor for autism spectrum disorder (ASD). The leading theory for a "female protective effect" (FPE) envisions males and females have "differing thresholds" under a "liability threshold model" (DT-LTM). Specifically, this model posits that females require either a greater number or larger magnitude of risk factors (i.e., greater liability) to manifest ASD, which is supported by the finding that a greater proportion of females with ASD have highly penetrant genetic mutations. Herein, we derive testable hypotheses from the DT-LTM for ASD, investigating heritability, familial recurrence, correlation between ASD penetrance and sex ratio, population traits, clinical features, the stability of the sex ratio across diagnostic changes, and highlight other key prerequisites. Our findings reveal that several key predictions of the DT-LTM are not supported by current data, requiring us to establish a different conceptual framework for evaluating alternate models that explain sex differences in ASD., Competing Interests: Declaration of interests J.N.C. receives royalties from Western Psychological Services for the commercial distribution of the social responsiveness scale, a quantitative measure of autistic traits implemented in some of the studies cited in this article., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)
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- 2022
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14. GABBR1 monoallelic de novo variants linked to neurodevelopmental delay and epilepsy.
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Cediel ML, Stawarski M, Blanc X, Nosková L, Magner M, Platzer K, Gburek-Augustat J, Baldridge D, Constantino JN, Ranza E, Bettler B, and Antonarakis SE
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- Humans, gamma-Aminobutyric Acid metabolism, HEK293 Cells, Epilepsy genetics, Intellectual Disability genetics, Nervous System Malformations, Neurodevelopmental Disorders genetics, Receptors, GABA-B genetics
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GABA
B receptors are obligatory heterodimers responsible for prolonged neuronal inhibition in the central nervous system. The two receptor subunits are encoded by GABBR1 and GABBR2. Variants in GABBR2 have been associated with a Rett-like phenotype (MIM: 617903), epileptic encephalopathy (MIM: 617904), and milder forms of developmental delay with absence epilepsy. To date, however, no phenotypes associated with pathogenic variants of GABBR1 have been established. Through GeneMatcher, we have ascertained four individuals who each have a monoallelic GABBR1 de novo non-synonymous variant; these individuals exhibit motor and/or language delay, ranging from mild to severe, and in one case, epilepsy. Further phenotypic features include varying degrees of intellectual disability, learning difficulties, autism, ADHD, ODD, sleep disorders, and muscular hypotonia. We functionally characterized the four de novo GABBR1 variants, p.Glu368Asp, p.Ala397Val, p.Ala535Thr, and p.Gly673Asp, in transfected HEK293 cells. GABA fails to efficiently activate the variant receptors, most likely leading to an increase in the excitation/inhibition balance in the central nervous system. Variant p.Gly673Asp in transmembrane domain 3 (TMD3) renders the receptor completely inactive, consistent with failure of the receptor to reach the cell surface. p.Glu368Asp is located near the orthosteric binding site and reduces GABA potency and efficacy at the receptor. GABA exhibits normal potency but decreased efficacy at the p.Ala397Val and p.Ala535Thr variants. Functional characterization of GABBR1-related variants provides a rationale for understanding the severity of disease phenotypes and points to possible therapeutic strategies., Competing Interests: Declaration of interests S.E.A. is a cofounder and CEO of Medigenome, Swiss Institute of Genomic Medicine; he is also a member of the Scientific Advisory Board of the “Imagine Institute”, Paris. E.R. is also a cofounder and medical director of Medigenome, Swiss Institute of Genomic Medicine. M.L.C. is an intern in the federally recognized clinical training program for Genetic Medicine of Medigenome. L.N. was supported by the grant NV19-07-00136 from the Ministry of Health of the Czech Republi, and The National Center for Medical Genomics (LM2018132) for support with the WES analyses., (Copyright © 2022 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)- Published
- 2022
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15. Examining shortened versions of the Social Responsiveness Scale for use in autism spectrum disorder prediction and as a quantitative trait measure: Results from a validation study of 3-5 year old children.
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Lyall K, Rando J, Toroni B, Ezeh T, Constantino JN, Croen LA, Garvin B, Piselli K, Connell J, Kaat AJ, and Newschaffer CJ
- Abstract
Background: The Social Responsiveness Scale (SRS) is a 65-item measure yielding a continuous score capturing autism-related traits. Scores based on SRS item subsets have been analytically examined but administration of shortened versions has not been evaluated prospectively., Objective: The goal of this study was to compare psychometric properties of two shortened versions of the SRS to the full 65-item SRS, in young children from both a clinical and general population setting., Methods: Study participants (aged 3-5 years) were drawn from the AJ Drexel Autism Institute clinic ( n = 154) and Kaiser Permanente Northern California ( n = 201) and block randomized to receive either the 16-item short SRS, a newly developed computer adaptive testing-SRS, or the published full-length SRS. Total scores across the three SRS administration methods were scaled to facilitate comparisons. Scores were plotted to assess distributional properties, while Receiver Operating Characteristic analysis was used to estimate Area Under the Curve (AUC) and address predictive ability., Results: Overall, distributional properties of the three administration methods were highly comparable, with shortened measures demonstrating similar ability to capture the range of the distribution and case non-case separation as the full SRS. In addition, AUC values were high (0.91-0.97) and comparable across the administration methods, though there was evidence of difference in predictive ability across measures for females (AUC for full SRS = 0.99 vs. 0.84 for short). Within individual comparisons of short versus full scores (available only for participants at the general population site) suggested underestimation of actual full SRS scores with the CAT-SRS., Conclusions: Our findings broadly support the construct validity and performance of shortened SRS versions examined here, though the full measure may be needed to more accurately assess traits consistent with ASD diagnosis in females. This work suggests opportunities for collection of ASD-related phenotype in settings where participant burden or feasibility considerations may have otherwise prohibited such measurement., Competing Interests: John N. Constantino receives royalties from Western Psychological Services for the commercial distribution of the Social Responsiveness Scale‐2. The remaining authors have declared that they have no competing or potential conflicts of interest., (© 2022 The Authors. JCPP Advances published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.)
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- 2022
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16. Infants later diagnosed with autism have lower canonical babbling ratios in the first year of life.
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Yankowitz LD, Petrulla V, Plate S, Tunc B, Guthrie W, Meera SS, Tena K, Pandey J, Swanson MR, Pruett JR Jr, Cola M, Russell A, Marrus N, Hazlett HC, Botteron K, Constantino JN, Dager SR, Estes A, Zwaigenbaum L, Piven J, Schultz RT, and Parish-Morris J
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- Humans, Infant, Longitudinal Studies, Reproducibility of Results, Autism Spectrum Disorder diagnosis, Autistic Disorder, Language Development Disorders diagnosis
- Abstract
Background: Canonical babbling-producing syllables with a mature consonant, full vowel, and smooth transition-is an important developmental milestone that typically occurs in the first year of life. Some studies indicate delayed or reduced canonical babbling in infants at high familial likelihood for autism spectrum disorder (ASD) or who later receive an ASD diagnosis, but evidence is mixed. More refined characterization of babbling in the first year of life in infants with high likelihood for ASD is needed., Methods: Vocalizations produced at 6 and 12 months by infants (n = 267) taking part in a longitudinal study were coded for canonical and non-canonical syllables. Infants were categorized as low familial likelihood (LL), high familial likelihood diagnosed with ASD at 24 months (HL-ASD) or not diagnosed (HL-Neg). Language delay was assessed based on 24-month expressive and receptive language scores. Canonical babble ratio (CBR) was calculated by dividing the number of canonical syllables by the number of total syllables. Generalized linear (mixed) models were used to assess the relationship between group membership and CBR, controlling for site, sex, and maternal education. Logistic regression was used to assess whether canonical babbling ratios at 6 and 12 months predict 24-month diagnostic outcome., Results: No diagnostic group differences in CBR were detected at 6 months, but HL-ASD infants produced significantly lower CBR than both the HL-Neg and LL groups at 12 months. HL-Neg infants with language delay also showed reduced CBR at 12 months. Neither 6- nor 12-month CBR was significant predictors of 24-month diagnostic outcome (ASD versus no ASD) in logistic regression., Limitations: Small numbers of vocalizations produced by infants at 6 months may limit the reliability of CBR estimates. It is not known if results generalize to infants who are not at high familial likelihood, or infants from more diverse racial and socioeconomic backgrounds., Conclusions: Lower canonical babbling ratios are apparent by the end of the first year of life in ASD regardless of later language delay, but are also observed for infants with later language delay without ASD. Canonical babbling may lack specificity as an early marker when used on its own., (© 2022. The Author(s).)
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- 2022
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17. Poverty and Developing Brain.
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Luby JL, Constantino JN, and Barch DM
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Evidence continues to mount that the brains of children living in poverty show tangible alterations. Our authors, all part of a team that studies the issue at Washington University School of Medicine, explain the challenges and the necessary steps needed to build a healthier and more productive society., (Copyright 2022 The Dana Foundation All Rights Reserved.)
- Published
- 2022
18. A Prospective Evaluation of Infant Cerebellar-Cerebral Functional Connectivity in Relation to Behavioral Development in Autism Spectrum Disorder.
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Hawks ZW, Todorov A, Marrus N, Nishino T, Talovic M, Nebel MB, Girault JB, Davis S, Marek S, Seitzman BA, Eggebrecht AT, Elison J, Dager S, Mosconi MW, Tychsen L, Snyder AZ, Botteron K, Estes A, Evans A, Gerig G, Hazlett HC, McKinstry RC, Pandey J, Schultz RT, Styner M, Wolff JJ, Zwaigenbaum L, Markson L, Petersen SE, Constantino JN, White DA, Piven J, and Pruett JR Jr
- Abstract
Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed based on social impairment, restricted interests, and repetitive behaviors. Contemporary theories posit that cerebellar pathology contributes causally to ASD by disrupting error-based learning (EBL) during infancy. The present study represents the first test of this theory in a prospective infant sample, with potential implications for ASD detection., Methods: Data from the Infant Brain Imaging Study ( n = 94, 68 male) were used to examine 6-month cerebellar functional connectivity magnetic resonance imaging in relation to later (12/24-month) ASD-associated behaviors and outcomes. Hypothesis-driven univariate analyses and machine learning-based predictive tests examined cerebellar-frontoparietal network (FPN; subserves error signaling in support of EBL) and cerebellar-default mode network (DMN; broadly implicated in ASD) connections. Cerebellar-FPN functional connectivity was used as a proxy for EBL, and cerebellar-DMN functional connectivity provided a comparative foil. Data-driven functional connectivity magnetic resonance imaging enrichment examined brain-wide behavioral associations, with post hoc tests of cerebellar connections., Results: Cerebellar-FPN and cerebellar-DMN connections did not demonstrate associations with ASD. Functional connectivity magnetic resonance imaging enrichment identified 6-month correlates of later ASD-associated behaviors in networks of a priori interest (FPN, DMN), as well as in cingulo-opercular (also implicated in error signaling) and medial visual networks. Post hoc tests did not suggest a role for cerebellar connections., Conclusions: We failed to identify cerebellar functional connectivity-based contributions to ASD. However, we observed prospective correlates of ASD-associated behaviors in networks that support EBL. Future studies may replicate and extend network-level positive results, and tests of the cerebellum may investigate brain-behavior associations at different developmental stages and/or using different neuroimaging modalities., (© 2021 The Authors.)
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- 2021
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19. Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018.
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Shaw KA, Maenner MJ, Bakian AV, Bilder DA, Durkin MS, Furnier SM, Hughes MM, Patrick M, Pierce K, Salinas A, Shenouda J, Vehorn A, Warren Z, Zahorodny W, Constantino JN, DiRienzo M, Esler A, Fitzgerald RT, Grzybowski A, Hudson A, Spivey MH, Ali A, Andrews JG, Baroud T, Gutierrez J, Hallas L, Hall-Lande J, Hewitt A, Lee LC, Lopez M, Mancilla KC, McArthur D, Pettygrove S, Poynter JN, Schwenk YD, Washington A, Williams S, and Cogswell ME
- Subjects
- Autism Spectrum Disorder epidemiology, Child, Preschool, Early Diagnosis, Epidemiological Monitoring, Female, Humans, Male, United States epidemiology, Autism Spectrum Disorder diagnosis, Population Surveillance
- Abstract
Problem/condition: Autism spectrum disorder (ASD)., Period Covered: 2018., Description of System: The Autism and Developmental Disabilities Monitoring Network is an active surveillance program that estimates ASD prevalence and monitors timing of ASD identification among children aged 4 and 8 years. This report focuses on children aged 4 years in 2018, who were born in 2014 and had a parent or guardian who lived in the surveillance area in one of 11 sites (Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin) at any time during 2018. Children were classified as having ASD if they ever received 1) an ASD diagnostic statement (diagnosis) in an evaluation, 2) a special education classification of ASD (eligibility), or 3) an ASD International Classification of Diseases (ICD) code. Suspected ASD also was tracked among children aged 4 years. Children who did not meet the case definition for ASD were classified as having suspected ASD if their records contained a qualified professional's statement indicating a suspicion of ASD., Results: For 2018, the overall ASD prevalence was 17.0 per 1,000 (one in 59) children aged 4 years. Prevalence varied from 9.1 per 1,000 in Utah to 41.6 per 1,000 in California. At every site, prevalence was higher among boys than girls, with an overall male-to-female prevalence ratio of 3.4. Prevalence of ASD among children aged 4 years was lower among non-Hispanic White (White) children (12.9 per 1,000) than among non-Hispanic Black (Black) children (16.6 per 1,000), Hispanic children (21.1 per 1,000), and Asian/Pacific Islander (A/PI) children (22.7 per 1,000). Among children aged 4 years with ASD and information on intellectual ability, 52% met the surveillance case definition of co-occurring intellectual disability (intelligence quotient ≤70 or an examiner's statement of intellectual disability documented in an evaluation). Of children aged 4 years with ASD, 72% had a first evaluation at age ≤36 months. Stratified by census-tract-level median household income (MHI) tertile, a lower percentage of children with ASD and intellectual disability was evaluated by age 36 months in the low MHI tertile (72%) than in the high MHI tertile (84%). Cumulative incidence of ASD diagnosis or eligibility received by age 48 months was 1.5 times as high among children aged 4 years (13.6 per 1,000 children born in 2014) as among those aged 8 years (8.9 per 1,000 children born in 2010). Across MHI tertiles, higher cumulative incidence of ASD diagnosis or eligibility received by age 48 months was associated with lower MHI. Suspected ASD prevalence was 2.6 per 1,000 children aged 4 years, meaning for every six children with ASD, one child had suspected ASD. The combined prevalence of ASD and suspected ASD (19.7 per 1,000 children aged 4 years) was lower than ASD prevalence among children aged 8 years (23.0 per 1,000 children aged 8 years)., Interpretation: Groups with historically lower prevalence of ASD (non-White and lower MHI) had higher prevalence and cumulative incidence of ASD among children aged 4 years in 2018, suggesting progress in identification among these groups. However, a lower percentage of children with ASD and intellectual disability in the low MHI tertile were evaluated by age 36 months than in the high MHI group, indicating disparity in timely evaluation. Children aged 4 years had a higher cumulative incidence of diagnosis or eligibility by age 48 months compared with children aged 8 years, indicating improvement in early identification of ASD. The overall prevalence for children aged 4 years was less than children aged 8 years, even when prevalence of children suspected of having ASD by age 4 years is included. This finding suggests that many children identified after age 4 years do not have suspected ASD documented by age 48 months., Public Health Action: Children born in 2014 were more likely to be identified with ASD by age 48 months than children born in 2010, indicating increased early identification. However, ASD identification among children aged 4 years varied by site, suggesting opportunities to examine developmental screening and diagnostic practices that promote earlier identification. Children aged 4 years also were more likely to have co-occurring intellectual disability than children aged 8 years, suggesting that improvement in the early identification and evaluation of developmental concerns outside of cognitive impairments is still needed. Improving early identification of ASD could lead to earlier receipt of evidence-based interventions and potentially improve developmental outcomes., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.
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- 2021
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20. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018.
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Maenner MJ, Shaw KA, Bakian AV, Bilder DA, Durkin MS, Esler A, Furnier SM, Hallas L, Hall-Lande J, Hudson A, Hughes MM, Patrick M, Pierce K, Poynter JN, Salinas A, Shenouda J, Vehorn A, Warren Z, Constantino JN, DiRienzo M, Fitzgerald RT, Grzybowski A, Spivey MH, Pettygrove S, Zahorodny W, Ali A, Andrews JG, Baroud T, Gutierrez J, Hewitt A, Lee LC, Lopez M, Mancilla KC, McArthur D, Schwenk YD, Washington A, Williams S, and Cogswell ME
- Subjects
- Autism Spectrum Disorder ethnology, Child, Epidemiological Monitoring, Ethnicity statistics & numerical data, Female, Geography, Humans, Male, Prevalence, Race Factors, Racial Groups statistics & numerical data, United States epidemiology, Autism Spectrum Disorder epidemiology, Health Status Disparities, Population Surveillance
- Abstract
Problem/condition: Autism spectrum disorder (ASD)., Period Covered: 2018., Description of System: The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts active surveillance of ASD. This report focuses on the prevalence and characteristics of ASD among children aged 8 years in 2018 whose parents or guardians lived in 11 ADDM Network sites in the United States (Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin). To ascertain ASD among children aged 8 years, ADDM Network staff review and abstract developmental evaluations and records from community medical and educational service providers. In 2018, children met the case definition if their records documented 1) an ASD diagnostic statement in an evaluation (diagnosis), 2) a special education classification of ASD (eligibility), or 3) an ASD International Classification of Diseases (ICD) code., Results: For 2018, across all 11 ADDM sites, ASD prevalence per 1,000 children aged 8 years ranged from 16.5 in Missouri to 38.9 in California. The overall ASD prevalence was 23.0 per 1,000 (one in 44) children aged 8 years, and ASD was 4.2 times as prevalent among boys as among girls. Overall ASD prevalence was similar across racial and ethnic groups, except American Indian/Alaska Native children had higher ASD prevalence than non-Hispanic White (White) children (29.0 versus 21.2 per 1,000 children aged 8 years). At multiple sites, Hispanic children had lower ASD prevalence than White children (Arizona, Arkansas, Georgia, and Utah), and non-Hispanic Black (Black) children (Georgia and Minnesota). The associations between ASD prevalence and neighborhood-level median household income varied by site. Among the 5,058 children who met the ASD case definition, 75.8% had a diagnostic statement of ASD in an evaluation, 18.8% had an ASD special education classification or eligibility and no ASD diagnostic statement, and 5.4% had an ASD ICD code only. ASD prevalence per 1,000 children aged 8 years that was based exclusively on documented ASD diagnostic statements was 17.4 overall (range: 11.2 in Maryland to 29.9 in California). The median age of earliest known ASD diagnosis ranged from 36 months in California to 63 months in Minnesota. Among the 3,007 children with ASD and data on cognitive ability, 35.2% were classified as having an intelligence quotient (IQ) score ≤70. The percentages of children with ASD with IQ scores ≤70 were 49.8%, 33.1%, and 29.7% among Black, Hispanic, and White children, respectively. Overall, children with ASD and IQ scores ≤70 had earlier median ages of ASD diagnosis than children with ASD and IQ scores >70 (44 versus 53 months)., Interpretation: In 2018, one in 44 children aged 8 years was estimated to have ASD, and prevalence and median age of identification varied widely across sites. Whereas overall ASD prevalence was similar by race and ethnicity, at certain sites Hispanic children were less likely to be identified as having ASD than White or Black children. The higher proportion of Black children compared with White and Hispanic children classified as having intellectual disability was consistent with previous findings., Public Health Action: The variability in ASD prevalence and community ASD identification practices among children with different racial, ethnic, and geographical characteristics highlights the importance of research into the causes of that variability and strategies to provide equitable access to developmental evaluations and services. These findings also underscore the need for enhanced infrastructure for diagnostic, treatment, and support services to meet the needs of all children., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.
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- 2021
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21. A MYT1L syndrome mouse model recapitulates patient phenotypes and reveals altered brain development due to disrupted neuronal maturation.
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Chen J, Lambo ME, Ge X, Dearborn JT, Liu Y, McCullough KB, Swift RG, Tabachnick DR, Tian L, Noguchi K, Garbow JR, Constantino JN, Gabel HW, Hengen KB, Maloney SE, and Dougherty JD
- Subjects
- Animals, Brain metabolism, Humans, Male, Mice, Nerve Tissue Proteins metabolism, Neurogenesis, Phenotype, Transcription Factors metabolism, Intellectual Disability genetics, Nerve Tissue Proteins genetics, Transcription Factors genetics
- Abstract
Human genetics have defined a new neurodevelopmental syndrome caused by loss-of-function mutations in MYT1L, a transcription factor known for enabling fibroblast-to-neuron conversions. However, how MYT1L mutation causes intellectual disability, autism, ADHD, obesity, and brain anomalies is unknown. Here, we developed a Myt1l haploinsufficient mouse model that develops obesity, white-matter thinning, and microcephaly, mimicking common clinical phenotypes. During brain development we discovered disrupted gene expression, mediated in part by loss of Myt1l gene-target activation, and identified precocious neuronal differentiation as the mechanism for microcephaly. In contrast, in adults we discovered that mutation results in failure of transcriptional and chromatin maturation, echoed in disruptions in baseline physiological properties of neurons. Myt1l haploinsufficiency also results in behavioral anomalies, including hyperactivity, muscle weakness, and social alterations, with more severe phenotypes in males. Overall, our findings provide insight into the mechanistic underpinnings of this disorder and enable future preclinical studies., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)
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- 2021
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22. Genetic counseling as preventive intervention: toward individual specification of transgenerational autism risk.
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Marrus N, Turner TN, Forsen E, Bolster D, Marvin A, Whitehouse A, Klinger L, Gurnett CA, and Constantino JN
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- Genetic Counseling, Humans, Parents, Prospective Studies, Autism Spectrum Disorder epidemiology, Autism Spectrum Disorder genetics, Autistic Disorder epidemiology, Autistic Disorder genetics
- Abstract
Background: Although autism spectrum disorders (ASD) are among the most heritable of all neuropsychiatric syndromes, most affected children are born to unaffected parents. Recently, we reported an average increase of 3-5% over general population risk of ASD among offspring of adults who have first-degree relatives with ASD in a large epidemiologic family sample. A next essential step is to investigate whether there are measurable characteristics of individual parents placing them at higher or lower recurrence risk, as this information could allow more personalized genetic counseling., Methods: We assembled what is to our knowledge the largest collection of data on the ability of four measurable characteristics of unaffected prospective parents to specify risk for autism among their offspring: (1) sub clinical autistic trait burden, (2) parental history of a sibling with ASD, (3) transmitted autosomal molecular genetic abnormalities, and (4) parental age. Leveraging phenotypic and genetic data in curated family cohorts, we evaluate the respective associations between these factors and child outcome when autism is present in the family in the parental generation., Results: All four characteristics were associated with elevation in offspring risk; however, the magnitude of their predictive power-with the exception of isolated rare inherited pathogenic variants -does not yet reach a threshold that would typically be considered actionable for reproductive decision-making., Conclusions: Individual specification of risk to offspring of adults in ASD-affected families is not straightforwardly improved by ascertainment of parental phenotype, and it is not yet clear whether genomic screening of prospective parents in families affected by idiopathic ASD is warranted as a clinical standard. Systematic screening of affected family members for heritable pathogenic variants, including rare sex-linked mutations, will identify a subset of families with substantially elevated transmission risk. Polygenic risk scores are only weakly predictive at this time but steadily improving and ultimately may enable more robust prediction either singly or when combined with the risk variables examined in this study., (© 2021. The Author(s).)
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- 2021
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23. Altered neuronal physiology, development, and function associated with a common chromosome 15 duplication involving CHRNA7.
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Meganathan K, Prakasam R, Baldridge D, Gontarz P, Zhang B, Urano F, Bonni A, Maloney SE, Turner TN, Huettner JE, Constantino JN, and Kroll KL
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- Humans, Male, Neurons, Phenotype, Chromosomes, Human, Pair 15 genetics, DNA Copy Number Variations, alpha7 Nicotinic Acetylcholine Receptor genetics
- Abstract
Background: Copy number variants (CNVs) linked to genes involved in nervous system development or function are often associated with neuropsychiatric disease. While CNVs involving deletions generally cause severe and highly penetrant patient phenotypes, CNVs leading to duplications tend instead to exhibit widely variable and less penetrant phenotypic expressivity among affected individuals. CNVs located on chromosome 15q13.3 affecting the alpha-7 nicotinic acetylcholine receptor subunit (CHRNA7) gene contribute to multiple neuropsychiatric disorders with highly variable penetrance. However, the basis of such differential penetrance remains uncharacterized. Here, we generated induced pluripotent stem cell (iPSC) models from first-degree relatives with a 15q13.3 duplication and analyzed their cellular phenotypes to uncover a basis for the dissimilar phenotypic expressivity., Results: The first-degree relatives studied included a boy with autism and emotional dysregulation (the affected proband-AP) and his clinically unaffected mother (UM), with comparison to unrelated control models lacking this duplication. Potential contributors to neuropsychiatric impairment were modeled in iPSC-derived cortical excitatory and inhibitory neurons. The AP-derived model uniquely exhibited disruptions of cellular physiology and neurodevelopment not observed in either the UM or unrelated controls. These included enhanced neural progenitor proliferation but impaired neuronal differentiation, maturation, and migration, and increased endoplasmic reticulum (ER) stress. Both the neuronal migration deficit and elevated ER stress could be selectively rescued by different pharmacologic agents. Neuronal gene expression was also dysregulated in the AP, including reduced expression of genes related to behavior, psychological disorders, neuritogenesis, neuronal migration, and Wnt, axonal guidance, and GABA receptor signaling. The UM model instead exhibited upregulated expression of genes in many of these same pathways, suggesting that molecular compensation could have contributed to the lack of neurodevelopmental phenotypes in this model. However, both AP- and UM-derived neurons exhibited shared alterations of neuronal function, including increased action potential firing and elevated cholinergic activity, consistent with increased homomeric CHRNA7 channel activity., Conclusions: These data define both diagnosis-associated cellular phenotypes and shared functional anomalies related to CHRNA7 duplication that may contribute to variable phenotypic penetrance in individuals with 15q13.3 duplication. The capacity for pharmacological agents to rescue some neurodevelopmental anomalies associated with diagnosis suggests avenues for intervention for carriers of this duplication and other CNVs that cause related disorders., (© 2021. The Author(s).)
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- 2021
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24. New guidance to seekers of autism biomarkers: an update from studies of identical twins.
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Constantino JN
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- Autism Spectrum Disorder diagnosis, Autism Spectrum Disorder genetics, Biomarkers, Endophenotypes, Humans, Twins, Monozygotic, Autistic Disorder diagnosis, Autistic Disorder genetics
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Background: The autism spectrum disorders (ASD) are common neuropsychiatric conditions of childhood for which the vast proportion of population risk is attributable to inheritance, and for which there exist few if any replicated biomarkers., Main Body: This commentary summarizes a set of recent studies involving identical (monozygotic, MZ) twins which, taken together, have significant implications for the search for biomarkers of inherited susceptibility to autism. A first is that variation-in-severity of the condition (above the threshold for clinical diagnosis) appears more strongly influenced by stochastic/non-shared environmental influences than by heredity. Second is that there exist disparate early behavioral predictors of the familial recurrence of autism, which are themselves strongly genetically influenced but largely independent from one another. The nature of these postnatal predictors is that they are trait-like, continuously distributed in the general population, and largely independent from variation in general cognition, thereby reflecting a developmental substructure for familial autism. A corollary of these findings is that autism may arise as a developmental consequence of an allostatic load of earlier-occurring liabilities, indexed by early behavioral endophenotypes, in varying permutations and combinations. The clinical threshold can be viewed as a "tipping point" at which stochastic influences and/or other non-shared environmental influences assert much stronger influence on variation-in-severity (a) than do the genetic factors which contributed to the condition in the first place, and (b) than is observed in typical development., Conclusion: Biomarkers identified on the basis of association with clinical symptom severity in ASD may reflect effects rather than causes of autism. The search for biomarkers of pathogenesis may benefit from a greater focus on traits that predict autism recurrence, among both clinical and general populations. In case-control studies, salient developmental liabilities should be systematically measured in both cases and controls, to avoid the erosion in statistical power (i.e., to detect differences) that can occur if control subjects carry sub-clinical aggregations of the same unmeasured traits that exert causal influences on the development of autism.
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- 2021
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25. Autism in neurofibromatosis type 1: misuse of covariance to dismiss autistic trait burden.
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Morris SM, Acosta MT, Garg S, Green J, Legius E, North K, Payne JM, Weiss LA, Constantino JN, and Gutmann DH
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- Child, Humans, Phenotype, Attention Deficit Disorder with Hyperactivity, Autism Spectrum Disorder complications, Autistic Disorder etiology, Neurofibromatosis 1 complications
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- 2021
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26. Brain function distinguishes female carriers and non-carriers of familial risk for autism.
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Eggebrecht AT, Dworetsky A, Hawks Z, Coalson R, Adeyemo B, Davis S, Gray D, McMichael A, Petersen SE, Constantino JN, and Pruett JR Jr
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- Adult, Autistic Disorder diagnostic imaging, Brain diagnostic imaging, Female, Heterozygote, Humans, Magnetic Resonance Imaging, Middle Aged, Risk Factors, Young Adult, Autistic Disorder genetics, Autistic Disorder physiopathology, Brain physiopathology, Genetic Predisposition to Disease
- Abstract
Background: Autism spectrum disorder (ASD) is characterized by high population-level heritability and a three-to-one male-to-female ratio that occurs independent of sex linkage. Prior research in a mixed-sex pediatric sample identified neural signatures of familial risk elicited by passive viewing of point light motion displays, suggesting the possibility that both resilience and risk of autism might be associated with brain responses to biological motion. To confirm a relationship between these signatures and inherited risk of autism, we tested them in families enriched for genetic loading through undiagnosed ("carrier") females., Methods: Using functional magnetic resonance imaging, we examined brain responses to passive viewing of point light displays-depicting biological versus non-biological motion-in a sample of undiagnosed adult females enriched for inherited susceptibility to ASD on the basis of affectation in their respective family pedigrees. Brain responses in carrier females were compared to responses in age-, SRS-, and IQ-matched non-carrier-females-i.e., females unrelated to individuals with ASD. We conducted a hypothesis-driven analysis focused on previously published regions of interest as well as exploratory, brain-wide analyses designed to characterize more fully the rich responses to this paradigm., Results: We observed robust responses to biological motion. Notwithstanding, the 12 regions implicated by prior research did not exhibit the hypothesized interaction between group (carriers vs. controls) and point light displays (biological vs. non-biological motion). Exploratory, brain-wide analyses identified this interaction in three novel regions. Post hoc analyses additionally revealed significant variations in the time course of brain activation in 20 regions spanning occipital and temporal cortex, indicating group differences in response to point light displays (irrespective of the nature of motion) for exploration in future studies., Limitations: We were unable to successfully eye-track all participants, which prevented us from being able to control for potential differences in eye gaze position., Conclusions: These methods confirmed pronounced neural signatures that differentiate brain responses to biological and scrambled motion. Our sample of undiagnosed females enriched for family genetic loading enabled discovery of numerous contrasts between carriers and non-carriers of risk of ASD that may index variations in visual attention and motion processing related to genetic susceptibility and inform our understanding of mechanisms incurred by inherited liability for ASD.
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- 2020
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27. On the Nature of Monozygotic Twin Concordance and Discordance for Autistic Trait Severity: A Quantitative Analysis.
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Castelbaum L, Sylvester CM, Zhang Y, Yu Q, and Constantino JN
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- Adolescent, Autistic Disorder genetics, Child, Child, Preschool, Databases, Factual, Databases, Genetic, Female, Gene-Environment Interaction, Genetic Association Studies, Genetic Predisposition to Disease genetics, Genotype, Humans, Male, Phenotype, Twins, Dizygotic genetics, Twins, Monozygotic genetics, Autism Spectrum Disorder genetics, Diseases in Twins genetics
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The characterizing features of autism spectrum disorder (ASD) are continuously distributed in nature; however, prior twin studies have not systematically incorporated this knowledge into estimations of concordance and discordance. We conducted a quantitative analysis of twin-twin similarity for autistic trait severity in three existing data sets involving 366 pairs of uniformly-phenotyped monozygotic (MZ) twins with and without ASD. Probandwise concordance for ASD was 96%; however, MZ trait correlations differed markedly for pairs with ASD trait burden below versus above the threshold for clinical diagnosis, with R
2 s on the order of 0.6 versus 0.1, respectively. Categorical MZ twin discordance for ASD diagnosis is rare and more appropriately operationalized by standardized quantification of twin-twin differences. Here we provide new evidence that although ASD itself is highly heritable, variation-in-severity of symptomatology above the diagnostic threshold is substantially influenced, in contrast, by non-shared environmental factors which may identify novel targets of early ASD amelioration.- Published
- 2020
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28. Expediting clinician assessment in the diagnosis of autism spectrum disorder.
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Sanchez MJ and Constantino JN
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- Adolescent, Adult, Behavior Observation Techniques, Child, Child, Preschool, Female, Humans, Male, Young Adult, Autism Spectrum Disorder diagnosis, Psychiatric Status Rating Scales standards
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Aim: To investigate a novel observational rating protocol designed to expedite clinical diagnosis of autism spectrum disorder (ASD)., Method: Two hundred and forty patients referred to a tertiary autism center (median age 8y 9mo, range 2y 6mo-34y 8mo; 188 males, 52 females) were rated using an adaptation of the Childhood Autism Rating Scale, Second Edition (CARS-2) based exclusively on patient observation (CARS-2
obs ). Scores were compared to expert diagnosis of ASD, parent-reported Social Responsiveness Scale, Second Edition (SRS-2) and, in a selected subset of patients, the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2)., Results: CARS-2obs distinguished patients with a clinical diagnosis of ASD from those with non-ASD neuropsychiatric disorders (mean score=18 vs 11.7, p<0.001). Severity ratings on the CARS-2obs correlated with the ADOS-2 (r=0.68, ρ=0.64) and SRS-2 (r=0.31, ρ=0.32). A CARS-2obs cutoff point equal to or greater than 16 demonstrated 95.8% specificity and 62.3% sensitivity in discriminating individuals with ASD from individuals without ASD in a specialty referral setting., Interpretation: The CARS-2obs allows the rapid acquisition of quantitative ratings of autistic severity by direct observation. Coupled with parent/teacher-reported symptoms and developmental history, the measure may contribute to a low-cost diagnostic paradigm in clinical and public health settings, where positive results might help reduce delays in diagnosis, and negative results could prompt further specialty assessment., What This Paper Adds: The Childhood Autism Rating Scale, Second Edition based on patient observation distinguished individuals with versus without autism spectrum disorder (ASD). A score equal to or greater than 16 on this assessment showed high specificity for a diagnosis of ASD., (© 2020 Mac Keith Press.)- Published
- 2020
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29. Corrigendum: Joint Attention and Brain Functional Connectivity in Infants and Toddlers.
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Eggebrecht AT, Elison JT, Feczko E, Todorov A, Wolff JJ, Kandala S, Adams CM, Snyder AZ, Lewis JD, Estes AM, Zwaigenbaum L, Botteron KN, McKinstry RC, Constantino JN, Evans A, Hazlett HC, Dager S, Paterson SJ, Schultz RT, Styner MA, Gerig G, Das S, Kostopoulos P, Schlaggar BL, Petersen SE, Piven J, and Pruett JR
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- 2020
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30. Neonatal CSF vasopressin concentration predicts later medical record diagnoses of autism spectrum disorder.
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Oztan O, Garner JP, Constantino JN, and Parker KJ
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- Arginine Vasopressin analysis, Arginine Vasopressin cerebrospinal fluid, Autism Spectrum Disorder cerebrospinal fluid, Autistic Disorder cerebrospinal fluid, Biomarkers cerebrospinal fluid, Female, Humans, Infant, Infant, Newborn, Male, Medical Records, Neuropeptides, Neurophysins analysis, Neurophysins cerebrospinal fluid, Oxytocin, Prospective Studies, Protein Precursors analysis, Protein Precursors cerebrospinal fluid, Social Behavior, Vasopressins cerebrospinal fluid, Autism Spectrum Disorder diagnosis, Autistic Disorder diagnosis, Vasopressins analysis
- Abstract
Autism spectrum disorder (ASD) is a brain disorder characterized by social impairments. ASD is currently diagnosed on the basis of behavioral criteria because no robust biomarkers have been identified. However, we recently found that cerebrospinal fluid (CSF) concentration of the "social" neuropeptide arginine vasopressin (AVP) is significantly lower in pediatric ASD cases vs. controls. As an initial step in establishing the direction of causation for this association, we capitalized upon a rare biomaterials collection of newborn CSF samples to conduct a quasi-prospective test of whether this association held before the developmental period when ASD first manifests. CSF samples had been collected in the course of medical care of 0- to 3-mo-old febrile infants ( n = 913) and subsequently archived at -70 °C. We identified a subset of CSF samples from individuals later diagnosed with ASD, matched them 1:2 with appropriate controls ( n = 33 total), and quantified their AVP and oxytocin (OXT) concentrations. Neonatal CSF AVP concentrations were significantly lower among ASD cases than controls and individually predicted case status, with highest precision when cases with comorbid attention-deficit/hyperactivity disorder were removed from the analysis. The associations were specific to AVP, as ASD cases and controls did not differ in neonatal CSF concentrations of the structurally related neuropeptide, OXT. These preliminary findings suggest that a neurochemical marker of ASD may be present very early in life, and if replicated in a larger, prospective study, this approach could transform how ASD is detected, both in behaviorally symptomatic children, and in infants at risk for developing it., Competing Interests: Competing interest statement: The Board of Trustees of the Leland Stanford Junior University filed a patent application related to biological measures studied herein (PCT/US2019/019029 “Methods for diagnosing and determining severity of an autism spectrum disorder”). This patent has not been granted, nor licensed, and no study author is receiving any financial compensation at this time.
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- 2020
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31. Insufficient Evidence for "Autism-Specific" Genes.
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Myers SM, Challman TD, Bernier R, Bourgeron T, Chung WK, Constantino JN, Eichler EE, Jacquemont S, Miller DT, Mitchell KJ, Zoghbi HY, Martin CL, and Ledbetter DH
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- Cohort Studies, Genetic Testing, Genotype, Humans, Reproducibility of Results, Autism Spectrum Disorder diagnosis, Autism Spectrum Disorder genetics, Uncertainty
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Despite evidence that deleterious variants in the same genes are implicated across multiple neurodevelopmental and neuropsychiatric disorders, there has been considerable interest in identifying genes that, when mutated, confer risk that is largely specific for autism spectrum disorder (ASD). Here, we review the findings and limitations of recent efforts to identify relatively "autism-specific" genes, efforts which focus on rare variants of large effect size that are thought to account for the observed phenotypes. We present a divergent interpretation of published evidence; discuss practical and theoretical issues related to studying the relationships between rare, large-effect deleterious variants and neurodevelopmental phenotypes; and describe potential future directions of this research. We argue that there is currently insufficient evidence to establish meaningful ASD specificity of any genes based on large-effect rare-variant data., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)
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- 2020
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32. Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2016.
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Maenner MJ, Shaw KA, Baio J, Washington A, Patrick M, DiRienzo M, Christensen DL, Wiggins LD, Pettygrove S, Andrews JG, Lopez M, Hudson A, Baroud T, Schwenk Y, White T, Rosenberg CR, Lee LC, Harrington RA, Huston M, Hewitt A, Esler A, Hall-Lande J, Poynter JN, Hallas-Muchow L, Constantino JN, Fitzgerald RT, Zahorodny W, Shenouda J, Daniels JL, Warren Z, Vehorn A, Salinas A, Durkin MS, and Dietz PM
- Subjects
- Child, Diagnostic and Statistical Manual of Mental Disorders, Female, Humans, Male, Prevalence, United States epidemiology, Autism Spectrum Disorder epidemiology, Population Surveillance
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Problem/condition: Autism spectrum disorder (ASD)., Period Covered: 2016., Description of System: The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance program that provides estimates of the prevalence of ASD among children aged 8 years whose parents or guardians live in 11 ADDM Network sites in the United States (Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin). Surveillance is conducted in two phases. The first phase involves review and abstraction of comprehensive evaluations that were completed by medical and educational service providers in the community. In the second phase, experienced clinicians who systematically review all abstracted information determine ASD case status. The case definition is based on ASD criteria described in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition., Results: For 2016, across all 11 sites, ASD prevalence was 18.5 per 1,000 (one in 54) children aged 8 years, and ASD was 4.3 times as prevalent among boys as among girls. ASD prevalence varied by site, ranging from 13.1 (Colorado) to 31.4 (New Jersey). Prevalence estimates were approximately identical for non-Hispanic white (white), non-Hispanic black (black), and Asian/Pacific Islander children (18.5, 18.3, and 17.9, respectively) but lower for Hispanic children (15.4). Among children with ASD for whom data on intellectual or cognitive functioning were available, 33% were classified as having intellectual disability (intelligence quotient [IQ] ≤70); this percentage was higher among girls than boys (39% versus 32%) and among black and Hispanic than white children (47%, 36%, and 27%, respectively) [corrected]. Black children with ASD were less likely to have a first evaluation by age 36 months than were white children with ASD (40% versus 45%). The overall median age at earliest known ASD diagnosis (51 months) was similar by sex and racial and ethnic groups; however, black children with IQ ≤70 had a later median age at ASD diagnosis than white children with IQ ≤70 (48 months versus 42 months)., Interpretation: The prevalence of ASD varied considerably across sites and was higher than previous estimates since 2014. Although no overall difference in ASD prevalence between black and white children aged 8 years was observed, the disparities for black children persisted in early evaluation and diagnosis of ASD. Hispanic children also continue to be identified as having ASD less frequently than white or black children., Public Health Action: These findings highlight the variability in the evaluation and detection of ASD across communities and between sociodemographic groups. Continued efforts are needed for early and equitable identification of ASD and timely enrollment in services., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Zachary Warren reports personal fees from Hoffman La Roche, grants and personal fees from Adaptive Technology Consulting, grants from Autism Speaks, grants from Cognoa, and grants from Simons Foundation; all fees and grants received are outside the submitted work. John Constantino reports a grant from Western Psychological Services outside the submitted work.
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- 2020
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33. Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years - Early Autism and Developmental Disabilities Monitoring Network, Six Sites, United States, 2016.
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Shaw KA, Maenner MJ, Baio J, Washington A, Christensen DL, Wiggins LD, Pettygrove S, Andrews JG, White T, Rosenberg CR, Constantino JN, Fitzgerald RT, Zahorodny W, Shenouda J, Daniels JL, Salinas A, Durkin MS, and Dietz PM
- Subjects
- Autism Spectrum Disorder epidemiology, Child, Preschool, Diagnostic and Statistical Manual of Mental Disorders, Early Diagnosis, Female, Humans, Male, Prevalence, United States epidemiology, Autism Spectrum Disorder diagnosis, Population Surveillance
- Abstract
Problem/condition: Autism spectrum disorder (ASD)., Period Covered: 2016., Description of System: The Early Autism and Developmental Disabilities Monitoring (Early ADDM) Network, a subset of the overall ADDM Network, is an active surveillance program that estimates ASD prevalence and monitors early identification of ASD among children aged 4 years. Children included in surveillance year 2016 were born in 2012 and had a parent or guardian who lived in the surveillance area in Arizona, Colorado, Missouri, New Jersey, North Carolina, or Wisconsin, at any time during 2016. Children were identified from records of community sources including general pediatric health clinics, special education programs, and early intervention programs. Data from comprehensive evaluations performed by community professionals were abstracted and reviewed by trained clinicians using a standardized ASD surveillance case definition with criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)., Results: In 2016, the overall ASD prevalence was 15.6 per 1,000 (one in 64) children aged 4 years for Early ADDM Network sites. Prevalence varied from 8.8 per 1,000 in Missouri to 25.3 per 1,000 in New Jersey. At every site, prevalence was higher among boys than among girls, with an overall male-to-female prevalence ratio of 3.5 (95% confidence interval [CI] = 3.1-4.1). Prevalence of ASD between non-Hispanic white (white) and non-Hispanic black (black) children was similar at each site (overall prevalence ratio: 0.9; 95% CI = 0.8-1.1). The prevalence of ASD using DSM-5 criteria was lower than the prevalence using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria at one of four sites that used criteria from both editions. Among sites where ≥60% of children aged 4 years had information about intellectual disability (intelligence quotient ≤70 or examiner's statement of intellectual disability documented in an evaluation), 53% of children with ASD had co-occurring intellectual disability. Of all children aged 4 years with ASD, 84% had a first evaluation at age ≤36 months and 71% of children who met the surveillance case definition had a previous ASD diagnosis from a community provider. Median age at first evaluation and diagnosis for this age group was 26 months and 33 months, respectively. Cumulative incidence of autism diagnoses received by age 48 months was higher for children aged 4 years than for those aged 8 years identified in Early ADDM Network surveillance areas in 2016., Interpretation: In 2016, the overall prevalence of ASD in the Early ADDM Network using DSM-5 criteria (15.6 per 1,000 children aged 4 years) was higher than the 2014 estimate using DSM-5 criteria (14.1 per 1,000). Children born in 2012 had a higher cumulative incidence of ASD diagnoses by age 48 months compared with children born in 2008, which indicates more early identification of ASD in the younger group. The disparity in ASD prevalence has decreased between white and black children. Prevalence of co-occurring intellectual disability was higher than in 2014, suggesting children with intellectual disability continue to be identified at younger ages. More children received evaluations by age 36 months in 2016 than in 2014, which is consistent with Healthy People 2020 goals. Median age at earliest ASD diagnosis has not changed considerably since 2014., Public Health Action: More children aged 4 years with ASD are being evaluated by age 36 months and diagnosed by age 48 months, but there is still room for improvement in early identification. Timely evaluation of children by community providers as soon as developmental concerns have been identified might result in earlier ASD diagnoses, earlier receipt of evidence-based interventions, and improved developmental outcomes., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. John Constantino reports grants from Western Psychological Services outside the submitted work.
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- 2020
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34. Quantitative trait variation in ASD probands and toddler sibling outcomes at 24 months.
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Girault JB, Swanson MR, Meera SS, Grzadzinski RL, Shen MD, Burrows CA, Wolff JJ, Pandey J, John TS, Estes A, Zwaigenbaum L, Botteron KN, Hazlett HC, Dager SR, Schultz RT, Constantino JN, and Piven J
- Subjects
- Adolescent, Child, Child, Preschool, Female, Humans, Infant, Male, Phenotype, Autism Spectrum Disorder genetics, Siblings
- Abstract
Background: Younger siblings of children with autism spectrum disorder (ASD) are at increased likelihood of receiving an ASD diagnosis and exhibiting other developmental concerns. It is unknown how quantitative variation in ASD traits and broader developmental domains in older siblings with ASD (probands) may inform outcomes in their younger siblings., Methods: Participants included 385 pairs of toddler siblings and probands from the Infant Brain Imaging Study. ASD probands (mean age 5.5 years, range 1.7 to 15.5 years) were phenotyped using the Autism Diagnostic Interview-Revised (ADI-R), the Social Communication Questionnaire (SCQ), and the Vineland Adaptive Behavior Scales, Second Edition (VABS-II). Siblings were assessed using the ADI-R, VABS-II, Mullen Scales of Early Learning (MSEL), and Autism Diagnostic Observation Schedule (ADOS) and received a clinical best estimate diagnosis at 24 months using DSM-IV-TR criteria (n = 89 concordant for ASD; n = 296 discordant). We addressed two aims: (1) to determine whether proband characteristics are predictive of recurrence in siblings and (2) to assess associations between proband traits and sibling dimensional outcomes at 24 months., Results: Regarding recurrence risk, proband SCQ scores were found to significantly predict sibling 24-month diagnostic outcome (OR for a 1-point increase in SCQ = 1.06; 95% CI = 1.01, 1.12). Regarding quantitative trait associations, we found no significant correlations in ASD traits among proband-sibling pairs. However, quantitative variation in proband adaptive behavior, communication, and expressive and receptive language was significantly associated with sibling outcomes in the same domains; proband scores explained 9-18% of the variation in cognition and behavior in siblings with ASD. Receptive language was particularly strongly associated in concordant pairs (ICC = 0.50, p < 0.001)., Conclusions: Proband ASD symptomology, indexed by the SCQ, is a predictor of familial ASD recurrence risk. While quantitative variation in social communication and restricted and repetitive behavior were not associated among sibling pairs, standardized ratings of proband language and communication explained significant variation in the same domains in the sibling at 24 months, especially among toddlers with an ASD diagnosis. These data suggest that proband characteristics can alert clinicians to areas of developmental concern for young children with familial risk for ASD.
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- 2020
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35. Cellular and molecular characterization of multiplex autism in human induced pluripotent stem cell-derived neurons.
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Lewis EMA, Meganathan K, Baldridge D, Gontarz P, Zhang B, Bonni A, Constantino JN, and Kroll KL
- Subjects
- Adolescent, Autistic Disorder genetics, Cell Differentiation genetics, Child, Preschool, Cluster Analysis, Family, Female, Gene Expression Regulation, Genotype, Humans, Infant, Infant, Newborn, Interneurons pathology, Male, Models, Biological, Neural Stem Cells metabolism, Pedigree, Phenotype, Pregnancy, Reproducibility of Results, Transcriptome genetics, Autistic Disorder pathology, Cell Communication, Induced Pluripotent Stem Cells pathology, Neurons pathology
- Abstract
Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with pronounced heritability in the general population. This is largely attributable to the effects of polygenic susceptibility, with inherited liability exhibiting distinct sex differences in phenotypic expression. Attempts to model ASD in human cellular systems have principally involved rare de novo mutations associated with ASD phenocopies. However, by definition, these models are not representative of polygenic liability, which accounts for the vast share of population-attributable risk., Methods: Here, we performed what is, to our knowledge, the first attempt to model multiplex autism using patient-derived induced pluripotent stem cells (iPSCs) in a family manifesting incremental degrees of phenotypic expression of inherited liability (absent, intermediate, severe). The family members share an inherited variant of uncertain significance (VUS) in GPD2 , a gene that was previously associated with developmental disability but here is insufficient by itself to cause ASD. iPSCs from three first-degree relatives and an unrelated control were differentiated into both cortical excitatory (cExN) and cortical inhibitory (cIN) neurons, and cellular phenotyping and transcriptomic analysis were conducted., Results: cExN neurospheres from the two affected individuals were reduced in size, compared to those derived from unaffected related and unrelated individuals. This reduction was, at least in part, due to increased apoptosis of cells from affected individuals upon initiation of cExN neural induction. Likewise, cIN neural progenitor cells from affected individuals exhibited increased apoptosis, compared to both unaffected individuals. Transcriptomic analysis of both cExN and cIN neural progenitor cells revealed distinct molecular signatures associated with affectation, including the misregulation of suites of genes associated with neural development, neuronal function, and behavior, as well as altered expression of ASD risk-associated genes., Conclusions: We have provided evidence of morphological, physiological, and transcriptomic signatures of polygenic liability to ASD from an analysis of cellular models derived from a multiplex autism family. ASD is commonly inherited on the basis of additive genetic liability. Therefore, identifying convergent cellular and molecular phenotypes resulting from polygenic and monogenic susceptibility may provide a critical bridge for determining which of the disparate effects of rare highly deleterious mutations might also apply to common autistic syndromes., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s). 2019.)
- Published
- 2019
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36. Adaptation of the Clinical Dementia Rating Scale for adults with Down syndrome.
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Lessov-Schlaggar CN, Del Rosario OL, Morris JC, Ances BM, Schlaggar BL, and Constantino JN
- Subjects
- Adolescent, Adult, Down Syndrome psychology, Female, Humans, Male, Middle Aged, Young Adult, Down Syndrome diagnosis, Mental Status and Dementia Tests standards
- Abstract
Background: Adults with Down syndrome (DS) are at increased risk for Alzheimer disease dementia, and there is a pressing need for the development of assessment instruments that differentiate chronic cognitive impairment, acute neuropsychiatric symptomatology, and dementia in this population of patients., Methods: We adapted a widely used instrument, the Clinical Dementia Rating (CDR) Scale, which is a component of the Uniform Data Set used by all federally funded Alzheimer Disease Centers for use in adults with DS, and tested the instrument among 34 DS patients recruited from the community. The participants were assessed using two versions of the modified CDR-a caregiver questionnaire and an in-person interview involving both the caregiver and the DS adult. Assessment also included the Dementia Scale for Down Syndrome (DSDS) and the Raven's Progressive Matrices to estimate IQ., Results: Both modified questionnaire and interview instruments captured a range of cognitive impairments, a majority of which were found to be chronic when accounting for premorbid function. Two individuals in the sample were strongly suspected to have early dementia, both of whom had elevated scores on the modified CDR instruments. Among individuals rated as having no dementia based on the DSDS, about half showed subthreshold impairments on the modified CDR instruments; there was substantial agreement between caregiver questionnaire screening and in-person interview of caregivers and DS adults., Conclusions: The modified questionnaire and interview instruments capture a range of impairment in DS adults, including subthreshold symptomatology, and the instruments provide complementary information relevant to the ascertainment of dementia in DS. Decline was seen across all cognitive domains and was generally positively related to age and negatively related to IQ. Most importantly, adjusting instrument scores for chronic, premorbid impairment drastically shifted the distribution toward lower (no impairment) scores.
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- 2019
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37. 22q11.2 duplication: a review of neuropsychiatric correlates and a newly observed case of prototypic sociopathy.
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Vyas S, Constantino JN, and Baldridge D
- Subjects
- Aggression, Attention Deficit and Disruptive Behavior Disorders genetics, Child, Chromosomes, Human, Pair 22 genetics, Chromosomes, Human, Pair 22 metabolism, Female, Humans, Male, Mental Disorders genetics, Abnormalities, Multiple genetics, Abnormalities, Multiple metabolism, Antisocial Personality Disorder genetics, Chromosome Duplication genetics, DiGeorge Syndrome genetics, DiGeorge Syndrome metabolism
- Abstract
Callous-unemotional (CU) traits are highly disabling behavioral characteristics, common predictors of delinquency and criminality, and pathognomonic for antisocial personality disorder. They are highly heritable, but their specific molecular genetic causes are unknown. Here, we briefly review the literature on neuropsychiatric correlates of 22q11.2 duplication and describe a newly identified case of a 737-kb microduplication within the low copy repeat (LCR) B-D region, involving a 13-yr-old early adoptee with mild developmental delay and severe, chronic antisocial behavior of early childhood onset. When psychiatric symptoms have been reported in relation to duplications in this specific region, 19% of the reports feature aggression-but never previously CU traits-as a component of the phenotype. We discuss the potential implications of gain of function in this chromosomal region for heritable origins of sociopathy and their possible relation to genetic influences on aggression., (© 2019 Vyas et al.; Published by Cold Spring Harbor Laboratory Press.)
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- 2019
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38. Behavioral predictors of autism recurrence are genetically independent and influence social reciprocity: evidence that polygenic ASD risk is mediated by separable elements of developmental liability.
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Pohl A, Jones WR, Marrus N, Zhang Y, Klin A, and Constantino JN
- Subjects
- Autism Spectrum Disorder diagnosis, Autism Spectrum Disorder genetics, Child, Preschool, Diseases in Twins diagnosis, Diseases in Twins genetics, Female, Genetic Predisposition to Disease, Humans, Male, Multifactorial Inheritance, Attention physiology, Autism Spectrum Disorder etiology, Diseases in Twins etiology, Endophenotypes, Motor Skills physiology, Parents, Social Behavior
- Abstract
The preponderance of causal influence on total population attributable risk for autism is polygenic in nature, but it is not known how such liability engenders the development of the syndrome. In 348 epidemiologically ascertained toddler twins, we explored associations between autistic traits and three robust, highly heritable predictors of familial autism recurrence: variation in attention, motor coordination, and parental autistic trait burden. We observed that these predictors-despite collectively accounting for over one third of variance in clinical recurrence-are genetically independent in early childhood, and jointly account for a comparable share of inherited influence on early reciprocal social behavior in the general population. Thus, combinations of what are otherwise discrete, inherited behavioral liabilities-some not specific to autism-appear to jointly mediate common genetic risk for autism. Linking genetic variants and neural signatures to these independent traits prior to the onset of the development of autism will enhance understanding of mechanisms of causation in familial autistic syndromes. Moreover, ongoing biomarker discovery efforts will benefit from controlling for the effects of these common liabilities, which aggregate in individuals with autism but are also continuously distributed in "controls". Finally, early inherited liabilities that participate in the early ontogeny of autistic syndromes represent parsimonious intervention targets for polygenic forms of the condition, and represent candidate trans-diagnostic endophenotypes of potential relevance to a diversity of neuropsychiatric syndromes.
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- 2019
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39. AMPA receptor GluA2 subunit defects are a cause of neurodevelopmental disorders.
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Salpietro V, Dixon CL, Guo H, Bello OD, Vandrovcova J, Efthymiou S, Maroofian R, Heimer G, Burglen L, Valence S, Torti E, Hacke M, Rankin J, Tariq H, Colin E, Procaccio V, Striano P, Mankad K, Lieb A, Chen S, Pisani L, Bettencourt C, Männikkö R, Manole A, Brusco A, Grosso E, Ferrero GB, Armstrong-Moron J, Gueden S, Bar-Yosef O, Tzadok M, Monaghan KG, Santiago-Sim T, Person RE, Cho MT, Willaert R, Yoo Y, Chae JH, Quan Y, Wu H, Wang T, Bernier RA, Xia K, Blesson A, Jain M, Motazacker MM, Jaeger B, Schneider AL, Boysen K, Muir AM, Myers CT, Gavrilova RH, Gunderson L, Schultz-Rogers L, Klee EW, Dyment D, Osmond M, Parellada M, Llorente C, Gonzalez-Peñas J, Carracedo A, Van Haeringen A, Ruivenkamp C, Nava C, Heron D, Nardello R, Iacomino M, Minetti C, Skabar A, Fabretto A, Raspall-Chaure M, Chez M, Tsai A, Fassi E, Shinawi M, Constantino JN, De Zorzi R, Fortuna S, Kok F, Keren B, Bonneau D, Choi M, Benzeev B, Zara F, Mefford HC, Scheffer IE, Clayton-Smith J, Macaya A, Rothman JE, Eichler EE, Kullmann DM, and Houlden H
- Subjects
- Adolescent, Adult, Brain diagnostic imaging, Child, Child, Preschool, Cohort Studies, Female, Heterozygote, Humans, Infant, Loss of Function Mutation, Magnetic Resonance Imaging, Male, Neurodevelopmental Disorders diagnostic imaging, Young Adult, Intellectual Disability genetics, Neurodevelopmental Disorders genetics, Receptors, AMPA genetics
- Abstract
AMPA receptors (AMPARs) are tetrameric ligand-gated channels made up of combinations of GluA1-4 subunits encoded by GRIA1-4 genes. GluA2 has an especially important role because, following post-transcriptional editing at the Q607 site, it renders heteromultimeric AMPARs Ca
2+ -impermeable, with a linear relationship between current and trans-membrane voltage. Here, we report heterozygous de novo GRIA2 mutations in 28 unrelated patients with intellectual disability (ID) and neurodevelopmental abnormalities including autism spectrum disorder (ASD), Rett syndrome-like features, and seizures or developmental epileptic encephalopathy (DEE). In functional expression studies, mutations lead to a decrease in agonist-evoked current mediated by mutant subunits compared to wild-type channels. When GluA2 subunits are co-expressed with GluA1, most GRIA2 mutations cause a decreased current amplitude and some also affect voltage rectification. Our results show that de-novo variants in GRIA2 can cause neurodevelopmental disorders, complementing evidence that other genetic causes of ID, ASD and DEE also disrupt glutamatergic synaptic transmission.- Published
- 2019
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40. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 4 Years - Early Autism and Developmental Disabilities Monitoring Network, Seven Sites, United States, 2010, 2012, and 2014.
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Christensen DL, Maenner MJ, Bilder D, Constantino JN, Daniels J, Durkin MS, Fitzgerald RT, Kurzius-Spencer M, Pettygrove SD, Robinson C, Shenouda J, White T, Zahorodny W, Pazol K, and Dietz P
- Subjects
- Child, Preschool, Female, Humans, Male, Prevalence, United States epidemiology, Autism Spectrum Disorder epidemiology, Public Health Surveillance
- Abstract
Problem/condition: Autism spectrum disorder (ASD) is estimated to affect up to 3% of children in the United States. Public health surveillance for ASD among children aged 4 years provides information about trends in prevalence, characteristics of children with ASD, and progress made toward decreasing the age of identification of ASD so that evidence-based interventions can begin as early as possible., Period Covered: 2010, 2012, and 2014., Description of System: The Early Autism and Developmental Disabilities Monitoring (Early ADDM) Network is an active surveillance system that provides biennial estimates of the prevalence and characteristics of ASD among children aged 4 years whose parents or guardians lived within designated sites. During surveillance years 2010, 2012, or 2014, data were collected in seven sites: Arizona, Colorado, Missouri, New Jersey, North Carolina, Utah, and Wisconsin. The Early ADDM Network is a subset of the broader ADDM Network (which included 13 total sites over the same period) that has been conducting ASD surveillance among children aged 8 years since 2000. Each Early ADDM site covers a smaller geographic area than the broader ADDM Network. Early ADDM ASD surveillance is conducted in two phases using the same methods and project staff members as the ADDM Network. The first phase consists of reviewing and abstracting data from children's records, including comprehensive evaluations performed by community professionals. Sources for these evaluations include general pediatric health clinics and specialized programs for children with developmental disabilities. In addition, special education records (for children aged ≥3 years) were reviewed for Arizona, Colorado, New Jersey, North Carolina, and Utah, and early intervention records (for children aged 0 to <3 years) were reviewed for New Jersey, North Carolina, Utah, and Wisconsin; in Wisconsin, early intervention records were reviewed for 2014 only. The second phase involves a review of the abstracted evaluations by trained clinicians using a standardized case definition and method. A child is considered to meet the surveillance case definition for ASD if one or more comprehensive evaluations of that child completed by a qualified professional describes behaviors consistent with the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR) diagnostic criteria for any of the following conditions: autistic disorder, pervasive developmental disorder-not otherwise specified (PDD-NOS, including atypical autism), or Asperger disorder (2010, 2012, and 2014). For 2014 only, prevalence estimates based on surveillance case definitions according to DSM-IV-TR and the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) were compared. This report provides estimates of overall ASD prevalence and prevalence by sex and race/ethnicity; characteristics of children aged 4 years with ASD, including age at first developmental evaluation, age at ASD diagnosis, and cognitive function; and trends in ASD prevalence and characteristics among Early ADDM sites with data for all 3 surveillance years (2010, 2012, and 2014), including comparisons with children aged 8 years living in the same geographic area. Analyses of time trends in ASD prevalence are restricted to the three sites that contributed data for all 3 surveillance years with consistent data sources (Arizona, Missouri, and New Jersey)., Results: The overall ASD prevalence was 13.4 per 1,000 children aged 4 years in 2010, 15.3 in 2012, and 17.0 in 2014 for Early ADDM sites with data for the specific years. ASD prevalence was determined using a surveillance case definition based on DSM-IV-TR. Within each surveillance year, ASD prevalence among children aged 4 years varied across surveillance sites and was lowest each year for Missouri (8.5, 8.1, and 9.6 per 1,000, for 2010, 2012, and 2014, respectively) and highest each year for New Jersey (19.7, 22.1, and 28.4 per 1,000, for the same years, respectively). Aggregated prevalence estimates were higher for sites that reviewed education and health care records than for sites that reviewed only health care records. Among all participating sites and years, ASD prevalence among children aged 4 years was consistently higher among boys than girls; prevalence ratios ranged from 2.6 (Arizona and Wisconsin in 2010) to 5.2 boys per one girl (Colorado in 2014). In 2010, ASD prevalence was higher among non-Hispanic white children than among Hispanic children in Arizona and non-Hispanic black children in Missouri; no other differences were observed by race/ethnicity. Among four sites with ≥60% data on cognitive test scores (Arizona, New Jersey, North Carolina, and Utah), the frequency of co-occurring intellectual disabilities was significantly higher among children aged 4 years than among those aged 8 years for each site in each surveillance year except Arizona in 2010. The percentage of children with ASD who had a first evaluation by age 36 months ranged from 48.8% in Missouri in 2012 to 88.9% in Wisconsin in 2014. The percentage of children with a previous ASD diagnosis from a community provider varied by site, ranging from 43.0% for Arizona in 2012 to 86.5% for Missouri in 2012. The median age at earliest known ASD diagnosis varied from 28 months in North Carolina in 2014 to 39.0 months in Missouri and Wisconsin in 2012. In 2014, the ASD prevalence based on the DSM-IV-TR case definition was 20% higher than the prevalence based on the DSM-5 (17.0 versus 14.1 per 1,000, respectively). Trends in ASD prevalence and characteristics among children aged 4 years during the study period were assessed for the three sites with data for all 3 years and consistent data sources (Arizona, Missouri, and New Jersey) using the DSM-IV-TR case definition; prevalence was higher in 2014 than in 2010 among children aged 4 years in New Jersey and was stable in Arizona and Missouri. In Missouri, ASD prevalence was higher among children aged 8 years than among children aged 4 years. The percentage of children with ASD who had a comprehensive evaluation by age 36 months was stable in Arizona and Missouri and decreased in New Jersey. In the three sites, no change occurred in the age at earliest known ASD diagnosis during 2010-2014., Interpretation: The findings suggest that ASD prevalence among children aged 4 years was higher in 2014 than in 2010 in one site and remained stable in others. Among children with ASD, the frequency of cognitive impairment was higher among children aged 4 years than among those aged 8 years and suggests that surveillance at age 4 years might more often include children with more severe symptoms or those with co-occurring conditions such as intellectual disability. In the sites with data for all years and consistent data sources, no change in the age at earliest known ASD diagnosis was found, and children received their first developmental evaluation at the same or a later age in 2014 compared with 2010. Delays in the initiation of a first developmental evaluation might adversely affect children by delaying access to treatment and special services that can improve outcomes for children with ASD., Public Health Action: Efforts to increase awareness of ASD and improve the identification of ASD by community providers can facilitate early diagnosis of children with ASD. Heterogeneity of results across sites suggests that community-level differences in evaluation and diagnostic services as well as access to data sources might affect estimates of ASD prevalence and age of identification. Continuing improvements in providing developmental evaluations to children as soon as developmental concerns are identified might result in earlier ASD diagnoses and earlier receipt of services, which might improve developmental outcomes., Competing Interests: Deborah Bilder reports personal fees from Audentes Therapeutics and personal fees from BioMarin Pharmaceuticals outside the submitted work. John Constantino receives royalties from Western Psychological Services for the commercial distribution of the Social Responsiveness Scale.
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- 2019
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41. Early behavioral indices of inherited liability to autism.
- Author
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Constantino JN
- Subjects
- Autistic Disorder diagnosis, Child, Preschool, Early Diagnosis, Female, Humans, Male, Autistic Disorder genetics, Genetic Predisposition to Disease
- Abstract
Objective: The observed heterogeneity of autism spectrum disorder (ASD)-and the diversity of rare germline mutations with which it has been associated-has been difficult to reconcile with knowledge of its pronounced heritability in the population., Methods: This article reviews and synthesizes recent family and developmental studies incorporating behavioral indices of inherited risk for ASD., Results: Autism may arise from critical combinations of early inherited neurobehavioral susceptibilities-some specific to autism, some not-each of which may be traceable to partially-independent sets of genetic variation. These susceptibilities and their respective genetic origins may not relate to the characterizing symptoms of autism (after it develops) in a straightforward way, and may account for "missing heritability" in molecular genetic studies., Conclusions: Within-individual aggregations of a finite set of early inherited neurobehavioral susceptibilities-each individually common in the population-may account for a significant share of the heritability of ASD. Comprehensive identification of these underlying traits my help elucidate specific early intervention targets in individual patients, especially if autism represents a developmental consequence of earlier-interacting susceptibilities. Scientific understanding of the early ontogeny of autism will benefit from epidemiologically-rigorous, genetically-informative studies of robust endophenotypic candidates whose inter-relationships in infancy are mapped and normed.
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- 2019
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42. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012.
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Christensen DL, Braun KVN, Baio J, Bilder D, Charles J, Constantino JN, Daniels J, Durkin MS, Fitzgerald RT, Kurzius-Spencer M, Lee LC, Pettygrove S, Robinson C, Schulz E, Wells C, Wingate MS, Zahorodny W, and Yeargin-Allsopp M
- Subjects
- Autism Spectrum Disorder ethnology, Child, Ethnicity statistics & numerical data, Female, Humans, Male, Prevalence, Risk Factors, United States epidemiology, Autism Spectrum Disorder epidemiology, Epidemiological Monitoring
- Abstract
Problem/condition: Autism spectrum disorder (ASD)., Period Covered: 2012., Description of System: The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system that provides estimates of the prevalence and characteristics of ASD among children aged 8 years whose parents or guardians reside in 11 ADDM Network sites in the United States (Arkansas, Arizona, Colorado, Georgia, Maryland, Missouri, New Jersey, North Carolina, South Carolina, Utah, and Wisconsin). Surveillance to determine ASD case status is conducted in two phases. The first phase consists of screening and abstracting comprehensive evaluations performed by professional service providers in the community. Data sources identified for record review are categorized as either 1) education source type, including developmental evaluations to determine eligibility for special education services or 2) health care source type, including diagnostic and developmental evaluations. The second phase involves the review of all abstracted evaluations by trained clinicians to determine ASD surveillance case status. A child meets the surveillance case definition for ASD if one or more comprehensive evaluations of that child completed by a qualified professional describes behaviors that are consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision diagnostic criteria for any of the following conditions: autistic disorder, pervasive developmental disorder-not otherwise specified (including atypical autism), or Asperger disorder. This report provides ASD prevalence estimates for children aged 8 years living in catchment areas of the ADDM Network sites in 2012, overall and stratified by sex, race/ethnicity, and the type of source records (education and health records versus health records only). In addition, this report describes the proportion of children with ASD with a score consistent with intellectual disability on a standardized intellectual ability test, the age at which the earliest known comprehensive evaluation was performed, the proportion of children with a previous ASD diagnosis, the specific type of ASD diagnosis, and any special education eligibility classification., Results: For 2012, the combined estimated prevalence of ASD among the 11 ADDM Network sites was 14.5 per 1,000 (one in 69) children aged 8 years. Estimated prevalence was significantly higher among boys aged 8 years (23.4 per 1,000) than among girls aged 8 years (5.2 per 1,000). Estimated ASD prevalence was significantly higher among non-Hispanic white children aged 8 years (15.3 per 1,000) compared with non-Hispanic black children (13.1 per 1,000), and Hispanic (10.2 per 1,000) children aged 8 years. Estimated prevalence varied widely among the 11 ADDM Network sites, ranging from 8.2 per 1,000 children aged 8 years (in the area of the Maryland site where only health care records were reviewed) to 24.6 per 1,000 children aged 8 years (in New Jersey, where both education and health care records were reviewed). Estimated prevalence was higher in surveillance sites where education records and health records were reviewed compared with sites where health records only were reviewed (17.1 per 1,000 and 10.4 per 1,000 children aged 8 years, respectively; p<0.05). Among children identified with ASD by the ADDM Network, 82% had a previous ASD diagnosis or educational classification; this did not vary by sex or between non-Hispanic white and non-Hispanic black children. A lower percentage of Hispanic children (78%) had a previous ASD diagnosis or classification compared with non-Hispanic white children (82%) and with non-Hispanic black children (84%). The median age at earliest known comprehensive evaluation was 40 months, and 43% of children had received an earliest known comprehensive evaluation by age 36 months. The percentage of children with an earliest known comprehensive evaluation by age 36 months was similar for boys and girls, but was higher for non-Hispanic white children (45%) compared with non-Hispanic black children (40%) and Hispanic children (39%)., Interpretation: Overall estimated ASD prevalence was 14.5 per 1,000 children aged 8 years in the ADDM Network sites in 2012. The higher estimated prevalence among sites that reviewed both education and health records suggests the role of special education systems in providing comprehensive evaluations and services to children with developmental disabilities. Disparities by race/ethnicity in estimated ASD prevalence, particularly for Hispanic children, as well as disparities in the age of earliest comprehensive evaluation and presence of a previous ASD diagnosis or classification, suggest that access to treatment and services might be lacking or delayed for some children., Public Health Action: The ADDM Network will continue to monitor the prevalence and characteristics of ASD among children aged 8 years living in selected sites across the United States. Recommendations from the ADDM Network include enhancing strategies to 1) lower the age of first evaluation of ASD by community providers in accordance with the Healthy People 2020 goal that children with ASD are evaluated by age 36 months and begin receiving community-based support and services by age 48 months; 2) reduce disparities by race/ethnicity in identified ASD prevalence, the age of first comprehensive evaluation, and presence of a previous ASD diagnosis or classification; and 3) assess the effect on ASD prevalence of the revised ASD diagnostic criteria published in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.
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- 2018
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43. Language delay aggregates in toddler siblings of children with autism spectrum disorder.
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Marrus N, Hall LP, Paterson SJ, Elison JT, Wolff JJ, Swanson MR, Parish-Morris J, Eggebrecht AT, Pruett JR Jr, Hazlett HC, Zwaigenbaum L, Dager S, Estes AM, Schultz RT, Botteron KN, Piven J, and Constantino JN
- Subjects
- Autism Spectrum Disorder physiopathology, Autism Spectrum Disorder psychology, Brain physiopathology, Child, Preschool, Female, Genetic Predisposition to Disease, Humans, Infant, Language Development Disorders physiopathology, Male, Prospective Studies, Autism Spectrum Disorder complications, Autism Spectrum Disorder genetics, Endophenotypes, Language Development Disorders complications, Language Development Disorders genetics, Siblings psychology
- Abstract
Background: Language delay is extremely common in children with autism spectrum disorder (ASD), yet it is unclear whether measurable variation in early language is associated with genetic liability for ASD. Assessment of language development in unaffected siblings of children with ASD can inform whether decreased early language ability aggregates with inherited risk for ASD and serves as an ASD endophenotype., Methods: We implemented two approaches: (1) a meta-analysis of studies comparing language delay, a categorical indicator of language function, and language scores, a continuous metric, in unaffected toddlers at high and low familial risk for ASD, and (2) a parallel analysis of 350 unaffected 24-month-olds in the Infant Brain Imaging Study (IBIS), a prospective study of infants at high and low familial risk for ASD. An advantage of the former was its detection of group differences from pooled data across unique samples; an advantage of the latter was its sensitivity in quantifying early manifestations of language delay while accounting for covariates within a single large sample., Results: Meta-analysis showed that high-risk siblings without ASD (HR-noASD) were three to four times more likely to exhibit language delay versus low-risk siblings without ASD (LR-noASD) and had lower mean receptive and expressive language scores. Analyses of IBIS data corroborated that language delay, specifically receptive language delay, was more frequent in the HR-noASD (n = 235) versus LR-noASD group (n = 115). IBIS language scores were continuously and unimodally distributed, with a pathological shift towards decreased language function in HR-noASD siblings. The elevated inherited risk for ASD was associated with lower receptive and expressive language scores when controlling for sociodemographic factors. For receptive but not expressive language, the effect of risk group remained significant even when controlling for nonverbal cognition., Conclusions: Greater frequency of language delay and a lower distribution of language scores in high-risk, unaffected toddler-aged siblings support decreased early language ability as an endophenotype for ASD, with a more pronounced effect for receptive versus expressive language. Further characterization of language development is warranted to refine genetic investigations of ASD and to elucidate factors influencing the progression of core autistic traits and related symptoms.
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- 2018
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44. Event-related potential (ERP) correlates of face processing in verbal children with autism spectrum disorders (ASD) and their first-degree relatives: a family study.
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Sysoeva OV, Constantino JN, and Anokhin AP
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- Adolescent, Adult, Child, Electroencephalography, Fathers, Humans, Male, Middle Aged, Siblings, Young Adult, Autism Spectrum Disorder physiopathology, Evoked Potentials, Face, Pattern Recognition, Visual
- Abstract
Background: Inherited abnormalities of perception, recognition, and attention to faces have been implicated in the etiology of autism spectrum disorders (ASD) including abnormal components of event-related brain potentials (ERP) elicited by faces., Methods: We examined familial aggregation of face processing ERP abnormalities previously implicated in ASD in 49 verbal individuals with ASD, 36 unaffected siblings (US), 18 unaffected fathers (UF), and 53 unrelated controls (UC). The ASD, US, and UC groups ranged in age from 12 to 21 years, the UF group ranged in age from 30 to 56 years. ERP responses to images of upright and inverted faces and houses were analyzed under disparate EEG reference schemes., Results: Face-sensitive features of N170 and P1 were readily observed in all groups. Differences between ASD and control groups depended upon the EEG reference scheme. Notably, the superiority of face over object for N170 latency was attenuated in ASD subjects, but not their relatives; this occurred exclusively with the average reference. The difference in N170 amplitude between inverted and upright faces was reduced in both ASD and US groups relative to UC, but this effect was significant only with the vertex reference. Furthermore, similar group differences were observed for both inverted faces and inverted houses, suggesting a lack of face specificity for the attenuation of the N170 inversion effect in ASD., Conclusion: The present findings refine understanding of face processing ERPs in ASD. These data provide only modest evidence for highly-selective ASD-sensitive ERP features, and underscore the sensitivity of these biomarkers to ERP reference scheme. These schemes have varied across published studies and must be accounted for in future studies of the relationship between these commonly acquired ERP characteristics, genotype, and ASD., Competing Interests: Individual informed consent was obtained from all subjects aged 18 and older and from parents of subjects below age 18. All subjects below age 18 who had capacity to provide assent were afforded opportunity to do so and were only included in the study if they gave assent.JNC receives royalties from Western Psychological Services for the commercial distribution of the Social Responsiveness Scale, a quantitative measure of autistic traits for ages 30 months through adulthood. OS and AA declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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- 2018
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45. Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014.
- Author
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Baio J, Wiggins L, Christensen DL, Maenner MJ, Daniels J, Warren Z, Kurzius-Spencer M, Zahorodny W, Robinson Rosenberg C, White T, Durkin MS, Imm P, Nikolaou L, Yeargin-Allsopp M, Lee LC, Harrington R, Lopez M, Fitzgerald RT, Hewitt A, Pettygrove S, Constantino JN, Vehorn A, Shenouda J, Hall-Lande J, Van Naarden Braun K, and Dowling NF
- Subjects
- Child, Female, Humans, Male, Prevalence, United States epidemiology, Autism Spectrum Disorder epidemiology, Population Surveillance
- Abstract
Problem/condition: Autism spectrum disorder (ASD)., Period Covered: 2014., Description of System: The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system that provides estimates of the prevalence of autism spectrum disorder (ASD) among children aged 8 years whose parents or guardians reside within 11 ADDM sites in the United States (Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin). ADDM surveillance is conducted in two phases. The first phase involves review and abstraction of comprehensive evaluations that were completed by professional service providers in the community. Staff completing record review and abstraction receive extensive training and supervision and are evaluated according to strict reliability standards to certify effective initial training, identify ongoing training needs, and ensure adherence to the prescribed methodology. Record review and abstraction occurs in a variety of data sources ranging from general pediatric health clinics to specialized programs serving children with developmental disabilities. In addition, most of the ADDM sites also review records for children who have received special education services in public schools. In the second phase of the study, all abstracted information is reviewed systematically by experienced clinicians to determine ASD case status. A child is considered to meet the surveillance case definition for ASD if he or she displays behaviors, as described on one or more comprehensive evaluations completed by community-based professional providers, consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) diagnostic criteria for autistic disorder; pervasive developmental disorder-not otherwise specified (PDD-NOS, including atypical autism); or Asperger disorder. This report provides updated ASD prevalence estimates for children aged 8 years during the 2014 surveillance year, on the basis of DSM-IV-TR criteria, and describes characteristics of the population of children with ASD. In 2013, the American Psychiatric Association published the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), which made considerable changes to ASD diagnostic criteria. The change in ASD diagnostic criteria might influence ADDM ASD prevalence estimates; therefore, most (85%) of the records used to determine prevalence estimates based on DSM-IV-TR criteria underwent additional review under a newly operationalized surveillance case definition for ASD consistent with the DSM-5 diagnostic criteria. Children meeting this new surveillance case definition could qualify on the basis of one or both of the following criteria, as documented in abstracted comprehensive evaluations: 1) behaviors consistent with the DSM-5 diagnostic features; and/or 2) an ASD diagnosis, whether based on DSM-IV-TR or DSM-5 diagnostic criteria. Stratified comparisons of the number of children meeting either of these two case definitions also are reported., Results: For 2014, the overall prevalence of ASD among the 11 ADDM sites was 16.8 per 1,000 (one in 59) children aged 8 years. Overall ASD prevalence estimates varied among sites, from 13.1-29.3 per 1,000 children aged 8 years. ASD prevalence estimates also varied by sex and race/ethnicity. Males were four times more likely than females to be identified with ASD. Prevalence estimates were higher for non-Hispanic white (henceforth, white) children compared with non-Hispanic black (henceforth, black) children, and both groups were more likely to be identified with ASD compared with Hispanic children. Among the nine sites with sufficient data on intellectual ability, 31% of children with ASD were classified in the range of intellectual disability (intelligence quotient [IQ] <70), 25% were in the borderline range (IQ 71-85), and 44% had IQ scores in the average to above average range (i.e., IQ >85). The distribution of intellectual ability varied by sex and race/ethnicity. Although mention of developmental concerns by age 36 months was documented for 85% of children with ASD, only 42% had a comprehensive evaluation on record by age 36 months. The median age of earliest known ASD diagnosis was 52 months and did not differ significantly by sex or race/ethnicity. For the targeted comparison of DSM-IV-TR and DSM-5 results, the number and characteristics of children meeting the newly operationalized DSM-5 case definition for ASD were similar to those meeting the DSM-IV-TR case definition, with DSM-IV-TR case counts exceeding DSM-5 counts by less than 5% and approximately 86% overlap between the two case definitions (kappa = 0.85)., Interpretation: Findings from the ADDM Network, on the basis of 2014 data reported from 11 sites, provide updated population-based estimates of the prevalence of ASD among children aged 8 years in multiple communities in the United States. The overall ASD prevalence estimate of 16.8 per 1,000 children aged 8 years in 2014 is higher than previously reported estimates from the ADDM Network. Because the ADDM sites do not provide a representative sample of the entire United States, the combined prevalence estimates presented in this report cannot be generalized to all children aged 8 years in the United States. Consistent with reports from previous ADDM surveillance years, findings from 2014 were marked by variation in ASD prevalence when stratified by geographic area, sex, and level of intellectual ability. Differences in prevalence estimates between black and white children have diminished in most sites, but remained notable for Hispanic children. For 2014, results from application of the DSM-IV-TR and DSM-5 case definitions were similar, overall and when stratified by sex, race/ethnicity, DSM-IV-TR diagnostic subtype, or level of intellectual ability., Public Health Action: Beginning with surveillance year 2016, the DSM-5 case definition will serve as the basis for ADDM estimates of ASD prevalence in future surveillance reports. Although the DSM-IV-TR case definition will eventually be phased out, it will be applied in a limited geographic area to offer additional data for comparison. Future analyses will examine trends in the continued use of DSM-IV-TR diagnoses, such as autistic disorder, PDD-NOS, and Asperger disorder in health and education records, documentation of symptoms consistent with DSM-5 terminology, and how these trends might influence estimates of ASD prevalence over time. The latest findings from the ADDM Network provide evidence that the prevalence of ASD is higher than previously reported estimates and continues to vary among certain racial/ethnic groups and communities. With prevalence of ASD ranging from 13.1 to 29.3 per 1,000 children aged 8 years in different communities throughout the United States, the need for behavioral, educational, residential, and occupational services remains high, as does the need for increased research on both genetic and nongenetic risk factors for ASD.
- Published
- 2018
- Full Text
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46. Prevention of child maltreatment: strategic targeting of a curvilinear relationship between adversity and psychiatric impairment.
- Author
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Constantino JN
- Published
- 2018
- Full Text
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47. Walking, Gross Motor Development, and Brain Functional Connectivity in Infants and Toddlers.
- Author
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Marrus N, Eggebrecht AT, Todorov A, Elison JT, Wolff JJ, Cole L, Gao W, Pandey J, Shen MD, Swanson MR, Emerson RW, Klohr CL, Adams CM, Estes AM, Zwaigenbaum L, Botteron KN, McKinstry RC, Constantino JN, Evans AC, Hazlett HC, Dager SR, Paterson SJ, Schultz RT, Styner MA, Gerig G, Schlaggar BL, Piven J, and Pruett JR Jr
- Subjects
- Autism Spectrum Disorder diagnostic imaging, Autism Spectrum Disorder physiopathology, Child, Preschool, Female, Humans, Infant, Longitudinal Studies, Magnetic Resonance Imaging trends, Male, Neural Pathways diagnostic imaging, Neural Pathways growth & development, Brain diagnostic imaging, Brain growth & development, Child Development physiology, Nerve Net diagnostic imaging, Nerve Net growth & development, Walking physiology
- Abstract
Infant gross motor development is vital to adaptive function and predictive of both cognitive outcomes and neurodevelopmental disorders. However, little is known about neural systems underlying the emergence of walking and general gross motor abilities. Using resting state fcMRI, we identified functional brain networks associated with walking and gross motor scores in a mixed cross-sectional and longitudinal cohort of infants at high and low risk for autism spectrum disorder, who represent a dimensionally distributed range of motor function. At age 12 months, functional connectivity of motor and default mode networks was correlated with walking, whereas dorsal attention and posterior cingulo-opercular networks were implicated at age 24 months. Analyses of general gross motor function also revealed involvement of motor and default mode networks at 12 and 24 months, with dorsal attention, cingulo-opercular, frontoparietal, and subcortical networks additionally implicated at 24 months. These findings suggest that changes in network-level brain-behavior relationships underlie the emergence and consolidation of walking and gross motor abilities in the toddler period. This initial description of network substrates of early gross motor development may inform hypotheses regarding neural systems contributing to typical and atypical motor outcomes, as well as neurodevelopmental disorders associated with motor dysfunction., (© The Author 2017. Published by Oxford University Press.)
- Published
- 2018
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48. Attention and motor deficits index non-specific background liabilities that predict autism recurrence in siblings.
- Author
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Mous SE, Jiang A, Agrawal A, and Constantino JN
- Abstract
Background: Recent research has demonstrated that subclinical autistic traits of parents amplify the effects of deleterious mutations in the causation of autism spectrum disorder (ASD) in their offspring. Here, we examined the extent to which two neurodevelopmental traits that are non-specific to ASD-inattention/hyperactivity and motor coordination-might contribute to ASD recurrence in siblings of ASD probands., Methods: Data from a quantitative trait study of 114 ASD probands and their brothers, 26% of whom also had ASD, were analyzed. Autistic trait severity was ascertained using the Social Responsiveness Scale-2, attention/hyperactivity problems using the Achenbach System of Empirically Based Assessment, and motor coordination (in a subset of participants) using the Developmental Coordination Disorder Questionnaire., Results: Among siblings (affected and unaffected), both categorical recurrence of ASD (Nagelkerke R
2 = 0.53) and quantitative ASD trait burden (R2 = 0.55) were predicted by sibling ADHD and motor coordination impairment scores, even though these traits, on average, were not elevated among the unaffected siblings., Conclusions: These findings in a clinical family cohort confirm observations from general population studies that inattention/hyperactivity and motor impairment-axes of behavioral development that are non-specific to ASD, and often appreciable before ASD is typically diagnosed-jointly account for over 50% of the variation in autistic impairment of siblings, whether ascertained quantitatively or categorically. This finding within a sibling design suggests that background ASD susceptibilities that are inherited but non-specific ("BASINS") may contribute to additive genetic liability in the same manner that ASD-specific susceptibilities (such as parental subclinical ASD traits and deleterious mutations) engender ASD risk.- Published
- 2017
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49. Redefining the endophenotype concept to accommodate transdiagnostic vulnerabilities and etiological complexity.
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Beauchaine TP and Constantino JN
- Subjects
- Attention Deficit Disorder with Hyperactivity diagnosis, Attention Deficit Disorder with Hyperactivity etiology, Autistic Disorder diagnosis, Autistic Disorder etiology, Endophenotypes, Humans, Mental Disorders diagnosis, Schizophrenia diagnosis, Schizophrenia etiology, Mental Disorders etiology
- Abstract
In psychopathology research, endophenotypes are a subset of biomarkers that indicate genetic vulnerability independent of clinical state. To date, an explicit expectation is that endophenotypes be specific to single disorders. We evaluate this expectation considering recent advances in psychiatric genetics, recognition that transdiagnostic vulnerability traits are often more useful than clinical diagnoses in psychiatric genetics, and appreciation for etiological complexity across genetic, neural, hormonal and environmental levels of analysis. We suggest that the disorder-specificity requirement of endophenotypes be relaxed, that neural functions are preferable to behaviors as starting points in searches for endophenotypes, and that future research should focus on interactive effects of multiple endophenotypes on complex psychiatric disorders, some of which are 'phenocopies' with distinct etiologies.
- Published
- 2017
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50. Infant viewing of social scenes is under genetic control and is atypical in autism.
- Author
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Constantino JN, Kennon-McGill S, Weichselbaum C, Marrus N, Haider A, Glowinski AL, Gillespie S, Klaiman C, Klin A, and Jones W
- Subjects
- Autistic Disorder psychology, Child, Preschool, Endophenotypes, Eye, Female, Humans, Infant, Male, Mouth, Siblings, Twins, Dizygotic genetics, Twins, Monozygotic genetics, Attention, Autistic Disorder genetics, Autistic Disorder physiopathology, Child Development, Face anatomy & histology, Fixation, Ocular genetics, Interpersonal Relations
- Abstract
Long before infants reach, crawl or walk, they explore the world by looking: they look to learn and to engage, giving preferential attention to social stimuli, including faces, face-like stimuli and biological motion. This capacity-social visual engagement-shapes typical infant development from birth and is pathognomonically impaired in children affected by autism. Here we show that variation in viewing of social scenes, including levels of preferential attention and the timing, direction and targeting of individual eye movements, is strongly influenced by genetic factors, with effects directly traceable to the active seeking of social information. In a series of eye-tracking experiments conducted with 338 toddlers, including 166 epidemiologically ascertained twins (enrolled by representative sampling from the general population), 88 non-twins with autism and 84 singleton controls, we find high monozygotic twin-twin concordance (0.91) and relatively low dizygotic concordance (0.35). Moreover, the characteristics that are the most highly heritable, preferential attention to eye and mouth regions of the face, are also those that are differentially decreased in children with autism (χ
2 = 64.03, P < 0.0001). These results implicate social visual engagement as a neurodevelopmental endophenotype not only for autism, but also for population-wide variation in social-information seeking. In addition, these results reveal a means of human biological niche construction, with phenotypic differences emerging from the interaction of individual genotypes with early life experience.- Published
- 2017
- Full Text
- View/download PDF
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