ALBERT

Description: Whole exome sequencing has proven to be a powerful tool for understanding the genetic architecture of human disease. Here we apply it to more than 2,500 simplex families, each having a child with an autistic spectrum disorder. By comparing affected to unaffected siblings, we show that 13% of de novo missense mutations and 43% of de novo likely gene-disrupting (LGD) mutations contribute to 12% and 9% of diagnoses, respectively. Including copy number variants, coding de novo mutations contribute to about 30% of all simplex and 45% of female diagnoses. Almost all LGD mutations occur opposite wild-type alleles. LGD targets in affected females significantly overlap the targets in males of lower intelligence quotient (IQ), but neither overlaps significantly with targets in males of higher IQ. We estimate that LGD mutation in about 400 genes can contribute to the joint class of affected females and males of lower IQ, with an overlapping and similar number of genes vulnerable to contributory missense mutation. LGD targets in the joint class overlap with published targets for intellectual disability and schizophrenia, and are enriched for chromatin modifiers, FMRP-associated genes and embryonically expressed genes. Most of the significance for the latter comes from affected females.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4313871/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4313871/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Iossifov, Ivan -- O'Roak, Brian J -- Sanders, Stephan J -- Ronemus, Michael -- Krumm, Niklas -- Levy, Dan -- Stessman, Holly A -- Witherspoon, Kali T -- Vives, Laura -- Patterson, Karynne E -- Smith, Joshua D -- Paeper, Bryan -- Nickerson, Deborah A -- Dea, Jeanselle -- Dong, Shan -- Gonzalez, Luis E -- Mandell, Jeffrey D -- Mane, Shrikant M -- Murtha, Michael T -- Sullivan, Catherine A -- Walker, Michael F -- Waqar, Zainulabedin -- Wei, Liping -- Willsey, A Jeremy -- Yamrom, Boris -- Lee, Yoon-ha -- Grabowska, Ewa -- Dalkic, Ertugrul -- Wang, Zihua -- Marks, Steven -- Andrews, Peter -- Leotta, Anthony -- Kendall, Jude -- Hakker, Inessa -- Rosenbaum, Julie -- Ma, Beicong -- Rodgers, Linda -- Troge, Jennifer -- Narzisi, Giuseppe -- Yoon, Seungtai -- Schatz, Michael C -- Ye, Kenny -- McCombie, W Richard -- Shendure, Jay -- Eichler, Evan E -- State, Matthew W -- Wigler, Michael -- P30 CA016359/CA/NCI NIH HHS/ -- T32 GM007266/GM/NIGMS NIH HHS/ -- U54 HD083091/HD/NICHD NIH HHS/ -- UL1 TR000142/TR/NCATS NIH HHS/ -- Canadian Institutes of Health Research/Canada -- Howard Hughes Medical Institute/ -- England -- Nature. 2014 Nov 13;515(7526):216-21. doi: 10.1038/nature13908. Epub 2014 Oct 29.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA. ; 1] Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA [2] Molecular &Medical Genetics, Oregon Health &Science University, Portland, Oregon 97208, USA. ; 1] Department of Psychiatry, University of California, San Francisco, San Francisco, California 94158, USA [2] Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520, USA. ; Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA. ; Department of Psychiatry, University of California, San Francisco, San Francisco, California 94158, USA. ; 1] Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520, USA [2] Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China. ; Child Study Center, Yale University School of Medicine, New Haven, Connecticut 06520, USA. ; Yale Center for Genomic Analysis, Yale University School of Medicine, New Haven, Connecticut 06520, USA. ; 1] Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China [2] National Institute of Biological Sciences, Beijing 102206, China. ; 1] Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA [2] New York Genome Center, New York, New York 10013, USA. ; 1] Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA [2] Department of Medical Biology, Bulent Ecevit University School of Medicine, 67600 Zonguldak, Turkey. ; Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York 10461, USA. ; 1] Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA [2] Howard Hughes Medical Institute, Seattle, Washington 98195, USA. ; 1] Department of Psychiatry, University of California, San Francisco, San Francisco, California 94158, USA [2] Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520, USA [3] Child Study Center, Yale University School of Medicine, New Haven, Connecticut 06520, USA [4] Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut 06520, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25363768" target="_blank"〉PubMed〈/a〉

Description: Higher order software (HOS) is a methodology for the specification and verification of large scale, complex, real time systems. The HOS methodology was implemented as FAME (front end analysis and modeling environment), a microprocessor based system for interactively developing, analyzing, and displaying system models in a low cost user-friendly environment. The nature of the model is such that when completed it can be the basis for projection to a variety of forms such as structured design diagrams, Petri-nets, data flow diagrams, and PSL/PSA source code. The user's interface with the analyzer is easily recognized by any current user of a structured modeling approach; therefore extensive training is unnecessary. Furthermore, when all the system capabilities are used one can check on proper usage of data types, functions, and control structures thereby adding a new dimension to the design process that will lead to better and more easily verified software designs.