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1. Bayesian optimization with evolutionary and structure-based regularization for directed protein evolution

Author

Trevor S. Frisby and Christopher James Langmead

Subjects
Protein design, Bayesian optimization, Regularization, Directed evolution, Rational design, Gaussian process regression, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Background Directed evolution (DE) is a technique for protein engineering that involves iterative rounds of mutagenesis and screening to search for sequences that optimize a given property, such as binding affinity to a specified target. Unfortunately, the underlying optimization problem is under-determined, and so mutations introduced to improve the specified property may come at the expense of unmeasured, but nevertheless important properties (ex. solubility, thermostability, etc). We address this issue by formulating DE as a regularized Bayesian optimization problem where the regularization term reflects evolutionary or structure-based constraints. Results We applied our approach to DE to three representative proteins, GB1, BRCA1, and SARS-CoV-2 Spike, and evaluated both evolutionary and structure-based regularization terms. The results of these experiments demonstrate that: (i) structure-based regularization usually leads to better designs (and never hurts), compared to the unregularized setting; (ii) evolutionary-based regularization tends to be least effective; and (iii) regularization leads to better designs because it effectively focuses the search in certain areas of sequence space, making better use of the experimental budget. Additionally, like previous work in Machine learning assisted DE, we find that our approach significantly reduces the experimental burden of DE, relative to model-free methods. Conclusion Introducing regularization into a Bayesian ML-assisted DE framework alters the exploratory patterns of the underlying optimization routine, and can shift variant selections towards those with a range of targeted and desirable properties. In particular, we find that structure-based regularization often improves variant selection compared to unregularized approaches, and never hurts.

Published
2021
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2. Harvestman: a framework for hierarchical feature learning and selection from whole genome sequencing data

Author

Trevor S. Frisby, Shawn J. Baker, Guillaume Marçais, Quang Minh Hoang, Carl Kingsford, and Christopher J. Langmead

Subjects
Feature selection, Hierarchical feature spaces, Knowledge graphs, Integer linear programming, Machine learning, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Supervised learning from high-throughput sequencing data presents many challenges. For one, the curse of dimensionality often leads to overfitting as well as issues with scalability. This can bring about inaccurate models or those that require extensive compute time and resources. Additionally, variant calls may not be the optimal encoding for a given learning task, which also contributes to poor predictive capabilities. To address these issues, we present Harvestman, a method that takes advantage of hierarchical relationships among the possible biological interpretations and representations of genomic variants to perform automatic feature learning, feature selection, and model building. Results We demonstrate that Harvestman scales to thousands of genomes comprising more than 84 million variants by processing phase 3 data from the 1000 Genomes Project, one of the largest publicly available collection of whole genome sequences. Using breast cancer data from The Cancer Genome Atlas, we show that Harvestman selects a rich combination of representations that are adapted to the learning task, and performs better than a binary representation of SNPs alone. We compare Harvestman to existing feature selection methods and demonstrate that our method is more parsimonious—it selects smaller and less redundant feature subsets while maintaining accuracy of the resulting classifier. Conclusion Harvestman is a hierarchical feature selection approach for supervised model building from variant call data. By building a knowledge graph over genomic variants and solving an integer linear program , Harvestman automatically and optimally finds the right encoding for genomic variants. Compared to other hierarchical feature selection methods, Harvestman is faster and selects features more parsimoniously.

Published
2021
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4. Asynchronous parallel Bayesian optimization for AI-driven cloud laboratories

Author

Christopher J. Langmead, Zhiyun Gong, and Trevor S. Frisby

Subjects
Statistics and Probability, AcademicSubjects/SCI01060, Deterministic algorithm, Computer science, Distributed computing, Cloud computing, 02 engineering and technology, Biochemistry, 03 medical and health sciences, Artificial Intelligence, Molecular Biology, 030304 developmental biology, computer.programming_language, Protocol (science), 0303 health sciences, SIMPLE (military communications protocol), business.industry, Bayesian optimization, Bayes Theorem, Python (programming language), 021001 nanoscience & nanotechnology, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, General Computational Biology, Asynchronous communication, Data quality, 0210 nano-technology, business, Laboratories, computer, Algorithms, Software
Abstract

Motivation The recent emergence of cloud laboratories—collections of automated wet-lab instruments that are accessed remotely, presents new opportunities to apply Artificial Intelligence and Machine Learning in scientific research. Among these is the challenge of automating the process of optimizing experimental protocols to maximize data quality. Results We introduce a new deterministic algorithm, called PaRallel OptimizaTiOn for ClOud Laboratories (PROTOCOL), that improves experimental protocols via asynchronous, parallel Bayesian optimization. The algorithm achieves exponential convergence with respect to simple regret. We demonstrate PROTOCOL in both simulated and real-world cloud labs. In the simulated lab, it outperforms alternative approaches to Bayesian optimization in terms of its ability to find optimal configurations, and the number of experiments required to find the optimum. In the real-world lab, the algorithm makes progress toward the optimal setting. Data availability and implementation PROTOCOL is available as both a stand-alone Python library, and as part of a R Shiny application at https://github.com/clangmead/PROTOCOL. Data are available at the same repository. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2021

5. Harvestman: a framework for hierarchical feature learning and selection from whole genome sequencing data

Author

Quang Minh Hoang, Christopher J. Langmead, Shawn James Baker, Guillaume Marçais, Trevor S. Frisby, and Carl Kingsford

Subjects
Computer science, 02 engineering and technology, Overfitting, computer.software_genre, Genome, Biochemistry, 0302 clinical medicine, Structural Biology, 0202 electrical engineering, electronic engineering, information engineering, lcsh:QH301-705.5, 0303 health sciences, Methodology Article, Applied Mathematics, Hierarchical feature spaces, Genomics, Computer Science Applications, Feature (computer vision), 030220 oncology & carcinogenesis, Feature selection, lcsh:R858-859.7, Data mining, Curse of dimensionality, Single-nucleotide polymorphism, Breast Neoplasms, lcsh:Computer applications to medicine. Medical informatics, Machine learning, 03 medical and health sciences, Breast cancer, Deep Learning, 020204 information systems, Knowledge graphs, medicine, Humans, 1000 Genomes Project, Molecular Biology, Selection (genetic algorithm), 030304 developmental biology, Whole genome sequencing, Whole Genome Sequencing, business.industry, Supervised learning, medicine.disease, ComputingMethodologies_PATTERNRECOGNITION, Integer linear programming, lcsh:Biology (General), Artificial intelligence, business, Classifier (UML), Feature learning, computer
Abstract

Background Supervised learning from high-throughput sequencing data presents many challenges. For one, the curse of dimensionality often leads to overfitting as well as issues with scalability. This can bring about inaccurate models or those that require extensive compute time and resources. Additionally, variant calls may not be the optimal encoding for a given learning task, which also contributes to poor predictive capabilities. To address these issues, we present Harvestman, a method that takes advantage of hierarchical relationships among the possible biological interpretations and representations of genomic variants to perform automatic feature learning, feature selection, and model building. Results We demonstrate that Harvestman scales to thousands of genomes comprising more than 84 million variants by processing phase 3 data from the 1000 Genomes Project, one of the largest publicly available collection of whole genome sequences. Using breast cancer data from The Cancer Genome Atlas, we show that Harvestman selects a rich combination of representations that are adapted to the learning task, and performs better than a binary representation of SNPs alone. We compare Harvestman to existing feature selection methods and demonstrate that our method is more parsimonious—it selects smaller and less redundant feature subsets while maintaining accuracy of the resulting classifier. Conclusion Harvestman is a hierarchical feature selection approach for supervised model building from variant call data. By building a knowledge graph over genomic variants and solving an integer linear program , Harvestman automatically and optimally finds the right encoding for genomic variants. Compared to other hierarchical feature selection methods, Harvestman is faster and selects features more parsimoniously.

Published
2021

6. Fold Family-Regularized Bayesian Optimization for Directed Protein Evolution

Author

Trevor S. Frisby and Christopher J. Langmead, Frisby, Trevor S., Langmead, Christopher J., Trevor S. Frisby and Christopher J. Langmead, Frisby, Trevor S., and Langmead, Christopher J.

Abstract

Directed Evolution (DE) is a technique for protein engineering that involves iterative rounds of mutagenesis and screening to search for sequences that optimize a given property (ex. binding affinity to a specified target). Unfortunately, the underlying optimization problem is under-determined, and so mutations introduced to improve the specified property may come at the expense of unmeasured, but nevertheless important properties (ex. subcellular localization). We seek to address this issue by incorporating a fold-specific regularization factor into the optimization problem. The regularization factor biases the search towards designs that resemble sequences from the fold family to which the protein belongs. We applied our method to a large library of protein GB1 mutants with binding affinity measurements to IgG-Fc. Our results demonstrate that the regularized optimization problem produces more native-like GB1 sequences with only a minor decrease in binding affinity. Specifically, the log-odds of our designs under a generative model of the GB1 fold family are between 41-45% higher than those obtained without regularization, with only a 7% drop in binding affinity. Thus, our method is capable of making a trade-off between competing traits. Moreover, we demonstrate that our active-learning driven approach reduces the wet-lab burden to identify optimal GB1 designs by 67%, relative to recent results from the Arnold lab on the same data.

Published
2020
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7. The Undiagnosed Diseases Network: Accelerating Discovery about Health and Disease

Author

Rachel B. Ramoni, John J. Mulvihill, David R. Adams, Patrick Allard, Euan A. Ashley, Jonathan A. Bernstein, William A. Gahl, Rizwan Hamid, Joseph Loscalzo, Alexa T. McCray, Vandana Shashi, Cynthia J. Tifft, Anastasia L. Wise, Christopher J. Adams, Mercedes E. Alejandro, Mashid S. Azamian, Carlos A. Bacino, Ashok Balasubramanyam, Hayk Barseghyan, Alan H. Beggs, Hugo J. Bellen, David Bernick, Anna Bican, David P. Bick, Camille L. Birch, Braden E. Boone, Lauren C. Briere, Donna M. Brown, Catherine A. Brownstein, Matthew Brush, Elizabeth A. Burke, Lindsay C. Burrage, Katherine R. Chao, Gary D. Clark, Joy D. Cogan, Cynthia M. Cooper, William J. Craigen, Mariska Davids, Jyoti G. Dayal, Esteban C. Dell’Angelica, Shweta U. Dhar, Katrina M. Dipple, Laurel A. Donnell-Fink, Naghmeh Dorrani, Daniel C. Dorset, David D. Draper, Annika M. Dries, Rachel Eastwood, David J. Eckstein, Lisa T. Emrick, Christine M. Eng, Cecilia Esteves, Tyra Estwick, Paul G. Fisher, Trevor S. Frisby, Kate Frost, Valerie Gartner, Rena A. Godfrey, Mitchell Goheen, Gretchen A. Golas, David B. Goldstein, Mary 'Gracie' G. Gordon, Sarah E. Gould, Jean-Philippe F. Gourdine, Brett H. Graham, Catherine A. Groden, Andrea L. Gropman, Mary E. Hackbarth, Melissa Haendel, Neil A. Hanchard, Lori H. Handley, Isabel Hardee, Matthew R. Herzog, Ingrid A. Holm, Ellen M. Howerton, Brenda Iglesias, Howard J. Jacob, Mahim Jain, Yong-hui Jiang, Jean M. Johnston, Angela L. Jones, Alanna E. Koehler, David M. Koeller, Isaac S. Kohane, Jennefer N. Kohler, Donna M. Krasnewich, Elizabeth L. Krieg, Joel B. Krier, Jennifer E. Kyle, Seema R. Lalani, Lea Latham, Yvonne L. Latour, C. Christopher Lau, Jozef Lazar, Brendan H. Lee, Hane Lee, Paul R. Lee, Shawn E. Levy, Denise J. Levy, Richard A. Lewis, Adam P. Liebendorder, Sharyn A. Lincoln, Carson R. Loomis, Richard L. Maas, Ellen F. Macnamara, Calum A. MacRae, Valerie V. Maduro, May Christine V. Malicdan, Laura A. Mamounas, Teri A. Manolio, Thomas C. Markello, Casey Martin, Paul Mazur, Alexandra J. McCarty, Allyn McConkie-Rosell, Thomas O. Metz, Matthew Might, Paolo M. Moretti, Jennifer L. Murphy, Donna M. Muzny, Michele E. Nehrebecky, Stan F. Nelson, J. Scott Newberry, John H. Newman, Sarah K. Nicholas, Donna Novacic, Jordan S. Orange, J. Carl Pallais, Christina G.S. Palmer, Jeanette C. Papp, Loren D.M. Pena, John A. Phillips, Jennifer E. Posey, John H. Postlethwait, Lorraine Potocki, Barbara N. Pusey, Amy K. Robertson, Lance H. Rodan, Jill A. Rosenfeld, Sarah Sadozai, Katherine E. Schaffer, Kelly Schoch, Molly C. Schroeder, Daryl A. Scott, Prashant Sharma, Edwin K. Silverman, Janet S. Sinsheimer, Ariane G. Soldatos, Rebecca C. Spillmann, Kimberly Splinter, Joan M. Stoler, Nicholas Stong, Kimberly A. Strong, Jennifer A. Sullivan, David A. Sweetser, Sara P. Thomas, Nathanial J. Tolman, Camilo Toro, Alyssa A. Tran, Zaheer M. Valivullah, Eric Vilain, Daryl M. Waggott, Colleen E. Wahl, Nicole M. Walley, Chris A. Walsh, Michael F. Wangler, Mike Warburton, Patricia A. Ward, Katrina M. Waters, Bobbie-Jo M. Webb-Robertson, Alec A. Weech, Monte Westerfield, Matthew T. Wheeler, Lynne A. Wolfe, Elizabeth A. Worthey, Shinya Yamamoto, Yaping Yang, Guoyun Yu, and Patricia A. Zornio

Subjects
0301 basic medicine, Knowledge management, Genotype, Genotyping Techniques, Best practice, Disease, 030105 genetics & heredity, Bioinformatics, 03 medical and health sciences, Rare Diseases, Diagnostic model, Common fund, Genetics, Humans, Metabolomics, Functional studies, Genetics (clinical), Information Dissemination, business.industry, Disease mechanisms, Sequence Analysis, DNA, United States, Research objectives, Data sharing, Phenotype, 030104 developmental biology, National Institutes of Health (U.S.), Commentary, business
Abstract

Diagnosis at the edges of our knowledge calls upon clinicians to be data driven, cross-disciplinary, and collaborative in unprecedented ways. Exact disease recognition, an element of the concept of precision in medicine, requires new infrastructure that spans geography, institutional boundaries, and the divide between clinical care and research. The National Institutes of Health (NIH) Common Fund supports the Undiagnosed Diseases Network (UDN) as an exemplar of this model of precise diagnosis. Its goals are to forge a strategy to accelerate the diagnosis of rare or previously unrecognized diseases, to improve recommendations for clinical management, and to advance research, especially into disease mechanisms. The network will achieve these objectives by evaluating patients with undiagnosed diseases, fostering a breadth of expert collaborations, determining best practices for translating the strategy into medical centers nationwide, and sharing findings, data, specimens, and approaches with the scientific and medical communities. Building the UDN has already brought insights to human and medical geneticists. The initial focus has been on data sharing, establishing common protocols for institutional review boards and data sharing, creating protocols for referring and evaluating patients, and providing DNA sequencing, metabolomic analysis, and functional studies in model organisms. By extending this precision diagnostic model nationally, we strive to meld clinical and research objectives, improve patient outcomes, and contribute to medical science.

Published
2017

8. A Recurrent De Novo Variant in NACC1 Causes a Syndrome Characterized by Infantile Epilepsy, Cataracts, and Profound Developmental Delay

Author

Kelly Schoch, Linyan Meng, Szabolcs Szelinger, David R. Bearden, Asbjorg Stray-Pedersen, Oyvind L. Busk, Nicholas Stong, Eriskay Liston, Ronald D. Cohn, Fernando Scaglia, Jill A. Rosenfeld, Jennifer Tarpinian, Cara M. Skraban, Matthew A. Deardorff, Jeremy N. Friedman, Zeynep Coban Akdemir, Nicole Walley, Mohamad A. Mikati, Peter G. Kranz, Joan Jasien, Allyn McConkie-Rosell, Marie McDonald, Stephanie Burns Wechsler, Michael Freemark, Sujay Kansagra, Sharon Freedman, Deeksha Bali, Francisca Millan, Sherri Bale, Stanley F. Nelson, Hane Lee, Naghmeh Dorrani, David B. Goldstein, Rui Xiao, Yaping Yang, Jennifer E. Posey, Julian A. Martinez-Agosto, James R. Lupski, Michael F. Wangler, Vandana Shashi, Wayne W. Grody, Samuel P. Strom, Eric Vilain, Joshua Deignan, Fabiola Quintero-Rivera, Sibel Kantarci, Sureni Mullegama, Sung-Hae Kang, Mercedes E. Alejandro, Carlos A. Bacino, Ashok Balasubramanyam, Lindsay C. Burrage, Gary D. Clark, William J. Craigen, Shweta U. Dhar, Lisa T. Emrick, Brett H. Graham, Neil A. Hanchard, Mahim Jain, Seema R. Lalani, Brendan H. Lee, Richard A. Lewis, Azamian S. Mashid, Paolo M. Moretti, Sarah K. Nicholas, Jordan S. Orange, Lorraine Potocki, Daryl A. Scott, Alyssa A. Tran, Hugo J. Bellen, Shinya Yamamoto, Christine M. Eng, Donna M. Muzny, Patricia A. Ward, Andrea L. Gropman, Yong-hui Jiang, Loren D.M. Pena, Rebecca C. Spillmann, Jennifer A. Sullivan, Nicole M. Walley, Alan H. Beggs, Lauren C. Briere, Cynthia M. Cooper, Laurel A. Donnell-Fink, Elizabeth L. Krieg, Joel B. Krier, Sharyn A. Lincoln, Joseph Loscalzo, Richard L. Maas, Calum A. MacRae, J. Carl Pallais, Lance H. Rodan, Edwin K. Silverman, Joan M. Stoler, David A. Sweetser, Chris A. Walsh, Cecilia Esteves, Ingrid A. Holm, Isaac S. Kohane, Paul Mazur, Alexa T. McCray, Matthew Might, Rachel B. Ramoni, Kimberly Splinter, David P. Bick, Camille L. Birch, Braden E. Boone, Donna M. Brown, Dan C. Dorset, Lori H. Handley, Howard J. Jacob, Angela L. Jones, Jozef Lazar, Shawn E. Levy, J. Scott Newberry, Molly C. Schroeder, Kimberly A. Strong, Elizabeth A. Worthey, Jyoti G. Dayal, David J. Eckstein, Sarah E. Gould, Ellen M. Howerton, Donna M. Krasnewich, Carson R. Loomis, Laura A. Mamounas, Teri A. Manolio, John J. Mulvihill, Anastasia L. Wise, Ariane G. Soldatos, Matthew Brush, Jean-Philippe F. Gourdine, Melissa Haendel, David M. Koeller, Jennifer E. Kyle, Thomas O. Metz, Katrina M. Waters, Bobbie-Jo M. Webb-Robertson, Euan A. Ashley, Jonathan A. Bernstein, Annika M. Dries, Paul G. Fisher, Jennefer N. Kohler, Daryl M. Waggott, Matt T. Wheeler, Patricia A. Zornio, Patrick Allard, Hayk Barseghyan, Esteban C. Dell’Angelica, Katrina M. Dipple, Matthew R. Herzog, Stan F. Nelson, Christina G.S. Palmer, Jeanette C. Papp, Janet S. Sinsheimer, Christopher J. Adams, Elizabeth A. Burke, Katherine R. Chao, Mariska Davids, David D. Draper, Tyra Estwick, Trevor S. Frisby, Kate Frost, Valerie Gartner, Rena A. Godfrey, Mitchell Goheen, Gretchen A. Golas, Mary 'Gracie' G. Gordon, Catherine A. Groden, Mary E. Hackbarth, Isabel Hardee, Jean M. Johnston, Alanna E. Koehler, Lea Latham, Yvonne L. Latour, C. Christopher Lau, Denise J. Levy, Adam P. Liebendorder, Ellen F. Macnamara, Valerie V. Maduro, Thomas C. Markello, Alexandra J. McCarty, Jennifer L. Murphy, Michele E. Nehrebecky, Donna Novacic, Barbara N. Pusey, Sarah Sadozai, Katherine E. Schaffer, Prashant Sharma, Sara P. Thomas, Nathanial J. Tolman, Camilo Toro, Zaheer M. Valivullah, Colleen E. Wahl, Mike Warburton, Alec A. Weech, Guoyun Yu, David R. Adams, William A. Gahl, May Christine V. Malicdan, Cynthia J. Tifft, Lynne A. Wolfe, Paul R. Lee, John H. Postlethwait, Monte Westerfield, Anna Bican, Rizwan Hamid, John H. Newman, John A. Phillips, Amy K. Robertson, and Joy D. Cogan

Subjects
Male, 0301 basic medicine, Microcephaly, Mutation, Missense, Biology, Cataract, Germline, 03 medical and health sciences, Neurodevelopmental disorder, Cataracts, Report, Intellectual Disability, Intellectual disability, Genetics, medicine, Humans, Missense mutation, Amino Acid Sequence, Child, Alleles, Genetics (clinical), Cerebral atrophy, Brain, Genetic Variation, Infant, medicine.disease, Magnetic Resonance Imaging, Neoplasm Proteins, Pedigree, 3. Good health, Repressor Proteins, Phenotype, 030104 developmental biology, Child, Preschool, Failure to thrive, Female, medicine.symptom, Spasms, Infantile, Genome-Wide Association Study
Abstract

Whole-exome sequencing (WES) has increasingly enabled new pathogenic gene variant identification for undiagnosed neurodevelopmental disorders and provided insights into both gene function and disease biology. Here, we describe seven children with a neurodevelopmental disorder characterized by microcephaly, profound developmental delays and/or intellectual disability, cataracts, severe epilepsy including infantile spasms, irritability, failure to thrive, and stereotypic hand movements. Brain imaging in these individuals reveals delay in myelination and cerebral atrophy. We observe an identical recurrent de novo heterozygous c.892C>T (p.Arg298Trp) variant in the nucleus accumbens associated 1 ( NACC1 ) gene in seven affected individuals. One of the seven individuals is mosaic for this variant. NACC1 encodes a transcriptional repressor implicated in gene expression and has not previously been associated with germline disorders. The probability of finding the same missense NACC1 variant by chance in 7 out of 17,228 individuals who underwent WES for diagnoses of neurodevelopmental phenotypes is extremely small and achieves genome-wide significance (p = 1.25 × 10 −14 ). Selective constraint against missense variants in NACC1 makes this excess of an identical missense variant in all seven individuals more remarkable. Our findings are consistent with a germline recurrent mutational hotspot associated with an allele-specific neurodevelopmental phenotype in NACC1 .

Published
2017

9. A Syndromic Neurodevelopmental Disorder Caused by De Novo Variants in EBF3

Author

Hsiao-Tuan Chao, Mariska Davids, Elizabeth Burke, John G. Pappas, Jill A. Rosenfeld, Alexandra J. McCarty, Taylor Davis, Lynne Wolfe, Camilo Toro, Cynthia Tifft, Fan Xia, Nicholas Stong, Travis K. Johnson, Coral G. Warr, Shinya Yamamoto, David R. Adams, Thomas C. Markello, William A. Gahl, Hugo J. Bellen, Michael F. Wangler, May Christine V. Malicdan, Christopher J. Adams, Mercedes E. Alejandro, Patrick Allard, Euan A. Ashley, Carlos A. Bacino, Ashok Balasubramanyam, Hayk Barseghyan, Alan H. Beggs, Jonathan A. Bernstein, David P. Bick, Camille L. Birch, Braden E. Boone, Lauren C. Briere, Donna M. Brown, Matthew Brush, Lindsay C. Burrage, Katherine R. Chao, Gary D. Clark, Joy D. Cogan, Cynthia M. Cooper, William J. Craigen, Jyoti G. Dayal, Esteban C. Dell'Angelica, Shweta U. Dhar, Katrina M. Dipple, Laurel A. Donnell-Fink, Naghmeh Dorrani, Dan C. Dorset, David D. Draper, Annika M. Dries, David J. Eckstein, Lisa T. Emrick, Christine M. Eng, Cecilia Esteves, Tyra Estwick, Paul G. Fisher, Trevor S. Frisby, Kate Frost, Valerie Gartner, Rena A. Godfrey, Mitchell Goheen, Gretchen A. Golas, David B. Goldstein, Mary 'Gracie' G. Gordon, Sarah E. Gould, Jean-Philippe F. Gourdine, Brett H. Graham, Catherine A. Groden, Andrea L. Gropman, Mary E. Hackbarth, Melissa Haendel, Rizwan Hamid, Neil A. Hanchard, Lori H. Handley, Isabel Hardee, Matthew R. Herzog, Ingrid A. Holm, Ellen M. Howerton, Howard J. Jacob, Mahim Jain, Yong-hui Jiang, Jean M. Johnston, Angela L. Jones, Alanna E. Koehler, David M. Koeller, Isaac S. Kohane, Jennefer N. Kohler, Donna M. Krasnewich, Elizabeth L. Krieg, Joel B. Krier, Jennifer E. Kyle, Seema R. Lalani, Lea Latham, Yvonne L. Latour, C. Christopher Lau, Jozef Lazar, Brendan H. Lee, Hane Lee, Paul R. Lee, Shawn E. Levy, Denise J. Levy, Richard A. Lewis, Adam P. Liebendorder, Sharyn A. Lincoln, Carson R. Loomis, Joseph Loscalzo, Richard L. Maas, Ellen F. Macnamara, Calum A. MacRae, Valerie V. Maduro, Laura A. Mamounas, Teri A. Manolio, Azamian S. Mashid, Paul Mazur, Allyn McConkie-Rosell, Alexa T. McCray, Thomas O. Metz, Matthew Might, Paolo M. Moretti, John J. Mulvihill, Jennifer L. Murphy, Donna M. Muzny, Michele E. Nehrebecky, Stan F. Nelson, J. Scott Newberry, John H. Newman, Sarah K. Nicholas, Donna Novacic, Jordan S. Orange, J. Carl Pallais, Christina G.S. Palmer, Jeanette C. Papp, Loren D.M. Pena, John A. Phillips, Jennifer E. Posey, John H. Postlethwait, Lorraine Potocki, Barbara N. Pusey, Rachel B. Ramoni, Lance H. Rodan, Sarah Sadozai, Katherine E. Schaffer, Kelly Schoch, Molly C. Schroeder, Daryl A. Scott, Prashant Sharma, Vandana Shashi, Edwin K. Silverman, Janet S. Sinsheimer, Ariane G. Soldatos, Rebecca C. Spillmann, Kimberly Splinter, Joan M. Stoler, Kimberly A. Strong, Jennifer A. Sullivan, David A. Sweetser, Sara P. Thomas, Cynthia J. Tift, Nathanial J. Tolman, Alyssa A. Tran, Zaheer M. Valivullah, Eric Vilain, Daryl M. Waggott, Colleen E. Wahl, Nicole M. Walley, Chris A. Walsh, Mike Warburton, Patricia A. Ward, Katrina M. Waters, Bobbie-Jo M. Webb-Robertson, Alec A. Weech, Monte Westerfield, Matt T. Wheeler, Anastasia L. Wise, Lynne A. Worthe, Elizabeth A. Worthey, Yaping Yang, Guoyun Yu, and Patricia A. Zornio

Subjects
Central Nervous System, Male, 0301 basic medicine, Ataxia, Developmental Disabilities, Biology, Speech Disorders, 03 medical and health sciences, 0302 clinical medicine, Neurodevelopmental disorder, Intellectual Disability, Report, Intellectual disability, Genetics, medicine, Humans, Abnormalities, Multiple, Genitalia, Global developmental delay, Child, Transcription factor, Genetics (clinical), Zinc finger, Infant, Newborn, Infant, Zinc Fingers, Syndrome, medicine.disease, Hypotonia, 030104 developmental biology, Neurodevelopmental Disorders, Child, Preschool, Mutation, Muscle Hypotonia, Homeobox, Female, medicine.symptom, 030217 neurology & neurosurgery, Transcription Factors
Abstract

Early B cell factor 3 (EBF3) is a member of the highly evolutionarily conserved Collier/Olf/EBF (COE) family of transcription factors. Prior studies on invertebrate and vertebrate animals have shown that EBF3 homologs are essential for survival and that loss-of-function mutations are associated with a range of nervous system developmental defects, including perturbation of neuronal development and migration. Interestingly, aristaless-related homeobox (ARX), a homeobox-containing transcription factor critical for the regulation of nervous system development, transcriptionally represses EBF3 expression. However, human neurodevelopmental disorders related to EBF3 have not been reported. Here, we describe three individuals who are affected by global developmental delay, intellectual disability, and expressive speech disorder and carry de novo variants in EBF3. Associated features seen in these individuals include congenital hypotonia, structural CNS malformations, ataxia, and genitourinary abnormalities. The de novo variants affect a single conserved residue in a zinc finger motif crucial for DNA binding and are deleterious in a fly model. Our findings indicate that mutations in EBF3 cause a genetic neurodevelopmental syndrome and suggest that loss of EBF3 function might mediate a subset of neurologic phenotypes shared by ARX-related disorders, including intellectual disability, abnormal genitalia, and structural CNS malformations.

Published
2017

10. Looking beyond the exome: a phenotype-first approach to molecular diagnostic resolution in rare and undiagnosed diseases

Author

Loren D.M. Pena, Yong-Hui Jiang, Kelly Schoch, Rebecca C. Spillmann, Nicole Walley, Nicholas Stong, Sarah Rapisardo Horn, Jennifer A. Sullivan, Allyn McConkie-Rosell, Sujay Kansagra, Edward C. Smith, Mays El-Dairi, Jane Bellet, Martha Ann Keels, Joan Jasien, Peter G. Kranz, Richard Noel, Shashi K. Nagaraj, Robert K. Lark, Daniel S.G. Wechsler, Daniela del Gaudio, Marco L. Leung, Laura G. Hendon, Collette C. Parker, Kelly L. Jones, David B. Goldstein, Vandana Shashi, Mercedes E. Alejandro, Carlos A. Bacino, Ashok Balasubramanyam, Bret L. Bostwick, Lindsay C. Burrage, Shan Chen, Gary D. Clark, William J. Craigen, Shweta U. Dhar, Lisa T. Emrick, Brett H. Graham, Neil A. Hanchard, Mahim Jain, Seema R. Lalani, Brendan H. Lee, Richard A. Lewis, Mashid S. Azamian, Paolo M. Moretti, Sarah K. Nicholas, Jordan S. Orange, Jennifer E. Posey, Lorraine Potocki, Jill A. Rosenfeld, Susan L. Samson, Daryl A. Scott, Alyssa A. Tran, Tiphanie P. Vogel, Jing Zhang, Hugo J. Bellen, Michael F. Wangler, Shinya Yamamoto, Christine M. Eng, Donna M. Muzny, Patricia A. Ward, Yaping Yang, Yong-hui Jiang, Nicole M. Walley, Alan H. Beggs, Lauren C. Briere, Cynthia M. Cooper, Laurel A. Donnell-Fink, Elizabeth L. Krieg, Joel B. Krier, Sharyn A. Lincoln, Joseph Loscalzo, Richard L. Maas, Calum A. MacRae, J. Carl Pallais, Lance H. Rodan, Edwin K. Silverman, Joan M. Stoler, David A. Sweetser, Chris A. Walsh, Cecilia Esteves, Ingrid A. Holm, Isaac S. Kohane, Paul Mazur, Alexa T. McCray, Matthew Might, Rachel B. Ramoni, Kimberly Splinter, David P. Bick, Camille L. Birch, Braden E. Boone, Donna M. Brown, Daniel C. Dorset, Lori H. Handley, Howard J. Jacob, Angela L. Jones, Jozef Lazar, Shawn E. Levy, J. Scott Newberry, Molly C. Schroeder, Kimberly A. Strong, Elizabeth A. Worthey, Jyoti G. Dayal, David J. Eckstein, Sarah E. Gould, Ellen M. Howerton, Donna M. Krasnewich, Laura A. Mamounas, Teri A. Manolio, John J. Mulvihill, Tiina K. Urv, Anastasia L. Wise, Ariane G. Soldatos, Matthew Brush, Jean-Philippe F. Gourdine, Melissa Haendel, David M. Koeller, Jennifer E. Kyle, Thomas O. Metz, Katrina M. Waters, Bobbie-Jo M. Webb-Robertson, Euan A. Ashley, Jonathan A. Bernstein, Annika M. Dries, Paul G. Fisher, Jennefer N. Kohler, Daryl M. Waggott, Matthew T. Wheeler, Patricia A. Zornio, Patrick Allard, Hayk Barseghyan, Esteban C. Dell'Angelica, Ani Dillon, Katrina M. Dipple, Naghmeh Dorrani, Emilie D. Douine, Ascia Eskin, Brent L. Fogel, Matthew R. Herzog, Hane Lee, Allen Lipson, Sandra K. Loo, Julian A. Martínez-Agosto, Stan F. Nelson, Christina G.S. Palmer, Jeanette C. Papp, Neil H. Parker, Janet S. Sinsheimer, Eric Vilain, Allison Zheng, Christopher J. Adams, Elizabeth A. Burke, Katherine R. Chao, Mariska Davids, David D. Draper, Tyra Estwick, Trevor S. Frisby, Kate Frost, Valerie Gartner, Rena A. Godfrey, Mitchell Goheen, Gretchen A. Golas, Mary G. Gordon, Catherine A. Groden, Mary E. Hackbarth, Isabel Hardee, Jean M. Johnston, Alanna E. Koehler, Lea Latham, Yvonne L. Latour, C. Christopher Lau, Denise J. Levy, Adam P. Liebendorfer, Ellen F. Macnamara, Valerie V. Maduro, Thomas C. Markello, Alexandra J. McCarty, Jennifer L. Murphy, Michele E. Nehrebecky, Donna Novacic, Barbara N. Pusey, Sarah Sadozai, Katherine E. Schaffer, Prashant Sharma, Sara P. Thomas, Nathanial J. Tolman, Camilo Toro, Zaheer M. Valivullah, Colleen E. Wahl, Mike Warburton, Alec A. Weech, Guoyun Yu, Andrea L. Gropman, David R. Adams, William A. Gahl, May Christine V. Malicdan, Cynthia J. Tifft, Lynne A. Wolfe, Paul R. Lee, John H. Postlethwait, Monte Westerfield, Anna Bican, Joy D. Cogan, Rizwan Hamid, John H. Newman, John A. Phillips, and Amy K. Robertson

Subjects
0301 basic medicine, Genotype, Biopsy, Infantile systemic hyalinosis, infantile neuroaxonal dystrophy, Biology, Polymorphism, Single Nucleotide, PLA2G6, Article, Frameshift mutation, whole exome sequencing, Infantile neuroaxonal dystrophy, 03 medical and health sciences, symbols.namesake, Rare Diseases, Exome Sequencing, medicine, Humans, Exome, Genetic Predisposition to Disease, Indel, Child, leukoencephalopathy with vanishing white matter, Genetics (clinical), Exome sequencing, Alleles, Genetic Association Studies, Genetics, Sanger sequencing, Whole genome sequencing, Whole Genome Sequencing, Genetic Diseases, Inborn, Infant, medicine.disease, ANTXR2, undiagnosed diseases network, 3. Good health, 030104 developmental biology, Phenotype, Molecular Diagnostic Techniques, Child, Preschool, EIF2B5, symbols, Female, infantile systemic hyalinosis
Abstract

PurposeTo describe examples of missed pathogenic variants on whole-exome sequencing (WES) and the importance of deep phenotyping for further diagnostic testing.MethodsGuided by phenotypic information, three children with negative WES underwent targeted single-gene testing.ResultsIndividual 1 had a clinical diagnosis consistent with infantile systemic hyalinosis, although WES and a next-generation sequencing (NGS)-based ANTXR2 test were negative. Sanger sequencing of ANTXR2 revealed a homozygous single base pair insertion, previously missed by the WES variant caller software. Individual 2 had neurodevelopmental regression and cerebellar atrophy, with no diagnosis on WES. New clinical findings prompted Sanger sequencing and copy number testing of PLA2G6. A novel homozygous deletion of the noncoding exon 1 (not included in the WES capture kit) was detected, with extension into the promoter, confirming the clinical suspicion of infantile neuroaxonal dystrophy. Individual 3 had progressive ataxia, spasticity, and magnetic resonance image changes of vanishing white matter leukoencephalopathy. An NGS leukodystrophy gene panel and WES showed a heterozygous pathogenic variant in EIF2B5; no deletions/duplications were detected. Sanger sequencing of EIF2B5 showed a frameshift indel, probably missed owing to failure of alignment.ConclusionThese cases illustrate potential pitfalls of WES/NGS testing and the importance of phenotype-guided molecular testing in yielding diagnoses.

Published
2017

11. MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome

Author

Julia Wang, Rami Al-Ouran, Yanhui Hu, Seon-Young Kim, Ying-Wooi Wan, Michael F. Wangler, Shinya Yamamoto, Hsiao-Tuan Chao, Aram Comjean, Stephanie E. Mohr, Norbert Perrimon, Zhandong Liu, Hugo J. Bellen, Christopher J. Adams, David R. Adams, Mercedes E. Alejandro, Patrick Allard, Euan A. Ashley, Mashid S. Azamian, Carlos A. Bacino, Ashok Balasubramanyam, Hayk Barseghyan, Alan H. Beggs, Jonathan A. Bernstein, Anna Bican, David P. Bick, Camille L. Birch, Braden E. Boone, Lauren C. Briere, Donna M. Brown, Matthew Brush, Elizabeth A. Burke, Lindsay C. Burrage, Katherine R. Chao, Gary D. Clark, Joy D. Cogan, Cynthia M. Cooper, William J. Craigen, Mariska Davids, Jyoti G. Dayal, Esteban C. Dell’Angelica, Shweta U. Dhar, Katrina M. Dipple, Laurel A. Donnell-Fink, Naghmeh Dorrani, Daniel C. Dorset, David D. Draper, Annika M. Dries, David J. Eckstein, Lisa T. Emrick, Christine M. Eng, Cecilia Esteves, Tyra Estwick, Paul G. Fisher, Trevor S. Frisby, Kate Frost, William A. Gahl, Valerie Gartner, Rena A. Godfrey, Mitchell Goheen, Gretchen A. Golas, David B. Goldstein, Mary G. Gordon, Sarah E. Gould, Jean-Philippe F. Gourdine, Brett H. Graham, Catherine A. Groden, Andrea L. Gropman, Mary E. Hackbarth, Melissa Haendel, Rizwan Hamid, Neil A. Hanchard, Lori H. Handley, Isabel Hardee, Matthew R. Herzog, Ingrid A. Holm, Ellen M. Howerton, Howard J. Jacob, Mahim Jain, Yong-hui Jiang, Jean M. Johnston, Angela L. Jones, Alanna E. Koehler, David M. Koeller, Isaac S. Kohane, Jennefer N. Kohler, Donna M. Krasnewich, Elizabeth L. Krieg, Joel B. Krier, Jennifer E. Kyle, Seema R. Lalani, Lea Latham, Yvonne L. Latour, C. Christopher Lau, Jozef Lazar, Brendan H. Lee, Hane Lee, Paul R. Lee, Shawn E. Levy, Denise J. Levy, Richard A. Lewis, Adam P. Liebendorfer, Sharyn A. Lincoln, Carson R. Loomis, Joseph Loscalzo, Richard L. Maas, Ellen F. Macnamara, Calum A. MacRae, Valerie V. Maduro, May Christine V. Malicdan, Laura A. Mamounas, Teri A. Manolio, Thomas C. Markello, Paul Mazur, Alexandra J. McCarty, Allyn McConkie-Rosell, Alexa T. McCray, Thomas O. Metz, Matthew Might, Paolo M. Moretti, John J. Mulvihill, Jennifer L. Murphy, Donna M. Muzny, Michele E. Nehrebecky, Stan F. Nelson, J. Scott Newberry, John H. Newman, Sarah K. Nicholas, Donna Novacic, Jordan S. Orange, J. Carl Pallais, Christina G.S. Palmer, Jeanette C. Papp, Loren D.M. Pena, John A. Phillips, Jennifer E. Posey, John H. Postlethwait, Lorraine Potocki, Barbara N. Pusey, Rachel B. Ramoni, Amy K. Robertson, Lance H. Rodan, Jill A. Rosenfeld, Sarah Sadozai, Katherine E. Schaffer, Kelly Schoch, Molly C. Schroeder, Daryl A. Scott, Prashant Sharma, Vandana Shashi, Edwin K. Silverman, Janet S. Sinsheimer, Ariane G. Soldatos, Rebecca C. Spillmann, Kimberly Splinter, Joan M. Stoler, Nicholas Stong, Kimberly A. Strong, Jennifer A. Sullivan, David A. Sweetser, Sara P. Thomas, Cynthia J. Tifft, Nathanial J. Tolman, Camilo Toro, Alyssa A. Tran, Zaheer M. Valivullah, Eric Vilain, Daryl M. Waggott, Colleen E. Wahl, Nicole M. Walley, Chris A. Walsh, Mike Warburton, Patricia A. Ward, Katrina M. Waters, Bobbie-Jo M. Webb-Robertson, Alec A. Weech, Monte Westerfield, Matthew T. Wheeler, Anastasia L. Wise, Lynne A. Wolfe, Elizabeth A. Worthey, Yaping Yang, Guoyun Yu, and Patricia A. Zornio

Subjects
0301 basic medicine, ved/biology.organism_classification_rank.species, Computational biology, Biology, Genome, Article, 03 medical and health sciences, 0302 clinical medicine, Databases, Genetic, Genetics, Humans, Model organism, FlyBase : A Database of Drosophila Genes & Genomes, Gene, Genetics (clinical), ved/biology, Genome, Human, Genetic Variation, Molecular Sequence Annotation, 030104 developmental biology, DECIPHER, Human genome, Zebrafish Information Network genome database, 030217 neurology & neurosurgery, Software
Abstract

One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research.

Published
2017

12. De Novo Truncating Variants in ASXL2 Are Associated with a Unique and Recognizable Clinical Phenotype

Author

Colleen E. Wahl, Ingrid A. Holm, Jonathan A. Bernstein, Mitja I. Kurki, Annika M. Dries, Alexander Hoischen, Patrick Allard, Janet S. Sinsheimer, J. Scott Newberry, Maysantoine A. El-Dairi, David R. Adams, Anna C. Need, Mitchell Goheen, Camilo Toro, Outi Kuismin, Andrea L. Gropman, Fanny Kortüm, Lindsay C. Burrage, Braden E. Boone, Nicole M. Walley, Lori H. Handley, Daryl A. Scott, Donna Muzny, Jane S. Bellet, Lance H. Rodan, Catherine Groden, Paul Mazur, Christina G.S. Palmer, Megan W. Butler, Azamian S. Mashid, Brendan Lee, Peter G. Kranz, Alexa T. McCray, Yaping Yang, Hane Lee, David A. Sweetser, Lynne A. Wolfe, Richard Alan Lewis, Sylvia Klinkenberg, Trevor S. Frisby, Lea Latham, Elizabeth A. Worthey, Michele Nehrebecky, William J. Craigen, Donna M. Brown, Constance T. R. M. Stumpel, Laura A. Mamounas, Michael F. Wangler, Lauren C. Briere, Alanna E. Koehler, Sarah Sadozai, Shinya Yamamoto, Kate Frost, Michael Freemark, Carson R. Loomis, Slavé Petrovski, Christine M. Eng, Barbara K. Burton, Hugo J. Bellen, Angela L. Jones, Esteban C. Dell Angelica, A. Bacino, Camille L. Birch, David Goldstein, Tran A. Alyssa, Joan M. Stoler, Yong-hui Jiang, Scott E. Hickey, Paul R. Lee, Jennifer A. Sullivan, William A. Gahl, Christopher J. Adams, Rebecca C. Spillmann, Katherine H. Kim, Daryl Waggott, Seema R. Lalani, Denise J. Levy, René Santer, May V. Malicdan, Donna Novacic, John H. Postlethwait, Kimberly Splinter, Laurel A. Donnell-Fink, Jean M. Johnston, Richard L. Maas, Alexandra J. McCarty, Gretchen Golas, Sarah K. Nicholas, Donna M. Krasnewich, David D. Draper, Cynthia J. Tifft, Cecilia Esteves, David M. Koeller, John A. Phillips, Chris A. Walsh, Palotie Aarno, Gary D. Clark, Howard J. Jacob, Katherine E. Schaffer, Magdalena Walkiewicz, Satu Korpi-Heikkila, Karin Oberndorff, David P. Bick, Isabel Hardee, Valerie Maduro, John J. Mulvihill, Elizabeth A. Burke, Thomas C. Markello, Yvonne L. Latour, Adam P. Liebendorder, Ashok Balasubramanyam, David J. Eckstein, Elizabeth L. Krieg, M. T. Cho, Teri A. Manolio, Katherine R. Chao, Alan H. Beggs, Patricia A. Zornio, Valerie Gartner, Chyau Yueh C Lau, Monte Westerfield, Issac S. Kohane, Jyoti G. Dayal, Rena A. Godfrey, Thomas O. Metz, John H. Newman, Brett H. Graham, Alec A. Weech, Joe Lazar, Mike Warburton, Anastasia L. Wise, Nicholas Stong, Shweta U. Dhar, Matthew R. Herzog, Joel B. Krier, Jennefer N. Kohler, Guoyun Yu, Neil A. Hanchard, Edwin K. Silverman, Christine M. Shuss, Kim A. Strong, Olli Pietilainen, Casey Martin, Mariska Davids, Prashant Sharma, Joseph Loscalzo, Lorraine Potocki, Nathanial J. Tolman, Joy D. Cogan, Matthew Might, Barbara N. Pusey, Naghmeh Dorrani, Sharyn A. Lincoln, Euan A. Ashley, Mahim Jain, Jennifer L. Murphy, Stan F. Nelson, Patricia A. Ward, Shawn Levy, Kelly Schoch, Katrina M. Dipple, Paul G. Fisher, Cynthia M. Cooper, Vandana Shashi, Juan C. Pallais, Martha Ann Keels, Jennifer E. Posey, Heather M. McLaughlin, Calum A. MacRae, Eric Vilain, Molly C. Schroeder, Mary E. Hackbarth, Sara P. Thomas, Lisa Emrick, Ariane Soldatos, Allyn McConkie-Rosell, Ellen Macnamara, Melanie J. Bonner, Hayk Barseghyan, Tyra Estwick, Alejandro E. Mercedes, Malik Alawi, Maja Hempel, Matthew T. Wheeler, Jordan S. Orange, Paolo M. Moretti, Brenda Iglesias, Rachel Ramoni, Loren D M Pena, Zaheer M. Valivullah, Mary 'Gracie' G. Gordon, Rizwan Hamid, Jeanette C. Papp, Dan C. Dorset, Jill A. Rosenfeld, RS: GROW - R4 - Reproductive and Perinatal Medicine, MUMC+: DA KG Polikliniek (9), Klinische Genetica, Klinische Neurowetenschappen, MUMC+: MA Med Staf Spec Neurologie (9), and Genetica & Celbiologie

Subjects
0301 basic medicine, Male, Developmental Disabilities, lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], ASXL2, Germline, glabellar nevus flammeus, 0302 clinical medicine, Intellectual disability, Tumours of the digestive tract Radboud Institute for Molecular Life Sciences [Radboudumc 14], Exome, whole-exome sequencing, Hypertelorism, Child, Exome sequencing, Genetics (clinical), Genetics & Heredity, Genetics, 11 Medical And Health Sciences, Syndrome, Phenotype, Hypotonia, developmental delay, intellectual disability, 030220 oncology & carcinogenesis, Child, Preschool, Muscle Hypotonia, medicine.symptom, Biology, macrocephaly, 03 medical and health sciences, Report, medicine, Humans, RNA, Messenger, Clinical phenotype, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Macrocephaly, Infant, Newborn, Infant, Correction, 06 Biological Sciences, medicine.disease, Human genetics, Megalencephaly, Repressor Proteins, 030104 developmental biology, Eyebrows, Bohring–Opitz syndrome
Abstract

Item does not contain fulltext The ASXL genes (ASXL1, ASXL2, and ASXL3) participate in body patterning during embryogenesis and encode proteins involved in epigenetic regulation and assembly of transcription factors to specific genomic loci. Germline de novo truncating variants in ASXL1 and ASXL3 have been respectively implicated in causing Bohring-Opitz and Bainbridge-Ropers syndromes, which result in overlapping features of severe intellectual disability and dysmorphic features. ASXL2 has not yet been associated with a human Mendelian disorder. In this study, we performed whole-exome sequencing in six unrelated probands with developmental delay, macrocephaly, and dysmorphic features. All six had de novo truncating variants in ASXL2. A careful review enabled the recognition of a specific phenotype consisting of macrocephaly, prominent eyes, arched eyebrows, hypertelorism, a glabellar nevus flammeus, neonatal feeding difficulties, hypotonia, and developmental disabilities. Although overlapping features with Bohring-Opitz and Bainbridge-Ropers syndromes exist, features that distinguish the ASXL2-associated condition from ASXL1- and ASXL3-related disorders are macrocephaly, absence of growth retardation, and more variability in the degree of intellectual disabilities. We were also able to demonstrate with mRNA studies that these variants are likely to exert a dominant-negative effect, given that both alleles are expressed in blood and the mutated ASXL2 transcripts escape nonsense-mediated decay. In conclusion, de novo truncating variants in ASXL2 underlie a neurodevelopmental syndrome with a clinically recognizable phenotype. This report expands the germline disorders that are linked to the ASXL genes.

Published
2016

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