Genetic Correlations Reveal the Shared Genetic Architecture of Transcription in Human Peripheral Blood
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
HCST (NM 001007469) Human Recombinant Protein Product Data
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for TP319253 HCST (NM_001007469) Human Recombinant Protein Product data: Product Type: Recombinant Proteins Description: Recombinant protein of human hematopoietic cell signal transducer (HCST), transcript variant 2 Species: Human Expression Host: HEK293T Tag: C-Myc/DDK Predicted MW: 7.3 kDa Concentration: >50 ug/mL as determined by microplate BCA method Purity: > 80% as determined by SDS-PAGE and Coomassie blue staining Buffer: 25 mM Tris.HCl, pH 7.3, 100 mM glycine, 10% glycerol Preparation: Recombinant protein was captured through anti-DDK affinity column followed by conventional chromatography steps. Storage: Store at -80°C. Stability: Stable for 12 months from the date of receipt of the product under proper storage and handling conditions. Avoid repeated freeze-thaw cycles. RefSeq: NP_001007470 Locus ID: 10870 UniProt ID: Q9UBK5 RefSeq Size: 521 Cytogenetics: 19q13.12 RefSeq ORF: 279 Synonyms: DAP10; KAP10; PIK3AP This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 HCST (NM_001007469) Human Recombinant Protein – TP319253 Summary: This gene encodes a transmembrane signaling adaptor that contains a YxxM motif in its cytoplasmic domain. The encoded protein may form part of the immune recognition receptor complex with the C-type lectin-like receptor NKG2D. As part of this receptor complex, this protein may activate phosphatidylinositol 3-kinase dependent signaling pathways through its intracytoplasmic YxxM motif. -
The Genetic Architecture of Economic and Political Preferences
1 The Genetic Architecture of Economic and Political Preferences Daniel J. Benjamin1*, David Cesarini2, Matthijs J.H.M. van der Loos3, Christopher T. Dawes4, Philipp D. Koellinger3, Patrik K.E. Magnusson5, Christopher F. Chabris6, Dalton Conley7, David Laibson8, Magnus Johannesson9, and Peter M. Visscher10 1Department of Economics, Cornell University, 480 Uris Hall, Ithaca, NY 14853, USA. 2Center for Experimental Social Science, Department of Economics, New York University, 19 W. 4th Street, New York, NY 10012, USA. 3 Department of Applied Economics, Erasmus Universiteit Rotterdam, Post Box 1738, 3000 DR Rotterdam, Netherlands. 4 Department of Politics, New York University, 19 W. 4th Street, New York, NY 10012, USA. 5 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, SE- 171 77 Stockholm, Sweden. 6 Department of Psychology, Union College, 807 Union Street, Schenectady, NY 12308, USA. 7 Department of Sociology, New York University, Department of Sociology, New York University, 295 Lafayette Street, 4th floor, New York, NY 10012, USA. 8 Department of Economics, Harvard University, Littauer Center, 1805 Cambridge Street, Cambridge, MA 02138, USA. 9 Department of Economics, Stockholm School of Economics, Box 6501, SE-113 83 Stockholm, Sweden. 10 University of Queensland Diamantina Institute, Level 4, R Wing, Princess Alexandra Hospital, Ipswich Road, Woolloongabba 4102, Australia. Classification: Social Sciences; Economic Sciences; Social Sciences; Political Sciences; Biological Sciences; Genetics. Manuscript information: 12 text pages, 1 Figure, 2 Tables. *To whom correspondence should be addressed: phone: +1 607 255 2355. E-mail: [email protected]. 2 Abstract: Preferences are fundamental constructs in all models of economic and political behavior and important precursors to many lifetime outcomes. -
An Introduction to Quantitative Genetics I Heather a Lawson Advanced Genetics Spring2018 Outline
An Introduction to Quantitative Genetics I Heather A Lawson Advanced Genetics Spring2018 Outline • What is Quantitative Genetics? • Genotypic Values and Genetic Effects • Heritability • Linkage Disequilibrium and Genome-Wide Association Quantitative Genetics • The theory of the statistical relationship between genotypic variation and phenotypic variation. 1. What is the cause of phenotypic variation in natural populations? 2. What is the genetic architecture and molecular basis of phenotypic variation in natural populations? • Genotype • The genetic constitution of an organism or cell; also refers to the specific set of alleles inherited at a locus • Phenotype • Any measureable characteristic of an individual, such as height, arm length, test score, hair color, disease status, migration of proteins or DNA in a gel, etc. Nature Versus Nurture • Is a phenotype the result of genes or the environment? • False dichotomy • If NATURE: my genes made me do it! • If NURTURE: my mother made me do it! • The features of an organisms are due to an interaction of the individual’s genotype and environment Genetic Architecture: “sum” of the genetic effects upon a phenotype, including additive,dominance and parent-of-origin effects of several genes, pleiotropy and epistasis Different genetic architectures Different effects on the phenotype Types of Traits • Monogenic traits (rare) • Discrete binary characters • Modified by genetic and environmental background • Polygenic traits (common) • Discrete (e.g. bristle number on flies) or continuous (human height) -
Evolution of Neural Fields for Visual Discrimination and Multiple Pole Balancing Tasks
Honda Research Institute Europe GmbH https://www.honda-ri.de/ NEATfields: Evolution of neural fields for visual discrimination and multiple pole balancing tasks Benjamin Inden, Yaochu Jin, Robert Haschke, Helge Ritter 2010 Preprint: This is an accepted article published in Genetic and Evolutionary Computation Conference. The final authenticated version is available online at: https://doi.org/[DOI not available] Powered by TCPDF (www.tcpdf.org) NEATfields: Evolution of neural fields for visual discrimination and multiple pole balancing tasks ABSTRACT networks can be optimized. However, a few challenges have We have developed a novel extension of the NEAT neuroevo- to be addressed. For example, it is desirable that the exist- lution method, termed NEATfields, to solve problems with ing function of a network should not be fully disrupted when large input and output spaces. NEATfields networks are lay- adding new elements to the network. It is also not obvious ered into two-dimensional fields of identical or similar sub- how to recombine neural networks with arbitrary topologies, networks with an arbitrary topology. The subnetworks are or genomes of neural networks with variable lengths. evolved with genetic operations similar to those used in the Another grand challenge in evolving neural networks is NEAT neuroevolution method. We show that information the scalability issue: the evolution of solutions for tasks of a processing within the neural fields can be organized by pro- large dimension. This problem is particularly serious when viding suitable building blocks to evolution. NEATfields can a direct encoding scheme is used for representing the neural solve a number of visual discrimination tasks and a newly network, where the length of the genome grows linearly with introduced multiple pole balancing task. -
Plasma Cells in Vitro Generation of Long-Lived Human
Downloaded from http://www.jimmunol.org/ by guest on September 24, 2021 is online at: average * The Journal of Immunology , 32 of which you can access for free at: 2012; 189:5773-5785; Prepublished online 16 from submission to initial decision 4 weeks from acceptance to publication November 2012; doi: 10.4049/jimmunol.1103720 http://www.jimmunol.org/content/189/12/5773 In Vitro Generation of Long-lived Human Plasma Cells Mario Cocco, Sophie Stephenson, Matthew A. Care, Darren Newton, Nicholas A. Barnes, Adam Davison, Andy Rawstron, David R. Westhead, Gina M. Doody and Reuben M. Tooze J Immunol cites 65 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription http://www.jimmunol.org/content/suppl/2012/11/16/jimmunol.110372 0.DC1 This article http://www.jimmunol.org/content/189/12/5773.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2012 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 24, 2021. The Journal of Immunology In Vitro Generation of Long-lived Human Plasma Cells Mario Cocco,*,1 Sophie Stephenson,*,1 Matthew A. -
Insights Into the Genetic Architecture of the Human Face
bioRxiv preprint doi: https://doi.org/10.1101/2020.05.12.090555; this version posted May 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Insights into the genetic architecture of the human face Julie D. White1†*, Karlijne Indencleef2,3,4†*, Sahin Naqvi5,6, Ryan J. Eller7, Jasmien Roosenboom8, Myoung Keun Lee8, Jiarui Li2,3, Jaaved Mohammed5,9, Stephen Richmond10, Ellen E. Quillen11,12, Heather L. Norton13, Eleanor Feingold14, Tomek Swigut5,9, Mary L. Marazita8,14, Hilde Peeters15, Greet Hens4, John R. Shaffer8,14, Joanna Wysocka5,9, Susan Walsh7, Seth M. Weinberg8,14,16, Mark D. Shriver1, Peter Claes2,3,15,17,18* Affiliations: 1Department of Anthropology, Pennsylvania State University, State College, PA, 16802, USA 2Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, 3000, Belgium 3Medical Imaging Research Center, UZ Leuven, Leuven, 3000, Belgium 4Department of Otorhinolaryngology, KU Leuven, Leuven, 3000, Belgium 5Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA 6Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA 7Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, 46202, USA 8Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, -
A Global Overview of Pleiotropy and Genetic Architecture in Complex Traits
bioRxiv preprint doi: https://doi.org/10.1101/500090; this version posted December 19, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A global overview of pleiotropy and genetic architecture in complex traits Authors: Kyoko Watanabe1, Sven Stringer1, Oleksandr Frei2, Maša Umićević Mirkov1, Tinca J.C. Polderman1, Sophie van der Sluis1,3, Ole A. Andreassen2,4, Benjamin M. Neale5-7, Danielle Posthuma1,3* Affiliations: 1. Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, The Netherlands. 2. NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Criminal Medicine, University of Oslo, Oslo, Norway 3. Department of Clinical Genetics, Section of Complex Trait Genetics, Neuroscience Campus Amsterdam, VU Medical Center, Amsterdam, the Netherlands. 4. Division of Mental health and addiction Oslo University hospital, Oslo, Norway 5. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA 6. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA 7. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA *Correspondence to: Danielle Posthuma, Department of Complex Trait Genetics, VU University, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands. Phone: +31 20 5982823, Fax: +31 20 5986926, Email: [email protected] Word count: Abstract 181 words, Main text 5,762 words and Methods 4,572 words References: 40 Display items: 4 figures and 2 tables Extended Data: 11 figures Supplementary Information: Text 3,401 words and 25 tables 1 1 bioRxiv preprint doi: https://doi.org/10.1101/500090; this version posted December 19, 2018. -
The Importance of Genetic Redundancy in Evolution
TREE 2688 No. of Pages 14 Trends in Ecology & Evolution Review The Importance of Genetic Redundancy in Evolution Áki J. Láruson,1,3,*,@ Sam Yeaman,2,@ and Katie E. Lotterhos1,3,@ Genetic redundancy has been defined in many different ways at different levels Highlights of biological organization. Here, we briefly review the general concept of redun- The use of the term ‘genetic redundancy’ dancy and focus on the evolutionary importance of redundancy in terms of the has changed over the past 100 years. number of genotypes that give rise to the same phenotype. We discuss the chal- The amount of redundancy in the lenges in determining redundancy empirically, with published experimental mapping of genotype to phenotype is a examples, and demonstrate the use of the C-score metric to quantify redun- critical parameter for evolutionary out- dancy in evolution studies. We contrast the implicit assumptions of redundancy comes under a range of models. in quantitative versus population genetic models, show how this contributes to Distinguishing the conceptual terms signatures of allele frequency shifts, and highlight how the rapid accumulation ‘genotypic redundancy’ and ‘segregating of genome-wide association data provides an avenue for further understanding redundancy’ promotes clearer language the prevalence and role of redundancy in evolution. to further the study of redundancy at all levels of evolutionary biology. History of Redundancy in Evolutionary Studies Empirical determination of redundancy is Redundancy describes a situation in which there is an excess of causal components in a system, challenging, but there are approaches above the minimum needed for its proper function. Understanding how redundancy is built into which allow for the quantitative inference of redundancy. -
Canalization in Evolutionary Genetics: a Stabilizing Theory?
Problems and paradigms Canalization in evolutionary genetics: a stabilizing theory? Greg Gibson1* and GuÈ nter Wagner2 ``The stability of the morphogenetic system is destroyed (rendered labile) due either to variation in environmental factors or to mutation. On the other hand, in the course of evolution stability is reestablished by the continuous action of stabilizing selection. Stabilizing selection produces a stable form by creating a regulating apparatus. This protects normal morphogenesis against possible disturbances by chance variation in the external environment and also against small variations in internal factors (i.e. mutations). In this case, natural selection is based upon the selective advantage of the norm (often the new norm) over any deviations from it.'' I.I. Schmalhausen, 1949 (p 79 of 1986 reprinted edition). Summary mechanisms that produce it, and that it has implications for Canalization is an elusive concept. The notion that both developmental and evolutionary biology.(4) However, biological systems ought to evolve to a state of higher stability against mutational and environmental perturba- evolutionary geneticists have devised a more limited mean- tions seems simple enough, but has been exceedingly ing for the term canalization. Whereas buffering refers to difficult to prove. Part of the problem has been the lack keeping a trait constant and hence to low variance about of a definition of canalization that incorporates an the mean, the associated evolutionary questions are whether evolutionary genetic perspective and provides a frame- or not traits can evolve such that they become less likely to work for both mathematical and empirical study. After briefly reviewing the importance of canalization in vary, and if so, whether particular ones have done so, and studies of evolution and development, we aim, with eventually to address how this has occurred. -
SCIENCE CHINA Genetic Architecture, Epigenetic Influence And
SCIENCE CHINA Life Sciences THEMATIC ISSUE: Autism October 2015 Vol.58 No.10: 958–967 • REVIEW • doi: 10.1007/s11427-015-4941-1 Genetic architecture, epigenetic influence and environment expo- sure in the pathogenesis of Autism YU Li1†, WU YiMing1† & WU Bai-Lin1,2* 1Children's Hospital of Fudan University, Institutes of Biomedical Science, Shanghai Medical College of Fudan University, Shanghai 200032, China; 2Claritas Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA Autism spectrum disorder (ASD) is a spectral neurodevelopment disorder affecting approximately 1% of the population. ASD is characterized by impairments in reciprocal social interaction, communication deficits and restricted patterns of behavior. Multiple factors, including genetic/genomic, epigenetic/epigenomic and environmental, are thought to be necessary for autism development. Recent reviews have provided further insight into the genetic/genomic basis of ASD. It has long been suspected that epigenetic mechanisms, including DNA methylation, chromatin structures and long non-coding RNAs may play important roles in the pathology of ASD. In addition to genetic/genomic alterations and epigenetic/epigenomic influences, environmental exposures have been widely accepted as an important role in autism etiology, among which immune dysregulation and gastro- intestinal microbiota are two prominent ones. autism spectrum disorder, genetic architecture, genomic disorder, gene mutation, copy number variants, single nucleo- tide variants, genetic pathways, -
Arxiv:1803.03453V4 [Cs.NE] 21 Nov 2019
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities Joel Lehman1†, Jeff Clune1, 2†, Dusan Misevic3†, Christoph Adami4, Lee Altenberg5, Julie Beaulieu6, Peter J Bentley7, Samuel Bernard8, Guillaume Beslon9, David M Bryson4, Patryk Chrabaszcz11, Nick Cheney2, Antoine Cully12, Stephane Doncieux13, Fred C Dyer4, Kai Olav Ellefsen14, Robert Feldt15, Stephan Fischer16, Stephanie Forrest17, Antoine Fr´enoy18, Christian Gagn´e6 Leni Le Goff13, Laura M Grabowski19, Babak Hodjat20, Frank Hutter11, Laurent Keller21, Carole Knibbe9, Peter Krcah22, Richard E Lenski4, Hod Lipson23, Robert MacCurdy24, Carlos Maestre13, Risto Miikkulainen26, Sara Mitri21, David E Moriarty27, Jean-Baptiste Mouret28, Anh Nguyen2, Charles Ofria4, Marc Parizeau 6, David Parsons9, Robert T Pennock4, William F Punch4, Thomas S Ray29, Marc Schoenauer30, Eric Schulte17, Karl Sims, Kenneth O Stanley1,31, Fran¸coisTaddei3, Danesh Tarapore32, Simon Thibault6, Westley Weimer33, Richard Watson34, Jason Yosinski1 †Organizing lead authors 1 Uber AI Labs, San Francisco, CA, USA 2 University of Wyoming, Laramie, WY, USA 3 Center for Research and Interdisciplinarity, Paris, France 4 Michigan State University, East Lansing, MI, USA 5 Univeristy of Hawai‘i at Manoa, HI, USA 6 Universit´eLaval, Quebec City, Quebec, Canada 7 University College London, London, UK 8 INRIA, Institut Camille Jordan, CNRS, UMR5208, 69622 Villeurbanne, France 9 Universit´ede Lyon, INRIA, CNRS, LIRIS UMR5205, INSA, UCBL, -
Human Social Genomics in the Multi-Ethnic Study of Atherosclerosis
Getting “Under the Skin”: Human Social Genomics in the Multi-Ethnic Study of Atherosclerosis by Kristen Monét Brown A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Epidemiological Science) in the University of Michigan 2017 Doctoral Committee: Professor Ana V. Diez-Roux, Co-Chair, Drexel University Professor Sharon R. Kardia, Co-Chair Professor Bhramar Mukherjee Assistant Professor Belinda Needham Assistant Professor Jennifer A. Smith © Kristen Monét Brown, 2017 [email protected] ORCID iD: 0000-0002-9955-0568 Dedication I dedicate this dissertation to my grandmother, Gertrude Delores Hampton. Nanny, no one wanted to see me become “Dr. Brown” more than you. I know that you are standing over the bannister of heaven smiling and beaming with pride. I love you more than my words could ever fully express. ii Acknowledgements First, I give honor to God, who is the head of my life. Truly, without Him, none of this would be possible. Countless times throughout this doctoral journey I have relied my favorite scripture, “And we know that all things work together for good, to them that love God, to them who are called according to His purpose (Romans 8:28).” Secondly, I acknowledge my parents, James and Marilyn Brown. From an early age, you two instilled in me the value of education and have been my biggest cheerleaders throughout my entire life. I thank you for your unconditional love, encouragement, sacrifices, and support. I would not be here today without you. I truly thank God that out of the all of the people in the world that He could have chosen to be my parents, that He chose the two of you.