Behavioral Genetics
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K-Model – an Evolutionary Algorithm with New Schema of Representation
K-MODEL – AN EVOLUTIONARY ALGORITHM WITH NEW SCHEMA OF REPRESENTATION Halina Kwasnicka Department of Computer Science, Wroclaw University of Technology Wyspianskiego 27, 50-370 Wroclaw, Poland tel. (48 71) 3202397, fax: (48 71)211018, e-mail: [email protected], http://www.ci.pwr.wroc.pl/~kwasnick ABSTRACT The paper presents an evolutionary model with pleiotropy and polygene effects, named K-Model. Genes are integer numbers and the linear dependence between phenes and genes are assumed. The redundant genes, i.e., not active ones during some period of evolution, are also included. Such representation allows the evolutionary algorithm to escape from local optima (evolutionary traps) and evolve quicker especially for multi-modal and multi-variable fitness functions. INTRODUCTION From centuries people learn observing natural processes. Usually natural processes are perceived as very skilful, and researchers willingly imitate them to obtain effective techniques. Optimisation techniques called Genetic Algorithms (GAs), or more generally, Evolutionary Algorithms (EAs), base on biological evolution. They imitate the Darwinian paradigm survival the fittest, and use a vocabulary borrowed from genetics. GAs search large spaces efficiently without complete knowledge of an objective function. They require only a limited number of values of objective function to guide their search process. Operators analogical to genetic ones as crossover and mutation are used for achieving a new area in the search space (new solutions). GAs act on the set of coded potential solutions (called chromosomes or individuals) and use the strategy of natural selection to achieve their aims. Proposed by J. Holland genetic algorithms, which we call here as Classic Genetic Algorithm (CGA) uses a binary alphabet for defining chromosomes. -
UNIT 3 QUASI EXPERIMENTAL DESIGN Factorial Design
UNIT 3 QUASI EXPERIMENTAL DESIGN Factorial Design Structure 3.0 Introduction 3.1 Objectives 3.2 Meaning of Quasi Experimental Design 3.3 Difference Between Quasi Experimental Design and True Experimental Design 3.4 Types of Quasi Experimental Design 3.4.1 Non-Equivalent Group Posttest only Design 3.4.2 Non-Equivalent Control Group Design 3.4.3 The Separate Pretest-post Test Sample Design 3.4.4 The Double Pretest Design 3.4.5 The Switching Replications Design 3.4.6 Mixed Factorial Design 3.4.7 Interrupted Time Series Design 3.4.8 Multiple Time Series Design 3.4.9 Repeated Treatment Design 3.4.10 Counter Balanced Design 3.5 Advantages and Disadvantages of Quasi Experimental Design 3.6 Let Us Sum Up 3.7 Unit End Questions 3.8 Glossary 3.9 Suggested Readings 3.0 INTRODUCTION For most of the history of scientific psychology, it has been accepted that experimental research, with its twin assets of random assignment and manipulation of the independent variable by the researcher, is the ideal method for psychological research. Some researchers believe this so strongly that they avoid studying important questions about human personality, sex differences in behaviour, and other subjects that do not lend themselves to experimental research. A few decades ago researchers in psychology were interested in applied psychology issues conducting research on how students learnt in school, how social factors influenced the behaviour of an individual, how to motivate factory workers to perform at a higher level etc. These research questions cannot be answered by lab experiments as one has to go to the field and the real life situation like the classroom etc., to find answers to the research issues mentioned above. -
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. -
Genetic Correlations Reveal the Shared Genetic Architecture of Transcription in Human Peripheral Blood
Hemani, G. (2017). Genetic correlations reveal the shared genetic architecture of transcription in human peripheral blood. Nature Communications, 8, [483]. https://doi.org/10.1038/s41467-017-00473- z Publisher's PDF, also known as Version of record License (if available): CC BY Link to published version (if available): 10.1038/s41467-017-00473-z Link to publication record in Explore Bristol Research PDF-document This is the final published version of the article (version of record). It first appeared online via Nature Publishing Group at https://www.nature.com/articles/s41467-017-00473-z . Please refer to any applicable terms of use of the publisher. University of Bristol - Explore Bristol Research General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/red/research-policy/pure/user-guides/ebr-terms/ ARTICLE DOI: 10.1038/s41467-017-00473-z OPEN Genetic correlations reveal the shared genetic architecture of transcription in human peripheral blood Samuel W. Lukowski 1, Luke R. Lloyd-Jones1,2, Alexander Holloway2, Holger Kirsten3,4, Gibran Hemani 5,6, Jian Yang 1,2, Kerrin Small 7, Jing Zhao8, Andres Metspalu9, Emmanouil T. Dermitzakis10, Greg Gibson 8, Timothy D. Spector7, Joachim Thiery4,11, Markus Scholz 3,4, Grant W. Montgomery1,12, Tonu Esko 9, Peter M. Visscher 1,2 & Joseph E. Powell1,2 Transcript co-expression is regulated by a combination of shared genetic and environmental factors. Here, we estimate the proportion of co-expression that is due to shared genetic variance. -
Heredity in Sarcoidosis: a Registry-Based Twin Study Thorax: First Published As 10.1136/Thx.2007.094060 on 5 June 2008
Sarcoidosis Heredity in sarcoidosis: a registry-based twin study Thorax: first published as 10.1136/thx.2007.094060 on 5 June 2008. Downloaded from A Sverrild,1 V Backer,1 K O Kyvik,2 J Kaprio,3 N Milman,4 C B Svendsen,5 S F Thomsen1 1 Department of Respiratory ABSTRACT Prevalence and incidence are dependent on age, sex Medicine, Bispebjerg University Background: Sarcoidosis is a multiorgan granulomatous and ethnicity.1 2 8–10 Hospital, Copenhagen, Denmark; The present understanding of the pathogenesis 2 Institute of Regional Health inflammatory disease of unknown aetiology. Familial Research Services and The clustering of cases and ethnic variation in the epidemiol- of the disease is that sarcoidosis is triggered by an Danish Twin Registry, University ogy suggests a genetic influence on susceptibility to the abnormal immune response to an environmental of Southern Denmark, Odense, agent in a genetically predisposed individual.1 3 disease. This paper reports twin concordance and Denmark; The Finnish Twin heritability estimates of sarcoidosis in order to assess the Genetic factors are considered to contribute to Cohort Study, Department of Public Health, University of overall contribution of genetic factors to the disease the development of sarcoidosis for two main 12610 Helsinki, Helsinki, Finland and susceptibility. reasons ; (1) epidemiological studies have Department of Mental Health Methods: Monozygotic and dizygotic twins enrolled in identified ethnicity as an important risk factor; and Alcohol Research, National the Danish and the Finnish population-based national twin and (2) familial clustering of cases has been Public Health Institute, Helsinki, 4 observed frequently over the last decades. -
Research Methods in Social Psychology 2 Antony S.R
9781405124003_4_002.qxd 10/31/07 2:54 PM Page 20 Research Methods in Social Psychology 2 Antony S.R. Manstead KEY CONCEPTS confederate confounding construct construct validity control group convergent validity cover story debriefing demand characteristics dependent variable discourse analysis experiment experimental group experimental scenario experimenter expectancy effects external validity factorial experiment field experiment Hawthorne effect hypothesis implicit measures independent variable interaction effect interaction process analysis (IPA) internal validity Internet experiments main effect manipulation check mediating variable meta-analysis 9781405124003_4_002.qxd 10/31/07 2:54 PM Page 21 one-shot case study operationalization participant observation post-experimental enquiry CHAPTER OUTLINE post-test only control group design quasi-experiment This chapter provides an overview of research methods in social psychology, from the develop- quota sample random allocation ment of theory to the collection of data. After describing three quantitative research strategies reactivity (survey research, experiments and quasi-experiments), the chapter briefly discusses qualitative reliability approaches, focusing on discourse analysis. There follows a description of the key elements of sampling experiments and of threats to validity in experimental research, and a discussion of problems simple random sample with experimental research in social psychology. The final section of the chapter contains a social desirability description of three methods of data collection (observation, self-report and implicit measures). survey research theory triangulation true randomized experiment unobtrusive measures validity variable Introduction How do social psychologists develop their theories? How do social psychologists go about testing their theories? Methods provide a means of translating a researcher’s ideas into actions. These ideas usually revolve around one or more questions about a phenomenon. -
National Longitudinal Study of Adolescent to Adult Health
Social, Behavioral, and Biological Linkages Across the Life Course Social, Behavioral, and Biological Linkages Across the Life Course School Sampling Frame = QED National Longitudinal Study of HS Adolescent to Adult Health HS HS HHS HS Feeder Feeder Feeder Feeder Feeder Sampling Frame of Adolescents and Parents N = 100,000+ (100 to 4,000 per pair of schools) Ethnic High Educ Samples National Academy of Sciences Workshop Black Disabled Sample February 10-11, 2014 Saturation Puerto Rican Samples from 16 Schools Chinese Guidelines for Returning Individual Results from Genome Research Using Population- Main Sample 200/Community Based Banked Specimens Genetic CubanCuban Samples Carolyn Tucker Halpern University of North Carolina at Chapel Hill Unrelated Pairs Identical Twins Fraternal Twins Full Sibs Half Sibs in Same HH Social, Behavioral, and Biological Linkages Across the Life Course Social, Behavioral, and Biological Linkages Across the Life Course In-School In-Home Administration Administration Race and Ethnic Diversity in Add Health Wave I School Adolescents Race/Ethnicity N Unwtd. % Students Parent 1994-1995 Admin in grades 7-12 90,118 17,670 Mexico 1,767 8.5 (79%) 144 20,745 Cuba 508 2.5 Wave II School Adolescents Central-South America 647 3.1 1996 Admin in grades 8-12 Puerto Rico 570 2.8 (88.6%) 128 14,738 China 341 1.7 Wave III Young Adults Philippines 643 3.1 Partners 2001-2002 Aged 18-26 1,507 Other Asia 601 2.9 (77.4%) 15,197 Black (Africa/Afro-Caribbean) 4,601 22.2 Wave IV Adults Non-Hispanic White (Eur/Canada) 10,760 52.0 IIV -
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) -
Segregation Distortion and the Evolution of Sex-Determining Mechanisms
Heredity (2010) 104, 100–112 & 2010 Macmillan Publishers Limited All rights reserved 0018-067X/10 $32.00 www.nature.com/hdy ORIGINAL ARTICLE Segregation distortion and the evolution of sex-determining mechanisms M Kozielska1,2, FJ Weissing2, LW Beukeboom1 and I Pen2 1Evolutionary Genetics, University of Groningen, Haren, The Netherlands and 2Theoretical Biology, University of Groningen, Haren, The Netherlands Segregation distorters are alleles that distort normal segre- selection pressure, allowing novel sex-determining alleles to gation in their own favour. Sex chromosomal distorters lead spread. When distortion leads to female-biased sex ratios, a to biased sex ratios, and the presence of such distorters, new masculinizing gene can invade, leading to a new male therefore, may induce selection for a change in the heterogametic system. When distortion leads to male-biased mechanism of sex determination. The evolutionary dynamics sex ratios, a feminizing factor can invade and cause a switch of distorter-induced changes in sex determination has only to female heterogamety. In many cases, the distorter- been studied in some specific systems. Here, we present a induced change in the sex-determining system eventually generic model for this process. We consider three scenarios: leads to loss of the distorter from the population. Hence, the a driving X chromosome, a driving Y chromosome and a presence of sex chromosomal distorters will often only be driving autosome with a male-determining factor. We transient, and the distorters may remain unnoticed. The role investigate how the invasion prospects of a new sex- of segregation distortion in the evolution of sex determination determining factor are affected by the strength of distortion may, therefore, be underestimated. -
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. -
Due to Broad-Sense Genetic Heritability
The Sociology of Heritability How (and Why) Sociologists Should Care About Heritability: Evidence from Misclassified Twins Dalton Conley* Emily Rauscher NYU & NBER NYU * To whom correspondence should be directed; 6 Washington Square North Room 20; New York, NY 10003. [email protected] The Sociology of Heritability Abstract We argue that despite many sociologists’ aversion to them, heritability estimates have critical policy relevance for a variety of social outcomes ranging from education to health to stratification. However, estimates have traditionally been plagued by genetic-environmental covariance, which is likely to be non-trivial and confound estimates of narrow-sense (additive) heritability for social and behavioral outcomes. Until recently, there has not been an effective way to address this concern and as a result, sociologists have largely dismissed the entire enterprise as methodologically flawed and ideologically-driven. Indeed, in a classic paper, Goldberger (1979) shows that by varying assumptions of the GE-covariance, a researcher can drive the estimated heritability of an outcome, such as IQ, down to zero or up close to one. Survey questions that attempt to measure directly the extent to which more genetically similar kin (such as monozygotic twins) also share more similar environmental conditions than, say, dizygotic twins, represent poor attempts to gauge a very complex underlying phenomenon of GE-covariance. Methods that rely on concordance between interviewer classification and self- report offer similar concerns about validity. In the present study, we take advantage of a natural experiment to address this issue from another angle: Misclassification of twin zygosity in the National Longitudinal Survey of Adolescent Health (Add Health). -
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,