Introducing Social Genetic Parameters to Quantify Bias in Polygenic Score Analyses
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Identifying Intergenerational Risk Factors for ADHD Symptoms Using Polygenic Scores in the Norwegian Mother, Father and Child Cohort
medRxiv preprint doi: https://doi.org/10.1101/2021.02.16.21251737; this version posted February 20, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Intergenerational transmission of ADHD Identifying intergenerational risk factors for ADHD symptoms using polygenic scores in the Norwegian Mother, Father and Child Cohort Jean-Baptiste Pingault1,2*, Wikus Barkhuizen1*, Biyao Wang1, Laurie J. Hannigan3,4, Espen Moen Eilertsen6, Ole A. Andreassen7, Helga Ask5, Martin Tesli5,7, Ragna Bugge Askeland4,5, George Davey Smith4,9, Neil Davies4,8,9, Ted Reichborn-Kjennerud5, †Eivind Ystrom5,6, †Alexandra Havdahl3,5,6 *Equal first authors †Equal last authors 1 Division of Psychology and Language Sciences, University College London, London, United Kingdom 2 MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College, London, United Kingdom 3 Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway 4 Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, United Kingdom 5 Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway 6 PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway 7 NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway 8 K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Norway 9 Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom Address correspondence to Dr. -
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. -
Common Genetic Variation and Schizophrenia Polygenic Risk
RESEARCH ARTICLE Neuropsychiatric Genetics Common Genetic Variation and Schizophrenia Polygenic Risk Influence Neurocognitive Performance in Young Adulthood Alex Hatzimanolis,1 Pallav Bhatnagar,2 Anna Moes,2 Ruihua Wang,1 Panos Roussos,3,4 Panos Bitsios,5 Costas N. Stefanis,6 Ann E. Pulver,1 Dan E. Arking,2 Nikolaos Smyrnis,6,7 Nicholas C. Stefanis,6,7 and Dimitrios Avramopoulos1,2* 1Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland 2McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 3Department of Psychiatry, Friedman Brain Institute and Department of Genetics and Genomics Science, Institute of Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 4James J. Peters Veterans Affairs Medical Center, Bronx, New York, New York 5Department of Psychiatry and Behavioral Sciences, Faculty of Medicine, University of Crete, Heraklion, Greece 6University Mental Health Research Institute, Athens, Greece 7Department of Psychiatry, Eginition Hospital, University of Athens Medical School, Athens, Greece Manuscript Received: 3 December 2014; Manuscript Accepted: 29 April 2015 Neurocognitive abilities constitute complex traits with consid- erable heritability. Impaired neurocognition is typically ob- How to Cite this Article: served in schizophrenia (SZ), whereas convergent evidence Hatzimanolis A, Bhatnagar P, Moes A, has shown shared genetic determinants between neurocognition Wang R, Roussos P, Bitsios P, Stefanis CN, and SZ. Here, we report a genome-wide association study Pulver AE, Arking DE, Smyrnis N, Stefanis (GWAS) on neuropsychological and oculomotor traits, linked NC, Avramopoulos D. 2015. Common to SZ, in a general population sample of healthy young males Genetic Variation and Schizophrenia ¼ (n 1079). -
Polygenic Methods and Their Application to Psychiatric Traits
This is the peer reviewed version of the following article: Wray, N. R., & Visscher, P. M. (2015). Quantitative genetics of disease traits. Wray, N. R., Lee, S. H., Mehta, D., Vinkhuyzen, A. A., Dudbridge, F., & Middeldorp, C. M. (2014). Research review: polygenic methods and their application to psychiatric traits. Journal of Child Psychology and Psychiatry, 55(10), 1068–1087. DOI: 10.1111/jcpp.12295 . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for self-archiving. Polygenic methods and their application to psychiatric traits Naomi R Wray1*, S Hong Lee1, Divya Mehta1, Anna AE Vinkhuyzen1, Frank Dudbridge2, Christel M Middeldorp3,4 1. The University of Queensland, Queensland Brain Institute, Queensland, Australia 4068 2. Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK 3. Department of Biological Psychology, Neuroscience Campus Amsterdam, VU University Amsterdam, The Netherlands 4. Department of Child and Adolescent Psychiatry, GGZ inGeest/VU University Medical Center, Amsterdam, The Netherlands * Corresponding author. [email protected] Abstract Background Despite evidence from twin and family studies for an important contribution of genetic factors to both childhood and adult onset psychiatric disorders, identifying robustly associated specific DNA variants has proved challenging. In the pre-genomics era the genetic architecture (number, frequency and effect size of risk variants) of complex genetic disorders was unknown. Empirical evidence for the genetic architecture of psychiatric disorders is emerging from the genetic studies of the last five years. Methods and scope We review the methods investigating the polygenic nature of complex disorders. -
The BRCA1 Tumor Suppressor
Journal of Translational Science Review Article ISSN: 2059-268X The BRCA1 tumor suppressor: potential long-range interactions of the BRCA1 promoter and the risk of breast cancer Beata Anna Bielinska* Department of Molecular and Translational Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Poland Abstract Breast cancer is a complex disease with different phenotypes associated with genetic and non-genetic risk factors. An aberrant expression of the BRCA1 tumor suppressor as well as dysfunction of BRCA1 protein caused by germline mutations are implicated in breast cancer aethiology. BRCA1 plays a crucial role in genome and epigenome stability. Its expression is auto regulated and modulated by various cellular signals including metabolic status, hypoxia, DNA damage, estrogen stimulation. The review describes breast cancer risk factors, the BRCA1 gene expression and functions as well as covers the role of long-range genomic interactions, which emerge as regulators of gene expression and moderators of genomic communication. The potential long-range interactions of the BRCA1 promoter (driven by polymorphic variant rs11655505 C/T, as an example) and their possible impact on the BRCA1 gene regulation and breast cancer risk are also discussed. Introduction efficiency of BRCA1 transcription [9]. However, to date the mechanism underlying effect of the BRCA1 promoter variant rs11655505 C>T Female breast cancer is one of the most common cancers worldwide remains unknown. with app.1.68 million cases diagnosed annually [1]. It is a complex disease characterized by molecular and phenotypic heterogeneity Besides high risk BRCA1/BRCA2 mutations, moderate risk observed both within populations and intra-individually, within mutations such as those found in DNA repair genes (CHEK2, ATM, single tumor cells in a spatiotemporal manner, as pointed out by PALB2) also demonstrate familial clustering. -
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. -
Within-Sibship GWAS Improve Estimates of Direct Genetic Effects
bioRxiv preprint doi: https://doi.org/10.1101/2021.03.05.433935; this version posted March 7, 2021. 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 4.0 International license. Within-sibship GWAS improve estimates of direct genetic effects Laurence J Howe1,2 *, Michel G Nivard3, Tim T Morris1,2, Ailin F Hansen4, Humaira Rasheed4, Yoonsu Cho1,2, Geetha Chittoor5, Penelope A Lind6,7,8, Teemu Palviainen9, Matthijs D van der Zee3, Rosa Cheesman10,11, Massimo Mangino12,13, Yunzhang Wang14, Shuai Li15,16,17, Lucija Klaric18, Scott M Ratliff19, Lawrence F Bielak19, Marianne Nygaard20,21, Chandra A Reynolds22, Jared V Balbona23,24, Christopher R Bauer25,26, Dorret I Boomsma3,27, Aris Baras28, Archie Campbell29, Harry Campbell30, Zhengming Chen31,32, Paraskevi Christofidou12, Christina C Dahm33, Deepika R Dokuru23,24, Luke M Evans24,34, Eco JC de Geus3,35, Sudheer Giddaluru36,37, Scott D Gordon38, K. Paige Harden39, Alexandra Havdahl40,41, W. David Hill42,43, Shona M Kerr18, Yongkang Kim24, Hyeokmoon Kweon44, Antti Latvala9,45, Liming Li46, Kuang Lin31, Pekka Martikainen47,48,49, Patrik KE Magnusson14, Melinda C Mills50, Deborah A Lawlor1,2,51, John D Overton27, Nancy L Pedersen14, David J Porteous25, Jeffrey Reid28, Karri Silventoinen47, Melissa C Southey17,52,53, Travis T Mallard39, Elliot M Tucker-Drob39, Margaret J Wright54, Social Science Genetic Association Consortium, Within Family -
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) -
LDL Polygenic Risk Scores in Practice
University of Copenhagen & Copenhagen University Hospital LDL polygenic risk scores in practice Anne Tybjærg-Hansen MD DMSc Professor, Chief Physician Copenhagen University Hospital and Faculty of Health and Medical Sciences, University of Copenhagen, Denmark FH Global Summit, Atlanta 211019 Outline • Introduction to polygenic risk score • Polygenic risk scores for LDL cholesterol and FH • Polygenic risk scores for CAD and FH – polygenic versus omnigenic scores • Take home message Introduction to polygenic risk score Genome-wide association study of LDL cholesterol (from GLGC) P<10-8 Willer CJ et al. Nat Genet 2013 Creating a polygenic score GWAS SNPs Individually: tiny effects ComBined: larger effect Effect of gene score on trait level 1.0 1.2 Odds ratio Genome-wide association study of LDL cholesterol Polygenic risk score P<10-8 Willer CJ et al. Nat Genet 2013 Genome-wide association studies (GWAS’) for lipids In sufficiently large samples, many ~ 100,000 previously non-significant loci would European ancestry become nominally significant, 95 loci although effect sizes are close to zero. ~ 600,000 ~85,000 non-Europeans ~200,000 >118 novel loci 7,898 non-Europeans (~268 known loci) 62 novel loci 30.4 mill SNPs Klarin et al. 2010 2013 2018 Polygenic risk scores for LDL cholesterol and Familial Hypercholesterolemia Rigshospitalet The Centre of Diagnostic Investigations Diagnostic criteria for FH 1/220 individuals • Definite FH: > 8 points • Probable FH: 6-8 points • Possible FH: 3-5 points • Unlikely FH: 0-2 points Mette Christoffersen 9 Rigshospitalet The Centre of Diagnostic Investigations Clinical vs. mutation diagnosis Polygenic cause? 20-40% High Lp(a)? 60-80 % Polygenic cause? Modified from Eur Heart J. -
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. -
Selection Against Variants in the Genome Associated PNAS PLUS with Educational Attainment
Selection against variants in the genome associated PNAS PLUS with educational attainment Augustine Konga,b,1, Michael L. Friggea, Gudmar Thorleifssona, Hreinn Stefanssona, Alexander I. Youngc, Florian Zinka, Gudrun A. Jonsdottira, Aysu Okbayd,e, Patrick Sulema, Gisli Massona, Daniel F. Gudbjartssona,b, Agnar Helgasona,f, Gyda Bjornsdottira, Unnur Thorsteinsdottira,g, and Kari Stefanssona,g,1 adeCODE genetics/Amgen Inc., Reykjavik 101, Iceland; bSchool of Engineering and Natural Sciences, University of Iceland, Reykjavik 101, Iceland; cWellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom; dDepartment of Applied Economics, Erasmus School of Applied Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, The Netherlands; eInstitute for Behavior and Biology, Erasmus University Rotterdam, 3062 PA Rotterdam, The Netherlands; fDepartment of Anthropology, University of Iceland, Reykjavik 101, Iceland; and gFaculty of Medicine, University of Iceland, Reykjavik 101, Iceland Edited by Andrew G. Clark, Cornell University, Ithaca, NY, and approved December 5, 2016 (received for review July 22, 2016) Epidemiological and genetic association studies show that genet- 62 cohorts were used to determine the weightings for POLYEDU. ics play an important role in the attainment of education. Here, we We computed POLYEDU for over 150,000 Icelanders who were investigate the effect of this genetic component on the reproduc- directly genotyped with chip arrays and imputed for additional tive history of 109,120 Icelanders and the consequent impact on sequence variants discovered through whole-genome sequencing the gene pool over time. We show that an educational attainment of 8,453 Icelanders (20) (Materials and Methods). POLYEDU was polygenic score, POLYEDU, constructed from results of a recent scaled to an SD of 1, hereafter referred to as standard units (SUs). -
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,