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Astri J. Lundervold

Other affiliations: Deaconess Hospital, Equinor, Haukeland University Hospital  ...read more
Bio: Astri J. Lundervold is an academic researcher from University of Bergen. The author has contributed to research in topics: Population & Attention deficit hyperactivity disorder. The author has an hindex of 55, co-authored 222 publications receiving 12818 citations. Previous affiliations of Astri J. Lundervold include Deaconess Hospital & Equinor.


Papers
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Journal ArticleDOI
22 Jun 2018-Science
TL;DR: It is demonstrated that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine, and it is shown that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures.
Abstract: Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

1,357 citations

Journal ArticleDOI
Jeanne E. Savage1, Philip R. Jansen1, Philip R. Jansen2, Sven Stringer1, Kyoko Watanabe1, Julien Bryois3, Christiaan de Leeuw1, Mats Nagel, Swapnil Awasthi4, Peter B. Barr5, Jonathan R. I. Coleman6, Katrina L. Grasby7, Anke R. Hammerschlag1, Jakob Kaminski4, Robert Karlsson3, Eva Krapohl8, Max Lam, Marianne Nygaard9, Chandra A. Reynolds10, Joey W. Trampush11, Hannah Young12, Delilah Zabaneh8, Sara Hägg3, Narelle K. Hansell13, Ida K. Karlsson3, Sten Linnarsson3, Grant W. Montgomery7, Grant W. Montgomery13, Ana B. Muñoz-Manchado3, Erin Burke Quinlan8, Gunter Schumann8, Nathan G. Skene3, Nathan G. Skene14, Bradley T. Webb5, Tonya White2, Dan E. Arking15, Dimitrios Avramopoulos15, Robert M. Bilder16, Panos Bitsios17, Katherine E. Burdick18, Katherine E. Burdick19, Katherine E. Burdick20, Tyrone D. Cannon21, Ornit Chiba-Falek, Andrea Christoforou22, Elizabeth T. Cirulli, Eliza Congdon16, Aiden Corvin23, Gail Davies24, Ian J. Deary24, Pamela DeRosse25, Pamela DeRosse26, Dwight Dickinson27, Srdjan Djurovic28, Srdjan Djurovic29, Gary Donohoe30, Emily Drabant Conley, Johan G. Eriksson31, Thomas Espeseth32, Nelson A. Freimer16, Stella G. Giakoumaki17, Ina Giegling33, Michael Gill23, David C. Glahn21, Ahmad R. Hariri34, Alex Hatzimanolis35, Alex Hatzimanolis36, Matthew C. Keller37, Emma Knowles21, Deborah C. Koltai34, Bettina Konte33, Jari Lahti31, Stephanie Le Hellard29, Todd Lencz26, Todd Lencz25, David C. Liewald24, Edythe D. London16, Astri J. Lundervold29, Anil K. Malhotra25, Anil K. Malhotra26, Ingrid Melle32, Ingrid Melle29, Derek W. Morris30, Anna C. Need38, William Ollier39, Aarno Palotie40, Aarno Palotie18, Aarno Palotie31, Antony Payton39, Neil Pendleton41, Russell A. Poldrack42, Katri Räikkönen31, Ivar Reinvang32, Panos Roussos20, Panos Roussos19, Dan Rujescu33, Fred W. Sabb43, Matthew A. Scult34, Olav B. Smeland32, Nikolaos Smyrnis36, Nikolaos Smyrnis35, John M. Starr24, Vidar M. Steen29, Nikos C. Stefanis35, Nikos C. Stefanis36, Richard E. Straub15, Kjetil Sundet32, Henning Tiemeier2, Aristotle N. Voineskos44, Daniel R. Weinberger15, Elisabeth Widen31, Jin Yu, Gonçalo R. Abecasis45, Ole A. Andreassen32, Gerome Breen6, Lene Christiansen9, Birgit Debrabant9, Danielle M. Dick5, Andreas Heinz4, Jens Hjerling-Leffler3, M. Arfan Ikram46, Kenneth S. Kendler5, Nicholas G. Martin7, Sarah E. Medland7, Nancy L. Pedersen3, Robert Plomin8, Tinca J. C. Polderman1, Stephan Ripke4, Stephan Ripke18, Stephan Ripke47, Sophie van der Sluis, Patrick Sullivan3, Patrick Sullivan48, Scott I. Vrieze12, Margaret J. Wright13, Danielle Posthuma1 
TL;DR: A large-scale genetic association study of intelligence identifies 190 new loci and implicates 939 new genes related to neurogenesis, neuron differentiation and synaptic structure, a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.
Abstract: Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

800 citations

Journal ArticleDOI
Martine Hoogman1, Janita Bralten1, Derrek P. Hibar2, Maarten Mennes, Marcel P. Zwiers, Lizanne S.J. Schweren3, Kimm J. E. van Hulzen1, Sarah E. Medland4, Elena Shumskaya1, Neda Jahanshad2, Patrick de Zeeuw5, Eszter Szekely6, Gustavo Sudre6, Thomas Wolfers1, Alberdingk M.H. Onnink1, Janneke Dammers1, Jeanette C. Mostert1, Yolanda Vives-Gilabert, Gregor Kohls, Eileen Oberwelland, Jochen Seitz, Martin Schulte-Rüther, Sara Ambrosino5, Alysa E. Doyle7, Alysa E. Doyle8, Marie F. Høvik9, Margaretha Dramsdahl10, Leanne Tamm11, Theo G.M. van Erp12, Anders M. Dale13, Andrew J. Schork13, Annette Conzelmann14, Annette Conzelmann15, Kathrin C. Zierhut15, Ramona Baur15, Hazel McCarthy16, Yuliya N. Yoncheva17, Ana Cubillo18, Kaylita Chantiluke18, Mitul A. Mehta18, Yannis Paloyelis18, Sarah Hohmann19, Sarah Baumeister19, Ivanei E. Bramati, Paulo Mattos20, Fernanda Tovar-Moll20, Pamela K. Douglas21, Tobias Banaschewski19, Daniel Brandeis, Jonna Kuntsi18, Philip Asherson18, Katya Rubia18, Clare Kelly16, Clare Kelly17, Adriana Di Martino17, Michael P. Milham22, Michael P. Milham23, Francisco X. Castellanos17, Francisco X. Castellanos23, Thomas Frodl24, Thomas Frodl16, Mariam Zentis24, Klaus-Peter Lesch15, Klaus-Peter Lesch25, Andreas Reif26, Paul Pauli15, Terry L. Jernigan13, Jan Haavik27, Jan Haavik9, Kerstin J. Plessen, Astri J. Lundervold9, Kenneth Hugdahl9, Kenneth Hugdahl27, Larry J. Seidman8, Larry J. Seidman28, Joseph Biederman8, Nanda Rommelse1, Dirk J. Heslenfeld29, Catharina A. Hartman3, Pieter J. Hoekstra3, Jaap Oosterlaan29, Georg von Polier, Kerstin Konrad, Oscar Vilarroya30, Josep Antoni Ramos-Quiroga30, Joan Carles Soliva30, Sarah Durston5, Jan K. Buitelaar1, Stephen V. Faraone9, Stephen V. Faraone31, Philip Shaw6, Paul M. Thompson2, Barbara Franke1 
TL;DR: Lifespan analyses suggest that, in the absence of well powered longitudinal studies, the ENIGMA cross-sectional sample across six decades of ages provides a means to generate hypotheses about lifespan trajectories in brain phenotypes.

749 citations

Journal ArticleDOI

730 citations

Journal ArticleDOI
TL;DR: A genome-wide analysis of unrelated adults with data on single nucleotide polymorphisms and detailed phenotypes on cognitive traits unequivocally confirms that a substantial proportion of individual differences in human intelligence is due to genetic variation, and is consistent with many genes of small effects underlying the additive genetic influences on intelligence.
Abstract: General intelligence is an important human quantitative trait that accounts for much of the variation in diverse cognitive abilities. Individual differences in intelligence are strongly associated with many important life outcomes, including educational and occupational attainments, income, health and lifespan. Data from twin and family studies are consistent with a high heritability of intelligence, but this inference has been controversial. We conducted a genome-wide analysis of 3511 unrelated adults with data on 549692 single nucleotide polymorphisms (SNPs) and detailed phenotypes on cognitive traits. We estimate that 40% of the variation in crystallized-type intelligence and 51% of the variation in fluid-type intelligence between individuals is accounted for by linkage disequilibrium between genotyped common SNP markers and unknown causal variants. These estimates provide lower bounds for the narrow-sense heritability of the traits. We partitioned genetic variation on individual chromosomes and found that, on average, longer chromosomes explain more variation. Finally, using just SNP data we predicted B1% of the variance of crystallized and fluid cognitive phenotypes in an independent sample (P=0.009 and 0.028, respectively). Our results unequivocally confirm that a substantial proportion of individual differences in human intelligence is due to genetic variation, and are consistent with many genes of small effects underlying the additive genetic influences on intelligence. Molecular Psychiatry advance online publication, 9 August 2011; doi:10.1038/mp.2011.85

618 citations


Cited by
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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

4,409 citations

01 Jan 2006
TL;DR: For example, Standardi pružaju okvir koje ukazuju na ucinkovitost kvalitetnih instrumenata u onim situacijama u kojima je njihovo koristenje potkrijepljeno validacijskim podacima.
Abstract: Pedagosko i psiholosko testiranje i procjenjivanje spadaju među najvažnije doprinose znanosti o ponasanju nasem drustvu i pružaju temeljna i znacajna poboljsanja u odnosu na ranije postupke. Iako se ne može ustvrditi da su svi testovi dovoljno usavrseni niti da su sva testiranja razborita i korisna, postoji velika kolicina informacija koje ukazuju na ucinkovitost kvalitetnih instrumenata u onim situacijama u kojima je njihovo koristenje potkrijepljeno validacijskim podacima. Pravilna upotreba testova može dovesti do boljih odluka o pojedincima i programima nego sto bi to bio slucaj bez njihovog koristenja, a također i ukazati na put za siri i pravedniji pristup obrazovanju i zaposljavanju. Međutim, losa upotreba testova može dovesti do zamjetne stete nanesene ispitanicima i drugim sudionicima u procesu donosenja odluka na temelju testovnih podataka. Cilj Standarda je promoviranje kvalitetne i eticne upotrebe testova te uspostavljanje osnovice za ocjenu kvalitete postupaka testiranja. Svrha objavljivanja Standarda je uspostavljanje kriterija za evaluaciju testova, provedbe testiranja i posljedica upotrebe testova. Iako bi evaluacija prikladnosti testa ili njegove primjene trebala ovisiti prvenstveno o strucnim misljenjima, Standardi pružaju okvir koji osigurava obuhvacanje svih relevantnih pitanja. Bilo bi poželjno da svi autori, sponzori, nakladnici i korisnici profesionalnih testova usvoje Standarde te da poticu druge da ih također prihvate.

3,905 citations