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Tonya White

Bio: Tonya White is an academic researcher from Erasmus University Rotterdam. The author has contributed to research in topics: Population & Brain morphometry. The author has an hindex of 60, co-authored 292 publications receiving 13269 citations. Previous affiliations of Tonya White include ETH Zurich & University of Minnesota.


Papers
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Journal ArticleDOI
Jeanne E. Savage1, Philip R. Jansen2, Philip R. Jansen1, 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 Hellard28, Todd Lencz26, Todd Lencz25, David C. Liewald24, Edythe D. London16, Astri J. Lundervold28, Anil K. Malhotra25, Anil K. Malhotra26, Ingrid Melle32, Ingrid Melle28, Derek W. Morris30, Anna C. Need38, William Ollier39, Aarno Palotie18, Aarno Palotie40, Aarno Palotie31, Antony Payton39, Neil Pendleton41, Russell A. Poldrack42, Katri Räikkönen31, Ivar Reinvang32, Panos Roussos19, Panos Roussos20, Dan Rujescu33, Fred W. Sabb43, Matthew A. Scult34, Olav B. Smeland32, Nikolaos Smyrnis35, Nikolaos Smyrnis36, John M. Starr24, Vidar M. Steen28, Nikos C. Stefanis36, Nikos C. Stefanis35, 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
Derrek P. Hibar1, Jason L. Stein1, Jason L. Stein2, Miguel E. Rentería3  +341 moreInstitutions (93)
09 Apr 2015-Nature
TL;DR: In this paper, the authors conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts.
Abstract: The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10(-33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

721 citations

Journal ArticleDOI
Paul M. Thompson1, Jason L. Stein2, Sarah E. Medland3, Derrek P. Hibar1  +329 moreInstitutions (96)
TL;DR: The ENIGMA Consortium has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected.
Abstract: The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

713 citations

Journal ArticleDOI
TL;DR: Intentions from developing a set of recommendations on behalf of the Organization for Human Brain Mapping are described and barriers that impede these practices are identified, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.
Abstract: Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.

544 citations

Journal ArticleDOI
TL;DR: It is shown that neuroticism’s genetic signal partly originates in two genetically distinguishable subclusters13 (‘depressed affect’ and ‘worry’), suggesting distinct causal mechanisms for subtypes of individuals.
Abstract: Neuroticism is an important risk factor for psychiatric traits, including depression1, anxiety2,3, and schizophrenia4-6. At the time of analysis, previous genome-wide association studies7-12 (GWAS) reported 16 genomic loci associated to neuroticism10-12. Here we conducted a large GWAS meta-analysis (n = 449,484) of neuroticism and identified 136 independent genome-wide significant loci (124 new at the time of analysis), which implicate 599 genes. Functional follow-up analyses showed enrichment in several brain regions and involvement of specific cell types, including dopaminergic neuroblasts (P = 3.49 × 10-8), medium spiny neurons (P = 4.23 × 10-8), and serotonergic neurons (P = 1.37 × 10-7). Gene set analyses implicated three specific pathways: neurogenesis (P = 4.43 × 10-9), behavioral response to cocaine processes (P = 1.84 × 10-7), and axon part (P = 5.26 × 10-8). We show that neuroticism's genetic signal partly originates in two genetically distinguishable subclusters13 ('depressed affect' and 'worry'), suggesting distinct causal mechanisms for subtypes of individuals. Mendelian randomization analysis showed unidirectional and bidirectional effects between neuroticism and multiple psychiatric traits. These results enhance neurobiological understanding of neuroticism and provide specific leads for functional follow-up experiments.

492 citations


Cited by
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01 Jan 2016
TL;DR: The using multivariate statistics is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

14,604 citations

Journal Article
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

9,847 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

Journal ArticleDOI
TL;DR: A triple network model of aberrant saliency mapping and cognitive dysfunction in psychopathology is proposed, emphasizing the surprising parallels that are beginning to emerge across psychiatric and neurological disorders.

2,712 citations