Dorret Irene Boomsma

Prof. Dr. Dorret Irene Boomsma

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Research Focus & Metrics

Academic Career Length

37Years (1990-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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2 September 2021 in Nature Genetics
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UVUrmo Võsa
Urmo Võsa
ACAnnique Claringbould
Annique Claringbould
HWHarm-Jan Westra
Harm-Jan Westra
MBMarc Jan Bonder
Marc Jan Bonder
PDPatrick Deelen
Patrick Deelen
LFLude Franke
Lude Franke

18 January 2021 in Nature Genetics
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AKAlexander Kurilshikov
Alexander Kurilshikov
CGCarolina Medina Gomez
Carolina Medina Gomez
RBRodrigo Bacigalupe
Rodrigo Bacigalupe
DRDjawad Radjabzadeh
Djawad Radjabzadeh
JWJun Wang
Jun Wang
AZAlexandra Zhernakova
Alexandra Zhernakova

26 January 2023 in Nature Genetics
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DDDitte Demontis
Ditte Demontis
GWG. Bragi Walters
G. Bragi Walters
GAGeorgios Athanasiadis
Georgios Athanasiadis
RWRaymond Walters
Raymond Walters
KTKaren Therrien
Karen Therrien
ABAnders D. Børglum
Anders D. Børglum

20 March 2020 in Science
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KGKatrina Grasby
Katrina Grasby
NJNeda Jahanshad
Neda Jahanshad
JPJodie Painter
Jodie Painter
LCLucía Colodro Conde
Lucía Colodro Conde
JBJanita Bralten
Janita Bralten
EGEnhancing NeuroImaging Genetics through Meta-Analysis Consortium (ENIGMA)—Genetics working group
Enhancing NeuroImaging Genetics through Meta-Analysis Consortium (ENIGMA)—Genetics working group
INTRODUCTION: The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE: To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS: We identified 369 nominally genome-wide significant loci ( P < 5 × 10 −8 ) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 360 loci for which replication data were available, 241 loci influencing surface area and 66 influencing thickness remained significant after replication, with 237 loci passing multiple testing correction ( P < 8.3 × 10 −10 ; 187 influencing surface area and 50 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation ( r G = −0.32, SE = 0.05, P = 6.5 × 10 −12 ), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 46 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION: This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function. Identifying genetic influences on human cortical structure.: ( A ) Measurement of cortical surface area and thickness from MRI. ( B ) Genomic locations of common genetic variants that influence global and regional cortical structure. ( C ) Our results support the radial unit hypothesis that the expansion of cortical surface area is driven by proliferating neural progenitor cells. ( D ) Cortical surface area shows genetic correlation with psychiatric and cognitive traits. Error bars indicate SE. IMAGE CREDITS: (A) K. COURTNEY; (C) M. R. GLASS

6 September 2021 in Nature Genetics
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JMJosine L. Min
Josine L. Min
GHGibran Hemani
Gibran Hemani
EHEilis Hannon
Eilis Hannon
KDKoen F. Dekkers
Koen F. Dekkers
JCJuan Castillo-Fernandez
Juan Castillo-Fernandez
CRCaroline L. Relton
Caroline L. Relton

16 January 2013 in European Journal of Human Genetics
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LBLinda Broer
Linda Broer
VCVeryan Codd
Veryan Codd
DNDale Nyholt
Dale Nyholt
JDJoris Deelen
Joris Deelen
MMMassimo Mangino
Massimo Mangino
DBDorret Irene Boomsma
Dorret Irene Boomsma