Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies

Winkler, Anderson M.; Kochunov, Peter; Blangero, John; Almasy, Laura; Zilles, Karl; Fox, Peter T.; Duggirala, Ravindranath; Glahn, David C.

doi:10.1016/j.neuroimage.2009.12.028

Cited by 1,016 publications

(984 citation statements)

References 76 publications

(80 reference statements)

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“…More than 3,500 native Englishspeaking adults with normal or corrected-to-normal vision were recruited from Harvard University, MGH, and the surrounding Boston communities. To avoid spurious effects resulting from population stratification, we restricted our analyses to 1,320 young adults (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) Imaging Data Processing. We used FreeSurfer (freesurfer.net) (49), version 4.5.0, a freely available, widely used, and extensively validated brain MRI analysis software package, to process the structural brain MRI scans and compute global and regional morphological measurements.…”

Section: Methodsmentioning

confidence: 99%

“…Although, in principle, any measure computable from different brain imaging modalities can be used as phenotypes in genetic studies, ideal candidate imaging traits should be heritable intermediate (or endo-) phenotypes (22)(23)(24), to uncover the genetic underpinnings of various neuropsychiatric disorders or biological processes of interest (25). However, due to the computational complexity and inordinate options of brain imaging measurements, few tools exist to enable efficient heritability-based screening of these phenotypes (26,27), and the exploration of their genetic basis has been limited to a small subset of the search space. All high-dimensional (whole-brain, voxel-/vertex-wise) heritability maps computed to date have relied on twin or pedigree data (28)(29)(30)(31)(32).…”

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confidence: 99%

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Massively expedited genome-wide heritability analysis (MEGHA)

Nichols

Lee

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time to our knowledge. As a demonstration of application, we conducted heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for largescale heritability-based screening and high-dimensional heritability profile construction.heritability | genome-wide complex trait analysis | imaging genetics | endophenotype | phenomics

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Massively expedited genome-wide heritability analysis (MEGHA)

Nichols

Lee

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…The significant contribution of surface area in our study might be supported by one or more of the following. First, individual differences in cortical gray matter volume were found to be more closely related to differences in cortical surface area than cortical thickness (Winkler et al, 2010). In addition, cortical surface area became dramatically larger during evolution while cortical thickness remained relatively conserved (Rakic, 2009).…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

“…Another possible issue is that most studies have examined cortical volume as a whole, while ignoring the two individual components that comprise it, cortical thickness and surface area. Cortical volume is the product of cortical thickness and surface area, which are genetically (Panizzon et al, 2009) and phenotypically (Winkler et al, 2010) independent.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

“…Given this gap in the literature, the respective contributions of surface area and cortical thickness to the NS trait should be investigated. This could not only help us to clarify the neural structure basis of the NS trait, but could also provide appropriate endophenotypes for imaging genetics studies (Winkler et al, 2010), to better understand the genetic-neural basis of the NS trait.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

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The cortical surface area of the insula mediates the effect of DBH rs7040170 on novelty seeking

Cui

et al. 2015

NeuroImage

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The cortical surface area of the insula mediates the effect of DBH rs7040170 on novelty seeking, NeuroImage (2015), doi: 10.1016/j.neuroimage. 2015.05.033 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT2 AbstractNovelty seeking (NS) is a personality trait important for adaptive functioning, but an excessive level of NS has been linked to psychiatric disorders such as ADHD and substance abuse. Previous research has investigated separately the neural and genetic bases of the NS trait, but results were mixed and neural and genetic bases have yet to be examined within the same study. In this study, we examined the interrelationships among the dopamine beta-hydroxylase (DBH) gene, brain structure, and the NS trait in 359 healthy Han Chinese subjects. We focused on the DBH gene because it encodes a key enzyme for dopamine metabolism, NS is believed to be related to the dopaminergic system and has been reported associated with DBH variation. Results showed a significant positive association between the cortical surface area of the left insula and NS score. Furthermore, the DBH genetic polymorphism at the SNP rs7040170 was strongly associated with both the surface area of the left insula and NS score, with G carriers having a larger left insula surface area and a higher NS score than AA homozygotes. Subsequent path analysis suggested that the insula partially mediated the association between the DBH gene and the NS trait. Our data provided the first evidence for the involvement of the insula in the dopamine-NS relationship.Future studies of molecular mechanisms underlying the NS personality trait and related psychiatric disorders should consider the mediation effect of the neural structure.

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Early Brain Overgrowth in Autism Associated With an Increase in Cortical Surface Area Before Age 2 Years

Hazlett

Poe²,

Gerig³

et al. 2011

Arch Gen Psychiatry

401

324

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Objective Brain enlargement has been observed in 2 year old children with autism but the underlying mechanisms are unknown. This longitudinal MRI study investigated early growth trajectories in brain volume and cortical thickness. Method Cerebral gray and white matter volumes and cortical thickness in children with autism spectrum disorder and controls were examined. Subjects were seen at approximately 2 years of age (autism = 59, controls = 38) and were rescanned approximately 24 months later at age 4–5 years (autism = 38, controls = 21). Results We observed generalized cerebral cortical enlargement in individuals with ASD at both 2 and 4 – 5 years of age. Rate of cerebral cortical growth across multiple brain regions and tissue compartments, in individuals with ASD, was parallel to that seen in controls, indicating that there was no increase in rate of cerebral cortical growth during this interval. No cerebellar differences were observed in ASD. After controlling for TBV, a disproportionate enlargement in temporal lobe white matter was observed in the ASD group. We found no differences in cortical thickness, but an increase in an estimate of surface area in the ASD group compared to controls for all cortical regions measured (temporal, frontal, and parietal-occipital). Conclusions Our longitudinal MRI study found generalized cerebral cortical enlargement in children with ASD, with a disproportionate enlargement in temporal lobe white matter. There was no difference from controls in the rate of brain growth for this age interval, indicating brain enlargement in ASD results from an increased rate of brain growth prior to age 2. The presence of increased cortical volume, but not cortical thickness, suggests that early brain enlargement may be associated with increased cortical surface area. Cortical surface area overgrowth in ASD may underlie brain enlargement and implicates a distinct set of pathogenic mechanisms.

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Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies

Cited by 1,016 publications

References 76 publications

Massively expedited genome-wide heritability analysis (MEGHA)

Massively expedited genome-wide heritability analysis (MEGHA)

The cortical surface area of the insula mediates the effect of DBH rs7040170 on novelty seeking

Early Brain Overgrowth in Autism Associated With an Increase in Cortical Surface Area Before Age 2 Years

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