Genetic patterns of correlation among subcortical volumes in humans: results from a magnetic resonance imaging twin study - PubMed (original) (raw)

Elizabeth Prom-Wormley, Christine Fennema-Notestine, Matthew S Panizzon, Michael C Neale, Terry L Jernigan, Bruce Fischl, Carol E Franz, Michael J Lyons, Allison Stevens, Jennifer Pacheco, Michele E Perry, J Eric Schmitt, Nicholas C Spitzer, Larry J Seidman, Heidi W Thermenos, Ming T Tsuang, Anders M Dale, William S Kremen

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Genetic patterns of correlation among subcortical volumes in humans: results from a magnetic resonance imaging twin study

Lisa T Eyler et al. Hum Brain Mapp. 2011 Apr.

Abstract

Little is known about genetic influences on the volume of subcortical brain structures in adult humans, particularly whether there is regional specificity of genetic effects. Understanding patterns of genetic covariation among volumes of subcortical structures may provide insight into the development of individual differences that have consequences for cognitive and emotional behavior and neuropsychiatric disease liability. We measured the volume of 19 subcortical structures (including brain and ventricular regions) in 404 twins (110 monozygotic and 92 dizygotic pairs) from the Vietnam Era Twin Study of Aging and calculated the degree of genetic correlation among these volumes. We then examined the patterns of genetic correlation through hierarchical cluster analysis and by principal components analysis. We found that a model with four genetic factors best fit the data: a Basal Ganglia/Thalamus factor; a Ventricular factor; a Limbic factor; and a Nucleus Accumbens factor. Homologous regions from each hemisphere loaded on the same factors. The observed patterns of genetic correlation suggest the influence of multiple genetic influences. There is a genetic organization among structures which distinguishes between brain and cerebrospinal fluid spaces and between different subcortical regions. Further study is needed to understand this genetic patterning and whether it reflects influences on early development, functionally dependent patterns of growth or pruning, or regionally specific losses due to genes involved in aging, stress response, or disease.

Copyright © 2010 Wiley-Liss, Inc.

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Figures

Figure 1

Figure 1

Representative parcellation of subcortical regions using Freesurfer software.

Figure 2

Figure 2

An example of the Cholesky parameterization used to determine genetic covariance of the 19 subcortical regions of interest.

Figure 3

Figure 3

Heatmap representing magnitude of genetic correlations between subcortical regions of interest and dendrogram representing results of a hierarchical cluster analysis. Warmer colors represent more positive genetic correlations.

References

    1. Abrahams BS, Tentler D, Perederiy JV, Oldham MC, Coppola G, Geschwind DH ( 2007): Genome‐wide analyses of human perisylvian cerebral cortical patterning. Proc Natl Acad Sci USA 104: 17849–17854. -PMC -PubMed
    1. Agartz I, Sedvall GC, Terenius L, Kulle B, Frigessi A, Hall H, Jonsson EG ( 2006): BDNF gene variants and brain morphology in schizophrenia. Am J Med Genet B Neuropsychiatr Genet 141: 513–523. -PubMed
    1. Akaike H ( 1987): Factor analysis and AIC. Psychometrika 52: 317–332.
    1. Amat JA, Bansal R, Whiteman R, Haggerty R, Royal J, Peterson BS ( 2008): Correlates of intellectual ability with morphology of the hippocampus and amygdala in healthy adults. Brain Cogn 66: 105–114. -PMC -PubMed
    1. Baare WF, Hulshoff Pol HE, Boomsma DI, Posthuma D, de Geus EJ, Schnack HG, van Haren NE, van Oel CJ, Kahn RS ( 2001): Quantitative genetic modeling of variation in human brain morphology. Cereb Cortex 11: 816–824. -PubMed

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