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Review
. 2007 Jun;28(6):488-501.
doi: 10.1002/hbm.20401.

Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function

David C Glahn  1 , Paul M Thompson, John Blangero
Affiliations
Review

Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function

David C Glahn et al. Hum Brain Mapp. 2007 Jun.

Abstract

It is vitally important to identify the genetic determinants of complex brain-related disorders such as autism, dementia, mood disorders, and schizophrenia. However, the search for genes predisposing individuals to these illnesses has been hampered by their genetic and phenotypic complexity and by reliance upon phenomenologically based qualitative diagnostic systems. Neuroimaging endophenotypes are quantitative indicators of brain structure or function that index genetic liability for an illness. These indices will significantly improve gene discovery and help us to understand the functional consequences of specific genes at the level of systems neuroscience. Here, we review the feasibility of using neuroanatomic and neuropsychological measures as endophenotypes for brain-related disorders. Specifically, we examine specific indices of brain structure or function that are genetically influenced and associated with neurological and psychiatric illness. In addition, we review genetic approaches that capitalize on the use of quantitative traits, including those derived from brain images.

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Figures

Figure 1
Figure 1
Genetic influences on human neuroanatomy. Ten monozygotic twin pairs (n = 20 subjects) are almost perfectly correlated in their gray matter distribution, with near identity in frontal (F), sensorimotor (S/M), and perisylvian language cortices. In contrast, 10 dizygotic twin pairs (n = 20) are significantly less alike in frontal cortices, but are 90–100% correlated for gray matter in perisylvian language‐related cortex, including supramarginal and angular territories and Wernicke's language area (W). The significance of these increased similarities, visualized in color, is related to the local intraclass correlation coefficents (r) (From Thompson et al., Nat Neurosci, 2001, 4(12), 1253–1258, © Nature Publishing Group, reproduced by permission). [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]
Figure 2
Figure 2
Power to detect genetic association. A power function for a hypothetical study involving the analysis of a single quantitative endophenotype studied in 1,000 unrelated subjects. The three lines represent the level of variant examination being undertaken running the gamut from the analysis of a single SNP (blue line) to the comprehensive analysis of a single gene (red line, 50 sequence variants) to the genome‐wide analysis of 5,000,000 markers (black line). Each power function is based on an experiment‐wide significance level of 0.05 as calculated by a standard Bonferroni correction. For a single SNP, we have 80% power to detect an association that accounts for as little as 0.8% of the total phenotypic variance of the endophenotype. For a gene with 50 variants, we have 80% power to detect an association that accounts for 1.7% of total variation, while for a genome‐wide association study, we require a large genetic signal that accounts for at least 3.7% of the total phenotypic variation before we achieve 80% power of detection. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]
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