Neural phenotypes of common and rare genetic variants

Abstract

Neuroimaging methods offer a powerful way to bridge the gaps between genes, neurobiology and behavior. Such investigations may be further empowered by complementary strategies involving chromosomal abnormalities associated with particular neurobehavioral phenotypes, which can help to localize causative genes and better understand the genetics of complex traits in the general population. Here we review the evidence from studies using these convergent approaches to investigate genetic influences on brain structure: (1) studies of common genetic variations associated with particular neuroanatomic phenotypes, and (2) studies of possible 'genetic subtypes' of neuropsychiatric disorders with very high penetrance, with a focus on neuroimaging studies using novel computational brain mapping algorithms. Finally, we discuss the contribution of behavioral neurogenetics research to our understanding of the genetic basis of neuropsychiatric disorders in the broader population.

Figure 1

Schematic of convergent strategies for investigating endophenotypes of major neuropsychiatric disorders: Common genes of small effect and rare genetic mutations of high penetrance may result in shared/overlapping neuroanatomic features.

Figure 2

Literature based-associations of the COMT Val¹⁵⁸Met polymorphism with brain structure and function in the general population (top panel) and in individuals with 22q11.2 deletions (bottom panel). Interestingly, the effects of the COMT Val¹⁵⁸Met polymorphism do not appear to be limited to frontal brain regions in individuals with 22q11.2 deletions, and indeed appear more pronounced in cerebellar regions. Figure created using PubBrain methodology (www.phenowiki.org/pubbrain).

Figure 3

Figure depicts lateral brain surfaces across three neurodevelopmental disorders, two of which are defined by contiguous gene deletion syndromes (22q11.2DS and Williams syndrome), and one by virtue of behavioral features (child onset schizophrenia). All are mapped using the same cortical pattern matching methodology (P M Thompson, Schwartz, & Toga, 1996; P. M. Thompson & Toga, 2002a; P. M. Thompson, Woods, Mega, & Toga, 2000).

Figure 4

Developmental effects on cortical thickness in 22q11.2DS. Correlations between medial cortical thickness and age are mapped locally and visualized, separately for children with 22q11.2 deletions and typically developing controls. Figure depicts correlational (r ) maps, indicating the strength of the association between cortical thickness and age across the lateral (top panel) and medial surface of the brain (bottom panel), separately for children and adolescents with 22q11.2DS (left) and typically developing controls (right). Red colors indicate brain regions showing the greatest age-associated cortical thinning.

Figure 5

Gene-Brain -Behavior Correlations in Fragile X. (A) Fragile X mental retardation protein (FMRP) protein correlations in females with FraX. (a) r-values (Spearman’s rank correlation) that correlate FMRP protein score with tissue volumes are mapped to show the direction of the correlation. The significance of these correlations is shown in (b). Blue colors show correlations that are significant at the uncorrected, voxel level. In the r-maps (a,b,c), red colors indicate regions that show volume reduction with reduction in FMRP protein, whereas blue colors indicate inverse correlations, i.e. greater volume excess associated with FMRP protein reduction. (adapted from A. D. Lee et al., 2007). (B) Surface-based correlational maps showing significant associations between regions of radial caudate nucleus expansion and (a) reduced FMRP levels, (b) Autism Behavior Checklist scores, and (c) Aberrant Behavior Checklist Stereotypy Subscale scores (adapted from Gothelf, Furfaro et al., 2007).

Figure 6

Cortical thickening in 22q11.2DS and Williams Syndrome. Maps contrast regions of excess in WS compared to demographically matched controls (top) and in 22q11.2DS (bottom). In patients with 22q11.2, no regions of increased cortical thickness were detected, relative to controls. In contrast, patients with WS showed a 10% increase in cortical thickness in perisylvian language-related cortex and adjacent temporal cortex.

Figure 7

Tensor-Based Morphometry Maps in Two Neurogenetic Syndromes. (A) Significance maps for group differences in local brain volume between FraX and controls. FraX subjects have significant excess volumes in the ventricles and caudate regions (blue colors in panel a). b) depicts significant volume deficits in medial occipital regions and some temporal lobe regions in subjects with FraX (adapted from A. D. Lee et al., 2007). (B) Maps depict neuroanatomic alterations in Williams Syndrome, after adjusting for individual differences in total brain volume. Panel a (top row) shows areas of relative volume preservation in WS, in prefrontal and orbitofrontal areas, anterior cingulate gyrus, inferior parietal regions at the parieto-occipital junction, fusiform gyrus, and cerebellum. Panel b (bottom row): Disproportionate reduction is seen in occipital areas, parietal lobes near the temporo-parietal junction, splenium and posterior body of the corpus callosum, thalamus and the basal ganglia, and midbrain (adapted from Chiang et al., 2007). Abbreviations: Cd: caudate nucleus; OL: occipital lobe; aCG: anterior cingulate gyrus; PF: prefrontal area, IP: inferior parietal region, BG: basal ganglia, PL: parietal lobe, TL: thalamus.