Effects of sex chromosome dosage on corpus callosum morphology in supernumerary sex chromosome aneuploidies

Abstract

Background: Supernumerary sex chromosome aneuploidies (sSCA) are characterized by the presence of one or more additional sex chromosomes in an individual's karyotype; they affect around 1 in 400 individuals. Although there is high variability, each sSCA subtype has a characteristic set of cognitive and physical phenotypes. Here, we investigated the differences in the morphometry of the human corpus callosum (CC) between sex-matched controls 46,XY (N =99), 46,XX (N =93), and six unique sSCA karyotypes: 47,XYY (N =29), 47,XXY (N =58), 48,XXYY (N =20), 47,XXX (N =30), 48,XXXY (N =5), and 49,XXXXY (N =6).

Methods: We investigated CC morphometry using local and global area, local curvature of the CC boundary, and between-landmark distance analysis (BLDA). We hypothesized that CC morphometry would vary differentially along a proposed spectrum of Y:X chromosome ratio with supernumerary Y karyotypes having the largest CC areas and supernumerary X karyotypes having significantly smaller CC areas. To investigate this, we defined an sSCA spectrum based on a descending Y:X karyotype ratio: 47,XYY, 46,XY, 48,XXYY, 47,XXY, 48,XXXY, 49,XXXXY, 46,XX, 47,XXX. We similarly explored the effects of both X and Y chromosome numbers within sex. Results of shape-based metrics were analyzed using permutation tests consisting of 5,000 iterations.

Results: Several subregional areas, local curvature, and BLDs differed between groups. Moderate associations were found between area and curvature in relation to the spectrum and X and Y chromosome counts. BLD was strongly associated with X chromosome count in both male and female groups.

Conclusions: Our results suggest that X- and Y-linked genes have differential effects on CC morphometry. To our knowledge, this is the first study to compare CC morphometry across these extremely rare groups.

Keywords: Aneuploidies; Corpus callosum; Sex chromosomes; Sexual differentiation; Statistical shape analysis.

Figure 1

Extraction and parameterization of corpus callosum boundary. Illustration of the (a) raw SPGR image, (b) extracted binary callosum, and (c) parametric boundary of callosum as represented in MATLAB.

Figure 2

Hofer-Frahm divisions and curvature boundary segments. A depiction of the (a) Hofer-Frahm subdivisions of the callosum based on diffusion image white matter tractography, (b) the representation of these divisions on the parametric boundary in MATLAB, and (c) the ten segments of the callosal boundary used to measure average local curvature.

Figure 3

Between-landmark distance analysis (BLDA) pipeline. (a) The locations of the 14 landmarks on the parametric boundary of the callosum, (b) example trace distances between pairwise landmarks, and (c) matrix representation of pairwise distance.

Figure 4

Average callosum shapes by karyotype. (a) Average callosum shapes along the karyotype spectrum and (b) an overlay of the average shapes.

Figure 5

Summary of chromosome dosage-metric associations. Coefficient matrices of the associations between the (a) area, (b) lower boundary curvature, (c) upper boundary curvature and (d-g) between-landmark distance and the (1) Y:X ratio spectrum, (2) number of X-chromosomes within female karyotypes (X#_F), (3) number of X-chromosomes within male karyotypes (X#_M), and (4) number of Y-chromosomes within male karyotypes (Y#_M). The interpretation of the coefficient in each intersection is simply that for every unit increase in the given spectrum, the metric of interest changes by the amount given by the coefficient (e.g., for every extra X chromosome in the male karyotype, the global area of the callosum decreases by approximately 8 mm²). A circle is placed in intersections containing coefficients significant at the 0.05 level prior to correction for multiple comparisons while a triangle is present for coefficients significant after FDR correction.

Figure 6

Global and subregion areas by karyotype. Boxplots of the global and regional callosal areas associated with each karyotype.

Figure 7

Groupwise coefficient matrices of area by karyotype. Groupwise comparisons of area by callosum region. The elements of each matrix contain the coefficient of Karyotype resulting from the multiple linear regression model Area_i = β₀ + β₁ Age_i + β₂ ICV_i + β₃ Karyotype_i + ϵ_i. The coefficient is essentially the slope of the least squares line between the areas of the two karyotypes after adjusting for age and ICV. To clarify the direction of the effect, we print the name of the karyotype with the greatest area in the intersection. A circle is placed in intersections containing coefficients significant at the 0.05 level prior to correction for multiple comparisons while a triangle is present for coefficients significant after FDR correction.

Figure 8

Average local curvature by karyotype. Boxplots of average local curvature of the callosal boundary by karyotype.

Figure 9

Groupwise coefficient matrices of average local curvature by karyotype. Groupwise comparisons of average curvature by callosum region. The elements of each matrix contain the coefficient of Karyotype resulting from the multiple linear regression model Curvature_i = β₀ + β₁ Age_i + β₂ ICV_i + β₃ Karyotype_i + ϵ_i. We print the name of the karyotype with the greatest area in the intersection to clarify the direction of the effect. A circle is placed in intersections containing coefficients significant at the 0.05 level prior to correction for multiple comparisons while a triangle is present for coefficients significant after FDR correction.

Figure 10

Circular representation of significant groupwise landmark distances. Each landmark is represented as a tile on the outer perimeter of the circle. If a between-landmark distance (BLD) is significantly different between two groups then a line color coded with the comparison is drawn between the landmark tiles. (a) Significant BLDs prior to multiple comparisons correction. (b) Significant BLDs surviving FDR.