Damaging de novo mutations diminish motor skills in children on the autism spectrum

Abstract

In individuals with autism spectrum disorder (ASD), de novo mutations have previously been shown to be significantly correlated with lower IQ but not with the core characteristics of ASD: deficits in social communication and interaction and restricted interests and repetitive patterns of behavior. We extend these findings by demonstrating in the Simons Simplex Collection that damaging de novo mutations in ASD individuals are also significantly and convincingly correlated with measures of impaired motor skills. This correlation is not explained by a correlation between IQ and motor skills. We find that IQ and motor skills are distinctly associated with damaging mutations and, in particular, that motor skills are a more sensitive indicator of mutational severity than is IQ, as judged by mutational type and target gene. We use this finding to propose a combined classification of phenotypic severity: mild (little impairment of either), moderate (impairment mainly to motor skills), and severe (impairment of both IQ and motor skills).

Keywords: IQ; autism; de novo mutation; genetics; motor skills.

Fig. 1.

Significance of correlations between measures of dn genetic damage and measures of motor skills and nvIQ of affected children. We used 11 measures of dn genetic damage shown as columns in the figure and 11 phenotypic measures (1 for nvIQ and 10 for MS extracted from 4 different phenotypic instruments) shown as rows. The genetic damage and phenotypic measures were defined on different subsets of the affected children in the SSC collection. To reflect this fact, we pasted counts to the labels as follows: “(100)” would mean the measure is defined on a subset of size 100, and “(20:100)” indicates in addition that of the 100 values the number of nonzeroes is 20; this second version is relevant for genetic variables that are counts of mutations in a child. We computed the correlation between each of the 11 genetic damage measures and the 11 phenotypic measures using only the children for which both the genetic damage and phenotypic measures were defined, and we tested whether the correlation was significantly different from 0. The resulting 11 by 11 table of P values is rendered graphically with rectangles whose size represents inversely the P value from the statistical test (large rectangle ∼ small P value) and whose color represents the sign of the correlation (blue ∼ positive, red ∼ negative). The meaning of both the sizes and colors can be gleaned from the figure’s key in the bottom left. A small dot is used when the correlation is strongly insignificant (P ≥ 0.10). In a similar fashion, the related SI Appendix, Fig. S1 shows the underlying computed correlations. For more background about this type of display, see Methods. Measures of genetic damage: The primary measure of genetic damage (A) is defined as the number of de novo LGDs (0, 1, 2, or 3) identified in an affected child. The variables in B–G differentiate LGDs according to indications that they may be damaging; these variables are defined only for children who have at least one LGD. B is the number (0 or 1) of de novo LGDs in a child affected by genes with more than one de novo LGD in the SSC (recurrent genes). C is the sum of the vulnerability scores of the genes affected by de novo LGDs in the child. D–G are defined as the number (0 or 1) of de novo LGDs that fall in four gene functional classes that have previously been implicated in autism’s etiology: FMRP target genes, embryonic genes, genes encoding chromatin modifiers, and CHD8 target genes. The remaining columns concern de novo missense mutations: H is the number of de novo missense mutations (0 up to 5) in an affected child, applied only to children without de novo LGD mutations, to prevent confounding with overpowering LGD effects. I, analogous to C, is the sum of vulnerability scores of genes affected by missense mutations in a child. J is the sum of VIPUR scores of missense mutations in a child. K is the product of vulnerability and VIPUR scores, exhibiting P values that neither score could achieve alone. Phenotypic measures: The top row represents nonverbal IQ (nvIQ). The other rows represent 10 different measures of motor skills available in the phenotypic database of the SSC. The labels are suffixed by abbreviations of the originating instruments: DCDQ, VABS, SRS, and ADI-R. See Methods for details.

Fig. 2.

Significance of the correlation between measures of genetic damage and adjusted measures of motor skills and IQ of affected individuals. This figure is similar to Fig. 1 (see legend for details), but the phenotypic measures have been adjusted as follows: the 10 motor skills measures (suffixed DCDQ, VABS, SRS, ADIR) are adjusted for nvIQ, sex, and age; nvIQ (Top) is adjusted for total_DCDQ, sex, and age. The main result is that the significance of the correlations largely survives these adjustments. This is evidence that the association of motor skills with the genetic variables cannot be reduced to the correlation between motor skills and nvIQ. (See SI Appendix, Fig. S2 for a similar graph showing the underlying adjusted correlations.)

Fig. 3.

Relationship between IQ and motor skills. The scatterplot shows nvIQ and total_DCDQ for n = 2,119 affected children with available exome data. The gray vertical lines show the cutoffs used to dichotomize the two measures (see the text for justification of the particular cutoffs). The four quadrants of the graph are labeled clockwise with letters A through D. Quadrant D is significantly underpopulated compared with what is expected under an assumption that the two measures are independent. Ignoring quadrant D, the arrows demonstrate increasing phenotypic severity (as a function of both nvIQ and total_DCDQ) between adjacent quadrants, with A < B < C.

Fig. 4.

Absence of association between measures of genetic damage and measures of core ASD phenotype. In this figure, all but the top two rows represent core ASD measures drawn from the ADI-R, ADOS, RBS, ABC, and SRS instruments (see Methods for explanations). The conclusion is that these measures largely lack significant correlations with measures of dn genetic damage. Some P values that approach 0.05 correspond to correlations that have the wrong sign (shown red) as the core ASD variables measure behavioral deficiency, hence should be positively correlated with mutational severity. The figure also shows nvIQ and total_DCDQ in the two top rows to provide a comparison what significant correlations would look like. The two rightmost columns show nvIQ and total_DCDQ as well to give evidence of their strongly significant correlations with the core ASD measures.