Sex-dependent prediction of autism

Abstract Autism has a global prevalence of 1%, with a male-to-female diagnosis ratio of roughly 4:1. Several models have been developed to predict autism using genetic information. However, the influence of biological sex on prediction outcomes remains underexplored. We present an ensemble model to predict autism, which integrates polygenic risk scores (PRSs), common genetic variants, and autism associated genes with the MSSNG whole genome sequencing (WGS) dataset. Following training, our model achieved an accuracy of 0.68, an area under the receiver operating curve (AUROC) of 0.72, and a recall of 0.77 on the test dataset. Notably, common variants contributed more significantly to autism prediction in males than females (p < 0.001), with accuracies of 0.69 and 0.66, respectively. The 16p11 locus emerged as particularly predictive for females (p < 0.001). Gene enrichment analysis using the Allen Brain Atlas revealed that expression of autism associated genes that were significant in females were enriched (FWER < 0.05) in the primary somatosensory cortex, inferior parietal cortex, and parietal neocortex during fetal development. By contrast, male autism associated gene expression was enriched (FWER < 0.05) in the dorsolateral prefrontal cortex and anterior cingulate cortex across developmental stages (fetal to adult). These findings underscore a sex-dependent role for common genetic variants in autism development. In doing so, they highlight the utility of ensemble models that incorporate common variation and biological sex for autism prediction.