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Autism spectrum disorder (ASD) is a developmental condition that affects approximately four times as many males as females, a strong sex bias that has not yet been fully explained. Understanding the causes of this biased prevalence may highlight novel avenues for treatment development that could benefit patients with diverse genetic backgrounds, and the expertise of sex differences researchers will be invaluable in this endeavor. In this review, I aim to assess current evidence pertaining to the sex difference in ASD prevalence and to identify outstanding questions and remaining gaps in our understanding of how males come to be more frequently affected and/or diagnosed with ASD. Though males consistently outnumber females in ASD prevalence studies, prevalence estimates generated using different approaches report male/female ratios of variable magnitude that suggest that ascertainment or diagnostic biases may contribute to the male skew in ASD. Here, I present the different methods applied and implications of their findings. Additionally, even as prevalence estimations challenge the degree of male bias in ASD, support is growing for the long-proposed female protective effect model of ASD risk, and I review the relevant results from recurrence rate, quantitative trait, and genetic analyses. Lastly, I describe work investigating several sex-differential biological factors and pathways that may be responsible for females’ protection and/or males’ increased risk predicted by the female protective effect model, including sex steroid hormone exposure and regulation and sex-differential activity of certain neural cell types. However, much future work from both the ASD and sex differences research communities will be required to flesh out our understanding of how these factors act to influence the developing brain and modulate ASD risk.

Autism spectrum disorder (ASD) is more prevalent in males, and the mechanisms behind this sex-differential risk are not fully understood. Two competing, but not mutually exclusive, hypotheses are that ASD risk genes are sex-differentially regulated, or alternatively, that they interact with characteristic sexually dimorphic pathways. Here we characterized sexually dimorphic gene expression in multiple data sets from neurotypical adult and prenatal human neocortical tissue, and evaluated ASD risk genes for evidence of sex-biased expression. We find no evidence for systematic sex-differential expression of ASD risk genes. Instead, we observe that genes expressed at higher levels in males are significantly enriched for genes upregulated in post-mortem autistic brain, including astrocyte and microglia markers. This suggests that it is not sex-differential regulation of ASD risk genes, but rather naturally occurring sexually dimorphic processes, potentially including neuron–glial interactions, that modulate the impact of risk variants and contribute to the sex-skewed prevalence of ASD. Autism spectrum disorder is approximately 4.5 times more likely to occur in boys than girls. Here, Werling, Geschwind and Parikshak characterized sexually dimorphic gene expression in the non-diseased, post-mortem, adult and prenatal human brain, and show genes expressed at higher levels in males are significantly enriched for genes upregulated in autistic brain.

Local genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions. However, accurate estimation of local genetic correlation remains challenging, due to linkage disequilibrium in local genomic regions and sample overlap across studies. We introduce SUPERGNOVA, a statistical framework to estimate local genetic correlations using summary statistics from genome-wide association studies. We demonstrate that SUPERGNOVA outperforms existing methods through simulations and analyses of 30 complex traits. In particular, we show that the positive yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically distinct genetic signatures with bidirectional local genetic correlations.

Whole-genome sequencing (WGS) has facilitated the first genome-wide evaluations of the contribution of de novo noncoding mutations to complex disorders. Using WGS, we identified 255,106 de novo mutations among sample genomes from members of 1902 quartet families in which one child, but not a sibling or their parents, was affected by autism spectrum disorder (ASD). In contrast to coding mutations, no noncoding functional annotation category, analyzed in isolation, was significantly associated with ASD. Casting noncoding variation in the context of a de novo risk score across multiple annotation categories, however, did demonstrate association with mutations localized to promoter regions. We found that the strongest driver of this promoter signal emanates from evolutionarily conserved transcription factor binding sites distal to the transcription start site. These data suggest that de novo mutations in promoter regions, characterized by evolutionary and functional signatures, contribute to ASD.

Background Genetic variants in the voltage-gated sodium channels SCN1A , SCN2A , SCN3A , and SCN8A are leading causes of epilepsy, developmental delay, and autism spectrum disorder. The mRNA splicing patterns of all four genes vary across development in the rodent brain, including mutually exclusive copies of the fifth protein-coding exon detected in the neonate (5N) and adult (5A). A second pair of mutually exclusive exons is reported in SCN8A only (18N and 18A). We aimed to quantify the expression of individual exons in the developing human brain. Methods RNA-seq data from 783 human brain samples across development were analyzed to estimate exon-level expression. Developmental changes in exon utilization were validated by assessing intron splicing. Exon expression was also estimated in RNA-seq data from 58 developing mouse neocortical samples. Results In the mature human neocortex, exon 5A is consistently expressed at least 4-fold higher than exon 5N in all four genes. For SCN2A , SCN3A , and SCN8A , a brain-wide synchronized 5N to 5A transition occurs between 24 post-conceptual weeks (2nd trimester) and 6 years of age. In mice, the equivalent 5N to 5A transition begins at or before embryonic day 15.5. In SCN8A , over 90% of transcripts in the mature human cortex include exon 18A. Early in fetal development, most transcripts include 18N or skip both 18N and 18A, with a transition to 18A inclusion occurring from 13 post-conceptual weeks to 6 months of age. No other protein-coding exons showed comparably dynamic developmental trajectories. Conclusions Exon usage in SCN1A , SCN2A , SCN3A , and SCN8A changes dramatically during human brain development. These splice isoforms, which alter the biophysical properties of the encoded channels, may account for some of the observed phenotypic differences across development and between specific variants. Manipulation of the proportion of splicing isoforms at appropriate stages of development may act as a therapeutic strategy for specific mutations or even epilepsy in general.

Background Whole-genome sequencing (WGS) analyses have found higher genetic burden in autistic females compared to males, supporting higher liability threshold in females. However, genomic evidence of sex differences has been limited to European ancestry to date and little is known about how genetic variation leads to autism-related traits within families across sex. Methods To address this gap, we present WGS data of Korean autism families ( n  = 2255) and a Korean general population sample ( n  = 2500), the largest WGS data of East Asian ancestry. We analyzed sex differences in genetic burden and compared with cohorts of European ancestry ( n  = 15,839). Further, with extensively collected family-wise Korean autism phenotype data ( n  = 3730), we investigated sex differences in phenotypic scores and gene-phenotype associations within family. Results We observed robust female enrichment of de novo protein-truncating variants in autistic individuals across cohorts. However, sex differences in polygenic burden varied across cohorts and we found that the differential proportion of comorbid intellectual disability and severe autism symptoms mainly drove these variations. In siblings, males of autistic females exhibited the most severe social communication deficits. Female siblings exhibited lower phenotypic severity despite the higher polygenic burden than male siblings. Mothers also showed higher tolerance for polygenic burden than fathers, supporting higher liability threshold in females. Conclusions Our findings indicate that genetic liability in autism is both sex- and phenotype-dependent, expanding the current understanding of autism’s genetic complexity. Our work further suggests that family-based assessments of sex differences can help unravel underlying sex-differential liability in autism.

Background The phenotypic outcomes of de novo variants (DNVs) in autism spectrum disorder (ASD) exhibit wide variability. To date, no study has comprehensively estimated DNV effects accounting for familial phenotypic background. Methods To evaluate DNV effects in a family-relative context, we defined within-family standardized deviations (WFSD) by subtracting phenotype scores of unaffected family members and standardizing the result. We applied this approach to 78,685 individuals from 21,735 families from ASD cohorts of diverse ancestries. We compared the distribution, associations with disruptive DNVs, and gene discovery results between WFSD and raw phenotype scores. We further performed outlier analysis based on WFSDs per gene to detect genes with high variability between families. Results We observed that ASD probands with disruptive DNVs exhibited greater behavioral symptoms and lower adaptive functioning relative to their within-family unaffected members. Compared to raw phenotype scores, WFSD provided clearer associations with DNVs and enabled greater yield in DNV-enriched gene discovery, including 18 novel ASD-associated genes. Outlier analysis identified 11 genes with high intrafamilial variability in phenotypic effects, influenced by mutation sites within functional domains or exons. Conclusions Familial DNV analysis provides accurate effect estimates, a reliable basis for predicting clinical outcomes, and precise support while minimizing confounding from family background. This approach improves the identification of ASD-associated genes with true phenotypic effects by reducing variability, as well as genes with genuine phenotypic heterogeneity across families driven by mutation site. These findings enhance our understanding of ASD phenotype variability and inform potential targets for intervention.

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden. This study presents a framework to evaluate rare and de novo variation from whole-genome sequencing (WGS). The work suggests that robust results from WGS studies will require large cohorts and strategies that consider the substantial multiple-testing burden.