The Importance of Large-Scale Genomic Studies to Unravel Genetic Risk Factors for Autism

Abstract

Autism spectrum disorder (ASD) is a common and highly heritable neurodevelopmental disorder. During the last 15 years, advances in genomic technologies and the availability of increasingly large patient cohorts have greatly expanded our knowledge of the genetic architecture of ASD and its neurobiological mechanisms. Over two hundred risk regions and genes carrying rare de novo and transmitted high-impact variants have been identified. Additionally, common variants with small individual effect size are also important, and a number of loci are now being uncovered. At the same time, these new insights have highlighted ongoing challenges. In this perspective article, we summarize developments in ASD genetic research and address the enormous impact of large-scale genomic initiatives on ASD gene discovery.

Keywords: autism spectrum disorder; gene discovery; genetic heterogeneity; genomic technologies; large patient cohorts; locus discovery.

Figure 1

Timeline of key events in ASD genetic research and discovery. Over the past 40 years, genetic studies have provided crucial information on the genetic architecture of ASD. Methods and findings are depicted on the right, while large-scale cohort initiatives are indicated on the left. GWAS = genome-wide association studies; WES = whole exome sequencing; WGS = whole genome sequencing.

Figure 2

Main findings on the genetic architecture of ASD. Technical breakthroughs, including high-throughput microarrays and sequencing platforms, have enabled the search for a wide spectrum of risk variants via genome-wide surveys in increasingly large patient cohorts (see Table 1 for details). The main findings include the following: (a) Rare de novo germline CNVs, protein-truncating variants (loss-of-function SNVs and indels), and damaging missense variants involving constrained genes are overtransmitted to probands compared to siblings without ASD, arise more frequently on the paternal allele (except for some CNVs, such as 16p11.2 CNVs), and may show pleiotropic effects, thus increasing the risk for other complex neuropsychiatry disorders. (b) Female ASD patients from both simplex and multiplex families show an excess of de novo and inherited risk variants as well as a higher ASD polygenic burden, consistent with a female protective effect. (c) De novo germline CNVs and damaging SNVs/indels are significantly depleted in multiplex families compared to simplex families. (d) Rare recessive, de novo noncoding, and somatic variants also contribute to the risk for ASD; however, gene discovery based on these types of variants alone has been underpowered. (e) Common genetic variants of small individual effect sizes acting en masse carry the majority of the population risk for ASD; however, only a small number of common variants robustly associated with ASD have been identified to date. Furthermore, there is evidence that common and rare variations act additively to confer liability to ASD. WES = whole exome sequencing; WGS = whole genome sequencing; CNVs = copy number variants; SNVs = single-nucleotide variants; indels = insertions/deletions.

Figure 3

Summary of recent exome and genome sequencing studies using different cohort designs for gene discovery in ASD. Overview of five recent large-scale WES and WGS studies: Ruzzo et al., 2019 (R) [77]; Satterstrom et al., 2020 (S) [18]; Fu et al., 2022 (F) [19]; Zhou et al., 2022 (Z) [21]; Trost et al., 2022 (T) [20]. The cohorts, experimental designs, total number of ASD cases analyzed, and the total number of high-confidence ASD risk genes identified [false discovery rate (FDR) ≤ 0.001–0.1 or p < 2.5 × 10⁻⁶] are shown. Together, these studies identified a total of 215 different high-confidence ASD risk genes. The Venn diagram shows the overlap in the number of high-confidence risk genes detected by these studies, highlighting the extreme genetic heterogeneity of ASD. Shared risk genes identified by at least four studies are shown.

Figure 4

Future directions for research and clinical care in ASD. Data from much larger sample sizes of patients and their families will facilitate the identification of the full spectrum of genetic variations associated with ASD. The integration of genomics with other omics datasets (such as epigenomics, transcriptomics, proteomics, metabolomics, and functional genomics) and the application of machine learning algorithms to combine multi-omics data and predict risk genes and treatment outcomes will offer unprecedented possibilities to enhance the current understanding of the causes and mechanisms of ASD and advance diagnosis and personalized treatment.