It's Not Just A Spectrum: Scientists Identify 4 Distinct Genetic Profiles Of Autism

Scientists have identified four distinct subtypes of autism, each with unique behavioral, developmental, and genetic characteristics. This shows that autism is not a single condition, but rather a collection of distinct profiles. This discovery could help personalize diagnosis and treatment, making them more accurate and effective for each person on the spectrum.

Autism is a complex neurodevelopmental condition that affects how a person communicates, interacts socially, and behaves. In recent years, the number of diagnoses of autism spectrum disorder (ASD) has grown rapidly.

Furthermore, with the expansion of diagnostic criteria, there is a wide diversity of profiles among people with autism, both in terms of behavior and their genetic basis. This means that, although two people have the same diagnosis, they can have very different characteristics, challenges, and needs.

Behind this diversity are dozens, if not hundreds, of genes that may be linked to autism. However, until now, science has not been able to clearly link which genetic patterns are associated with which types of clinical manifestations.

This is precisely what a major new study, published in the journal Nature Genetics, sought to investigate. Using detailed data from more than 5,000 autistic children participating in the Simons Foundation-funded SPARK study, a team of researchers from Princeton University combined advanced genetic analyses with a computational model to identify distinct subtypes of autism, both clinically and biologically.

Rather than focusing on just one or two symptoms at a time, the scientists used a "person-centered" approach, taking into account more than 230 characteristics per participant, such as level of social interaction, repetitive behavior, cognitive abilities, the presence of other mental health conditions (such as anxiety or ADHD), and developmental milestones (such as walking and talking).

With this, they were able to identify four clear subtypes of autism, each with a specific developmental pattern, behavioral traits, and, most interestingly, distinct genetic signatures.

The first group, called Social and Behavioral Challenges, includes children with classic autism symptoms, such as difficulties with social interaction and repetitive behaviors, but who generally develop at the expected time in other areas, such as language and motor skills. These children often have other associated conditions, such as ADHD, anxiety, and depression. This is the largest group, representing about 37% of the sample.

The second subtype, Mixed ASD with Developmental Delay, is characterized by delays in skills such as walking or talking, but with fewer psychiatric symptoms such as anxiety or irritability. The name "mixed" reflects the diversity of social and behavioral symptoms within this group, which corresponds to about 19% of the children studied.

The third group, called Moderate Challenges, comprises individuals who present milder and less impactful symptoms of the autism spectrum. They tend to reach developmental milestones as expected and, in most cases, do not have other psychiatric conditions. Approximately 34% of the children analyzed belong to this group.

Finally, the Broadly Affected subtype represents children with the most severe and widespread symptoms. They present significant developmental delays, severe social communication difficulties, marked repetitive behaviors, and multiple associated conditions, such as mood instability, anxiety, and depression. This is the smallest group, accounting for approximately 10% of the sample.

This study analyzed data from over 5,000 children with autism to better understand the different profiles within the spectrum. Using an advanced computational model, researchers grouped participants based on 239 behavioral and developmental characteristics, such as social skills, language, and developmental milestones, and identified four distinct groups (or "subclasses") of autism. Each group has its own combination of difficulties and characteristics. For example, some children had more difficulty walking or talking in infancy, while others had greater social or behavioral challenges. The graph in letter b shows how each group performed in seven major developmental areas; the closer to 1, the more difficulty the group had; the closer to -1, the less difficulty. In letters c and d, we see graphs comparing the average age at which children began walking or talking and their scores on an autism screening questionnaire (SCQ), also compared to siblings without autism, who served as a control group. Statistical analyses indicate the extent to which these differences were significant, using symbols such as stars to highlight when a difference is important. All of this helps demonstrate that autism can present itself in very different ways, with unique trajectories in both behavior and biology.

The most innovative part of the study was that it demonstrated that each of these subtypes also has a distinct genetic pattern. For example, children in the Broadly Affected group had a high rate of de novo mutations, meaning genetic alterations that arise spontaneously and were not inherited from their parents.

The Mixed ASD and Developmental Delay group, on the other hand, were more likely to have inherited rare genetic variants. Although both subgroups share developmental delays, their biological causes appear to be quite different.

Furthermore, scientists found that the timing of these genes' involvement in brain development also varies between subtypes. In some cases, such as in the Social and Behavioral Challenges group, the genetic alterations affect genes that only become active later in childhood.

This helps explain why some children are only diagnosed after a few years, when challenges begin to become more evident.

Princeton University researchers Aviya Litman, Olga Troyanskaya, and Chandra Theesfeld are among the study's co-authors. Credit: Denise Applewhite, Princeton University.

These findings show that autism is not a single condition with a single cause, but rather a cluster of conditions with multiple biological and clinical pathways. This new understanding could change how autism is diagnosed, treated, and even researched in the future.

Instead of seeking a "universal cure," science can begin to consider personalized approaches tailored to the characteristics and needs of each autism profile.

By separating the autism spectrum into subtypes based on detailed genetic and clinical data, researchers have paved the way for more precise and effective medicine, and, most importantly, for more equitable, empathetic, and appropriate care for each individual on the spectrum.

READ MORE:

Decomposition of phenotypic heterogeneity in autism reveals underlying genetic programs

Aviya Litman, Natalie Sauerwald, LeeAnne Green Snyder, Jennifer Foss-Feig, Christopher Y. Park, Yun Hao, Ilan Dinstein, Chandra L. Theesfeld, and Olga G. Troyanskaya

Nature Genetics, 57, pages 1611–1619 (2025).

DOI: 10.1038/s41588-025-02224-z

Abstract: 

Unraveling the phenotypic and genetic complexity of autism is extremely challenging yet critical for understanding the biology, inheritance, trajectory and clinical manifestations of the many forms of the condition. Using a generative mixture modeling approach, we leverage broad phenotypic data from a large cohort with matched genetics to identify robust, clinically relevant classes of autism and their patterns of core, associated and co-occurring traits, which we further validate and replicate in an independent cohort. We demonstrate that phenotypic and clinical outcomes correspond to genetic and molecular programs of common, de novo and inherited variation and further characterize distinct pathways disrupted by the sets of mutations in each class. Remarkably, we discover that class-specific differences in the developmental timing of affected genes align with clinical outcome differences. These analyses demonstrate the phenotypic complexity of children with autism, identify genetic programs underlying their heterogeneity, and suggest specific biological dysregulation patterns and mechanistic hypotheses.