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Autism genetics: The faces behind the data

image of Autism rainbow art
Autism is a developmental disorder that often goes undiagnosed, leaving many people without the support and therapies they may need. Cold Spring Harbor Laboratory scientists have taken a data-based approach to deciphering the mysteries of autism. Their work would be impossible were it not for the support of thousands of volunteer families who share CSHL’s hope to help improve the lives of people with autism. AI generated image: © Lucija -
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More than 5.4 million adults in the U.S. have autism. That’s about the populations of Los Angeles and Phoenix combined. Autism, or autism spectrum disorder (ASD), is a developmental disability that affects how a person experiences the world around them. If this definition sounds broad, there’s a reason for that. Autism manifests in many different ways.

“ASD is characterized by deficits in reciprocal social interactions, restricted interest, and repetitive behaviors,” says Cold Spring Harbor Laboratory (CSHL) Professor Ivan Iossifov. “It’s a very prevalent disorder, with one in a hundred girls and even more boys diagnosed.”

Despite its prevalence, there’s still a lot we don’t know about autism. Research has shown that early diagnosis, therapy, and treatment of co-occurring conditions can improve quality of life for people with autism. But without understanding its cause, many people aren’t diagnosed until later in life—if ever at all.

At CSHL, scientists research autism with a common goal in mind. They aim to help improve the lives of people with autism and their families.

A recent episode of Cold Spring Harbor Laboratory’s At the Lab podcast breaks down decades of autism research in less than three minutes.

Looking in the right places

Professor Iossifov has been working on the genetics side of autism research for years. His most recent innovation, a tool called Genotypes and Phenotypes in Families (GPF), may one day enable researchers to make new discoveries about the disorder’s roots.

As a young scientist, however, Iossifov started his career with a very different subject in mind. “In high school and college, I was interested in computers,” he says. “Biology was completely foreign to me.”

After earning his master’s degree in computer science, Iossifov was introduced to computational biology. He enrolled in a Ph.D. program at Columbia University and began developing software that could extract information about molecular interactions from large chunks of text in scientific research journals.

“I started working on autism with the hope that the functional knowledge we’d extracted from articles could help us make progress with autism genetics,” he says. “Not only autism genetics— related disorders, too—but that’s where I started.”

Autism is a tricky disorder to research, in part because it’s so diverse. As hinted earlier, ASD can manifest with a wide range of symptoms and severities. Some people with the disorder face significant day-to-day challenges, including learning disabilities. They may be unable to speak, live on their own, or complete basic tasks without help. Others have little or no difficulty communicating, do extremely well in school, and have no problem living by themselves. Yet, despite the broad spectrum, some traits are common among people with autism, including sensitivity to stimuli, anxiety, difficulty in social situations, and special interests.

While at Columbia, Iossifov began collaborating with CSHL Professor Michael Wigler, a molecular biologist researching how spontaneous changes in DNA could lead to autism. Wigler and his team would make a big breakthrough in autism research in the 2000s. Using a new method of genomic analysis on a small collection of data, they discovered that spontaneous, or de novo, genetic mutations were a higher risk factor for autism than previously thought.

CSHL Professor Michael Wigler outlines his “hypotheses of maximum hope.”

When Iossifov graduated, Wigler recruited him to join CSHL as a fellow. Around that time, Wigler’s colleagues were spearheading an effort to build a bigger dataset for continued research of de novo mutations in families that had only one child with autism. By 2011, this dataset, called the Simons Simplex Collection (SSC) from the Simons Foundation Autism Research Initiative (SFARI), would include genetic information on 2,659 volunteer families.

Iossifov began putting together projects using data collected from the SSC and a new technique called next-generation sequencing. “Everybody was crazy about next-generation sequencing at the time,” he says. “We successfully executed a large-scale project generating and analyzing whole-exome sequences for all 9,000 individuals from the SSC.” (The exome is the part of the genome thought to hold the key to most genetic disorders.) This effort would set the stage for more groundbreaking projects to come.

In 2016, SFARI launched SPARK, the largest study of autism ever, with over 275,000 participants. Using SFARI family datasets, Wigler and Iossifov have made a series of important discoveries. In 2021, they found that de novo mutations contribute to autism even more in children whose siblings do not also have the condition. In 2023, they found that siblings with autism share more of their father’s genome than previously thought.

Today, Iossifov’s powerful new tool, ​​GPF, helps researchers organize and analyze large-scale family datasets like SPARK and the SSC. It allows them to explore genetic variants more easily and upload their data securely to share it with the wider scientific community.

Using GPF, a scientist investigating a specific gene could look for it with the tool’s search function and see all variants in that gene across numerous autism genetics datasets. They could see how many families have variants in that gene, how many people have autism in each family, and how it manifests. A researcher could also search for specific variants across all genes in all datasets.

It took decades of research to identify the first genetic markers of autism. This platform could help researchers find new markers much faster. And that could make earlier diagnoses a reality for many families unknowingly living with autism.

How is autism passed down in families? It’s complicated. This video offers a few possible clues using easy-to-follow infographics.

From awareness to acceptance and support

“The science part of my mind wonders how amazing it would be if they could track down the genetic markers of autism,” LaVell Juricich tells Marina Sarris of the Simons Foundation. Juricich is a SPARK participant who wasn’t formally diagnosed with autism until she was 58. “If they could test infants, and give them the support they need immediately, how amazing it would be for that person. They wouldn’t have to suffer from not knowing that they have autism.”

Rates of autism diagnoses in children are rising, according to the Centers for Disease Control and Prevention. In 2020, about one in every 36 children was diagnosed by age 8—up from one in 44 in 2018. The trend is likely due in part to increases in awareness and testing.

At the same time, acceptance of autism is also increasing. “In my view, autism doesn’t have to be a strength or a deficit,” SPARK participant Mary Lipiec tells Sarris. “It’s just a thing I have, a natural variation.”

The term ‘neurodiversity’ was coined in the 1990s by a sociologist living with autism. Neurodiverse individuals, especially those with autism, may struggle to act in a way that suppresses or “masks” behaviors that come naturally to them but fall outside social norms. Masking can be uncomfortable, stressful, and exhausting. It often happens subconsciously, especially among those who are unaware they have autism.

Wigler and Iossifov’s work is made possible by thousands of volunteer families who support autism genetics research through the Simons Foundation. Video: SPARK

Research has shown that autism may be severely underdiagnosed in adults. ASD wasn’t even listed in the Diagnostic and Statistical Manual of Mental Disorders (DSM) psychiatric handbook until 1980. Women and people of color tend to be diagnosed later than white males, giving them fewer opportunities for intervention and therapy. A study from 2022 suggests that nearly 80 percent of women with autism are not diagnosed as of age 18.

CSHL research stands to bring that percentage down. Iossifov and Wigler’s work in identifying and categorizing genetic markers of autism has already allowed for more precise diagnoses. Today, they continue to build upon science and society’s growing knowledge of autism. In effect, they’re teaching us not only about the condition—but also about ourselves.

“I think about how much of my life was wasted on masking and not being who I am, and not really knowing who I am,” Juricich says. “If I had known and I had gotten support when I was younger, things would have been so much easier.”

In time, Wigler and Iossifov’s efforts should help more people with autism get the support they may need when they need it most.

Written by: Margaret Osborne, Science Writer | | 516-367-8455

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