research studies related to autism

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Autism and Developmental Disorders Research Program

Welcome to the website of the  Autism and Developmental Disorders Research Program (ADDRP) , Lucile Packard Children's Hospital at Stanford University.  This Stanford autism research program is based in the  Department of Psychiatry and Behavioral Sciences  at the Stanford University School of Medicine.

ADDRP focuses on improving the quality of life of individuals with autism spectrum disorder and/or intellectual disabilities. Through research methods that range from clinical trials, neuroimaging investigations, behavioral analysis to basic science methods, the researchers at ADDRP are committed to developing effective treatment strategies and identifying the causes of these conditions.

Our main research aim is to better understand the basic neurobiology of autism and other developmental disorders while furthering our understanding of how genetic and environmental factors may contribute to the onset and progression of these disorders. With this aim in mind, we conduct a variety of research studies and clinical trials of novel behavioral and biological therapies in hopes of developing effective interventions for the treatment of core features of these disorders.

Acknowledgements

The Stanford Autism and Developmental Disorders Research Program would like to thank the children, as well as their parents and families, for contributing to research. The joint effort to better understand and provide therapies for developmental disorders is not possible without their past and continued involvement.

Stanford ADDRP would also like to ackowledge financial support from the following organizations:

• National Institutes of Health
• Autism Speaks
• Simons Foundation
• John and Marcia Goldman Foundation
• Stanford Bio-X
• Child Health Research Institute
• The Teresa and Charles Michael Endowed Fund for Autism Research and Education
• The Mosbacher Family Fund for Autism Research
• PTEN Research Foundation
• The Bernard/Fung Family Fund for Autism Research at Stanford

In the News

2/2/22  Stanford Team Finds Benefits to Online Autism Treatment

7/16/21  Program improves resilience for parents of kids with autism

8/6/19 Stanford Trial Shows Parents Can Learn Therapy to Help Their Children With Autism Learn to Speak

8/5/19 One therapy bests others at motivating kids with autism to speak

5/1/19  Hormone reduces social impairment in kids with autism

3/6/19  Nature versus nurture in autism

2/2/18: Mechanical forces being studied by Stanford researchers may underlie brain's development and some diseases

7/12/17: Oxytocin improves social abilities in some kids with autism

4/10/17: Autism researchers seek teens, young adults for drug trial

9/21/2016: The seekers: Why parents try fringe therapies for autism

8/16/2016:  Automating genetic analysis helps keep up with rapid discovery of new diseases

7/22/2015 : Low levels of hormone linked to social deficit in autism

10/27/2014 : Group classes teach parents effective autism therapy, study finds

8/4/2014 : Blood-oxytocin levels in normal range in children with autism, study finds

11/14/2013 : Stanford drug trial seeks participants with autism spectrum disorder

8/13/2012 : Stanford researchers investigate the emotional side of autism

5/29/2012 : Antioxidant Shows Promise as Treatment for Certain Features of Autism, Study Finds (reprinted in ScienceDaily)

Spring 2012 : Autism Answers - Parents run experiments to see what works

9/2/2011 : Spotting autism's unique shape in the brain

7/30/2011 : Autism Risks: Genes May Not Play Biggest Role

1/25/2010 : Stanford/Packard autism researchers seek twins for brain-imaging study  

Related Pages & Events

Upcoming events.

16th Annual Pivotal Response Treatment (PRT) Conference  UPDATE: Conference has been postponed.

SAVE THE DATE! 

The Stanford Neurodiversity Summit

9/22/24-9/24/24 at the Li Ka Shing Conference Center, Stanford, CA

2024 Bay Area Adult Autism/DD Conference

12/7/24 at the Li Ka Shing Conference Center, Stanford, CA. Registration coming soon! 

18th Annual Autism Update

3/22/25  at Li Ka Shing Conference Center, Stanford,CA 

Autism Parent Support Group

Meets on second Mondays from 7-8:30pm (Sept.-June).  Please  email us  to be added to the our to the monthly email with participant zoom information.

• Stanford Autism Center
• Lucile Packard Children’s Hospital Stanford
• Early Support Program for Autism
• Child Psychiatry
• Stanford Clinical Trials Directory
• Center for Interdisciplinary Brain Sciences Research
• Stanford Brain Development Project
• Stanford Program for Psychometrics and Measurement-Based Care

Autism Research in 2022

Written by staff and the SAB of the Autism Science Foundation

COVID Recovery Was Slow, But Scientific Progress Continues

After two grueling pandemic years, 2022 saw a return to quasi-normalcy in both the world at large and in the world of autism research. Although the pandemic was incredibly challenging for autism families and researchers, the pivot to telehealth led to advances in online autism diagnoses, mental health supports, and interventions that will likely benefit autistic people across the globe for years to come.

Autism scientists working in diverse areas of research made important strides this year and continued to gain valuable insights into every facet of autism. They also identified more effective ways to support people on the spectrum. Researchers developed a better understanding of the unique needs and priorities of specific groups of autistic people , better-defined links between biological mechanisms and behavior , and disparities in autism diagnosis and treatment.

This work was only possible because of families like yours: You actively participated in important research studies. You agreed to have your data shared with others. You donated. You advocated. Your U.S. tax dollars supported $100 million of NIH grants funded in 2022 . 

Autism science simply cannot progress without your continued partnership. Earlier this year, ASF launched a “ Participate in Research ” directory to match families with research studies that meet your needs and interests. Many of these studies offer compensation, and can also provide valuable information and resources to aid your family member. The goal is to use the information gleaned from research to improve the real lives of real people, both now and in the future.

Here’s a little bit of what 2022 taught us.

Early identification leads to earlier diagnosis, but diagnosis happens at all ages

• Developmental milestones are skills that most children reach at a certain age and are used by healthcare providers to track progress. This year, the CDC updated these milestones to track what 75% of children can do by certain ages, rather than 50%, causing some pushback. In addition, the CDC added new time points as well as markers that might predict an autism diagnosis. 1
• In autism, reaching developmental milestones can be delayed from months to years. Delays are often more severe and variable in those with co-occurring intellectual disability and a rare genetic variant. New research reinforced the need to focus on milestones and the importance of early intervention.: If you notice your infant is struggling with new skills, tell your healthcare provider. 2
• Language skills in infants are an important predictor of an ASD diagnosis. Recent work from the ASF-supported Baby Siblings Research Consortium (BSRC) showed that maternal education levels and early gestures are important predictors of these language skills, suggesting markers for intervention. 3
• Researchers have suggested that early behaviors that are predictive of a later diagnosis may be part of a larger “developmental cascade,” where, for example, the trajectory from laying to sitting to language may be disrupted. These are intertwined behavioral and neurobiological networks that affect how a person with autism functions. 4
• There are now multiple biomarkers under investigation. Some are better than others at not just autism diagnosis, but the response to intervention. 5 In the future, they can be used to promote earlier diagnosis and more objective measures of the effectiveness of interventions.

Key takeaways: Parents and clinicians should monitor developmental milestones early in life. Early signs are not a substitute for a diagnosis, but some supports and interventions can be provided that allow for an improvement of trajectories across the lifespan.

Parent-mediated interventions and training – they work.

• A review of 30 studies showed promising results from parent-mediated interventions, but improvements in studies are still needed. 6
• Parent-mediated interventions can be used for teaching everything from core autism symptoms to self-care like tooth brushing. 7
• Autism interventions can and should be customized to culture and race. 8,9
• Some parent-mediated interventions have been tested successfully in a hybrid format, leading the way for others to investigate their effect on parent and child outcomes. 10
• While some have suggested parents only recognize the weaknesses in their children, recent research strongly notes that parents know their child’s strengths and use those strengths to help support their family.  Educators also note these strengths in the classroom. 11,12
• Siblings play an important role in the outcome of autistic individuals, while they also experience unique challenges themselves. 13,14

Key takeaways: Parents and caregivers often feel helpless when they are concerned about their child’s development and are facing long waiting lists for services. New research shows that providing support is beneficial for both the parents and the child outcome, and elevates strengths while mitigating support challenges. Further research should continue to explore the role of sibling relationships and support.

The brain has a distinct “signature” and sensory issues are on the front line

• One type of immune cell of the brain called the microglia has been known to affect cell communication, shape, and number. Researchers have now determined when and where these cells are expressed during development, laying the foundation for research into a critical brain cell type. 17
• The greatest differences in gene expression in the brain are in sensory areas like the visual cortex. 15 This may explain the almost universal problems in sensory processing that autistic individuals experience, and why sensory problems are so common in ASD. 18
• The visual area, specifically the occipital cortex, was also enlarged at young ages, more so in kids who have siblings with a diagnosis, demonstrating that genetic heritability plays a role in brain activity involved in sensory processing in families. 19
• A new marker of sensory processing was detected: differences in the activity of a neurotransmitter called GABA. GABA commonly slows down the activity of brain cells, which is important when they are too active, indicating this neurotransmitter is critical for sensory processing. Changing the activity of GABA neurons can alleviate sensory problems in autistic individuals. 20
• In addition, changes in the thickness of different cortical regions may influence sensory responses, depending on whether there is overstimulation or understimulation. 21
• Another brain region called the amygdala may relate to anxiety in autistic people. Certain areas of the amygdala are different in size, 22 and can explain variability in anxiety. 23   There is also disruption in connectivity from the amygdala to outside regions, 24 which may also explain how anxiety interacts with autism features.
• Rather than examining one autism feature at a time, it seems that ability to make gains or show potential for change over time is correlated to differences in brain structure. Markers of change over time are also linked to genes associated with ASD. 25 Targets of intervention based on biological markers may need to focus on sensitivity to change rather than a specific number on an instrument per se.
• The use of biological tools has increased this year. These tools include induced pluripotent stem cells (IPSCs) and organoids that are based on cells from individuals with different forms of ASD. Studies have looked at different types of autism (idiopathic and genetically-based) and identified creation of new brain cells as a common biological mechanism. 26 New studies also used novel tools to improve the validity of these cell-based systems. 27
• Animal models can be used to identify mechanisms by which genes and environmental factors exert their influence over behavior. Right now, there are hundreds of animal models of ASD, but not all of them are used appropriately to understand ASD. The ability of the model to recapitulate both the biology and behavior involved in ASD is essential. 28

Key takeaways: While different brain regions are specialized in their function, they interconnect and turn on and off in synchrony. Researchers need better models of human neurobiology, including better animal models, to understand the core and associated autism features, from sensory dysfunction to GI issues. If you want to learn more about research involving the brains of people with autism, sign up for more information at Autism BrainNet .

Genetic markers start to explain phenotype.

• The presence of rare genetic variants and common variants tend to funnel people into groups defined by intellectual disability (ID) or high educational attainment. 29,30 Scientists have identified and characterized two major types of genetic variation associated with ASD. Rare genetic variants are commonly associated with lower cognitive function and profound autism, but that is not always the case. 31  Even with hundreds of thousands of samples, scientists have still not found a direct gene – outcome linkage.  However, genetics are still important.  Genetic findings can help identify specific needs leading to appropriate supports.
• Certain types of gene mutations can explain associations with features like psychosis, 32 as well as obesity and depression. 33
• Five new variants were identified that are not linked to intellectual or developmental disability (IDD), but are linked to other neuropsychiatric issues besides ASD. 31,34 Therefore, rare ASD or DD gene mutations usually lead to some sort of deleterious outcome.
• There is a significant overlap between ASD genes and genes associated with developmental disorders in general. Researchers suggest that autism specificity may be the result of when the gene is expressed. For example, in developmental disorders, genes are expressed in progenitor cells while in ASD they may be expressed in developing neurons. 35  
• Other studies have not found any ASD-specific gene, they show linkage to neurodevelopmental problems in general, and can be grouped based on what cells are affected. 35
• There are shared pathways between ASD and other neuropsychiatric disorders. 36  
• Studies have shown linkages between epilepsy, ASD and ADHD. 37

Key takeaways: Genetic markers associated with ASD are also associated with other developmental conditions like ADHD and intellectual disability, as well as comorbid conditions like obesity. Two major types of genetic markers, rare and common variations, may represent biomarkers of two different phenotypes, but there is overlap, and rare and common variants are likely mixed in most people. Genetic research is important for a better understanding of ASD and the development of individualized approaches for supports.

But genetics doesn’t tell it all.

• Parental genetics and environmental factors are intertwined on a biological level. Genes associated with depression in parents are also linked to ASD. 38
• Maternal immune infections are an established risk factor for ASD. However, the genetics of children with and without maternal immune challenges during pregnancy are different. 39
• Studies in Norway offer a unique perspective of gestational exposures by banking blood taken mid-pregnancy during usual obstetrical visits. One study has shown that certain cytokines, or markers of immune activity, are elevated during pregnancy in both boys and girls with autism, particularly in girls. It’s unclear what role these cytokines play collectively or individually, or where they came from in the first place. 40
• Where you live can affect the role of genes vs. environment, evidenced by environmental factors playing a bigger role in heritability in certain areas of Sweden and the U.K. 41
• Genetics and the environment clearly interact when it comes to the influence of an ASD diagnosis. For example, pesticide exposure exacerbated the effects of the autism CHD8 gene on rodent behavior. 42
• The role of environmental factors may depend not just on a diagnosis but on specific autism traits. 43
• Given that autism is likely part of a larger developmental disorder spectrum, regulation of toxic chemicals which are harmful to development must be expanded. 44

Key takeaways: The role of environmental factors in ASD has often been disassociated with genetics when it should be integrated into the understanding of autism’s causes, behavioral features, and interventions.

Biological sex plays a role.

• Studies replicated this year showed that females with autism have a higher burden of rare genetic mutations. In addition, research is demonstrating that females with an autism diagnosis also show a higher level of “common” variations. 29,45
• The effect of higher levels of common variation in females extends to even undiagnosed members of ASD-impacted families, demonstrating that females carrying ASD genetic variation are resilient. 45
• The two above studies implicate an important role of the female protective effect but do not explain all of the differences in diagnosis. 46
• Some scientists have wondered if biases in instruments used to inform a diagnosis play a role in the sex difference. One study used a mathematical algorithm to eliminate the difference in M:F diagnostic differences, but still, females show different behavioral profiles. This further reiterates that instruments should be used to inform, not make a diagnosis, and that autism is more than a yes or no diagnosis. 47
• Clinicians may miss an autism diagnosis in females because of camouflage. Females are also more likely to camouflage, which means they (consciously or unconsciously) pretend to fit in as a typically-developing girl. This leads to lower quality of life. 48
•  Intellectual disability plays a bigger role in autism features in girls vs. boys. 49
•  New genetic mutations involving the X chromosome were identified – and these mutations are more likely to occur in females. 35
• Sex differences in brain region size can be attributed to gene expression patterns. In other words, brain differences in males and females with ASD are due, in part, to underlying genetics. 50

Key takeaways: Females with ASD show different biological and behavioral profiles and are understudied in research and underserved in the community. Future research should aim to include more females to better understand their unique needs and provide targeted support.

It’s still not over, but families are in a better place than a year ago.

• Despite a rocky start at the height of the pandemic in 2020 and 2021, opportunities to receive autism diagnoses, mental health supports, and interventions via telehealth have been improved, and polished, and are not only acceptable to families and clinicians but are effective. 51-57
• Families and clinicians were happier with remote diagnosis and evaluation when the diagnosis was clear; in cases where there was some ambiguity, it caused frustration. 58,59
• While many families and individuals experienced a mental health decline during the pandemic, some exhibited resiliency under social distancing guidelines. 60 The differences could be due to the degree to which services were lost, coping styles, and pre-existing mental health attributes. 61

Key takeaways: Autism families suffered during the pandemic, but it also allowed for new approaches to be developed that may ultimately improve practice – including hybrid clinical services, holistic family support, and more comprehensive diagnostic practices.

It’s not all about the asd.

• Individuals with ASD experience higher levels of anxiety, GI issues, epilepsy, and other developmental disorders like ADHD compared to those without a diagnosis.
• While not a core autism symptom, anxiety is linked to insistence on sameness in toddlers with ASD, which indicates a similar underlying mechanism. 62
• Gastrointestinal issues plague people with autism, and there are few options for treatment. The gastrointestinal microbiome has been a target for intervention for autism symptoms, although studies are still ongoing. 63 GI issues were the focus of a major NIH-funded meeting this year .
• Suicide risk is higher in ASD. 64
• Sleep problems, while mostly studied in children, are now shown to follow kids into adolescence and adulthood. 65
• There is a high degree of overlap in the brain activity profiles between ADHD and ASD kids. Differences are mostly seen when symptom severity is accounted for. ADHD and ASD show more similarities in the brain than differences. 66
• Behavioral profiles between ADHD and ASD are also similar. 67
• Mental health concerns are present in adolescents and adults with ASD with cognitive inflexibility strongly linked to compromised mental health. 68,69 Cognitive inflexibility, which is different than cognitive ability, is how someone shifts their attention from one thing to another based on what is going on around them. This may be a focus for future mental health interventions.
• Unfortunately there are no strong individual-level predictors in childhood of mental health issues in adults, but some factors that may lead to better mental health are better living skills and higher IQ. 70

Key takeaways: Outside the core features of autism listed in the DSM5, individuals experience a wide range of associated features, ranging from psychiatric issues to medical comorbidities. For many individuals, these associated features are highly debilitating.

Biases in underserved communities are getting more attention.

• A recent analysis showed a reduction of the disparities in the age of ASD diagnosis for Black and Hispanic children over the last four years, but a difference still exists. 71
• This is likely due to provider bias, but not necessarily diagnostic instrument biases. The standard diagnostic tools are not biased toward race or sex. 72
•  Lessons learned from the pandemic reiterate the need for intense community engagement, flexibility, and an understanding that a holistic approach – rather than one focused on ASD – is necessary for working with underserved communities 73,74 .
• A culturally-adapted parent training program delivered by Black providers was effective in the Black community and could be a model for future engagement efforts. 8
• Only 25% of intervention studies report the ethnic and racial makeup of their participants, 75 indicating that researchers need to do a better job of deliberately including racial and ethnic minorities, recruiting them as research leads and coordinators, and including them on boards for scientific review. 76
• Low socioeconomic status contributes to social and communication deficits in young children with ASD. 77

Key takeaways: Racial and ethnic biases are still pervasive in autism research and diagnosis, and we need a holistic approach to support families in all aspects of their lives beyond just autism symptoms. Scientists must continue to focus on the deliberate inclusion of these groups in both research and career training to better serve all individuals with autism.

On a final note, there has been a lot of debate this year about the language used to describe autism. 78-81 There is a diversity of experiences with autism and likely to be a diversity of perspectives. Families and scientists should use scientifically accurate terms to best describe the wide range of autistic people and their symptoms. 82   What that is may differ from person to person, and situation to situation, which means context and preference need to be considered as well.

1.         Zubler JM, Wiggins LD, Macias MM, et al. Evidence-Informed Milestones for Developmental Surveillance Tools. Pediatrics 2022; 149 (3).

2.         Kuo SS, van der Merwe C, Fu JM, et al. Developmental Variability in Autism Across 17 000 Autistic Individuals and 4000 Siblings Without an Autism Diagnosis: Comparisons by Cohort, Intellectual Disability, Genetic Etiology, and Age at Diagnosis. JAMA Pediatr 2022; 176 (9): 915-23.

3.         Pecukonis M, Young GS, Brian J, et al. Early predictors of language skills at 3 years of age vary based on diagnostic outcome: A baby siblings research consortium study. Autism Res 2022; 15 (7): 1324-35.

4.         Bradshaw J, Schwichtenberg AJ, Iverson JM. Capturing the complexity of autism: Applying a developmental cascades framework. Child Dev Perspect 2022; 16 (1): 18-26.

5.         Webb SJ, Naples AJ, Levin AR, et al. The Autism Biomarkers Consortium for Clinical Trials: Initial Evaluation of a Battery of Candidate EEG Biomarkers. Am J Psychiatry 2022: appiajp21050485.

6.         Conrad CE, Rimestad ML, Rohde JF, et al. Parent-Mediated Interventions for Children and Adolescents With Autism Spectrum Disorders: A Systematic Review and Meta-Analysis. Front Psychiatry 2021; 12 : 773604.

7.         Fenning RM, Butter EM, Macklin EA, et al. Parent Training for Dental Care in Underserved Children With Autism: A Randomized Controlled Trial. Pediatrics 2022; 149 (5).

8.         Kaiser K, Villalobos ME, Locke J, Iruka IU, Proctor C, Boyd B. A culturally grounded autism parent training program with Black parents. Autism 2022; 26 (3): 716-26.

9.         Rivera-Figueroa K, Marfo NYA, Eigsti IM. Parental Perceptions of Autism Spectrum Disorder in Latinx and Black Sociocultural Contexts: A Systematic Review. Am J Intellect Dev Disabil 2022; 127 (1): 42-63.

10.       Brian J, Solish A, Dowds E, et al. “Going Mobile”-increasing the reach of parent-mediated intervention for toddlers with ASD via group-based and virtual delivery. J Autism Dev Disord 2022; 52 (12): 5207-20.

11.       Mirenda P, Zaidman-Zait A, Cost KT, et al. Educators Describe the “Best Things” About Students with Autism at School. J Autism Dev Disord 2022.

12.       Wilkinson E, Vo LTV, London Z, Wilson S, Bal VH. Parent-Reported Strengths and Positive Qualities of Adolescents and Adults with Autism Spectrum Disorder and/or Intellectual Disability. J Autism Dev Disord 2022; 52 (12): 5471-82.

13.       Rosen NE, Schiltz HK, Lord C. Sibling Influences on Trajectories of Maladaptive Behaviors in Autism. J Clin Med 2022; 11 (18).

14.       Mokoena N, Kern A. Experiences of siblings to children with autism spectrum disorder. Front Psychiatry 2022; 13 : 959117.

15.       Gandal MJ, Haney JR, Wamsley B, et al. Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD. Nature 2022; 611 (7936): 532-9.

16.       Chen Y, Dai J, Tang L, et al. Neuroimmune transcriptome changes in patient brains of psychiatric and neurological disorders. Mol Psychiatry 2022.

17.       Menassa DA, Muntslag TAO, Martin-Estebane M, et al. The spatiotemporal dynamics of microglia across the human lifespan. Dev Cell 2022; 57 (17): 2127-39 e6.

18.       Wiggins LD, Tian LH, Rubenstein E, et al. Features that best define the heterogeneity and homogeneity of autism in preschool-age children: A multisite case-control analysis replicated across two independent samples. Autism Res 2022; 15 (3): 539-50.

19.       Girault JB, Donovan K, Hawks Z, et al. Infant Visual Brain Development and Inherited Genetic Liability in Autism. Am J Psychiatry 2022; 179 (8): 573-85.

20.       Huang Q, Pereira AC, Velthuis H, et al. GABA(B) receptor modulation of visual sensory processing in adults with and without autism spectrum disorder. Sci Transl Med 2022; 14 (626): eabg7859.

21.       Habata K, Cheong Y, Kamiya T, et al. Relationship between sensory characteristics and cortical thickness/volume in autism spectrum disorders. Transl Psychiatry 2021; 11 (1): 616.

22.       Seguin D, Pac S, Wang J, et al. Amygdala subnuclei volumes and anxiety behaviors in children and adolescents with autism spectrum disorder, attention deficit hyperactivity disorder, and obsessive-compulsive disorder. Hum Brain Mapp 2022; 43 (16): 4805-16.

23.       Andrews DS, Aksman L, Kerns CM, et al. Association of Amygdala Development With Different Forms of Anxiety in Autism Spectrum Disorder. Biol Psychiatry 2022; 91 (11): 977-87.

24.       Lee JK, Andrews DS, Ozturk A, et al. Altered Development of Amygdala-Connected Brain Regions in Males and Females with Autism. J Neurosci 2022; 42 (31): 6145-55.

25.       Pretzsch CM, Schafer T, Lombardo MV, et al. Neurobiological Correlates of Change in Adaptive Behavior in Autism. Am J Psychiatry 2022; 179 (5): 336-49.

26.       Connacher R, Williams M, Prem S, et al. Autism NPCs from both idiopathic and CNV 16p11.2 deletion patients exhibit dysregulation of proliferation and mitogenic responses. Stem Cell Reports 2022; 17 (6): 1380-94.

27.       Revah O, Gore F, Kelley KW, et al. Maturation and circuit integration of transplanted human cortical organoids. Nature 2022; 610 (7931): 319-26.

28.       Silverman JL, Thurm A, Ethridge SB, et al. Reconsidering animal models used to study autism spectrum disorder: Current state and optimizing future. Genes Brain Behav 2022; 21 (5): e12803.

29.       Antaki D, Guevara J, Maihofer AX, et al. A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex. Nature Genetics 2022; 54 (9): 1284-92.

30.       Warrier V, Zhang X, Reed P, et al. Genetic correlates of phenotypic heterogeneity in autism. Nature Genetics 2022; 54 (9): 1293-304.

31.       Zhou A, Cao X, Mahaganapathy V, et al. Common genetic risk factors in ASD and ADHD co-occurring families. Hum Genet 2022.

32.       Brownstein CA, Douard E, Mollon J, et al. Similar Rates of Deleterious Copy Number Variants in Early-Onset Psychosis and Autism Spectrum Disorder. Am J Psychiatry 2022; 179 (11): 853-61.

33.       Birnbaum R, Mahjani B, Loos RJF, Sharp AJ. Clinical Characterization of Copy Number Variants Associated With Neurodevelopmental Disorders in a Large-scale Multiancestry Biobank. JAMA Psychiatry 2022; 79 (3): 250-9.

34.       Shimelis H, Oetjens MT, Walsh LK, et al. Prevalence and Penetrance of Rare Pathogenic Variants in Neurodevelopmental Psychiatric Genes in a Health Care System Population. American Journal of Psychiatry 2022: appi.ajp.22010062.

35.       Wang T, Kim CN, Bakken TE, et al. Integrated gene analyses of de novo variants from 46,612 trios with autism and developmental disorders. Proc Natl Acad Sci U S A 2022; 119 (46): e2203491119.

36.       Murtaza N, Cheng AA, Brown CO, et al. Neuron-specific protein network mapping of autism risk genes identifies shared biological mechanisms and disease-relevant pathologies. Cell Rep 2022; 41 (8): 111678.

37.       Carson L, Parlatini V, Safa T, et al. The association between early childhood onset epilepsy and attention-deficit hyperactivity disorder (ADHD) in 3237 children and adolescents with Autism Spectrum Disorder (ASD): a historical longitudinal cohort data linkage study. Eur Child Adolesc Psychiatry 2022.

38.       Havdahl A, Wootton RE, Leppert B, et al. Associations Between Pregnancy-Related Predisposing Factors for Offspring Neurodevelopmental Conditions and Parental Genetic Liability to Attention-Deficit/Hyperactivity Disorder, Autism, and Schizophrenia: The Norwegian Mother, Father and Child Cohort Study (MoBa). JAMA Psychiatry 2022; 79 (8): 799-810.

39.       Nudel R, Thompson WK, Borglum AD, et al. Maternal pregnancy-related infections and autism spectrum disorder-the genetic perspective. Transl Psychiatry 2022; 12 (1): 334.

40.       Che X, Hornig M, Bresnahan M, et al. Maternal mid-gestational and child cord blood immune signatures are strongly associated with offspring risk of ASD. Mol Psychiatry 2022; 27 (3): 1527-41.

41.       Reed ZE, Larsson H, Haworth CMA, et al. Mapping the genetic and environmental aetiology of autistic traits in Sweden and the United Kingdom. JCPP Adv 2021; 1 (3): e12039.

42.       Jimenez JA, Simon JM, Hu W, et al. Developmental pyrethroid exposure and age influence phenotypes in a Chd8 haploinsufficient autism mouse model. Sci Rep 2022; 12 (1): 5555.

43.       Isaksson J, Ruchkin V, Aho N, Lundin Remnelius K, Marschik PB, Bolte S. Nonshared environmental factors in the aetiology of autism and other neurodevelopmental conditions: a monozygotic co-twin control study. Mol Autism 2022; 13 (1): 8.

44.       Volk HE, Ames JL, Chen A, et al. Considering Toxic Chemicals in the Etiology of Autism. Pediatrics 2022; 149 (1).

45.       Wigdor EM, Weiner DJ, Grove J, et al. The female protective effect against autism spectrum disorder. Cell Genomics 2022; 2 (6): 100134.

46.       Dougherty JD, Marrus N, Maloney SE, et al. Can the “female protective effect” liability threshold model explain sex differences in autism spectrum disorder? Neuron 2022; 110 (20): 3243-62.

47.       Burrows CA, Grzadzinski RL, Donovan K, et al. A Data-Driven Approach in an Unbiased Sample Reveals Equivalent Sex Ratio of Autism Spectrum Disorder-Associated Impairment in Early Childhood. Biol Psychiatry 2022; 92 (8): 654-62.

48.       Ross A, Grove R, McAloon J. The relationship between camouflaging and mental health in autistic children and adolescents. Autism Res 2022.

49.       Saure E, Castren M, Mikkola K, Salmi J. Intellectual disabilities moderate sex/gender differences in autism spectrum disorder: a systematic review and meta-analysis. J Intellect Disabil Res 2022.

50.       Floris DL, Peng H, Warrier V, et al. The Link Between Autism and Sex-Related Neuroanatomy, and Associated Cognition and Gene Expression. American Journal of Psychiatry 2022: appi.ajp.20220194.

51.       Rosen V, Blank E, Lampert E, et al. Brief Report: Telehealth Satisfaction Among Caregivers of Pediatric and Adult Psychology and Psychiatry Patients with Intellectual and Developmental Disability in the Wake of Covid-19. J Autism Dev Disord 2022; 52 (12): 5253-65.

52.       Talbott MR, Lang E, Avila F, Dufek S, Young G. Short report: Experiences of Caregivers Participating in a Telehealth Evaluation of Development for Infants (TEDI). J Autism Dev Disord 2022; 52 (12): 5266-73.

53.       Adler EJ, Schiltz HK, Glad DM, et al. Brief Report: A Pilot Study Examining the Effects of PEERS(R) for Adolescents Telehealth for Autistic Adolescents. J Autism Dev Disord 2022; 52 (12): 5491-9.

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Genetic contributions to autism spectrum disorder

1 Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway

2 Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway

3 Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway

M. Niarchou

4 Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA

A. Starnawska

5 The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark

6 Department of Biomedicine, Aarhus University, Denmark

7 Center for Genomics for Personalized Medicine, CGPM, and Center for Integrative Sequencing, iSEQ, Aarhus, Denmark

8 College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE

C. van der Merwe

9 Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, MA, USA

10 Department of Psychiatry, Autism Research Centre, University of Cambridge, UK

Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism, complemented by epigenetic and transcriptomic findings. The clinical heterogeneity of autism is mirrored by a complex genetic architecture involving several types of common and rare variants, ranging from point mutations to large copy number variants, and either inherited or spontaneous ( de novo ). More than 100 risk genes have been implicated by rare, often de novo , potentially damaging mutations in highly constrained genes. These account for substantial individual risk but a small proportion of the population risk. In contrast, most of the genetic risk is attributable to common inherited variants acting en masse , each individually with small effects. Studies have identified a handful of robustly associated common variants. Different risk genes converge on the same mechanisms, such as gene regulation and synaptic connectivity. These mechanisms are also implicated by genes that are epigenetically and transcriptionally dysregulated in autism. Major challenges to understanding the biological mechanisms include substantial phenotypic heterogeneity, large locus heterogeneity, variable penetrance, and widespread pleiotropy. Considerable increases in sample sizes are needed to better understand the hundreds or thousands of common and rare genetic variants involved. Future research should integrate common and rare variant research, multi-omics data including genomics, epigenomics, and transcriptomics, and refined phenotype assessment with multidimensional and longitudinal measures.

Definition of autism

Kanner defined autism in 1943 with detailed case descriptions of children showing social aloofness, communication impairments, and stereotyped behaviors and interests, often accompanied by intellectual disability (ID) (Kanner, 1943 ). A year later, Asperger independently published an article on children presenting marked difficulties in social communication and unusually circumscribed and intense interests, despite advanced intellectual and language skills (Asperger, 1944 ). Three decades later, Wing and Gould united Asperger and Kanner's descriptions and conceptualized a spectrum of autistic conditions (Wing and Gould, 1978 , 1979 ).

The onset of autism is during the first years of life, although symptoms may not be fully apparent or recognized until later (American Psychiatric Association, 2013 ). Autism is a heterogeneous and complex group of conditions with considerable variation in core symptoms, language level, intellectual functioning, and co-occurring psychiatric and medical difficulties. Subtype diagnoses such as childhood autism and Asperger's syndrome were previously used to specify more homogeneous presentations, but were unstable over time within individuals and used unreliably by clinicians (Lord et al., 2020 ). Current editions of the major diagnostic manuals have replaced the subtypes with an overarching autism spectrum disorder diagnosis and instead require specification of key sources of heterogeneity; language level, intellectual functioning, and co-occurring conditions (APA, 2013 ; World Health Organization, 2018 ).

Epidemiology

Prevalence estimates of autism have steadily increased from less than 0.4% in the 1970s to current estimates of 1–2% (Fombonne, 2018 ; Lyall et al., 2017 ). The increase is largely explained by broadening diagnostic criteria to individuals without ID and with milder impairments, and increased awareness and recognition of autistic traits (Lord et al., 2020 ; Taylor et al., 2020 ). There are marked sex and gender differences in autism (Halladay et al., 2015 ; Warrier et al., 2020 ). The male-to-female ratio is approximately 4:1 in clinical and health registry cohorts but closer to 3:1 in general population studies with active case-finding (Loomes, Hull, & Mandy, 2017 ) and 1–2:1 in individuals with moderate-to-severe ID (Fombonne, 1999 ; Yeargin-Allsopp et al., 2003 ). The mechanisms underlying the sex difference are mostly unknown, and hypotheses include a female protective effect (aspects of the female sex conferring resilience to risk factors for autism), prenatal steroid hormone exposure, and social factors such as underdiagnosis and misdiagnosis in women (Ferri, Abel, & Brodkin, 2018 ; Halladay et al., 2015 ).

Co-occurring conditions are the rule rather than the exception, estimated to affect at least 70% of people with autism from childhood (Lai et al., 2019 ; Simonoff et al., 2008 ). Common co-occurring conditions include attention-deficit hyperactivity disorder (ADHD), anxiety, depression, epilepsy, sleep problems, gastrointestinal and immune conditions (Davignon, Qian, Massolo, & Croen, 2018 ; Warrier et al., 2020 ). There is an elevated risk of premature mortality from various causes, including medical comorbidities, accidental injury, and suicide (Hirvikoski et al., 2016 ).

Autism is also associated with positive traits such as attention to detail and pattern recognition (Baron-Cohen & Lombardo, 2017 ; Bury, Hedley, Uljarević, & Gal, 2020 ). Further, there is wide variability in course and adulthood outcomes with regard to independence, social relationships, employment, quality of life, and happiness (Howlin & Magiati, 2017 ; Mason et al., 2020 ; Pickles, McCauley, Pepa, Huerta, & Lord, 2020 ). Rigorous longitudinal studies and causally informative designs are needed to determine the factors affecting developmental trajectories and outcomes.

Environmental factors

Twin studies suggest that 9–36% of the variance in autism predisposition might be explained by environmental factors (Tick, Bolton, Happé, Rutter, & Rijsdijk, 2016 ). There is observational evidence for association with pre- and perinatal factors such as parental age, asphyxia-related birth complications, preterm birth, maternal obesity, gestational diabetes, short inter-pregnancy interval, and valproate use (Lyall et al., 2017 ; Modabbernia, Velthorst, & Reichenberg, 2017 ). Mixed results are reported for pregnancy-related nutritional factors and exposure to heavy metals, air pollution, and pesticides, while there is strong evidence that autism risk is unrelated to vaccination, maternal smoking, or thimerosal exposure (Modabbernia et al., 2017 ). It is challenging to infer causality from observed associations, given that confounding by lifestyle, socioeconomic, or genetic factors contributes to non-causal associations between exposures and autism. Many putative exposures are associated with parental genotype (e.g. obesity, age at birth) (Gratten et al., 2016 ; Taylor et al., 2019a , Yengo et al., 2018 ), and some are associated both with maternal and fetal genotypes (e.g. preterm birth) (Zhang et al., 2017 ). Studies triangulating genetically informative designs are needed to disentangle these relationships (Davies et al., 2019 ; Leppert et al., 2019 ; Thapar & Rutter, 2019 ).

Twin and pedigree studies

In 1944, Kanner noted that parents shared common traits with their autistic children, introducing the ‘broader autism phenotype’ (i.e. sub-threshold autistic traits) and recognizing the importance of genetics (Harris, 2018 ; Kanner, 1944 ). Thirty years later, twin studies revolutionized the field of autism research (Ronald & Hoekstra, 2011 ).

Twin studies were the first to demonstrate the heritability of autism. In 1977, the first twin-heritability estimate was published, based on a study of 10 dizygotic (DZ) and 11 monozygotic (MZ) pairs (Folstein & Rutter, 1977 ). Four out of the 11 MZ pairs (36%) but none of the DZ pairs were concordant for autism. Subsequently, over 30 twin studies have been published, further supporting the high heritability of autism (Ronald & Hoekstra, 2011 ). A meta-analysis of seven primary twin studies reported that the heritability estimates ranged from 64% to 93% (Tick et al., 2016 ). The correlations for MZ twins were at 0.98 [95% confidence interval (CI) 0.96–0.99], while the correlations for DZ twins were at 0.53 (95% CI 0.44–0.60) when the autism prevalence rate was assumed to be 5% (based on the broader autism phenotype) and increased to 0.67 (95% CI 0.61–0.72) when the prevalence was 1% (based on the stricter definition) (Tick et al., 2016 ). Additionally, family studies have found that the relative risk of a child having autism relates to the amount of shared genome with affected relatives ( Fig. 1 ) (Bai et al., 2019 ; Constantino et al., 2013 ; Georgiades et al., 2013 ; Grønborg, Schendel, & Parner, 2013 ; Risch et al., 2014 ; Sandin et al., 2014 ).

Relative risk of autism by degree of relatedness with a person with autism. Relative risk for full and half siblings, and full cousins was provided in Hansen et al. ( 2019 ). Relative risk for half first cousins was estimated based on Xie et al. ( 2019 ). GS, genome shared.

Early twin and pedigree studies demonstrated that the biological relatives of individuals with autism who did not meet the criteria for an autism diagnosis themselves commonly showed elevated autistic traits such as communication and social interaction difficulties (Le Couteur et al., 1996 ), indicating that the heritability is not restricted to the traditional diagnostic boundaries of autism. Twin studies also indicate that although social communication and repetitive behavior trait dimensions each show strong heritability, there is a limited genetic correlation between them (e.g. for a review, see Ronald & Hoekstra, 2011 ). Further, twin studies have found substantial genetic overlap between autistic traits and symptoms of other psychiatric conditions, including language delay (e.g. Dworzynski et al., 2008 ), ID (e.g. Nishiyama et al., 2009 ), ADHD (e.g. Ronald, Edelson, Asherson, & Saudino, 2010 ), and anxiety (e.g. Lundström et al., 2011 ) (for a review, see Ronald & Hoekstra, 2014 ). Moreover, twin and family studies indicate that the sibling recurrence rate of autism is lower in female than male siblings (Palmer et al., 2017 ; Werling & Geschwind, 2015 ), suggesting the female protective effect hypothesis as a potential explanation for the male preponderance in the diagnosis of autism. The hypothesis was supported by results showing that the siblings of autistic females had a higher likelihood of high autistic trait scores and autism than the siblings of autistic males (Ferri et al., 2018 ; Palmer et al., 2017 ; Robinson, Lichtenstein, Anckarsäter, Happé, & Ronald, 2013 ), consistent with females having a higher liability threshold.

Genetic variants differ in the frequency at which they occur in the population (e.g. rare v. common), the type (i.e. SNPs/CNVs/translocations and inversions/indels), and whether they are inherited or de novo . Here, we summarize the findings on genetic risk for autism from linkage and candidate gene studies, common and rare genetic variation studies, epigenomics, and transcriptomics. A glossary of important terms is in Box 1 .

Candidate gene association study: A study that examines the association between a phenotype and a genetic variant chosen a priori based on knowledge of the gene's biology or functional impact.

Complex trait: A trait that does not follow Mendelian inheritance patterns, but is likely the result of multiple factors including a complex mixture of variation within multiple genes.

Copy number variant (CNV): Deletion or duplication of large genomic regions.

de novo mutation: A mutation that is present in the offspring but is either absent in parents or is present only in parental germ cells.

DNA methylation (DNAm): Epigenetic modification of DNA characterized by the addition of a methyl group (-CH 3 ) to the 5 th position of the pyrimidine ring of cytosine base resulting in 5-methylcytosine (5mC).

Epigenetics: The science of heritable changes in gene regulation and expression that do not involve changes to the underlying DNA sequence.

Epigenome-Wide Association Study (EWAS): A study that investigates associations between DNA methylation levels quantified at tens/hundreds of thousands of sites across the human genome, and the trait of interest.

Genome-Wide Association Study (GWAS): A study scanning genome-wide genetic variants for associations with a given trait.

Genetic correlation: An estimate of the proportion of variance shared between two traits due to shared genetics.

Heritability: An estimate of the proportion of variation in a given trait that is due to differences in genetic variation between individuals in a given population.

Heritability on the liability scale : A heritability estimate adjusted for the population prevalence of a given binary trait, typically disorders.

Genetic linkage studies: A statistical method of mapping genes of heritable traits to their chromosomal locations by using chromosomal co-segregation with the phenotype.

Mendelian inheritance: When the inheritance of traits is passed down from parents to children and is controlled by a single gene for which one allele is dominant and the other recessive.

Methylation Quantitative Trait Locus (mQTL): A SNP at which genotype is correlated with the variation of DNA methylation levels at a nearby ( cis- mQTL) or distal ( trans- mQTL) site.

Phenotype: The observable characteristics of an individual.

Polygenic risk score (PRS): An estimate of an individual's genetic liability for a condition calculated based on the cumulative effect of many common genetic variants.

Single nucleotide polymorphism (SNP): A single base pair change that is common (>1%) in the population.

Single nucleotide variant (SNV): A variation in a single nucleotide without any limitation of frequency.

SNP heritability: The proportion of variance in a given phenotype in a population that is attributable to the additive effects of all SNPs tested. Typically, SNPs included have a minor allele frequency >1%.

Linkage and candidate gene studies

Initial linkage studies were conducted to identify chromosomal regions commonly inherited in affected individuals. Susceptibility loci implicated a range of regions, but only two have been replicated (Ramaswami & Geschwind, 2018 ): at chromosome 20p13 (Weiss, Arking, Daly, & Chakravarti, 2009 ) and chromosome 7q35 (Alarcón, Cantor, Liu, Gilliam, & Geschwind, 2002 ). Lack of replication and inconsistent findings were largely due to low statistical power (Kim & Leventhal, 2015 ). Candidate gene association studies identified over 100 positional and/or functional candidate genes for associations with autism (Bacchelli & Maestrini, 2006 ). However, there was no consistent replication for any of these findings (Warrier, Chee, Smith, Chakrabarti, & Baron-Cohen, 2015 ), likely due to limitations in study design (e.g. low statistical power, population diversity, incomplete coverage of variation within the candidate genes, and false positives arising from publication bias) (Ioannidis, 2005 ; Ioannidis, Ntzani, Trikalinos, & Contopoulos-Ioannidis, 2001 ). The advancement of genome-wide association studies (GWAS) and next-generation sequencing techniques has significantly enhanced gene and variant discovery.

Common genetic variation

The SNP-heritability (proportion of variance attributed to the additive effects of common genetic variants) of autism ranges from 65% in multiplex families (Klei et al., 2012 ) to 12% in the latest Psychiatric Genomics Consortium GWAS ( Fig. 2 a ) (Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium, 2017 ; Grove et al., 2019 ). Variation is largely attributable to sample heterogeneity and differences in methods used to estimate SNP-heritability.

Variance explained by different classes of genetic variants in autism. ( a ) Donut chart of the variance explained by different classes of variants. The narrow-sense heritability (82.7%, Nordic average, shades of green) has been estimated using familial recurrence data from Bai et al. ( 2019 ). The total common inherited heritability (12%) has been estimated using LDSC-based SNP-heritability (additive) from Grove et al. ( 2019 ) and the total rare inherited heritability (3%) has been obtained from Gaugler et al. ( 2014 ). The currently unexplained additive heritability is thus 67.7% (total narrow-sense heritability minus common and rare inherited heritabilities combined). This leaves a total of 17.3% of the variance to shared and unique environmental estimates (Bai et al., 2019 ). The term environmental refers to non-additive and non-inherited factors that contribute to variation in autism liability. Of this, de novo missense and protein-truncating variants (Satterstrom et al., 2020 ) and variation in non-genic regions (An et al., 2018 ) together explain 2.5% of the variance. Whilst de novo variation can be inherited in some cases (germline mutation in the parent) and thus shared between siblings, it is unlikely that this will be shared by other related individuals, and thus unlikely to be included in the narrow-sense heritability in Bai et al. ( 2019 ). This is likely to be a lower-bound of the estimate as we have not included the variance explained by de novo structural variants and tandem repeats. Additionally, non-additive variation accounts for ~4% of the total variance (Autism Sequencing Consortium et al., 2019 ). Thus, ~11% of the total variance is currently unaccounted for, though this is likely to be an upper bound. ( b ) The variance explained is likely to change in phenotypic subgroups. For instance, the risk ratio for de novo protein-truncating variants in highly constrained genes (pLI > 0.9) is higher in autistic individuals with ID compared to those without ID (point estimates and 95% confidence intervals provided; Kosmicki et al., 2017 ). ( c ) Similarly, the proportion of the additive variance explained by common genetic variants is higher in autistic individuals without ID compared to autistic individuals with ID (Grove et al., 2019 ). Point estimates and 95% confidence intervals provided.

Early GWASs of autism were underpowered, partly due to overestimating potential effect sizes. Grove et al. ( 2019 ) conducted a large GWAS of autism combining data from over 18 000 autistic individuals and 27 000 non-autistic controls and an additional replication sample. They identified five independent GWAS loci ( Fig. 3 ). Another recent study (Matoba et al., 2020 ) identified a further novel locus by meta-analyzing the results from Grove et al. ( 2019 ) with over 6000 case-pseudocontrol pairs from the SPARK cohort by employing a massively parallel reporter assay to identify a potential causal variant (rs7001340) at this locus which regulates DDH2 in the fetal brain. The sample sizes are still relatively small compared to other psychiatric conditions (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2020 ; Howard et al., 2019 ), though ongoing work aims to double the sample size and identify additional loci.

Karyogram showing the 102 genes implicated by rare variant findings at a false discovery rate of 0.1 or less (Satterstrom et al., 2020 ) and the five index SNPs identified in GWAS (Grove et al., 2019 ) of autism.

Using genetic correlations and polygenic score analyses, studies have identified modest shared genetics between autism and different definitions of autistic traits in the general population (Askeland et al., 2020 ; Bralten et al., 2018 ; Robinson et al., 2016 ; Taylor et al., 2019 b ). There is some evidence for developmental effects, with greater shared genetics in childhood compared to adolescence (St Pourcain et al., 2018 ). These methods have also identified modest polygenic associations between autism and other neurodevelopmental and mental conditions such as schizophrenia, ADHD, and major depressive disorder, related traits such as age of walking, language delays, neuroticism, tiredness, and self-harm, as well as risk of exposure to childhood maltreatment and other stressful life events (Brainstorm Consortium et al., 2018 ; Bulik-Sullivan et al., 2015 ; Grove et al., 2019 ; Hannigan et al., 2020 ; Lee et al., 2019 , b ; Leppert et al., 2019 ; Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013 ; Warrier & Baron-Cohen, 2019 ). Notably, autism is positively genetically correlated with measures of intelligence and educational attainment (EA) (Bulik-Sullivan et al., 2015 ; Grove et al., 2019 ), an observation supported by polygenic score association (Clarke et al., 2016 ). Polygenic Transmission Disequilibrium Tests have identified an over-transmission of polygenic scores for EA, schizophrenia, and self-harm from parents to autistic children, but an absence of such over-transmission to non-autistic siblings (Warrier & Baron-Cohen, 2019 ; Weiner et al., 2017 ), suggesting that these genetic correlations are not explained by ascertainment biases or population stratification. However, a genetic correlation does not necessarily imply a causal relationship between the two phenotypes and may simply index biological pleiotropy. Causal inference methods such as Mendelian randomization can be used to disentangle such relationships (Davies et al., 2019 ; Pingault et al., 2018 ).

The relatively low SNP-heritability in autism compared to other psychiatric conditions may partly be due to phenotypic heterogeneity. In an attempt to reduce phenotypic heterogeneity, Chaste et al. ( 2015 ) identified 10 phenotypic combinations to subgroup autistic individuals. Family-based association analyses did not identify significant loci, and SNP-heritability for the subgroups was negligent. It is unclear if reducing phenotypic heterogeneity increases genetic homogeneity, and investigating this in larger samples is warranted. Another study identified no robust evidence of genetic correlation between social and non-social (restricted and repetitive behavior patterns) autistic traits (Warrier et al., 2019 ). A few studies have investigated the common variant genetic architecture of social and non-social autistic traits in individuals with autism (Alarcón et al., 2002 ; Cannon et al., 2010 ; Cantor et al., 2018 ; Lowe, Werling, Constantino, Cantor, & Geschwind, 2015 ; Tao et al., 2016 ; Yousaf et al., 2020 ) and in the general population (St Pourcain et al., 2014 ; Warrier et al., 2018 , 2019 ), but replication of the identified loci is needed.

Diagnostic classification is another source of heterogeneity: SNP-heritability of Asperger's syndrome (ICD-10 diagnosis) was twice (0.097 ± 0.001) that of childhood autism and unspecified pervasive developmental disorders (Grove et al., 2019 ) [due to overlap in subtype diagnoses, a hierarchy was used: childhood autism>atypical autism>Asperger's syndrome>unspecified subtypes (Grove et al., 2019 )]. Supporting this, polygenic scores for intelligence and EA had larger loadings in the Asperger's syndrome and childhood autism subgroups compared to other subgroups (Grove et al., 2019 ). Additionally, the SNP-heritability of autism (all subtypes) without co-occurring ID diagnosis (0.09 ± 0.005) was three times that of autism with ID (Grove et al., 2019 ) ( Fig. 2 c ).

Rare genetic variation

Rare genetic variants confer significant risk in the complex etiology of autism. They are typically non-Mendelian, with substantial effect sizes and low population attributable risk. It is estimated that ~10% of autistic individuals have been diagnosed with an identifiable rare genetic syndrome characterized by dysmorphia, metabolic, and/or neurologic features (Carter & Scherer, 2013 ; Tammimies et al., 2015 ). Associated syndromes include the 15q11-q13 duplication of the Prader-Willi/Angelman syndrome, fragile X syndrome, 16p11.2 deletion syndrome, and 22q11 deletion syndrome (Sztainberg & Zoghbi, 2016 ). Prevalence estimates for autism vary widely between genetic syndromes; for example, 11% in 22q11.2 deletion syndrome and 54% in Cohen's syndrome (Richards, Jones, Groves, Moss, & Oliver, 2015 ). Of note, estimating the prevalence of autism in the context of genetic syndromes is complex (Havdahl et al., 2016 ; Richards et al., 2015 ).

The rate of gene discovery in autism is a linear function of increasing sample size (De Rubeis et al., 2014 ). Early studies implicated nine genes in the first 1000 autism cases (Neale et al., 2012 ; Sanders et al., 2012 ), increasing to 27 and 33 associated genes from separate analyses of Simons Simplex Collection and Autism Sequencing Consortium (ASC) samples (De Rubeis et al., 2014 ; Iossifov et al., 2014 ). Integrating these samples using the TADA framework implicated a total of 65 autism genes (Sanders et al., 2015 ).

The MSSNG initiative analyzed whole genomes from 5205 individuals ( N cases  = 2636), and identified 61 autism-risk genes, of which 18 were new candidates (Yuen et al., 2017 ). More recently, the largest whole-exome sequencing analysis to date conducted by the ASC ( N  = 35 584, N cases  = 11 986) identified 102 autism-associated genes ( Fig. 3 ), many of which are expressed during brain development with roles in the regulation of gene expression and neuronal communication (Satterstrom et al., 2020 ). Rare CNVs and SNVs associated with autism have pleiotropic effects, thus increasing the risk for other complex disorders such as schizophrenia, ADHD, ID, and epilepsy (Gudmundsson et al., 2019 ; Satterstrom et al., 2019 , 2020 ).

CNVs can impact one or multiple genes and can occur at common or rare frequencies in a population. All CNVs associated with autism have been rare. Recurrent CNVs are among the most convincing rare inherited risk variations for autism, and have a prevalence of about 3% in affected patients (Bourgeron, 2016 ). In comparison, approximately 4–10% of autistic individuals have de novo deletions or duplications (Bourgeron, 2016 ; Pinto et al., 2010 ; Sebat et al., 2007 ) frequently mapped to established risk loci 1q21.1, 3q29, 7q11.23, 15q11.2-13, and 22q11.2 (Sanders et al., 2015 ). A higher global frequency of de novo CNVs is observed in idiopathic autism cases from simplex families (10%) compared to multiplex families (2%) and controls (1%) (Halladay et al., 2015 ; Itsara et al., 2010 ; Sebat et al., 2007 ). Inherited CNVs can be present in unaffected siblings and parents, suggesting a model of incomplete penetrance dependent on the dosage sensitivity and function of the gene(s) they affect (Vicari et al., 2019 ).

Damaging SNVs include nonsense, frameshift, and splice site mutations (collectively referred to as protein-truncating variants, or PTVs), and missense variants. Rare inherited variants have a smaller average effect size and reduced penetrance compared to de novo pathogenic mutations. Early studies on whole exomes from trios established a key role for de novo germline mutations in autism. Whilst analysis in smaller sample sizes indicated only modest increase in de novo mutation rates in autism cases (Neale et al., 2012 ), the rate rose significantly in excess of expectation as the sample size increased (De Rubeis et al., 2014 ; Iossifov et al., 2014 ). Most recently, the ASC observed a 3.5-fold case enrichment of damaging de novo PTVs and a 2.1-fold enrichment for damaging de novo missense variants (Satterstrom et al., 2020 ), concluding that all exome de novo SNVs explain 1.92% of the variance in autism liability (Satterstrom et al., 2020 ) ( Fig. 2 a ).

Comparatively, the ASC discovered a 1.2-fold enrichment of rare inherited damaging PTVs in cases compared to unaffected siblings (Satterstrom et al., 2020 ). Similarly, recent whole-genome analysis found no excess of rare inherited SNVs, and no difference in the overall rate of these variants in affected subjects compared to unaffected siblings (Ruzzo et al., 2019 ).

New advancements

It is estimated that de novo mutations in protein-coding genes contribute to risk in ~30% of simplex autism cases (Yuen et al., 2017 ; Zhou et al., 2019 ). However, recent work has also shown that de novo mutations in non-coding regions of the genome (particularly gene promoters) contribute to autism (An et al., 2018 ; Zhou et al., 2019 ). Adapting machine learning techniques may be key to providing novel neurobiological insights to the genetic influences on autism in the future (An et al., 2018 ; Ruzzo et al., 2019 ; Zhou et al., 2019 ). Additionally, rare tandem repeat expansions in genic regions are more prevalent among autism cases than their unaffected siblings, with a combined contribution of ~2.6% to the risk of autism (Trost et al., 2020 ).

Common and rare variant interplay

The largest component of genetic risk is derived from common variants of additive effect with a smaller contribution from de novo and rare inherited variation ( Fig. 2 a ) (de la Torre-Ubieta, Won, Stein, & Geschwind, 2016 ; Gaugler et al., 2014 ). Notably, KMT2E was implicated in both the latest GWAS (Grove et al., 2019 ) and exome sequencing (Satterstrom et al., 2020 ) analyses. It is hypothesized that common genetic variation in or near the genes associated with autism influences autism risk, although current sample sizes lack the power to detect the convergence of the two (Satterstrom et al., 2020 ).

Whilst higher SNP-heritability is observed in autistic individuals without ID ( Fig. 2 b ), de novo PTVs in constrained genes are enriched in autistic individuals with ID ( Fig. 2 a ). However, the genetic architecture of autism is complex and diverse. For example, common genetic variants also contribute to risk in autistic individuals with ID and in autistic individuals carrying known large-effect de novo variants in constrained genes (Weiner et al., 2017 ). Furthermore, an excess of disruptive de novo variants is also observed in autistic individuals without co-occurring ID compared to non-autistic individuals (Satterstrom et al., 2020 ).

Epigenetics

DNA methylation (DNAm), an epigenetic modification, allows for both genetic and environmental factors to modulate a phenotype (Martin & Fry, 2018 ; Smith et al., 2014 ). DNAm affects gene expression, regulatory elements, chromatin structure, and alters neuronal development, functioning, as well as survival (Kundaje et al., 2015 ; Lou et al., 2014 ; Peters et al., 2015 ; Sharma, Klein, Barboza, Lohdi, & Toth, 2016 ; Yu et al., 2012 ; Zlatanova, Stancheva, & Caiafa, 2004 ). Additionally, putative prenatal environmental risk factors impact the offspring's methylomic landscape (Anderson, Gillespie, Thiele, Ralph, & Ohm, 2018 ; Cardenas et al., 2018 ; Joubert et al., 2016 ), thus providing a plausible molecular mechanism to modulate the neurodevelopmental origins of autism.

Autism Epigenome-Wide Association Study (EWAS) meta-analysis performed in blood from children and adolescents from SEED and SSC cohorts ( N cases  = 796, N controls  = 858) identified seven differentially methylated positions (DMPs) associated ( p  < 10 × 10 −05 ) with autism, five of them also reported to have brain-based autism associations. The associated DMPs annotated to CENPM , FENDRR , SNRNP200 , PGLYRP4 , EZH1 , DIO3 , and CCDC181 genes, with the last site having the largest effect size and the same direction of association with autism across the prefrontal cortex, temporal cortex, and cerebellum (Andrews et al., 2018 ). The study reported moderate enrichment of methylation Quantitative Trait Loci (mQTLs) among the associated findings, suggesting top autism DMPs to be under genetic control (Andrews et al., 2018 ). These findings were further extended by the MINERvA cohort that added 1263 neonatal blood samples to the meta-analysis. The SEED-SSC-MINERvA meta-EWAS identified 45 DMPs, with the top finding showing the consistent direction of association across all three studies annotated to ITLN1 (Hannon et al., 2018 ). The MINERvA sample was also used for EWAS of autism polygenic score, hypothesizing that the polygenic score-associated DNAm variation is less affected by environmental risk factors, which can confound case–control EWAS. Elevated autism polygenic score was associated with two DMPs ( p  < 10 × 10 −06 ), annotated to FAM167A / C8orf12 and RP1L1 . Further Bayesian co-localization of mQTL results with autism GWAS findings provided evidence that several SNPs on chromosome 20 are associated both with autism risk and DNAm changes in sites annotated to KIZ , XRN2 , and NKX2-4 (Hannon et al., 2018 ). The mQTL effect of autism risk SNPs was corroborated by an independent study not only in blood, but also in fetal and adult brain tissues, providing additional evidence that autism risk variants can act through DNAm to mediate the risk of the condition (Hammerschlag, Byrne, Bartels, Wray, & Middeldorp, 2020 ).

Since autism risk variants impact an individual's methylomic landscape, studies that investigate DNAm in the carriers of autism risk variants are of interest to provide insight into their epigenetic profiles. A small blood EWAS performed in 52 cases of autism of heterogeneous etiology, nine carriers of 16p11.2del, seven carriers of pathogenic variants in CHD8 , and matched controls found that DNAm patterns did not clearly distinguish autism of the heterogeneous etiology from controls. However, the homogeneous genetically-defined 16p11.2del and CHD8 +/− subgroups were characterized by unique DNAm signatures enriched in biological pathways related to the regulation of central nervous system development, inhibition of postsynaptic membrane potential, and immune system (Siu et al., 2019 ). This finding highlights the need to combine genomic and epigenomic information for a better understanding of the molecular pathophysiology of autism.

It must be noted that a very careful interpretation of findings from peripheral tissues is warranted. DNAm is tissue-specific and therefore EWAS findings obtained from peripheral tissues may not reflect biological processes in the brain. Using the mQTL analytical approach may reduce this challenge, as mQTLs are consistently detected across tissues, developmental stages, and populations (Smith et al., 2014 ). However, not all mQTLs will be detected across tissues and will not necessarily have the same direction of effect (Smith et al., 2014 ). Therefore, it is recommended that all epigenetic findings from peripheral tissues are subjected to replication analyses in human brain samples, additional experimental approaches, and/or Mendelian randomization to strengthen causal inference and explore molecular mediation by DNAm (Walton, Relton, & Caramaschi, 2019 ).

EWASs performed in post-mortem brains have typically been conducted using very small sample sizes, due to limited access to brain tissue (Ladd-Acosta et al., 2014 ; Nardone et al., 2014 ). One of the largest autism EWAS performed in post-mortem brains (43 cases and 38 controls) identified multiple DMPs ( p  < 5 × 10 −05 ) associated with autism (31 DMPs in the prefrontal cortex, 52 in the temporal cortex, and two in the cerebellum) (Wong et al., 2019 ), and autism-related co-methylation modules to be significantly enriched for synaptic, neuronal, and immune dysfunction genes (Wong et al., 2019 ). Another post-mortem brain EWAS reported DNAm levels at autism-associated sites to resemble the DNAm states of early fetal brain development (Corley et al., 2019 ). This finding suggests an epigenetic delay in the neurodevelopmental trajectory may be a part of the molecular pathophysiology of autism.

Overall, methylomic studies of autism provide increasing evidence that common genetic risk variants of autism may alter DNAm across tissues, and that the epigenetic dysregulation of neuronal processes can contribute to the development of autism. Stratification of study participants based on their genetic risk variants may provide deeper insight into the role of aberrant epigenetic regulation in subgroups within autism.

Transcriptomics

Transcriptomics of peripheral tissues.

Gene expression plays a key role in determining the functional consequences of genes and identifying genetic networks underlying a disorder. One of the earliest studies on genome-wide transcriptome (Nishimura et al., 2007 ) investigated blood-derived lymphoblastoid cells gene expression from a small set of males with autism ( N  = 15) and controls. Hierarchical clustering on microarray expression data followed by differentially expressed gene (DEG) analysis revealed a set of dysregulated genes in autism compared to controls. This approach was adopted (Luo et al., 2012 ) to investigate DEGs in a cohort of 244 families with autism probands (index autism case in a family) known to carry de novo pathogenic or variants of unknown significance and discordant sibling carriers of non-pathogenic CNVs. From genome-wide microarray transcriptome data, this study identified significant enrichment of outlier genes that are differentially expressed and reside within the proband rare/ de novo CNVs. Pathway enrichment of these outlier genes identified neural-related pathways, including neuropeptide signaling, synaptogenesis, and cell adhesion. Distinct expression changes of these outlier genes were identified in recurrent pathogenic CNVs, i.e. 16p11.2 microdeletions, 16p11.2 microduplications, and 7q11.23 duplications. Recently, multiple independent genome-wide blood-derived transcriptome analysis (Filosi et al., 2020 ; Lombardo et al., 2018 ; Tylee et al., 2017 ) showed the efficiency of detecting dysregulated genes in autism, including aberrant expression patterns of long non-coding RNAs (Sayad, Omrani, Fallah, Taheri, & Ghafouri-Fard, 2019 ).

Transcriptomics of post-mortem brain tissue

Although blood-derived transcriptome can be feasible to study due to easy access to the biological specimen, blood transcriptome results are not necessarily representative of the transcriptional machinery in the brain (GTEx Consortium, 2017 ). Hence, it is extremely hard to establish a causal relationship between blood transcriptional dysregulations and phenotypes in autism. A landmark initiative by Allen Brain Institute to profile human developing brain expression patterns (RNA-seq) from post-mortem tissue enabled neurodevelopmental research to investigate gene expression in the brain (Sunkin et al., 2013 ). Analyzing post-mortem brain tissue, multiple studies identified dysregulation of genes at the level of gene exons impacted by rare/ de novo mutations in autism (Uddin et al., 2014 ; Xiong et al., 2015 ), including high-resolution detection of exon splicing or novel transcript using brain tissue RNA sequencing (RNA-seq). High-resolution RNA-seq enabled autism brain transcriptome analysis on non-coding elements, and independent studies identified an association with long non-coding RNA and enhancer RNA dysregulation (Wang et al., 2015 ; Yao et al., 2015 ; Ziats & Rennert, 2013 ).

Although it is difficult to access post-mortem brain tissue from autistic individuals, studies of whole-genome transcriptome from autism and control brains have revealed significantly disrupted pathways ( Fig. 4 ) related to synaptic connectivity, neurotransmitter, neuron projection and vesicles, and chromatin remodeling pathways (Ayhan & Konopka, 2019 ; Gordon et al., 2019 ; Voineagu et al., 2011 ). Recently, an integrated genomic study also identified from autism brain tissue a component of upregulated immune processes associated with hypomethylation (Ramaswami et al., 2020 ). These reported pathways are in strong accordance with numerous independent autism studies that integrated genetic data with brain transcriptomes (Courchesne, Gazestani, & Lewis, 2020 ; Uddin et al., 2014 ; Yuen et al., 2017 ). A large-scale analysis of brain transcriptome from individuals with autism identified allele-specific expressions of genes that are often found to be impacted by pathogenic de novo mutations (Lee et al., 2019 a ). The majority of the studies are in consensus that genes that are highly active during prenatal brain development are enriched for clinically relevant mutations in autism (Turner et al., 2017 ; Uddin et al., 2014 ; Yuen et al., 2017 ). Recently, a large number (4635) of expression quantitative trait loci were identified that were enriched in prenatal brain-specific regulatory regions comprised of genes with distinct transcriptome modules that are associated with autism (Walker et al., 2019 ).

Most commonly reported three pathways (Ayhan & Konopka, 2019 ; Gordon et al., 2019 ; Voineagu et al., 2011 ) associated with autism. ( a ) The synaptic connectivity and neurotransmitter pathway involves genes (yellow rectangular box) within presynaptic and postsynaptic neurons. Neurotransmitter transport through numerous receptors is an essential function of this pathway; ( b ) the chromatin remodeling pathway involves binding of remodeling complexes that initiate the repositioning (move, eject, or restructure) of nucleosomes that potentially can disrupt gene regulation; and ( c ) the neural projection pathway [adapted from Greig, Woodworth, Galazo, Padmanabhan, & Macklis ( 2013 )] involves the projection of neural dendrite into distant regions and the migration of neuronal cells through ventricular (VZ) and subventricular zones (SVZ) into the different cortical layers (I-VI).

Single-cell transcriptomics

Recent advancement of single-cell transcriptomics enables the detection of cell types that are relevant to disorder etiology. A recent case–control study conducted single-cell transcriptomics analysis on 15 autism and 16 control cortical post-mortem brain tissues generating over 100 000 single-cell transcriptomics data (Velmeshev et al., 2019 ). Cell-type analysis revealed dysregulations of a specific group of genes in cortico-cortical projection neurons that correlate with autism severity (Velmeshev et al., 2019 ). Deciphering cell-type identification has future implications, in particular for the implementation of precision medicine. However, single-cell technology is at very early stages of development and computationally it is still very complex to classify cell-type identity.

The emergence of CRISPR/Cas9 genome editing technology can potentially become an effective tool in future therapeutics of genetic conditions associated with autism. Although introducing and reversing DNA mutation is becoming a mature technology within in vitro systems, much work needs to be done for in vivo use of genome editing. Single-cell OMICs is another emerging field that has the potential to decipher developmental (spatio-temporally) brain cell types that are associated with autism. Identifying cell clusters and defining cell identity is a major computational challenge. Artificial intelligence can significantly improve these computational challenges to identify the molecular associations of autism at the single-cell level.

Clinical and therapeutic implications

In some, but not all, best practice clinical guidelines, genetic tests such as fragile X testing, chromosomal microarray, and karyotype testing are part of the standard medical assessment in a diagnostic evaluation of autism to identify potentially etiologically relevant rare genetic variants (Barton et al., 2018 ). The guidelines vary with respect to whether genetic testing is recommended for all people with autism, or based on particular risk factors, such as ID, seizures, or dysmorphic features. The DSM-5 diagnosis of autism includes a specifier for associated genetic conditions (APA, 2013 ). Although genetic test results may not usually have consequences for treatment changes, the results could inform recurrence risk and provide families with access to information about symptoms and prognosis. In the future, gene therapy, CRISPR/Cas9, and genome editing technologies may lead to the gene-specific design of precision medicine for rare syndromic forms of autism (Benger, Kinali, & Mazarakis, 2018 ; Gori et al., 2015 ).

Given that a substantial proportion of the genetic liability to autism is estimated to be explained by the cumulative effect of a large number of common SNPs, polygenic scores have gained traction as potential biomarkers. However, the predictive ability of polygenic scores from the largest autism GWAS to date is too low to be clinically useful. The odds ratio when comparing the top and bottom polygenic score decile groups is only 2.80 (95% CI 2.53–3.10) (Grove et al., 2019 ). Additionally, polygenic scores based on the samples of European ancestry do not translate well in populations with diverse ancestry (Palk, Dalvie, de Vries, Martin, & Stein, 2019 ).

Genetic testing can in the future become useful for informing screening or triaging for diagnostic assessments or identifying who may be more likely to respond to which type of intervention (Wray et al., 2021 ). Genetics may also help identify individuals with autism who are at a high risk of developing co-occurring physical and mental health conditions or likely to benefit from treatments of such conditions. A top research priority for autistic people and their families is addressing co-occurring mental health problems (Autistica, 2016 ), which may sometimes be the primary treatment need as opposed to autism per se . Genomics may also be helpful to repurpose existing treatments and better identify promising treatments. There are active clinical trials to repurpose drugs in autism (Hong & Erickson, 2019 ). Moreover, genetics can be used to identify social and environmental mediating and moderating factors (Pingault et al., 2018 ), which could inform interventions to improve the lives of autistic people.

Notably, there are important ethical challenges related to clinical translation of advances in genetics, including concerns about discriminatory use, eugenics concerning prenatal genetic testing, and challenges in interpretation and feedback (Palk et al., 2019 ). People with autism and their families are key stakeholders in genetic studies of autism and essential to include in discussions of how genetic testing should be used.

Conclusions and future directions

Recent large-scale and internationally collaborative investigations have led to a better understanding of the genetic contributions to autism. This includes identifying the first robustly associated common genetic variants with small individual effects (Grove et al., 2019 ) and over 100 genes implicated by rare, mostly de novo , variants of large effects (Sanders et al., 2015 ; Satterstrom et al., 2020 ). These and other findings show that the genetic architecture of autism is complex, diverse, and context-dependent, highlighting a need to study the interplay between different types of genetic variants, identify genetic and non-genetic factors influencing their penetrance, and better map the genetic variants to phenotypic heterogeneity within autism.

Immense collaborative efforts are needed to identify converging and distinct biological mechanisms for autism and subgroups within autism, which can in turn inform treatment (Thapar & Rutter, 2020 ). It is crucial to invest in multidimensional and longitudinal measurements of both core defining traits and associated traits such as language, intellectual, emotional, and behavioral functioning, and to collaboratively establish large omics databases including genomics, epigenomics, transcriptomics, proteomics, and brain connectomics (Searles Quick, Wang, & State, 2020 ). Indeed, large-scale multi-omic investigations are becoming possible in the context of large population-based family cohorts with rich prospective and longitudinal information on environmental exposures and developmental trajectories of different neurodevelopmental traits. Finally, novel methods (Neumeyer, Hemani, & Zeggini, 2020 ) can help investigate causal molecular pathways between genetic variants and autism and autistic traits.

Acknowledgements

We thank the Psychiatric Genomics Consortium, Anders Børglum, and Elise Robinson for their support and advice.

Financial support

Alexandra Havdahl was supported by the South-Eastern Norway Regional Health Authority (#2018059, career grant #2020022) and the Norwegian Research Council (#274611 PI Ted Reichborn-Kjennerud and #288083 PI Espen Røysamb). Maria Niarchou was supported by Autism Speaks (#11680). Anna Starnawska was supported by The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark (R155-2014-1724). Varun Warrier is supported by the Bowring Research Fellowship (St. Catharine's College, Cambridge), the Templeton World Charity Foundation, Inc., the Autism Research Trust, and the Wellcome Trust. Celia van der Merwe is supported by the Simons Foundation NeuroDev study (#599648) and the NIH R01MH111813 grant.

Conflict of interest

Efficacy and Safety of Alpha-2 Agonists in Autism Spectrum Disorder: A Systematic Review

• Published: 13 September 2024

Cite this article

• Alan D. Kaye 1 ,
• Abigail M. Green 2 ,
• Joseph Tremblay Claude II 2 ,
• Charles P. Daniel 2 ,
• Jada F. Cooley 2 ,
• Kelly R. Sala 3 ,
• Pooja Potharaju 4 ,
• Ross Rieger 4 ,
• Shilpadevi Patil 4 ,
• Shahab Ahmadzadeh 4 &
• Sahar Shekoohi   ORCID: orcid.org/0009-0007-4545-4523 4  

This analysis is a systematic literature review assessing efficacy and adverse effects of three alpha-2 agonists for the symptomatic management of autism spectrum disorder (ASD).

The present investigation involved an extensive systematic search for eligible studies in PubMed, Embase, Cochrane Library, and Google Scholar. Nine studies, collectively incorporating 226 patients, were assessed.

The results demonstrated promising indications for use of alpha-2 agonists in the symptomatic management of autism spectrum disorders, including improvement of hyperactivity, impulsivity, attention deficit symptoms, irritability, and stereotypies in many of the participants studied.

The present investigation encourages physicians to consider treatment outcomes of clonidine, guanfacine, and lofexidine to determine the most effective management of ASD-related symptoms and to minimize adverse effects. However, our review cannot provide definitive treatment protocols related to various study limitations.

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Kaye, A.D., Green, A.M., Claude, J.T. et al. Efficacy and Safety of Alpha-2 Agonists in Autism Spectrum Disorder: A Systematic Review. Adv Ther (2024). https://doi.org/10.1007/s12325-024-02980-0

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• Published: 15 June 2022

Genetics of autism spectrum disorder: an umbrella review of systematic reviews and meta-analyses

• Shuang Qiu 1 ,
• Yingjia Qiu 2 ,
• Yan Li 3 &
• Xianling Cong   ORCID: orcid.org/0000-0002-5790-4188 1  

Translational Psychiatry volume  12 , Article number:  249 ( 2022 ) Cite this article

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• Autism spectrum disorders

Autism spectrum disorder (ASD) is a class of neurodevelopmental conditions with a large epidemiological and societal impact worldwide. To date, numerous studies have investigated the associations between genetic variants and ASD risk. To provide a robust synthesis of published evidence of candidate gene studies for ASD, we performed an umbrella review (UR) of meta-analyses of genetic studies for ASD (PROSPERO registration number: CRD42021221868). We systematically searched eight English and Chinese databases from inception to March 31, 2022. Reviewing of eligibility, data extraction, and quality assessment were performed by two authors. In total, 28 of 5062 retrieved articles were analyzed, which investigated a combined 41 single nucleotide polymorphisms (SNPs) of nine candidate genes. Overall, 12 significant SNPs of CNTNAP2 , MTHFR , OXTR , SLC25A12 , and VDR were identified, of which associations with suggestive evidence included the C677T polymorphism of MTHFR (under allelic, dominant, and heterozygote models) and the rs731236 polymorphism of VDR (under allelic and homozygote models). Associations with weak evidence included the rs2710102 polymorphism of CNTNAP2 (under allelic, homozygote, and recessive models), the rs7794745 polymorphism of CNTNAP2 (under dominant and heterozygote models), the C677T polymorphism of MTHFR (under homozygote model), and the rs731236 polymorphism of VDR (under dominant and recessive models). Our UR summarizes research evidence on the genetics of ASD and provides a broad and detailed overview of risk genes for ASD. The rs2710102 and rs7794745 polymorphisms of CNTNAP2 , C677T polymorphism of MTHFR , and rs731236 polymorphism of VDR may confer ASD risks. This study will provide clinicians and healthcare decision-makers with evidence-based information about the most salient candidate genes relevant to ASD and recommendations for future treatment, prevention, and research.

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Introduction.

Autism spectrum disorder (ASD) is a group of neurodevelopmental conditions characterized by early-onset dysfunctions in communication, impairments in social interaction, and repetitive and stereotyped behaviors and interests [ 1 ]. Patients develop ASD-related symptoms when they are 12−18 months of age, and diagnosis is generally made at the age of 2 years [ 2 ]. In 2010, 52 million people had been diagnosed with ASD worldwide, which was equivalent to a population prevalence of 7.6 per 1000 or 1 in 132 persons [ 3 ]. ASD is the leading cause of disability in children under 5 years, and people with ASD may require high levels of support, which is costly and thus leads to substantial economic, emotional, and physical burdens on affected families [ 3 ].

Due to the lack of clinical and epidemiological evidence for an ASD cure, researchers have focused on better understanding ASD and advancing risk prediction and prevention [ 3 ]. The causes of ASD are complex and multifactorial, with several associated genes and environmental risk factors [ 4 ]. A previous umbrella review (UR) of environmental risk factors for ASD showed that several maternal factors, including advanced age (≥35 years), chronic hypertension, preeclampsia, gestational hypertension, and being overweight before or during pregnancy, were significantly associated with ASD risk, without any signs of bias [ 5 , 6 ]. Accumulating twin- and family based studies further indicate that genetic factors play critical roles in ASD, such that the concordance rate among monozygotic twins is higher (60–90%) than that among dizygotic twins (0–30%) [ 7 , 8 ]. The heritability of ASD has been estimated to be 50%, indicating that genetic factors are the main contributors to the etiology of ASD [ 8 ].

To date, numerous studies investigating the association between genetic variants and ASD risk have been published [ 9 , 10 , 11 ]. Most of these studies focused on identifying single nucleotide polymorphisms (SNPs) of candidate genes associated with ASD risk. However, these SNP studies had small sample sizes and, therefore, low statistical power to demonstrate statistically significant effects of low-risk susceptibility genes, leading to inconsistent conclusions. Although meta-analyses have been conducted to resolve this problem, single SNPs or genes have usually been investigated.

An UR collects and evaluates multiple systematic reviews and meta-analyses conducted on a specific research topic, provides a robust synthesis of published evidence, and considers the importance of effects found over time [ 12 ]. In addition, the results of UR studies may increase the predictive power with more precise estimates [ 13 ]. Thus, we aimed to perform an UR study of all the systematic reviews and meta-analyses that have been published, assessing candidate genes associated with ASD risk. This study will provide clinicians and healthcare decision-makers with evidence-based information about candidate genes of ASD and recommendations for future prevention and research in less time than would otherwise be required to locate and examine all relevant research individually.

Literature search strategy and eligibility criteria

We systematically searched the PubMed, EMBASE, PsycINFO, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Sinomed, and Wanfang databases from inception to March 31, 2022. The databases were searched using the following strategy: (autis* [All Fields] OR autism* [All Fields] OR autistic* [All Fields] OR ASD [All Fields] OR autism spectrum disorder* [All Fields] OR PDD-NOS [All Fields] OR PDDNOS [All Fields] OR unspecified PDD [All Fields] OR PDD [All Fields] OR pervasive developmental disorder* [All Fields] OR pervasive developmental disorder not otherwise specified [All Fields] OR Asperger* [All Fields] OR Asperger* syndrome [All Fields]) AND (gene* [All Fields] OR genom* [All Fields]) AND (systematic review [All Fields] OR meta-analysis [All Fields]). Authors S. Qiu and Y. Qiu independently conducted literature searches for potential articles included in this review. The references of the relevant articles were manually searched to identify and incorporate eligible studies.

We included meta-analyses of family based and case-control studies that examined associations between ASD and potential risk genes. We only included meta-analyses that reported either effect estimates of individual study or the data necessary to calculate these estimates. We excluded meta-analyses if (1) risk genes were used for screening, diagnostic, or prognostic purposes; (2) a study examined ASD as a risk factor for other medical conditions; (3) a study included fewer than three original studies investigating the association between risk genes and ASD; and (4) a study with missing information after the corresponding author, whom we contacted through email, failed to provide the required information. All articles retrieved were first organized in the reference manager software (Endnote 9, Clarivate Analytics, New York, NY, USA), and duplicates were deleted. S. Qiu and Y. Qiu chose eligible articles by screening the titles, abstracts, and full article texts independently. Disagreements were resolved through a discussion with a third investigator (Y. Li) until a consensus was reached.

Data extraction and quality assessment

From each eligible meta-analysis, we extracted the first author, publication year, genetic risk factors examined, number of studies, number of ASD cases and participants, study-specific relative risk estimates (odds ratio [ OR ]) with the corresponding 95% confidence interval ( CI ), sample size of cases and controls, genotype and allele counts, and individual study designs (case-control, family based or mixed [case-control and family based]). We used the ‘assessment of multiple systematic reviews’ tool, consisting of 11 items, to assess the methodological quality of the meta-analyses [ 14 ]. Data extraction and quality assessment were independently conducted by S. Qiu and Y. Qiu. Disagreements were resolved via a discussion with a third investigator (Y. Li) until a consensus was reached.

Data analysis

In agreement with previous URs, we performed a statistical analysis using a series of tests that were previously developed and reproduced [ 13 , 15 , 16 ]. If more than one meta-analysis on the same research question was eligible, the most recent meta-analysis was retained for the main analysis. For each eligible meta-analysis, we calculated the summary-effect size with 95% CI [ 17 ]. We also calculated the 95% prediction interval ( PI ) to explain the between-study heterogeneity and to assess the uncertainty of a new study [ 18 , 19 ]. Heterogeneity between studies was assessed using the Chi-squared test based Q-statistic and quantified using the I 2 -statistic [ 20 , 21 ]. If there was no substantial statistical heterogeneity ( P  > 0.10, I 2  ≤ 50%), data were pooled using a fixed-effect model; otherwise, heterogeneity was evaluated using a random-effect model [ 22 ]. The Hardy–Weinberg equilibrium (HWE) of meta-analyses in the control group was analyzed using Chi-squared tests. Additionally, small-study effects were evaluated using Egger’s regression asymmetry test. P -values < 0.10 were considered to indicate the presence of small-study effects [ 23 , 24 ]. The Chi-squared test was used to assess the presence of excess significance, which evaluated whether the observed number of studies with significant results ( P  < 0.05) was greater than the expected number [ 22 , 25 ]. All statistical analyses were performed using RStudio 3.6.2. Statistical significance was set at P  < 0.05, except where otherwise specified.

Determining the credibility of evidence

In line with previous URs, we categorized the strength of the evidence of risk genes for ASD into five levels: convincing (class I), highly suggestive (class II), suggestive (class III), weak (class IV), and not significant [ 5 , 26 , 27 , 28 ]. Criteria for the level of evidence included the number of ASD cases, P -values by random effects model, small-study effects, excess significance bias, heterogeneity ( I² ), and 95% CI .

This review was prospectively registered with PROSPERO (registration number: CRD42021221868).

Description of eligible meta-analyses

A total of 5062 articles were identified through an initial search. After removing duplicates, the titles and abstracts of 3182 articles were screened for eligibility. Of the remaining 66 articles that were reviewed in full, 28 eligible articles were selected for data extraction (Fig. 1 ).

Flow chart of literature identification and selection.

The characteristics of the selected studies are presented in Table 1 . Of the 28 included reviews, eight were on methylenetetrahydrofolate reductase ( MTHFR ) [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]; four each on solute carrier family 6 member 4 ( SLC6A4 ) [ 37 , 38 , 39 , 40 ] and contactin associated protein 2 ( CNTNAP2 ) [ 41 , 42 , 43 , 44 ]; three each on oxytocin receptor ( OXTR ) [ 45 , 46 , 47 ] and reelin ( RELN ) [ 48 , 49 , 50 ]; two each on gamma-aminobutyric acid type A receptor subunit beta3 ( GABRB3 ) [ 51 , 52 ], solute carrier family 25 member 12 ( SLC25A12 ) [ 53 , 54 ], and vitamin D receptor ( VDR ) [ 55 , 56 ]; and one on catechol-o-methyltransferase ( COMT ) [ 39 ] (one meta-analysis was on both COMT and SLC6A4 ). These studies were published from 2008 to 2021 and considered the associations between 41 SNPs in nine candidate genes and ASD risk. For quality assessment, 22 articles that scored 5−8 were rated as ‘moderate quality’, and six that scored < 5 were rated as ‘low quality’. Seventeen studies (60.7%) performed the HWE check (Table 1 ). With respect to the study design, 14 (64.3%) studies synthesized case-control studies, two (7.1%) included family based studies, and eight (28.6%) used both case-control and family based studies (Table 1 ).

Summary-effect sizes and significant findings

The results of the associations between the 41 SNPs and ASD risks reported in the meta-analyses are presented in Table 2 under five different genetic models: allelic model (mutant allele vs. wild-type allele), dominant model (mutant homozygote + heterozygote vs. wild-type homozygote), heterozygote model (heterozygote vs. wild-type homozygote), homozygote model (mutant homozygote vs. wild-type homozygote), and recessive model (mutant homozygote vs. wild-type homozygote + heterozygote).

Only one meta-analysis on the rs2710102 polymorphism of CNTNAP2 showed that the polymorphism was associated with ASD susceptibility in allelic, homozygote, and recessive models [ 44 ]. This meta-analysis also found that the rs7794745 polymorphism of CNTNAP2 was associated with an increased risk of ASD in dominant and heterozygote models [ 44 ].

All four meta-analyses reported no significant association between the A1298C polymorphism of MTHFR and ASD risk. All eight meta-analyses on the C677T polymorphism of MTHFR showed that the polymorphism was associated with ASD susceptibility in allelic and heterozygote models [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Seven meta-analyses found that the C677T polymorphism was associated with an increased risk of ASD in dominant [ 29 , 31 , 32 , 33 , 34 , 35 , 36 ] and homozygote [ 29 , 30 , 31 , 33 , 34 , 35 , 36 ] models. Five meta-analyses found that the C677T polymorphism was associated with an increased risk of ASD in the recessive model [ 29 , 30 , 31 , 33 , 34 ].

For OXTR , 19 SNPs were summarized. LoParo et al. [ 45 ] found that the mutant allele of rs2268491, wild-type allele of rs237887, and mutant allele of rs7632287 were risk-inducing SNPs of ASD. In addition, Kranz et al. [ 46 ] found that the mutant allele of rs237889 was associated with ASD risk.

Regarding SLC25A12 , both Aoki et al. [ 53 ] and Liu et al. [ 54 ] found that the mutant alleles of rs2056202 and rs2292813 significantly increased ASD risk in family-based and mixed studies. We excluded the results of the associations between rs2292813 and ASD risk based on the case-control design reported by Liu et al. [ 54 ], as the authors included only two case–control studies.

Sun et al. [ 55 ] found that the rs2228570 polymorphism of VDR was associated with an increased ASD risk in homozygote and recessive models, while Yang et al. [ 56 ] did not find significant associations in any genetic model. Both authors [ 55 , 56 ] found that the rs731236 polymorphism of VDR was significantly associated with ASD risk in allelic, homozygote, and recessive models. Sun et al. [ 55 ] found that the rs731236 polymorphism was significantly associated with ASD risk in the dominant model. Both Sun et al. [ 55 ] and Yang et al. [ 56 ] found that the mutant allele of rs7975232 of VDR was significantly associated with a decreased ASD risk (Table 2 ). There were no significant SNPs in COMT , GABRB3 , RELN , and SLC6A4 .

When more than one meta-analysis on the same research question was eligible, the most recent one was retained for the main analysis. After comparing the publication year and sample size of each meta-analysis, 11 meta-analyses were retained for further analysis, of which two each study were on RELN and MTHFR , and one each was on CNTNAP2 , COMT , GABRB3 , OXTR , SLC25A12 , SLC6A4 , and VDR . We extracted the allele and genotype frequencies of each SNP in case and control groups from the original research for further analysis. However, the allele and genotype frequencies of some SNPs in the compared groups could not be extracted from the original research that did not contain the information, and we could not obtain this information from the corresponding authors of the studies. Finally, we analyzed the data of 20 SNPs with allele frequencies in 10 meta-analyses from 117 original studies and 16 SNPs with genotype frequencies in eight meta-analyses from 101 original studies. Associations were measured using five different genetic models (Tables 3 , 4 ).

We found that the rs2710102 polymorphism of CNTNAP2 was associated with a decreased ASD risk in the allelic ( OR  = 0.849, 95% CI  = 0.734–0.981, P  = 0.0263), homozygote ( OR  = 0.668, 95% CI  = 0.470–0.950, P  = 0.0248), and recessive ( OR  = 0.715, 95% CI  = 0.563–0.909, P  = 0.0062) models. In addition, we found that the mutant allele of rs7794745 ( CNTNAP2 ) increased ASD risk based on the dominant ( OR  = 1.300, 95% CI  = 1.109–1.523, P  = 0.0012) and heterozygote ( OR  = 1.275, 95% CI  = 1.081–1.504, P  = 0.0039) models. The C677T polymorphism of MTHFR was associated with an increased ASD risk in the allelic ( OR  = 1.799, 95% CI  = 1.303–2.483, P  = 0.0004), dominant ( OR  = 1.959, 95% CI  = 1.402–2.738, P  < 0.0001), heterozygote ( OR  = 1.767, 95% CI  = 1.343–2.330, P  < 0.0001), and homozygote ( OR  = 1.795, 95% CI  = 1.158–2.782, P  = 0.0089) models. The rs607755 polymorphism of RELN was associated with an increased ASD risk in the allelic ( OR  = 1.316, 95% CI  = 1.029–1.683, P  = 0.0284), dominant ( OR  = 1.520, 95% CI  = 1.061–2.178, P  = 0.0226), heterozygote ( OR  = 1.483, 95% CI  = 1.016–2.165, P  = 0.0411), and homozygote ( OR  = 1.816, 95% CI  = 1.051–3.136, P  = 0.0324) models. The rs731236 polymorphism of VDR was associated with an increased ASD risk in the allelic ( OR  = 1.297, 95% CI  = 1.125–1.494, P  = 0.0003), dominant ( OR  = 1.304, 95% CI  = 1.082–1.571, P  = 0.0053), homozygote ( OR  = 1.741, 95% CI  = 1.258–2.409, P  = 0.0008), and recessive ( OR  = 1.613, 95% CI  = 1.187–2.190, P  = 0.0022) models. In addition, we found that the mutant allele of rs7975232 ( VDR ) decreased ASD risk ( OR  = 0.823, 95% CI  = 0.681–0.993, P  = 0.0425) based on the allelic model. There was no significant association between the other SNPs and ASD risk (all P  > 0.05; Table 4 ).

As for the results of PI , the null value was excluded in only four SNPs of rs2710102 ( CNTNAP2 ) under the allelic, homozygote, and recessive models; rs7794745 ( CNTNAP2 ) under the heterozygote model; rs607755 ( RELN ) and rs731236 ( VDR ) under the allelic and homozygote models (Table 4 ). When evaluating small-study effects using Egger’s regression asymmetry test, evidence for statistically significant small-study effects in the meta-analyses was identified in some SNPs. Supporting evidence included a meta-analysis on A1298C ( MTHFR ) under the allelic, dominant, and heterozygote models; a meta-analysis on C677T ( MTHFR ) under the five genetic models; a meta-analysis on rs20317 ( GABRB3 ) under the dominant and heterozygote models; one each on rs736707 ( RELN ) and rs1544410 ( VDR ) under the recessive and allelic models, respectively; and three meta-analyses on rs607755 ( RELN ), 5-HTTLPR ( SLC6A4 ), and rs7975232 ( VDR ) under the heterozygote model ( P  < 0.10).

Hints of excess-statistical-significance bias were observed in rs2710102 ( CNTNAP2 ) under the allelic, homozygote, and recessive models; rs4680 ( COMT ) under the allelic model; rs20317 ( GABRB3 ) under the heterozygote model; A1298C ( MTHFR ) under allelic, dominant, heterozygote, and recessive models; C677T ( MTHFR ) under homozygote and recessive models; rs736707 ( RELN ) under allelic, dominant, and homozygote models; 5-HTTLPR ( SLC6A4 ) under allelic and recessive models; rs11568820 ( VDR ) under the dominant model; and rs731236 ( VDR ) under the heterozygote model, with statistically significant ( P  < 0.05) excess of positive studies (Table 4 ).

We categorized the strength of the evidence of 20 SNPs for ASD into five levels. According to the criteria for the level of evidence, for rs2710102 ( CNTNAP2 ), the P -value based on the random effects model was significant at P  < 0.05 under allelic, homozygote, and recessive models. Between-study heterogeneity was not significant ( P  > 0.10, I²  < 50.0%), the 95% PI did not exclude the null value, and there was no excess significance bias ( P  > 0.05) under the five genetic models. For rs7794745 ( CNTNAP2 ), the P -value based on the random effects model was significant at P  < 0.05 under dominant and heterozygote models. For C677T ( MTHFR ), there was a total of 2147 ASD cases, which was > 1000, and the P -value based on the random effects model was significant at P  < 10 –3 under allelic, dominant, and heterozygote models. Moreover, it was significant at P  < 0.05 under the homozygote model. Between-study heterogeneity was large ( I²  > 50.0%) under the five genetic models, the 95% PI did not exclude the null value under the five genetic models, and there was no excess significance bias ( P  > 0.05) under allelic, dominant, and heterozygote models. For rs731236 ( VDR ), there was a total of 1088 ASD cases, which was >1000, the P -value based on the random effects model was significant at P  < 10 –3 under allelic and homozygote models, and the P -value was significant at P  < 0.05 under dominant and recessive models. Between-study heterogeneity was not significant ( P  > 0.10, I²  < 50.0%), the 95% PI excluded the null value, and there was no small-study effect ( P  > 0.10) and excess significance bias ( P  > 0.05) under the five genetic models (Table 4 ). Thus, the rs2710102 ( CNTNAP2 ) was graded as weak evidence (class IV) under allelic, homozygote, and recessive models; rs7794745 ( CNTNAP2 ) was graded as weak evidence (class IV) under dominant and heterozygote models; the C677T ( MTHFR ) was graded as suggestive evidence (class III) under allelic, dominant, and heterozygote models; C677T ( MTHFR ) was graded as weak evidence (class IV) under the homozygote model; VDR (rs731236) was graded as suggestive evidence (class III) under allelic and homozygote models; and VDR (rs731236) was graded as weak evidence (class IV) under dominant and recessive models.

This UR summarizes evidence on the genetic basis of ASD. Our study design provides a robust and significant synthesis of published evidence and increases the conclusive power with more precise estimates. Overall, 12 significant SNPs of CNTNAP2 , MTHFR , OXTR , SLC25A12 , and VDR were identified from 41 SNPs of nine candidate genes in 28 meta-analyses. Of those, associations with suggestive evidence (class III) were the C677T polymorphism of MTHFR (under allelic, dominant, and heterozygote models) and rs731236 polymorphism of VDR (under allelic and homozygote models). Associations with weak evidence (class IV) were the rs2710102 polymorphism of CNTNAP2 (under allelic, homozygote, and recessive models), rs7794745 polymorphism of CNTNAP2 (under dominant and heterozygote models), C677T polymorphism of MTHFR (under homozygote model), and rs731236 polymorphism of VDR (under dominant and recessive models).

ASD remains a ‘disease of theories’, as multiple genes and environmental risk factors are probably involved in its pathogenesis. However, to date, the etiology and pathological mechanism of ASD are still unknown [ 57 ]. The genetic architecture of ASD is complex. Moreover, most research in this field has focused on candidate genes, primarily those with a plausible role in the known underlying pathophysiology, including mitochondrial dysfunction, abnormal neurodevelopment, and dysfunction of synapse formation and stability during neurodevelopment [ 58 , 59 ].

CNTNAP2 is a member of neurexin superfamily and is a synaptic protein [ 60 ]. It plays a major role in neural development, crucial for neural circuit assembly [ 61 ]. CNTNAP2 mutations may be linked to the abnormal behavior of ASD by altering synaptic neurotransmission, functional connectivity, and neuronal network activity [ 61 , 62 ]. The rs2710102 and rs7794745 are two common non-coding variants in CNTNAP2 , with four and three meta-analyses reporting the associations with ASD, respectively. The results of the meta-analysis by Uddin et al. were inconsistent with the other authors’ [ 44 ]. We further re-analyzed and categorized the strengths of evidence. Both the rs2710102 and rs7794745 polymorphisms of CNTNAP2 were associated with decreased risk of ASD. The rs2710102 was graded as having a weak association with ASD under allelic, homozygote, and recessive models. The rs7794745 was graded as having a weak association with ASD under dominant and heterozygote models. Therefore, it is likely that the rs2710102 and rs7794745 polymorphisms of CNTNAP2 influence the risk of ASD.

MTHFR is one of the most frequently-researched genes in ASD, with four and eight meta-analyses for A1298C [ 29 , 31 , 32 , 33 ] and C667T [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ] polymorphisms, respectively. The A1298C and C667T polymorphisms of MTHFR are associated with reduced enzymatic activity, which affects folate metabolism, and, consequently, fetal brain development [ 29 , 32 , 33 ]. Dysfunction of the brain is indicated in ASD etiology; thus, MTHFR has been the focal point of investigation in this disorder. The meta-analysis by Li et al. was selected because it was the most recent among the examined meta-analyses [ 34 ]. The genotype distributions of the A1298C and C667T polymorphisms of MTHFR in the control group were not found in the HWE, which may be due to selection bias, population stratification, and genotyping errors within the original studies. We found no significant association between the A1298C polymorphism of MTHFR and ASD risk in the five genetic models, which was consistent with the four meta-analyses, indicating that the A1298C polymorphism of MTHFR may not be a risk SNP of ASD. We found that the C667T polymorphism of MTHFR was associated with an increased risk of ASD, graded as having suggestive association under allelic, dominant, and heterozygote models and weak association under the homozygote model. Thus, the C667T polymorphism of MTHFR may confer ASD risk.

OXTR, a neuropeptide gene, is also one of the most frequently-studied genes associated with ASD [ 45 ]. Oxytocin plays an important role in a range of human behaviors, including affiliative behavior to social bonding, and is differentially expressed in the blood of individuals with autism compared to that of non-autistic individuals [ 45 , 63 ]. Three meta-analyses investigated 19 SNPs and ASD risk. Of these, only rs2254298 and rs53576 were analyzed in two meta-analyses [ 45 , 46 ], and the remaining SNPs were unique in one meta-analysis. Three SNPs (rs2268491, rs237887, and rs7632287) were significantly associated with ASD risk [ 45 , 46 ]; however, we failed to determine the credibility of the evidence because of the lack of original data.

RELN encodes a large secreted extracellular matrix protein considered to be involved in neuronal migration, brain structure construction, synapse formation, and stability during neurodevelopment [ 59 ]. Fatemi et al. found decreased levels of reelin mRNA and protein and increased levels of reelin receptors in the brain and plasma of individuals with autism [ 64 ]. Dysfunction of the reelin signaling pathway has been found in ASD, schizophrenia, epilepsy, bipolar disorder, mental retardation, depression, Alzheimer’s disease, and lissencephaly [ 59 , 65 ]. Genetic association studies have been conducted to investigate the associations between SNPs within RELN and ASD with conflicting results. None of the three meta-analyses found significant associations [ 48 , 49 , 50 ]. The meta-analysis by Hernández-García et al. was retained for further analysis of the original studies after comparing publication years and sample sizes of the three meta-analyses [ 50 ]. Hernández-García et al. did not find a significant association between RELN and ASD risk [ 50 ]. In our analysis, because there was no substantial statistical heterogeneity under the five genetic models (all P  > 0.10, I 2  ≤ 50%), a fixed model was applied to pool the effect size. We found that the rs607755 of RELN was associated with ASD risk in allelic, dominant, heterozygote, and homozygote models. This inconsistent result was caused by different pooling methods, indicating that it is necessary to perform an UR to provide a robust synthesis of published evidence and evaluate the importance of genetic factors related to ASD. Our UR results showed that the rs607755 of RELN was not significant when we categorized the strength of the evidence. Thus, it may not be a risk factor for ASD.

SLC25A12 encodes the mitochondrial aspartate/glutamate carrier of the brain, a calcium-binding solute carrier located in the inner mitochondrial membrane that is expressed principally in the heart, brain, and skeletal muscle [ 66 , 67 ]. Rossignol et al. found that individuals with ASD had a significantly higher prevalence of mitochondrial diseases than that of controls, indicating the involvement of mitochondrial dysfunction in ASD [ 58 ]. Thus, an increasing number of genetic studies on ASD have focused on SLC25A12 . However, the results on the association between SNPs of SLC25A12 and ASD risk are inconsistent. Two meta-analyses were performed by Aoki et al. [ 53 ] and Liu et al. [ 54 ], and despite differences in the number of studies between the two meta-analyses, both found a higher risk of ASD in individuals with the mutant allele of rs2056202 or rs2292813. However, we failed to determine the credibility of the evidence because of a lack of original data.

Vitamin D plays a significant role in brain homeostasis, neurodevelopment, and immunological modulation, and its deficiency has been reported in children with ASD [ 68 ]. Hence, changes in the genes involved in the transport or binding of vitamin D may be associated with ASD risk. Notably, vitamin D exerts its effects on genes via the VDR gene, to which changes may be an underlying risk factor for ASD. Sun et al. [ 55 ] and Yang et al. [ 56 ] performed meta-analyses to pool the effect size of inconsistent conclusions from original studies on the associations between SNPs in VDR and ASD risks. We further re-analyzed and categorized the strengths of evidence. The rs731236 polymorphism of VDR was associated with an increased risk of ASD, graded as having a suggestive association under allelic and homozygote models and a weak association under dominant and recessive models without small-study effects, excess significance bias, and large heterogeneity. It is likely that the VDR rs731236 polymorphism influences the risk of ASD.

Our study has some limitations. First, associations between several SNPs and ASD risks under five genetic models or in different populations were not fully assessed in our UR, partly due to insufficient original data. Second, our UR is limited by significant heterogeneity that may be caused by population stratification, study design, and differences in the pattern of linkage disequilibrium structure. Finally, ASD is a complex disorder with different causative factors (multiple genetic and environmental factors). We did not investigate the involvement of environmental factors in ASD. Despite these limitations above, our UR includes its prospective registration with PROSPERO, an extensive search strategy, clear criteria of inclusion and exclusion, duplicated processing by two authors, accurate quality assessment, systematic assessment and critical comparison of meta-analyses, and consistent standards for re-analysis of original data.

In conclusion, our UR summarizes evidence on the genetics of ASD and provides a broad and detailed overview of risk genes for ASD. The rs2710102 and rs7794745 polymorphisms of CNTNAP2 , C677T polymorphism of MTHFR , and rs731236 polymorphism of VDR may confer ASD risk. This study will aid clinicians in decision-making through the use of evidence-based information on the most salient candidate genes relevant to ASD and recommendations for future treatment, prevention, and research.

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This study was funded by the Science and Technology Department of Jilin Province (grant number: 20200601010JC).

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Qiu, S., Qiu, Y., Li, Y. et al. Genetics of autism spectrum disorder: an umbrella review of systematic reviews and meta-analyses. Transl Psychiatry 12 , 249 (2022). https://doi.org/10.1038/s41398-022-02009-6

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Graduate Students Invited to Apply for Research Grants

September 10, 2024

By: Organization for Autism Research

Categories: Self-Advocates , Research , OAR News

OAR invites graduate students to submit research proposals for the annual  Graduate Research Grant Program . Opened on September 9, OAR has started accepting proposals from students pursuing graduate studies in the United States and abroad. The maximum award for master’s candidates is $1,000, while doctoral and post-doctoral candidates are eligible for a maximum award of $2,000.

Interested students should first review the  2025 Request for Proposals and OAR’s  funding guidelines , then apply online . Proposals will be accepted through February 3, 2025.

Since the program was established in 2004, OAR has awarded over $329,266 in grants to more than 190 graduate research studies. In 2024, OAR awarded five students with grant awards totaling $7,199. OAR hopes to build on this success in 2025, continuing its commitment to support the next generation of applied autism researchers.

OAR’s Scientific Council will evaluate the proposals it receives for scientific and technical merit. Review criteria for the evaluation include:

• Significance:  Does the study address an important problem? How will it advance scientific knowledge in the field?
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OAR’s Board of Directors will grant awards based on these evaluations and the recommendations of the Scientific Council. OAR will announce grant recipients in May 2025 and make the awards in July 2025.

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Systematic review finds suicide rate considerably higher for people with autism

by University of Queensland

University of Queensland-led research has found people on the autism spectrum are almost 3 times more likely to die by suicide compared to non-autistic people. The study was published in Psychiatry Research .

Dr. Damian Santomauro from UQ's School of Public Health and the Queensland Centre for Mental Health Research led a team which conducted a systematic review of nearly 1,500 international research papers.

"We aimed to quantify the risk, mortality and burden of suicide among people on the autism spectrum ," Dr. Santomauro said.

"There were several alarming findings in this study, including the fact people on the autism spectrum but without intellectual disability were more than 5 times more likely to die by suicide compared to people not on the autism spectrum.

"In 2021, the total years of life lost to the increased risk of suicide in the autistic community exceeded those lost to cocaine use, rabies or testicular cancer across the total global population. And almost 2% of all suicide deaths globally in 2021 could have been avoided if the risk for death by suicide for autistic people was not elevated."

Dr. Santomauro said there were likely many reasons for the higher associated risk.

"People on the autism spectrum often experience bullying, social rejection, stigma and discrimination—all risk factors for depressive disorders," he said.

"There can also be other challenges for autistic people that impact their educational progress, employment, independent living and peer relationships."

Dr. Santomauro said the findings showed a critical need for interventions and prevention strategies.

"Measures to reduce risk factors for suicide among autistic people would substantially reduce the fatal burden of suicides globally and the health burden experienced by people on the autism spectrum," he said.

"Studies like this one are important, to get an idea of how issues impact people on the autism spectrum. Without these estimates there would be no gauge for policy makers or service providers on the mortality and burden of suicide for autistic people."

The study also involved researchers from Deakin University, La Trobe University, the University of Leicester, the Institute for Health Metrics and Evaluation and the University of Washington.

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New Research Shows Dungeons & Dragons Can Help People with Autism “Conquer” Social Situations and Find Their “Inner Hero”

New research has found that playing Dungeons and Dragons could be beneficial for people with autism by providing a safe space to engage in social interactions away from some of the challenges they face in their daily lives.

Previous research showed positive mental health benefits for those playing the immersive role-playing game (often shortened to D&D), but this is the first study to focus on the potential social, health, and quality of life benefits for people with autism.

“There are many myths and misconceptions about autism, with some of the biggest suggesting that those with it aren’t socially motivated or don’t have any imagination,” said Dr. Gray Atherton, Lecturer in Psychology at the University of Plymouth and the study’s lead author in a press release . “Dungeons and Dragons goes against all that, centering around working together in a team, all of which takes place in a completely imaginary environment.”

Previous Research Showed the Benefits of Playing Dungeons and Dragons and other Board Games

In July, The Debrief covered a study showing the potential mental health benefits of playing Dungeons and Dragons . According to that study, D&D players experienced positive mental health correlations in escapism, exploration of self, creative expression, social support, and routine. The authors of that particular study said that these correlations translated to an overall better mental health profile, indicating that participating in the game had a range of possible benefits. Still, that study did not specifically look at the potential benefits for people with autism.

In this latest study, Dr. Atherton and colleagues noted that a previous study had found that people with autism enjoyed many types of board games “because they took the pressure off the uncertainty around meeting and interacting with people, removing the need for small talk.” According to the study authors, reading social cues and navigating social situations has often been cited as one of the challenges facing this community, leading to potential negative perceptions about them.

“Autism comes with several stigmas, and that can lead to people being met with judgment or disdain,” explained Dr. Liam Cross, a Lecturer in Psychology in Plymouth and the study’s senior author. According to Cross, these types of stigmas and negative perceptions often occur “because people have a picture in their minds of how a person with autism should behave.” He explains that this situation exists because those inaccurate perceptions are based on “neurotypical experiences.”

As a result, the researcher says that this situation can cause people with autism to withdraw from social situations or even avoid them entirely. At the same time, the parents of younger people with autism worry that their children can disappear into fictional worlds to escape the judgment of others.

“We also hear from lots of families who have concerns about whether teenagers with autism are spending too much time playing things like video games,” Dr. Atherton explained.

Players Experienced “A Sense of Innate Kinship” With Their Fellow D & D Adventurers

Curious if the benefits of playing interactive board games and a penchant for fantasy might be used to help these folks better navigate or even thrive in social situations, the researchers decided to test the reactions to people with autism playing Dungeons and Dragons.

According to the published study, volunteers with autism were initially divided into small groups. Each group had an assigned dungeon master, meaning someone who guided the volunteers on their D&D quests. Those sessions took place over six weeks.

At the conclusion of these fictional adventures, the volunteers were interviewed individually to measure their overall experience. Specifically, the researchers asked them “about the ways they felt their autism might have interacted with their experiences and, in turn, whether taking part in the game impacted their lives.”

In those interviews, the volunteers first spoke about their overall desires and motivations, especially regarding social situations and the challenges they faced in such situations. At the top of the list was the commonly reported experience that those desires “were often in direct conflict” with their actual experiences. The study subjects said this resulted in them hiding or otherwise attempting to “mask” their autism from others.

However, after playing D & D, the study volunteers reported experiencing entirely new feelings and experiences. For example, many of the study participants told the researchers that playing D & D, which, by its nature, takes place in a fictional, fantasy environment, provided them with an unusually friendly environment. As a result, they said that they felt safer being themselves. They also reported that they very quickly felt “a sense of innate kinship” with others taking part in the adventure, which was not usually present in everyday social situations.

“Understanding common issues linked to activities inside and outside of the game allowed them to relax without feeling pressure to act in a certain way,” explains the press release announcing the study, “and as a result, they felt included in – and able to better contribute to – the group’s interactions.”

Some Benefits Lasted Beyond Six Week Trial

According to the researchers, one of the more unexpected responses came when they conducted follow-up communications with the study volunteers. Specifically, many of the respondents said they were motivated to take some of the traits from their fictional character “outside of the game,” which made them feel differently about themselves in a positive way.

Scientists Have Designed the World’s First True ‘One-Way Glass’ Using the Magic of Metamaterials

“Those taking part in our study saw the game as a breath of fresh air, a chance to take on a different persona and share experiences outside of an often-challenging reality,” Dr. Atherton explained. “That sense of escapism made them feel incredibly comfortable, and many of them said they were now trying to apply aspects of it in their daily lives.”

While the study was the first to look for potential benefits of playing Dungeons and Dragons for people with autism, especially in terms of social benefits, the researchers believe their work can offer families and medical professionals who support this community a new tool to help them learn to navigate social situations and improve their overall sense of confidence and self-worth. According to the release, this includes helping people with autism “find their inner hero.”

“Our studies have shown that there are everyday games and hobbies that autistic people do not simply enjoy but also gain confidence and other skills from,” said Dr. Cross. “It might not be the case for everyone with autism, but our work suggests it can enable people to have positive experiences that are worth celebrating.”

The study “ A critical hit: Dungeons and Dragons as a buff for autistic people ” was published  in the scientific journal Autism .

Christopher Plain is a Science Fiction and Fantasy novelist and Head Science Writer at The Debrief. Follow and connect with him on X , learn about his books at plainfiction.com , or email him directly at [email protected] .