# A Genetic Bridge Between Medicine and Neurodiversity for Autism

Claire S. Leblond, Thomas Rolland, Eli Barthome, Zakaria Mougin, Mathis Fleury, Christine Ecker, Stéf Bonnot-Briey, Freddy Cliquet, Anne-Claude Tabet, Anna Maruani, et al.

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Claire S. Leblond, Thomas Rolland, Eli Barthome, Zakaria Mougin, Mathis Fleury, et al. A Genetic Bridge Between Medicine and Neurodiversity for Autism. Annual Review of Genetics, 2024, 58 (1), pp.487-512. 10.1146/annurev-genet-111523-102614. pasteur-05129876

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Annual Review of Genetics

A Genetic Bridge Between
Medicine and Neurodiversity
for Autism

Claire S. Leblond,<fnref n="1" /> Thomas Rolland,<fnref n="1" /> Eli Barthome,<fnref n="1" />
Zakaria Mougin,<fnref n="1" /> Mathis Fleury,<fnref n="1" /> Christine Ecker,<fnref n="2" />
Stéf Bonnot-Briey,<fnref n="3" /> Freddy Cliquet,<fnref n="1" />
Anne-Claude Tabet,<fnref n="1" />,<fnref n="4" /> Anna Maruani,<fnref n="1" />,<fnref n="5" />
Boris Chaumette,<fnref n="1" />,<fnref n="6" />,<fnref n="7" />,<fnref n="8" /> Jonathan Green,<fnref n="9" />
Richard Delorme,<fnref n="1" />,<fnref n="5" /> and Thomas Bourgeron<fnref n="1" />

ANNUAL
REVIEWS **CONNECT**

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Annu. Rev. Genet. 2024. 58:487–512

The *Annual Review of Genetics* is online at
genet.annualreviews.org

https://doi.org/10.1146/annurev-genet-111523-102614

Copyright © 2024 by the author(s). This work is
licensed under a Creative Commons Attribution 4.0
International License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are credited.
See credit lines of images or other third-party
material in this article for license information.

<figure><img src="image_4.png" /></figure>

<footnote n="1">¹Human Genetics and Cognitive Functions, Institut Pasteur, CNRS UMR3571, Institut Universitaire de France, Université Paris Cité, Paris, France;
email: thomas.bourgeron@pasteur.fr</footnote>

<footnote n="2">²Department of Child and Adolescent Psychiatry, University Hospital of the Goethe University, Frankfurt am Main, Germany</footnote>

<footnote n="3">³Fédération AUTOP-H, Paris, France</footnote>

<footnote n="4">⁴Department of Genetics, Cytogenetics Unit, Robert Debré Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France</footnote>

<footnote n="5">⁵Department of Child and Adolescent Psychiatry, Robert Debré Hospital, Assistance
Publique-Hôpitaux de Paris, Paris, France</footnote>

<footnote n="6">⁶Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Université Paris Cité, Paris, France</footnote>

<footnote n="7">⁷Groupe Hospitalier Universitaire-Paris Psychiatry and Neurosciences, Hôpital Sainte Anne, Paris, France</footnote>

<footnote n="8">⁸Department of Psychiatry, McGill University, Montreal, Quebec, Canada</footnote>

<footnote n="9">⁹Division of Psychology and Mental Health, University of Manchester and Royal Manchester Children's Hospital, Manchester, United Kingdom</footnote>
## Keywords

autism, neurodiversity, common genetic variants, rare genetic variants, participative research, multidisciplinary research

## Abstract

Autism represents a large spectrum of diverse individuals with varying underlying genetic architectures and needs. For some individuals, a single de novo or ultrarare genetic variant has a large effect on the intensity of specific dimensions of the phenotype, while, for others, a combination of thousands of variants commonly found in the general population are involved. The variants with large impact are found in up to 30% of autistic individuals presenting with intellectual disability, significant speech delay, motor delay, and/or seizures. The common variants are shared with those found in individuals with attention-deficit/hyperactivity disorder, major depressive disorders, greater educational attainment, and higher cognitive performance, suggesting overlapping genetic architectures. The genetic variants modulate the function of chromatin remodeling and synaptic proteins that influence the connectivity of neuronal circuits and, in interaction with the environment of each individual, the subsequent cognitive and personal trajectory of the child. Overall, this genetic heterogeneity mirrors the phenotypic diversity of autistic individuals and provides a helpful bridge between biomedical and neurodiversity perspectives. We propose that participative and multidisciplinary research should use this information to understand better the assessment, treatments, and accommodations that individuals with autism and families need.

## INTRODUCTION

Medicine is moving from a patient-centric view to a global health population perspective with the aim of reducing the need for treatments by increasing prevention. It is also slowly moving away from responding to disease categories and toward dimensions of functioning that can be more easily linked to underlying biological systems and to the real lives of the individuals being treated (70, 130). These paradigm shifts open new opportunities to reconsider both the medical view of autism and its compatibility with neurodiversity (10, 17, 113). In medical practice, autism is part of the broader category of neurodevelopmental disorders (NDDs), which have a high prevalence (15–20% of the general population), early onset, and persistence as well as frequent co-occurrence with mental health conditions (e.g., anxiety or mood disorders, risk for suicide, and substance use disorders) (133, 164). NDDs include subdiagnostic categories such as autism, attention-deficit/hyperactivity disorder (ADHD), and intellectual disability (ID) (https://icd.who.int/en). Most of these conditions have relatively high heritability (57), but there is still a limited understanding of the mechanisms involved in the transition from well-being to illness throughout the life course. Individuals with NDDs and their families also experience greater discrimination and stigma compared to neurotypical individuals, further affecting their mental well-being (139).

Within NDDs, according to the *Diagnostic and Statistical Manual of Mental Disorders*, Fifth Edition (DSM-5), a diagnosis of autism spectrum disorder can be given when there are persistent deficits in social communication and interactions across multiple contexts, as well as restricted, repetitive, stereotyped patterns of behaviors, interests, or activities. Hyper- and hyporeactivity are also included in the DSM-5 definition of autism. The worldwide prevalence of autism is 1 out of every 100 individuals (165) and may reach 1 out of 36 children in the United States (90). The median percentage of individuals with autism and ID is 33% (165). There is a lasting debate about whether autism should be considered a prototypic category, a very heterogeneous condition, or part of a continuum of neurodiversity in the general population (Figure 1a). There is no need,
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Models of autism

<figure><img src="image_6.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>c</figcaption></figure>

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<figure><img src="image_9.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>e</figcaption></figure>

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Figure 1

The clinical and genetic heterogeneity of autism. (a) Reconciling prototypical autism and the dimensionality of autistic traits and manifestations. (b, top) Variance in autistic traits involves a different contribution of de novo, rare, and common inherited variants, as well as environmental factors, in each individual. (Bottom) Examples of genetic and environmental factors and their contributions are shown. (c) Genetic variants in different genes can contribute to autism, and genetic variants in the same gene can be associated with a variety of co-occurring symptoms or with no symptoms. (d) A reaction norm describes the pattern of phenotypic expression of a single genotype across a range of environments. Individuals with G1 have increased functionality/autonomy in environment B and even more so in environment C. Individuals with genotype G2 have higher functionality/autonomy in environment B. Individuals with genotype G3 have low functionality/autonomy in all environments. For these individuals, innovation is needed to find an environment that will increase their functionality/autonomy. (e) The Waddington epigenetic landscape illustrates that the functionality or autonomy of each individual depends on several factors that each are involved in a deviation from the norm, ultimately leading to different autistic traits with different levels of intensity. Abbreviations: AQ, Autism Spectrum Quotient; BP, bipolar disorder; ID, intellectual disability; NS, no symptoms; SCZ, schizophrenia; SRS, Social Responsiveness Scale. Figure adapted from images created with BioRender.com.

however, to oppose dimensional and categorical views of autism, and the phenotype emerges from
the transaction between biology and environment in early life (17, 60). Autism can certainly be
usefully seen as a prototypical and distinct category (100). By contrast, studies using quantitative
tools such as the Autism Spectrum Quotient (AQ) (12) or the Social Responsiveness Scale (SRS)
(20) can evidence autism as a quantitative extreme of a neurodevelopmental continuum (118).
Coleman & Gillberg (30) have introduced the term *autismS* to highlight the large diversity of
clinical presentations displayed by individuals diagnosed with autism. Subsequently, they added
the term *autismS+* to emphasize that autism rarely comes in an isolated form: Individuals with
autism display associated medical conditions more frequently than the general population (77),
for example, intellectual disability, gastrointestinal problems, sleep difficulties, and epilepsy. Such
conditions can represent a burden on the everyday life of individuals with autism, and they are
not sufficiently detected, diagnosed, and studied (25). Finally, Gillberg (58) introduced the term
ESSENCE (early symptomatic syndromes eliciting neurodevelopmental clinical examinations) to
highlight the connection between various neurodevelopmental conditions and the importance of early identification to promote early interventions. Even the coherence of autism as a syndrome remains a debated question with no consensus on whether the two core aspects of autism—social impairment and restricted, repetitive behaviors—are tightly related to each other or exist independently (23). Because of these difficulties in delineating autism, a genotype-first approach has been proposed as a fruitful strategy for describing the biological differences underlying autism as well as identifying effective and personalized interventions (27, 37). This genotype-first approach, however, needs to emphasize the genomic architecture as a complex system rather than simple, linear single-gene effects; it should serve as a starting point to a systemic approach that integrates brain development within a personal and environmental context. In this review, we highlight how the genotype-first approach does not reduce the complexity of autism but, on the contrary, sheds light on what could be specific to autism (beyond the related mental health issues) and what could be shared with other conditions.

## THE HERITABILITY AND GENETIC ARCHITECTURE OF AUTISM

Autism has a high heritability of more than 80% (8). Heritability is a complex notion that can be a source of confusion, and it is therefore important to briefly define it. Heritability is a statistical measure that describes the proportion of variation in a trait within a population that is due to genetic differences among individuals. Heritability estimates range from 0 to 1, where a heritability of 0 suggests that genetic factors do not contribute, and all variability is due to environmental factors or stochastic events. A heritability of 1 suggests that genetic factors alone account for all the observed variation in the trait, with no influence of environmental factors. Heritability can be measured for categorical (e.g., autism versus no autism) or quantitative traits (e.g., intensity of autistic dimensions). It is crucial to note that heritability is specific to a level of observation in a particular population in a particular environment and does not indicate the degree to which a trait is genetically determined at an individual level. For example, the high heritability of autism does not preclude its genetic heterogeneity and interindividual differences (Figure 1b).

The human genome is made of approximately 3 billion base pairs, and two individuals have on average 3 million variants that differ between them. For some individuals, a single variant can have a large effect on the phenotype (the so-called monogenic forms of autism), but in any case both genetic and environmental contexts will contribute to the phenotype. Genetics is often considered to be a fatality (e.g., “it is in my DNA”; “it is written in my genes”). As a matter of fact, each gene (more precisely each genetic variant) can have a different range of qualitative and quantitative phenotypic impacts (Figure 1c). Even in the apparently monogenic forms of autism, a heterogeneity of phenotype is well documented (5). Such key information on the functioning of individuals is rarely available in cohorts based solely on NDD categories (which are presently limited to affected versus nonaffected). One proposition is to use instruments such as the International Classification of Functioning, Disability and Health (ICF) (18) or the Vineland Adaptive Behavior Scales (VABS) to better inform on individuals’ ability to adapt behaviors/emotions in their everyday life. Such adaptive/functioning skills can be considered intermediate phenotypes between genetic variations and autism and inform us about the complex developmental processes that lead to autism.

A better recognition of genetic and phenotypic heterogeneity can have clinical implications since different individuals with the same clinical presentation may have distinct underlying genetic causes, requiring tailored diagnostics and care. A largely understudied complexity can exist in the interrelationships between genetic and environmental factors in determining traits. In other words, for every genotype, phenotypic trait, and environmental variable, a different reaction norm can exist (Figure 1d).
Genetic variations do not directly code for autism, but they can modulate intermediate cellular/circuit phenotypes that downstream will influence how neuronal circuits are connected and how the cognition of a child will develop. For example, a subset of individuals with autism can have micro- or macrocephaly (122), hypo- or hypersensitivity (85), motor impairments (15), atypicality in eye tracking (74), atypical neural responses to faces (91), difficulties with detecting emotions (11), circadian and sleep difficulties (78), and seizures (111). Compared to the general population, they can also have higher skills in systemizing (i.e., the cognitive process of analyzing the underlying rules that govern the behavior of a system) (149). Some of these conditions and traits have relatively high heritability, and relatives of individuals with autism also display some of these features. For example, the ability to read the mind in the eyes and to detect emotions has a twin heritability of 0.28 (146). In 5-month-old infants, early preference for faces versus nonfacial objects has a heritability of 0.46 (115). In toddlers, the heritability of preference for eye and mouth looking is over 0.86 (33). Consistent with the continuum hypothesis, relatives of individuals with autism have higher autistic traits compared to the general population (32), and siblings of children with autism have on average less advanced linguistic and motor skills compared to siblings of children with typical development (55).

An additional degree of complexity is the developmental trajectory of each individual and the variability of the autistic phenotype through the life course (56). For example, Fountain et al. (49), who followed 6,975 children with autism aged 2 to 14 years, observed six distinct patterns of social, communication, and repetitive behaviors. These trajectories displayed significant heterogeneity in developmental pathways, and some children whose traits were less intensely expressed at first assessment tended to develop with fewer difficulties than those with more intensely expressed traits (49). Still, trait intensity was not a deterministic predictor, as ~10% of the latter group developed into a profile with fewer functioning challenges. Socioeconomic factors were correlated with trajectory outcomes, similar to all chronic disorders in children. Pickles et al. (114) reported a similar heterogeneity of developmental trajectories by following 192 children initially referred for autism between ages 2 and 19. Almost a quarter of the children (22%) began the study in the group with the most underdeveloped language skills but made rapid gains by age 6, attaining age equivalents slightly lower than or similar to those of typically developed individuals. Understanding which combination of risk and resilience factors can influence phenotypic variability and whether traits develop into symptoms could help to improve the trajectory of individuals with autism.

# THE GENETIC ORCHESTRA OF AUTISM: DE NOVO, RARE, AND COMMON VARIANTS AT PLAY

## De Novo and Rare Variants Associated with Autism

The first strategy to identify genes for autism was to analyze candidate genes, which were selected due to their expression patterns/functions in the brain or locations within or close to a chromosomal rearrangement observed in individuals with autism. This allowed researchers to identify de novo [sometimes only present in the brain (39)] and rare inherited genetic variants associated with autism and the whole spectrum of associated features. The first genes—*NLGN3*, *NLGN4X*, *SHANK3*, and *NRXN1*—were all coding for synaptic proteins, suggesting that a difference in brain connectivity could be causative in autism (7, 43, 71). The functional impact of these genetic variations on synapse and neuronal physiology was reminiscent of those observed for other NDD genes such as *MECP2* and *FMR1*, which are responsible for Rett and fragile X syndromes, respectively (169). With the technical advances in whole-exome sequencing/whole-genome sequencing (WES/WGS), it was soon possible to investigate all the coding/noncoding regions of the human genome. Such unbiased approaches have allowed the identification of additional genes
associated with autism/NDDs (4, 53, 137, 157, 167). Several databases (SPARK/SFARI1, DBD, SysNDD, DDG2P) have been developed to provide extensive information on these genes. An updated list of more than 1,800 high-confidence NDD (HC-NDD) genes can be downloaded on the GeneTrek website (Figure 2a; Supplemental Table 1). The unbiased WES/WGS approach also confirmed that the biological functions of the autism-associated genes are synaptic plasticity and homeostasis. Not all proteins associated with autism are located at the synapse; some act as chromatin-remodeling or transcriptional regulatory factors, and others are involved in protein synthesis and degradation (Figure 2b). Interestingly, many of these regulators are modulated by synaptic activity, and this activity-dependent property makes them key players of synaptic consolidation and plasticity (21). Not surprisingly, genes associated with autism are also susceptibility genes for epilepsy, a medical condition observed in 7-19% of individuals with autism compared to approximately 1% in the general population (111).

While many genes have now been robustly associated with autism, little is known about their spectrum of variations within and across NDD categories as well as the prevalence of variants impacting their function in individuals without a diagnosis of NDD. Thus, the existence of genes specific to autism has been questioned (99, 102). To identify autism-/ID-predominant genes, several studies have investigated genotype-phenotype relationships by comparing large cohorts of individuals with autism, ID, or developmental delay (DD) (53, 124, 145). As expected, autistic individuals carrying variants in ID-/DD-predominant genes had a more significant delay in early developmental milestones (e.g., age of walking) and global cognitive development compared to carriers of variants in genes predominantly associated with autism. A new metric, called Evaluation of Autism Gene Link Evidence (EAGLE; see Figure 2b), is available at the SFARI gene website and indicates whether genes are preferentially associated with autism compared to other NDD conditions (125). A large-scale study on >13,000 autistic individuals and >200,000 undiagnosed individuals estimated the odds ratio (OR) of genetic variants that lead to the loss of function (LoF) of 185 genes robustly associated with autism (119). On average, LoF variants in autism-associated genes had an OR of 16, with only a few genes (e.g., *SCN2A*, *SYNGAP1*, *GRIN2B*, and *CHD8*) for which LoF was only observed in autistic individuals. Such genotype-phenotype approaches have limitations: (a) The level of cognitive development is not always considered in cohorts of autistic individuals, and (b) the level of autistic traits is largely unknown in cohorts of individuals with ID (102). In addition, the cognitive development of individuals with autism is difficult to ascertain using generic cognitive tests (159). Even for individuals carrying similar genetic causes (e.g., *ADNP*, *CHD8*, *DYRK1A*, *GRIN2B*, or *SCN2A*), there is considerable heterogeneity in developmental milestones and outcomes within each gene group (6). This clinical heterogeneity between individuals carrying similar genetic variations could be the consequence of different environmental contexts and of different genetic backgrounds made of common variants.

## Common Variants Associated with Autism

Using genome-wide association studies (GWASs), researchers analyze a large number of individuals to find genetic markers that are commonly found in the general population [usually single-nucleotide polymorphisms (SNPs)] and more frequently in individuals with autism compared to those without the diagnosis. In contrast to the de novo or rare variants, common variants individually confer a very low OR for autism (<1.2), and large samples are required to detect differences between autistic individuals and controls. The most recent GWAS for autism (63) leveraged a sufficient sample size (18,000 cases and 28,000 controls) to identify common genetic variations associated with autism over the genome-wide significance threshold. Interestingly, the genes prioritized from common variant analyses are enriched in genes associated with autism carrying de novo and rare genetic variations. However, the overlap is limited, suggesting that there
a

HC-NDD n = 1,826

- SPARK/SFARI1 n = 243
- SysNDD definitive n = 1,609
- DDG2P definitive n = 756
- DBD Tier1 and AR n = 382
- Intersection n = 76

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

b

<figure><img src="image_14.png" /></figure>

Node label color

Gene EAGLE >11
Gene Not EAGLE >11

Node color

- Synapse
- Chromatin
- Synapse and chromatin

Border color

- Epilepsy
- No epilepsy

Node shape and size

- No autism OR available
- Autism OR

<figure><img src="image_15.png" /></figure>

(Caption appears on following page)
HC-NDD proteins and their interactions. (a) Venn diagram of HC-NDD genes and their properties. The HC-NDD genes (https://genetrek.pasteur.fr/) are composed of those listed on the SPARK/SFARI1, DBD, SysNDD, and DDG2P databases. Only genes with clear evidence for genetic association were selected. EAGLE genes are those considered strong or definitive genes for autism (EAGLE score > 11). *CNTN6* and *SHANK1* are genes highlighted by EAGLE scores as definitive for autism but are not listed in the HC-NDD genes from the SPARK/SFARI1, DBD, SysNDD, and DDG2P databases. (b) Protein-protein interaction network of EAGLE strong/definitive proteins associated with autism. Only proteins directly interacting with EAGLE proteins (red) and included in the HC-NDD list are displayed. Interactions were extracted from the STRING database and filtered for the highest-confidence physical interactions from experiments or databases. Interactions from the mouse and human interactomes were merged. Autism ORs were extracted from Rolland et al. (119). See **Supplemental Text** for additional information. Abbreviations: AR, autosomal recessive inheritance; EAGLE, Evaluation of Autism Gene Link Evidence; HC-NDD, high-confidence neurodevelopmental disorder; NDD, neurodevelopmental disorder; OR, odds ratio; SPARK, Simons Powering Autism Research for Knowledge.

are partially common mechanisms that exist for common and rare variants in autism. Individually, common variants have small effects, but they are numerous. One way to sum up their collective contribution to autism susceptibility is to study the heritability captured by the SNPs (h2). Grove et al. (63) showed that currently common variants associated with autism capture 11% of heritability and pointed to neuronal functions and brain development, consistent with the role of the genes highlighted by the rare variant studies. Another way to analyze common variants associated with autism is to estimate their additive effect recapitulated through the calculation of polygenic score (PGS). The PGS allows the identification of individuals who accumulate alleles associated with a phenotype. As expected, the PGS for autism has been confirmed to be significantly higher in independent cohorts of autistic individuals compared to undiagnosed individuals (83, 150). Within families, the inheritance of this PGS for autism is also biased toward autistic individuals compared to their nonautistic siblings (153). Warrier et al. (150) found that the PGS for ADHD and educational attainment increased and reduced, respectively, the intensity of autistic traits in individuals with autism. Schlag et al. (126) investigated the link between PGS and types of social interaction difficulties. They found that the PGS for ADHD, major depression, and autism was strongly linked to peer problems (e.g., social withdrawal or being bullied), while the PGS for schizophrenia was linked to low prosociality (e.g., behaviors that are intended to benefit others) (126).

Finally, another way to consider the influence of common variants is to explore to what extent they are shared by other traits (Figure 3). Such genetic correlation studies indicate that genetic variants associated with autism are shared with those identified for schizophrenia, ADHD, major depressive disorders, greater educational attainment, systemizing, and higher intellectual quotient (IQ). Altogether, these results suggest an overlap in the genetic architecture of child- and adult-onset mental health conditions. Remarkably, the genetic architectures of autism and ADHD reveal striking similarities but also differences, including strong, discordant polygenic associations with educational attainment, with a positive correlation in autism and a negative one in ADHD (142). Mattheisen et al. (92) identified seven loci shared by autism and ADHD and five loci differentiating them. Individuals diagnosed with both autism and ADHD were double loaded with genetic variants associated with both conditions.

By contrast, the variants associated with autism are negatively correlated to those associated with subjective well-being (63) or to high scores on the empathy quotient (148). Such genetic results could be misinterpreted as an empathy deficit in autism. The link between autism and empathy is obviously more complex. While autistic individuals have, on average, difficulties detecting emotions (i.e., cognitive empathy), they might have no deficit in the ability to respond with an appropriate emotion or maybe even a higher emotional response to another person's mental state (i.e., affective empathy). A study investigating the equilibrium between cognitive and affective empathy showed that some autistic individuals might have affective empathy dominance and others cognitive empathy dominance (127). Further analyses are warranted to understand how
a

<figure><img src="image_16.png" /></figure>

Individual polygenic scores
$PGS_i = \sum_{j \in SNPs} x_{ij} \beta_j$

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

b

<figure><img src="image_19.png" /></figure>

Neurodevelopmental conditions

Personal traits

Psychiatric traits

(Caption appears on following page)
<caption>**Figure 3** (*Figure appears on preceding page*)</caption>

Common variants associated with autism. (a) Learning from GWAS analyses. A trait's SNP-based heritability is the proportion of variance explained by additive effects of the observed SNPs. $b_{gy}^2$ is the SNP-based heritability for a trait $y$, where each SNP $j$ has an additive effect on the trait $\beta_j$. Here, $x_{ij}$ encodes the number of alternative alleles an individual $i$ has at polymorphic site $j$. Individual polygenic scores summarize the effect of genetic variants on individuals. Genetic correlation quantifies the shared genetic factors between 2 traits: $y_1$ and $y_2$. $\rho_g$ denotes the genetic covariance. (b) Genetic correlations between autism, common co-occurring neurodevelopmental conditions, and psychiatric and personal traits. Estimates and tests were performed with LDSC (24). Asterisks indicate a significant genetic correlation after FDR correction. The GWASs used to perform genetic correlations are listed in the **Supplemental Text**. Abbreviations: ADHD, attention-deficit/hyperactivity disorder; BMI, body mass index; FDR, false discovery rate; GWAS, genome-wide association study; LDSC, linkage disequilibrium score regression; RMET, Reading the Mind in the Eyes Test; SNP, single-nucleotide polymorphism.

**Supplemental Material** >

genetic variants influence individuals' empathy skills and why such abilities are atypical in autistic individuals (101).

GWASs highlight that common variants associated with autism are frequent in the population and shared by many traits, but larger sample sizes from more diverse populations are warranted to disentangle the complex polygenic architecture of autism. Integrating models of developmental pathways, functional information such as transcriptomics (152), and brain imaging (147), as well as longitudinal information from the individuals in their environmental contexts, might also provide biological insights on how common and rare variants influence the likelihood of having a diagnosis of autism.

## THE KEY ROLE FOR GENE DOSAGE AND GENE REGULATION IN AUTISM

To understand the role of these genetic variants associated with autism requires an exploration of their primary functional consequences. In most cases, genetic variations associated with autism do not lead to a complete absence of the gene product but to a modulation of the gene dosage and regulation (135).

### Patterns of Expression of Genes Associated with Autism During Human Brain Development

Genes associated with autism have different biological functions and also display different patterns of expression during human brain development (Figure 4; Supplemental Table 2). Genes involved in chromatin remodeling have higher levels of expression during the massive cell proliferation at the fetal stage (before postconception week 16). They orchestrate how neurons proliferate and migrate to their appropriate locations. By contrast, genes that are coding for synaptic proteins display increased expression starting from fetal life to after birth. They participate in the establishment of brain circuits and their consolidation throughout the life of the individual. The connections between gene expression patterns and the developmental trajectories of autistic individuals remain unclear. Carriers of genetic variations in genes expressed early during brain development are, however, more likely to have autism and ID (72). Genes associated with autism are preferentially expressed in neurons but not in a specific neuronal type or brain region, suggesting again heterogeneity in autism and the necessity to look at the way in which gene expression is linked to strengths and weaknesses (e.g., mental health issues) in autism.

### Gene Dosage

With the exception of the genes on the X and Y chromosomes in males, the vast majority of genes are present in two copies, one on the paternal and one on the maternal chromosome. A striking feature of the genetics of autism is that most of the de novo and rare genetic variants identified in
<figure><img src="image_20.png" /><figcaption>Figure 4</figcaption></figure>

Profiles of the EAGLE protein-coding genes. (Top) An expression profile of EAGLE genes is shown using a weighted gene coexpression network analysis performed on BrainSpan developmental RNA sequencing data including only human neocortical regions (112). There are three expression trajectories based on coexpression modules that represent genes that share highly similar expression patterns during cortical development. Expression trajectory 1 contains 23 EAGLE genes that are downregulated during cortical development; M2 and M3 modules are from Parikshak et al. (112). Expression trajectory 2 contains 9 EAGLE genes that are upregulated during cortical development; M16 and M17 modules from Parikshak et al. (112). Expression trajectory 3 contains 33 EAGLE genes for which the expression trajectory is stable during cortical development or genes that are not coexpressed. (Bottom) Genes that are part of each trajectory are described using a color matrix. Grey dotted line indicates birth. See **Supplemental Text** for additional information. Abbreviations: AUTISMP, autism predominant genes; DDp, developmental delay predominant genes; EAGLE, Evaluation of Autism Gene Link Evidence; HC, high-confidence.

autism impact the function of a single copy of the gene in a dominant manner. For example, copy number variants (CNVs), which are losses (deletions) or gains (duplications) of genetic material, impact the typical gene dosage. Such an increase or decrease of gene copy could have different consequences: uncovering other genetic/epigenetic variations on the remaining allele, affecting the dosage of the protein, or interfering with a subtle stoichiometry of the different subunits in protein complexes (135). In the human genome, some genes are intolerant to LoF genetic variations (n = 4,357) and/or dosage sensitive (haploinsufficient genes n = 2,987; triplosensitive genes n = 1,559) (31), while others are less constrained. The loss of one copy of these LoF-intolerant genes is often associated with an average loss of 2.6 ± 0.24 IQ points (42). While the effect of the deletions on the liability to have autism was mostly driven by the impact on general cognitive ability, in contrast, duplications were associated with autism independently of IQ (42). Some regions of the genome have opposite phenotypes when they are deleted or duplicated. Deletion of 7q11.23 results in Williams syndrome, which is characterized by mild to moderate ID with relative cognitive strengths in verbal short-term memory and in language and extreme weakness in visuospatial construction, as well as anxiety, ADHD, and overfriendliness. By contrast, duplication results in severely delayed speech and expressive language but with relative strength in

**Supplemental Material** >
visuospatial construction and often no ID (108). On chromosome 16p11.2, deletions and duplications have been associated with an increased probability of ID and psychiatric disorder. There is higher frequency of ADHD in children with the duplication than those with the deletion (OR = 2.7, p = 0.04) as well as a higher frequency of overall psychiatric disorders (OR = 2.8, p = 0.02) and psychotic symptoms (OR = 4.7, p = 0.02). However, no differences in the prevalence of autism were found between deletion and duplication carriers (104). On chromosome 22q13.3, deletions including the *SHANK3* gene are associated with Phelan-McDermid syndrome, characterized by neonatal hypotonia, minor dysmorphic features, absent to severely delayed speech, global DD, and autism. Duplications of the same region were identified in an autistic boy who learned language precociously and in individuals with ADHD (43, 64).

## Regulatory Variants

Among the 3 million variants that are different from one individual to another, the vast majority falls in non-protein-coding regions with limited functional annotations. Still, such variants are suspected to play a role in the regulation of gene expression. In particular, variants in regulatory sites could mimic, to a lesser extent, coding variants disrupting protein structure by affecting gene dosage. Zhou et al. (166) investigated this hypothesis for rare noncoding variants in regulatory sites and showed a significantly increased burden of de novo variants disrupting transcriptional regulation or RNA-binding protein regulation in autistic individuals (compared to nonautistic siblings). In addition, other studies demonstrated an excess of de novo noncoding genetic variations in autistic individuals, particularly in the promoter regions (3) and in conserved sequences (138). Moreover, de novo noncoding variants were enriched in processes related to neuronal functions in autistic individuals (166). Regarding rare inherited genetic variations, they are biased in their transmission: Paternal structural variants falling into regulatory elements tend to be overtransmitted to autistic offspring compared to their nonautistic siblings (22). Regarding more common regulatory variants, public databases such as Genotype-Tissue Expression (GTEx) and, more recently, meta-analyses using brain expression data sets such as MetaBrain (35) have gathered thousands of expression quantitative trait loci (eQTLs) in adult and fetal brain (154). eQTLs are SNPs associated with changes in gene expression levels and are thus putative regulatory variants. Hickman et al. (67) identified variants at eQTLs with significantly different allele distributions between autistic and undiagnosed individuals, suggesting that common regulatory variants contribute to autism. In addition, common regulatory variants could interact with rare deleterious variants through the variation of expression levels of both mutated and unmutated gene copies. Changes in methylation patterns and in the proportion of functional transcripts could affect the penetrance of deleterious coding variants and modify their impact on the organism (26). Better understanding of the impact of common variants could potentially help us disentangle the phenotypic heterogeneity observed in autism. There are multiple challenges in refining the functional characterization of noncoding mutations, but the development of deep-learning methods predicting functional impact and the improvement of functional annotation of the noncoding genome (ENCODE) are encouraging (41).

## The Sex Difference in Autism

The reasons why there is a difference in the prevalence of autism between boys and girls remain largely unexplained. At the phenotypic level, girls have on average different cognitive profiles with, for example, higher scores in the Reading the Mind in the Eyes Test (62) and lower autistic traits compared to boys (160). At the gene expression level, 1,000-3,000 genes display differential expression in the male and female brain (105), but it has not yet been demonstrated that this
sexual dimorphism in gene expression has an effect on the liability to have autism. At the genetic variation level, monogenic forms of autism are more frequent in autistic girls compared to autistic boys (119), but most of the time, for each gene category, males and females are equally distributed (e.g., there is an equal number of females and males in the carriers of genetic variants in *SHANK3* or *SCN2A*). Autistic females also have a higher load of autism PGS compared to males (156). Such results suggest that, for large-effect genetic variants and for high levels of PGS for autism, the susceptibility to autism does not differ between boys and girls. It suggests that boys could be more sensitive to medium levels of rare genetic variants and PGS for autism compared to girls. An alternative hypothesis is that females with medium levels of genetic variants could actually be autistic but less diagnosed than males. This could be due to a male bias in the symptom weighting currently implemented in the algorithms used to make an autism diagnosis (14), and girls, more than boys, may be using coping strategies as camouflage to conceal their autistic symptoms (34). Such girls struggling with undiagnosed autism or using camouflage often develop depression, anxiety, or poor self-esteem (34).

# IMPACT OF THE GENETIC VARIANTS ASSOCIATED WITH AUTISM ON BRAIN STRUCTURE AND CONNECTIVITY

## Brain Structure

Differences in brain structure have been proposed to constitute intermediate phenotypes linking genetic variation to interindividual differences in behaviors. For example, an enlarged brain volume, particularly in the frontal lobes, emerges as one of the frequently observed brain structural phenotypes in autism (38). While considerable research exists on this phenomenon, uncertainties remain regarding its prevalence at birth and in children, as well as its persistence in adolescents and adults with autism (84) and the homogeneity of the increased volume to all substructures. The existing body of literature on autism reveals notable variations in surface-based morphometric measures such as cortical thickness, surface area, and gyrification. Predominantly, significant developmental differences in cortical thickness have been identified in key autism regions such as the superior temporal cortex and cingulate gyrus, with reports indicating both increases and decreases in thickness (13, 45, 46) (Figure 5). However, the literature also presents inconsistencies, with some studies reporting thicker cortices, others indicating thinner cortices, and some finding no significant differences in autistic individuals (44, 46, 95, 116, 168). To establish their putative genomic underpinnings, some studies have identified genes with a cortical expression pattern that closely resembles the topology of observed differences in neuroanatomy (46, 116, 120). For instance, a recent study by Ecker et al. (46) revealed that differences in cortical thickness were functionally associated with distinct sensory peculiarities in individuals with autism, especially in brain regions expressing genes involved in excitatory rather than inhibitory neurotransmission.

Using a gene-first approach, a recent review has also summarized brain structural MRI (sMRI) findings in individuals carrying high-impact variants in genes associated with autism (52). On average, brain sMRI abnormalities were reported for half of the participants, but each gene had a different penetrance [for example, for *PTEN*, there were 60 carriers and 48% with brain abnormalities (n = 60; 48%), *SHANK3* (n = 50; 50%), *SYNGAP1* (n = 12; 67%), *CHD8* (n = 9; 33%), *ARID1B* (n = 6; 100%), *ADNP* (n = 10; 60%), *POGZ* (n = 12; 58%), *MED13L* (n = 5; 60%), *SCN2A* (n = 5; 60%), *DYRK1A* (n = 3; 33%), and *CHD2* (n = 4; 25%)]. Thus, sMRI findings were highly variable, with no clear patterns emerging, although white matter abnormalities were the most common. Notably, for most of the genes, the sMRI abnormalities were not always present in the carriers, suggesting again the importance of the genetic/environmental context in the expression of a phenotype.
a

<figure><img src="image_21.png" /></figure>

b

<figure><img src="image_22.png" /></figure>

c

<figure><img src="image_23.png" /></figure>

d

<caption>**Figure 5**</caption>

Neurological differences between autistic and neurotypical individuals. (a) Structural brain atypicalities based on cortical thickness. (b, left) Alpha and gamma power patterns. (Right) Autistic individuals exhibit a delayed N170 component (brain responses to faces) in an EEG. (c) Atypicalities in brain connectivity: The resting-state functional connectivity of the default mode network and DTI showed reduced white matter integrity in all language-associated tracts. (d) Different brain networks: Default mode network, social cognition, and sensorimotor. Abbreviations: ACC, anterior cingulate cortex; DTI, diffusion tensor imaging; EEG, electroencephalogram; FA, fractional anisotropy; l., left side of the brain; mPFC, medial prefrontal cortex; MTG, middle temporal gyrus; PCC, posterior cingulate cortex; r., right side of the brain; SMA, supplementary motor area; STS, superior temporal sulcus. Figure adapted from images created with BioRender.com.

## Brain Connectivity

Because genes associated with autism are involved in synaptic plasticity and homeostasis, brain connectivity is a phenotype of interest that has been studied in humans using functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalogram (EEG). Resting-state fMRI (rsfMRI) studies unveiled an atypical connectivity in the default mode network (DMN) but also in brain regions crucial for emotional and sensory processing. The DMN is a network of brain regions that are active when an individual is not focused on the outside world (144). Studies using dynamic functional connectivity (dFC), which measures how neural activity patterns across the whole brain evolve over time, showed differences correlating with specific autism traits as well as with cognitive differences in autism (151). Notably, a recent study focusing on preterm infants has revealed atypical patterns of dynamic connectivity associated with differences in social, sensory, and repetitive behaviors, as assessed by scores on the Quantitative Checklist for Autism in Toddlers (Q-CHAT) at 18 months of age (50). In a meta-analysis of DTI studies, autistic individuals (n = 831) showed significantly lower fractional anisotropy or higher mean diffusivity across language-associated tracts than neurotypical individuals (n = 836) (87), but this was not always observed across studies.
Moreau et al. (98) showed that both rare and common genetic variations influence functional connectivity and that the effect sizes of CNVs were correlated with their previously reported effects on cognitive abilities. Similar to structural studies, recent studies assessed the association between transcriptomics and brain connectivity (16, 117). For instance, Rasero et al. (117) identified two subtypes of connectivity patterns: Subtype 1 (n = 348) exhibited hypoconnectivity (less average connectivity than typically developing control participants), and subtype 2 exhibited hyperconnectivity (n = 284). The two connectivity subtypes did not differ in structural brain-imaging metrics or in behavioral scores. The hyperconnectivity in subtype 2 concerned the thalamus, putamen, and precentral gyrus and showed network alterations in somatomotors and DMNs. The authors indicate that genes involved in the excitation/inhibition balance are enriched in these regions (117).

Finally, EEG can provide direct measures of neuronal responses, and resting-state EEG (rsEEG) has been proposed as a possible autism biomarker (54, 106). In a recent meta-analysis of rsEEG, autistic individuals exhibited reduced alpha and greater gamma powers compared to neurotypical individuals. This could reflect a greater inhibition of task-irrelevant cortical areas (reduced alpha oscillation) and higher maintenance of memory contents and representation of objects (greater gamma oscillation) (54, 106). However, a recent study from Li et al. (88) revealed that autistic individuals without ID show typical resting-state activity, highlighting the high heterogeneity in autism. Despite some inconsistencies, a systematic literature review suggested moderate support for a general pattern of long-range underconnectivity distinguishing autistic from neurotypical individuals (107). Autism is associated with atypical face processing, and the N170 event-related potential (ERP) has been considered the most evidenced neural marker of this trait. A meta-analysis of 23 studies (374 autistic individuals and 359 typically developed individuals) suggested that, on average, N170 latencies to faces are delayed in individuals with autism, but amplitudes did not differ from controls (75). A recent study from Mason et al. (91) showed that the delay in N170 latency positively correlated with PGS for autism.

Overall, brain-imaging studies highlight the absence of one single atypical brain region or network found in all autistic individuals. Prediction algorithms for detecting autism suggest that connectivity is more informative compared to anatomical features (136), and, in the future, we might be able to identify, as in genetics, what is specific to autism and what is shared among different conditions (93).

## WHAT TO DO WITH GENETICS INFORMATION?

The process of autism diagnosis in children can mostly be summarized as follows: (a) a prediagnosis phase when parents realize their child's difference or when a child has higher liability to have an NDD compared to the general population (e.g., siblings of a child with an NDD, preterm babies, or presence of a brain abnormalities identified during pregnancy), (b) a during-diagnosis phase in which an accurate assessment by a trained professional is obtained, and (c) a postdiagnosis phase when caregivers seek out the most appropriate services for the child (81). Sometimes, a final diagnosis is made during adulthood or later in life (68). There is no genetic test for autism. However, genomic analysis may be useful for a proportion of autistic individuals to reduce the diagnostic odyssey, to better understand the origin of interpersonal differences, to identify undetected somatic diseases, to innovate personalized support to improve mental health, and, finally, to better apprehend neurodiversity in humans.

## Reducing the Diagnostic Odyssey

Symptoms of autism manifest in early childhood and can typically be recognized before 24 months of age (2). However, the process of obtaining an autism diagnosis and connecting to early
interventions and support services is often challenging. This process involves the interaction of multiple individuals, including parents, children, pediatricians, specialists, therapists, social workers, and educators, as well as factors including the social and medical systems, family dynamics, and knowledge of autism (81). The mean age at first diagnosis is between 4 and 6 years old (141) with great disparities owing to the heterogeneity of autism, the presence of additional medical conditions, gender, and social or ethnic diversity. Genetic tests can detect genetic variants that explain most or part of the autistic traits and/or the medical co-occurrences and therefore greatly reduce the time to diagnosis. Overall, parents are glad to be informed of a genetic finding, seeing that the benefits outweigh the drawbacks (79). But they also flag certain disadvantages and limitations such as a lack of medical treatments related to the genetic finding and a sense of finality, which can increase anxieties concerning additional symptoms associated with the genetic finding and the child's ability to live independently in the future. Parents and autistic individuals also ask for a detailed, balanced, sensitively written report that values practical, personalized recommendations based on the individual's strengths and difficulties (158). The development of collaborative psychiatric-genetics consultations is a great approach that improves the personalized support of autistic individuals (128).

## The Yield of Positive Genetic Findings in Autism

Following the strict guidelines of the American College of Medical Genetics and Genomics (ACMG), a genetic test is indicated as positive when at least one genetic variant is robustly considered clinically relevant and involved in the symptoms of an individual. In the last decade, short-read sequencing became the preferred method for the molecular diagnosis of NDDs. Indeed, WES outperformed chromosomal microarray analysis with a yield of positive findings that are now estimated at 17% [95% confidence interval (CI): 11–25%] for autism (132). Few studies compared the yield of genetic tests for autistic individuals with and without ID. The Simons Powering Autism Research for Knowledge (SPARK) initiative (https://sparkforautism.org/) dissected the odds of having an autism-related genetic result in 21,149 autistic individuals and showed that they are 1.7-fold higher in females and 2- to 3-fold higher in participants presenting ID, significant speech delay, congenital anomaly, motor delay, or seizures (161). The increased molecular diagnostic rate in autism and ID was similar in a study by Husson et al. (69), with a diagnostic rate of 30% (95%CI: 20–43%) in autistic individuals with ID and 16% (95%CI: 11–22%) in autistic individuals without ID. Currently, the added value of WGS compared to WES is modest (5.5% of novel positive findings; 95%CI: 2.4–11.4%) (89), driven essentially by structural variations (such as inversions, translocations, and CNVs) not detected by previous methods. However, WGS will be the method of choice in the next few years because of its competitive prices, its improved coverage of the exons, and an increased knowledge of the contribution of regulatory variants outside the protein-coding region of the genome. Overall, using the current pipeline of analysis, up to 30% of individuals with autism receive a positive genetic result, although the heritability estimate for autism is over 80%. This gap is explained by two main reasons: (a) Not all genes associated with autism are identified, especially those for which variants have a lower effect on the liability to display the condition (76, 167), and (b) current genetic tests are robust to detect genetic variants with high impact but fail to detect the polygenic contribution of frequent variants at the individual level. Some autistic individuals, especially those without ID, might have a few rare variants associated with autism but a high contribution of common variants.

## Identifying Previously Undetected Somatic Diseases

Genetic tests can identify the causes of frequently co-occurring medical conditions with autism, such as metabolic disorders, epilepsy, and sleep abnormalities, that worsen the quality of life of
autistic individuals. Several metabolic defects have higher rates in autism compared to the general population: phenylketonuria, creatine deficiency syndromes, histidinemia, adenylosuccinate lyase deficiency, metabolic purine disorders, and nucleotidase superactivity (110). Individuals with mitochondrial disorders could also present symptoms commonly seen in individuals with autism (121). Inborn errors of metabolism are often detected very early in babies, but some milder forms might remain undetected and be diagnosed later (162). For epilepsy, depending on the genetic variant found, treatment varies to correct the specific defect leading to seizures (91). Sleep disturbances are highly prevalent in autism/NDDs (40). Some genetic variants associated with NDDs can affect the circadian processes and sleep duration/quality. For example, genetic variants in *RAI1* cause Smith-Magenis syndrome [Online Mendelian Inheritance in Man (OMIM) 182290] and inverted melatonin (darkness hormone) production (36). A low blood/saliva melatonin level is frequently observed in autism (in up to 25% of individuals), and in some cases a genetic variant can disrupt melatonin synthesis (94, 109). Such genetic information could be a very early indication that melatonin supplementation is needed, which has been repeatedly shown to improve sleep disturbance via reduced bedtime resistance and increased total sleep time (163). When a genetic test is performed, the ACMG suggests looking into a specific group of 81 genes, which are linked to conditions that are actionable, meaning that there are steps that can be taken to reduce the impact or severity of the outcomes. The overlap between ACMG actionable genes (96) and HC-NDD genes includes 12 genes categorized in 3 groups: cancer-related (*BRCA1*, *PMS2*, *PTEN*, *SDHB*, *SDHD*, *SMAD4*, *TSC1*, and *TSC2*), cardiac/vascular-related (*COL3A1* and *TTN*), and inborn errors of metabolism-related (*BTD* and *OTC*). Depending on genetic findings, the clinical recommendations differ. For instance, pathogenic variants in *PTEN*, *TSC1*, or *TSC2* are associated with Cowden or tuberous sclerosis syndromes (OMIM 191100 and 613254), in which autism-like behavior can be observed, as well as epilepsy (131). The identification of genetic events in these genes indicates the need for frequent and deeper investigations, clinical imaging, and specific medications (119, 155).

## Innovation in Personalized Care and Education

After the return of a positive genetic test, the next step is referral to a referent center and, when possible, to a support group of families whose children share the same genetic diagnostic. These associations, such as the FamilieSCN2A Foundation (https://www.scn2a.org/) and the Phelan-McDermid Foundation (https://pmsf.org/), are key actors for child and parental support and promoting research (103). They also actively take part in communicating information through social media (134) as well as the development of concerned individual registries (97) and clinical guidelines (140). Regarding current pharmacological treatments, from 12% to 35% of individuals with autism are frequently treated with multiple medications, including off-label use (e.g., use of antipsychotic medications in younger children) (1). However, none of these medications are specific to individuals with autism (9). Integrating genetic information could lead to new medications targeting the biological pathways at the origins of some of the autistic traits and the medical co-occurrences (6, 37, 51, 143). Regarding nonpharmacological approaches (59), integrating information on the genetic profile might also help to tailor the quantity and the type of interventions. Currently, very few studies have investigated whether genetic tests could be used to aid intervention decisions. Li et al. (86) investigated the genetic modifiers of intervention outcomes (measured by the SRS) in 207 children, of which 105 received social skills group training and 102 received standard care. Individuals who were carriers of a clinically relevant variant improved significantly less after standard care than noncarriers, but no difference was found for those who received social skills group training. In the future, studies will explore whether genetics can
help in identifying personalized approaches using a specific combination of pharmacological and educational strategies.

# FUTURE DIRECTIONS TO BRIDGE MEDICINE AND NEURODIVERSITY IN AUTISM

Whatever the model used—prototypic autism, continuity of autistic traits, or autism S+—the conceptualization of autism has dramatically changed over the years (65), and autistic individuals currently represent a large spectrum of diverse individuals with different genetic architecture and needs. Innovation in support services (which may include access to the right type of, e.g., sleep medication and educational facilities) is needed for a subgroup of autistic individuals, and this does not preclude the concept of neurodiversity (130), which should be included in research (28), more precisely in participatory research (66) with the active contribution of autistic adults and young adolescents and family members of autistic individuals (129). A major concern of many autistic individuals is the possibility that genetic data could potentially be used to eradicate autism (123). There is currently a consensus that the use of genetic information such as PGS to select embryos is impracticable and unethical (48), but the threat of eugenics should be considered extremely seriously, and it will depend largely on how the ethical, legal, and social implications of genetics are managed (82). In this review, we highlighted that the concept of a gene for autism is obviously a reductionist view and a source of confusion and exclusion. However, this information could be an asset for early intervention: not aiming to modify neurodiverse behavior to fit in with “normal” behavior, as has been one approach taken in the past, but to optimize the environment around the child so that their neurodivergence can flourish in the best possible way (61). For example, there is a need for more research on how sensory information is received, filtered, and computed in autistic individuals. A better knowledge of the mechanisms and the modifiers of the gene × environment relationship that shape sensory particularities very early in life should improve the quality of life of autistic individuals (73). One approach to bridge medical and neurodiversity paradigms is to use the continuous and diagnostically neutral view of the ICF (19). The ICF may provide a means to harmonize measures of functionality/autonomy independently of autistic symptoms, offering the possibility to reconsider a more neurodiversity-affirmative lens (18). The human genome is diverse, and its diversity will influence the way we receive, filter, analyze, and experience the outside world. Paradoxically, genetics could be an asset in the recognition that diversity is the rule, not the exception (29). One strength of neurodiversity lies in the differences between people, for example, in the problem-solving and creativity that drive much of human invention and empathy (47). Is autism a single color, a network of many colors, or a continuous spectrum (80)? As a society, a reworking toward a shared language in this area will be valuable since language matters. On the other hand, as Matthew Belmonte very nicely stated: “Whether or not any individual is labeled autistic oughtn’t to matter, as long as every individual gets the assessments, treatments, and reasonable accommodations that their particular neurodevelopmental profile demands. . . . Let’s focus on what individuals and families need” (15, p. 1583).

# DISCLOSURE STATEMENT

The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

# AUTHOR CONTRIBUTIONS

T.B. conceived the review. C.S.L., T.R., E.B., and M.F. aggregated data and designed figures. T.B., C.S.L., T.R., E.B., Z.M., and M.F. were involved in writing sections of the manuscript. All authors were involved in revisions.
ACKNOWLEDGMENTS

The authors thank the members of the Human Genetics and Cognitive Functions laboratory and Jo Bervoets for helpful comments and discussions and Simon Malesys for updating the GeneTrek website. This work was funded by Institut Pasteur, Université Paris Cité, the GenMed Labex, and AIMS-2-TRIALS, which received support from the Innovative Medicines Initiative 2 Joint Undertaking under grant 777394, and the Inception program (Investissement d'Avenir grant ANR-16-CONV-0005). This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant 847818 (CANDY), and from Horizon Europe under grant 101057385 (R2D2-MH). Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. J.G. is a United Kingdom National Institute for Health and Care Research (NIHR) Senior Investigator and supported by the NIHR Manchester Biomedical Research Centre (NIHR203308). Any views expressed are those of the authors and not necessarily those of the funder.

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