# Genomic and Developmental Models to Predict Cognitive and Adaptive Outcomes in Autistic Children

Vincent-Raphaël Bourque, MD; Zoe Schmilovich, PhD; Guillaume Huguet, PhD; Jade England, MD; Adeniran Okewole, MD; Cécile Poulain, MSc; Thomas Renne, MSc; Martineau Jean-Louis, MSc; Zohra Saci, PhD; Xinhe Zhang, MSc; Thomas Rolland, PhD; Aurélie Labbé, PhD; Jacob Vorstman, MD, PhD; Guy A. Rouleau, MD, PhD; Simon Baron-Cohen, PhD; Laurent Mottron, MD, PhD; Richard A. I. Bethlehem, PhD; Varun Warrier, PhD; Sébastien Jacquemont, MD

<figure><img src="image_1.png" /></figure>

Supplemental content

**IMPORTANCE** Although early signs of autism are often observed between 18 and 36 months of age, there is considerable uncertainty regarding future development. Clinicians lack predictive tools to identify those who will later be diagnosed with co-occurring intellectual disability (ID).

**OBJECTIVE** To predict ID in children diagnosed with autism.

**DESIGN, SETTING, AND PARTICIPANTS** This prognostic study involved the development and validation of models integrating genetic variants and developmental milestones to predict ID. Models were trained, cross-validated, and tested for generalizability across 3 autism cohorts: Simons Foundation Powering Autism Research, Simons Simplex Collection, and MSSNG. Autistic participants were assessed older than 6 years of age for ID. Study data were analyzed from Month Year to Month Year.

**EXPOSURES** Ages at attaining early developmental milestones, occurrence of language regression, polygenic scores for cognitive ability and autism, rare copy number variants, de novo loss-of-function and missense variants impacting constrained genes.

**MAIN OUTCOMES AND MEASURES** The out-of-sample performance of predictive models was assessed using the area under the receiver operating characteristic curve (AUROC), positive predictive values (PPVs), and negative predictive values (NPVs).

**RESULTS** A total of 5633 autistic participants (mean [SD] age, X [X] years; X majority sex) were included in this analysis. On average, participants were diagnosed with autism at 4 years of age and assessed for ID at 10 to 11 years of age, with 13.7% to 22.0% of participants being diagnosed with ID. The model integrating all predictors yielded an AUROC of 0.653 (95% CI, 0.625-0.681), and this predictive performance was cross-validated and generalized across cohorts. This modest performance reflected that only a subset of individuals carried large-effect variants, high polygenic scores, or presented delayed milestones. However, combinations of genetic variants that are typically not considered clinically relevant by diagnostic laboratories achieved PPVs of 55% and correctly identified 10% of individuals developing ID. The addition of polygenic scores to developmental milestones specifically improved NPVs rather than PPVs. Notably, the ability to stratify ID probabilities using genetic variants was up to 2-fold higher in individuals with delayed milestones compared with those with typical development.

**CONCLUSIONS AND RELEVANCE** Results of this prognostic study suggest that the growing number of neurodevelopmental condition-associated variants cannot, in most cases, be used alone for predicting ID. However, models combining different classes of variants with developmental milestones provide clinically relevant individual-level predictions that could be useful for targeting early interventions.

JAMA Pediatr. doi:10.1001/jamapediatrics.2025.0205
Published online April 21, 2025.

**Author Affiliations:** Author affiliations are listed at the end of this article.

**Corresponding Author:** Sébastien Jacquemont, MD (sebastien.jacquemont@umontreal.ca), and Vincent-Raphaël Bourque, MD (raphael.bourque@mail.mcgill.ca), CHU Sainte-Justine Pediatric Hospital and Research Centre, 3175 Chem de la Côte-Sainte-Catherine, Montréal, QC H3T 1C5, Canada.
Parents of children diagnosed with autism in early childhood invariably question their child's future cognitive and adaptive development: will they be able to communicate, interact socially, and live independently?¹

Early signs of autism, including differences in social communication and repetitive or restricted behavior, often manifest around 18 months of age.²,³ These first concerns may be accompanied by delays in language development, and in a more limited proportion of individuals, by delays in motor milestones.⁴ Diagnoses of autism made at 18 to 36 months are stable in most cases,⁵⁻⁷ yet significant uncertainty remains regarding future development.⁸ Autistic individuals can later display a broad diversity of strengths⁹ and disabilities, which might not directly correspond to their early developmental presentation.¹⁰ This is exemplified by intellectual disability (ID), a clinical diagnosis characterized by impairments in both cognitive ability and adaptive functioning, which occurs in approximately 10% to 40% of autistic individuals¹¹,¹² and can only be diagnosed with relative certainty after age 6 years.¹³⁻¹⁶

Interventions have been reported to benefit some autistic individuals,⁸,¹⁷ guided by general principles of maximizing potential, minimizing barriers, and optimizing the person-environment fit.¹⁸ However, the current wait-and-see approach¹⁹ often overlooks a child's specific strengths and challenges until there is a significant mismatch²⁰,²¹ between the individual's abilities, the environmental demands, and the provided support,¹⁸ which may result in higher levels of stress, school failure, and social misunderstandings. As such, anticipating cognitive and adaptive profiles may be key to determining what specific interventions are best offered, to whom, when, and at what intensity.²²

Recent genetic research suggests a potential for polygenic scores (PGS) and rare genetic variants—including copy number variants (CNVs; deletions or duplications) and single nucleotide gene-disrupting variants—as predictors of cognitive and adaptive profiles in autistic individuals.²³⁻²⁵ However, even strong statistical associations derived from group-level comparisons do not necessarily translate into clinically relevant individual-level predictions.²⁶ Currently, clinicians attempt to intuitively integrate genetic findings with the surveillance of developmental milestones⁸ but thus far without the assistance from any predictive models, as are increasingly used in other medical fields.²⁷⁻³¹

Here, we aimed to develop and validate models integrating genetic variants and early developmental milestones to predict the probability of developing ID in toddlers and young children receiving a diagnosis of autism.

We asked the following: (1) does combining different classes of genetic variants improve ID prediction? (2) does the integration of genetic variants with developmental milestones outperform milestones alone? and (3) what are the cognitive and adaptive dimensions more precisely predicted by genetic variants and developmental milestones?

**Key Points**

**Question** What is the predictive performance of combining genetic variants and developmental milestones to predict intellectual disability (ID) in autistic children?

**Findings** In this prognostic study including 5633 autistic participants, a model combining 5 classes of genetic variants and early developmental milestones yielded an area under the receiver operating characteristic curve of 0.65, with this predictive performance cross-validated and generalized across cohorts. The model yielded positive predictive values of 55%, accurately identifying 10% of ID cases; the ability to stratify ID probabilities using genetic variants was up to 2-fold higher in individuals with delayed milestones compared with those with typical development.

**Meaning** Results suggest that models that integrate genetic and developmental information could be implemented in clinical settings to help anticipate developmental trajectories in autism and target early interventions.

## Methods

### Ethics and Reporting Standards

This study was approved by the CHU Sainte-Justine Research Centre institutional review board. All participants provided written informed consent. This study adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guidelines³² and the Polygenic Risk Score Reporting Standards (PRS-RS).³³

### Sample Selection

We included participants from 3 cohorts: the Simons Foundation Powering Autism Research (SPARK)³⁴ version WES1-2-3, Simons Simplex Collection (SSC),³⁵ and MSSNG.³⁶ Inclusion criteria were as follows: a professional diagnosis of autism spectrum disorder according to *Diagnostic and Statistical Manual of Mental Disorders* Fifth Edition, Text Revision, or corresponding categories from previous editions, based on a caregiver report or self-report; genetic data from the proband and both parents meeting quality control; documented information on ID; and age at least 6 years for greater stability of this assessment.³⁷ Analyses of developmental milestones were done in the subset of individuals for whom all milestones were available.

### Outcome

In the SPARK cohort, ID was determined based on a caregiver report of a professional diagnosis (eMethods in Supplement 1). We validated that cognitive and adaptive measures, where available from medical records, were strong predictors of this report (eTable 1 in Supplement 2). In the SSC and MSSNG cohorts, ID was inferred from nonverbal IQ data using a threshold below 70.³⁸ In a subsample of the SSC cohort where data were consistently available, we conducted a secondary analysis on the subscales from the IQ and the Vineland Adaptive Behavior Scales.³⁹ Developmental milestones (motor, lan-
<figure><img src="image_2.png" /><figcaption>Figure 1. Schematic Overview of the Study</figcaption></figure>

A, Study rationale. There is often a long period of uncertainty between parents' initial concerns, an autism diagnosis, and the identification of co-occurring intellectual disability (ID) in 10% to 40% of autistic children. Genetic testing is frequently conducted at or before the diagnosis of autism. Therefore, predictive models that integrate genetic testing results with developmental milestones could help clinicians provide parents with accurate information about their child's expected developmental trajectory such as to offer the child appropriate support. B, Study flowchart. We trained models to predict ID in the Simons

Foundation Powering Autism Research (SPARK) cohort, estimating their out-of-sample predictive performance using cross-validation. Additionally, we evaluated the generalizability of these models in the Simons Simplex Collection (SSC) and MSSNG cohorts. AUROC indicates area under the receiver operating characteristic curve.

a bottom-decile PGS. In individuals with a higher probability due to a de novo LOF variant, the top and bottom decile PGS were associated with predicted probabilities of ID of less than 34.0% and greater than 43.0%, respectively, as compared with less than 17.1% and greater than 23.2% in those without any rare variant. In other words, combining rare and common variants improved discrimination between low vs high probabilities of ID.

## Evaluating PPVs and NPVs

To evaluate the clinical relevance of this model, we examined trade-offs between PPVs and sensitivity, as well as between NPVs and specificity (eAppendix in Supplement 1), across the full range of combinations of genetic variants (Figure 2C and D).

We observed that the addition of each class of variant into the model (Figure 2C) consistently improved PPVs. Certain combinations of variants reached a PPV of 46.8%. In other words, 46.8% of the individuals (X of X) predicted to have ID by the model had a diagnosis of ID (2-fold increase compared with the baseline prevalence of 20.8%). Such high-liability combinations of variants occurred in a small proportion of individuals, thus correctly identifying 10% (sensitivity) of individuals who will develop ID. Conversely, this curve illustrates that the integration of all genetic variants also increases the number of identified children among all those having ID (ie, sensitivity) from 6% (X of X) when using only polygenic scores

to 30% (X of X) when incorporating all variants, while maintaining a stable PPV of approximately 30%.

Gains in NPVs (Figure 2D) were more modest. The NPVs reached 86.0%, as observed at the lowest 10% of specificity, compared with the 79.2% baseline prevalence of not having ID. This suggests that genetic variants, when used in isolation, are more effective at predicting the presence of ID (Figure 2C) than excluding it (Figure 2D).

Many variants (with mild-to-moderate effect sizes) integrated in our model would either not be reported by most diagnostic laboratories or be reported as variants of unknown significance. Therefore, we compared our model's PPVs with that of variants that would currently be reported to clinicians as pathogenic by diagnostic laboratories. The carrier status of deletions or de novo LOF variants disrupting DD-associated genes⁴⁵ had a PPV of 49.8%, similar to that of the model integrating all classes of variants, albeit with a 3-fold smaller sensitivity (3.3%) (eTable 4 in Supplement 2). This indicates that models integrating different classes of genetic variants achieve PPVs similar to those of currently reported pathogenic variants in a 3-fold larger group of individuals (ie, improved sensitivity).

## Sensitivity Analyses

The eMethods in Supplement 1 showed that the model's predictive performance was not influenced by environmental factors (ie, removing individuals with prenatal exposure to alco-

<footnote>ªIndividuals with complete milestones data.</footnote>
<table><caption>Table. Participant Characteristicsª</caption><thead><tr><th rowspan="2">Characteristic</th><th rowspan="2">Cohort used for training and cross-validation, SPARK (n = 4085)</th><th colspan="2">Cohorts used for generalization</th></tr><tr><th>SSC (n = 1183)</th><th>MSSNG (n = 365)</th></tr></thead><tbody><tr><td>Profile of participants</td><td></td><td></td><td></td></tr><tr><td>Sex, No. (%)</td><td></td><td></td><td></td></tr><tr><td>Female</td><td>X (20.2)</td><td>X (12.8)</td><td>X (22.5)</td></tr><tr><td>Male</td><td>X (X)</td><td>X (X)</td><td>X (X)</td></tr><tr><td>Age at parents' first concern, median (IQR), mo</td><td>18.0 (12.0-30.0)</td><td>18.0 (14.0-30.0)</td><td>18.0 (12.0-30.0)</td></tr><tr><td>Age at diagnosis of autism, median (IQR), y</td><td>4.25 (2.83-7.00)</td><td>NA</td><td>NA</td></tr><tr><td>Age at ID assessment, median (IQR), y</td><td>11.3 (8.42-14.9)</td><td>9.67 (7.92-12.8)</td><td>10.1 (8.21-12.9)</td></tr><tr><td>Outcome</td><td></td><td></td><td></td></tr><tr><td>Diagnosis of ID, No. (%)</td><td>X (20.8)</td><td>X (22.0)</td><td>X (13.7)</td></tr><tr><td>Developmental predictors, median (IQR)</td><td></td><td></td><td></td></tr><tr><td>Age at independent walking (general population 90th percentile: 18 mo)</td><td>13.0 (12.0-15.0)</td><td>12.0 (11.0-15.0)</td><td>13.0 (12.0-16.0)</td></tr><tr><td>Delayed, No. (%)</td><td>X (16.7)</td><td>X (10.4)</td><td>X (16.8)</td></tr><tr><td>Age at first word (general population 90th percentile: 15 mo)</td><td>14.0 (11.0-24.0)</td><td>18.0 (12.0-30.0)</td><td>18.0 (12.0-36.0)</td></tr><tr><td>Delayed, No. (%)</td><td>X (48.6)</td><td>X (65.4)</td><td>X (66.9)</td></tr><tr><td>Age at first phrase (general population 90th percentile: 24 mo)</td><td>24.0 (18.0-42.0)</td><td>36.0 (24.0-48.0)</td><td>36.0 (24.0-48.0)</td></tr><tr><td>Delayed, No. (%)</td><td>X (60.3)</td><td>X (78.5)</td><td>X (77.4)</td></tr><tr><td>Language regression (occurs on average at 21.8 mo in autism), No. (%)<sup>51</sup></td><td>X (22.7)</td><td>X (15.4)</td><td>X (16.6)</td></tr><tr><td>Bladder control, mo</td><td>42.0 (36.0-54.0)</td><td>43.0 (36.0-54.0)</td><td>45.5 (36.0-54.0)</td></tr><tr><td>Bowel control, mo</td><td>48.0 (36.0-60.0)</td><td>48.0 (37.0-60.0)</td><td>48.0 (36.0-60.0)</td></tr><tr><td>Impacting constrained genes | impacting DD genes (2971 genes | 285 genes, genome-wide)</td><td></td><td></td><td></td></tr><tr><td>Carriers of genetic variants, No. (%)</td><td></td><td></td><td></td></tr><tr><td>Deletions</td><td></td><td></td><td></td></tr><tr><td>Impacting constrained genes</td><td>X (3.3)</td><td>X (2.3)</td><td>X (1.1)</td></tr><tr><td>Impacting DD genes</td><td>X (0.8)</td><td>X (0.4)</td><td>X (0.5)</td></tr><tr><td>Duplications</td><td></td><td></td><td></td></tr><tr><td>Impacting constrained genes</td><td>X (4.8)</td><td>X (4.4)</td><td>X (4.7)</td></tr><tr><td>Impacting DD genes</td><td>X (0.7)</td><td>X (0.6)</td><td>X (1.1)</td></tr><tr><td>De novo missense variants with MPC ≥2</td><td></td><td></td><td></td></tr><tr><td>Impacting constrained genes</td><td>X (3.0)</td><td>X (4.1)</td><td>X (3.3)</td></tr><tr><td>Impacting DD genes</td><td>X (1.5)</td><td>X (2.1)</td><td>X (1.6)</td></tr><tr><td>De novo LOF variants</td><td></td><td></td><td></td></tr><tr><td>Impacting constrained genes</td><td>X (7.1)</td><td>X (11.4)</td><td>X (7.4)</td></tr><tr><td>Impacting DD genes</td><td>X (3.4)</td><td>X (3.9)</td><td>X (2.5)</td></tr><tr><td>Any of the aforementioned variants</td><td></td><td></td><td></td></tr><tr><td>Impacting constrained genes</td><td>X (17.3)</td><td>X (20.6)</td><td>X (16.2)</td></tr><tr><td>Impacting DD genes</td><td>X (6.2)</td><td>X (6.9)</td><td>X (5.8)</td></tr></tbody></table>

Abbreviations: DD, developmental disorder; ID, intellectual disability; LOF, loss of function; MPC, X; NA, not applicable; SPARK, Simons Foundation Powering Autism Research; SSC, Simons Simplex Collection.

ª Autistic individuals were included from 3 distinct cohorts: SPARK, SSC, and MSSNG. For each milestone, we present the median (IQR) and the percentage delayed as compared with the 90th general population percentile. Constrained genes are intolerant to gene variants and therefore likely to have deleterious impacts on development.⁴² DD genes represent a smaller set of genes previously associated with severe developmental disorders.⁴⁵ Delayed milestones were defined based on the 90th general population percentile.⁵²

hol or drugs, oxygen supplementation at birth, intraventricular hemorrhage, meningitis, or encephalitis (eTable 5 in Supplement 2), verbal abilities (ie, removing nonspeaking individuals) (eTable 5 in Supplement 2), changing predictive algorithm (random forest vs logistic regression) (eTable 6 in Supplement 2), or telescoping effects⁵⁵ on milestones reporting (eTable 7 in Supplement 2).

## Integrating Genetic Variants and Developmental Milestones to Predict Outcome

When a child is referred for atypical development, clinicians commonly take into account developmental history to assess

the probability of developing ID, based on normative data and their clinical experience. We aimed to evaluate the predictive informativeness of genetic test results on the background of this clinical information.

First, we tested the predictive value of developmental history, by sequentially including in a model the age of attaining 5 milestones, as well as the presence of language regression, in the order these typically occur (Figure 3A, right panel).⁵² As expected, the predictive performance increased as we added developmental milestones that are achieved at older ages, closer to the diagnosis of ID.
<figure><img src="image_3.png" /><figcaption>Figure 2. Genetic Analyses to Predict Intellectual Disability (ID) in Autistic Individuals</figcaption></figure>

A, Predictive performance of combining different classes of genetic variants, assessed using cross-validation in the Simons Foundation Powering Autism Research (SPARK) cohort and evaluated for generalizability in the Simons Simplex Collection (SSC) and MSSNG cohorts. The predictive performance of each model is tested using two complementary methods: the likelihood-ratio test, and the area under the curve (AUROC) with its 95% CI. Genetic variants were included sequentially into the models: polygenic scores (PGS) for cognitive ability and autism, deletions and duplications, de novo loss-of-function (LOF) and missense variants. Rare variants were annotated based on constrained genes alone or combined with developmental disorder (DD) genes. Benjamini-Yekutieli adjustment for multiple comparisons was used. B, Probability estimates of ID for carriers of rare variants in constrained genes are shown (colored curve) as a function of their common variant background, measured by PGS for cognitive ability. Shaded areas representing 95% CIs. Dashed lines indicate the average probability of ID for carriers of each class of rare variant, as well as for individuals who do not carry any rare variant. The baseline probability (0.208) corresponds to the prevalence of ID in the total

sample, irrespective of genetic variants. C. Positive predictive values achieved at different probability thresholds reflect the trade-off with the model's sensitivity. The horizontal gray dashed line represents the baseline prevalence of ID in the sample. Annotations show that for a given sensitivity value (displayed for 10%, 25%, and 50% sensitivities), models integrating different classes of genomic variants yield progressively higher positive predictive values. Shaded areas represent 95% CIs. The area under the curve (AUC) reflects the overall ability to predict ID and should be compared with the sample's baseline ID prevalence (20.8%). D. As a counterpart to panel C, Negative predictive values achieved at different probability thresholds reflect a trade-off with the model's specificity. The AUCs reflect the overall ability to predict the absence of ID, and should be compared with the sample's baseline prevalence of not having ID (79.2%).

<footnote>aP < .001.</footnote>

<footnote>bP < .01.</footnote>
Then, we assessed the added predictive value of genetics (Figure 3A, left panel) at each developmental stage, compared with milestones alone. The added predictive value of genetic variants was contingent on the performance of milestones, such that it decreased when milestones occurring at an older age were included in the model, suggesting that genetic variants contribute more prediction early in development when fewer milestones have been achieved; thus, less cumulative developmental information is available. We then focused on a model integrating the ages of walking and first words as these milestones are typically observed at the time of referral for autism assessment. The predictive performance was AUROC = 0.637 (95% CI, 0.608-0.667). The sequential additions of polygenic scores, copy number variants, and de novo coding variants into the model were respectively associated with increments in AUROC of 0.008 (likelihood-ratio test, P = 1.0 × 10⁻³), 0.013 (P = 1.6 × 10⁻⁵), and 0.016 (P = 9.3 × 10⁻³). This complete model had an AUROC of 0.653 (95% CI, 0.625-0.681).

### Evaluating PPVs and NPVs

To understand the strengths and limitations of this model for clinical predictions, we examined PPVs and NPVs across combinations of genetic and developmental trajectories (Figures 3B and C). We expected that incorporating genetic information alongside developmental milestones would primarily improve PPVs, as clinicians generally consider the carrier status of a genetic variant more clinically significant than its absence. However, we observed that the addition of PGS to developmental milestones improved NPVs (Figure 3C). Certain combinations of variants (or their absence) and milestones reached a NPV of 89%.

### Stratification of Probability Estimates

We aimed to assess predictive accuracy in clinical situations where children are referred for specific concerns. We, therefore, computed probability estimates within 4 groups that represent common reasons for referral to developmental clinics, such as delayed (>90th percentile) motor or language milestones or language regression.⁵²

The observed frequencies of ID for each group were con-
cordant with the probabilities of ID predicted by the model
(Figure 2D). The stratification between low vs high probabili-
ties provided by genetic variants was increased 2-fold in in-
dividuals with significant developmental delays (probabili-
ties ranging from 16.7%-47.1%) compared with those without
such delays (7.1%-22.1%).

### Out-of-Sample Generalization

The predictive performances provided by the combination of
developmental milestones and genetic variants were gener-
alized to SSC (AUROC = 0.739; 95% CI, 0.694-0.784) and
MSSNG (0.726; 95% CI, 0.639-0.813) (eTable 6 in Supple-
ment 2).

Differential Prediction of Cognitive and Adaptive Dimensions

The diverse adaptive and cognitive profiles of autistic indi-
viduals are more complex than the category of ID. Indeed, con-
sistent with previous reports,⁵⁶,⁵⁷ we observed a nonlinear re-
lationship between IQ and Vineland adaptive scales (eMethods
in Supplement 1), which highlighted that a proportion of au-
tistic individuals with typical-range IQ (>70) had greater im-
pairment in adaptive functioning than would be expected
based on their IQ.

Therefore, we asked whether genetic variants and early milestones were more predictive of some defining features of ID rather than others: verbal and nonverbal IQ, as well as the 4 Vineland subscales of adaptive functioning (Figure 4 and eTable 8 in Supplement 2). We evaluated this in a subset of autistic individuals from the SSC sample. Significant milestones explained on average 4-fold more variance than genetics, with on average 4.8% of variance explained by milestones vs 1.1% for the genomic variants assessed in this study (including significant effects only). All classes of rare and common variants, as well as age of walking, were more predictive of nonverbal IQ, which is often difficult to assess in autistic individuals.³⁸

Discussion

Leveraging the broad spectrum of combinations of genetic vari-
ants and developmental milestones observed in autistic indi-
viduals, we developed models that provide PPVs of up to 55%,
accurately identifying 10% of individuals who will develop ID.
Although the addition of genetic variants to developmental
milestones especially improved the identification of individu-
als who will not develop ID (NPVs), the ability to stratify the
probabilities of ID using genetic variants was 2-fold greater in
individuals with delayed milestones compared with those with
typical development.

Our approach builds on previous studies showing that the
effects of rare variants are modulated by an individual's ge-
netic background of common variants.²³,⁵³,⁵⁸,⁵⁹ This was pre-
viously shown in the 22q11.2 deletion, where the predictive
power of PGS for ID was higher in carriers (due to an elevated
baseline prevalence) compared with the general population.⁵³
Here, we extended this approach to all autistic individuals. The
inclusion of multiple combinations of rare and common vari-
ants—without any phenotypic information—resulted in a rela-
tive 2-fold increased probability, PPVs of 46.8% compared with
a baseline probability of 20.8%.

Probability prediction in other medical fields such as car-
diovascular health relies on quantitative tools that integrate
multiple variables, each contributing modestly but collec-
tively achieving clinically significant predictions.⁶⁰ Next steps
to improve predictive performances of neurodevelopmental
outcomes will similarly require the integration of iterative as-
sessments, comprehensive developmental scales,¹³ familial and
environmental factors, as well as a saturated map of all ge-
netic variants—each contributing incrementally to predictive
performance. Although the heritability of ID related to addi-
<figure><img src="image_4.png" /><figcaption>Figure 4. Differential Prediction of Adaptive and Cognitive Dimensions Using Genetic Variants and Developmental Milestones</figcaption></figure>

The predictive performance of developmental (A) and genetic (B) predictors varies across cognitive and adaptive dimensions, as shown by their distinct patterns of explained variance (R²). Significance is evaluated against the null model. All subscales and predictors were evaluated in the same sample of 1054 participants from the Simons Simplex Collection cohort, except for the optional

subscales or motor skills, which was assessed in a subset of 160 participants where motor impairments were suspected by examiners.

tive and transmitted variants⁶¹ is high, a considerable portion of genetic variants contributing to ID remains unidentified. Current, polygenic scores explain approximately 5% of variance in general cognitive ability,⁶² which is low compared with other traits (with similar heritability) such as height where association studies have reached saturation and provide PGS explaining up to 40% of variance.⁶³ Improved knowledge of rare, common, as well as lesser known intermediate-frequency genetic variants involved in neurodevelopmental conditions, along with association studies performed across diverse ancestries, is expected to increase the predictive power of such models.

The pleiotropy of genetic variants, influencing multiple developmental phenotypes, such as autism, ID,⁶⁴ as well as developmental milestones,²³ may represent an opportunity to improve predictive models by using developmental milestones as proxies for currently unmeasured genomic liabilities (eg, unidentified gene-disrupting variants may be present in children with significantly delayed milestones). Conversely, we showed that genetic variants bring additional predictive information (albeit modest) compared with milestones alone. Although genetic variants alone led to greater gains in PPVs (Figure 2C), incorporating genetic data, particularly PGS, with developmental milestones improved NPVs (Figure 3C). This suggests that PPVs from genetic variants overlap with those from developmental milestones, whereas their NPV—specifically from polygenic scores—is more orthogonal to developmental milestones.

## Limitations

This study has some limitations. First, retrospective evaluation of milestones may be susceptible to recall biases or tele-

scoping effects, where developmental milestones can be remembered by caregivers as more recent than they occurred.⁵⁵ We found, however, no evidence of such effects. Such parental report mirrors current practices, where developmental history is gathered retrospectively by clinicians. Moreover, retrospective motor milestones have previously been shown to be reliable as compared to prospective inquiry.⁶⁵ Second, models were trained and cross-validated on parent reports of a clinical diagnosis of ID in the SPARK cohort, and their out-of-sample validity were examined on IQ scores less than 70 in the SSC and MSSNG cohorts. Although these approaches differ, the consistency of results across datasets further highlights the generalizability of the models. Third, participation bias⁶⁶ may have led to an overrepresentation of individuals without ID, including possibly those individuals who are carriers of variants in known autism/ID genes, because significant ID may exclude some individuals from an autism diagnosis and/or participation in research.⁶⁷ This could reduce the predictive performance of models for predicting ID. Indeed, it was previously shown that in the SPARK cohort, more than 25% of autistic individuals carrying de novo LOF variants in a set of autism-associated genes had an IQ in the typical range.²⁴ Fourth, due to the current underrepresentation of diverse ancestries in genomics research, it is likely that the models would underperform in diverse ancestries.⁶⁸

## Conclusions

This prognostic study highlights the feasibility of using predictive models to assist clinicians in their assessment of chil-

<footnote>aP < .05 (Benjamini-Yekutieli adjustment for multiple comparisons).</footnote>
dren referred for autism. Models can provide families with
probability estimates for different developmental trajecto-
ries and the corresponding levels of uncertainty tied to the in-

terpretation of developmental milestones and genetic find-
ings in the context of autism.

ARTICLE INFORMATION

**Accepted for Publication:** February 1, 2025.

**Published Online:** April 21, 2025.
doi:10.1001/jamapediatrics.2025.0205

**Open Access:** This is an open access article distributed under the terms of the CC-BY License. © 2025 Bourque VR et al. JAMA Pediatrics.

**Author Affiliations:** CHU Sainte-Justine Pediatric Hospital and Research Centre, Université de Montréal, Montréal, Québec, Canada (Bourque, Huguet, England, Poulain, Renne, Jean-Louis, Saci, Jacquemont); Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom (Bourque, Okewole, Zhang, Baron-Cohen, Bethlehem, Warrier); Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada (Bourque, Schmilovich, Rouleau); Département de Psychiatrie, Université de Montréal, Montréal, Québec, Canada (Bourque, Mottron); Département de Pédiatrie, Université de Montréal, Montréal, Québec, Canada (England, Jacquemont); Département de Biochimie et Médecine Moléculaire, Université de Montréal, Montréal, Québec, Canada (Poulain, Renne); Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Paris, France (Rolland); HEC Montréal, Montréal, Québec, Canada (Labbé); The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada (Vorstman); The Neuro, Montreal Neurological Institute-Hospital, McGill University, Montréal, Québec, Canada (Rouleau); Centre de Recherche, Évaluation et Intervention en Autisme (CRÉIA), Rivière-des-Prairies Hospital, CIUSSS du Nord-de-l'Île-de-Montréal, Montréal, Québec, Canada (Mottron); Department of Psychology, University of Cambridge, Cambridge, United Kingdom (Bethlehem, Warrier).

**Author Contributions:** Drs Jacquemont and Bourque had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Warrie and Jacquemont jointly supervised this work.

**Concept and design:** Bourque, Okewole, Zhang, Bethlehem, Warrier, Jacquemont.

**Acquisition, analysis, or interpretation of data:** Bourque, Schmilovich, Huguet, England, Okewole, Poulain, Renne, Jean-Louis, Saci, Rolland, Labbé, Vorstman, Rouleau, Baron-Cohen, Mottron, Warrier, Jacquemont.

**Drafting of the manuscript:** Bourque, Okewole, Bethlehem.

**Critical review of the manuscript for important intellectual content:** All authors.

**Statistical analysis:** Bourque, Schmilovich, England, Okewole, Labbé.

**Obtained funding:** Baron-Cohen.

**Administrative, technical, or material support:** Bourque, Schmilovich, Huguet, Poulain, Jean-Louis.

**Supervision:** Baron-Cohen, Bethlehem, Warrier, Jacquemont.

**Conflict of Interest Disclosures:** Dr Bourque reported receiving personal fees from Quebec Health Research Fund-Quebec Ministry of Health Training program and personal fees from the

Montreal Neurological Institute (The Neuro)- University of Cambridge Neuroscience Research Collaboration during the conduct of the study. Dr Vorsman reported receiving consultant fees from Nobias Therapeutics Inc during the preparatory phase of a clinical trial outside the submitted work. Dr Mottron reported receiving grants from M & R Gosselin Research Chair, IBM Canada, and Les Petit's Tresors Foundation; having a patent for Mardaga issued, 3 books published, and a patent for Asadis issued; and receiving conference and/or expert opinion fees from X and X. Dr Bethlehem reported holding equity in and being a cofounder of Centile Bioscience Inc. Dr Jacquemont reported receiving grants from Canadian Institute of Health Research, the National Institutes of Health, and the Jeanne et J.-Louis-Lévesque Foundation outside the submitted work. No other disclosures were reported.

**Funding/Support:** This research was supported by a Brain Canada Multi-Investigator Research Initiative Team Grant, a Canadian Institutes of Health Research (CIHR) grant 159734, and grants from the National Institutes of Health (NIH) 1U01MH119690 and 1U01MH119739 (Dr Jacquemont); the Montreal Neurological Institute- University of Cambridge Neuroscience Research Collaboration (Drs Warrier and Jacquemont); a training scholarship from Quebec Health Research Funds-Health (FRQS) for resident physicians pursuing a career in research (Dr Bourque); grant 214322(Z)18(Z) from the Wellcome Trust (Drs Baron-Cohen and Warrier); the Autism Centre of Excellence (ACE) at Cambridge, SFARI, the Templeton World Charitable Fund, the MRC, and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (Drs Baron-Cohen and Warrier); the National Institute for Health Research (NIHR) Applied Research Collaboration East of England; Calcul Quebec (http://www.calculquebec.ca) and Compute Canada (http://www.computecanada.ca).

**Role of the Funder/Sponsor:** The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

**Disclaimer:** Any views expressed are those of the author(s) and not necessarily those of the funders.

**Data Sharing Statement:** See Supplement 3.

**Additional Contributions:** We thank all of the families in Simons Foundation Powering Autism Research (SPARK), the SPARK clinical sites, and SPARK staff; we appreciate obtaining access to phenotypic and genetic data on SFARI Base; we thank the families participating in MSSNG as well as the generosity of the donors who support this program; we acknowledge the resources of MSSNG (www.mss.ng), Autism Speaks, and The Centre for Applied Genomics at The Hospital for Sick Children, Toronto, Canada; we thank all the families at the participating Simons Simplex Collection (SSC) sites, as well as the principal investigators (A. Beaudet, XX, R. Bernier, XX, J. Constantino, XX, E. Cook, XX, E. Fombonne, XX, D. Geschwind, XX, R. Goin-

Kochel, XX, E. Hanson, XX, D. Grice, XX, A. Klin, XX, D. Ledbetter, XX, C. Lord, XX, C. Martin, XX, D. Martin, XX, R. Maxim, XX, J. Miles, XX, O. Ousley, XX, K. Pelphrey, XX, B. Peterson, XX, J. Piggot, XX, C. Saulnier, XX, M. State, XX, W. Stone, XX, J. Sutcliffe, XX, C. Walsh, XX, Z. Warren, XX, E. Wijsman, XX). Beyond usual salary, no one received financial compensation for their contribution.

**Additional Information:** For the purpose of Open Access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript version arising from this submission. The results leading to this publication have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and EFPIA and AUTISM SPEAKS, Autistica, and SFARI.

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